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1996 Deterministic analysis and simulation of runoff in urban catchments Rouzbehani Abdolmohammad Ghafouri University of Wollongong
Recommended Citation Ghafouri, Rouzbehani Abdolmohammad, Deterministic analysis and simulation of runoff in urban catchments, Doctor of Philosophy thesis, Department of Civil and Mining Engineering, University of Wollongong, 1996. http://ro.uow.edu.au/theses/1236
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DETERMINISTIC ANALYSIS AND SIMULATION OF RUNOFF IN URBAN CATCHMENTS
A thesis submitted in fulfilment of the requirements for the award of the degree of
DOCTOR OF PHILOSOPHY
from
UNIVERSITY OF WOLLONGONG
Hfr$y M&y HWflf w w w
R A Ghafouri, BSc, MSc Shahid Chamran & Shiraz Universities, Iran
Department of Civil and Mining Engineering
1996 DECLARATION
I hereby certify that the work presented in this thesis has been carried out in the Department of Civil and Mining Engineering of the University of Wollongong and has not been submitted for any other degree.
R A Ghafouri
ACKNOWLEDGEMENT
I wish to thank the Ministries of Higher Education and Construction Jihad of the Islamic Republic of Iran for the overseas scholarship, granting me the opportunity to increase my knowledge and experience through this research .
I wish to express my sincere thanks to Associate Professor Michael Boyd for his supervision of the thesis, constructive suggestions and quick response in reading the manuscripts while this research was on.
I wish to thank Associate Professor Geoff O'Loughlin for providing most of the catchment and rainfall-runoff data for the project. I like to thank Mr Ian Thorpe from Water Board's Southern Regional Office for providing the project with cadastral maps of Maroubra and Strathfield urban catchments. I like to thank Mr Jim Britton for his assistance in field works for the project.
I wish to thank my English teacher, Mr Geoff Cowell, from Wollongong English Language Centre for his continuous assistance in editing the manuscript and his proof reading of the final draft.
Finally my great thank goes to my devoted wife, Farzaneh, and my son, Shahab, and my lovely daughter, Shima for their cooperation, patience and support while doing this research.
i LIST OF PUBLICATIONS
1- Ghafouri R A and M J Boyd ( 1993). " Uncertainty in Estimation of Time of Concentration of Catchments ". The First Iranian Students' Conference in Australia. UNSW, 12 April 1993.
2- Ghafouri R A ( 1994). " Simulation of Frequent Runoff in Urban Catchments ". Water Down Under 94, Workshop W4, IE Aust. Adelaide 21-25 Nov. 1994.
3- Ghafouri R A (1995). " Spatial Effects of New Developments in Urban Catchments". 12th Canadian Hydrotechnical Conference. Ottawa June 1-3, 1995, pp. 359-368.
4- Ghafouri R A & M J Boyd ( 1995). " Urban Hydrology Models-Perspectives and Applications ". Regional Conference on Water Resources Management ( WRM'95), Isfahan, IRAN, August 28-30, 1995, pp. 647-655.
5- Ghafouri R.A. (1996). " Application of MOUSE Model on Impervious Area Runoff Simulation in Australia". 7th International Conference on Urban Storm Drainage (ICUSD). Hannover, Germany, Sept. 9-13, 1996 (Accepted).
ii ACRONYMS
AQUALM : Australian QUALiry Model , an integrated water quality and streamflow model Phillips et al. (1992)
AUSQUAL Australian QUALity (White et al. 1992)
EXTRAN : Extended TRANSport , Roesner et al. (1981). a hydraulic flow routing model capable of solving flow depth and velocity in closed conduits or open channels with different cross sections. It works with SWMM or separately.
EXTRAN-XP: the EXTRAN model accompanied by an eXPert graphical system (WP Software 1988).
ILLUDAS-SA : The modified ELLUDAS in South Africa, (Watson 1981).
ILLUDAS: ILLinois Urban Stormwater Area Simulator (Terstriep and Stall 1974).
ILSAX : Some enhancements added to ELLUDAS-SA to conform with Australian conditions. (O'Loughlin 1988).
MMOUSE: A Modification to MOUSE in the present study.
MOUSE : Modelling of Urban SEwers, A hydrologic-hydraulic model for urban sewer analysis (DHI1988).
MOUSE SIMULATOR : The MOUSE model built in a system of telecommunication for Real Time Control and operation of combined sewer system in Europe
(Lindberg et al. 1993).
RAFTS-XP: Runoff Analysis and How Training System (Goyen et al. 1991).
iii RatHGL-XP : Rational Hydraulic Grade Line - eXPert graphic system, (Messner and Goyen 1985-a).
RORB : (Laurenson & Mein, 1988).
RSWM : Regional Storm Water Model (Goyen et al. 1991).
SCS : Soil Conservation Service
SWMM : StormWater Management Model, U.S. Environmental Protection Agency (Huber etal. 1981).
TRRL : Transport and Road Research Laboratory (UK Transport and Road Research Laboratory 1963, 1976).
WASSP : WAllingford Stormwater Simulation Program, (U.K. National Water Council
1981).
WBNM : Watershed Bounded Network Model, (Boyd et al. 1987).
WBNM-94: The enhanced version of WBNM for urban catchment runoff simulation,
(Boyd etal. 1994).
iv NOTATIONS u : kinematic viscosity, m2/s a: velocity distribution factor p0: density of water for free surface flow At: time step, Seconds
AX : distance between computational nodes in the pipe, m [(2/3R)(dR/dy)+(l/A)(dA/dy)] : channel shape function 10 Ii : 1-hour rainfall with return period of 10 years A : cross section, m2 A : total area of subcatchment A: catchment area, ha , mile2 or Km2 Abs. Error : Absolute Error ao : The speed of sound in water API: Antecedent Precipitation Index ARI: Average recurrence Interval AVE.: Average B: Runoff width B.C.: Boundary Conditions BD: Burst Duration C :the runoff coefficient C : Combined area runoff event C.V.: Coefficient of Variation Cio : The 10 year ARI runoff coefficient C\o' the pervious area runoff coefficient with 10 year ARI
Catch.: Catchment CD: Concentrated Development Cf: frequency factor in the Rational method
Ck: Creek Com. or COM.: Computed Comb.: Combined
Cr: rate runoff coefficient
CR: routing coefficient
v Cv: the volumetric runoff coefficient D: diameter, m D.C. MPV.: Directly Connected Impervious Areas dc : water depth on the road crown, m dg and dp : gutter and pavement greatest depths, m Dia.: Diameter dt: time step dy : change in depth e : pipe wall thickness
Er: Young's modulus of elasticity f: dimensionless watershed conveyance factor F : flow correction factor f: the fraction impervious (0.0 to 1.0)
kt f = fc + ( f0 - fc). e" : Horton's Infiltration Equation F(t) B.C.: Time function Boundary Conditions F.A.: Frequency Analysis F: a coefficient related to the selected unit for A, 1/360 if A in ha and 0.278 if A in
Km2 F: the final infiltration capacity of soil or saturated hydraulic conductivity , rnm/hr FI: Total impervious area of each subcatchment FIC: Directly connected impervious area of each subcatchment Fixed B.C.: Fixed Boundary Conditions g : acceleration gravity, m/s2 H: Height in rating curve Hmax : maximum water level in pits or manholes HRF : Hydrologic Reduction Factor
HRFIMP : HRF of impervious areas
HRFPER : HRF of pervious areas I: rainfall intensity during Tc with return period of T years
I: Surface Slope I: the average rainfall rate, rnm/hr I: Impervious Area runoff event
vi I: impervious areas, % i: rainfall intensity Ieff(t): me effective rainfall If: friction slope IFD : Intensity Frequency Duration IL: Initial Loss
ILIMP : initial loss of impervious areas
ILPER : initial loss of pervious areas IMP : directly connected impervious areas, % IMPV. imperviousness percentage, total lo : bottom slope k : No of floods in partial duration series k : pipe wall roughness, mm K : the lag time, minute KW: Kinematic Wave DW: Diffusive Wave DYN.W: Dynamic Wave L: flow path length, m L: the total distance along the main channel from the point being considered to the upstream watershed boundary MAN.: Manning coefficient (in MOUSE Manual) Min.: Minute Mini.: Minimum n : Manning's Roughness Coefficient n*: surface roughness or retardance coefficient N: No of years of record np and ng : gutter and pavement Manning's roughness coefficients obs. or OBS.: observed P: rainfall depth, mm p : rainfall intensity, innVhr P5: sum of 5 previous day rainfall PER. pervious area runoff volume
vii pm : mean rainfall intensity, mm/hr q : runoff rate, mm/hr Q : the peak flow, m3/s Q : runoff volume, mm over catchment Q: the lateral flow Q: the peak flow of UH Qo : the initial discharge, m3/ s Qacc: Accumulated flow
Qb: the original flood peak before urbanisation expansion
QE (t): Evaporation Qi (t): Infiltration Q: the estimated discharge by Manning's equation Qmax: maximum flood discharge in pipes or channels Qp : catchment flood peak qp : subcatchment flood peak
QS(t): Storage Qt: the discharge after the time t, m3/ s
QW(t): Wetting R(t): Rain R.E. : Relative Error S : energy line slope, m/m S : slope, m/m S.D. : Standard Deviation S: the soil storage capacity, mm SCD: Separate Concentrated Developments
S0: longitudinal slope, m/m SSQ : Sum of Squares VOLobs-: Observed Volume VOL sim : Simulated Volume STD : Standard Deviation t: overland flow time, minutes
t: time
viii t: duration of rainfall T1....T13: Trial No. 1 to Trial No. 13 TAD : Time Area Diagram TB: The time base, minutes Tc : time of concentration, minute Tp : Time to peak of rainfall Tr : rainfall duration, minute
TR: the time of rise Tt, Travel time in the pipe UCWI: an antecedent wetness index UD: Uniform Development V: velocity, m/s Vfull, Vf, Vl/4f, Vl/2f, V3/4f,: The pipe velocity for full and partly full conditions
W50 and W75: the width of the hydrograph at 50% and 75% of the Q, minutes
Cy: Runoff coefficient related to the return period of y x : longitudinal axis, m X: Average y : Computed depth y : flow depth, m
yj+1 and yj: the successive depths in iteration
yr: year Zg and Zp: gutter and pavement cross-slopes, m/m
ix ABSTRACT
Among many consequences of urbanisation, frequent flooding is outstanding as the most common problem resulting from land use change. To discharge surface runoff from cities the Rational method was introduced as one of the first formulas to design drainage pipes/waterways. In the beginning the formula was applied as a deterministic model according to the few available records of rainfall events, however later when recorded data was more commonly available the application changed to a statistical method. After more than a century from the introduction of the Rational method by Mulvaney, this formula still is the centre of attention among practitioners. Despite the simplicity of its application the dilemma of its parameters is still outstanding. The Runoff Coefficient and the Time of Concentration are two ill-defined parameters of the formula which question its accuracy. However, as a design tool in urban stormwater drainage the Rational method is still most commonly used.
In the last two decades the deterministic models received more attention than simple formulas because of the availability of digital computers. Many urban hydrology/hydraulic computer models have been developed and applied on urban catchments to evaluate their drainage network's performance and efficiency. The basic requirement of these models is observed data for calibration of their parameters which has always been an obstacle in their wide application by urban drainage designers. In the early stages the development of deterministic models was intended to obtain a design tool to replace simple methods like the Rational formula. However, the majority of applications of these models has been evaluation and performance of the existing systems since their introduction to the industry.
The present study aims to establish a relation between deterministic and statistical interpretations of the Rational method to achieve more accurate design flood peak regarding land classification of urban catchments. The study follows specifically three broad objectives of flood peak estimation; using the Rational formula, hydrograph
simulation using MOUSE model, and design flood estimation using both temporal patterns and design rainfall. The study determines whether the application of a deterministic model such as MOUSE along with design rainfalls and temporal patterns
x from ARR 87 Vol. 2 can produce similar results to frequency analysis of flood peaks. To achieve more insights into the proportion of rainfall which transforms to runoff, the main theme of the study concentrates on the runoff coefficient, which runs through the thesis from both deterministic and statistical points of view.
Both deterministic and statistical evaluation were carried out on the runoff coefficient of the Rational formula. The parameters of the formula: including the time of concentration, Tc, and runoff coefficient were studied using observed rainfall-runoff data for five urban catchments in Sydney.
Values of Tc for the catchments were estimated using three methods including flow velocity, typical minimum time ofrise, and lag time. Time ofrise i n the urban catchments was found to be dependent on rainfall temporal pattern, so it is not a good indicator of Tc. On the other hand, lag times for both impervious and combined events were found to be independent of flood size which shows the stability of Tc in the urban catchments. Regarding these results, the velocity method proposed in Australian Rainfall and Runoff 1987, ARR87, can give reasonable estimates of Tc in ungauged urban catchments.
The rate runoff coefficient was selected as a suitable surrogate for all the effective abstractions from rainfall to produce the flood peak. This coefficient was calculated using recorded values of average rainfall intensities and flood peaks. The average rainfall intensity was calculated during the bursts and also during the Tc of the catchment.
The integration of deterministic values with statistical values was performed by relating the average observed values of runoff coefficient during the catchment's times of concentration and 2-yr return period runoff coefficient from ARR87. It was concluded that ARR87 estimates for 2-yr return period are correlated very well with the observed values with a coefficient of determination of 0.85. The integration of deterministic values of runoff coefficients with a statistical method can be useful because it incorporates the effects of both soil type and time of concentration of catchments in the statistical method ofARR87.
xi Besides deterministic evaluation, runoff coefficient was studied from the view point of statistics using partial duration series of flood peaks as well. Log Pearson Type HI was fitted to the partial duration series of flood peaks and for return periods of 1.2.5 and 10 years flood peaks were calculated using the distribution. Design rainfalls were scaled off the IFD curves resulting from ARR87 partial duration series of design rainfall for the catchments time of concentration.
For different return periods, it was concluded that statistical runoff coefficient remains constant in the catchments with light soils and has an increasing trend in the catchments with heavy soils.
Generally speaking ARR87 overestimates runoff coefficient. However, for catchments with light soil type the overestimate is higher than that in the catchments with heavy soil type. On average, the magnitude of overestimation is 84% and 31 % for catchments with light and heavy soil types respectively. Comparison of deterministic runoff coefficient with statistical showed that in catchments with light soils estimation of flood peaks with return period up to 10 years can be performed considering only impervious area of catchments. In catchments with medium soils only 1-yr flow can be assumed to generate from impervious areas and for higher return periods the incorporation of pervious areas is necessary. Both pervious and impervious areas should be considered for flood computation of 1 year return period and higher in catchments with heavy soil type.
Regarding complex models, the MOUSE model from Danish Hydraulics Institute was calibrated for four catchments on impervious area runoff events. Three indices were considered for calibration of the model including: volume, flood peak and time to peak. Volume and flood peak were simulated by adjustment of the Hydrological Reduction Factor, HRF, while for time to peak, the Manning roughness coefficient was adjusted. In this part of the study the coefficients of determination denote that at least 94% of the runoff volume variations could be explained by the model. The closeness of the simulated and the observed time to peak of hydrographs shows that the magnitude of the
Manning's roughness coefficient is reflected correctly by the model.
xii Combined runoff from both pervious and impervious areas is important in Australian urban catchments and should be considered in network design .and stormwater management. Generally, simulation of combined runoff events in the MOUSE model is performed using Level B Module. In this module excess rainfall is calculated by a water balance equation which incorporates infiltration equation, evaporation and storage data. The excess rainfall is transformed to a hydrograph at each subcatchment outlet by using Kinematic wave equation. The MOUSE model at this level averages the effects of pervious and impervious areas of each subcatchment, however these areas have different responses in producing runoff. Besides the combination of pervious and impervious areas, the process of rainfall excess is very data intensive and the involved parameters have interactions which makes the calibration time consuming and unstable. Because of interactions, the magnitudes of the calibrated parameters have no physical interpretation.
The MOUSE model was modified (MMOUSE) for excess rainfall calculation, and separated storages for impervious and pervious areas. To convey the separated pervious area runoff to the catchment drainage system a fictitious conduit and a dummy manhole were added to the catchment drainage system at the outlet of each subcatchment. The beginning of this conduit is the dummy manhole and the ending is the real manhole.
In MMOUSE the excess rainfall of pervious areas is calculated according to the concept of the runoff coefficient for pervious areas and different initial losses for pervious and impervious areas. In this method runoff is calculated from two parallel storages of impervious and pervious areas separately and is added at manholes. The delay between response time of pervious and impervious areas is considered by different times of concentration for them.
Despite using only one parameter to calibrate runoff model, the MMOUSE indicated better results for flood peak and volume compared with the original model (MOUSE). With the proposed method there is a rninimum interaction between parameters and the calibration process is very time efficient. This method gives the practitioners the opportunity to use a complex model like MOUSE and a simple concept like runoff
xiii coefficient in urban drainage practice. Using this method the model can be calibrated on a few observed events and be applied in design situation.
The proposed method was tested on two catchments where combined runoff was frequently observed. The results for flood peak simulation were satisfactory. Time to peak and overall hydrograph shape were simulated very well.
Using the runoff volume resulting from simulation of impervious areas of the catchments, and the observed total runoff, runoff from pervious areas of the catchments was calculated. On average the ratio of pervious areas runoff to total runoff in two catchments with clay soil type was calculated equal to 37%. This fact should be useful in water quality and sediment transport studies in Australian urban areas with heavy soil type.
The pervious area runoff coefficient was investigated in relation to rainfall depth, rainfall intensity and sum of rainfall forfive days before the occurrence of the event (P5) which is an index of API. No significant correlation was found between the pervious area runoff coefficient and rainfall depth or intensity or P5. When sum of the P5 and event rainfall was used as API, the correlation coefficient changed very significantly for one catchment while for the other it did not change at all.
The HRF of the pervious area ( HRFPER), calibrated by the MMOUSE, was studied in relation to the pervious area runoff coefficient. They are just slightly different ways of expressing the same concepts. Runoff coefficient was calculated based on the ratio of pervious runoff to total catchment area, but HRFPER was calculated by the ratio of pervious runoff to the pervious area of the catchment.
Deterministic simulation of design flood peaks using both design rainfalls and temporal patterns was accomplished using both MOUSE and the MMOUSE in four catchments. Design rainfalls were considered during the time of concentration of catchments and were distributed over the time using temporal patterns from ARR87 Vol. 2. For simulation of design floods in the catchments with light soil type MOUSE Level-A was
xiv used while for the heavy soil type catchments MMOUSE which incorporates pervious areas runoff coefficient was used. The median values of Hydrologic Reduction Factor for pervious areas ( HRFPER) of the catchments were used for design excess rainfall calculations.
Comparison of the ratios of 10 and 5 years flood to 2 years flood for three methods of ARR87, MMOUSE and frequency analysis showed that the frequency analysis and the MMOUSE results are close together and different from those of ARR87. It is concluded that deterministic simulation of design floods ( MMOUSE + Design Rainfall + Temporal Patterns) can produce similar/related values as frequency analysis of flood peaks. However, the results of deterministic simulation were generally overestimated compared with those of frequency analysis. Apart from the different basis of two methods, deterministic for MMOUSE and statistical for frequency analysis, the reasons for overestimation of deterministic simulation can be either high value of HRFPER or inappropriate temporal patterns. Using MMOUSE two other HRFPER values were examined to obtain closer design floods to the frequency analysis. It was concluded that in MMOUSE when HRFPER equals zero the resulting design flood magnitudes approach the frequency analysis results. In other words, assuming both correctness and representativeness of the currently available temporal patterns, in deterministic simulation of design flood ,up to 10 years return period, the incorporation of pervious areas is not required, however, for runoff volume simulation they should be considered.
The results of this study should be useful for both water quantity and quality investigations. Rate runoff coefficient should be used along with the Rational method for stormwater flood peak estimation. Volumetric runoff coefficient should be used for estimation of runoff volume in conjunction with MMOUSE model for impervious and pervious areas of urban catchments.
xv TABLE OF CONTENTS
ACKNOWLEDGEMENT i LIST OF PUBLICATIONS ii ACRONYMS Hi NOTATIONS v ABSTRACT x TABLE OF CONTENTS xvi LIST OF TABLES xxiv LIST OF FIGURES xxix
CHAPTER ONE 1-1 1. INTRODUCTION 1-1 1.1. STORMWATER DRAINAGE NETWORKS 1-3 1.2. THE IMPORTANCE OF DESIGN FLOOD PEAK IN URBAN CATCHMENTS 1-4 1.3. THE OBJECTIVES OF THE CURRENT STUDY 1-4 1.3.1. FLOOD PEAK, THE RATIONAL FORMULA AND RUNOFF COEFFICIENT 1-5 1.3.2. DETERMINISTIC MODELLING OF RUNOFF HYDROGRAPH 1-5 1.3.3. DESIGN RAINFALL, TEMPORAL PATTERNS AND DETERMINISTIC SIMULATION OF DESIGN FLOODS 1-7
CHAPTER TWO 2-1 2. LITERATURE REVIEW AND THEORETICAL BACKGROUND 2-1 2.1. HISTORY OF URBAN HYDROLOGY DEVELOPMENT 2-1 2.1.1. HYDROLOGIC PROCESSES VARIATIONS IN URBAN CATCHMENTS 2-1 2.1.2. HYDRAULIC FEATURES OF URBAN CATCHMENTS 2-4 2.2. SIMPLE FORMULAS FOR ESTIMATION OF RUNOFF RATES IN URBAN CATCHMENTS 2-5 2.2.1. UNIT HYDROGRAPH METHOD 2-5 2.2.2. FLOOD FREQUENCY ANALYSIS AND REGIONALISATION 2-6 2.2.3. SCS METHOD 2-7
xvi 2.3.THE RATIONAL FORMULA 2-8 2.3.1. THE WALLINGFORD RATIONAL METHOD 2-9 2.3.2. THE TWO-VALUE RATIONAL METHOD 2-10 2.3.3. Two MAJOR INTERPRETATIONS OF RUNOFF COEITICIENT 2-10 2.3.3.1. DETERMINISTIC INTERPRETATION 2-11 2.3.3.2. STATISTICAL INTERPRETATION 2-12 2.3.3.3. THE NECESSITY OF RESEARCH ON THE DETERMINISTIC RUNOFF COEFFICIENT 2-13 2.4. DETERMINISTIC RUNOFF COEFFICIENT 2-14 2.4.1. RUNOFF COEFFICIENT AND EFFECTIVE PARAMETERS 2-15 2.4.2. DIMENSIONAL ANALYSIS AND RUNOFF COEFFICIENT FORMULATION 2-16 2.5. MODELLING OF URBAN CATCHMENT HYDROLOGY 2-19 2.6. AUSTRALIAN URBAN HYDROLOGY MODELS 2-20 2.6.1. RAINFALL-RUNOFF ROUTING MODELS 2-21 2.6.1.1. RAFTS-XP 2-22 2.6.1.2. RORB 2-23 2.6.1.3. WBNM 2-23 2.6.1.4. RATHGL-XP 2-24 2.6.2. URBAN CATCHMENT HYDROLOGIC - HYDRAULIC MODELS 2-24 2.6.2.1. JLSAX 2-24 2.6.2.1.1. HYDROLOGIC MODULE 2-25 2.6.2.1.2. HYDRAULIC MODULE - STEADY STATE FLOW ASSUMPTION 2-26 A). TRAVEL TIME 2-26 B). Prr ENTRIES 2-27 c). PIPE AND OPEN CHANNELS HYDRAULICS 2-27 2.6.2.1.3. DETENTION BASIN COMPUTATION 2-28 2.6.2.1.4. REQUIRED DATA 2-28 2.7. OVERSEAS URBAN HYDROLOGIC MODELS 2-29 2.7.1. WASSP 2-29 2.7.2. SWMM 2-30 2.7.3. MOUSE 2-31 2.7.3.1. HYDROLOGIC MODULE 2-32 A). LEVEL A 2-32 B). LEVEL B 2-32
xvii 2.7.3.2. HYDRAULIC MODULE - UNSTEADY STATE FLOW CALCULATION - KW - DW-DYN.W 2-35 A). KW APPROXIMATION 2-37 B). DW APPROXIMATION 2-37 C). DYN.W APPROXIMATION 2-37 D). ENERGY LOSSES IN JUNCTIONS 2-38 E). INITIAL AND BOUNDARY CONDITIONS 2-38 F). WATER LEVEL ABOVE GROUND LEVEL 2-39 2.7.3.3. METHOD OF SOLUTION OF FLOW EQUATIONS 2-39 2.7.3.4. REQUIRED DATA 2-40 2.7.4. COMPARISON OF JLSAX AND MOUSE 2-40 2.8. SUMMARY 2-42
CHAPTER THREE 3-1 3. CATCHMENTS AND DATA 3-1 3.1.MAR0UBRA 3-2 3.1.1. CATCHMENT AND PIPE NETWORK 3-2 3.1.2. RAINFALL AND DISCHARGE MEASUREMENT STATIONS 3-10 3.1.3. RAINFALL AND STORMFLOW DATA 3-12 3.2. JAMISON PARK 3-14 3.2.1. CATCHMENT AND PIPE NETWORK 3-14 3.2.2. RAINFALL AND DISCHARGE MEASUREMENT STATIONS 3-18 3.2.3. RAINFALL AND STORMFLOW DATA 3-18 3.3. FISHER'S GHOST CREEK 3-21 3.3.1. CATCHMENT AND PIPE NETWORK 3-21 3.3.2. RAINFALL AND DISCHARGE MEASUREMENT STATIONS 3-26 3.3.3. RAINFALL AND STORMFLOW DATA 3-28 3.4. STRATHFIELD 3-30 3.4.1. CATCHMENT AND PIPE NETWORK 3-30 3.4.2. RAINFALL AND DISCHARGE MEASUREMENT STATIONS 3-34 3.4.3. RAINFALL AND STORMFLOW DATA 3-35 3.5. CRANEBROOK 3-38 3.5.1. CATCHMENT AND PIPE NETWORK 3-38
xviii 3.5.2. RAINFALL AND DISCHARGE MEASUREMENT STATIONS 3-41
3.5.3. RAINFALL AND STORMFLOW DATA 3-41
CHAPTER FOUR 4-1
4. DETERMINISTIC EVALUATION OF THE RATIONAL METHOD 4-1
4.1. TIME OF CONCENTRATION 4-1 4.1.1. ARR87 METHOD 4-3
4.1.1.1. OVERLAND FLOW TRAVEL TIME 4-3
4.1.1.2. GUTTER FLOW TRAVEL TIME 4-3
4.1.1.3. PIPE FLOW TRAVEL TIME 4-7
4.1.2. MAROUBRA 4-8
4.1.3. JAMISON PARK 4-11
4. l .4. FISHER'S GHOST CREEK 4-13
4.1.5. STRATHFIELD 4-15
4.1.6. CRANEBROOK 4-16
4.2. ESTIMATE OF TIME OF CONCENTRATION USING OBSERVED RAINFALL -
STREAMFLOW 4-17
4.2.1. MINIMUM TIME OF RISE 4-17
4.2.1.1. MAROUBRA 4-20
4.2.1.2. JAMISON PARK 4-22
4.2.1.3. FISHER'S GHOST CREEK 4-26
4.2.1.4. STRATHFIELD 4-28
4.2.1.5. CRANEBROOK 4-31
4.2.2. EFFECT OF TEMPORAL PATTERN OF RAINFALL ON THE TIME OF RISE 4-32
4.2.2.1. MAROUBRA 4-33 4.2.2.2. FISHER'S GHOST CREEK 4-36
4.2.3. LAG ANALYSIS METHOD 4-39
4.2.3.1. MAROUBRA 4-46
4.2.3.2. JAMISON PARK 4-49
4.2.3.3. FISHER'S GHOST CREEK 4-52
4.2.3.4. STRATHFIELD 4-55
4.2.3.5. CRANEBROOK 4-59
4.2.4. SUMMARY RESULTS OF TIME OF CONCENTRATION 4-61
4.3. RUNOFF COEFFICIENT 4-64
xix 4.3.1. ARR87 RUNOFF ESTIMATION METHOD 4-64 4.3.2. RUNOFF COEFFICIENT AND SYSTEM THEORY 4-65 4.3.2.1. ESTIMATES OF THE RUNOFF COEFFICIENT USING OBSERVED DATA 4-67 4.3.2.2. THE VARIATIONS OF RUNOFF COEFFICIENT 4-76 4.3.2.3. THE RELATION OF FLOOD PEAK AND AVERAGE RAINFALL INTENSITY' ....4-76 4.3.2.4. INTEGRATION OF DETERMINISTIC RUNOFF COEFFICIENT IN A STATISTICAL METHOD 4-83 4.4. SUMMARY OFTHE RATIONAL METHOD 4-84
CHAPTER FIVE 5-1 5. STATISTICAL EVALUATION OF THE RATIONAL METHOD 5-1 5.1. PARTIAL DURATION SERIES SPECIFICATIONS 5-2 5.1.1. BASE DISCHARGE SELECTION IN PARTIAL DURATION SERIES 5-3 5.1.2. FREQUENCY ANALYSIS OF PARTIAL DURATION SERIES OF FLOOD PEAKS .... 5-7 5.2. DESIGN RAINFALL 5-10 5.3. STATISTICAL RUNOFF COEFFICIENT 5-10 5.4. COMPARISON OF STATISTICAL RUNOFF COEFFICIENT AND ARR87 METHOD5- 11 5.5. COMPARISON OF STATISTICAL AND DETERMINISTIC RUNOFF COEFFICIENT 5-16 5.6. SUMMARY 5-17
CHAPTER SIX 6-1 6. THE MOUSE MODEL 6-1 6.1. INTRODUCTION 6-1 6.2. CATCHMENT AND PIPE DATA FILE 6-2 6.2.1. RAINFALL DATA 6-8 6.2.2. HYDROLOGY DATA 6-9 6.2.3. HYDRAULICS DATA 6-9 6.3. SENSITIVITY ANALYSIS OF MOUSE 6-12 6.3.1. RUNOFF MODEL LEVEL A 6-12 6.3.1.1. TIME OF CONCENTRATION 6-13 6.3.1.2. TIME AREA DIAGRAM (TAD) 6-16 6.3.1.3. HYDROLOGIC REDUCTION FACTOR (HRF) 6-19 6.3.2. PIPE FLOW MODEL 6-24
XX 6.3.2.1. INTRODUCING CIRCULAR MANHOLE INSTEAD OF SQUARE PIT 6-25 6.3.2.2. MANNING'S ROUGHNESS COEFFICIENT 6-28 6.3.2.3. SIMULATION OF GRATE ENTRY LIMITATION/MANHOLE OPENING 6-31 6.3.2.4. HEAD LOSS IN MANHOLES 6-34 6.3.2.5. BOUNDARY CONDITIONS EFFECTS (FIXED/TIME FUNCTION) 6-37 6.3.2.6. TIME STEP IN SIMULATION 6-41 6.3.2.7. KINEMATIC AND DYNAMIC WAVE APPROXIMATION 6-43 6.4. SUMMARY 6-49
6.4.1. HYDROLOGIC PARAMETERS SENsm\rrY STUDY RESULTS 6-49 6.4.1.1. TIME OF CONCENTRATION 6-49 6.4.1.2. TIME AREA DIAGRAM 6-50 6.4.1.3. HYDROLOGIC REDUCTION FACTOR 6-50
6.4.2. HYDRAULIC PARAMETERS SENSmVITY STUDY RESULTS 6-50 6.4.2.1. MANNING'S ROUGHNESS COEFHCIENT 6-51 6.4.2.2. GRATE ENTRY HYDRAULIC LOSS 6-51 6.4.2.3. MANHOLE HEAD LOSS 6-51 6.4.2.4. BOUNDARY CONDITIONS EFFECTS 6-51 6.4.2.5. TIME STEP IN SIMULATION 6-52 6.4.2.6. SIMULATION METHODS 6-52
CHAPTER SEVEN 7-1
7. SIMULATION OF IMPERVIOUS AREA RUNOFF AND URBANISATION EFFECTS USING MOUSE 7-1 7.1. CALIBRATION OF THE MODEL 7-1
7.1.1. HYDROLOGIC MODULE 7-2 7.1.2. HYDRAULICS MODULE 7-3 7.1.3. COMPARISON OF SIMULATED AND OBSERVED VOLUMES AND PEAKS 7-4 7.2. VERIFICATION OF THE PARAMETERS 7-6
7.3. CATCHMENTS SIMULATED 7-6
7.3.1. MAROUBRA 7-6
7.3.1. 1. VERIFICATION OF THE CALIBRATED PARAMETERS 7-9 7.3.1.1.1. VOLUME OF RUNOFF 7-9 7.3.1.1.2. FLOOD PEAK 7-9
7.3.1.1.3. TIME TO PEAK OF HYDROGRAPH 7-10
xxi 7.3.1.2. EVALUATION OF SURCHARGE AND FLOODING FOR THE HEAVIEST OBSERVED RAINFALL 7-15 7.3.2. JAMISON PARK 7-20 7.3.2.1. INPUT DATA 7-20 7.3.2.2. CALIBRATION OF THE MODEL 7-22 7.3.3. FISHER'S GHOST CREEK 7-28 7.3.3.1. INPUT DATA 7-28 7.3.3.2. CALIBRATION OF THE MODEL 7-28 7.3.4. CRANEBROOK 7-35 7.3.4.1. INPUT DATA 7-35 7.3.4.1. CALIBRATION OF MODEL 7-35 7.4. INVESTIGATION OF URBANISATION EFFECT ON FLOOD PEAK USING MOUSE 7-40 7.4.1. SPATIAL DISTRIBUTION EFFECTS OF NEW DEVELOPMENT ON FLOOD PEAK 7-41 7.4.2. SPATIAL DISTRIBUTION AND EVENT SIZE 7-43 7.5. SUMMARY 7-45
CHAPTER EIGHT 8-1
8. MODIFICATION OF MOUSE MODEL IN COMBINED RUNOFF SIMULATION 8-1 8.1. MOUSE RUNOFF MODULE- LEVELB 8-2 8.1.1. LIMITATION OF PARAMETERS RANGE IN MOUSE 8-3 8.1.2. ASSOCIATED LAND USE WITH LEVEL-B IN MOUSE 8-3 8.1.3. SIMULATION OF COMBINED HYDROGRAPH IN MOUSE LEVEL-B 8-4 8.1.4. APPLICATION OF LEVEL-B 8-4 8.2. INVESTIGATION OF ALTERNATIVE SOLUTIONS FOR COMBINED EVENTS 8-8 8.2.1. PROPOSED SOLUTION FOR COMBINED EVENTS 8-10 8.2.2. IMPLEMENTATION OF THE PROPOSED METHOD 8-12 8.3. CATCHMENTS ANALYSED 8-13 8.3.1. JAMISON PARK 8-13
8.3.1.1. COMPARISON OF MOUSE AND THE PROPOSED METHOD 8-14 8.3.1.2. SIMULATION RESULTS 8-17 8.3.2. FISHER'S GHOST CREEK 8-21 8.3.2.1. SIMULATION RESULTS 8-21
rxii 8.4. SUMMARY 8-25
CHAPTER NINE 9-1
9. PERVIOUS AREA RUNOFF AND SIMULATION OF DESIGN FLOODS USING MODIFIED MOUSE 9-1 9.1. PERVIOUS AREA RUNOFF 9-2 9.1.1. RELATION OF PERVIOUS AREA RUNOFF AND TOTAL RUNOFF 9-7 9.1.2. PERVIOUS AREA RUNOFF COEFHCIENT 9-9 9.2. INTER-RELATION OF HRF AND RUNOFF COEFHCIENT 9-13 9.3. APPLICATION OF MODIFIED MOUSE IN DESIGN FLOOD ESTIMATION 9-16 9.3.1. TEMPORAL PATTERN 9-16 9.3.2. LAND USE AND SIMULATION OF DESIGN FLOOD 9-18 9.3.3. EXCESS RAINFALL 9-18 9.3.4. SIMULATION OF DESIGN FLOODS 9-19 9.3.5. COMPARISON OF THE METHODS OF DESIGN FLOOD ESTIMATION 9-22 9.3.6. INVESTIGATION OF PERVIOUS AREAS CONTRIBUTION IN DESIGN FLOOD PEAKS 9-24 9.4. SUMMARY 9-26
CHAPTER TEN 10-1 10. SUMMARY AND CONCLUSIONS 10-1
REFERENCES
APPENDICES
xxiii LIST OF TABLES
Table 2.1. Gauged urban catchment descriptions ( from Jones and Lawson 1992 and Philips 1995) 2-13 Table 2.1. Comparison of capabilities of ILSAX and MOUSE 2-42
Table 3.1. Characteristics of the gauged urban catchments in Sydney 3-1 Table 3.2. Land use of Maroubra catchment 3-6 Table 3.3. The characteristics of the main pipe line of the Maroubra catchment 3-10 Table 3.4. Rating curve of the gauging station - Maroubra 3-11 Table 3.5. Rainfall and runoff record - Maroubra (After Bufill 1989) 3-13 Table 3.6. The characteristics of the main pipe line of Jamison Park catchment 3-18 Table 3.7. Rainfall and Runoff record - Jamison Park (After Bufill 1989) 3-19 Table 3.8. Land use of Fisher's Ghost Creek Catchment 3-23 Table 3.9. The main waterway characteristics of Fisher's Ghost Creek catchment 3-23 Table 3.10. Fisher's Ghost Creek rating table at Bradbury (from DWR) 3-26 Table 3.11. Rainfall and Runoff record - Fisher's Ghost Creek (After Bufill 1989).... 3-29 Table 3.12. Land use of Strathfield catchment 3-34 Table 3.13. Main pipe line specifications of Strathfield catchment 3-34 Table 3.14. Rainfall and Runoff record - Strathfield (After Bufill 1989) 3-36 Table 3.15. The main pipe line specifications of Cranebrook catchment 3-41
Table 4.1. Time of concentration empirical formulas and classification of required input (fromMcCuen 1989) 4-2 Table 4.2. Average gutter flow travel time estimates using Fig. 14.9 from ARR87- Maroubra (2-year return period rainfall) 4-7 Table 4.3. Velocity estimate in main pipe line - Maroubra 4-10 Table 4.4. Travel time estimation for different pipe filling conditions - Maroubra 4-10 Table 4.5. Comparison of pipes travel times - Maroubra 4-11 Table 4.6. Main pipe line travel time- Jamison Park catchment 4-12 Table 4.7. Main waterway flow travel times- Fisher's Ghost Creek 4-14 Table 4.8. Main pipe line travel times - Strathfield 4-15 Table 4.9. Main pipe line trave times - Cranebrook 4-16 Table 4.10. Summary results of ARR87 method (Fig. 14.9 from ARR87 +Manning's formula) of travel time estimation 4-17 Table 4.11. Maroubra: Time ofrise an d burst duration 4-21
xxiv Table 4.12. Jamison Park - Impervious area runoff events 4-23 Table 4.13. Jamison Park - Combined runoff events 4-24 Table 4.14. Fisher's Ghost Creek - Impervious area runoff events 4-26 Table 4.15. Fisher's Ghost Creek - Combined events 4-27 Table 4.16. Strathfield - Total events 4-29 Table 4.17. Cranebrook - Total events 4-31 Table 4.18. A summary of results of estimated time of concentration by typical minimum and average time ofrise method- Minutes 4-32 Table 4.19. Maroubra time ofrise an d time to peak of rainfall 4-35 Table 4.20. Time ofrise an d time to peak of rainfall 4-36 Table 4.21. Time ofrise an d time to peak of rainfall - Fisher's Ghost Creek, Minutes 4-37 Table 4.22. Comparison of lags calculated by recession and hyetograph-hydrograph analysis - Maroubra 4-41 Table 4.23. Lag - flood size relationships^ = a + b Q) 4-43 Table 4.24. Lag time computation - Maroubra 4-48 Table 4.25. Lag time computation for impervious runoff events - Jamison Park 4-50 Table 4.26. Lag time computation for combined events - Jamison Park 4-51 Table 4.27. Lag time computation for impervious area runoff - Fisher's Ghost Creek 4-54 Table 4.28. Lag time computation for combined events - Fisher's Ghost Creek 4-54 Table 4.29. Comparison of the first and second lag times on recessions - Fisher's Ghost Creek 4-54 Table 4.30. Lag time computation for impervious area runoff- Strathfield 4-57 Table 4.31. Lag time computation for combined events - Strathfield 4-57 Table 4.32. Lag time computation - Cranbrook 4-59 Table 4.33. The summary results of estimated lag time by recession analysis - Minutes4-60 Table 4.34. Comparison of estimated time of concentration with lag time, Minutes... 4-62 Table 4.35. Frequency factor for runoff coefficient 4-65 Table 4.36. 2-yr runoff coefficient of the catchments using ARR87 method 4-65 Table 4.37. Problems solving using systems approaches (From ODonnell 1986) 4-66 Table 4.38. Average rainfall intensity and runoff coefficient for the bursts and time of concentration -Maroubra 4-68 Table 4.39. Jamison Park - Impervious area runoff events 4-70 Table 4.40. Jamison Park - Combined runoff events 4-71 Table 4.41. Fisher's Ghost Creek - Impervious area runoff events 4-72 Table 4.42. Fisher's Ghost Creek - combined events 4-72
XXV Table 4.43. Strathfield - Total events 4-73 Table 4.44. Cranebrook - Total events 4-75 Table 4.45. Statistics of rate runoff coefficient-BD Rain 4-76 Table 4.46. Statistics of rate runoff coefficient- Tc Rain 4-76 Table 4.47. Correlation of flood peak and average rainfall intensity during the bursts 4-78 Table 4.48. Correlation of flood peak and average rainfall intensity during time of concentration 4-78 Table 4.49. Runoff coefficient of 2-yr return period by ARR87 method and the average value from the observed data - % 4-84
Table 5.1. Comparison of Partial Duration Series Indexes 5-4 Table 5.2. Number of floods in the partial duration series 5-5 Table 5.3. Partial duration series of flood peak, average rainfall intensity and runoff coefficient 5-5 Table 5.4. Partial duration series of flood peak, average rainfall intensity and runoff coefficient 5-6 Table 5.5. Statistics of partial duration series of flood peaks (logarithms) 5-7 Table 5.6. Magnitude of flood peaks from frequency analysis-nr/s 5-7 Table 5.7. Magnitude of average rainfall intensities during time of concentration-rnm/hr5-10 Table 5.8. Statistical runoff coefficients 5-11 Table 5.9. Runoff coefficient from ARR87 and Frequency Analysis 5-12 Table 5.10. Runoff coefficient from ARR87 and Frequency Analysis 5-12 Table 5.11. Magnitude of flood peaks from ARR87 and frequency analysis-m3/s 5-13 Table 5.12. Runoff coefficient by Statistical and Deterministic Approaches 5-17
Table 6.1. Physical characteristics of sub catchments- Maroubra 6-3 Table 6.2. Characteristics of manholes-Maroubra 6-4 Table 6.3. Pipes and conduits characteristics - Maroubra 6-5 Table 6.4. Hydrology data file - Maroubra 6-10 Table 6.5. Hydraulics datafile - Maroubra 6-11 Table 6.6. Sensitivity test of time of concentration (Maroubra, 1703 8 3) 6-13 Table 6.7 Comparison the effect of ARR87 and Chow's Formula for Gutter flow time of concentration 6-14 Table 6.8. TAD effect on the subcatchments hydrograph - Maroubra 6-17 Table 6.9. TAD effects on flood peak and time to peak of hydrograph at the outlet- Maroubra 6-17
xxvi Table 6.10. Calculation of HRF 6-19 Table 6.11. Sensitivity of directly connected impervious areas in simulation of event 170383 - Maroubra 6-20 Table 6.12. Test of the effect of pits/manholes on maximum discharge and elevation - Maroubra (Hmax : m, Qmax: m3/s, Time: Minutes) 6-27 Table 6.13. Sensitivity of roughness coefficient (Hmax : m, Qmax: m3/s, Time: Minutes)6-30 Table 6.14. Boundary conditions effects (Hmax : m, Qmax: m3/s, Time: Minutes).... 6-40 Table 6.15. The results of KW and DYN.W for a small event simulation (Hmax : m, Qmax: m3/s, Time: Minutes) 6-44 Table 6.16. The results of DYN.W simulation of a large event and pressurised flow (Hmax : m, Qmax: m3/s, Time: Minutes) 6-45 Table 6.17. The results of KW and DYN.W for simulation of a medium event (Hmax : m, Qmax: m3/s, Time: Minutes) 6-46
Table 7.1. Continuity balance of inflowing and diverted hydrographs in MOUSE 7-5 Table 7.2. The calibration results of MOUSE on Maroubra catchment 7-7 Table 7.3. Verification results of calibration- Maroubra 7-10 Table 7.4. Comparison of the results of MOUSE simulation with those of the other models for event 051184- Maroubra 7-15 Table 7.5. Comparison of volumes resulting from two sets of modelling, mm- Jamison Park 7-23 Table 7.6. The results of thefirst trial calibration - Jamison Park 7-24 Table 7.7. The results of runoff volume simulation - FGC 7-30 Table 7.8. Effects of varying open natural channel roughness coefficient - Fisher's Ghost Creek 7-31 Table 7.9. The overall results of calibration - Fisher's Ghost Creek 7-32 Table 7.10. Comparison of simulated volumes - Cranebrook 7-36 Table 7.11. The results of calibration - Cranebrook 7-37 Table 7.12. The effects of event size and spatial distribution of new developments- Maroubra 7-43 Table 7.13. The effects of catchment size and spatial distribution of new developments7-44 Table 7.14. The effect of overall urbanisation in catchments on flood peak 7-45 Table 7.15. The summary results of calibration of MOUSE - Level A 7-46 Table 7.16. Results of volume simulation 7-48 Table 7.17. Results of flood peak simulation 7-48 Table 7.18. Results of time to peak simulation 7-48
Table 8.1. Global input values of hydrologic level B in MOUSE (DHI 1988) 8-2
xxvii Table 8.2. Calibration of Runoff Module Level B on Jamison Park Catchment 8-5 Table 8.3. Calibration results of combined events using MOUSE Level B - Jamison Park8-6 Table 8.4. Calibration results of combined events using MMOUSE - Jamison Park... 8-15 Table 8.5. Simulation results of combined events using MMOUSE- Jamison Park .... 8-18 Table 8.6. Simulation results of combined events using MMOUSE-Fisher's Ghost Creek8-22
Table 9.1. Separated impervious and pervious runoff, Jamison Park 9-3 Table 9.2. Separated impervious and pervious runoff, Fisher's Ghost Creek 9-3 Table 9.3. Regression equations of rainfall-runoff of the catchments 9-7 Table 9.4. Pervious area runoff coefficient, HRF and P5-Jamison Park 9-9 Table 9.5. Pervious area runoff coefficient, HRF and P5-Fisher's Ghost Creek 9-10
Table 9.6. Median and mean values of HRFPER used in design floods simulation 9-18 Table 9.7. Design floods simulated using modified MOUSE model, m3/s 9-19 Table 9.8. Design flood magnitudes from frequency analysis, ARR87 and MMOUSE, m3/s 9-20 Table 9.9. Design flood magnitudes from frequency analysis, ARR87 and MMOUSE, m3/s 9-20 Table 9.10. Comparison of design flood ratios 9-23 Table 9.11. Comparison MOUSE, MMOUSE with frequency analysis 9-23 Table 9.12. Comparison MMOUSE with frequency analysis 9-24 Table 9.13. Adjusting F.A. and MMOUSE Results- Jamison Park 9-25 Table 9.14: Adjusting F.A. and MMOUSE Results- Fisher's Ghost Creek 9-25
xxviU LIST OF FIGURES
Fig. 2.1. Schematic amendments of a rural catchment after urbanisation 2-2 Fig. 2.2. Runoff coefficient estimate in urban catchments (from ARR87 ) 2-12 Fig. 2.3. Pipe direction change 2-38
Fig. 3.1. The locality of the urban catchments 3-1 Fig. 3.2. Maroubra Catchment 3-5 Fig. 3.3. Maroubra Catchment cadastral map 3-7 Fig. 3.4. Maroubra main pipe line longitudinal profile- Government contract (Water Board) 3-8 Fig. 3.5. Rating curve of the gauging station - Maroubra 3-11 Fig. 3.6. Jamison Park catchment Map 3-15 Fig. 3.7. Fisher's Ghost Creek Catchment Boundary 3-22 Fig. 3.8. The Fisher's Ghost Creek rating curve at Bradbury park (From DWR) 3-28 Fig. 3.9. Strathfield catchment boundaries 3-32 Fig. 3.10. Cranebrook catchment boundaries and pipe network 3-39
Fig. 4.1. Gutter and roadway profile with vertical kerb(After ARR87) 4-4 Fig. 4.2. Design charts for gutter flow travel time( Fig. 14.9 ARR87) 4-5 Fig. 4.3. Cross section of Lach Maree street - Maroubra 4-9 Fig. 4.4. Illustration of burst duration and time ofrise of hydrograph 4-18 Fig. 4.5. Variation of runoff hydrograph with duration of rainfall based on the Rational Method theory (After French et al. 1974) 4-19 Fig. 4.6. Theoretical estimation of time of concentration -uniform rainfall 4-20 Fig. 4.7. Maroubra Impervious area runoff 4-22 Fig. 4.8. Jamison Park impervious area runoff events(some points are overlaid) 4-25 Fig. 4.9. Jamison Park combined events(some points are overlaid) 4-25 Fig. 4.10. Fisher Ghost Creek impervious runoff event 4-27 Fig. 4.11. Fisher's Ghost Creek combined events 4-28 Fig. 4.12. Strathfield impervious area runoff 4-30 Fig. 4.13. Strathfield combined events 4-30 Fig. 4.14. Cranebrook time of rise 4-31 Fig. 4.15. The extremes theoretical temporal pattern of rainfall 4-33
XXIX Fig. 4.16. Maroubra: Relation of time of rise and time to peak of rainfall 4-34 Fig. 4.17. Time ofrise versu s time to peak of rainfall for impervious area runoff - Fisher's Ghost Creek 4_3g Fig. 4.18. Time ofrise versu s time to peak of rainfall for combined runoff events - Fisher's Ghost Creek 4_38 Fig. 4.19. Time ofrise an d time to peak of rainfall for both cases of runoff- Fisher's Ghost Creek 4.39 Fig. 4.20. Lag estimation methods- (a): hydrograph - hyetograph analysis, (b): recession analysis 4_42 Fig. 4.21. K-Q relations 4.44 Fig. 4.22. K-Q Relations 4-45 Fig. 4.23. K - Q relationship 4-46 Fig. 4.24. Recession analysis- Maroubra 4-49 Fig. 4.25. Recession analysis - Jamison Park 4-52 Fig. 4.26. Recession analysis - Fisher's Ghost Creek 4-55 Fig. 4.27. Recession analysis- Strathfield 4-58 Fig. 4.28. Recession analysis- Cranbrook 4-60 Fig. 4.29. Comparison of time of concentration methods - (a): average time ofrise, (b) : typical minimum time ofrise and (c): ARR87 method 4-63 Fig. 4.30. Correlation of rainfall and flood peak-(a) Maroubra, (b): Cranebrook 4-79 Fig. 4.31. Correlation of rainfall and flood peak for Jamison Park -(a) impervious area runoff events, (b): combined events 4-80 Fig. 4.32. Correlation of rainfall and flood peak for Fisher's Ghost Creek -(a) impervious area runoff events, (b): combined events 4-81 Fig. 4.33. Correlation of rainfall and flood peak for Strathfield-(a) impervious area runoff events, (b): combined events 4-82 Fig. 4.34. Comparison of observed runoff coefficient calculated by using rainfall intensity during Tc and ARR87 method (the regression line is forced through the origins) 4-84
Fig. 5.1. Frequency Distribution Fitted to the Partial Duration Series of Flood Peaks ..5-8 Fig. 5.2. Frequency Distribution Fitted to the Partial Duration Series of Flood Peaks.. 5-9 Fig. 5.3. Runoff Coefficient by ARR87 and Frequency Analysis 5-14 Fig. 5.4. Runoff Coefficient by ARR87 and Frequency Analysis 5-15
Fig. 6.1. Layout of pipe drainage network - Maroubra 6-3 Fig. 6.2. The longitudinal profile of the main pipe line -Maroubra 6-6 Fig. 6.3. The longitudinal profile of the steepest pipe line of the catchment - Maroubra6-6
XXX Fig. 6.4. The longitudinal profile of the Council line - Maroubra 6-7 Fig. 6.5. The longitudinal profile of the Council line - Maroubra 6-7 Fig. 6.6. Sample Rainfall hyetograph - Maroubra, 17.03.1983 6-8 Fig. 6.7. Superimposed hydrographs for testing the effect of Tc 6-15 Fig. 6.8. Superimposed hydrographs with two methods of Tc estimate 6-16 Fig. 6.9. TAD options in MOUSE 6-17 Fig. 6.10. The effect of TAD selection on hydrograph shape 6-18 Fig. 6.11. The effect of TAD selection on hydrograph shape 6-18 Fig. 6.12. Sensitivity of flood hydrograph to HRF magnitude-030378 6-21 Fig. 6.13. Sensitivity of flood hydrograph to HRF magnitude- 170383 6-21 Fig. 6.14. Sensitivity of flood hydrograph to HRF magnitude- 210578 6-22 Fig. 6.15. Sensitivity of flood hydrograph to HRF magnitude- 190679 6-22 Fig. 6.16. Sensitivity of flood hydrograph to HRF magnitude- 200679 6-23 Fig. 6.17. Correlation of FIC/FI and flood volume - Maroubra 6-23 Fig. 6.18. Correlation of FIC/FI and flood peak - Maroubra 6-24 Fig. 6.19. Modified pit to consider grate entry loss 6-25 Fig. 6.20. Flooding and surcharge - n = 0.017 6-29 Fig .6.21. Small surcharge - n = 0.012 6-29 Fig. 6.22. The effect of roughness coefficient on simulated outlet hydrograph 6-31 Fig. 6.23. Flooding and surcharge in the pipe 16-15 - manhole diameters equal to 0.64 m and pipe diameter 0.305 m 6-32 Fig. 6.24. Flooding and surcharge in the pipe 16-15 - manhole diameters equal to 1.00 m and pipe diameter 0.305 m 6-33 Fig. 6.25. Free surface flow in the pipe 16-15, manhole diameters equal to 0.64 m and pipe diameter 1.00 m 6-34 Fig. 6.26. Schematic receiving manhole in Maroubra network 6-35 Fig. 6.27. Water level in pipe 16-15, no outlet head loss 6-36 Fig. 6.28. Water level in pipe 16-15, orificing outlet 6-36 Fig. 6.29. Orificing type of manhole outlet 6-37 Fig. 6.30. Water level in pipe 16-15, time function B.C 6-38 Fig. 6.31. Water level in pipe 16-15, fixed B.C 6-39 Fig. 6.32. Superimposed hydrographs simulated with fixed/time function B.C 6-40 Fig. 6.33. The effect of time step on simulation 6-42 Fig. 6.34. DYN.W and KW solution of water surface profile 6-47 Fig. 6.35. DYN.W and KW solution 6-48
xxxi Fig. 7.1. MOUSE calibration results on Maroubra - (a) volume, (b) flood peaks and (c) time to peak 7_8 Fig. 7.2. Verification of flood volume- Maroubra 7-12 Fig. 7.3. Verification of flood peak - Maroubra 7-12 Fig. 7.4. Verification of time to peak-Maroubra 7-12 Fig. 7.5. Typical malfunction of recorded data in Maroubra catchment- (a) advancement of runoff, (b) retardance of runoff 7-13 Fig. 7.6. Samples of calibrated and verified hydrographs- (a) calibrated, (b) verified.. 7-14 Fig. 7.7. Superimposed simulated and observed hydrographs of the heaviest observed rainfall -Maroubra 7-16 Fig. 7.8. Surcharged and flooded pits on 051184 - Maroubra 7-18 Fig. 7.9. Longitudinal profile of Maroubra network along with simulated maximum water surface profile of event 051184 7-19 Fig. 7.10. Comparison of observed and computed events for Jamison Park-(a) volume, (b) flood peak and (c) time to peak 7-25 Fig. 7.11. Superimposed simulated hydrographs by MOUSE and the observed- Jamison Park 7-27 Fig. 7.12. Comparison of observed and computed events for Fisher's Ghost Creek-(a) volume, (b) flood peak and (c) time to peak 7-33 Fig. 7.13. Superimposed simulated hydrographs by MOUSE and the observed- Fisher's Ghost Creek 7-34 Fig. 7.14. Illustration of MOUSE calibration on Cranebrook -(a) volume-(b) flood Peak (c) time to peak 7-38 Fig. 7.15. Superimposed simulated hydrographs by MOUSE and the observed- Cranebrook 7-39 Fig. 7.16. Possible misrepresentation of hyetograph to MOUSE - Cranebrook 7-40 Fig. 7.17. Land use Development Scenarios 7-42
Fig. 8.1. The results of simulation using MOUSE Level-B in Jamison Park 8-7 Fig. 8.2. Loss models used to estimate rainfall excess (From ARR1987) 8-9 Fig. 8.3. The proposed loss model used in MMOUSE 8-9 Fig. 8.4. The results of simulation using MMOUSE in Jamison Park 8-16 Fig. 8.5. Comparison of observed and computed combined events for Jamison Park using MMOUSE-(a) volume, (b) flood peak and (c) time to peak 8-19 Fig. 8.6. Combined events simulation using MMOUSE -Jamison Park 8-20 Fig. 8.7. Comparison of observed and computed combined events for Fisher's Ghost Creek using MMOUSE-(a) volume, (b) flood peak and (c) time to peak 8-23 Fig. 8.8. Combined events simulation using MMOUSE- Fisher's Ghost 8-24 xxxii Fig. 9.1. Comparison of rainfall with runoff from pervious and impervious areas- (a): Jamison Park, (b): Fisher's Ghost Creek 9-4 Fig. 9.2. Pervious area runoff and rainfall relationship- Jamison Park 9-6 Fig. 9.3. Pervious area runoff and rainfall relationship- Fisher's Ghost Creek 9-7 Fig. 9.4. Relation of pervious area runoff and total runoff 9-8 Fig. 9.5. The relation of pervious area runoff coefficient with rainfall and P5 -Jamison Park 9-11 Fig. 9.6. The relation of pervious area runoff coefficient with rainfall and P5- Fisher's Ghost Creek 9-12 Fig. 9.7. The correlation of HRF and runoff coefficient- Jamison Park 9-14 Fig. 9.8. The correlation of HRF and runoff coefficient- Fisher's Ghost Creek 9-15 Fig. 9.9. Temporal patterns of design rainfalls for ARI < 30 years, Zone 1, (ARR87 Vol. 2) 9-17 Fig. 9.10. Design floods using frequency analysis, ARR87 and MMOUSE 9-21 Fig. 9.11. The results of application of Modified MOUSE model in the Cranebrook catchment 9-22 Fig. 9.12. Contribution of pervious areas in design flood peak 9-25
xxxiii CHAPTER ONE
INTRODUCTION Chapter! Introduction 1-1
CHAPTER ONE
1. INTRODUCTION
Urban area flooding due to the increase of urbanisation is becoming a serious problem in many countries including Australia. Changing pervious areas of rural and natural catchments to impervious surfaces, such as roads, residential and industrial sites, is the main cause of the increase in the proportion of runoff to rainfall that puts the downstream areas in danger of flooding. Inundation of urban areas causes inconvenience for traffic and pedestrians. In some cases residential, commercial or industrial areas will be inundated during major floods and damage to properties such as flooding of basements or lower floors of building is considerable. The problem of flooding in urban catchments becomes worse if drainage systems are used for carrying both sewage and stormwater. Surcharging of sewer systems during wet periods causes overall pollution within urban areas. In this chapter the main distinctions between urban catchments and rural ones are highlighted in general. The aim is to give readers an idea about the physical characteristics of urban catchments and the importance of accuracy for design flood peak estimates.
During the process of urbanisation many hydrologic components will be changed because of deforestation, land grading, road making and building. Almost all the changes within urban areas are in the direction of surface flow increase. Cutting trees to make space for buildings or roads has two consequences. Firstly there will be no interception when rainfall starts initially, which results in the acceleration of catchment response, and secondly evapotranspiration will be decreased because of the deforestation. Following such changes in urban areas, the surface flow component in the water balance model will be higher than that of natural conditions.
The increase in runoff volume is another outcome of urbanisation. Developing impervious areas in any form and deforestation adversely affect infiltration and increase Chapterl Introduction 1-2
runoff volume. Any decrease in the infiltration will be directly reflected in ground water storage shortage.
The quick response of urban catchments because of impervious areas and lined. shortened waterways, causes an increase in flood peaks. The frequency of flood peaks will go up causing trouble for traffic and residents. Decreasing the roughness coefficient of natural waterways through lining, accelerates the flow and results in sharp and flashy floods in urban catchments. The comparison of flood peaks for pre and post urbanisation is shown clearly in much research conducted on urbanisation effects on floods. In the work performed by Bhaskar (1988), it was concluded that in an urban catchment with an initial developed area of 17.5% in 1946, the magnitude of Instantaneous Unit Hydrograph peak flow had more than doubled by 1973 when urbanisation in the catchment approached 60%. Meanwhile the time to peak of runoff reduced by more than 50%. In another study which was carried out on two urban catchments in the A.C.T. (Australia) it was shown that a 30% reduction in catchment storage and lag time occurred due to the urbanisation in thefirst catchment . The average runoff from the first catchment was six times greater than that of the latter (Codner et al. 1988).
One of the important factors which varies widely in urban catchments is the proportion of rainfall which transforms to runoff or the so-called Runoff Coefficient. Impervious surfaces in the form of directly connected areas, effective areas, or indirectly connected areas, after abstraction of a small part of rainfall as initial loss, start generating runoff. Estimation of the runoff coefficient for these areas is not difficult and it is normally between 0.85 - 0.90. The percentage of directly connected impervious areas is referred to as hydraulically effective impervious areas in most investigations and is found to be equal to the slope of the fitted line between rainfall and runoff (Bufill and Boyd 1990). The study by Miller (1978) in the USA indicated that the slope of rainfall versus runoff for rainfall up to 38 mm was equal to the percentage of directly connected areas which were measured by a precise photo mosaic of the catchment.
Pervious areas of urban catchments, depending on the soil type, are important in runoff generation especially during large storms. Developing a runoff coefficient for these areas, such as rural catchments, is not an easy task for real storms. Chapterl Introduction 1-3
1.1. Stormwater Drainage Networks
For new developments, residential or industrial, a drainage network for evacuating stormwater should be considered. The network should be designed for the ultimate percentage of urbanisation, to reduce the inconvenience caused by rainfall over the catchment as development proceeds.
Another factor is the sewage network installed beneath urban regions for carrying sanitation water. Although in some countries such as the USA, UK, and some parts of Europe dual purpose systems are still in use to cope with both sewage and stormwater, new policy in urban drainage design is based on separate systems. Australia is one of the pioneering countries in the development of separate systems for runoff and sewage. The cost of separate systems is very high, but it has the advantage of being environmentally safe because therisk of pollution due to surcharging and flooding during the wet periods is prevented. During some floods, sewage treatment plants fail to process the influent, and receiving waters will deteriorate.
The estimated annual expenditure for urban drainage in Australia in 1984 was A$150 million (Pilgrim 1986). Highway construction costs in California during the 5 year period, 1985 - 1989, were US$ 900,000,000. The money allocated for highway drainage is one fourth of the total cost of roads in the USA (Linsley, 1986). To enhance Sydney's stormwater system and control water quality of receiving waters till the year 2010, a budget of A$6.25 billion has been allocated. This programe covers The Blue Mountains and the Illawarra regions as well ( Spry et al. 1992). Design criteria for urban catchments are divided into two main branches, namely minor and major systems. The minor system normally consists of underground pipes or open channels located on one or both sides of streets. This system is supposed to carry the design flow peak with a return period of 2-10 years. The magnitude of the return period depends on the land use of urban catchments. Normally for commercial areas the design return period will be higher than that ofresidential areas . Major systems consist of roadways and sometimes sidewalks which are assumed to handle major floods during heavy rainfalls normally with a return period of 100 years. Chapterl Introduction 1-4
While developing a new urban area, precautions should be taken to prevent downstream flooding because of land use changes upstream. Trying to keep natural waterways to infiltrate stormwater, and also the building of detention basins, off-line or on-line, are common measures to protect downstream flood liable areas.
1.2. The Importance of Design Flood Peak in Urban Catchments
Design flood peak is the key factor for sizing the pipe diameter, channel dimensions, road characteristics and height of buildingfloors to prevent inundation. Although in designing detention basins hydrograph volume and shape are required, the degree of reduction in flood peak is the major concern, so a precise estimate of inflow peak is quite important. Both overestimation and underestimation of design flood peaks cause economic loss. The cost for minor and major public works within urban and rural areas is estimated at A$600 million per year in Australia, while the proportion of small structures in rural and urban areas is 70% of that total ( Pilgrim 1986). Investing this huge amount of money in the underground pipes and miscellaneous structures needs as precise an estimate of flood peak as possible. For instance, during the study for upgrading the stormwater network in Barcelona, Spain, the difference in the project costs was 60 million Pesos (A$600,000) when two different approaches to flood peak estimates were selected (Mujeriogo et al.
1987).
1.3. The Objectives of The Current Study
As stated above, the estimate of flood peak discharge is very important in the proper design of drainage networks and minor public works within urban catchments. This study considers two major hydrologic design indices in urban catchments, including flood peak and flood hydrograph estimation using both deterministic and statistical analysis. Deterministic analysis of runoff in urban catchments paves the way for a better understanding of runoff formation and the sources where runoff originates from. The deterministic analysis of runoff will help in achievement of more accurate design parameters in the statistical approach. The study will shed light on the hydrologic and hydraulic parametersrepresenting the two main land classifications of urban catchments, impervious and pervious areas. The role of these land uses in urban catchment flood estimation, in conjunction with soil type and also frequency of events, are the issues Chapterl Introduction 1-5
which will be investigated in the present study by analysis of rainfall-runoff, frequency analysis of flood peaks, and deterministic simulation of events. The study follows specifically three broad objectives of flood peak estimation; using the Rational formula, hydrograph simulation using MOUSE, and design flood estimation using both temporal patterns and design rainfall. To achieve more insights in the proportion of rainfall which transforms to runoff, the main theme of the study concentrates on the runoff coefficient, which runs through the thesis from both deterministic and statistical points of view.
1.3.1. Flood peak, the Rational formula and runoff coefficient
In conjunction with the flood peak, the most widely used method, the Rational formula, will be evaluated from both deterministic and statistical points of view. The study is seeking the possible relation between statistical and deterministic runoff coefficients in the Rational method. Two important factors of this method, the time of concentration and the runoff coefficient, will be derived by use of observed data from five gauged urban catchments in Sydney. The variations in runoff coefficient on pervious and impervious areas of urban catchments will be studied, and effective parameters such as average rainfall intensity during the time of concentration and during the burst duration will be discussed. The effect of Antecedent Moisture Conditions, AMC, of the catchments on runoff coefficient variations will also be considered. The statistical interpretation of runoff coefficient will be investigated through partial duration series of flood peaks and design rainfalls using Intensity-Frequency-Duration curves from ARR87. The method presented in ARR87 for urban catchment runoff coefficient determination will be evaluated using the results of both deterministic and statistical studies.
13.2. Deterministic modelling of runoff hydrograph
The second part of the study is concerned with hydrograph modelling in the above catchments using the MOUSE model. This model is a complex hydrologic and hydraulic package for simulation of runoff in urban areas. As in thefirst part of the study, the effect of including pervious and impervious areas in the simulation, and their significance will be evaluated. The MOUSE hydrologic module consists of two levels of surface runoff simulation called level A and B. Level A considers only the impervious areas of urban catchments in surface runoff simulation, which in many cases seems to be Chapterl Introduction 1-6
sufficient, especially in congested urban regions. Level B computes runoff for both impervious and pervious areas and obviously needs more detailed data than level A. Considering the fact that level A needs less input data, the sufficiency of this level for simulating of surface runoff from both pervious and impervious areas will be investigated.
Calculation of the excess rainfall from pervious areas in the level B of MOUSE uses a multi parameter water balance model which includes evaporation, storage and infiltration. The interactions of the parameters involved in the model and lack of sufficient data on soil physical characteristics and runoff make the use of this part of the model very limited (Harremoes et al. 1993).
In Australian conditions the occurrence of combined runoff from both pervious and impervious areas of urban catchments is very common. On the other hand the data from gauged catchments are insufficient to generalise the MOUSE model parameters for application in design situations. The present study is seeking an innovative and simple approach for calculation of excess rainfall to be used in complex hydraulic models such as MOUSE. The study will concentrate on reducing the number parameters of excess rainfall calculation. Allowing practitioners to benefit from both the simplicity of runoff coefficient concept for excess rainfall calculation and the complexity of the hydrodynamic MOUSE model for drainage system design and management is one of the goals of the present study.
The MOUSE model is generally developed for combined systems, both sanitary water and surface runoff in urban catchments. However, the applicability of the model to separate urban drainage networks which are common in Australia is evaluated in the current study.
1.3.3. Design rainfall, temporal patterns and deterministic simulation of design floods
Application of design rainfalls and temporal patterns in conjunction with deterministic models is a common practice in hydrologic investigations, however the accuracy of the approach when compared with other conventional methods especially in urban Chapterl Introduction 1-7
catchments is dubious. The reason for this uncertainty is the difference between the philosophies of deterministic and statistical models. The former is based on measurable parameters of land and meteorologic phases while the latter is based on random variates and probability concepts. In the former the threshold conditions for occurrence of an event eg. a flood are known and deterministic, but in the latter they are unknown and probabilistic.
The present study aims to determine whether the application of a deterministic model such as MOUSE along with design rainfalls and temporal patterns from ARR 87 Vol. 2 can produce similar results to frequency analysis of flood peaks. The results of simulation will be compared with the frequency analysis results for flood peaks, and the sufficiency of the model in producing similar flood peaks will be evaluated. CHAPTER TWO
LITERATURE REVIEW AND THEORETICAL BACKGROUND Chapter 2 Literature Review And Theoretical Background 2-1
CHAPTER TWO
2. LITERATURE REVIEW AND THEORETICAL BACKGROUND
In this chapter the specific characteristics of urban catchments are highlighted. The simple flood peak and hydrograph models are reviewed. Among these models the Rational formula and its modifications are emphasised. Urban catchment hydrologic and hydraulic behaviours are considered by reviewing the well-known overseas and Australian models. These models and their application to Australian urban catchments are presented.
2.1. History of Urban Hydrology Development
Biswas (1972) states that the history of hydrologic knowledge goes back to 600 B.C. Subsurface masonry storm drains were constructed at least 3000 years ago, and the first sewers were for stormwater control in the 19th century (Walesh 1989). In a historical view, Chow (1962) stated that the preparation of a table expressing the relation between the diameter and slope of a circular outlet sewer and the size of the drainage area goes back to 1852. He has cited the publication of the Burkli - Zieler formula by the Swiss hydraulic engineer in 1880.
Although the history of urban hydrology goes back nearly 150 years, serious studies of surface water due to urbanisation, especially after rainfall and streamflow data acquisition in urban catchments, has only started recently. "Over the last 25 years, increased attention has been devoted to the hydrology of land use changes in general branch of the subject of urban hydrology" (Hall 1984).
2.1.1. Hydrologic processes variations in urban catchments
The hydrologic aspects of urban catchments are closely related to their land use pattern.
Generally land use in urban areas consists of impervious and pervious regions. This duality of surfaces makes hydrologic assessment more difficult than in rural watersheds. Chapter 2 Literature Review And Theoretical Background 2-2
The assessment of collected data in urban catchments should be performed with care, because runoff may generate from either impervious, or pervious areas, or both.
Impervious areas of urban catchments may be classified into three types. The first type is the directly connected impervious areas. These areas are directly connected to drainage systems and may include roads, streets, parking lots, pathways and sidewalks. These directly connected impervious areas generate runoff during most storms and are also mentioned as hydraulically effective areas in urban hydrology jargon. The second type is indirectly connected impervious or supplementary areas. These areas are surrounded by grassed or pervious areas e.g. tennis courts, houses and villas. The third type is semi- impervious areas which have recently aroused considerable interest in new urban stormwater management. Besides the convenience which the latter provide for users, they absorb some rainfall and reduce the flood volume and peak. Typical of this kind of surface are sidewalks and parking lots tiled with bricks instead of concrete or asphalt.
Perhaps the most comprehensive way to show urban catchment characteristics in contrast with those in a rural area is the qualitative evaluation of the hydrologic cycle in two hypothetical catchments with the same meteorological conditions (Fig. 2.1 ).
( a): rural ( b ): urban
Fig. 2.1. Schematic amendments of a rural catchment after urbanisation
As illustrated in Fig. 2.1, urbanisation causes many changes in the land surface and the geometry of the streamflow network. The main component of the hydrologic cycle is precipitation which is the same in both cases. Generally urbanisation causes land phase Chapter 2 Literature Review And Theoretical Background 2-3
changes in the hydrologic cycle, and normally some variations are expected in the land phase parameters, such as infiltration and interflow.
The abstraction of rainfall before reaching the ground surface because of the tree canopy and buildings is called interception in the hydrologic cycle. This component is negligible during an event, but in the computation of annual water balance is a major factor. It may capture more than 25% of annual rainfall which finally will be returned to the atmosphere in the form of evaporation (Linsley et al. 1982). For frequent and small storms 38% of annual precipitation in spruce forests of England was measured as interception ( Hewlett 1982). In the urban environment, because of scarcity of grass and bush areas, this component is not significant.
Evaporation extracts the water held in the soil profile during and after rainfall. Evapotranspiration is the absorption and release of the soil water by plants. These phenomena will be greatly diminished in urban catchments because of deforestation and development of impervious areas such as roads, buildings and industrial sites.
Due to the irregularity of land surface, especially in rural catchments, precipitation firstly fills the surface micro, small, and large depressions and then surface runoff occurs. The trapped water in depressions infiltrates into the ground or is evaporated. In urban areas these processes are reduced because of land grading, compacting and the increase in impervious areas. A quantitative form of depression storage is initial loss, which according to Australian Rainfall and Runoff practice, should be 5-10 mm for rural, and 1- 3 mm for urban catchments (ARR87).
The key factor in computing surface runoff over pervious areas is infiltration. This well defined phenomenon, because of its importance, is included in the most generally known computer models of urban and rural watersheds mostly in the form of the Horton equation (refer to section 2.6.2.1.1. for the equation components). This factor is directly connected to ground water recharge. Increases in urbanisation adversely affect the degree of infiltrated water.
The most important practice in rural and urban hydrology is surface runoff computation.
The final output of a catchment is streamflow which in rural catchments consists of Chapter 2 Literature Review And Theoretical Backs round 2-4
subsurface and surface water. Surface runoff is dominant in urban catchment stormwater because of the changes in land cover and decrease in infiltration. One of the definite effects of urbanisation is the great increase in flood peaks. Urban development and the consequent flooding problems, usually in older parts of cities, are experienced all around the world (Cordery et al. 1990). In a study by Cherkauer (1975) it was demonstrated that two urban and rural catchments with areas of 7.5 and 9.7 Km2 with similar primary geologic conditions and under the same precipitation produced greatly different runoff rates. The urban catchment in spite of a smaller area produced a peak flow 2.5 times greater than the rural one. Studies by Bhaskar (1988) and Codner et al. (1988) showed an increase in the flood peak and runoff volume equal to two and six fold respectively when compared with those of pre-urbanisation or rural catchments. Regarding flood frequency, the occurrence of high frequency floods, of say a 1-2 yr return period, increases, but generally there is no difference for low frequency flood such as 100 yr or more between rural or urban areas (Hollis 1975). The increase in high frequency floods is because of impervious area development which causes problems in cities.
2.1.2. Hydraulic features of urban catchments
Covering the surface of natural watershed with impervious material such as asphalt, tiles, and concrete which are used in the construction of roads, buildings, and parking lots means that the overland flow reaches the channels in a shorter time compared with pre- urbanisation conditions. The travel time decreases because retarding surfaces such as forest, turf, bare and ploughed soil are replaced by fairly smooth surfaces. Furthermore, substitution of natural waterways by sewers or lined channels decreases the coefficient of roughness and causes higher velocity than that of natural stream networks. The most obvious effect of the above modifications is shorter lag time (the time span between centroid of rainfall excess and the peak of hydrograph), and time of concentration in urban catchments compared with those in rural areas.
Increase in surface runoff in urban areas accelerates erosion in natural channels in these catchments. Among many recent and older studies such as Graf (1977) and Booth (1990), the wide ranging work by Hammer (1972) is the most important. He compared 72 urban and rural catchments and concluded that most urban channels had doubled in area and some had increased up to 3.8 times. However, rate of erosion declines in urban Chapter 2 Literature Review And Theoretical Background 2-5
catchments and will be negligible after 30 years, he found. Nanson and Young (1981) found a 2-3 fold increase in the cross-sectional area of small streams due to urbanisation in Wollongong. More than half of the natural streams in Melbourne have been channelized or piped and the remainder have been severely eroded (Ruthefurd & Ducatel 1994).
2.2. Simple Formulas for Estimation of Runoff Rates in Urban Catchments
Simple formulas have applicability in design situations for both small and large catchments for sizing culverts, small sewers, and generally wherever application of complex models is not economic, not possible or is time consuming. It may be thought that due to the existence of continuous conceptual models, the simple formulas are now obsolete, and there is no necessity to utilize them. However, lack of sufficient data for modelling always justifies usage of simple models.
2.2.1. Unit hydrograph method
If required data are available, the unit hydrograph method along with design rainfall used in rural watersheds can be applied to urban catchments as well.
Due to lack of the necessary data, several researchers have tried to develop a synthetic unit hydrograph for urban catchments by considering their physical characteristics, such as percentage of imperviousness. Rao et al. (1975) presented a conceptual instantaneous unit hydrograph, IUH, for urban catchments. A regional dimensionless IUH for urban catchments has been investigated by Hossain et al. (1978).
The most comprehensive work on synthetic 10 min- UH for urban watersheds is the study done by Espy and Altman(1978) based on data of 41 basins located in different parts of the U.S.A. Kibler (1982) has reported the Espy 10-minute UH equations as follows:
a23 25 018 1 57 TR = 3.1L S-°- I- f -
3 a96 107 Q = 31.62* 10 A TR"
3 95 TB = 125.89 * 10 A Q "° Chapter 2 Literature Review And Theoretical Backs round 2-6
3 093 92 W50=16.22*10 A Q-°
3 a79 )78 W75 = 3.24*10 A Q^ where:
L: the total distance along the main channel from the point being considered to the upstream watershed boundary, ft
S: the main channel slope, ft/ft
I: impervious areas, % f: dimensionless watershed conveyance factor
A: the catchment area, mile2
TR: the time of rise
Q: the peak flow of UH(ftVsec)
TB: The time base, minutes
W50 and W75: the width of the hydrograph at 50% and 75% of the Q, minutes
" perhaps the biggest unknown in the Espy UH method is the watershed conveyance factor, f " (Kibler 1982). The conveyance factor is related to the percentage of impervious cover of the watershed in the Espy method.
2.2.2. Flood frequency .analysis and regionalisation
Providing of stormflow data are available, flood frequency analysis can be performed for flood peaks at measuring stations. The results of the analysis could be integrated into a regionalised frequency curve which is applicable to ungauged catchments. Knee (1990) used multiple regression to estimate the flood peak for ungauged urban catchments in the Australian Capital Territory (ACT). Utilising 173 urban stations in 30 metropolitan areas, Driver & Troutman (1989) developed some regression models which were used to estimate urban runoff quantity and quality throughout the USA. Frequency analysis of flood peak was carried out to determine the runoff coefficient in the Rational method along with rainfall intensity values with the same return period in Australian urban catchments (French et al. 1974). Chapter 2 Literature Review And Theoretical Background 2-7
Normally, urban catchments are small in area, so they have a brief times of concentration which is a major factor in assigning design rainfall duration. To select design rainfall of the relevant return period, isohyetal maps of short duration rainfall or Intensity- Frequency-Duration- (IFD) curves may be used. IFD can be constructed by means of available recordings from rain gauge data. In the case of limited data, empirical formulas based on the assumption that the shorter the duration the more intense the rainfall, can be employed. For any given frequency, the intensity is related to the duration by an equation similar to the following (Gupta 1984).
A ' ~ (t + B)n where: i: intensity t: duration A, B and n : constants depending on the frequency and climatic conditions
Short duration rainfall data down to 5 minutes, is available from IFD curves throughout Australia (ARR87). For durations of between 5 minutes and 1 minute a method has recently been proposed for Australia by Kennedy & Minty (1992).
2.2.3. SCS Method
The Soil Conservation Service, SCS, has developed a method based on the physical characteristics of catchment, soil group and soil wetness conditions, the so-called Antecedent Moisture Conditions. This method generally was proposed for rural catchments, butrecently afte r some modification is now applicable in urban catchments. The modified version of this model is presented in Technical Release No. 55 (U.S. Dept. of Agriculture, SCS 1986). This method is mostly applied in the USA. Chapter 2 Literature Rexiew And Theoretical Background 2-8
2.3.The Rational Formula
This formula was originally developed by Mulvaney(1851) in Ireland for urban catchments, but later was applied to rural areas. According to Pilgrim and Cordery (1980) this is the most widely used formula in rural and urban areas for minor public works in Australia. The formula is presented in ARR87 and is recommended in a recent manual for Queensland urban drainage design (QUDM 1993). The general form of the formula is given below:
Q=FCIA where Q : the peak flow, m3/s I: the rainfall intensity during the time of concentration of the catchment, mm/hr A: catchment area, ha or Km2 C : the runoff coefficient F: a coefficient related to the selected unit for A, 1/360 if A in ha and 0.278 if A in Km2
According to ARR 1977, the assumptions made in the development and application of this formula are as follows:
• Rainfall is uniform over the catchment • Duration of rainfall is equal to the time of concentration of catchment • Both the return period of rainfall and flood peak are the same.
This formula has undergone many modifications and interpretations since its development (Cordery et al. 1993). Although this formula is generally used for flood peak estimation for sizing pipe and culvert diameters, it is modified for the hydrograph of the flood as well. The modified rational method is used to facilitate design of detention or retention basins in urban catchments. It assumes triangular or trapezoidal shapes for the hydrograph, and also assumes a design rainfall duration equal to the time of concentration of the catchment, and does not account for rainfall before or after it (Walesh 1989).
Although the Rational method explanation is found in every text book of hydrology, the description by Gupta (1989) is the most complete and is briefly presented here. The basic equation is given as :
Q=QCIA Chapter 2 Literature Review And Theoretical Background 2-9
Where: Q: peak rate of flow
Cf: frequency factor C: runoff coefficient I: rainfall intensity during Tc with return period of T years A : drainage area
C and Cf are dimensionless coefficients, and their product should not exceed one. The frequency factor, Q, is taken as unity for storms with a return period of 2-10 years, and it changes to 1.25 for return periods of up to 100 years. However, in ARR87 if the value
of 100-year runoff coefficient, Cioo, (which is 1.2 times of 10-year runoff coefficient, CI0) exceeds one it should be set equal one. In addition to these, in both deterministic and statistical use of the Rational formula if the value of Tc is out the value of C could be other than one.
To estimate the runoff coefficient, the U.S. Dept. of Transportation (1979) has prepared a set of curves based on the Mitci formula. This formula relates runoff coefficient to the degree of imperviousness, P, and to the antecedent rainfall using t, which is actually the time from the beginning of rainfall to the time of occurrence of the design rainfall of duration Tc. The formula has the form;
0.98t 0.78t C~(4.54+t)P + (31.17+t)(1~P)
2.3.1. The Wallingford Rational method
A complete description of the different functions of the Wallingford procedure is presented by Hall (1984). This collaborative research program was carried out in the U.K. between 1974 and 1981. A modified version of the Rational method for homogeneous areas of up to 150 hectares is presented in this method, which is applicable manually or in a computerised form. The computer program includes the facility for simulating stormwater overflow. The Wallingford Rational method which is mostly applied in the UK is presented as follows:
Qp = 2.78 Cv CRIA where:
Cv: the volumetric runoff coefficient Chapter 2 Literature Review And Theoretical Background 2-10
CR: routing coefficient Qp: peak discharge, L/s I: the average rainfall rate, mm/hr A : the total catchment area, ha
For total catchment area Cv = PR / 100 where, PR is the percentage of runoff and is estimated by the following formula:
PR = 0. 829 IMP + 25.0 Soil + 0.078 UCWI - 20.7
IMP : directly connected impervious areas, % Soil: soil index (map available for the U.K.) UCWI: an antecedent wetness index
If impervious areas alone is being considered then,
Cv = PR / IMP
For design purpose CR = 1.3 is recommended.
2.3.2. The two-value Rational method
This method is proposed by Argue (1984) which combines both impervious and pervious areas of urban catchments to calculate the maximum peak flow. The method combines the time area diagram of pervious and impervious areas of the catchment. For the full- area the time of concentration is estimated for the most remote point of the catchment regardless of land use. For the part-area the time of concentration is calculated for the most remote point of the directly-connected impervious area in the catchment. The peak flow rates of the two methods above will be compared and the maximum will be selected for design purposes (Argue 1986).
2.3.3. Two major interpretations of runoff coefficient
Depending on the runoff coefficient, the Rational method has been interpreted in two different ways. The first view point is deterministic and the second is statistical (Aitken
1973). Chapter 2 Literature Review And Theoretical Background 2-11
2.3.3.1. Deterministic interpretation
Historically the Rational formula has been considered as a model which describes a deterministic relation between a rainfall event and the resulting runoff event (Aitken 1973). In some applications of this formula, the runoff coefficient has been assumed to be constant, however, it varies from storm to storm because of the soil moisture conditions of the catchment. The Rational formula can not be applied to an individual storm unless an appropriate runoff coefficient is calculated for that storm depending on the rainfall intensity and catchment soil wetness conditions. "The Rational method when used as a deterministic model to analyse different storms of the same rainfall intensity, often requires non-constant coefficients of runoff to simulate observed runoff rates. This characteristics is caused by the effects of different antecedent moisture conditions which normally exist at the start of different storms" (ARR87). The formula in the deterministic case has the form
Q = 0.278 C I A where: Q: peak discharge, m3/s C: Runoff coefficient, dimensionless I: average rainfall intensity, mm/hr A: Catchment area, Km2
To monitor the variations of the runoff coefficient, current records of variations of rainfall intensity and Antecedent Moisture Conditions are required. To reject the deterministic interpretation of the formula, French et al. (1974) used four tests and found that the model failed when compared with observed data. The tests were as follows: a) Comparison of the volumetric and rate runoff coefficients b) Relation between the time ofrise an d duration of rainfall c) Constant runoff coefficient independent of rainfall intensity d) Independence of runoff coefficient from rainfall temporal pattern
Recently Akan (1988) has mentioned the deterministic dimension of the formula, and by coupling overland flow and infiltration equations has derived the variation of the runoff coefficient in a rectangular pervious basin. Chapter 2 Literature Review And Theoretical Background 2-12
2.3.3.2. Statistical interpretation
With regard to the statistical interpretation of the Rational formula, researchers including Schaake (1967), Aitken(l973) and French et al. (1974), concluded that the Rational formula is determinislically poor and could only be applied in design situations. Assuming equal recurrence intervals for flood peaks and rainfall intensity, the formula has been proposed in the form:
Qy = 0.278CyI,Tt.y)A
Where Cy is related to the return period and I is related to the same return period and time of concentration. This formula is only for design situations and must not be applied to real storms.
To estimate Cy in the above formula, firstly a flood frequency analysis must be performed on an individual station or regional basis. Then by comparison of rainfall intensity and peak flow with the same recurrence interval, Cy can be calculated. In some cases maps can be used, or regional formulas where the regionalisation of the runoff coefficient has already been carried out.
Statistical application of the Rational formula is recommended in Australian Rainfall and Runoff (1987). The coefficient of runoff for northern and southern parts of Australia has the variations shown in Fig. 2.2 for urban catchments. This graph applies only for estimating Cio, and for the other return periods must be modified based on the values presented in ARR87.
0 2 0.4 0.6 0.8 1-0 Fraction. Impervious. /
Fig. 2.2. Runoff coefficient estimate in urban catchments ( from ARR87 ) Chapter 2 Literature Review And Theoretical Background 2-13
The relationship shown in Fig. 2.2 has been criticized by some researchers and organizations. A report from the N.C.D.C (1989) has shown gross underestimation when results of ARR87 were compared to those of modelling with real data. Deen and Lawrence (1985) have also reported runoff coefficients greater than one in northern Australia which are not predictable by ARR87. In ARR87 if the value of C, exceeds one it should be set equal to one.
This figure was prepared based on experience of drainage authorities and evidence from a few gauged catchments (ARR87). To check the accuracy of Fig. 2.2, data from a total of six catchments were used for frequency analysis, including four in Melbourne, one in Canberra and one in Sydney (Jones and Lawson 1992, Phillips 1995). The list of these
catchments is presented in Table 2.1 and also C!0is plotted in Fig. 2.2.
Table 2.1. Gauged urban catchment descriptions ( from Jones and Lawson 1992 and Philips 1995)
No. Location Area, ha '°I„ mm/hr 1 Powells Ck, Strathfield, NSW 231 48.9 2 Box Hill Main Drain, Box Hill, VIC 113 28.0 3 Vine St Main Drain, Braybrook, VIC 70 29.0 4 Ashmore Ave Main Drain, Mordialloc, VIC 53 26.5 5 Gardenia Road Main Drain, Doncaster, VIC 80 28.1 6 Yarralumla Creek, Mawson, ACT 382-400 32.2
The present study focuses on the evaluation of both deterministic and statistical sides of the Rational method using observed data offive gauge d catchments in Sydney. The study seeking possiblerelation betwee n the deterministic and statistical interpretation of runoff coefficient in the Rational formula. Regarding wide application of the method introduced in ARR87 (Fig. 2.2) for estimation of runoff coefficient in urban catchments, the method is evaluated using the results of both deterministic and statistical approaches.
2.3.3.3. The necessity of research on the deterministic runoff coefficient
Two questions which arise when the deterministic application of the Rational formula is proposed are, is the deterministic interpretation necessary, and how can it be applied? Chapter 2 Literature Review And Theoretical Background 2-14
To answer this question the scarcity of runoff data compared with the relative availability of rainfall data should be noted. In cases where there are very few years of runoff data to carry out individual or regional flood frequency studies to compute runoff coefficient, the statistical method has limitations.
If record length is short and a statistical analysis can not be carried out much information on the Rational method can still be obtained by considering individual storms, and treating the Rational as a deterministic method. Thus the major factors which have a considerable effect on runoff generation have to be considered. This approach should integrate the major rainfall and catchment characteristics to show the variation of the
runoff coefficient during storms.
The deterministic approach could provide the basis for statistical analysis. In this way, after evaluation of different cases, rainfall intensity and catchment surface wetness, designers could use the design rainfall to predict discharge at the point being studied. The degree of wetness of the catchment is a prominent factor which affects the runoff coefficient especially when the pervious area of an urban catchment is considered.
2.4. Deterministic Runoff Coefficient
To evaluate the effect of physical parameters and meteorologic phenomena on the runoff coefficient the most measurable meteorologic parameter, rainfall intensity, and the most effective land phase parameter, soil storage capacity, should be considered. Rainfall intensity has interaction with soil infiltrability, and based on Hortonian theory of runoff generation, when rainfall intensity exceeds the infiltration capacity or unsaturated hydraulic conductivity, runoff occurs. The hydraulic conductivity is a function of soil moisture and decreases when soil moisture increases. Measurement and evaluation of unsaturated hydraulic conductivity is very difficult and time consuming, furthermore heterogeneity of soil in catchment makes it nearly impossible to include it in runoff
models. The reality is that runoff coefficient varies from storm to storm and assuming a constant and fixed value for it in a deterministic approach is not accurate. The amount of generated runoff based on design rainfall depends on the wetness or dryness of Chapter 2 Literature Review And Theoretical Background 2-15
catchment surface, thus considering this parameter related to variation of runoff coefficient is one step closer to the real catchment's response.
According to the dimensional analysis, runoff coefficient is a dimensionless variable and measurable factors which have the most significant effects on its variations to be sought. On the other hand, the parameters should not have interaction with each other. For example, initial infiltration with final infiltration or upper soil storage with lower soil storage have interactions, so in the dimensional analysis less important parameters should be excluded.
2.4.1. Runoff coefficient and effective parameters
The ratio of rainfall which transforms to runoff depends on two major series of parameters related to atmosphere and land phase. To avoid the dilemma of interaction, more significant parameters should be incorporated in determination of runoff
coefficient.
Average rainfall intensity and Antecedent Precipitation Index (API) are the atmosphere parameters to be considered. The average rainfall intensity can be taken either during the time of concentration of the catchment or over the whole storm duration. In the former the rate runoff coefficient is sought while in the latter the volumetric runoff coefficient is considered. As API sum of rainfall depth over 5 previous days is common practice in
rainfall-runoff analysis.
Soil storage and final infiltration capacity are two significant parameters to represent land phase in runoff coefficient estimation. Soil moisture level causes runoff coefficient to vary from storm to storm. The rain accumulates in the soil profile and decreases soil infiltration towards itsfinal valu e or saturated hydraulic conductivity. Soil storage or water holding capacity for every soil is different, but for some types of catchment cover and soil type some figures resulting from rainfall-runoff modelling are available
(Boughton 1984).
The soil storage and infiltration capacity have interaction, because when the storage is low the infiltration is high and vice versa. The interaction vanishes when soil storage is at its maximum level and the infiltration at the rninimum which is final infiltration or Chapter 2 Literature Review And Theoretical Background 2-16
saturated hydraulic conductivity of the soil. If the storage and infiltration are assumed fixed in runoff coefficient estimation the variations of runoff rate is associated with the API and average rainfall intensity.
2.4.2. Dimensional analysis and runoff coefficient formulation
Dimensional analysis is a method to evaluate the relationship between variables in a physical process. When establishment of a theoretical and mathematical interpretation of a physical process is impossible or difficult, this method can be used. The method is applicable to every problem dealing with real situations in applied science including the organisation, planning and analysis of experimental investigations (Douglas 1969). Dimensional analysis is used when variables are known and equations are not. By evaluating the dimension of every variable in the system of MLT ( Mass, Length and Time) or FLT ( Force, Length and Time) every equation can be tested for dimensional homogeneity. "The principal assumption of the dimensional analysis is that the physical quantity being investigated is a function of other known quantities." ( Granger 1985). Dimensional analysis has been applied to fluid mechanic problems successfully and most of dimensional parameters which are used in describing the nature of flow have been derived and investigated by this method. The well known parameters in this group are Reynolds, Froude, Euler, Mach, Weber, and Cauchy numbers. Before considering the possibility of dimensional analysis application in thefield of hydrology , it is worth giving some definitions about dimensional and dimensionless variables.
Dimensionless Variable - such as strain, Reynolds Number Dimensional Constants - such as the velocity of light Dimensional Variables - such as mass, torque and velocity Dimensionless Constants - such as ' Numeric' - they are constants which derive from physical quantities e.g. TL = 3.14...
According to the above definitions, the runoff coefficient will be considered as a dimensionless variable. Because has no dimension and varies with API and rainfall intensity.
There are two methods of dimensional analysis available which are mostly used in fluid mechanics - Buckingham and Rayleigh methods. The first method is based on the Chapter 2 Literature Review And Theoretical Background 2-17
Buckingham Pi Theorem ,first explicitly stated by Buckingham in 1914 (Isaacson and Isaacson 1975). The second method is an easy approach for analysing the behaviour of fluid motion. In the Rayleigh method, if f be a function of properties Q„ . since the equation is to be homogeneous then
a b c r faQ. Q2 Q3 .-.Qn where a, b,c,. . . , r are to be determined from the fact that the arrangement of quantities
Qn must be reducible to the dimension of f. This method is easier in application than Buckingham Pi theorem.
Despite the popularity and application of this method in hydraulic processes, there is no serious report in hydrologicfield, excep t few examples found in texts about dimensional analysis principles (Douglas 1969). The reason for this scarcity of application may be the complexity of the hydrologic processes compared with those of hydraulics. However, the method can be applied to empirical or theoretical equations in hydrology to seek the dimensional homogeneity. While investigating effective parameters on certain phenomena e.g. run off process, it could be employed because hydrologic processes despite their complexity, are physical and have dimensions.
To derive a formula for runoff coefficient which accounts for catchment soil wetness and rainfall intensity the following parameters should be considered:
API: the sum of 5 previous day rainfall, mm I: the average rainfall intensity mm/hr F: the final infiltration capacity of soil or saturated hydraulic conductivity , mm/hr S: the soil storage capacity, mm
The introducing of "F "and "S" has two advantages. Firstly in the form of I/F and API/S they are useful to have a dimensionless equation for C, and secondly the variation of soil moisture is shown by the ratio of API/S to some degree. According to the dimensional analysis theory, C cannot be a function of API and I only, because C is dimensionless, but the product of API and I is dimensional. Bearing this in mind and according to the Rayleigh method we can write
CaAPI,I,F, S Chapter 2 Literature Review And Theoretical Background 2-18
C = A(API)a(I)b(F)c(S)e
M° L°T° = (L)a ( LT1)b ( LT1)c ( L )e
ForL= 0 0 = a+b+c+e a=-e
For T = 0 0 = - b -c b= -c
C= <|> ( ( API/S)a (I/F) b )
or
C = k(( API/S) *(VF)b)X
The general form of the equation after lumping the powers, Xa and Xb, is as follows:
C = k ( API/S) A (I/F ) B
The coefficients of the above formula ( k, A and B) can be derived provided the availability of accurate data. They can be derived using observed data of API, I and C. S and F should be known for catchments under study. The stormflow data in urban catchments always include impervious area runoff, so they can not be used directly to derive the unknown coefficients of the deterministic equation. Runoff from pervious areas can be obtained using urban catchment rainfall-runoff modelling. In some urban hydrology models such as MOUSE (DHJ 1988), separate simulation of impervious areas runoff is possible and the difference with combined events will give the pervious area runoff. However, the best way to calculate the unknown coefficients of the runoff coefficient equation is using the measured values of S and F along with rainfall -runoff data which was not available for this study. Furthermore, modelling cannot help because the parameters range is very limited and also parameters have interactions (refer to Chapter 8), so the derivation of coefficients was not possible in the present study. Chapter 2 Literature Review And Theoretical Background 2-19
2.5. Modelling of Urban Catchment Hydrology
Compared with rural watersheds urban catchments are very complex in physical characteristics and runoff production. The existence of considerable impervious areas and many man-made features make the study of the runoff generation mechanism more complicated than that of rural catchments. Furthermore, the hydraulics of drainage networks of urban catchments is another unique feature which adds more complexity to rainfall-runoff analysis. The difference between response time of impervious and pervious areas of an urban catchment makes the prediction of the total time of concentration of the catchment more difficult than that of a rural catchment.
Study of urban catchment hydrology by employing mathematical models could make some presently vague aspects of runoff generation and flooding clear. Despite the complexity of models, the simple Rational method is wisely used and is incorporated in some models such as RatHGL (Messner and Goyen 1985-a ) and WASSP (U.K. National Water Council 1981).
This part of the study is mainly concerned with a review of the current urban hydrologic models developed in Australia and overseas. In recent years design practice in urban catchment hydrology has been mainly performed using these models. Although the lack of adequate observed data in urban catchments in Australia is a big hurdle to the evaluation of the models, the popularity of the models ignores the data paucity. The continuing increase in urbanisation is the other reason for the popularity of these models. The evaluation of flooding in downstream and stormwater management systems needs a comprehensive tool for verifying flood prone areas, designing detention basins and preventing inundation of downstream areas due to urbanisation. Some urban hydrologic models are able to handle this job and anticipate the consequences of different actions which are adopted in urban catchments. Despite the existence of integrated models for performing the above tasks, the problem of simulating the flood hydrograph and peak has still not been solved. Flood estimation and simulation is the basis of these models because every measure taken in catchments including detention basin, water surface profile computation and network design, will be performed according to the design or observed flood hydrographs. Chapter 2 Literature Review And Theoretical Background 2-20
There are different models which have been developed specifically for urban catchments in Australia and overseas. The overseas models mainly have a water quality parameter estimation subroutine. The philosophy behind the development of these models is the verification of urban runoff effects on receiving waters. Most of these countries have dual purpose drainage systems which carry urban runoff and domestic sewage together. The problems of surcharge and overflow of sewers in residential or commercial areas, eutrophication of lakes and deterioration ofriver water quality are common experience in these countries.
The most well known model in this category is SWMM (Huber et al. 1981). This model was developed under the auspices of the Environmental Protection Agency (EPA) of the USA and much research has been done on it to improve the hydrologic, hydraulics and water quality simulation parts of the model. In Europe like in the USA, most urban drainage networks carry both sewage and stormwater. The pioneering work was done by the Danish on an integrated catchment model called MOUSE (DHI 1988). A well known urban hydrologic model for evaluating existing networks and designing new systems in the UK is the Wallingford procedure, called WASSP (National Water Council 1981)
2.6. Australian Urban Hydrology Models
During the last two decades there have been many attempts to use overseas models directly or to develop or to modify models which are suitable for Australian urban catchments (Aitken 1975). From the beginning, two distinct features have always affected both the selection of overseas models and the development of models in Australia. Thefirst problem is the existence of separate systems for urban stormwater and sewage water collection in Australia. The second problem is the soil type underlying the urban areas in different cities in Australia with high variations in infiltration characteristics. For instance, the UK Transport and Road Research Laboratory model (TRRL) was considered to be included in ARR1977, but was rejected because of the lack of pervious area runoff simulation (O'Loughlin & Goyen 1990). Besides the above features, the variations in climatological conditions from the northern to the southern parts of Australia cause quite different flooding characteristics in urban catchments. Chapter 2 Literature Review And Theoretical Background 2-21
Considering the above artificial and natural features in this country the estimation and forecasting of floods has priority over water quality in urban areas. The vicinity of the major cities in Australia to the Pacific Ocean is another factor that has retarded water quality study of urban catchments.
The evaluation of current Australian urban catchment models apart from modified or adopted models such as ILSAX (O'Loughlin, 1988) or developed models such as RAFTS-XP (Goyen et al. 1991), RatHGL (Messner and Goyen 1985-a), WBNM (Boyd et al. 1987), RORB (Laurenson & Mein, 1988) shows that all of them deal with the hydrology and hydraulics of urban catchments and there is no specific model developed for water quality prediction. A model called AUSQUAL has been developed recently and is being applied to urban catchments in Sydney (White et al. 1992), but it has not yet been marketed like the other Australian models.
2.6.1. Rainfall-runoff routing models
Generally these models are lumped in structure and are used for simulation of the hydrograph at a project location. Although these models were developed for application in rural catchments at first, they are, after some modifications, currently being applied in urban catchments. Three well-known models in this category within Australia and overseas are RAFTS- XP (Goyen & Varley 1990), RORB (Laurenson & Mein 1993) and WBNM (Boyd et al. 1994). These models use methods of rainfall excess computation for surface runoff and river routing approaches to consider attenuation of flood peak in rivers. The accompanying Expert System with some of these models has made them more efficient and more useful than conventional methods of computer programming.
The expert system (XP) is a type of software which tries to extract data for the problem posed by users (Mein and Goyen 1988). The XP is a system by which the communication with computers is very efficient The conventional method of working with computers does not allow the user to think about the different choices in solving a problem, and sometimes the results are biased because of limited interaction between user and computer. In the conventional method the user has no knowledge of the internal structure of the program and does not know its hmitations. Most recent models have Chapter 2 Literature Review And Theoretical Background 2-22
been written in an expert system environment, and most previous models have been revised and are available with XP. This system includes a knowledge base, an inference engine and a user interface. The XP makes models more user friendly than conventional methods. Installation, calibration and verification of models can be implemented very quickly . This system has WIMP (Window, Icon, Mouse, Pull - down menu) facilities which makes working with the model easier. This system is very useful for designers who have not enough time to go through a model's structure and find its limitations. The XP saves a lot of time and energy in the application of the model.
The Expert System has been used frequently to calibrate stormwater models such as SWMM (Baffaut et al. 1989). Phillips et al. (1992) used XP in an integrated water quality and streamflow model called AQUALM-XP. Models such as MOUSE (DHI 1988), RAFTS-XP(Goyen et al. 1991) and RatHGL (Messner and Goyen 1985-a) use XP as their driving system. With this system, creation of the stream network on the screen is very easy and hydrologic benefits are tangible.
2.6.1.1. RAFTS-XP
This is an event model. The general structure is based on storage and routing processes in the catchment. The contribution of the catchment to the production of runoff is considered according to the isochronal method. The maximum division of each subarea is 10 and the maximum time base of the hydrograph is 300 times greater than the time increment of routing. It should be noted that the routing increment should be divisible into the time increment of rainfall temporal pattern. This model can be applied to rural or fully urbanised catchments. RAFTS-XP uses an expert system shell and incorporates CAD. The expert system allows the user to increase productivity by increasing the efficiency of data entry. The inclusion of this system in RAFTS has made it more user friendly than the previous version, RSWM (Goyen et al. 1991).
Data requirements are in accordance with data availability. This model is appropriate for a straightforward runoff estimate. Input data can be upgraded to include more detailed information about the catchment. In addition to rainfall, runoff and any other catchment data to simulate rainfall losses, this model needs some field measurement of hydraulic conductivity in saturation conditions and also sorptivity of dominant soil types in each Chapter 2 Literature Reviev. And Theoretical Background 2-23
subcatchment. This model is more suitable for designing trunk drainage and stormwater master plan studies in urban catchments (O'Loughlin and Goyen 1990). It has been applied on 2 hectares to several thousand square Kilometres areas. This model can handle both natural and synthetic data to reproduce historical events and estimate design floods.
2.6.1.2. RORB
Like the RAFTS model the RORB (Laurenson & Mein, 1988) program is mainly for simulation offloods o n large catchments, say over 10 Km2, in which catchment and trunk drains orriver systems have a significant storage capacity (O'Loughlin and Goyen 1990). The model simulates rainfall excess through nonlinear storages for assigned subcatchments, towards the catchment outlet. The estimation of rainfall excess is based on introducing an initial loss and loss rate to the model in the process of calibration.
2.6.1.3. WBNM
Developed by Boyd et al. (1987), WBNM is the simplest rainfall-runoff routing model which showed its capability in modelling large floods in small and large catchments (Boyd 1981). There is only one parameter to be calibrated when linear response for catchments is assumed. Simulation of linear or nonlinear behaviour of the catchments is feasible with the model. The application of this model is mainly in rural watersheds; however, recently it has been modified for urban catchments and is called WBNM94 (Boyd et al. 1994). The main catchment is divided into subcatchments and the hydrograph is simulated by the aid of two coefficients called transformation and transmission for transforming of rainfall to runoff in subcatchments and transmitting of runoff inrivers towards the outlet. In a comparative study the capabilities of the two models RORB and WBNM were found to be the same; however, WBNM requires considerably less data than does RORB regarding the definition of subcatchment
structures (Boyd 1983). Chapter 2 Literature Review And Theoretical Background 2-24
2.6.1.4. RatHGL-XP
This model has been developed for design and analysis of stormwater pipe networks (Messner and Goyen 1985-a). The model is capable of evaluating existing systems. It is suitable for designing new stormwater networks to size the pipe based on any design return period discharge. This model uses flood peak only. The statistical form of the Rational formula as presented in ARR87 and ARR1977 is utilised as the hydrologic part of the model. The temporal pattern of rainfall is not an important factor in this model because of the use of the Rational formula. The only outcome of the hydrologic models is the runoff peak. In contrast to the other urban catchment model, RatHGL does not need much detailed rainfall and runoff data.
The modelling of an urban catchment necessarily includes modelling of catchment hydraulics. The full hydraulic conditions should be considered in pipe networks in urban catchments. The analysis of a single pipe is simple, but in a network due to surcharging and overflow, the problem becomes very complicated and it requires a computer model. RatHGL has had some success in modelling hydraulic components in urban catchments (Messner & Goyen 1985-b).
2.6.2. Urban catchment hydrologic - hydraulic models
The only effort in this category in Australia was made by O'Loughlin (1988) who introduced the ILSAX model. ILSAX is a deterministic model which simulates the land phase hydrology of both pervious and impervious areas of urban catchments. This model can simulate the hydraulics of both open channels and closed conduits of urban catchments based on steady state flow conditions.
2.6.2.1. ILSAX
The ELSAX model, developed by O'Loughlin (1988) is a comprehensive tool for design, investigation and evaluation of urban drainage networks in Australia, This model, by integrating hydrologic and hydraulic aspects of urban catchments, simulates the flood hydrographs at any sub-catchment outlet. The predecessor of this model is the Illinois Urban Stormwater Area Simulator, ILLUDAS, (Terstriep and Stall, 1974) which after modification in South Africa by Watson(1981) was named BLLUDAS-SA. The Chapter 2 Literature Review And Theoretical Background 2-25
ELLUDAS-SA model after being modified and tailored for Australian conditions is known as ILSAX (O'Loughlin, 1988). The ELSAX model is mentioned as an urban stormwater design tool in ARR87. It has been extensively tested in gauged catchments since its development in 1986. Studies done by Vale et al. (1986), Clare (1988), Haig (1988), O'Loughlin et al. (1991) and Gallen (1991) are some examples of the application of the model, mostly in Sydney urban gauged catchments. Regarding the popularity of ELSAX in urban stormwater modelling in Australia, the structure of the model is
explained in the following sections.
2.6.2.1.1. Hydrologic module
ILSAX generates surface flow hydrograph using an excess rainfall hyetograph and the time-area method for both impervious and pervious areas of a catchment separately. Direct estimates of time of concentration of each sub catchment, or the necessary land surface data, e.g. slope, length and roughness coefficient, are required as input to the model. The shape of the time-area diagram in ILSAX is linear; however, concave and convex shapes are expected in real catchments as well. Considering the different response time of impervious, paved, and pervious, grassed, areas a lag time should be introduced into the model for the pervious area hydrograph. The magnitude of lag time is
subjective and is adjusted by calibration of the model.
Initial loss and loss rate are considered in the model to compute excess rainfall. Initial loss in the form of depression storage for a paved area is subtracted from the rainfall hyetograph and the remaining rainfall is in excess. For pervious areas the problem is quite complex because of the variable loss rate due to movement of water in the soil environment. ILSAX uses Horton's infiltration equation accompanied by four curves which are related to different soil types of the catchments according to the classification of the US Soil Conservation Service (1975). Besides soil types, catchment wetness in the form of Antecedent Moisture Condition, AMC, is taken into account along with each soil infiltration curve. Infiltration capacity by Horton's formula is computed in the form
of Diminishing and Constant components in the model in the form given below:
f=fc + (f0-fc).e Chapter 2 Literature Review And Theoretical Background 2-26
b Diminishing component: (f0 - fc). e" Constant component: fc
Using the above definition and considering the actual depth of infiltration, AF, which is fraction of I. At overtime step, At, Watson (1981) derived the following formula :
kAt Fcap = d-e- ).[(f0-f ) / k - Fd] + fc.At
where Fd is accumulated diminishing infiltration, which is estimated by the following equation at each time step.
AF/ Fd = Fd - [ ^cap ] • ( AFcap - fc . At)
Fcap is the total capacity of infiltration, and AFcap is the capacity over time step of At.
Hydrographs of paved and grassed areas and the hydrograph provided by the user are combined and the total hydrograph will enter the pit. If the capacity of the pit exceeds the inflow hydrograph, it is routed through the pipe after adding to the upstream pipe flow; otherwise a part of the hydrograph will be admitted into the pipe and the rest of it will be considered as bypass flow which will be guided to a downstream pit or out of the system. This process is one of the major advantages of the ELSAX model over its predecessors- ELLUDAS and ILLUDAS- SA.
2.6.2.1.2. Hydraulic module - steady state flow assumption
The hydraulic part of the ELSAX model is fairly simple in terms of pipe flow, but rather complicated in computing bypass flow and time of entry.
a). Travel time
To compute the flood hydrographs of paved and grassed areas, ELSAX needs the time of entry or time of concentration of each sub-catchment. There are three different options in the model for the Tc estimate including: providing the model with a direct value of Tc; entering path length and slope in order to use the ELLUDAS- SA method of paved area
Tc estimation; and finally providing the model with slope, length, gutter flow factor and surface roughness coefficient to compute Tc based on ARR87 method. ELLUDAS -SA Chapter 2 Literature Review And Theoretical Background 2-2 ~
uses Manning's equation for gutter flow entry time while ARR87 employs more complex relationships which are described in Chapter 4.
For estimating time of entry of grassed areas, ILSAX uses the kinematic wave equation for overland flow by Ragan and Duru (1972) which is described in Chapter 4. ILSAX uses the value of a retardance coefficient and average rainfall intensity in the overland flow travel time computation.
b). Pit entries
One of the major points in ILSAX urban hydrologic modelling is the pit entry options which are provided with the model. Different options including no inlet restriction, on- grade inlet and sag inlet are available to model pit entries as close to real situations as possible. ILSAX applies the pit capacity equations to every ordinate of the arriving hydrograph and computes the bypass hydrograph which will be guided to a downstream
pit after adding a lag time.
c). Pipe and open channels hydraulics
Despite the existence of complete and complex hydraulic models for simulating flow within pipe systems, ILSAX employs the simplest method. Full pipe flow under atmospheric pressure, like an open channel, is assumed and solved based on steady state flow conditions. While the assumption of steady state for design situation causes no problem, except the overestimation of pipe diameter, its application in evaluation of existing systems is quite unrealistic. ILSAX truncates the flow exceeding pipe capacity and guides it downstream, while the remaining flow is admitted into the pipe system under atmospheric pressure which is not accurate because in the real situation there is an
exerted pressure due to head of water in pit.
Different cross sections of closed conduits and open channels including circular, rectangular, trapezoidal and irregular natural streams can be introduced to the model. The Colebrook-White equation is used in the model to estimate velocity as given below:
V = -0.87 V2g .D.S. loge [k/3.7 D + 2.51 W-fo 2g.D.Sl Chapter 2 Literature Review And Theoretical Background 2-28
Where: D : diameter, m S : energy line slope, m/m k : pipe wall roughness, mm 1): kinematic viscosity, m2/s g : the acceleration of gravity, m/s2
2.6.2.1.3. Detention basin computation
ELSAX uses the modified Puis method to calculate outflow of detention basins within urban catchments. The method is based on the continuity equation which for a time step At is written as follows:
I - Q = dS/dt
(I, + I,+I)/2 - (Q, +Qi+1)/2 = (Swl - S,)/ At
After rearranging the above equation and putting the known terms on the left side we have:
Ii + Ii+1-Qi + 2S/At = Q,+1 + 2Si+l/At
ILSAX Plot facilities provide visual comparison between inflow and outflow of a detention basin.
2.6.2.1.4. Required data
Although the model can accept system data for Evaluation, Design or both, of an urban catchment, here we study the required data for system Evaluation to be comparable with those of the other models.
The catchment and pipe data file include general and detailed information about sub catchments and pipe networks. For sub-catchment land surface, users must provide the model with the total area, percentage or area of paved and grassed areas, the grassed and paved area entry times and the lag time of the grassed area. The area or percentage of the supplementary paved area or detached impervious areas, eg houses or villas, will be required for modelling as well. Entry time of runoff from the grassed or paved area can be calculated by built in equations if the necessary data are given to the model. Pipe network data input consists of pipe diameter, slope, length and wall roughness. Wall roughness will be introduced to the model depending on the material of the pipe. Rainfall Chapter 2 Literature Review And Theoretical Background 2-29
hyetograph and stormflow hydrograph with short time increment are necessary to simulate the hydrograph and compare it to the observed.
2.7. Overseas Urban Hydrologic Models
In this section three well-known overseas urban hydrologic models are discussed. The first model is SWMM (Huber et al. 1988). The second model is MOUSE (DHI 1988) and the last is WASSP (U.K. National Water Council 1981). Thefirst tw o models have been used in most countries including Australia for design purposes (O'Loughlin and Goyen 1990). MOUSE has been extensively used for sewer investigation in Australia (Lindberg et al. 1992). In spite of strenuous attempts to search for the available data bases, no report was found on the application of WASSP in Australia.
2.7.1. WASSP
The formula presented in section 2.3.1. and three other modules are gathered in a model called WASSP (U.K. National Water Council 1981). The computer program includes the facility to simulate stormwater overflow. This method consists of three methods
including; hydrograph, optimisation and simulation.
In the hydrograph method a computer-based approach is used for modelling of the
below-ground and above-ground phases of runoff separately.
The optimisation method consists of a computer-based technique to obtain pipe diameter, depth, gradient, and minimum construction cost using discrete differential
dynamic programming techniques.
The simulation method is for evaluation of performance of both existing systems and the proposed design under surcharged conditions. It can be used for overflow computation,
on-line and off-line detention tank simulation, and pumping station design.
The above methods may be applied to both separate and combined sewerage systems. No allowances are made for rural areas that may contribute to an urban drainage Chapter 2 Literature Review And Theoretical Background 2-30
network, and for water quality modelling as well. A complete description about different functions of the Wallingford procedure is presented by Hall (1982).
2.7.2. SWMM
The Storm Water Management Model, SWMM, was developed under the sponsorship of the Environmental Protection Agency, EPA, in the USA in 1969-71. Since 1971 this model has been applied and modified in urban catchments in order to simulate water quantity and quality and its impacts on receiving waters. The latest version of this model called SWMM-IV, is a sophisticated hydrologic and hydraulic urban model (Huber et al. 1988). Applications of this model are numerous, including Khlifa et al. (1984) and Baffaut et al. (1987). Baffaut et al. (1987) used expert system to calibrate SWMM. This model was applied in Australian urban catchments with some success by Vale et al. (1986) and O'Loughlin et al. (1991 ).
Generally the model consists of three principal computational blocks including, Runoff, Transport and Storage/Treatment. The Runoff block simulates both quantity and quality of any kind of precipitation, rain or snow, for periods from minutes to years. The Transport block routes the stormwater/sewage through the system using kinematic wave approximation both quantitatively and qualitatively. The Storage/Treatment block simulates the routed flows and pollutants through a dry- or wet-weather treatment plant. There are many blocks in SWMM-IV, but they are mostly service blocks. Implementation of kinematic wave in the Transport block in SWMM exhibits limitations when modelling surcharge and overflow in the system.
To overcome the limitations of kinematic wave in simulation of surcharge and flooding in stormwater networks, EXTRAN was developed by Roesner et al. (1981). This model works either in conjunction with SWMM-IV or separately. The model includes a hydraulic flow routing model capable of solving flow depth and velocity in closed conduits or open channels with different cross sections. EXTRAN receives the input hydrograph from the Runoff block or the user's data file. The EXT-RAN-XP is accompanied by an expert system which makes the model more efficient and user friendly (WP Software 1988). Using the expert system to calibrate the SWMM Runoff block, Chapter 2 Literature Review And Theoretical Background 2-31
Baffaut et al. (1987) could improve the shape and timing of the simulated hydrographs when compared with the conventional manual calibration of the model.
2.7.3. MOUSE
This model was developed by the Danish Hydraulics Institute (DHI 1988). This is a hydrologic-hydraulic model applicable for urban catchments only. This model is used extensively for sewer design in Australia but little attention has been paid to the design of drainage stormwater network (Lindberg et al. 1992). The hydrologic part of the model deals with simulation of runoff in two ways: a simple one based on a time area diagram and a complex one based on kinematic flow theory and continuity equation. The hydraulic part of the model simulates flow routing within closed conduits or open channels. To compute depth and velocity of flow within waterways three options are available in MOUSE. Thefirst i s the Kinematic wave which is mostly applied to partly full flow conditions. The second is the Diffusive wave to consider backwater and surcharge in the systems; and the last is the Dynamic wave for a full hydrodynamic solution of flow within pipes. Water quality modelling and prediction is also included in the MOUSE model (DHI 1988). The package was released in 1986 and 300 copies of the model are installed all around the world. In Australia it was recently considered as a stormwater drainage system design tool (Lindberg and Jorgensen 1986, Lindberg and Carr 1992). The MOUSE package is the most widely used for analysing urban drainage systems in Europe. (Gustafsson et al. 1993).
New applications of this model, MOUSE SIMULATOR, are in Real Time Control (RTC) of combined sewer systems in Europe (Lindberg et al. 1993). It has been applied to a catchment with an area of 200 Km2 for on-line operation of a treatment plant in Sweden (Gustafsson et al. 1993). The hydrology and hydraulics modules of the model are explained and a comparison is made with ELSAX model in the following sections. Chapter 2 Literature Review And Theoretical Background 2-32
2.7.3.1. Hydrologic module
Hydrologic computation in MOUSE is performed based on arelatively simple , level A, and a fairly complex model, level B. Selection of the level of hydrologic computation depends on the runoff nature of the catchment. The hydrologic module produces a hydrograph for each subcatchment at the outlet manhole.
a). Level A
This level of runoff computation applies only to directly connected impervious areas of a catchment. Hydrograph generation is based on the combination of rainfall excess and time-area diagram. The required parameters for this level are initial loss, hydrologic reduction factor, time of concentration and type of time-area diagram. Three types of time-area diagram including; linear, concave and convex, are available in MOUSE which can be selected by the user based on the shape of the sub-catchment. Time of concentration must be calculated and given to the model as input. The Hydrologic Reduction Factor, HRF, is used to show the ratio of contributing impervious area to total impervious area of urban catchments. Typical values of this parameter ranges between 0.80 - 0.95. Initial loss is the amount of rainfall whichfills up the depression storage, which normally is between 0.05 - 1.00 mm for impervious areas.
b). Level B
This is arelatively comple x hydrologic computation from both the view point of the intensive necessary data and computation method. Level B is used for simulation of combined runoff from both pervious and impervious areas. This level of the model after a decade of development is rarely used by practitioners unless for special purposes like inflow from rural catchments or infiltration studies (Harremoes et al. 1993). Each sub catchment will be divided into three different surface types including; impermeable, semipermeable and permeable surfaces. These three surface types are divided into five subareas which arerecognisable fo r the model when computing surface runoff. The total runoff is the sum of runoff from the five subareas. Flow length and width will be calculated for each subarea according to the ratio of length to width of the total subcatchment. Surface runoff for each subarea will be computed based on the global or specified losses. The effective rainfall is written as: Chapter 2 Literature Review And Theoretical Background 2-33
Idf(t) = R(t) - QE (t) - Qw (t) - Q,( t) - Qs( t)
Where:
Icff(t): the effective rainfall R(t) :Rain
QE (t): Evaporation Qw (t): Wetting Q,( t): Infiltration
Qs( t): Storage
Evaporation, QE (t), is a continuous loss and assumed constant due to the time; however. it is negligible during rainfall. Wetting, Qw (t), is a temporary loss for wetting the surface which will be satisfied at the beginning of rainfall after evaporation is subtracted. Storage loss, Qs(t), is considered for filling the depressions and holes on the surface. It starts after infiltration but before the occurrence of runoff. The infiltration loss, Qi(t), is a variably continuous loss which is considered in the model by Horton's infiltration equation. After considering all the possible losses, surface runoff will be calculated for each time step.
Hydrograph generation at this level is based on kinematic wave equations assuming equal depth of water on the subcatchment surface with steady flow conditions. Runoff is determined using Manning's equation and the depth by the continuity equation in the following form:
Q(t) = MAN. B I m y573
Q(t) = dy/dt. A Where: MAN.: Manning number B : Runoff width I: Surface Slope y : Computed depth dt: time step dy : change in depth
An implicit solution of the above equations keeps the computation steady even at large time steps. A time step of between 30 and 120 seconds is recommended to keep the accuracy of the computation. Considering the significant contribution of pervious areas in runoff generation in urban catchments in Australian conditions and also the scarcity of the data required to calibrate Chapter 2 Literature Review And Theoretical Background 2-34
and apply the Level B for calculation of runoff from both pervious and impervious areas, alternative solutions were sought for combined runoff simulation. In application of Level B there many parameters to be calculated or calibrated. Besides the limitation of the available data, the interaction of these parameters creates many problems. For example, the calibrated parameter's magnitude in most applications of rainfall runoff models has no physical interpretation and finding optimum values of parameters for catchments are found to be very difficult ( Johnston and Pilgrim 1976). Until now the workable solution to these complexities is reduction in the number of parameters. As an example of model simplification to reduce the number of parameters the SFB model with three parameters was derived from Boughton' model which has eight parameters ( Boughton 1984). However, there are still lots of interactions between storage and infiltration unless doing some necessary flow separation to reduce the persisting interaction ( Sharifi and Boyd
1994).
The MOUSE model at this level lumps the runoff responses of pervious and impervious areas of each subcatchment and transforms them to a hydrograph by using kinematic wave, however these areas have different responses in producing runoff. The responses of impervious and pervious areas both in producing runoff and in traveling down the subcatchments are fast and slow respectively. The importance of separate simulation of these areas are already recognised by Wittenberg (1975), Diskin et al. (1978), Diskin (1980) and Bufill (1989). Besides the combination of pervious and impervious areas in the MOUSE model, the process of rainfall excess calculation is very data intensive and the involved parameters have interactions which make the calibration time consuming and unstable. Because of interactions, the magnitudes of the calibrated parameters have no physical interpretation.
In the present study the MOUSE model is modified ( MMOUSE) for both excess rainfall calculation, and separated storages for impervious and pervious areas. To convey the separated pervious area runoff to the catchment drainage system a fictitious conduit and a dummy manhole were added to the catchment drainage system at the outlet of each subcatchment. The beginning of this conduit is the dummy manhole and the ending is the real manhole. Chapter 2 Literature Review And Theoretical Background 2-35
In MMOUSE the excess rainfall of pervious areas is calculated according to the concept of the runoff coefficient for pervious areas and different initial losses for pervious and impervious areas. In this method runoff is calculated from two parallel storages of impervious and pervious areas separately and is added at manholes. The delay between response time of pervious and impervious areas is considered by different times of concentration for them ( Chapter 8).
2.7.3.2. Hydraulic module - unsteady state flow calculation - KW - DW - DYN.W
Three different options are available in the model to simulate pipe or channel flow including Kinematic Wave ( KW ), Diffusive Wave ( DW ) and Dynamic Wave (DYN.W) methods. The user can choose the computational level relevant to the type of flow conditions. Branched or looped systems can be modelled in MOUSE. Both DYN.W and DW can calculate backwater effects but not KW. However, none of the three approaches include hydraulic jump energy loss. The solution technique is the same for
the three approximations.
The pipe flow model simulates free surface flows, surcharging and flooding/storage on the surface. Head losses in manholes and control structures eg weirs and pumps, are accounted for in the model. Modelling of the combination of super and sub critical flow
can be performed when DYN.W or DW approaches are used.
The general equation of motion accompanied by the continuity equation are used after
some modifications in the model as follows: dQ/dx + dA/dt = 0
2 dQ/dt + d(a Q /A)/ 3x +g A 3y/3x +gAL = g A Io I KW I I DW I I _DYN. W I
Where:
Q : flow rate, m /s A : cross section, m Chapter 2 Literature Review And Theoretical Background 2-36
y : flow depth, m g : acceleration gravity, m/s2 x : longitudinal axis, m t: time, S a: velocity distribution factor Io: bottom slope If: friction slope
Saint Venant equations are for free surface flow computation of flow rate and depth variation in a pipe or channel. The velocity distribution factor, a , at a cross section is defined as follows: a = A/Q2J V2dA A If I0 is small then it can be expressed as
Io - dy/dx - dh/Bx
Using h instead of y results in the following equation which is used for free surface pipe flow
2 dQ/dt + a ( a Q /A)/ 3x +g A dh/dx = gAIf
The term ( g A dh/dx) is assigned for both pressure and gravity forces.
The friction slope, L, can be computed from one of the following relationships:
L = x/pgR
2 i *n If=QIQI(MAN) /A R
Where MAN is Manning number and R is the hydraulic radius.
Overcapacity pipe flow can be modelled by the free surface flow pipe equation assuming a fictitious slot in the top of the pipe. This idea, known as Preissmann slot, is employed in MOUSE to simulate pressurised pipe flow. The continuity equation in this case changes to the form of
2 dQ/dx +Q/p . ap /dx + g.Ao/a . dy/dx = 0
Where: Ao: pipe cross section of pipe flow without excess pressure y : water depth Chapter 2 Literature Review And Theoretical Background 2-37
p : density of water under pressure and 2 a = a0/V(l+ao7ar )
a=VEr.e/po.d Where: ao: speed of sound in water Er: Young's modulus of elasticity e : pipe wall thickness
po: density of water for free surface flow a). KW approximation
In partly full steep pipes, the flow is controlled mainly by the balance between friction force and gravity forces. The inertia and pressure terms in the equation of momentum in this condition are less effective and the equation reduces to the following if Manning's formula is used.
m ,n Q = MAN A R I0
KW is independent of downstream conditions (super critical flow i.e., Froude No. > 1) and generally is not suitable for pressurised flow.
b). DW approximation
If the pressure term is included in the momentum equation, then downstream conditions could be introduced to the model and simulation of backwater effects are possible.
c). DYN.W approximation
All the effective forces exerted on flow are considered while the flow remains sub- critical by the full hydrodynamic Saint Venant equation, m super critical flow the equations are gradually reduced to DW approximation. Chapter 2 Literature Review And Theoretical Background 2-38
d). Energy losses in junctions
The energy loss is computed in MOUSE when flow enters manholes and when it leaves them. Three possible losses due to differences in levels of inlet and outlet pipe, changes in direction and contraction are considered. In the catchments under study it is assumed that inlet, outlet and the manhole bottom are at the same level due to unavailability of required data. There is no contraction loss and the only loss is due to change in the flow direction. In this case loss is a function of the angles between the inlet/outlet pipes as shown in Fig. 2.3.
2 2 2 2 £dir = ( Q, / Q M ) ( 9,3 / 90° ) + ( Q2 / Q ^ )( 923 / 90° ) + ...
Then the loss will be equal to
2 HL=Cdir*Vom /2
PIPE! Q1=V1.A1
Q3=V3.A3
PIPE 3
Q2=V2.A2
Fig. 2.3. Pipe direction change
e). Initial and boundary conditions
MOUSE at the beginning of computation automatically sets the flow depth equal to 10% of the pipe diameter and flow is calculated based on Manning's formula. The pipe flow model assumes the depth of flow equal to 2% of diameter during dry periods to keep the
numerical solution stable. Chapter 2 Literature Review And Theoretical Background 2-39
Boundary conditions can be constant or variable with respect to time at manholes. For the catchment outlet, constant water depth or time function, e.g. changing water level, can be considered.
f). Water level above ground level
In contrast to the ELSAX model, in MOUSE there is no bypass flow option. If water level in a manhole exceeds its top elevation and reaches to ground level an artificial basin with an area of 1000 times that of the manhole area will be assumed by the model. The water will be kept in this basin until the inflow to the manhole decreases. It should be noted that originally MOUSE was developed for combined sewer system design and in such a system overflow means the failure of the system, which should be prevented as much as possible. In real catchments flow in excess of manhole capacity moves down the system and probably enters the system later. Bearing this in mind, the application of MOUSE in a separate system, e.g. in Australian conditions, should be performed cautiously. Heavy bypass flow may cause problems on roads for traffic and pedestrians. In the case of overflow which is shown by MOUSE, designers should route the bypass flow down the system and make sure that the width of flow on the road is not excessive. The distance between pits in this case should be designed properly to prevent inconvenience to traffic. The maximum flow spread for traffic and pedestrian crossings
are 2.5 and 1.0 m respectively (Argue 1986).
2.7.3.3. Method of solution of flow equations
The computational grid for thefinite differenc e solution of flow equations in MOUSE is
based on the following conditions:
V. At < AX Where V: velocity, m/s At: time step, S AX : distance between computational nodes in the pipe, m
For a given flow, the distance between computational nodes is calculated based on At and a minimum of three computational grids will be established for each pipe by the model. An implicit finite difference scheme, 6-point Abbott-Scheme, of the governing Chapter 2 Literature Review And Theoretical Background 2-40
equations is introduced in the model to solve Q, flow rate, and h, water level, at each time step.
2.7.3.4. Required data
The required data in MOUSE depends on the level of hydrologic modelling. Generally for level A much less data is needed than for level B. Sewer system data is the same for both levels. The sewer data includes diameter, slope, length and roughness. The geometry of manhole/pits or any other structure and its coordinates are needed as input. The model prepares the stormwater network based on the above data. The elevation of the bottom and the top of each node, manhole/pit , is needed for consideration of surcharge and flooding.
The required data for each subcatchment consists of total area, pervious area, impervious area and surface slope and length of each sub catchment. Impervious areas are divided into connected and disconnected areas. Connected impervious areas include mainly roads and pathways and disconnected impervious areas consist of houses and villas. Pervious areas can be planted or unplanted and the soil type of the catchment should be introduced to the model. A more detailed explanation of the data required with Level B is presented in Chapter 8.
2.7.4. Comparison of ILSAX and MOUSE
A qualitative comparison of ILSAX and MOUSE is presented in Table 2.1. In some cases both models are similar, but some strong and weak points can be found in each model. Hydrologically speaking, both models are fairly similar, except MOUSE uses KW approximation to route excess rainfall in Level B. ILSAX uses TAD and lag time for both cases of runoff from pervious and impervious areas. TAD in ILSAX is linear while in MOUSE it could be linear, concave and convex. Both models use the same infiltration equation. ILSAX considers API, catchment soil moisture conditions, while this is not
incorporated in MOUSE.
In the hydraulic section of the models, unsteady flow simulation of branched/looped systems makes MOUSE closer to reality than ILSAX. A considerable head loss in urban drainage systems occurs in junctions, gully pits and manholes, which are not Chapter 2 Literature Review And Theoretical Background 2-41
incorporated in ELSAX, but are in MOUSE. Bypass flow to pits or manholes is diverted to downstream pits via gutters in ELSAX while in MOUSE the bypass flow is accumulated in a fictitious storage and will be released later into the same pits/manholes. This process in ELSAX is more realistic than in MOUSE. In ILSAX two drainage systems including above ground and below ground could be defined while in MOUSE only the below ground system is available. However, flooding and surcharge could be easily detected and managed in MOUSE by manipulating pipe characteristics or routing the flow downstream via gutters.
Introducing urban networks to MOUSE is much easier than in ILSAX. In MOUSE every junction has definite coordinates by which it is easily located. Tidal effects or any other downstream conditions could be investigated using MOUSE.
In the present study MOUSE is selected as the model to simulate the runoff hydrograph. This study is mostly concerned with simulation of runoff by use of runoff coefficient. This parameter is introduced in MOUSE by HRF which is close to the runoff coefficient definition. In Chapter 8 it is shown that runoff from pervious areas of an urban catchment could be simulated using HRF for those areas. Implementation of the proposed method was only possible using the MOUSE model. However unsteady pipe flow simulation was another main reason for the adoption of MOUSE. Chapter 2 Literature Review And Theoretical Background 2-42
Table 2.1. Comparison of capabilities of ILSAX and MOUSE
Item ELSAX MOUSE
Infiltration Equation Horton Horton (Level B) HRF (Level A) API Yes No TAD Linear Linear/Nonlinear Runoff Routing TAD + Lag time KW approximation (Level B) TAD+ Tc (Level A) Drainage Network Branched Branched/Looped Junction Location Should be defined to the It is introduced only by model coordinates Pipe flow Equation Steady Unsteady( KW, DW, DYN.W) Head loss in manholes No Yes Excess flow to Will be guided to Will be stored in fictitious manholes/pits downstream storage and released later to the same manholes/pits Urban Drainage Above and below ground Below ground only Networks Investigation of Tidal No Yes effect on systems Grate entry loss Built in functions Could be introduced to available the model by modified top cross section of manholes/pits
2.8. Summary
Hydrologic processes vary in urban catchments, because of man-made structures and land use changes. The most tangible consequence of urbanisation is the increase in surface runoff. The development of impervious areas in urban catchments is the main reason for surface runoff increase. However, modifications made on natural streams including, lining, straightening and piping, cause lower surface water travel time and increase the flood peak compared to pre-urbanisation.
In contrast to rural watershed, there are many points of interest in urban catchments which should be considered from the viewpoint of hydrology and hydraulics. Design procedures should consider the special physical characteristics of urban catchments. The Chapter 2 Literature Review And Theoretical Background 2-43
outstanding features in these catchments are impervious areas and rapid response of flooding.
Different approaches have been applied to estimate the flood peak and hydrograph in urban catchments. Among these methods the Rational formula is widely used in Australia and other countries. Despite all the criticisms of the formula, variations or modifications to it, statistically or deterministically, are incorporated in models or are in use as handy formulas to estimate flood peak. To design infrastructures in urban catchments, the formula could be used simply by estimation of the time of concentration and runoff coefficient.
The use of a statistical interpretation of the Rational formula is recommended in ARR87. A formula is proposed for runoff coefficient estimation which considers the fraction of impervious area of catchments. Average rainfall intensity during the time of concentration should be used with the formula. A good approximation of time of concentration can be made through calculation of gutter, pipe or channel travel times in the urban catchment.
When the record lengths of gauged urban catchments are short, it is difficult to apply the statistical method unless regional values of runoff coefficient and rainfall intensities are available. However, recorded events of rainfall-runoff are available and can test validity of the Rational as a deterministic formula, which may assist in statistical application.
In urban hydrology modelling, Australian rainfall-runoff models are ahead of those of other countries. However, these model are lumped in structure and mostly are useful for trunk drainage and detention basin design.
In the urban hydrologic-hydraulic models category, Australians have made a considerable contribution by introducing ELSAX to the market. This is a comprehensive model which simulates hydrologic and hydraulic behaviour of urban catchments. The inclusion of formulas to simulate gully pits and other measures of stormwater drainage networks is an outstanding feature of this model. The main shortcomings of the model are the assumption of steady state flow in urban stormwater networks and also the lack of a water quality prediction module. Chapter 2 Literature Review And Theoretical Background 2-44
Among the overseas urban hydrologic-hydraulic models, SWMM has aroused the most interest in Australia. The model consists of three main blocks which simulate runoff and route it through the system to a treatment plant. The assumption of kinematic wave approximation while routing the flow in sewers is the main limitation of this model in simulating surcharge and flooding within urban catchments. This is overcome by introducing EXTRAN which accepts runoff from the Runoff block and routes it through the system using a full hydrodynamic solution.
The model focussed on in the present study is MOUSE. This is a comprehensive hydrologic and hydraulic urban model which includes a water quality prediction module as well. Two options for hydrologic simulation of pervious and impervious areas and full hydrodynamic solution of unsteady flow make the model appropriate for a more realistic study of stormwater networks. The model is widely used in Europe for urban stormwater and sewage network design, evaluation and management. The model is extensively used in Australia for sewer design. The adequacy of the model in simulating stormwater runoff hydrographs in Australian urban catchments with separated systems of stormwater and
sewerage has not been tested until the present study.
Due to the complexities and interactions of rainfall-runoff models parameters, attempts are made to reduce number of required parameters. This study is mostly concerned with simulation of runoff by use of runoff coefficient. This parameter is introduced in MOUSE by HRF for impervious areas. The HRF concept is close to the runoff coefficient definition, so with some modifications on time of concentration and initial loss it can be applied to pervious areas of urban catchments. Implementation of the proposed method was only possible using the MOUSE model. However, unsteady pipe flow simulation was another main reason for the adoption of MOUSE. In Chapter 8 it is shown that runoff from pervious areas of an urban catchment could be simulated using HRF for
those areas. CHAPTER THREE
CATCHMENTS AND DATA Chapter 3 Catchments And Data 3-1
CHAPTER THREE
3. CATCHMENTS AND DATA
In this chapter the available rainfall-runoff data of gauged catchments and some of their physical characteristics are presented. The accuracy of the catchment boundaries and networks has been checked by visiting the catchments and removing possible inconsistencies in the networks. The 2000 and 4000 scale orthophoto maps were used to check the boundaries and to compute land surface and waterway longitudinal slopes. The list of the catchments and their locality are presented in Table 3.1 and Fig. 3.1.
Table 3.1. Characteristics of the gauged urban catchments in Sydney
No. Catchment Area, Impervious Years of No. of Vpman Vpmini Km2 Fraction Record events m3/s m3/s 1 Maroubra 0.57 0.29 9 39 2.12 0.078 2 Jamison Pk 0.22 0.35 6 85 1.92 0.007 3 Fisher's Ghost Ck 2.14 0.27 7 23 15.65 1.040 4 Strathfield 2.34 0.50 11 78 16.61 2.780 5 Cranebrook 0.115 0.38 2 28 0.834 0.010
Fig. 3.1. The locality of the urban catchments Chapter 3 Catchments And Data 3-2
3.1. Maroubra
This catchment is located in the eastern part of Sydney with an area of 57 hectares. This is a completely developed urban area. This catchment is instrumented for research purposes by the University of New South Wales. Rainfall and runoff have been monitored in this catchment since 1977 (Plate No. 3.1 to 3.3).
3.1.1. Catchment and pipe network
The Maroubra catchment boundary and drainage network are shown in Fig. 3.2. The catchment land use is presented in Table 3.2. The ratio of road and sidewalk surfaces to the total area of the catchment is 17% which shows the directly connected impervious area ( Bufill 1989). These areas were directly measured by Bufill by visiting the catchment. This catchment is totally sewered and there is no open channel except a short section, less than 25 m, at the outlet on which the water level measurement device is installed ( Plate No. 3.4). The cadastral map of the catchment which is used to measure the directly connected impervious areas, mainly roads and sidewalks, is illustrated in Fig. 3.3. A copy of the government contract for the construction of the main pipe line was made available by the Sydney Water Board ( Fig. 3.4). This map was used to determine pipe slope and length, and also the ground surface elevation at each pit in order to model the catchment. The soil type of the pervious areas of the catchment is deep sand. Plates 3.5 and 3.6 show an exposed part of the grassed areas of Anzac Parade and a construction site in the catchment. The characteristics of the catchment main pipe line are presented in Table 3.3. Clui/'teiJ < '<;,'( l:n;cnt<. And Dnui 3-3
• 4i^>i • • -
\ - \ - - ••es
Plate 3.1. Part of Anzac PDE- Maroubra catchment
Plate 3.2. The steepest part of the catchment- Maroubra Chapter 3 Can luihiiis And Ihiln 3 4
GS**?-i.
-----<*'4VWl*fpSs54jBBT.-r.. r> •>•
L* • - - -
.'/
Plate 3.3. A combination of pervious and impervious areas- Maroubra
J#M> :.£. ::• • ^MV •••"•-•'• - -
is. 'ASte- -M -: ° . \ii
Plate 3.4. Measuring station on open channel - Maroubra Chaj2ter_3_ Catchments And Data 3-5
O
N 1 A 16 33
3 29r °f^nem boundaries txrWTT 281 ? *™ba/xtones PiPedn*aje 27/ 33 ^anchTm/rxxJerxjT^er o p,uviom(rtt=r A Saving station JpO ;on sco/eimeter
^
Fig. 3.2. Maroubra Catchment Chapter 3 Caich.merus And Dau: 3-^
Table 3.2. Land use of Maroubra catchment
Land Use Area, Km2 Ratio,%
Residential 0.35 61
Commercial 0.04 7
Roads & Sidewalks 0.17 30
Parks 0.01 2 Chapter 3 ALMMMM ''••- Data 3-7
•o, \ •f.\
\%. *-y Vfr \\& ! \ // \ \ - - / /,~ A \ v. /AAA \ ; " A>'A •<*',-.[• A <>> C^- \ \\ W J>\:f ^'-'* A\*> •' < A ;H •t' >A \\ \ AA
•<•.
1 .,-. / AA A>A\y\-. t. \v V ^'.' A^ \ AAA % V 4. /? S~ -~.l /* / // /'/ \v. // / , Vv\ A'' '"' v^/ *-' / / ~Jfi\\\\^3M \ • '-A v \. \ „ MMM*A ^£ --i \ ,•• •'•' -v % -fXA AA^ V*^^vv#> * / ^ / ,. OfT? *\ \-A^yy^ ^VA^ %£
\ A'' A. v
AA-^ .-A'.".
fy\\ <,^ v 7"/ ?:<>/A A\ AAA J
^^^•V / V / vv/ M"l\ • \
\ *'-\ >\.^- r Fig. 3.3. Maroubra Catchment cadastral map Chapter 3
tA
14-l-C- CAHOCN STRCCT
0 P SS-if OP it}!?! --~-'K- °r»-ff ••-'•.- any- -
J; GAXRCTT S" a 1/1
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PLAN .<~*
fU-1^
5 illi-OHAJNACC £ASCW - • O'- RC5 —cr* Rt5 — - Ctr £_ ffi7>"C£ i^'-C - 5* !t! ii i :'J
tSJ-33 1*3 73 ?| /,nfi» /.n^ /.n.?50 -l,n]Bl3 /*i •/rf»J
:;— ... —- "8 Pice ~-f-S««pr- li :. ..
I ?t JO 1, rca > U ! : trvrt 4 -'I i l I. > > t M 111) c i-
I DCP»«TV4C«T 34" PuSuC WOlktS LnNGlTUOlNAL SECTION ~ATO» SU"»Lr» sCwCAAGC awo. " 5c*tJS RANQWICK S.W.DRASNAGC
.C""C*4 » MAROUSfU JUNCTION CHANNEL, ST0RE?i'5"r TC ANZAO.F5 PLAN AND SECTION '
«r t*4r>444(tn FD» ^ueucwokes I - 132'X* !•• , Fig. 3.4. Maroubra main pipe line longitudinal profile- Government contract (Water Board) Chapter 3 Catelinicnt\ And Data 3-V
, , .1 ._, .fS>~pr.~ -- "*<5S3.
Plate 3.5. The exposed soil type of the catchment- Maroubra
E- =.=7 ^^^ WSSSM BHip
Plate 3.6. A construction site in the catchment- drilling deep sandy soil- Maroubra Chapter 3 Catchments And Data 3-10
Table 3.3. The characteristics of the main pipe line of the Maroubra catchment
Branch Length, m Dia., m Slope, % 33-32 101.2 0.45 0.89 32-31 156.2 0.45 1.00 31-29 62.6 1.22 0.39 29-28 93.1 1.22 0.28 28-27 84.8 1.22 0.21 27-22 101.7 1.22 0.19 22-21 93.9 1.372 0.22 21-17 35.4 1.372 0.48 17-8 96.4 1.372 0.73 8-4 100.5 1.372 0.30 4-3 107 1.524 0.51 3-2 242.6 1.372 0.46 Total 1275.4
3.1.2. Rainfall and discharge measurement stations
Rainfall is measured by means of three pluviographs located around the catchment (Fig. 3.2). The record of these pluviographs was averaged by Bufill (1989) using the Theissen polygon. In this research the averaged rainfall was also used in both rainfall-runoff analysis and modelling. The time step of the averaged rainfall intensity is three minutes which is suitable for the hydrologic analysisregarding th e catchment size.
Runoff is measured at the catchment outlet by using a water level recorder installed on the open channel. (Plate No. 3.4). The time step of the available water level records is the same as that of the rainfall and equal to three minutes. The recorded water levels are transformed to discharge using the rating curve of the open channel control section. The rating curve is presented in Table 3.4. and Fig. 3.5. The control section lies on a brick- concrete channel cross section, so it is stable with respect to time. Chapter 3 Catchments And Data 3-11
Table 3.4. Rating curve of the gauging station - Maroubra
Gauge Height, mm Discharge, rn3/s 53 0.001 58 0.003 61 0.004 76 0.008 87 0.011 91 0.017 99 0.028 107 0.040 125 0.057 152 0.096 182 0.156 213 0.232 244 0.317 274 0.430 305 0.595 366 0.966 427 1.218 488 1.586 548 2.039 610 2.577
0.0 0.5 1.0 1.5 2.0 £.5 3.0 Q - CMS
Fig. 3.5. Rating curve of the gauging station - Maroubra Chapter 3 Catchments And Data 3-12
3.1.3. Rainfall and stormflow data
A total of 39 synchronised rainfall-runoff events were available for this catchment. In the analysis of rainfall-runoff data the nature of runoff is very important, in other words the source of runoff for every individual event should be known. Bufill (1989) compared the runoff ratio with the fraction of impervious area of the catchment, 0.29, and concluded that no runoff was generated on the pervious areas of this catchment during the record because of deep sandy soil. Table 3.5. shows the rainfall and runoff depth and the nature of runoff for this catchment. By comparison of hydrographs with hyetograpghs of these events, Bufill (1989) computed initial losses and subtracted them from total rainfalls and derived runoff ratios based on runoff depths. For some events making decisions about the magnitude of initial loss was difficult and she derived the runoff ratios by dividing runoff depth to rainfall depth directly, so it does not necessarily mean that for these events initial losses were zero ( Table 3.5). The computed runoff ratios which incorporate initial losses are virtually the same as the volumetric runoff coefficients for bursts; however, the exact volumetric runoff coefficient is calculated using total rainfall depth. The present study focuses on the rate runoff coefficient calculated based on the average rainfall intensity during the bursts or time of concentration and the corresponding flood peaks (Chapter 4). It should be noted that the categorisation by Bufill (1989) has no serious effect on the results of this study because of the difference between rate and volumetric runoff coefficients (Refer to Section 4.3.2. Chapter 4). Chapter 3 Catchments And Data 3-13
Table 3.5. Rainfall and runoff record - Maroubra (After Bufill 1989)
Date Total Rain, Initial Loss, Runoff, Runoff Ratio I/C* No. mm mm mm
1 010377 48.16 8.20 0.1703 2 050377 8.87 1.43 0.1612 3 030378 35.66 6.54 0.1834 4 170378 6.81 1.20 0.67 0.1194 5 180378 42.64 1.05 8.18 0.1967 6 190378 11.25 2.94 0.2613 7 270378 5.66 0.85 0.1502 8 080478 36.90 5.94 0.1610 9 130478 18.90 3.74 0.1979 10 180578 11.25 1.70 0.1511 11 210578 12.25 0.56 2.60 0.2224 12 210578B 12.60 2.33 0.1849 13 290578 128.67 19.20 0.1499 14 130678 76.92 12.09 0.1572 15 190679 40.43 5.73 0.1416 16 200679 11.51 0.93 1.40 0.1323 17 170383 35.46 5.23 0.1475 18 180683 5.74 1.00 0.1742 19 051184 169.50 1.78 25.38 0.1513 20 061184 3.82 0.71 0.1859 21 061184B 18.35 2.84 0.1548 22 081184 93.76 0.72 13.58 0.1460 23 111184 33.23 5.48 0.1649 24 111284 20.80 1.59 4.57 0.2379 25 010585 10.44 1.99 0.1906 26 081185 22.96 2.76 0.1202 27 271285 27.30 5.13 0.1879 28 160186 114.81 18.45 0.1607 29 120486 27.36 5.10 3.86 0.1734 30 040187 18.68 3.30 3.86 0.2510 31 030787 15.65 3.22 0.2058 32 201087 38.28 7.34 0.1917 33 231087 19.73 0.49 4.25 0.2209 34 130288 82.88 12.66 0.1528 35 250388 26.32 3.61 0.1372 36 020488 136.47 23.35 0.1711 37 070488 27.89 4.94 0.1771 38 280488 227.15 46.72 0.2057 39 150688 68.83 10.31 0.1498 * I/C : Impervious Area Runoff only/ Combined Runoff Chapter 3 Catchments And Data 3-14
3.2. Jamison Park
This catchment is located in the western suburbs of Sydney with an area of 0.22 Km2. The history of the development of this area goes back to 1970. The measuring rainfall and runoff stations were established by the University of Technology Sydney, UTS, in 1983.
3.2.1. Catchment and pipe network
The Jamison park map is shown in Fig. 3.6. This is a completely sewered catchment. The catchment land use, measured by using 1/1000 scale map, consists of a small park area (part of Jamison Park), plus 200 detached dwellings and streets ( Haig 1989). The soil type of the catchment is typically clay with a slow rate of infiltration. Plate Nos. 3.7 to 3.9 show some of the catchment views. The main pipe line characteristics are presented in Table 3.6. The accuracy of the reported diameters was checked by visiting the catchment. The main gutter of the catchment at the top end of the main pipeline was surveyed for calculating the average slope. There was no difference between the average slope calculated using map and the survey results. ChapterM -MA.::..
catchment zcorccr succctcnmer.' 4? T.cmyp^
Fig. 3.6. Jamison Park catchment Map Chapter 3 Catchments And Data 3-16
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•
Plate 3.7. Looking east - main line of the network- Jamison Park catchment
Plate 3.8. Direct measurement of pit depth- Jamison Park catchment Chapter 3 Catehnienis And Data 3-1
Plate 3.9. Interceptor drains to guide pervious area runoff to the network- Jamison Park catchment ^_L« v
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Plate 3.10. Rainfall-Runoff measuring station - Jamison Park catchment Chapter 3 _ Catchments And Data 3-18
Table 3.6. The characteristics of the main pipe line of Jamison Park catchment
Branch Length.m Dia., m Slope, % D1-D2 40 0.45 2.75 D2-D3 13 0.45 2.00 D3-D4 23 0.45 2.50 D4-D5 52 0.45 3.45 D5-D6 43 0.45 2.57 D6-D7 47 0.60 3.45 D7-D8 39 0.60 2.67 D8-D9 33 0.60 4.27 D9-D10 90 0.90 2.51 D10-D11 60 0.90 1.00 D11-D12 42.5 0.90 1.33 D12-D13 22 1.2 0.42 D13-D14 83 1.2 0.65 D14-D15 65 1.35 0.47 D15-D16 12 1.35 0.47 D16-D17 13 1.54 0.50 D17-D18 110 1.54 0.238 D18-END 62 1.54 0.238 TOTAL 849.5
3.2.2. Rainfall and discharge measurement stations
The catchment rainfall is measured by a pluviometer located in Jamison Park. To prevent vandalism, the pluviometer funnel is posted on a 5.65 m steel pipe which guides rainfall to a tipping bucket raingauge housed in a small room. Runoff is measured by a pressure sensing unit located in a stilling well near the 1540 mm diameter outlet pipe ( Plate No. 3.10).
3.2.3. Rainfall and stormflow data
The available simultaneous rainfall and runoff events for this catchment are presented in Table 3.7. More than 50% of the events are combined runoff events which delineates the role of the catchment pervious areas in runoff production. Time step of the digitised rainfall charts is mostly 10 minutes which is too long for rainfall-runoff analysis in this small catchment. As with Maroubra catchment, runoff ratio was used by Bufill (1989) as an index to separate the combined events from impervious area runoff events in Jamison Park. (Table 3.7) Chapter 3 Catchments And Data 3-19
Table 3.7. Rainfall and Runoff record - Jamison Park ( After Bufill 1989)
Date Total Rain, Initial Loss, Runoff, Runoff Ratio I/C* No. mm mm mm
1 150383 8.00 2.14 0.2675 I 2 170383 44.80 13.32 0.2973 I 3 210383 53.82 10.0 31.45 0.7177 C 4 271183 10.60 0.2 6.11 0.5875 C 5 301183 14.60 12.25 0.8390 C 6 131283 7.00 4.55 0.6500 C 7 120184 9.80 3.86 0.3939 C 8 140284 17.20 6.87 0.3994 C 9 150284 11.20 1.0 8.66 0.8490 C 10 170284 12.60 4.32 0.3429 I 11 220384 24.20 3.20 16.13 0.7681 C 12 190684 36.60 15.58 0.4257 C 13 270784 79.80 2.0 68.47 0.8801 c 14 100884 8.80 5.04 0.5727 c 15 071184 29.00 16.31 0.5624 c 16 111184 16.00 14.91 0.9319 c 17 291184 22.00 9.14 0.4155 c 18 300485 18.27 8.34 0.4565 c 19 251185 34.60 0.6 8.42 0.2476 I 20 261185 13.20 2.44 0.1848 I 21 301185 25.40 6.27 0.2469 I 22 131285 36.40 21.52 0.5912 c 23 141285 13.80 9.73 0.7051 c 24 040186 17.20 10.0 1.92 0.2667 I 25 030686 4.80 0.2 1.92 0.4174 c 26 290986 34.00 3.0 11.58 0.3735 c 27 031086 3.80 1.17 0.3079 I 28 091086 32.20 6.2 12.62 0.4854 c 29 121186 62.20 4.8 32.65 0.5649 c 30 191186 14.80 1.6 8.12 0.6152 c 31 261186 6.80 1.2 2.21 0.3946 c 32 171286 3.60 1.8 0.26 0.1444 I 33 010187 28.40 1.2 5.73 0.2107 I 34 100287 8.40 3.6 1.22 0.2542 I 35 210287 2.80 0.4 0.39 0.1625 I 36 010387 49.60 1.4 9.14 0.1896 I 37 020387 36.20 8.52 0.2354 I 38 220687 6.00 0.8 0.68 0.1306 I 39 100887 4.60 0.77 0.1674 I 40 130887 4.40 0.6 0.78 0.2053 I 41 130887B 5.20 0.88 0.1692 I 42 170887 5.60 0.8 1.17 0.2438 I 43 180887 47.60 0.8 15.51 0.3314 I 44 190887 4.40 0.4 1.29 0.3225 I 45 270887 56.60 2.4 28.95 0.5322 c 46 300887 20.60 8.18 0.3971 c 47 060987 1.80 0.4 0.24 0.1714 I 48 231087 4.60 1.06 0.2304 I 49 241087 2.40 0.87 0.3625 c 50 241087B 14.40 2.60 0.1806 I Chapter 3 Catchments And Data 3-20
51 091187 8.60 1.4 1.94 0.2694 I 52 111187 41.60 2.6 15.81 0.4054 C 53 011287 3.40 1.2 0.60 0.2727 I 54 291287 8.00 1.4 2.26 0.3424 I 55 010188 16.20 5.99 0.3698 c 56 020188 2.60 1.03 0.3962 c 57 040188 2.80 0.59 0.2107 I 58 160188 35.60 1.0 5.78 0.1671 I 59 210188 9.00 3.8 2.59 0.4981 c 60 230188 8.80 2.2 3.06 0.4636 c 61 240188 5.80 0.8 1.50 0.3000 I 62 070288 4.60 1.06 0.2304 I 63 080288 2.40 0.87 0.3625 c 64 280288 4.40 1.08 0.2455 I 65 200388 3.00 0.6 0.12 0.3000 I 66 210388 1.420 0.8 1.00 0.2941 I 67 220388 2.20 0.47 0.2136 I 68 230388 5.12 0.8 1.12 0.2593 I 69 250388 1.40 0.42 0.3000 I 70 010488 9.80 0.8 2.55 0.2833 I 71 030488 25.40 1.2 9.87 0.4079 c 72 040488 19.00 0.6 8.02 0.4221 c 73 040488B# 4.96 1.4 1.79 0.4972 c 74 060488 2.60 0.2 0.26 0.1083 I 75 070488 21.60 9.24 0.4278 c 76 080488 28.00 15.92 0.5686 c 77 090488 1.00 0.31 0.3100 I 78 100488 17.00 0.4 9.69 0.5837 c 79 110488 6.00 0.6 2.73 0.5056 c 80 180488 0.60 0.33 0.5500 c 81 190488 2.60 1.40 0.78 0.65 c 82 280488 22.80 2.8 7.12 0.3560 I 83 290488 105.20 74.24 0.7057 c 84 300488 111.00 87.96 0.8144 c 85 080588 1.80 0.05 0.0278 I * I/C : Impervious Area Runoff/ Combined Runoff # The second storm on the same date Chapter 3 Catchments And Data 3-21
3 J. Fisher's Ghost Creek
This catchment is located in the suburb of Bradbury in the Campbelltown area. It has an area of 2.14 Km2. This catchment mostly consists of residential and park areas with a density of 15 to 20 dwellings per hectare. (Bufill 1989).
3.3.1. Catchment and pipe network
The catchment boundaries are shown in Fig. 3.7. The catchment land use is presented in Table 3.8. The figures in the Table 3.8. and impervious area fraction are gathered from different sources including; catchment layouts, aerial photos and inspection of the catchment by Vale(1986). The catchment network, especially along the main and longest waterway, consists of circular pipes and open natural channels. The soil type of the catchment is mainly red clay overlaid on shale with an approximate depth of 0.2 m. The natural channel bed is mostly rocky. Plates 3.11 to 3.14. show some special features of the catchment. The main waterway characteristics are presented in Table 3.9. This part of the data is taken from Vale's thesis (1986). The assumed roughness coefficients of channel beds by Vale (1986) for simulation of flow by SWMM model were used in the present study (Chapters 4 and 7). Most of the open waterways consist of naturally irregular channels for which the estimate of roughness coefficient is difficult. The reliability of the assumed roughness coefficients is examined by both lag analysis and modelling in this study (Chapters 4 and 7). Chapter 3 Catchments And Data 3-22
= 0' > * 5 — ^
S S 5 7. 5 -
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Fig. 3.7. Fisher's Ghost Creek Catchment Boundary Chapter 3 Catchrr.eiv.-. And M..\. .
Table 3.8. Land use of Fisher's Ghost Creek Catchment
Land Use Area,Km2 Ratio Ac
Residential 1.63 76
Commercial 0.04 2
Roads 0.24 12
Parks 0.23 10
Table 3.9. The main waterway characteristics of Fisher's Ghost Creek catchment
Branch Type Length, Depth*/ n Base Side Bed m Dia.,m width.m Slope, 1/z Slope, % 479 - 480 pipe 90 0.45 0.012 4.00 475 - 479 n 71 0.53 0.012 4.12 465 - 469 n 102 0.68 0.013 3.32 445-449 " 81 0.90 0.013 1.99 415-419 channel 83 1.50 0.050 1.0 0.167 2.65 395 - 399 n 170 2.00 0.045 1.0 0.10 3.76 385 - 389 pipe 75 1.35 0.013 3.13 365 - 369 ti 244 1.35 0.013 2.09 355 - 359 ti 68 1.35 0.013 1.50 285 - 289 channel 102 4.00 0.05 1.5 0.50 3.24 265 - 269 n 252 4.00 0.07 2.5 0.50 2.86 205 - 209 n 96 5.00 0.08 2.0 0.50 3.96 165-169 II 55 5.00 0.08 2.0 0.50 1.58 135 - 139 n 173 5.00 0.055 2.5 0.25 1.58 125 -129 ti 137 6.00 0.05 1.0 0.33 2.48 55-59 M 180 6.00 0.05 2.0 0.667 2.50 45-49 pipe 131 2.50 0.012 1.34 15 -19 channel 60 6.00 0.025 2.0 0.333 0.20 05-09 n 1 6.00 0.04 3.0 0.25 1.00 * Depth to bankfull ( haptei 3
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Plate 3.11. A residential area - Fisher's Ghost Creek
Plate 3.12. Combination of pipe and open natural channels - Fisher's Ghost Creek Chapter 3 ('iii' lunents And Data 3-25
Plate 3.13. Natural reserve within the catchment - Fisher's Ghost Creek
Plate 3.14. Rocky bed open channel - Part of main waterway - Fisher's Ghost Creek Chapter 3 Catchments And Data 3-26
3.3.2. Rainfall and discharge measurement stations
Rainfall is recorded by two pluviometers located near the catchment outlet and top. Runoff is measured by means of a sloping crested weir and a stilling well. Plates 3.15. and 3.16. show the rainfall and runoff measuring stations. A detailed stage-discharge table was prepared for the weir by Department of Water Resources, DWR. a summary of which is presented in Table 3.10 and shown in Fig. 3.8 ( Vale 1986). Considering the stability of the control section, the rating curve does not change with respect to time.
Table 3.10. Fisher's Ghost Creek rating table at Bradbury (from DWR)
Gauge Height, m Discharge, m3/ s 0.1 0.0001 0.2 0.0316 0.3 0.159 0.4 0.422 0.5 0.860 0.6 1.50 0.7 2.41 0.8 3.53 0.9 4.81 1.0 6.25 1.1 7.87 1.2 9.61 1.3 11.5 1.4 13.5 1.5 15.6 1.6 17.7 1.7 20.0 1.8 22.5 1.9 24.9 2.0 27.3 Chapter 3 Catchments And Data 3-2'
'.. . A'f'O mil • it EIHIlil»:ir-:....„rrTTiuV • • ••.!.11IM~.: •fit :C"gi:^^jtfc:ir22mMliKC mini HI* 'i. IIIIII , . . e:^iia.':»bimnnn Si jfati, unit HEMMr1'1*51 -SM^ IHIIHH mm _ •••••aimh&iiiiiiaiiuiisESEiai inim !!!! ••••••aiitnii IIIIIIIIIIIIHII iKi-iL- -u-i.i-1: JBBS5! iiiiiiiiiiiiiiid:"iieiiBiiiiiniii ••••••••••••••Ill I ••'CIIIIIIIIIIIIIMI.-•Minimi -leeiera:::* >rs8i!iiEaiiiiiiiiiiiiii».:.Miii}i :;:aieM«»r tt?XWaaaaaauaaaaaaaaaaiaaaaiaaiakkj- U~A, o imiHiMiifiBMeiiiiir iBR-naMirgaaiaDaasBaaaaaiiaaaBii
Plate 3.15. Rainfall measuring station near the catchment outlet - Fisher's Ghost
Creek
Plate 3.16. Water level measurement - looking downstream - Fisher's Ghost Creek Chapter 3 Catchments And Data 3-2S
Q - CMS
Fig. 3.8. The Fisher's Ghost Creek rating curve at Bradbury park ( From DWR)
3.3.3. Rainfall and Stormflow Data
A total of 23 measured rainfall and runoff events were available for this study which are presented in Table 3.11. The time step of rainfall and runoff is 3 minutes which is fairly suitable for analysis in this catchment. The rainfall data which have already been weighted using Theissen polygon by Bufill (1989) are used in this study directly. Chapter 3 Catchments And Data 3-29
Table 3.11. Rainfall and Runoff record - Fisher's Ghost Creek (After Bufill 1989)
Date Total Rain, Initial Loss, Runoff, Runoff Ratio I/C* No. mm mm mm
1 040581 12.46 0.6 4.10 0.3448 I 2 191081 19.05 7.83 0.4110 C 3 021181 39.57 17.59 0.4445 C 4 251281 13.54 4.77 0.3523 I 5 050383 25.82 2.12 0.0821 I 6 170383 19.05 2.0 4.32 0.2528 I 7 200383 66.69 40.57 0.6083 c 8 271183 25.61 6.77 0.2643 I 9 131283 15.58 3.62 0.2323 I 10 260184 24.84 1.5 6.90 0.2951 I 11 070284 19.65 3.20 0.1628 I 12 081184 21.71 10.77 0.4961 c 13 091184 19.94 10.93 0.5481 c 14 111184 21.70 7.03 0.3240 I 15 081285 20.02 5.7 5.80 0.4042 c 16 150186 54.41 21.84 0.4014 c 17 060886 86.53 57.32 0.6624 c 18 181186 14.84 4.24 0.2857 I 19 161087 23.22 5.09 0.2192 I 20 241087 84.62 37.43 0.4423 c 21 280488 170.55 91.96 0.5392 c 22 240588 39.67 1.5 10.19 0.2672 I 23 050688 116.99 57.58 0.4922 c * I/C : Impervious Area Runoff only / Combined Runoff Chapter 3 Catchments And Data 3-30
3.4. Strathfield
This is a fully developed catchment with an area of 2.34 Km2 located in Strathfield, a southern suburb of Sydney. This catchment was instrumented by the University of New South Wales in 1958. Plates 3.17 and 3.18 show some parts of the catchment.
3.4.1. Catchment and pipe network
The catchment boundary map is presented in Fig. 3.9. This catchment has a fully piped drainage network which ends in a U-shaped lined channel at the outlet (Plate 3.19). The closed conduits of the drainage system consist of circular and oval cross-section pipes. The catchment land use is depicted in Table 3.12. The total impervious fraction area of the catchment, directly connected impervious and supplementary impervious areas, was determined as equal to 50% by Aitken (1970) using 1:100 aerial photographs. Bufill (1989) estimated that the fraction of directly connected impervious area of the catchment was equal to 31 % using analysis rainfall and runoff. Considering the total percentage of roads and commercial areas, 23%, according to Bufill (1989), it is concluded that the remaining 8% consists of the impervious areas ofresidential places is connected to the drainage system. The 8% impervious area could be directly in the form of pathways, parking lots or probably in the form of roof-to-gutter connection systems. The main pipe
line specifications are presented in Table 3.13. Chapter 3 Catchments And Data 3 51
Plate 3.17. Residential areas ending in commercial area - Strathfield ,1^
VAN0) VANUY' X
Plate 3.18. Commercial area of the catchment - Strathfield Chapter 3 Catchments An J [)<::,_:
2 <»
u ^ = - —' x = ' < ~ ' •
* • :'A?A i "' «'—->\- If AA_
:J.A/>ArQ rT^^=^'I AVJI i| 1L_C I'\ J-l Jf-M?A A " M'7" "j1".-^--"' rA^^Oj^---^ r i iT MrHfTK^i ; 3 ML .' ii. _,| EMrlj
- -'; iC" " 'At1: AAA:1" TiP^Ti^r^-"" -^Sr^F—A • i^nn:.s. -'fc.r :zr.t A-5.' 1 J I r: ••r^NMl j SIHS
Fig. 3.9- Strathfield catchment boundaries Chapter 3 Call hmcnts And Data 3-33
Plate 3.19. U-shape outlet channel and water level recorder - Strathfield
Plate 3.20. A pluviograph near the catchment outlet - Strathfield Chapter 3 Catchments And Data 3-34
Table 3.12. Land use of Strathfield catchment
Land Use Area, Km2 Ratio,%
Residential 1.79 76
Commercial 0.05 2
Roads 0.48 21
Parks 0.02 1
Table 3.13. Main pipe line specifications of Strathfield catchment
Branch Type# Length, Depth*Widtn/ Slope, m Dia., % m T-U U 39.3 3.2*2.0 3.6 U-V O 44.2 3.2*2.0 7.58 V-W O 271.3 2.7*1.8 6.8 W-X O 91.5 2.5*1.8 6.8 X-Y O 139.0 2.4*1.8 6.8 Y-Z O 118.3 2.0*1.8 9.7 Z-ZA 0 190.9 1.9*1.8 9.7 ZA-ZB 0 132.6 1.9*1.8 6.13 ZB-ZC c 129.3 1.7 8.33 ZC-ZD c 52.1 1.7 8.33 ZD-ZE c 23.8 1.5 8.33 ZE-ZF c 110.4 1.2 10.0 ZF-ZG c 170.4 1.2 9.32 ZG-ZH c 47.3 1.1 16.67 ZH-ZJ c 37.8 1.1 12.8 ZJ-ZK c 88.4 1.1 9.50 TOTAL 1686.6 #U
3.4.2. Rainfall and Discharge Measurement Stations
A water level recorder was installed at the U-shaped outlet channel by UNSW in 1958. The stream gaugings have been carried out since 1973 by UNSW and a rating curve with an extrapolation of up to 3.0 m was prepared. Two installed raingauges by UNSW have been operating in this catchment since 1977. One of these raingauges is located near the outlet and the other near the catchment centre ( Plate 3.20). The records of these Chapter 3 Catchments And Data 3-35 raingauges are weighted by Bufill (1989) using the Theissen method which have been directly used in this study as well.
3.4.3. Rainfall and Stormflow Data
A total of 78 simultaneous rainfall-runoff events were available for this study (Table 3.14). The time step of the rainfall hyetographs and hydrographs varies from 3 to 10 minutes. In contrast to the other catchments, the runoff ratio was calculated using total rainfall and disregarding initial loss by Bufill (1989). She stated that the probable introduced error due to the large time step of rainfall hyetographs was the main reason for ignoring the initial loss. In spite of a fairly lengthy record for this catchment, the number of combined events is very low which shows the highly pervious soil type in the catchment. The soil type of the catchment isfine san d / sandy loam. Chapter 3 Catchments And Data 3-36
Table 3.14. Rainfall and Runoff record - Strathfield ( After Bufill 1989)
Date Totd Rain, Runoff, Runoff Ratio I/C* No. mm mm
1 210277 10.70 2.69 0.2514 2 070477 12.50 3.39 0.2712 3 080677 9.70 2.45 0.2526 4 030977 5.95 1.86 0.3126 5 270977 7.77 3.32 0.4273 6 270378 3.20 1.16 0.3625 7 040978 5.55 2.31 0.4162 8 070978 6.53 2.76 0.4227 9 311078 4.27 1.59 0.3724 10 170379 18.70 2.45 0.1310 11 260779 5.65 1.93 0.3416 12 100180 9.40 2.58 0.2745 13 130180 9.82 3.27 0.3330 14 140180 6.15 1.42 0.2309 15 121080 12.00 3.99 0.3325 16 161280 3.37 1.01 0.2997 17 291280 15.90 4.20 0.2642 18 100281 8.95 2.01 0.2246 19 020381 29.55 8.69 0.2941 20 020481 6.95 1.43 0.2058 21 040481 2.50 1.02 0.4080 22 04048IB 5.25 1.66 0.3162 23 220581 5.00 0.89 0.1780 24 22058IB 20.20 6.70 0.3317 25 220581C 5.30 1.51 0.2849 26 061181 8.70 0.89 0.1022 27 121281 9.78 2.10 0.2147 28 191281 8.37 2.37 0.2832 29 251281 12.87 3.98 0.3092 30 310182 14.20 4.24 0.2986 31 210382 11.85 4.33 0.3654 32 210382B 2.00 1.63 0.8150 C 33 210382C 3.50 1.91 0.5457 c 34 250382 32.25 17.00 0.5271 c 35 280382 5.55 2.19 0.3946 1 36 270982 17.53 6.46 0.3685 37 031282 5.57 1.71 0.3070 38 151282 3.65 1.89 0.5178 c 39 100283 7.20 1.44 0.2000 40 160383 99.19 32.24 0.3250 41 210583 57.47 25.62 0.4458 42 180683 4.20 1.43 0.3404 43 030983 4.82 1.39 0.2884 44 290983 4.88 1.55 0.3176 45 191083 13.45 3.54 0.2632 46 131283 11.22 4.51 0.4020 47 281283 9.47 3.09 0.3263 48 080184 26.33 7.40 0.2810 49 180284 10.85 4.40 0.4055 50 220384 11.62 5.42 0.4664 51 230384 5.20 1.52 0.2937 I 52 070484 42.39 11.87 0.2800 I 53 080984 11.80 3.36 0.2847 I 54 061184 3.30 1.23 0.3727 I 55 081184 171.21 90.10 0.5263 C 56 111184 16.60 6.61 0.3982 I 57 240385 11.12 2.80 0.2518 I 58 030485 5.32 1.55 0.2914 I 59 230485 6.55 2.68 0.4092 I 60 300485 9.50 5.63 0.5926 C 61 010585 6.10 3.86 0.6328 c 62 231085 12.12 4.46 0.3680 I 63 271085 4.20 1.48 0.3524 I 64 081185 6.85 2.29 0.3343 I 65 261185 10.00 3.69 0.3690 I 66 271185 3.80 3.16 0.8316 c 67 161285 8.60 2.69 0.3128 I 68 161285B 7.10 2.26 0.3183 I 69 150186 26.67 6.78 0.2542 I 70 160186 7.90 2.97 0.3759 I 71 120286 8.70 3.30 0.3793 I 72 090386 6.32 1.94 0.3700 I 73 040886 361.62 298.46 0.8253 c 74 130288 101.66 48.26 0.4747 I 75 030488 120.70 66.61 0.5519 c 76 280488 322.55 249.27 0.7728 c 77 240588 23.74 6.58 0.2772 I 78 040788 123.20 94.89 0.7702 c * I/C : Impervious Area Runoff only / Combined Runoff Chapter 3 Catchments And Data 3-38
3.5. Cranebrook
This is a small catchment with a total area of 11.5 hectares located approximately 4 Km to the north of Penrith town centre. The land use consists of 31% paved areas, 62% grassed areas and 7% supplementary paved areas (Gallen 1990). A combination of aerial photos and work-as-Executed drawings were used to determine land use pattern in this catchment (Gallen 1990). Adding the percentage of the paved area to the supplementary paved area, the total fraction of imperviousness of the catchment becomes 38%. The soil type of the catchment is clay with high potential for runoff generation. The catchment instrumentation was performed by UTS in 1986.
3.5.1. Catchment and pipe network
This is a totally sewered catchment. The catchment boundaries and pipe network are shown in Fig. 3.10. At the time of Gallen's study (1990) 40 properties out of a total of 120, were vacant, so the catchment has undergone many changes since then. Obviously the land use changes directly affect the runoff characteristics. However, the analysed data in the present study belong to the years before 1990. The main pipe line specifications are presented in Table 3.15. In Plates 3.21 to 3.22 some of the catchment sites are illustrated. Chapter 3 Catchments And Data 3-39
Fig. 3.10. Cranebrook catchment boundaries and pipe network Chapter 3 ( . ,•• \>:,1 Ih;:,. 3-i'
***S
A^^^Ar:v.'-M^t^.
Plate 3.21. Catchment boundaries at the top - Cranebrook
£*• WW J/JAS V^^K'.* \ -r-
\ ; 2 -
• ' : A, »./— A- ; :•. AA:-:,iA:A 'A: A^ i A Plate 3.22. Direct measurement of pit depths - Cranebrook Chapter 3 Catchments And Data 3-41 Table 3.15. The main pipe line specifications of Cranebrook catchment Branch Length, Dia., m Slope, % m 1-2 10 0.375 7.35 2-3 14 0.375 3.2 3-4 36 0.375 4.9 4-5 38 0.450 4.9 5-6 20 0.375 2.35 6-7 30.5 0.450 0.85 7-12 13 0.450 1.04 12-13 9 0.450 1.04 13-16 64 0.450 6.38 16-17 32 0.450 7.2 17-19 59.3 0.450 4.93 19-21 52 0.600 2.00 21-23 23 0.675 1.4 23-26 59 0.675 1.8 26-43 67 0.750 2.5 43-44 35 0.825 1.5 44-Outlet 10 0.900 1.1 Total 571.8 3.5.2. Rainfall and discharge measurement stations The catchment 900 mm diameter outlet pipe discharges to a flume in which the water level is measured by means of a stilling well and a float. Rainfall is measured by a pluviograph located in a brick shelter where the water leveller and other equipment are kept. Rainfall is caught by a receiving area posted above the shelter and connected to the pluviograph. Runoff from the catchment ends in a detention basin located at the outlet. (Plate No. 3.23 and 3.24). The time increments of the available rainfall data are mostly 5 and 10 minutes which are too coarse for this small size catchment. 3.5.3. Rainfall and stormflow data A total of 28 simultaneous hyetographs and hydrographs were made available for this study by UTS. Table 3.16. shows the depth of runoff and rainfall and also runoff ratio for each event. Chapter 3 ' '<..'. i''y.i'".t> And Data 3-41 Plate 3.23. Rainfall - Runoff measuring station - Cranebrook Plate 3.24. A detention basin receives catchment runoff - Cranebrook Chapter 3 Catchments And Data 3-43 Table 3.16. Rainfall and Runoff record of Cranebrook catchment Date Total Rain, Runoff, Runoff Ratio I/C* No. mm mm 1 191087 36.2 13.43 0.371 C 2 231087 4.2 0.66 0.158 3 191087B# 27.8 8.05 0.289 4 201087 3.0 1.23 0.409 C 5 201187 1.0 0.22 0.222 6 161087 2.6 1.34 0.516 C 7 151187 1.6 0.04 0.028 8 111187 13.8 6.69 0.485 C 9 211087 3.4 0.39 0.115 10 111187B 1.4 0.90 0.640 C 11 311187 2.4 0.08 0.034 12 281087 1.2 0.21 0.176 13 300388 3.2 0.24 0.076 14 310388 6.2 0.50 0.080 15 050388 6.6 0.30 0.044 16 240588 1.2 0.20 0.169 17 250388 0.6 0.09 0.148 18 240588 7.6 4.02 0.529 c 19 200388 4.6 0.74 0.161 20 200188 7.0 0.77 0.109 21 190388 3.2 0.23 0.072 22 040488 2.4 0.53 0.220 23 160188 21.6 1.24 0.057 24 080488 23.8 5.04 0.212 25 070488 15.4 1.54 0.100 26 220388 2.0 0.20 0.101 27 250388 0.6 0.09 0.149 28 280288 5.6 0.56 0.100 * I/C : Impervious Area Runoff only / Combined Runoff # The second storm on the same date CHAPTER FOUR DETERMINISTIC EVALUATION OF THE RATIONAL METHOD Chapter 4 Deterministic Evaluation of The Rational Method 4-1 CHAPTER FOUR 4. DETERMINISTIC EVALUATION OF THE RATIONAL METHOD The Rational formula as presented in ARR87 is applied to the catchments under study. To use the Rational formula, two necessary parameters including runoff coefficient and time of concentration of the catchment should be calculated. These two ill-defined parameters are very important in the application of the formula. To evaluate the proposed method in ARR87,firstly tim e of concentration, Tc, and runoff coefficient for the catchments are calculated according to ARR87, and secondly these values are compared with those derived based on the observed data. The ARR87 does not incorporate the soil type of the catchments in runoff coefficient calculation and it is the designer who should make provisions to consider soil type. The use of engineering judgement would cause discrepancies in flood peak design criteria, so other avenues must be sought to incorporate deterministic elements in the statistical methods. In the way towards standardisation of practice, the possiblerelationship betwee n deterministic values and statistical values of runoff coefficient is investigated in this chapter. 4.1. Time of Concentration One of the important factors in application of the Rational formula is the estimation of the time of concentration (Tc) of catchment. Tc is the time required for a particle of water to travel from the most distant part of the catchment to the point at which the discharge estimate is required. Many empirical formulae have been proposed for estimation of Tc within Australia and other countries in the world, but they often contain considerable uncertainty. It is desirable to compare the formulas with the observed hydrographs to determine the best formula (French et al. 1974). McCuen(1989), lists eleven methods of estimating time of concentration (Table 4.1). Some of these methods are designed primarily for overland flow, channel and pipe flow which are called mixed methods. Eagleson's method, which assumes a partially sewered watershed is a mixed method because it has a channel flow component as the input. The main limitation of any Chapter 4 Deterministic Evaluation of The Rational Method 4-2 empirical formula, except those which directly deal with overland and pipe flows, is that it is supposed to be applied in the vicinity of the region that it has been derived from. Using Kinematic Wave Theory to consider the effects of the temporal pattern of rainfall on Tc, Overton (1976) has proposed equations to estimate Tc for simple catchments including rectangular, triangular and converging surfaces. He also recommended a numerical method based on observed hydrograph and hyetograph to compute the weighted average of lag time and Tc. Lag time is the time between centre of mass of hyetograph of excess rainfall and the peak of surface runoff hydrograph. In this case a conversion factor equal to 1.67, based on analysis of a triangular hydrograph, is used to compute Tc (McCuen 1989). Analytical solutions of Tc for three different temporal patterns including; uniform, early peak, and late peak have been presented by Ball (1991). He has proved when the rainfall pattern is late peak or early peak, Tc is 1.22 and 0.81 times as much as that for uniform rainfall. These solutions are applicable for rectangular catchments and only for overland flow. Analytical solutions are suitable for simple catchments, and in real cases the catchments' shapes are complex, and the temporal patterns of rainfall in the work by Ball (1991) are very rare. Table 4.1. Time of concentration empirical formulas and classification of required input (from McCuen 1989) Method* Equation Overland Flow Channel Flow Pi jeFlow R S L I R S L I R s L I * * Carter t,= 1.7Lm0-6Sm-0-3 112 * * * * Eagleson t„ = 0.0001852 Lf n K^Sf 29 14 * * * * Espey-Winslow t. = 0.52 5 333 * * * Federal Aviation tr = 0.03(1.1-C)L°- S-°- Agency * * * * Kinematic wave t„ = 0.01567 L0-6n0.6r0.4Sf-0.33 * * * Kerby-Hathawa} t^O.O^l^n0-4^-0-235 Kirpich(PA) t„ = 0.00002167Lf°-77Sf-0-5 * * KirpichfTN) ^ = 0.00013Lf0-77Sf-0-385 * * SCS lag t„ = 0.000877U0-8(1000/CN-9)0-7S-°-5 * * * 5 * * * SCS velocity tr = 0.0002778X(LfkS-°- ) 13 3 Van Sickle t„ = 0.009167Lf° Lm01 Sf-0°65 * * * R : Resistance S : Slope L : Length I: Rainfall # These methods use a mix of units Chapter 4 Deterministic Evaluation of The Rational Method 4-3 4.1.1. ARR87 method It is possible to combine overland flow, kinematic wave, with pipe and channel flow travel times to estimate Tc for an urban catchment. Runoff in impervious area events only uses gutter flow travel time from ARR87 ( Fig. 14.9) plus pipe flow travel time from Manning's formula. This is referenced to as ARR87 method hereafter. 4.1.1.1. Overland flow travel time Overland flow travel time can be estimated by the kinematic wave equation proposed by Ragan and Duru (1972) in the form of t = 6.94 (L.n*)06 /1 °4 S°3 where t: overland flow time, Minutes L: flow path length, m n*: surface roughness orretardance coefficient I: rainfall intensity, mm/hr S : slope, m/m Overland flow travel time is dependent on the rainfall intensity in the above formula, so it can be solved by iteration or preparation of a table including values of t.I04 versus duration (ARR87). 4.1.1.2. Gutter flow travel time The following formula is proposed in ARR87 to estimate gutter flow travel time (Fig. 4.1) Q = QABC - QDBF + QDEF - QGEH = 8/3 m 8 8/3 1/2 = 0.375F[ (Zg/ng) (dg d, ) + (Zp/np) (dp * - dc )] So The original form of the above formula is the equation of Izzard (1946) for a triangular channel as given below: Chapter 4 Deterministic Evaluation of The Rational Method 4-4 Q = 0.375 Fd8/J So"2 Z/n where Q : the total flow rate, m3/s F : flow correction factor Zg and Zp: gutter and pavement cross-slopes, m/m np and ng: gutter and pavement Manning's roughness coefficients dg and dp: gutter and pavement the greatest depths, m dc: water depth on the road crown, m S0: longitudinal slope, m/m C zg Fig. 4.1. Gutter and roadway profile with vertical kerb( After ARR87) Given the flow depth d, the cross section area A and flowrate Q can be calculated. The gutter flow travel time is then given by length, L, divided by flow velocity (Q/A). With uniform lateral inflow along the gutter, the average velocity occurs at 60% of its length towards the pit (ARR87). Besides the above formula two design charts are presented in ARR87 to estimate gutter travel time which are actually special cases of the formula (Fig. 4.2). To use the formula trial and error is required. A flowrate is estimated by the Rational formula and the time will be calculated by the formula. The process will be repeated until the estimated time and the calculated time match. CJMIIULLA. Deterministic Evaluation of The Rational Method 4-5 ^"' A*' O^ s _ - — "" 005 / ^ — **" / ^, —"* _ 1.6 «4 | 1 / - -- - 0.02 u 0 > OJ ' " " ' - - - ^Se""-"o"bos*lo~5*i 0.1 0.2 0 i 0.2 03 Flowr.le (m'/j] Flowrat* (m'/ll Fig. 4.2. Design charts for gutter flow travel time( Fig. 14.9 ARR87) The travel time in the gutter can also be estimated by Newton's method applied to Manning's equation. The following formula is proposed by Chow et al. (1988) for different channel cross sections. This formula and Manning's equation are solved simultaneously to minimise the error of f = Oj-Q YH-i = yj - {(l-Q/Qj)/[(2/3R)(dR/dy)+(l/A)(dA/dy)]} where: Q: the lateral flow, cfs/ft Q: the estimated discharge by Manning's equation yj+i and yj: the successive depths in iteration [(2/3R)(dR/dy)+(l/A)(dA/dy)]: channel shape function Considering the geometric characteristics of the gutters, vertical kerb, the following formula is solved with the above formula to minimise the error f. 73 2 1/2 Q = (1.49/2n) (Z/V )/ (2(l+Vl+Zg ))2/3 (So) Chapter 4 Deterministic Evaluation of The Rational Method 4-6 The depth of flow and flow velocity are estimated in each foot along the length of gutter, and travel times are summed. The average velocity and total travel time are estimated for the gutter section of the longest flow route to the catchment outlet. Selection of flood peak for gutter flow travel time is a matter of trial and error like that of section 4.1.1.1. for overland flow. Because of short duration of records and small size of events, in this study average values of time of concentration are of concern, so short duration rainfalls with an average return period of 2 year were used to estimate flood peaks. Trial rainfall durations were used until the trial and calculated time of concentration agreed. The flood peaks were used to estimate travel time using Chow's formula. The subcatchment area in this case will be computed based on gutter length and half road width (a rectangle). Runoff coefficient is assumed equal to one. Following the notation in Fig. 4.1. the gutter specifications are given as Length = 252 m S0 = 0.0033 m/m Zg = 0.14 m/m Zp = 0.015 m/m Wg = 0.47 m Wp = 3.88 m ng = 0.012, Manning's roughness coefficient for smooth concrete (Chow 1957) np = 0.014, Manning's roughness coefficient for asphalt (Chow 1957) dg = 0.15 m Gutter type = Vertical kerb These specifications, except length and So, are adopted for the other catchments under study. Normally runoff is conveyed by gutters at the top of urban catchments because flow rate is small. The computation example for the Maroubra catchment is presented in Table 4.2. Chapter 4 Deterministic Evaluation of The Rational Method 4-7 Table 4.2. Average gutter flow travel time estimates using Fig. 14.9 from ARR87- Maroubra (2-year return period rainfall) Duration, 5 6 7 8 9 10 Minutes Rain, mm/hr 125 100 95 90 85 80 Qp, m3/s 0.03823 0.03058 0.02905 0.02752 0.02599 0.02446 OP* 60%, m3/s 0.02294 0.01835 0.01743 0.01651 0.01559 0.01468 Travel time, 9.66 9.60 9.56 9.53 9.49* 9.43 Minutes * Selected as gutter flow travel time To compare Fig. 14.9 from ARR87 with Chow's formula, the selected discharge from Table 4.2., 0.02599 m3/s, was divided by length of the gutter to get unit length discharge. The unit length discharge and So were used for total gutter travel time estimation. For the above gutter, 9.14 Minutes was adopted as the gutter flow travel time using Chow's formula. The flood peak for 9-minute rainfall duration, n equal to 0.012 and the same gutter length and S0 were used in the formula. The results of both methods are very close, so for all catchments in this study the Fig. 14.9 from ARR87 was adopted. Although Chow's method estimates flow velocity from the beginning to the end of the gutter, it uses only one single cross section and in some cases the depth of flow becomes greater than the gutter depth, while in the ARR87, computation of flow velocity in a composite cross section of asphalt and concrete is available and runoff can spread to centerline of streets. Besides the composite cross section ARR87 ignores the flow over kerbs. 4.1.1.3. Pipe flow travel time The travel time estimation method in ARR87 only covers overland flow and gutter flow. For pipe flow travel time the selection of discharge is unknown. Although some engineers assume a full running pipe, in real situations it may not happen in all cases. Generally the flow in the pipe route varies from the beginning to the end. The magnitude of flow within the pipes depends on the flow which enters into the system via pits. This is Chapter 4 Deterministic Evaluation of The Rational Method 4-8 time consuming and requires the entering flows to be computed for the pit at each sub catchment outlet. In this study average pipe travel times are assumed to be 1/4, 1/2, 3/4 and to be completely full all the way down the catchment. Furthermore, the pipe section of the longest route of each catchment is divided into four sections in this study. The first section of the pipe flow length is assumed to run a quarter full, the second half full, the third three quarter full and the last section completely full. The part full velocity of each section is computed based on the velocity of the full discharge of the pipe (Peavy et al. 1985). 4.1.2. Maroubra The characteristics of this catchment are presented in Chapter 3. The longest route from the outlet to the most remote point of the catchment is considered along the main pipe line which ends in a gutter of length 252m. The gutter ends in the playground of Maroubra Junction Public school which should be considered as overland flow for part of the longest waterway route. As discussed in Chapter (3) there was no runoff from pervious areas of this catchment during the record because of the deep sandy soil type. Furthermore, the school play ground is so flat that considering overland flow makes the total travel time so long it affects the results unreasonably. The gutter along the streets at the top of the catchment are vertical kerbed with 15 cm of depth and 0.015 m/m cross-sectional slope. The longitudinal slopes depend on the topography which are estimated from the orthophoto map or direct surveying of gutter slopes. In Fig. 4.3. one of the street cross-sections is presented. Chapter 4 Deterministic Evaluation of The Rational Method 4-9 fl IMA " °»* 047 -*+ > Fig. 4.3. Cross section of Lach Maree street - Maroubra The characteristics of the main pipe line of the Maroubra catchment, full and partly full velocities, are presented in Table 4.3. Manning roughness coefficient for these pipes due to the great age of the system (more than 50 years) is assumed to be 0.017 (Chow 1957). Pipe travel time for this catchment was estimated based on the two described methods in Sec. 4.1.1.3. (Tables 4.3 and 4.4). The comparison of different pipefilling condition s and divided partly full pipes shows that the quarter full pipe travel time is 40% larger than that of the full pipe (Table 4.5.). This difference (diminishes to 5% when 3/4 full is considered. Travel time for 1/2 full and divided partly full pipes is almost equal, i.e. 16 Minutes, and they are 18% larger than that of full condition. It is reasonable to apply full pipe travel time to large and 1/4 full to small frequent events. Practically there is no major difference between 3/4 and full pipe travel times, because 25% reduction in pipe filling only causes a 5% increase in travel time. The adoption of 1/2 full condition or divided partly full seems to give the best estimate of average travel time, so total pipe flow time is equal to 16 Minutes. By adding to this value the gutter flow travel time equal to 9.49 Minutes, total travel time of this catchment or time of concentration, is estimated equal to 25.5 Minutes. Computation of pipe travel time will be performed based on the half full condition for the other catchments. The main waterway of the catchments should only consist of circular cross sectional pipes, otherwise, divided partly full will be used. Chapter 4 Deterministic Evaluation of The Rational Method 4-10 Table 4.3. Velocity estimate in main pipe line - Maroubra Branch Length, Dia., m Slope, vfull> Vpartly Travel Pipe m % m/s full, m/s Time, s Filling 33-32 101.2 0.45 0.89 1.293 0.918 110 1/4 32-31 156.2 0.45 1.00 1.371 0.973 160 1/4 31-29 62.6 1.22 0.39 1.664 1.181 53 1/4 29-28 93.1 1.22 0.28 1.410 1.199 78 1/2 28-27 84.8 1.22 0.21 1.221 1.038 82 1/2 27-22 101.7 1.22 0.19 1.162 0.988 103 1/2 22-21 93.9 1.372 0.22 1.352 1.284 73 3/4 21-17 35.4 1.372 0.48 1.997 1.897 19 3/4 17-8 96.4 1.372 0.73 2.463 2.340 41 3/4 8-4 100.5 1.372 0.30 1.579 1.500 67 3/4 4-3 107 1.524 0.51 2.208 2.208 48 Full 3-2 242.6 1.372 0.46 1.955 1.955 124 Full Total 1275.4 958 Table 4.4. Travel time estimation for different pipefilling condition s - Maroubra Branch Vf, Tt*of v1/4fi Ttof v1/2fi Ttof v3/4f Ttof m/s full m/s 1/4 full m/s 1/2 full ,m/s 3/4 full pipe,s pipe, s pipe, s pipe, s 33-32 1.293 78 0.918 110 1.099 92 1.228 82 32-31 1.371 114 0.973 160 1.165 134 1.302 120 31-29 1.664 38 1.181 53 1.414 44 1.581 40 29-28 1.410 66 1.001 93 1.199 78 1.340 69 28-27 1.221 69 0.867 98 1.038 82 1.160 73 27-22 1.162 87 0.825 123 0.988 103 1.104 92 22-21 1.352 69 0.960 98 1.149 82 1.284 73 21-17 1.997 18 1.418 25 1.697 21 1.897 19 17-8 2.463 39 1.749 55 2.094 46 2.340 41 8-4 1.579 64 1.121 90 1.342 75 1.500 67 4-3 2.208 48 1.568 68 1.877 57 2.098 51 3-2 1.955 124 1.388 175 1.662 146 1.857 131 Total 814 1148 960 858 * Tt: Travel time Chapter 4 Deterministic Evaluation of The Rational Method 4-11 Table 4.5. Comparison of pipes travel times - Maroubra Pipes Condition 1/4 full 1/2 full 3/4 full full divided and partly full Travel Time, 19.1 16.0 14.3 13.6 16.1 Minutes % Change with 40 18 5 0 18 respect to full pipes 4.1.3. Jamison Park The physical characteristics of this catchment are presented in Chapter 3. The longest route from the outlet to the most remote point of the catchment is considered along the main pipe line which ends in a gutter of length 220 m and longitudinal slope of 4.08%. Although in this catchment more than half of the recorded events are combined runoff events, along the longest waterway of the catchment there are no pervious areas to consider overland flow travel time (Chapter 3). The gutter flow travel time is calculated to be equal to 2.4 Minutes using the gutter specifications and the adopted method of Section 4.1.1.2. The flood peak for this gutter is 0.031 m3/s which is based on a 2-yr-5- minute rainfall. Durations of less than 5 Minutes is not available for the catchment, so travel time resulting from 5-minute rainfall was considered. The main pipe route specifications of the catchment and travel time along each part are presented in Table 4.6. The pipe travel time is calculated to be equal to 7.8 Minutes, assuming a Manning roughness coefficient equal to 0.017 and half full pipe conditions all the way down the catchment.. Total travel time of the catchment is estimated to be equal to 10.2 Minutes. Chapter 4 Deterministic Evaluation of The Rational Method 4-12 Table 4.6. Main pipe line travel time- Jamison Park catchment Length, Branch Dia., m Slope, VFulI' Vl/2 Travel m % m/S Full, Time, m/S Sec. D1-D2 40 0.45 2.75 2.27 1.93 21 D2-D3 13 0.45 2.00 1.94 1.65 8 D3-D4 23 0.45 2.50 2.17 1.84 13 D4-D5 52 0.45 3.45 2.55 2.17 24 D5-D6 43 0.45 2.57 2.20 1.87 23 D6-D7 47 0.60 3.45 3.08 2.62 18 D7-D8 39 0.60 2.67 2.71 2.30 17 D8-D9 33 0.60 4.27 3.43 2.92 11 D9-D10 90 0.90 2.51 3.45 2.93 31 D10-D11 60 0.90 1.00 2.18 1.85 32 D11-D12 42.5 0.90 1.33 2.51 2.13 20 D12-D13 22 1.2 0.42 1.71 1.45 15 D13-D14 83 1.2 0.65 2.12 1.80 46 D14-D15 65 1.35 0.47 1.95 1.66 39 D15-D16 12 1.35 0.47 1.95 1.66 7 D16-D17 13 1.54 0.50 2.20 1.87 7 D17-D18 110 1.54 0.238 1.52 1.29 85 D18-END 62 1.54 0.238 1.52 1.29 48 TOTAL 849.5 465 Chapter 4 Deterministic Evaluation of The Rational Method 4-13 4.1.4. Fisher's Ghost Creek The main waterway of this catchment consists of pipes, natural and man-made open channels details of which are given in Chapter 3 (Table 3.9). To compute travel time in the main waterway, it is divided into four sections. As shown in the Maroubra catchment the result of 1/2 full pipes is very close to divided partly full pipes. Due to the existence of open channels in some sections, the full or partly full discharge of each downstream pipe, immediately after each open channel, was adopted as the maximum discharge in the channel as well. For example, for channel sections 205-209 three quarters of the full discharge of the pipes 45-49 was assumed to compute velocity in the channel (Table 4.7). This pipe has a diameter of 2.5 m and is located near the outlet of the catchment. The full capacity of this pipe is computed to equal 34.6 m^/s (using Manning's formula and pipe specifications of waterway sections 45-49 in Table 3.9) which isreasonable a s an index discharge of the upstream channels for computing travel time. For channel sections 415-419 and 395-399 a quarter of the full discharge of pipes 385-389 is assumed. The full discharge of the pipe is 9.45 mVs. In Table 4.7 type, length and travel time of different sections are presented. Thefirst pipe section, 479-480, belongs to the first sub catchment which collects the runoff of gutters. The longest gutter along this pipe has a length of 150 m and bed slope of 7.5%. The gutter travel time is 1.4 Minutes using flood peak equal to 0.022 m3/sresulting from a 2-yr-5-minute rainfall and ARR87 formula. The other gutter specifications and adopted method are just the same as those of Section 4.1.1.2. Total travel time of the impervious areas of the catchment is estimated as equalling 15.0 Minutes. Chapter 4 Deterministic Evaluation of The Rational Method 4-14 Table 4.7. Main waterway flow travel times- Fisher's Ghost Creek Type Branch Length VFull' VPartly Travel Pipe m/S Full, m/S Time, Filling m Sec. 479 - 480 pipe 90 3.88 2.76 32.6 1/4 475 - 479 ft 71 4.40 3.12 22.8 1/4 465 - 469 ft 102 4.30 3.05 33.4 1/4 445-449 ti 81 4.01 2.85 28.4 1/4 415-419 channel 83 1.903 1.339 62.0 1/4 395 - 399 i? 170 2.072 1.462 116.3 1/4 385 - 389 pipe 75 6.60 5.61 13.4 1/2 365 - 369 M 244 5.39 4.58 53.3 1/2 355 - 359 II 68 4.57 3,88 17.5 1/2 285 - 289 channel 102 3.57 2.996 34.0 1/2 265 - 269 H 252 2.638 2.449 102.9 3/4 205 - 209 tf 96 2.703 2.51 38.2 3/4 165 - 169 11 55 1.917 1.782 30.9 3/4 135-139 11 173 2.21 2.053 84.3 3/4 125 - 129 If 137 3.00 3.00 45.7 Full 55-59 It 180 3.367 3.367 53.5 Full 45-49 pipe 131 7.05 7.05 18.6 Full 15-19 channel 60 1.96 1.96 30.6 Full 05-09 II 1 2.357 2.357 0.4 Full Total 2171 818.8 Chapter 4 Deterministic Evaluation of The Rational Method 4-15 4.1.5. Strathfield This catchment is a fully developed catchment with an area of 2.34 Km2 located in Strathfield west of Sydney (Chapter 3). The longest pipe line characteristics and travel time are presented in Table 4.8. The main pipe line comprises of circular pipes at the beginning and oval cross section pipes in the middle to almost the end and finally a U shape cross section channel at the end. This combination makes the computation of average travel time difficult. Although the oval cross section pipe has the best hydraulic efficiency, but they are not common practice in urban drainage any more because of the difficulty in their construction (Anis et al. 1980). The calculated pipe travel time is equal to 10.6 Minutes. The longest pipe line ends in a gutter of length of 470 m and longitudinal slope of 4.3%. There is no overland flow path along the longest route. The gutter flow travel time is calculated to be equal to 4.47 Minutes using ARR87 formula and the gutter specifications of Section 4.1.1.2. The flood peak was equal to 0.075 m3/s computed by using a 2-yr-5-Minute rainfall. Total travel time along the longest route of the catchment is 15.0 Minutes. Table 4.8. Main pipe line travel times - Strathfield Branch Type# Length, Dia., m Slope, vFull' VPartly Travel Pipe m % m/S Full, Time, Filling m/S Sec. ZJ-ZK C 88.4 1.1 9.50 2.376 1.687 52.4 1/4 ZH-ZJ C 37.8 1.1 12.8 2.758 1.958 19.3 1/4 ZG-ZH c 47.3 1.1 16.67 3.147 2.234 21.2 1/4 ZF-ZG c 170.4 1.2 9.32 2.572 1.826 93.3 1/4 ZE-ZF c 110.4 1.2 10.0 2.664 1.891 58.4 1/4 ZD-ZE c 23.8 1.5 8.33 2.822 2.399 9.9 1/2 ZC-ZD c 52.1 1.7 8.33 3.007 2.556 20.4 1/2 ZB-ZC c 129.3 1.7 8.33 3.007 2.556 50.6 1/2 ZA-ZB 0 132.6 1.9*1.8 6.13 3.016 3.016 44.0 Full Z-ZA 0 190.9 1.9*1.8 9.7 3.794 3.794 50.3 Full Y-Z 0 118.3 2.0*1.8 9.7 3.794 3.794 31.2 Full X-Y 0 139.0 2.4*1.8 6.8 3.177 3.177 43.8 Full W-X 0 91.5 2.5*1.8 6.8 3.177 3.177 28.8 Full V-W 0 271.3 2.7*1.8 6.8 3.177 3.177 85.4 Full u-v 0 44.2 3.2*2.0 7.58 3.565 3.565 12.4 Full T-U u 39.3 3.2*2.0 3.6 2.457 2.457 16.0 Full TOTAL 1686.6 637.4 # U : U shape channel, O : Oval cross section pipe, C: Circular cross section pipe Chapter 4 Deterministic Evaluation of The Rational Method 4-16 4.1.6. Cranebrook This is a small catchment which has no overland flow along the longest watercourse. The gutter length along the main pipe route to the catchment boundary is equal to 140 m with a longitudinal slope of 2.6%. The gutter flow travel time estimated by ARR87 formula is equal to 2.0 Minutes using the same gutter specifications and adopted method as those presented in Section 4.1.1.2. The flood peak was equal 0.019 m3/s resulting from a 2-yr- 5-Minute rainfall. The pipe travel time is estimated as equal to 4.6 Minutes (Table 4.9). It should be noted that half full pipe discharge is used here because all the cross sections of the pipes are circular. Total travel time or time of concentration for this catchment is estimated as equal to 6.6 Minutes The summary results of the travel time as estimated by the ARR87 method proposed are presented in Table 4.10. Table 4.9. Main pipe line trave times - Cranebrook Branch Length, Dia., m Slope, VFull' Vl/2 Travel m % m/S Full, Time, m/S Sec. 1-2 10 0.375 7.35 3.29 2.80 4 2-3 14 0.375 3.2 2.17 1.84 8 3-4 36 0.375 4.9 2.69 2.29 16 4-5 38 0.450 4.9 3.03 2.58 15 5-6 20 0.375 2.35 1.86 1.58 13 6-7 30.5 0.450 0.85 1.26 1.07 29 7- 12 13 0.450 1.04 1.40 1.19 11 12-13 9 0.450 1.04 1.40 1.19 8 13-16 64 0.450 6.38 3.46 2.94 22 16-17 32 0.450 7.2 3.68 3.13 10 17-19 59.3 0.450 4.93 3.04 2.58 23 19-21 52 0.600 2.00 2.35 2.00 26 21-23 23 0.675 1.4 2.13 1.81 13 23-26 59 0.675 1.8 2.41 2.05 29 26-43 67 0.750 2.5 3.05 2.59 26 43-44 35 0.825 1.5 2.51 2.13 16 44-Outlet 10 0.900 1.1 2.28 1.94 5 Total 571.8 274 Chapter 4 Deterministic Evaluation of The Rational Method 4-17 Table 4.10. Summary results of ARR87 method (Fig. 14.9 from ARR87 +Manning's formula) of travel time estimation Catchment Overland Gutter Flow Pipe Row Total Travel Flow Time, Travel Time, Travel Time, Time, Minutes Minutes Minutes Minutes Maroubra - 9.5 16.0 25.5 Jamison Pk - 2.4 7.8 10.2 Fisher's Ghost - 1.4 13.6 15.0 Strathfield - 4.4 10.6 15.0 Cranbrook - 2.0 4.6 6.6 4.2. Estimate of Time of Concentration Using Observed Rainfall - Streamflow In this study the following methods have been used for the catchment Tc estimate using the observed data. Thefirst one is the so called Typical Minimum Time of Rise which is suggested by French et al. (1974) mainly for rural catchments. The relation between time ofrise of hydrograph and duration of rainfall is studied in this method to decide the best estimate for Tc. The second method is the well known Lag Method. In this method recession limbs of hydrographs are plotted on a semi-log paper and slopes are evaluated to compute lag time of surface runoff. 4.2.1. Minimum time of rise Time of rise or time to peak of a hydrograph from the beginning of excess rainfall on the catchment, TR, could represent the time when the whole catchment is contributing to surface runoff generation. The time of rise is plotted versus the burst duration and the typical minimum time of rise would be taken as the best estimate of time of concentration of each catchment (French et al. 1974). The events are evaluated in each catchment based on the nature of generated runoff regarding impervious areas or the whole catchment. The burst durations are selected to cover the intense portion of the storms which produce the particular peak discharges (Fig. 4.4.). Chapter 4 Deterministic Evaluation of The Rational Method 4-18 * <~ I C\J MRROUBRR 1—4 C3 cn d - DfiTE;D10377 OBS. F3_ CO <_3 oo I d ED =* o CD I i d 10 20 30 40 50 TR TIME - Min aslD1 Fig. 4.4. Illustration of burst duration and time ofrise of hydrograph Theoretically when the duration of uniform rainfall, Tr, is more than Tc, equilibrium is reached and TR = Tc. The variations of time ofrise relate d to duration of rainfall and Tc for a rectangular catchment are demonstrated in Fig. 4.5. These patterns forrise time are not exactly expected inreal catchment s with non uniform temporal or spatial rainfall. The minimum time ofrise has been proposed by French et al. (1974) as the most suitable measure of Tc, but the partial area effect when the rainfall duration is less than time of concentration may cause underestimation of Tc by this method. Regarding this limitation, a trend like that shown in Fig. 4.6. for uniform rainfall is expected if the most intense parts of storms, burst durations, are plotted versus time ofrise of the resulting peaks. The line of equal value in this figure separates those events where their burst durations are less than the time of concentration. In Fig. 4.6 empty circles show the events with burst duration less than time of concentration and vice versa for thefilled ones. Chapter 4 Deterministic Evaluation of The Rational Method 4-ly (a) p,q pm Case Tr < Tc (mm/hr) Cr = qp/pm < Q/P = Cv 0 Tr Tc (Tr + Tc) (b) p,q Case Tr = Tc (mm/hr) Cr = qp/pm = Q/P = Cv 0 Tr = Tc 2Tc (Cj Case Tr > Tc p.q (mm/hr) Cr = qp/pm = Q/P = Cv 0 Tc Tr Tr + Tc Tr: rainfall duration, minute Tc : time of concentration, minute pm : mean rainfall intensity, mm/hr qp : peak runoff rate, mm/hr P : rainfall depth, mm Q : runoff volume, mm over catchment Cv : volumetric runoff coefficient Cr: rate runoff coefficient q : runoff rate, mm/hr p: rainfall intensity, mm/hr Fig. 4.5. Variation of runoff hydrograph with duration of rainfall based on the Rational Method theory (After French et al. 1974) Chapter 4 Deterministic Evaluation of The Rational Method 4-20 TR Tc BD>TC • BD Tc BD Fig. 4.6. Theoretical estimation of time of concentration -uniform rainfall 4.2.1.1. Maroubra Time ofrise an d burst duration for 75 individual rises and peaks within the 39 observed events are presented in Table 4.11. To examine the trend in Fig. 4.6 with real data, time ofrises versus burst durations are plotted in Fig. 4.7. This figure indicates that many events have similar times ofrise but different burst durations. The time ofrise ranges between 6 and 36 Minutes. According to the criteria of the above method, 6 Minutes is taken as a typical minimum time ofrise as Tc. The average of the time ofrises, for BD > TR, is 13.6 Minutes. Chapter 4 _—_ Deterministic Evaluation of The Rational Method 4-21 Table 4.11. Maroubra: Time ofrise an d burst duration Date Time Burst Date Time Burst No. of Rise Duration No. of Rise Duration (TR), (BD), (TR), (BD), Minutes Minutes Minutes Minutes 1 010377 18 63 40 111284 24 29 2 II 12 21 41 010585 12 15 3 050377 9 12 42 081185 9 15 4 030378 12 18 43 271285 21 18 5 170378 9 90 44 160186 24 24 6 180378 12 15 45 M 9 21 7 n 12 12 46 ft 24 30 8 ti 18 27 47 120486 18 27 9 n 15 39 48 040187 15 12 10 190378 9 9 49 030787 12 24 11 n 9 12 50 231087 27 45 12 n 6 12 51 201087 12 24 13 270378 6 9 52 130288 12 15 14 080478 12 24 53 it 9 24 15 II 6 9 54 ti 9 21 16 it 9 30 55 tt 9 12 17 180578 12 21 56 ti 9 21 18 210578 21 18 57 n 9 18 19 210578B 15 24 58 250388 12 21 20 290578 15 48 59 II 21 36 21 it 18 42 60 020488 12 27 22 n 27 47 61 n 12 18 23 n 9 18 62 n 6 18 24 n 30 63 63 n 15 24 25 130678 12 27 64 it 9 21 26 ti 12 15 65 it 9 24 27 130478 21 39 66 tt 6 24 28 n 15 15 67 it 6 16 29 II 18 24 68 n 6 6 30 190679 18 36 69 tt 9 18 31 200679 9 21 70 n 12 15 32 170383 12 30 71 it 15 24 33 180683 15 15 72 n 9 21 34 051184 12 60 73 070488 18 15 35 061184 12 24 74 280488 18 33 36 061184B 36 42 75 150688 27 30 37 061184 9 12 38 111184 12 24 39 n 12 12 Chapter 4 Deterministic Evaluation of The Rational Method 4-22 oo a .1 OJ C. m CD 0.0 20.0 4=0.0 60.0 60.0 100.0 120.0 BD - Min. Fig. 4.7. Maroubra Impervious area runoff 4.2.1.2. Jamison Park In Jamison Park catchment the time ofrise variation s have been studied for two cases. In Tables 4.12 and 4.13 time ofrise an d burst duration are presented for both impervious area runoff and for combined impervious and pervious area events. The typical minimum time ofrise fo r impervious areas is 5 Minutes. Making decision on the typical minimum time ofrise o f combined events is difficult, because there are 5 times ofrise equal to 4 or 5 Minutes, but 14 observations of 10 Minute time ofrise. There is no clear definition for typical minimum time ofrise and it is mostly a matter of judgement. In this catchment 10 Minutes was considered as a typical minimum time ofrise, firstly because for combined events, contribution of some pervious areas are expected and longer time of rise is concluded. (Figs 4.8 and 4.9). Average times ofrise for both cases of impervious areas runoff and combined events,, for BD > TR, were found to be 15.5 and 16.5 Minutes respectively. Chapter 4 Deterministic Evaluation of The Rational Method 4-25 Table 4.12. Jamison Park - Impervious area runoff events Date Time Burst Date Time Burst No. of Rise Duration No. of Rise Duration (TR), (BD), (TR). (BD), Minutes Minutes Minutes Minutes 1 150383 20 30 19 060987 10 30 2 170383 6 25 20 231087 15 35 3 170284 20 40 21 241087B 5 20 4 251185 20 40 22 011287 5 20 5 261185 10 30 23 291287 20 30 6 040186 10 20 24 240188 5 15 7 171286 5 20 25 070288B 15 35 8 010187 20 40 26 280288 5 25 9 100287 5 10 27 200388 10 45 10 210287 10 30 28 210388 5 15 11 010387 95 60 29 220388 10 10 12 020387 30 220 30 230388 5 10 13 220687 40 40 31 250388 5 10 14 100887 30 40 32 010488 15 20 15 130887 10 20 33 060488B 10 40 16 130887B 20 60 34 090488 10 10 17 170887 50 50 35 280488 15 40 18 180887 60 80 36 080588 10 10 Chapter 4 Deterministic Evaluation of The Rational Method 4-24 Table 4.13. Jamison Park - Combined runoff events Date Time Burst Date Time Burst No. of Rise Duration No. of Rise Duration (TR), (BD), (TR), (BD), Minutes Minutes Minutes Minutes 1 210383 10 20 20 261186 7.5 25 2 271183 30 110 21 270887 30 50 3 131283 15 20 22 241087 10 20 4 120184 20 40 23 111187 40 80 5 140284 25 40 24 010188 5 15 6 150284 10 20 25 020188 5 30 7 220384 10 40 26 210188 5 20 8 190684 50 50 27 230188 35 25 9 270784 80 70 28 080288 10 20 10 100884 10 40 29 030488 20 40 11 071184 12.5 32.5 30 040488 5 15 12 111184 10 20 31 040488B 4 6 13 291184 10 60 32 070488 10 40 14 131285 30 60 33 080488 30 20 15 141285 30 50 34 100488 10 40 16 030686 10 70 35 110488 10 40 17 091086 10 10 36 180488 10 5 18 121186 20 40 37 190488 20 5 19 191186 30 40 38 290488 30 40 Chapter 4 Deterministic Evaluation of The Rational Method 4-2* o.o 20.0 lio.o eo.o eo.o 100.0 120.0 BD - Min. Fig. 4.8. Jamison Park impervious area runoff events(some points are overlaid) 0.0 20.0 to.o eo.o eo.o IOO.O 120.0 BD - Min. Fig. 4.9. Jamison Park combined events(some points are overlaid) Chapter 4 Deterministic Evaluation of The Rational Method 4-26 4.2.1.3. Fisher's Ghost Creek For this catchment time ofrise and burst duration have been investigated separately for both impervious area runoff and combined events. Time of rise of impervious runoff events could represent the time of concentration of the impervious area drainage system, which includes both gutter and pipe flow travel time. Time of rise of combined events includes overland flow travel time besides those of gutter and pipe flow, and could be taken as the whole time of concentration of the catchment. In Tables 4.14 and 4.15 the time ofrise and the burst duration are presented for both cases. As is shown in Fig. 4.10 the minimum time ofrise for impervious runoff events is equal to 18 Minutes. There is an observation of 12 Minutes but it could not be taken as typical. For combined events 39 Minutes is selected as a typical minimum time ofrise (Fig . 4.11). The average values of time of rises of both cases, for BD > TR, were found to be equal to 24.6 and 55.1 Minutes respectively. Table 4.14. Fisher's Ghost Creek - Impervious area runoff events Date Time Burst Date Time Burst No. of Rise Duration No. of Rise Duration (TR), (BD), (TR), (BD), Minutes Minutes Minutes Minutes 1 040181 21 42 9 111184/1 24 27 2 050383 18 45 10 111184/2 24 30 3 170383 12 36 11 181186 30 42 4 271183/1 42 54 12 161087/2 21 54 5 271183/2 27 29 13 251281 30 39 6 271183/3 18 27 14 131284 18 30 7 271183/4 24 45 15 260184 42 129 8 070284 18 48 16 240588 - 39 Chapter 4 Deterministic Evaluation of The Rational Method 4- Table 4.15. Fisher's Ghost Creek - Combined events Date Time Burst Date Time Burst No. of Rise Duration No. of Rise Duration (TR), (BD), (TR), (BD), Minutes Minutes Minutes Minutes 1 191081 128 180 8 150186/2 24 48 2 021181 39 27 9 060886/1 - 24 3 200383 45 45 10 060886/2 45 99 4 081184 117 189 11 241087/1 - 45 5 091184 78 117 12 241087/2 - 120 6 081285 18 45 13 280488 - 27 7 150186/1 18 45 14 050688 39 75 oo =3" c= / + -4- d — O OJ ~ c en /++ co 1- + i A + + ct CO /-i-++/ - •+- to — / + CO CD ~ O CD r i i 1 1 1 o.o yo.o ao.o 120.0 teo. o 200.0 2*i0.0 BD - Min. Fig. 4.10. Fisher Ghost Creek impervious runoff event Chapter 4 Deterministic Evaluation of The Rational Method 4-28 o.o yo.o eo.o 120.0 ieo.0 200.0 240.0 BD - Min. Fig. 4.11. Fisher's Ghost Creek combined events 4.2.1.4. Strathfield The majority of observations in this catchment are for impervious areas. However, study of the burst duration and time ofrise wa s performed for both impervious area runoff and combined events. The computed data are presented in Table 4.16 for two cases. The estimated typical minimum time ofrise fo r impervious areas and combined events are 7.0 and 15.0 Minutesrespectively (Fig . 4.12 and 4.13). It should be noted that for combined events the estimation is subjective because of the large scatter of the observations. The average values of time ofrises fo r both cases were calculated as equal to 12.4 and 17.3 Minutes (for BD>TR). Chapter 4 Deterministic Evaluation of The Rational Method 4-29 Table 4.16. Strathfield - Total events Date Time Burst Date Time Burst No. of Rise Duration No. of Rise Duration (TR), (BD), (TR), (BD), Minutes Minutes Minutes Minutes 1 210277 7 9 39 100283 7 12 2 070477 11 15 40 160383 21 36 3 080677 12 24 41 180683 12 27 4 030977 11 21 42 030983 10 21 5 270977 14 21 43 290983 8 12 6 270378 4 6 44 191083 16 39 7 040978 19 33 45 131283 30 45 8 070978 21 51 46 281283 7 33 9 311078 7 6 47 080184 14 36 10 170379 28 27 48 180284 30 45 11 260779 45 30 49 220384 20 42 12 100180 12 24 50 230384 8 15 13 130180 7 24 51 080984 12 18 14 141080 8 21 52 061184 12 21 15 121080 16 24 53 111184 16 27 16 161280 7 9 54 240385 18 39 17 291280 15 33 55 030485 5 24 18 100281 8 27 56 230485 24 39 19 020381 14 45 57 300485* 21 15 20 020481 24 39 58 010585* 20 30 21 040481 7 21 59 231085 15 27 22 04048 IB 6 24 60 271085 7 12 23 220581 10 12 61 081185 12 15 24 22058IB 24 21 62 261185 12 36 25 220581C 18 33 63 271185 10 39 26 061181 3 15 64 161285 16 27 27 121281 8 27 65 161285B 9 27 28 191281 7 18 66 150186 6 18 29 251281 12 36 67 160186 15 36 30 310182 14 18 68 120286 6 30 31 210382 20 39 69 090386 8 15 32 210382B* 7 3 70 081184* 21 54 33 210382C* 15 12 71 271185* 9 42 34 250382* 23 39 72 040886* 21 9 35 280382 7 9 73 030488* 15 57 36 270982 14 45 74 280488* 24 30 37 031282 8 15 75 040788* 18 57 38 151282* 10 24 - - - - * Combined events Chapter 4 Deterministic Evaluation of The RuHo>\^_\le: r i~- —r 0.0 50.0 60.0 L0.0 20.0 30.0 HO.O BD - M in. Fig. 4.12. Strathfield impervious area runoff BD - Min. Fig. 4.13. Strathfield combined events Chapter 4 Deterministic Evaluation of The Rational Method 4-31 4.2.1.5. Cranebrook A total of 15 events out of 28 available observations were found suitable for analysing time ofrise an d burst duration in this catchment. Thetime incremen t of rainfall for some events is too large to distinguish the bursts which causerise i n the hydrographs. The number of combined events is too low to decide the typical minimum time ofrise fo r them, so all the events are plotted on one graph (Table 4.17. and Fig. 4.14.). The typical minimum time ofrise is equal to 5.0 Minutes for impervious area runoff events, and for combined events is 10.0 Minutes; however only three observations were available. The average time ofrises for the above cases were calculated as equal to 6.3 and 16.7 Minutes respectively. Table 4.17. Cranebrook - Total events Date Time Burst Date Time Burst No. of Rise Duration No. of Rise Duration (TR), (BD), (TR), (BD), Minutes Minutes Minutes Minutes 1 161087* 20 50 9 200388 5 12 2 191087 10 20 10 220388 5 25 3 191087B* 20 100 11 300388 10 20 4 211087 5 25 12 210388 5 15 5 281087 5 20 13 040488 5 10 6 111187* 10 85 14 070488 10 55 7 200188 5 35 15 080488 5 20 8 190388 5 45 * Combined events rvi CD d ~ •+• •+- ro O c S~ «—1 °"- CT i 2A / + + + CD O / +H- 4- + + + a« — o CO 1 1 l 1 1 0.0 20.0 40.0 60.0 60.0 100.0 120.0 BD - Min. Fig. 4.14. Cranebrook time of rise Chapter 4 Deterministic Evaluation of The Rational Method 4-32 Theoretically, according to Fig. 4.4., for uniform rainfall intensity the time of rise increases until the burst duration becomes equal to Tc, but when the burst duration becomes greater than Tc, time of rise remains constant. In all catchments, the points plotted on theright side of the line of equal value of burst duration and time of rise. Also there were many points with the same time of rise but different burst durations. This trend is repeated for small and large times of rise, which makes use of time ofrise as a measure of the time of concentration difficult. The main reason for this variation is the temporal pattern of rainfall which affects the time of rise. In the next section the interrelation between time ofrise an d the temporal pattern of rainfall is discussed. The summary results of time of concentration estimated by minimum time of rise method along with the averages are presented in Table 4.18. Table 4.18. A summary of results of estimated time of concentration by typical minimum and average time ofrise method- Minutes Catchment Impervious Area Combined Runoff Events Maroubra 6.0/13.6* - Jamison Park 5.0/15.5 10.0/16.5 Fisher's Ghost Creek 18.0/24.6 39.0/55.1 Strathfield 7.0/12.4 15.0/17.3 Cranebrook 5.0/6.3 10.0/16.7 * (typical minimum / average) 4.2.2. Effect of temporal pattern of rainfall on the time of rise If a catchment responds rapidly to rainfall, such as a small urban catchment with small detention storage, then an early peak storm could give a small time ofrise, whil e for a late peak storm a long time of rise is expected. Similar effects have been found for catchment response time, Tc, by Ball (1991). He concluded that response time of early and late peak rainfall, Fig. 4.15, is 0.81 and 1.22 times as much as that of uniform rainfall. He showed that besides being dependent on the catchment characteristics, response time is related to temporal pattern of rainfall as well. The dependency of Tc on depth of rainfall has been reported by Chui et al. (1990) as well. A similar (but not the same) result was found in the present study, where the time of rise was found to be related to the rainfall temporal pattern. Chapter 4 Deterministic Evaluation of The Rational Method 4-33 Early Peak Rain . - Late Peak Rain — Uniform Rain — Rain, mm/hr Time, hr Fig. 4.15. The extremes theoretical temporal pattern of rainfall Generally there is a logical relation between temporal pattern of rainfall and time of rise of hydrograph. When the rainfall is late peak a flattened hydrograph and a slow, long time ofrise wil l occur. On the other hand, if the rainfall pattern is an early peak one, a sharp hydrograph with rapid, short time of rise is expected. To investigate this phenomena with real data, the time to peak of rainfall hyetograph is taken as an index of earliness or lateness of temporal pattern. The relation of time ofrise an d time to peak of rainfall are studied for two catchments. 4.2.2.1. Maroubra The values of time of rise and time to peak of rainfall for 75 events have been Presented in Table 4.19 and plotted on Fig. 4.16. The increasing trend of time ofrise with regard to time to peak of rainfall is obvious, but there are some groups of observations in Fig. 4.16 with similar times ofrise and different times to peak of rainfall. The numerals above the observations indicate their frequency. However the maximum frequency of occurrence of observation of every group is on the line of equal value (30.6% of events, Table 4.20) Chapter 4 Deterministic Evaluation of The Rational Method 4-34 V '+* + '+ '£ V l+ i+ v ? ? 'Vl + + V + s+ J+ V \ J+ •+ ijj 2 •+% % + 4- i, 3y*i+ + 0.0 10.0 20.0 30.0 "tO.O 50.0 60.0 Tp - Min. Fig. 4.16. Maroubra: Relation of time ofrise and time to peak of rainfall Chapter 4 Deterministic Evaluation of The Rational Method 4-35 Table 4.19. Maroubra time of rise and time to peak of rainfall No. Date TR, Tp, No. Date TR, Tp, Minutes Minutes Minutes Minutes 1 010377 18 18 39 M 12 12 2 tt 12 12 40 111284 24 12 3 050377 9 6 41 010585 12 12 4 030378 12 18 42 081185 9 12 5 170378 9 9 43 271285 21 18 6 180378 12 9 44 160186 24 15 7 n 12 6 45 it 9 15 8 tt 18 18 46 n 24 15 9 it 15 15 47 120486 18 21 10 190378 9 6 48 040187 15 9 11 ti 9 9 49 030787 12 6 12 n 6 9 50 231087 27 24 13 270378 6 6 51 201087 12 18 14 080478 12 12 52 130288 12 6 15 ti 6 6 53 n 9 12 16 ti 9 3 54 n 9 12 17 180578 12 12 55 II 9 6 18 210578 21 24 56 it 9 9 19 210578B 15 15 57 ti 9 9 20 290578 15 18 58 250388 12 18 21 ti 18 18 59 it 21 36 22 tt 27 39 60 020488 12 12 23 tt 9 9 61 tt 12 12 24 it 30 60 62 it 6 12 25 130678 12 9 63 t» 15 15 26 tt 12 9 64 tt 9 15 27 130478 21 24 65 tt 9 6 28 ti 15 12 66 ti 6 9 29 ti 18 21 67 ti 6 6 30 190679 18 24 68 it 6 3 31 200679 9 18 69 tt 9 12 32 170383 12 6 70 ti 12 6 33 180683 15 9 71 ti 15 12 34 051184 12 30 72 II 9 12 35 061184 12 24 73 070488 18 15 36 061184B 36 36 74 280488 18 27 37 061184 9 9 75 150688 27 18 38 111184 12 15 Chapter 4 Deterministic Evaluation of The Rational Method MM Table 4.20. Time ofrise an d time to peak of rainfall No. of % Relation of TRand Tp*, Minutes events 1 1.3 TR = Tp+12 3 4.0 TR=Tp+9 8 10.7 TR = Tp+6 13 17.3 TR = Tp + 3 23 30.6 TR = Tp 13 17.3 TR = Tp -3 7 9.3 TR = Tp-6 2 2.6 TR=Tp-9 2 2.6 TR=Tp-12 1 1.3 TR = Tp-15 1 1.3 TR=Tp-18 1 1.3 TR = Tp - 30 * TR: Time of rise and Tp: Time to peak of rainfall 4.2.2.2. Fisher's Ghost Creek The possible relation between time ofrise o f hydrograph and time to peak of rainfall in this catchment is investigated (Table 4.21). Generally times ofrise fo r the combined events are longer than those of impervious area runoff events. The relation between time ofrise and time to peak of rainfall is shown in Figs. 4.17, 4.18 and 4.19 for impervious area runoff, combined runoff and both impervious and combined runoff events respectively. The relations in all cases confirm the dependency of time ofrise o n temporal pattern of rainfall. Chapter 4 Deterministic Evaluation of The Rational Method 4-37 Table 4.21. Time ofrise and time to peak of rainfall - Fisher's Ghost Creek, Minutes Date Time of Rise, Burst Duration, Time to Peak of Minutes Minutes Rainfall, Minutes 040581 21 48 39 050383 18 36 15 170383/1* 18 33 27 170383/2* 12 33 9 271183/1 42 54 36 271183/2 27 30 12 271183/3 18 27 21 271183/4 24 45 21 070284 18 48 15 111184/1 24 27 9 111184/2 24 30 18 181186 30 42 27 161087/2 21 60 42 251281 30 36 18 131283 18 27 12 260184 42 129 45 191081** 128 179 114 021181** 39 54 30 200383** 45 63 30 081184** 117 189 111 091184** 78 114 30 081285** 18 45 30 150186/1** 18 54 9 150186/2** 24 54 15 060886/2** 45 93 15 050688** 39 78 51 * Multi bursts rainfall ** Combined events Chapter 4 Deterministic Evaluation of The Rational Method 4-38 Tp - Min. Fig. 4.17. Time of rise versus time to peak of rainfall for impervious area runoff Fisher's Ghost Creek '5r OJ O C3 — CD CM O • s- CO -t- O — 1 OJ at CO -t- CO + "" + + o CD 1 1 1 1 1 0.0 20.0 to.o 60. o eo.o 100.0 120.0 Tp - Min. Fig. 4.18. Time ofrise versus time to peak of rainfall for combined runoff events - Fisher's Ghost Creek Chapter 4 Deterministic Evaluation of The Rational Method 4-39 8 FC CD 8~ cOJa O ca — c —' i — CD H^ 8 CO + CO tj> O ~ CO i °o:0 20. Tp - Min. Fig. 4.19. Time of rise and time to peak of rainfall for both cases of runoff- Fisher's Ghost Creek Figures 4.16 to 4.19 show that because of the effect of temporal pattern of rainfall on the occurrence time of flood peak, time ofrise is not a good indicator of the catchment time of concentration. 4.2.3. Lag analysis method Lag analysis is the most common approach of Tc estimation when rainfall and runoff data are available. Lag time is defined as either the time span between centroids of rainfall excess hyetograph and surface runoff hydrograph or the time between centroid of rainfall excess and the peak of hydrograph. In other words lag time is an average value of travel time of surface water from all parts of the catchment to the outlet. From the previous section (4.2.2) it was concluded that the estimation of Tc by using TR has limitations because of the rainfall temporal pattern effect, so lag time analysis is another alternative to be considered. To calculate lag time in urban catchments, accurately synchronised rainfall hyetographs and runoff hydrographs are required. Noticing the small size of the urban catchments under study (Chapter 3), every malfunction in hyetographs in recording the start of rainfall could directly affect the computed lag. To avoid such an uncertainty, recession analysis of falling limbs of Chapter 4 Deterministic Evaluation of The Rational Method 4-40 hydrograph is an alternative which does not need a rainfall hyetograph. The advancement or retardance of hyetographs and hydrographs in time has no effect on the results. Assuming a linear reservoir, lag time is the time span between centroids of hyetograph and hydrograph and this is also equal to the lag computed by recession analysis. In real catchments there might be non-linearity, delay between commencement of rainfall and rise of hydrograph and also an error in timing. If an error in timing exists, the lag measured between centroids of the hyetograph-hydrograph can be adjusted by this delay, and the modified lag value should equal the value computed from recession analysis. In this study the lag computed from recessions is used because of its simplicity and independence on the timing error of hyetograph-hydrograph. The assumption of a linear reservoir system is tested using 7 isolated events in the Maroubra catchment (Table 4.22). The average and standard deviation of lag time using recession are equal to 9.63 and 2.12 Minutes, while for hyetograph- hydrograph are 8.25 and 1.7 Minutes respectively. The lag values resulting from the difference in the centroids of hyetographs- hydrographs are reduced by subtraction of individual delay times for each event. Considering the similarity of the average and standard deviation of lag times for the two methods, it is concluded that the assumption of linear reservoir system is reasonable and the results of lag computation by recession analysis could be used directly. A sample of the analysed hydrographs along with the correspondent recession is depicted in Fig. 4.20. Chapter 4 Deterministic Evaluation of The Rational Method 4-41 Table 4.22. Comparison of lags calculated by recession and hyetograph-hydrograph analysis - Maroubra Date Kby Kby Kby Delay, Modified Recession Centroids, centroid & Minutes K , Minutes Minutes peak, Minutes 170383 9.18 16.3 9.82 6 10.3 081185 7.6 17.9 11.80 12 5.90 180683 8.04 12.95 9.33 6 6.95 130478 13.05 23.4 18.33 15 8.40 180578 12.20 10.44 1.40 0 10.44 270378 8.86 7.90 0.65 0 7.90 150688 8.48 7.84 3.50 0 7.84 AVE. 9.63 - - - 8.25 S.D. 2.12 - - - 1.70 Lag computation using recession analysis is performed by plotting the falling limb of the hydrograph against time on semi-log paper. The slope of a straight line through the points shows the lag time K. The lag time equation is given as: Qt = Qoe ** Where: Qo : the initial discharge, m3/ s 3 Qt: the discharge after the time t, m / s t: the elapsed time, minute K: the lag time, minute Normally the various slopes of the falling limb in a rural catchment show the rate of depletion of surface runoff, interflow and baseflow. However, in a fully urbanised catchment the variations of the slope along the recession should be considered carefully, especially when there is no groundwater and interflow discharge into the drainage system. In some cases there might be no groundwater and stormflow may consist of surface runoff and interflow only. On the other hand stormflow may contain surface Chapter 4 Deterministic Evaluation of The Rational Method 4-42 runoff and groundwater without interflow. Furthermore, sediment deposition in pipes or channels may affect results for low discharges. The results of lag analysis of the catchments are presented in the following sections. *; <_ _- o n lr MRRnUBRR i DRTE!l703S3 o i i az S_ i c_ U-i Min CO _J> I \i "! I,' ED OO i < i i; II ~T 1— 0 -»{ K" 40 80 120 160 200 6 Min TIME Min (a): hydrograph - hyetograph analysis 10 -3a - CMS (b): recession analysis Fig. 4.20. Lag estimation methods- (a): hydrograph - hyetograph analysis, (b) recession analysis Chapter 4 Deterministic Evaluation of The Rational Method 4-43 The possible relation of lag time to flood size was investigated for the catchments in this study. Jin (1993) found that basin lag is adversely related to flood peak velocity in 6 rural catchments. In the current study, the analysis offive urba n catchments did notreveal an y significant relation between lag and size of flood. In Table 4.23 the results of simple regression analysis between lag time and flood size are presented. The individual correlation as well as the overall correlation are very low in most cases. In Fisher's Ghost creek for combined events, the coefficient of determination shows an improvement. There are two possible reasons for this improvement: thefirst i s the rural nature of the catchment, e.g. natural channels and reserves which make the catchment more rural than urban, the second reason is the small number of events. However, overall regression between catchments for combined events does not show any relationship between lag time and flood size (Table 4.23). Average lag time for each catchment is calculated and the ratios of K/Kave were plotted versus flood peaks (Figs. 4.21 to 4.23). The uniform scatter around the K= Kave line denotes the independence of lag on the flood size in these urban catchments. It is concluded that lag time is a catchment physical property which is not affected by either temporal pattern of rainfall or flood size. Noting that the lag time is an average travel time for flow from all parts of the catchment, the time of concentration should be larger than the lag time. The average time of concentration of catchments is obtained by multiplying the average lag time by 1.417 (McCuen 1989). In the following section lag time is calculated for each catchment using recession analysis. Table 4.23. Lag - flood size relationships(K = a + b Q) Catchment Runoff Type a b R2 N* Maroubra Impervious 12.79 -2.56 0.048 47 Combined - - - - Jamison Pk Impervious 9.51 -0.87 0.003 12 Combined 19.28 -2.99 0.011 28 F.G.C. Impervious 26.41 -2.59 0.171 14 Combined 48.91 -2.41 0.396 9 Strathfield Impervious 11.95 0.23 0.021 32 Combined 28.71 -0.50 0.058 6 Crane Bk Impervious 7.79 -7.59 0.412 6 Combined - - - - Overall Impervious 10.96 0.51 0.053 Ill Combined 20.72 0.22 0.007 43 * N : Number of observations, R2: Coefficient of determination Q : flood peak, m3/s Chapter 4 Deterministic Evaluation of The Rar K / KKV* K«V4B BE nrf* _ s- _ o oo O.BO I.OO Q. rr>9/4B (a): Maroubra impervious area runoff K / K4SV444 K — Kavs "_ • .26 0.60 O, rr.3/4. (b): Jamison Park - impervious area runoff _UK/ Kava. Q, mS/« (c): Jamison Park - combined events Fig. 4.21. K-Q relations Chapter 4 Deterministic Evaluation of The Rational Method 4-45 K / KMV. « — Ksva o.oo 1.00 2.00 3.00 4.00 0.00 o oo O. mS/a (a): Fisher's Ghost Creek - impervious area runoff 11 K / K.vi K — K.v. (b): Fisher's Ghost Creek - combined events ESC K 1 Kiiva 3.00 1 2 OO at. (_, ac E ac OKI a_ _0 ac 0H K K«v» 1.00 W3 — ac_c' CM ac C"B ' _c an" ~__ o.oo 1 1 IO oo 20.00 Q. m3/i (c): Strathfield - impervious area runoff Fig. 4.22. K-Q Relations Chapter 4 Deterministic Evaluation of The Rational Method 4-46 ao K / K«v» 3 OO 2.00 — 1.00 K - K»v O.OO O.OO B.OO 10.00 16 OO 20.00 a. m3/« (a): Strathfield - combined events '•' K / Kava 1.SO OD K — K.v. I.OO O.SO — O.OO 1 O.OO O.IO 0.20 0.30 0.40 0.50 _. m3/B (b): Cranebrook - impervious area runoff Fig. 4.23. K-Q relationship 4.2.3.1. Maroubra In this catchment 47 falling limbs are analysed to compute the lag time and its variations (Table 4.24). Two samples of recessions are illustrated in Fig. 4.24. The slope of the first lines shows the lag times of surface runoff. The change in the slopes when discharge is decreasing is easily distinguishable. Considering the source of runoff for these two events which is an impervious area, and also the absence of a groundwater table in the vicinity Chapter 4 Deterministic Evaluation of The Rational Method 4-47 of the drainage pipe, the variation of slope could be related either to the partial clogging of pipes by sediments or infiltration and leakage of rainfall to pipes. In case of sediment clogging, the stored surface water will slowly be released after thefirst part . A study of cutoff discharge, Qt, for individual events could make the recognition of the source of the second part of the recession easier. The average cutoff discharge is equal to 0.15 m3/s which is equivalent to 27 cm of height in the outlet pipe. Regarding this depth of water at the outlet pipe, the contribution of infiltrated water seems more realistic than the slow release of water from accumulated sediment. The average value of lag time for this catchment is equal to 10.83 Minutes with a standard deviation of 3.44 Minutes and coefficient of variation of 32%. Chapter 4 _— Deterministic Evaluation of The Rational Method 4-48 Table 4.24. Lag time computation - Maroubra 3 Row Qo, m /s Qt, m3/s t, Minutes K, Minutes 1 0.61 0.030 26 8.63 2 0.48 0.084 18 10.33 3 0.09 0.025 18 13.93 4 0.46 0.056 25 11.87 5 0.74 0.052 29 10.92 6 0.65 0.058 21 8.69 7 0.38 0.042 18 8.17 8 0.68 0.085 21 10.10 9 0.39 0.059 27 14.30 10 0.82 0.080 24 10.31 11 0.72 0.052 27 10.27 12 0.90 0.066 33 12.63 13 0.78 0.060 27 10.53 14 1.00 0.100 24 10.42 15 0.50 0.075 27 14.23 16 1.10 0.200 18 10.56 17 0.42 0.130 18 15.35 18 0.61 0.042 21 7.85 19 0.30 0.080 30 22.70 20 0.45 0.170 18 18.49 21 1.01 0.062 36 12.90 22 0.95 0.100 27 12.00 23 1.25 0.170 14 7.02 24 1.17 0.150 14 6.82 25 0.49 0.056 24 11.06 26 1.05 0.170 39 21.42 27 1.02 0.055 24 8.22 28 0.90 0.150 15 8.37 29 1.02 0.270 18 13.54 30 1.05 0.120 27 12.45 31 1.00 0.045 39 12.58 32 0.80 0.055 21 7.84 33 0.62 0.040 24 8.76 34 0.48 0.450 21 8.87 35 0.40 0.420 21 9.32 36 0.80 0.480 24 8.53 37 0.60 0.400 21 7.75 38 0.95 0.150 18 9.75 39 1.01 0.140 18 9.11 40 1.02 0.190 12 7.14 41 0.28 0.055 15 9.22 42 0.32 0.500 18 9.70 43 1.00 0.700 24 9.03 44 0.76 0.190 15 10.82 45 0.52 0.048 21 8.81 46 1.02 0.055 18 6.16 47 1.35 0.290 18 11.70 Chapter 4 Deterministic Evaluation of The Rational Method 4-49 Date : 130478 a - CMS 1 — .01 .001 1 10 20 30 40 SO SO 70 Tlm« Min. Date c 070488 10 Fig. 4.24. Recession analysis- Maroubra 4.2.3.2. Jamison Park Lag time is computed for both impervious runoff and combined events in this catchment (Tables 4.25 and 4.26). Two samples of recessions are presented in Fig. 4.25. Regardless of runoff sources during the analysis of lag time for this catchment, only two different slopes on therecessions wer e distinguishable. The bivariation of the slope for two types of runoff shows the domination of surface runoff in this catchment. The change in the direction of falling limbs is because of either accumulation of sediment in the measuring Chapter 4 Deterministic Evaluation of The Rational Method 4-50 station or infiltrated water into the system. The average value of cutoff discharge of both cases of impervious and combined events is 0.13 m3/s which is equivalent of 28 cm of height in the 1.54 m diameter outlet pipe. The average lag times for impervious area runoff and combined events are equal to 9.15 and 17.74 Minutes respectively. The standard deviations are 3.09 and 9.7 Minutes and the coefficient of variations are 34% and 55% for impervious and combined events. The lag time of combined events is much longer than that of impervious events. In section 4.2.1.2 it was mentioned that there is no overland flow at the top end of the main flow path of this catchment, and therefore it is not included in Tc, but overland flow adds to various points along the flow path. Table 4.25. Lag time computation for impervious runoff events - Jamison Park 3 3 Row Qo, m /s Qt, m /s t, Minutes K, Minutes 1 0.320 0.066 15 9.50 2 0.088 0.032 10 9.89 3 0.460 0.090 25 15.32 4 0.290 0.080 15 11.65 5 0.140 0.520 10 10.10 6 0.250 0.400 15 8.19 7 0.750 0.120 20 10.91 8 0.600 0.080 20 9.93 9 0.480 0.080 10 5.58 10 0.460 0.055 10 4.71 11 0.550 0.068 20 9.57 12 0.480 0.090 7.5 4.48 Chapter 4 Deterministic Evaluation of The Rational Method 4-51 Table 4.26. Lag time computation for combined events - Jamison Park Row Qo, m3/s Qt, m3/s t, Minutes K, Minutes 1 0.95 0.150 20 10.84 2 0.30 0.080 18 13.62 3 0.48 0.090 30 17.92 4 0.28 0.070 20 14.43 5 0.52 0.150 20 16.09 6 0.44 0.085 50 30.41 7 0.45 0.078 20 11.41 8 0.22 0.070 20 17.47 9 0.28 0.130 20 26.07 10 0.35 0.070 20 12.43 11 0.70 0.110 20 10.81 12 0.16 0.070 40 48.39 13 0.14 0.050 30 29.14 14 1.70 0.290 40 22.62 15 0.40 0.070 30 17.21 16 0.80 0.120 10 5.27 17 0.50 0.080 50 27.28 18 0.28 0.080 30 23.95 19 0.60 0.100 50 27.91 20 0.39 0.120 10 8.48 21 0.39 0.110 10 7.90 22 0.75 0.300 25 27.28 23 1.20 0.290 30 21.12 24 0.61 0.100 15 8.30 25 0.20 0.058 15 12.12 26 0.13 0.037 15 11.94 27 0.75 0.130 10 5.71 28 0.38 0.150 10 10.76 Chapter 4 Deterministic Evaluation of The Rational Method 4-52 Date : 170383 (Impervious) Q - CMS .01 —| I | I | I 1 ! • 10 20 30 AO eo so Tlm» - Min. Date : 040488 (Combined) O - CMS .01 AO Fig. 4.25. Recession analysis - Jamison Park 4.2.3.3. Fisher's Ghost Creek In this catchment recession is studied for both cases of impervious area runoff and combined events (Tables 4.27 and 4.28). For both cases of runoff two different slopes are separable on recessions. For impervious area runoff events the first slope isrelated to Chapter 4 Deterministic Evaluation of The Rational Method 4-53 impervious surface detention and the second one is related to pipe/channel storage. However, for combined events thefirst slop e is related to surface detention of pervious and impervious areas and the second one to a combination of channel/pipe, interflow and perhaps groundwater storages. Considering the soil type of the catchment and underlying impermeable shale the second slope should include the interflow storage effect. The average cutoff discharges and equivalent heights are 0.4, 1.27 m3/s and 0.38, 0.51 m for impervious area runoff and combined events respectively. The computed lag times by considering thefirst and second recession slopes are presented in Table 4.29. The second lag for both cases is larger than thefirst, bu t has a lower coefficient of variation. This study is concerned with thefirst slop e for both cases of runoff for estimation of travel time. The average lag times for impervious and combined events are calculated as 18.7 and 31.2 Minutes respectively. The standard deviation of impervious runoff lag is equal to 8.6 Minutes which is smaller than that of for combined events (17.8 Minutes). The coefficient of variation is equal to 46% and 57% for impervious and combined events. Two recession samples are demonstrated in Fig. 4.26. Chapter 4 Deterministic Evaluation of The Rational Method 4-54 Table 4.27. Lag time computation for impervious area runoff - Fisher's Ghost Creek 3 3 Row Qo, m /s Qt, m /s t, Minutes K, Minutes 1 1.00 0.50 12 17.31 2 3.60 0.43 30 14.12 3 5.00 0.40 45 17.82 4 2.80 0.10 134 40.22 5 3.80 0.35 45 18.87 6 3.20 0.28 36 14.78 7 2.90 0.45 22 11.81 8 3.40 0.30 26 10.71 9 3.50 1.05 15 12.46 10 1.00 0.30 25 20.76 11 1.60 0.24 37 19.50 12 4.20 0.65 36 19.29 13 5.00 0.21 33 10.41 14 1.00 0.29 42 33.93 Table 4.28. Lag time computation for combined events - Fisher's Ghost Creek 3 3 Row Qo, m /s Qt, m /s t, Minutes K, Minutes 1 4.20 0.49 37 17.22 2 1.40 0.52 58 58.56 3 17.00 1.70 25 10.86 4 8.50 1.05 47 22.47 5 8.00 2.80 28 26.67 6 3.00 0.52 93 53.07 7 9.00 0.76 124 50.17 8 5.00 0.65 53 25.98 9 10.00 2.90 20 16.16 Table 4.29. Comparison of thefirst and second lag times on recessions - Fisher's Ghost Creek Parameter Impervious Area Runoff Combined events Lag 1st 2nd 1st 2nd Ave., 18.7 40.2 31.2 85.6 Minutes S.D., 8.6 15.0 17.8 43.5 Minutes CV. % 46 37.3 57 51 Chapter 4 Deterministic Evaluation of The Rational Method 4-55 Date c 070284 (Impervious) Q - CM IO .01 Date c 060886 (Combined) Q - CM: 1Q Fig. 4.26. Recession analysis - Fisher's Ghost Creek 4.2.3.4. Strathfield The majority of the recorded events in this catchment come from impervious areas. However, the analysis of the recession of combined events shows completely different results from those of impervious events (Tables 4.30 and 4.31). The average of lag time is 13.02 and 23.93 Minutes for impervious and combined events respectively. The standard deviation of lag times of impervious runoff is equal to 5.42 Minutes which is Chapter 4 Deterministic Evaluation of The Rational Method 4-56 smaller than that of combined events (13.05 Minutes). The coefficient of variations of lag time is fairly high and equal to 42% and 55% for impervious and combined events. Recession analysis of two falling limb samples is demonstrated in Fig. 4.27. The average cutoff discharge for impervious area runoff events is equal to 0.4 m3/s while this figure for combined events rises to 2.30 m/s. Considering the high magnitude of cutoff discharge of combined events and the fine sand soil type of the catchment, the assumption of contribution of infiltrated water to pipe flow seems realistic. Chapter 4 Deterministic Evaluation of The Rational Method 4- 57 Table 4.30. Lag time computation for impervious area runoff - Strathfield 3 Row Qo, m /s Qt, m3/s t, Minutes K, Minutes 1 7.80 0.12 40 9.58 2 2.50 0.30 60 28.30 3 3.80 0.22 44.5 15.62 4 0.85 0.02 40 10.67 5 5.40 0.17 34 9.83 6 2.60 0.27 30 13.25 7 2.50 0.35 24.5 12.46 8 5.80 0.21 48 14.46 9 2.60 0.49 16 9.59 10 2.50 0.28 24 10.96 11 2.50 0.12 40 13.17 12 2.40 0.34 12 6.14 13 2.50 0.44 15 8.63 14 2.80 0.28 20 8.69 15 1.80 0.40 12 7.98 16 5.10 0.16 80 23.11 17 7.60 1.50 32 19.72 18 2.60 0.27 30 13.25 19 1.20 0.046 30 9.20 20 7.60 0.42 32 11.05 21 7.80 1.80 12 8.18 22 2.60 0.08 60 17.24 23 2.50 0.26 35 15.46 24 4.20 0.08 56 14.14 25 2.80 0.16 15 5.24 26 5.80 0.075 48 11.04 27 4.80 0.06 54 12.32 28 2.60 0.085 85 24.85 29 18.00 2.10 39 18.15 30 9.00 0.52 51 17.89 31 6.10 0.31 27 9.06 32 7.20 0.96 15 7.44 Table 4.31. Lag time computation for combined events - Strathfield 3 3 Row Qo, m /s Qt, m /s t, Minutes K, Minutes 1 5.80 1.90 55 49.28 2 17.00 2.60 48 25.56 3 13.00 4.20 15 13.28 4 15.00 4.50 21 17.44 5 3.80 0.190 54 18.03 6 2.80 0.38 40 20.03 Chapter 4 Deterministic Evaluation of The Rational Method 4-53 Date : 070488 (Impervious) a - CMS 10 i — -i r io 20 30 AO SO T!m» Date c 030488 (Combined) Q - CMS IOO io — Fig. 4.27. Recession analysis- Strathfield Chapter 4 Deterministic Evaluation of The Rational Method 4- 59 4.2.3.5. Cranebrook A total of 29 events for this catchment were available and on 15 of them recession analysis was performed (Table 4.32). Because of the small magnitude of discharge, most of the recessions were unsuitable for lag analysis. Among the 15 suitable events lag time 3 for 6 impervious area runoff events with starting discharge, Q0, of 0.07 m /s were averaged. (Table 4.32). The remaining impervious runoff events have very small starting discharges, mainly less than 0.07 m3/s which makes the lag too long. Three combined event lags were averaged to have an estimation of combined events travel time. The average lag time and standard deviation for impervious area runoff events is 6.1 and 1.2 Minutes, while for combined events they are 11.9 and 0.5 Minutesrespectively. Th e coefficient of variation of impervious and combined events are 19% and 4% respectively. Two samples of recession for this catchment are illustrated in Fig. 4.28. The average cutoff discharge of the selected impervious area runoff events is equal to 0.01 mVs which is equivalent to 9.0 cm height in the outlet pipe. Thesefigures for combined events are 0.03 m3/s and 12.0 cm which show infiltration of water or release of water from accumulated sediment into the system. The summary of results of the lag method for all the catchments is presented in Table 4.33. Table 4.32. Lag time computation - Cranbrook 3 3 Row Qo, m /s Qt, m /s t, Minutes K, Minutes 1 0.022 0.011 10.0 14.43 2 0.031 0.013 24.5 28.19 3 0.580 0.026 35.5 11.43** 4 0.180 0.020 26.0 11.83** 5 0.200 0.013 12.5 4.57* 6 0.380 0.013 17.5 5.18* 7 0.180 0.010 17.5 6.05* 8 0.190 0.010 20.0 6.79* 9 0.580 0.052 30.0 12.44** 10 0.078 0.022 10.0 7.90* 11 0.050 0.016 20.0 17.55 12 0.260 0.021 16.0 6.36* 13 0.065 0.080 55.0 26.25 14 0.018 0.092 10.0 14.90 15 0.120 0.016 110.0 54.59 * Selected impervious area runoff lags Combined events Chapter 4 Deterministic Evaluation of The Rational Method 4-6'i Date : 190388 (Impervious) a - CMS .1 — .01 -d .001 IO 20 3D 40 Tim. - Min. Date : 161087" (Combined) Q - CMS .01 — .001 IO 20 SO 4-0 SO SO s - Min. Fig. 4.28. Recession analysis- Cranbrook Table 4.33. The summary results of estimated lagtime b y recession analysis - Minutes Catchment Impervious Area Runoff Combined Evenit s AVE. STD C.V.% AVE. STD C.V.% Maroubra 10.83 3.44 32 - - - Jamison Park 9.15 3.09 34 17.74 9.70 55 Fisher's Ghost 18.70 8.60 46 31.2 17.80 57 Ck Strathfield 13.02 5.42 42 23.93 13.05 55 Cranebrook 6.1 1.2 19 11.9 0.50 4 Chapter 4 Deterministic Evaluation of The Rational Method 4-61 4.2.4. Summary results of time of concentration The results of estimates of time of concentration by the selected methods are presented in Table 4.34. The computed lag times are multiplied by a constant of 1.417 to estimate time of concentration of catchments. This constant is proposed based on the lagtime an d time of concentration of a SCS triangular hydrograph when lag time is defined according to the time span between centroids of excess rainfall and direct runoff hydrograph (McCuen 1989). When the time difference between centroid of excess rainfall and the hydrograph peak is considered, the magnitude of the constant increases to 1.67 for the same triangular hydrograph. Generally lag time is an average travel time for all points on catchments, but Tc is the travel time from farthest point. Comparison of the results of minimum time of rise method with the Tc derived by lag time (lag time* 1.417) shows that the minimum time of rise underestimates for both impervious and combined events (Fig. 4.29(b)). Average time of rise shows a good agreement with Tc from the lag method for both impervious and combined events (Fig. 4.29(a)). However, time ofrise was found to be dependent on the temporal pattern of rainfall. In all the catchments except Maroubra, Tc estimates from gutter and pipe flow velocities, using the ARR87 method, show reasonable results for impervious area travel times when compared to those of Tc from lag values. The Tc from lag values for each of four catchments is slightly greater than that of ARR87 (Table 4.33 and Fig. 4.29(c)). The Pipe flow travel time for these catchments was estimated assuming half full pipe all the way down the catchments. It is concluded that the assumption of half full pipe for average travel time estimation overestimates velocity and decreases pipe travel times slightly. The difference between Tc by lag time and ARR87 for Fisher's Ghost Creek is much higher than for the other catchments. The waterway of this catchment consists of 1309 m of open natural channel which is 60% of the total length of the main waterway. As previously mentioned the natural channel segments are replaced with trapezoidal cross section channels and only by considering the roughness coefficient can the velocity be estimated. The large difference between Tc by the lag method and ARR87 for this catchment, 26.5 versus 15.0 Minutes, shows that velocities in open natural channel Chapter 4 Deterministic Evaluation of The Rational Method 4-62 segments are overestimated by the ARR87 method ( Fig. 14.9 in ARR87 and Manning's formula). Estimation of velocity in irregular natural open channels is a difficult task and the value of the roughness coefficient should be selected properly. In this calculation the roughness values used by Vale (1986) were used with Manning's equation (Chapter 3). In Maroubra, Table 4.33, the Tc by lag time is much less than that of ARR87 (15.3 versus 25.3 Minutes). The ARR87 uses gutter flow and pipe flow travel times for Tc of impervious area of catchment. In this catchment the magnitude of gutter flow travel time is much higher compared with the others (9.3 Minutes, Table 4.10 ) which is because of the very small longitudinal slope of the gutter (0.33%). Pipe flow travel time for Maroubra is 16.0 Minutes which accords well with the Tc by lag time, 15.3 Minutes. Table 4.34. Comparison of estimated time of concentration with lag time, Minutes Catchment Runoff ARR87* Min. / Ave. K Tc = Type time of rise K*1.417 Maroubra Impervious 25.3 6.0/13.6 10.83 15.3 Combined - - - - Jamison Pk Impervious 10.2 5.0/15.5 9.15 13.0 Combined - 10.0/16.5 17.74 25.1 F.G.C. Impervious 15.0 18.0/24.6 18.7 26.5 Combined - 39.0/55.1 31.2 44.2 Strathfield Impervious 15.0 7.0/12.4 13.02 18.4 Combined - 15.0/17.3 23.93 33.9 Cranebrook Impervious 6.6 5.0 / 6.3 6.1 8.6 Combined - 10.0/16.7 11.9 16.9 * Fig. 14.9 in ARR87 for gutter flow travel time plus Manning's formula for pipe/open channels The magnitudes of time of concentration by the lag time method are adopted for further study on the catchments. There are two reasons for this adoption. Firstly, the derivation of lag times is based on observed data, and secondly total time of concentration of catchments is achievable with this method through lag analysis for combined events. It is also concluded that when observed data are not available the ARR87 could give reasonable estimates of the Tc for impervious areas of catchments provided that special consideration is paid to gutter flow travel time. Considering the Maroubra catchment, the Tc by lag time is much less than that of ARR87 method (15.3 versus 25.3 Minutes). In this catchment the magnitude of gutter flow travel time was much higher compared with Chapter 4 Deterministic Evaluation of The Rational Method 4-63 the others (9.3 Minutes, Table 4.10 ) which is because of the very small longitudinal slope of the gutter (0.33%). Pipe flow travel time for Maroubra was calculated equal to 16.0 Minutes which accords well with the Tc by lag time, 15.3 Minutes. y — -4.03B -4- I.OOTx r"2 — O.rw A4?4». TR. mm eo.o « -. ^y^ /7 so.o — - i-.e.-v. /A *o.a ! N\ /A A so.o Ay 20.0 . lA OB ~ 10.0 "t \ ' | | ' i • i • 1 O.O TOO ao.o 3 O.O 40.0 SO.O o. min (a): average time of rise •m Min. TR, mtn SO.O — (b): typical minimum time of rise wn ARRB7, mini (c): ARR87 method Fig. 4.29. Comparison of time of concentration methods - (a): average time of rise, (b): typical minimum time ofrise an d (c): ARR87 method Chapter 4 Deterministic Evaluation of The Rational Method 4454 4.3. Runoff Coefficient Runoff coefficient is an ill-defined, and uncertain parameter which is essential in the Rational formula application. It is related to rainfall intensity and duration, and also to catchment soil type and current soil moisture conditions. It varies during a storm, and it is difficult to anticipate between two rainfall periods as well. In the past it has been investigated both statistically and deterministically. Although the latter approach has received less attention than the former due to the complexity of rainfall intensity interaction and soil characteristics such as hydraulic conductivity, it should not be overlooked. Deterministic estimation of runoff coefficient is applicable in evaluation of historical events and in real time forecasting. 4.3.1. ARR87 runoff estimation method This is a statistical estimation of the runoff coefficient which is used in design situations. The estimates of the runoff coefficient in ARR87 are carried out based on the straight lines presented in Fig. 2.2 (Chapter2). These curves are partially based on measured data and there is no specific provision for soil type of catchment. Furthermore, the link between the coefficients and the time of concentration has not been established (Mein and Goyen 1988). Regarding this figure two formulas are proposed by ARR87 as follows: C10=0.9f + C\0(l-f) C'io = 0.1+0.0133 (10I,-25) where Cio: the 10 year ARI runoff coefficient C1^ : the pervious area runoff coefficient f: the fraction impervious (0.0 to 1.0) 10 Ii : 1-hour rainfall with return period of 10 years For the other recurrence intervals, frequency factors (Fy) are provided by ARR87. (Table 4.35). Chapter 4 Deterministic Evaluation of The Rational Method 4-65 Cy = Fy C10 Soil type and catchment slope are not considered in the above formulas, and it is the designer's responsibility to modify the derived runoff coefficients based on the current conditions of catchments. The results of the application of this method on the five study catchments are presented in Table 4.36. Table 4.35. Frequency factor for runoff coefficient ARI Fy 1 0.8 2 0.85 5 0.95 10 1.00 20 1.05 50 1.15 100 1.20 Table 4.36. 2-yr runoff coefficient of the catchments using ARR87 method Catchment Area, Impv.Area 10I„ Runoff Coefficient 2 Km Fraction, mm/hr c2 % Impv. Area Perv. Area Total Maroubra 0.57 29 65 0.90 0.63 0.602* Jamison 0.22 35 44 0.90 0.35 0.463 F.G.C. 2.14 27 47 0.90 0.39 0.450 Strathfield 2.34 50 52 0.90 0.39 0.580 Cranebrook 0.115 38 44 0.90 0.35 0.477 * C2 = C10* 0.85 4.3.2. Runoff coefficient and system theory For system approaches O'Donnell (1986) identified two methods including; Analysis and Synthesis. Referring to Table 4.37, the analysis method tries to identify the response function, h(t), regarding input, x(t), and output, y(t). This method does not describe the physical interaction between different parameters on input and output, and only tries to establish a relation between them. The established relation is used for prediction or Cliapter 4 Deterministic Evaluation of The Rational Method 4-66 detection of output or input of the system. On the other hand the synthesis method is a thorough simulation of the physical phenomena of the system regarding the physical interactions between the variables. This method also uses input and output to recognise the best response function. Table 4.37. Problems solving using systems approaches (From O'Donnell 1986) Type of Problem Input, x(t) Response, h(t) Output, y(t) Prediction V V 7 Analysis Identification V 7 V ; Detection 7 V Synthesis Simulation V 7 V In the present study the analysis method is used to derive the response function between rainfall intensity and the flood peak which is called rate runoff coefficient. The rate runoff coefficient includes the effect of rainfall intensity, API, flood routing, soil type and all the possible parameters on both input and output. On the other hand volumetric runoff coefficient is based on runoff and rainfall depth and only includes the effect of API. In volumetric evaluation, the maximum magnitude of runoff coefficient is 1.0, which might occur under saturation conditions with minor flood routing effect. For rate runoff coefficient there is no limit on the runoff coefficient, and magnitudes greater than unity are expected whenever peak flow is greater than the rainfall rate. High rate runoff coefficients, also greater than one are reported by Jones and Lawson (1992) in the urban catchments of Darwin because of high intensity of storm events and high localised storm structures. In the statistical inteipretation of the Rational formula, the runoff coefficient is considered to include the effect of API, initial loss, infiltration and channel storage. In the statistical approach the runoff coefficient is calculated based on frequency analysis of flood peaks and rainfall intensity and these have the same return period (Pilgrim and McDermott 1980). The runoff coefficient in this case increases with return period, but the time of concentration of the catchment remains constant. Chapter 4 Deterministic Evaluation of The Rationed Method 4-67 4.3.2.1. Estimates of the runoff coefficient using observed data In the present study a deterministic interpretation of rate runoff coefficient is pursued. Rate runoff coefficient is estimated for two cases. Firstly the average rainfall intensity during the most intense burst and the resulting flood peak are used to calculate the rate runoff coefficient. Secondly average rainfall intensity during the time of concentration of the catchment, adopted from recession analysis, and flood peak are used to calculate the runoff coefficient. The relation of the peak discharge and rainfall intensity for two cases of runoff, impervious area runoff and combined runoff, are studied separately. Full details of the events and the calculated runoff coefficient are given in Tables 4.38 - 4.44. Rate runoff coefficients of greater than one were observed in some of the catchments which shows that it could happen in other catchments in Australia and it is not unique to Darwin. (Refer to Jones and Lawson 1992). Chapter 4 Deterministic Evaluation of The Rational Method 4-68 Table 4.38. Average rainfall intensity and runoff coefficient for the bursts and time of concentration -Maroubra Date Qp, Burst Average Runoff Average Runoff 3 m /s Duration Rain Coefficient Rain Coefficient (BD), Intensity Intensity in Burst, in Tc, Minutes mm/hr mm/hr 010377 1.03 63 17.63 0.369 29.9 0.217 tt 0.683 21 25.9 0.166 28.5 0.151 050377 0.57 12 26.23 0.137 * - 030378 1.65 18 69.00 0.151 75.3 0.138 170378 0.23 90 4.01 0.362 10.85 0.134 180378 0.49 15 18.90 0.164 18.90 0.164 ii 0.235 12 11.60 0.128 - - ti 0.59 27 18.20 0.205 25.69 0.145 ti 1.55 39 29.31 0.334 59.42 0.165 190378 0.39 9 27.40 0.090 - - ti 0.72 12 16.00 0.284 - - n 0.71 12 17.85 0.251 - - 270378 0.62 9 37.30 0.105 - - 080478 0.43 24 11.80 0.230 19.52 0.139 ti 0.15 9 8.20 0.115 - - ti 0.45 30 17.00 0.167 27.2 0.104 180578 0.94 21 30.60 0.194 38.20 0.155 210578 0.83 18 24.50 0.214 27.62 0.190 210578B 0.87 24 24.42 0.225 33.89 0.162 290578 0.71 48 10.10 0.544 14.73 0.304 n 0.85 42 11.00 0.488 18.96 0.283 n 0.76 47 11.80 0.406 19.53 0.246 it 0.57 18 7.80 0.461 9.11 0.395 tt 0.23 63 9.70 0.150 24.16 0.060 130678 1.08 27 26.60 0.256 32.27 0.211 n 0.73 15 26.70 0.173 26.70 0.173 130478 0.49 39 10.60 0.292 16.73 0.185 II 0.66 15 18.20 0.229 18.20 0.229 it 0.64 24 14.50 0.279 19.20 0.210 190679 1.41 36 40.50 0.220 53.46 0.166 200679 0.41 21 22.40 0.116 24.68 0.105 170383 2.12 30 70.40 0.190 110.32 0.121 180683 0.61 15 14.19 0.271 14.19 0.271 051184 1.81 60 96.70 0.118 164.75 0.069 061184 0.31 24 6.40 0.306 8.00 0.245 061184B 0.35 42 9.00 0.245 12.56 0.176 061184 0.078 12 5.30 0.093 - - 111184 0.444 24 13.60 0.206 18.15 0.153 n 0.90 12 35.51 0.160 - - Chapter 4 Deterministic Evaluation of The Rational Method 4-69 111284 1.29 29 24.60 0.331 34.39 0.237 010585 1.28 15 35.10 0.230 35.10 0.230 081185 1.27 15 78.00 0.103 78.00 0.103 271285 1.37 18 50.00 0.173 55.50 0.156 160186 0.50 24 23.13 0.136 27.83 0.113 n 1.32 21 52.80 0.158 65.61 0.127 ti 0.53 30 13.10 0.255 20.10 0.166 120486 1.61 27 45.20 0.225 57.04 0.178 040187 1.21 12 29.20 0.262 - . 030787 1.30 24 27.00 0.304 34.61 0.237 231087 1.45 45 21.30 0.430 36.14 0.253 201087 1.13 24 21.60 0.330 29.40 0.243 130288 0.86 15 34.20 0.159 34.20 0.159 ti 0.69 24 13.90 0.313 21.20 0.205 n 0.76 21 30.80 0.156 38.11 0.126 II 0.31 12 22.89 0.085 - - ti 0.503 21 9.00 0.353 11.87 0.267 n 0.507 18 11.50 0.278 13.39 0.239 250388 1.08 21 45.10 0.151 54.20 0.126 II 0.711 36 11.60 0.387 15.87 0.283 020488 0.40 27 16.60 0.152 19.62 0.129 n 0.38 18 11.70 0.205 16.39 0.146 n 0.803 18 34.00 0.149 35.56 0.143 Tt 1.15 24 33.90 0.214 50.68 0.143 IT 1.28 21 30.70 0.263 42.00 0.192 TI 0.32 24 6.40 0.316 17.50 0.115 II 0.34 24 5.40 0.397 10.83 0.198 TI 0.25 16 16.40 0.096 16.40 0.096 II 0.12 6 10.10 0.075 - - II 0.74 18 14.70 0.477 16.92 0.276 It 1.11 15 35.60 0.197 35.60 0.197 IT 0.83 24 23.90 0.219 39.65 0.132 IT 0.63 21 24.70 0.161 31.76 0.125 070488 1.36 15 33.40 0.257 33.40 0.257 280488 1.38 33 62.20 0.140 66.00 0.132 150688 1.29 30 26.60 0.306 42.00 0.194 * The burst duration is less than time of concentration Chapter 4 _ Deterministic Evaluation of The Rational Method 4- 70 Table 4.39. Jamison Park - Impervious area runoff events Date Qp, Burst Average Runoff Average Runoff 3 m /s Duration Rain Coefficient Rain Coefficient (BD), Intensity in Intensity Burst, in Tc, Minutes mm/hr mm/hr 150383 0.328 30 16.00 0.335 23.2 0.213 170383 0.526* 25 30.72 0.280 35.2 0.224 170284 0.738 40 18.90 0.639 33.0 0.336 251185 0.555* 40 18.90 0.481 31.2 0.268 261185 0.460 30 26.40 0.285 38.4 0.180 040186 0.451 20 51.00 0.144 51.0 0.133 171286 0.128 20 10.80 0.194 13.6 0.141 010187 0.535 40 20.40 0.429 32.6 0.247 100287 0.224 10 14.40 0.255 21.6 0.156 210287 0.113 30 5.60 0.330 7.8 0.218 010387 0.188 60 8.60 0.358 12.0 0.236 020387 0.196 220 4.63 0.692 7.8 0.379 220687 0.092 40 7.50 0.201 11.2 0.124 100887 0.046 40 2.70 0.278 3.0 0.230 130887 0.050 20 3.60 0.228 3.6 0.209 130887B 0.040 60 2.20 0.297 3.6 0.167 170887 0.072 50 3.12 0.378 4.8 0.226 180887 0.398 80 8.10 0.794 17.4 0.344 060987 0.023 30 2.80 0.134 3.6 0.097 231087 0.065 35 5.14 0.207 6.4 0.153 241087B 0.280 20 12.60 0.364 16.0 0.263 011287 0.077 20 4.80 0.262 5.6 0.207 291287 0.079 30 5.60 0.230 7.8 0.153 240188 0.268 15 16.80 0.261 16.8 0.240 070288B 0.065 35 5.14 0.207 6.4 0.153 280288 0.098 25 7.20 0.223 8.0 0.185 200388 0.055 45 3.20 0.281 4.8 0.172 210388 0.080 15 4.80 0.273 4.8 0.251 220388 0.071 10 12.00 0.097 12.0 0.089 230388 0.472 10 29.76 0.260 29.8 0.238 250388 0.062 10 7.19 0.141 7.2 0.129 010488 0.016* 20 9.60 0.027 11.2 0.022 060488B 0.031 40 3.60 0.141 5.6 0.083 090488 0.034 10 6.00 0.093 6.0 0.085 280488 0.250 40 7.20 0.568 13.6 0.276 080588 0.007 10 1.20 0.096 9.6 0.011 * The observed peak modified by hydrograph separation to consider the effect of the burst only Chapter 4 Deterministic Evaluation of The Rational Method 4-71 Table 4.40. Jamison Park - Combined runoff events Date QP, Burst Average Runoff Average Runoff 3 m /s Duration Rain Coefficient Rain Coefficient (BD), Intensity Intensity in Burst, in Tc, Minutes mm/hr nun/hr 210383 0.801* 20 23.39 0.560 21.6 0.558 271183 0.151 110 4.14 0.597 7.2 0.316 131283 0.382 20 15.6 0.401 10.8 0.532 120184 0.389 40 13.20 0.483 16.8 0.349 140284 0.688 40 22.2 0.507 29.2 0.354 150284 0.484 20 12.00 0.660 8.4 0.867 220384 0.342 40 6.90 0.810 8.8 0.585 190684 0.370 50 12.24 0.495 14.0 0.397 270784 1.418* 70 19.37 1.197 24.0 0.889 100884 0.287 40 13.20 0.345 14.4 0.300 071184 1.399 32.5 50.95 0.449 61.9 0.340 111184 0.884 20 30.60 0.472 20.4 0.652 291184 0.382 60 10.40 0.601 18.4 0.312 131285 1.924 60 34.00 0.926 45.6 0.635 141285 0.793 50 15.36 0.845 19.6 0.609 030686 0.145 70 3.94 0.602 7.6 0.287 091086 0.169* 10 14.40 0.192 9.2 0.276 121186 0.278* 40 10.20 0.446 12.4 0.337 191186 0.366 40 11.70 0.512 12.8 0.430 261186 0.759 25 16.32 0.761 16.3 0.701 270887 0.483 50 9.12 0.867 13.6 0.534 241087 0.083 20 6.00 0.227 4.8 0.260 111187 0.765 80 16.80 0.745 25.2 0.456 010188 1.139 15 43.20 0.431 - - 020188 0.069 30 5.28 0.214 8.6 0.121 210188 0.572 20 27.00 0.347 - - 230188 0.319 25 11.52 0.453 11.5 0.417 080288 0.083 20 6.00 0.227 - - 030488 0.779 40 11.70 1.090 15.2 0.771 040488 0.346 15 9.60 0.589 - - 040488B 0.502 6 40.00 0.205 - - 070488 0.133 40 4.20 0.518 5.2 0.385 080488 0.507 20 19.20 0.434 - - 100488 0.369 40 7.50 0.805 9.6 0.578 110488 0.098 40 3.90 0.411 4.8 0.307 180488 0.060 5 7.20 0.137 - - 190488 0.120 5 7.20 0.273 - - 290488 1.839 40 24.60 1.223 32.0 0.865 * The observed peak modified by hydrograph separation to consider the effect of the burst only Chapter 4 Deterministic Evaluation of The Rational Method 4-^2 Table 4.41. Fisher's Ghost Creek - Impervious area runoff events Date Qp, Burst Average Runoff Average Runoff m3/s Duration, Rain Coefficient Rain Coefficient (BD), Intensity Intensity in Burst inTc, Minutes mm/hr mm/hr 040181 3.47 42 8.60 0.608 11.79 0.448 050383 3.68 45 34.43 0.161 56.04 0.099 170383 2.44* 36 17.38 0.211 23.82 0.155 271183/1 3.80 54 13.45 0.426 21.77 0.265 271183/2 1.48 29 11.34 0.197 11.34 0.199 271183/3 1.11 27 8.00 0.209 8.00 0.211 271183/4 1.04 45 6.39 0.245 10.05 0.157 070284 4.18 48 24.48 0.258 40.12 0.158 111184/1 3.30 27 22.80 0.218 22.80 0.220 111184/2 4.31 30 22.40 0.290 24.53 0.267 181186 5.45 42 20.42 0.402 29.05 0.285 161087/2 5.49 54 14.49 0.571 26.53 0.314 251281 3.00 39 15.78 0.287 19.71 0.231 131284 3.30 30 24.18 0.205 26.80 0.188 260184 2.89 129 11.55 0.377 22.73 0.193 240588 3.06* 39 14.84 0.311 19.18 0.242 * Modified peak Table 4.42. Fisher's Ghost Creek - combined events Date Qp, Burst Average Runoff Average Runoff m3/s Duration, Rain Coefficient Rain Coefficient (BD), Intensity Intensity in Burst inTc, Minutes mm/hr mm/hr 191081 4.75 180 6.35 1.128 13.37 0.541 021181 5.96 27 21.14 0.425 - - 200383 7.81 45 18.44 0.638 18.44 0.644 081184 3.60 189 6.70 0.810 14.24 0.384 091184 3.99 117 9.78 0.615 17.50 0.347 081285 5.16 45 26.65 0.292 26.65 0.295 150186/1 8.49 45 27.34 0.468 27.34 0.473 150186/2 11.85* 48 29.96 0.597 34.44 0.524 060886/1 7.89 24 23.58 0.505 - - 060886/2 8.49 99 13.38 0.956 18.36 0.704 241087/1 6.82 45 17.60 0.584 17.60 0.589 241087/2 9.15 120 12.16 1.135 22.27 0.625 280488 3.44* 27 21.10 0.245 - - 050688 15.65 75 34.06 0.692 44.85 0.530 * Modified peak Chapter 4 _— Deterministic Evaluation of The Rational Method 4- 75 Table 4.43. Strathfield - Total events Date Qp, Burst Average Runoff Average Runoff m3/s Duration, Rain Coefficient Rain Coefficient (BD), Intensity Intensity in Burst inTc, Minutes mm/hr mm/hr 210277 9.23 9 71.33 0.199 - - 070477 9.65 15 50.00 0.297 - - 080677 5.59 24 24.25 0.354 29.26 0.294 030977 4.00 21 19.83 0.310 20.16 0.305 270977 6.43 21 22.21 0.445 25.72 0.384 270378 4.40 6 32.00 0.211 - - 040978 3.57 33 10.09 0.544 14.39 0.381 070978 4.40 51 7.67 0.882 18.09 0.374 311078 4.04 6 31.50 0.197 - - 170379 3.57 27 34.40 0.160 44.32 0.124 260779 3.68 30 11.05 0.512 15.96 0.354 100180 6.55 24 23.50 0.428 29.71 0.339 130180 6.91 24 24.56 0.433 29.84 0.356 141080 3.91 21 17.57 0.342 20.53 0.297 121080 8.54 24 23.97 0.548 34.53 0.380 161280 3.46 9 22.50 0.236 - - 291280 7.94 33 28.60 0.427 45.49 0.268 100281 4.72 27 19.89 0.365 26.04 0.279 020381 11.75 45 35.00 0.516 50.77 0.356 020481 3.14 39 10.69 0.452 22.11 0.218 040481 2.78 21 7.14 0.599 8.27 0.517 04048IB 3.50 24 8.55 0.629 10.98 0.490 220581 4.29 12 25.00 0.264 - - 22058IB 7.88 21 38.60 0.314 38.33 0.316 220581C 3.26 33 9.64 0.520 13.91 0.360 061181 3.44 15 34.80 0.152 - - 121281 4.92 27 21.37 0.354 22.39 0.338 191281 7.48 18 27.92 0.412 27.92 0.412 251281 6.82 36 21.46 0.489 33.52 0.313 310182 8.78 18 33.71 0.400 33.71 0.400 210382 6.93 39 18.23 0.584 26.22 0.406 210382B* 4.13 3 20.23 0.314 - - 210382C* 4.90 12 15.27 0.49 - - 250382* 15.20 39 23.75 0.984 27.66 0.845 280382 4.8 9 35.98 0.205 - - 270982 8.57 45 23.17 0.569 37.61 0.350 031282 4.22 15 21.26 0.305 - - 151282* 3.68 24 9.12 0.62 - - 100283 4.38 12 34.56 0.195 - - 160383 16.61 36 77.47 0.330 130.83 0.195 Chapter 4 p Deterministic Evaluation of The Rational Method 4-~4 180683 3.38 27 9.33 0.557 12.13 0.428 030983 3.57 21 13.78 0.398 15.76 0.348 290983 5.02 12 24.38 0.317 - _ 191083 8.99 39 20.69 0.668 39.01 0.354 131283 9.49 45 15.00 0.973 22.19 0.657 281283 6.38 33 16.01 0.613 18.82 0.521 080184 9.55 36 32.24 0.455 47.97 0.306 180284 7.04 45 14.47 0.748 25.14 0.430 220384 10.44 42 16.60 0.967 30.74 0.522 230384 3.46 15 20.69 0.257 - - 080984 6.86 18 38.42 0.274 38.42 0.274 061184 3.59 21 9.42 0.586 10.73 0.514 111184 10.31 27 32.85 0.482 39.04 0.406 240385 4.68 39 17.11 0.420 20.07 0.358 030485 4.29 24 13.20 0.500 15.52 0.425 230485 7.44 39 10.04 1.139 16.18 0.707 300485* 7.46 15 38.00 0.302 - - 010585* 6.12 30 12.19 0.772 12.19 0.772 231085 11.15 27 26.94 0.636 39.87 0.430 271085 4.20 12 20.75 0.311 - - 081185 6.46 15 27.15 0.366 - - 261185 5.69 36 16.67 0.525 27.16 0.322 271185 4.04 39 5.39 1.152 8.34 0.745 161285 3.90 27 13.80 0.434 18.93 0.317 161285B 11.76 27 15.78 1.146 20.67 0.875 150186 5.62 18 60.40 0.143 60.40 0.143 160186 6.41 36 13.20 0.746 21.93 0.449 120286 5.59 30 17.37 0.495 21.80 0.394 090386 15.42 15 25.17 0.942 - - 081184* 16.00# 54 45.27 0.543 58.96 0.417 271185* 4.82 42 5.43 1.365 6.33 1.171 040886* 11.91# 9 63.4 0.289 - - 030488* 13.98 57 21.28 1.010 35.38 0.607 280488* 12.30# 30 34.88 0.542 34.88 0.542 040788* 16.22# 57 15.98 1.560 27.23 0.916 * Combined events # Modified peak Chapter 4 Deterministic Evaluation of The Rational. Table 4.44. Cranebrook - Total events Date Qp, Burst Average Runoff Average Runoff m3/s Duration, Rain Coefficient Rain Coefficient (BD), Intensity Intensity in Burst in Tc, Minutes mm/hr mm/hr 161087* 0.038 50 1.68 0.708 1.80 0.660 191087 0.834 20 9.60 2.717 16.80 1.553 191087B* 0.336 100 1.68 6.256 - - 211087 0.076 25 5.00 0.475 10.80 0.220 281087 0.021 20 3.60 0.182 4.80 0.137 111187* 0.146 85 1.00 4.613 1.60 2.854 200188 0.364 35 10.63 1.071 14.40 0.791 190388 0.010 45 2.93 0.107 4.80 0.065 200388 0.309 12 17.60 0.549 22.40 0.431 220388 0.017 25 4.32 0.123 6.00 0.089 300388 0.027 20 5.40 0.156 7.20 0.117 210388 0.040 15 4.0 0.313 - - 040488 0.313 10 14.40 0.680 14.40 0.680 070488 0.062 55 5.01 0.387 8.40 0.231 080488 0.354 20 14.40 0.769 16.80 0.659 * Combined events Chapter 4 Deterministic Evaluation of The Rational Method 4-76 4.3.2.2. The variations of runoff coefficient The average, standard deviation and coefficient of variation of runoff coefficient are shown in Tables 4.45 and 4.46 for average rainfall intensity during the burst and during the time of concentration of catchments respectively. For all the catchments, and also for both impervious and combined runoff, coefficient of variation of runoff coefficient are smaller when average rainfall intensity during the time of concentration is considered. The smaller values of coefficient of variation show the stability of response time and time of concentration in the urban catchments under study. (Table 4.45). Table 4.45. Statistics of rate runoff coefficient- BD Rain Catchment No. of Impervious Area Runoff Combined Events events AVE. S.D. CV AVE. S.D. CV. Maroubra 75/0* 0.234 0.106 0.451 . - _ Jamison Pk 36/38 0.310 0.181 0.585 0.554 0.272 0.491 FGC 16/14 0.311 0.133 0.428 0.649 0.276 0.425 Strathfield 63/12 0.483 0.245 0.507 0.773 0.427 0.552 Cranebrook 12/3 0.627 0.721 0.115 0.386 0.285 0.738 * Numerator is the number of impervious area runoff events. Denominator is the number of combined events Table 4.46. Statistics of rate runoff coefficient- Tc Rain Catchment No. of Impervious Area Runoff Combined Events events AVE. S.D. CV. AVE. S.D. CV. Maroubra 63/0* 0.181 0.63 0.35 - - - Jamison Pk 36/30 0.203 0.89 0.437 0.481 0.201 1 0.418 FGC 16/11 0.227 0.081 0.356 0.514 0.129 0.251 Strathfield 48/7 0.390 0.141 0.360 0.753 0.255 0.338 i Cranebrook 11/3 0.452 0.449 0.994 - * Numerator is the number of impervious area runoff events. Denominator is the number of combined events 4.3.2.3. The relation of flood peak and average rainfall intensity The correlation of the observed flood peak discharge with average rainfall intensity during both the burst and during the time of concentration of the catchments were studied using simple regression equations. Only the burst durations equal to or greater Chapter 4 Deterministic Evaluation of The Rational Method 4-77 than the time of concentrations were considered in the calculation The results are presented in Tables 4.47 and 4.48 for average rainfall during the burst durations and the time of concentration respectively. The coefficients of determination for all the catchments when average rainfall during the time of concentration is used are significantly greater than those using the burst duration. This difference denotes the importance of time of concentration in flood peak estimates in urban catchments using the Rational formula. In other words, when rainfall duration is greater than the time of concentration of the catchment, the flood peak magnitude is more related to the maximum average intensity during the Tc, rather than the burst duration. The scatter diagram and regression equations are shown for flood peak and Tc rain in Figs. 4.30 to 4.33. Chapter 4 Deterministic Evaluation of The Rational Method 4-78 Table 4.47. Correlation of flood peak and average rainfall intensity during the bursts- (Qp = a + b(IBDRain)) Catchment Runoff a b R2 N* Type Maroubra Impervious 0.312 0.020 0.611 75 Combined - - - _ Jamison Pk Impervious 0.042 0.014 0.571 36 Combined 0.071 0.030 0.531 38 F.G.C Impervious 1.754 0.088 0.253 16 Combined 2.479 0.255 0.425 14 Strathfield Impervious 3.850 0.104 0.236 63 Combined 6.213 0.138 0.221 12 Crane Bk Impervious 0.011 0.027 0.413 15 Combined - - - - * N : Number of observations, R2: Coefficient of determination Table 4.48. Correlation of flood peak and average rainfall intensity during time of concentration - (Qp = a + b(Ijc Rajn)) Catchment Runoff a b R2 N* Type Maroubra Impervious 0.397 0.014 0.637 63 Combined - - - - Jamison Pk Impervious -0.01 0.014 0.787 36 Combined 0.011 0.033 0.697 30 F.G.C. Impervious 1.797 0.062 0.318 16 Combined 0.183 0.328 0.755 11 Strathfield Impervious 3.536 0.110 0.525 48 Combined 5.825 0.216 0.620 7 Crane Bk Impervious -0.080 0.026 0.498 13 Combined - - - - * N : Number of observations, R2: Coefficient of determination Chapter 4 Deterministic Evaluation of The Rational Method 4-~;* SI Qp, m3/s 3.00 y - 0.397 -t- 0.014x r"2 - 0.837 A 2.00 — 1.00 — 33*1 m O.OO | i ] i ] O.O so.o 100.0 1SO.O 20O.0 Tc flain. mm/hr (a): Maroubra HE QP. m3|i 1.00 - -0.060 + 0.026X r~2 - 0.4B6 0.90 y w O.BO 0.70 — /I O.BO A 0.50 / y 0.40 "A 0.30 S3 SB O.20 0.10 A S E myymm as O.OO I 1 0.0 10.0 20.0 30 Tc Rain, mm/hr (b): Cranebrook Fig. 4.30. Correlation of rainfall and flood peak-(a) Maroubra, (b): Cranebrook Chapter 4 Deterministic Evaluation of The Rational Method 4-8n EB Qp, m3/s 1.00 ~T y - -0.O1O * 0.014X r"2 - 0.787 0.90 0.80 0.70 0.80 0.50 0.40 0.30 0.20 0.10 0.00 1 1 I 1 , 1 1 1 1 1 I 1 ' 0.0 10.0 20.0 30.0 40.0 SO.O 80.0 7O.0 BOO To Rain, mm/hr (a): impervious area runoff events '* Qp, m3/s 2.00 1.60 — LOO — 0.50 — O.OO —F 1 1 1 1 1 1 1 1 1 1 O.O 10.0 20.0 30.0 40.0 50.0 80.0 70.0 Tc Rain, mm/hr (b): combined events Fig. 4.31. Correlation of rainfall and flood peak for Jamison Park -(a) impervious area runoff events, (b): combined events Chapter 4 Deterministic Evaluation of The Rational Method 4- s} ±. Qp, ma/a B.OO 6 OO y — 1.797 •*• 0.082X r"2 - O 318 4.00 — 3.00 2.00 — LOO — O.OO —T | ! | , | , | , , , 1 O.O 10.0 20.0 30.0 40.0 50.0 SO.O To Rain, mm/hr (a): Impervious area runoff events BE Qp, mS/a 20.00 y - 0.183 + O.S28X r"2 - 0.755 A\ 18.00 16.00 14.00 / A 12.00 — r 10.00 ^~ a B.OO •-> A X ^ m B.OO A _ mA 4.00 - 2.00 A - / 0.00 ' I ' I ' I i I • i 0.0 10.0 20.0 30.0 40.0 50.0 60.0 Tc Rain, mm/hr (b): Combined events Fig. 4.32. Correlation of rainfall and flood peak for Fisher's Ghost Creek -(a) impervious area runoff events, (b): combined events Chapter 4 Deterministic Evaluation of The Rational Method 4-82 K Qp, m3/» 20.00 y - 3.336 + O.HOx r-2 - 0.626 15.00 — 10.00 — 5.00 — 0.00 -i | i | I | i | i | ; , ; O.O 20.0 40.0 eo.o so.o 100.0 120.0 140.0 Tc Rain, mm/hr (a): impervious area runoff events wi Qp, mS/a 20.00 y y - S.826 + 0.218X r"2 - 0.620 15.00 — OB y <^y ^y m 10.00 — y y iSJ B.OO BO - O.OO ' I ' I ' I ' \ ' ; 0.0 10.0 20.0 SO.O 40.0 SO.O 80.0 70.0 Tc Rain, mm/hr (b): combined events Fig. 4.33. Correlation of rainfall and flood peak for Strathfield-(a) impervious area runoff events, (b): combined events Chapter 4 Deterministic Evaluation of The Rational Method 4-81 4.3.2.4. Integration of deterministic runoff coefficient in a statistical method The comparison of flood peaks with average rainfall intensities during time of concentration indicated better correlation than those with average rainfall intensities over burst durations in all catchments ( Tables 4.47 and 4.48). The maximum values of runoff coefficient, calculated based on average rainfall intensity during the Tc, were selected in each year and were averaged over the years of record. These average values are compared with 2-yr return period runoff coefficients estimated by the ARR87 method (Table 4.49). The correlation of the average observed runoff coefficients with those of ARR87 is depicted in Fig. 4.34. The coefficient of determination shows that 85% of the observed runoff coefficient could be explained by ARR87 method. In other words the deterministic values from observed data are correlated very well with statistical values from ARR87 method. In catchments with short record of rainfall-runoff, the average of the maximum observed value of runoff coefficient in each year can be used to estimate 2-yr return period runoff coefficient from ARR87 method. In cases of very short records when averaging maximum annual observed values of runoff coefficient is not possible and gives a bias result, the average rainfall intensities from recorded events ( rainfall and runoff) should be used. The average of recorded rainfall intensities during the catchment time of concentration should be compared with 2-yr return period design rainfall and a proper representative observed runoff coefficient to be selected. For estimation of higher return period runoff coefficient the frequency factor from ARR87 can be used. Bearing in mind that there is no specific provision for soil type of catchment in ARR87 and also the lack of link between the coefficients and the time of concentration in the Rational method (Mein and Goyen 1988), the established relation is a useful tool to cover these deficiencies. The advantage of this practice is the deterministic basis of computation which includes the effect of soil type and hydraulic characteristics of the catchment via time of concentration. In this way a deterministic method (observed values) is related to the statistical method (ARR87). Using this method can save time and money to avoid setting up station and obtaining long term data. Chapter 4 Deterministic Evaluation of The Rational Method 4-84 Table 4.49. Runoff coefficient of 2-yr return period by ARR87 method and the average value from the observed data - % Method Maroubra Strathfield Jamison Park Fisher's Ghost Ck MPV. Comb. IMPV. Comb. IMPV. Comb. IMPV. Comb. Observed 0.248 - 0.505 0.824 0.273 0.415 0.303 0.482 ARR87 0.220 0.602 0.380 0.650 0.270 0.463 0.210 0.451 Impervious 29 50 35 27 Fraction, % i - ARR87C2 = 0.85Obs.C2 0.8 - R2 = 0.85 U 0.6- 90 tf tf 0.4- < 0.2 - y y y I i 1 0- r ii 1 1 1 () 0.2 0.4 0.6 0.8 1 Observed C2 Fig. 4.34. Comparison of observed runoff coefficient calculated by using rainfall intensity during Tc and ARR87 method (the regression line is forced through the origins) 4.4. Summary of the Rational method In order to evaluate the current method of flood peak estimation in urban catchments (ARR87), a deterministic approach to the runoff coefficient for the Rational method was applied to the catchments under study. Two important parameters of the Rational formula including time of concentration and runoff coefficient were investigated using observed rainfall and streamflow. Time of concentration of catchments was estimated by three common methods consisting of the velocity method, typical minimum time ofrise, an d lag time. Among these three Chapter 4 Deterministic Evaluation of The Rational Method 4-85 methods, lag time was found to be the best and most consistent one for catchment time of concentration estimates. The assumption of linear reservoir system was in agreement with recession analysis of hydrographs to compute the lag time for impervious areas of the catchments. The velocity method could be considered suitable in un^au<>ed catchments provided that roughness coefficient is estimated precisely. Velocity of half full pipe conditions could give reasonable results for average travel time estimates. Rate runoff coefficient was selected as a suitable surrogate for all the effective abstractions and attenuations on flood peak. This coefficient was calculated using both the average rainfall intensities and flood peaks by substituting them in the Rational formula. The average rainfall intensity was calculated during both the bursts and during the time of concentration of the catchments. It was concluded that when burst duration is greater than time of concentration of catchments, the flood peak magnitude is more related to the maximum average rainfall intensity during the Tc rather than to the burst duration. This emphasises the fact that time of concentration is a crucial parameter in the Rational formula. The integration of deteministic values with statistical values was performed by relating the average observed values of runoff coefficient, computed using both observed average rainfall intensity during the catchment's times of concentration and observed flood peaks, and 2-yr return period runoff coefficient from ARR87. It was concluded that ARR87 estimates for 2-yr return period are correlated very well with the observed values. The integration of deterministic values of runoff coefficients with a statistical method can be useful because it incorporates the effects of both soil type and time of concentration of catchments in the statistical method of ARR87. The established relationship between statistical and deterministic values can compensate for deficiencies with the ARR87 regarding catchment's soil type and therealtion of time of concentration of catchments and runoff coefficient. CHAPTER FIVE STATISTICAL EVALUATION OF THE RATIONAL METHOD Chapter 5 Statistical Evaluation of The Rational Method 5-1 CHAPTER FIVE 5. STATISTICAL EVALUATION OF THE RATIONAL METHOD In design situations the statistical mode of the Rational formula is preferred. The main difference between the statistical and deterministic Rational formulas is related to the runoff coefficient. In deterministic mode it would be expected that runoff coefficient is related to the catchment wetness and rainfall intensity and it is suitable for real storms. On the other hand, in statistical mode the effect of catchment wetness is included in Cy value and it is applicable to design storms only. Due to the difficulty in calculation and identifying catchment wetness, researchers including Schaake (1967), Aitken (1973) and French et al. (1974), concluded that the Rational formula is deterministically poor and could only be applied statistically in design situations. The ARR 1987 method for urban catchment runoff coefficient estimation is a statistical approach. In this interpretation runoff coefficient varies with return period according to the variations of flood peak and rainfall with the same return periods. The ARR87 method was proposed based on experience of drainage authorities and was supported by data from six gauged catchments located in NSW, VTC and ACT. There has not been enough relevant data to check the validity of the method since the time the method was suggested. The data should conform with the Rational method theory and procedure ( eg average rainfall during time of concentration and flood peak). Considering lack of processed data to evaluate runoff coefficient in the Rational formula. Munro(1956) commented " The literature abounds with tabulations of graphs of C for various conditions, but few are observed from reliable evidence.." The present study provides a deterministic basis for the Rational formula parameters using the observed data which are processed to conform with the theory behind the formula as much as possible ( Chapter 4). Making this basic data available, the parameters of the Rational formula can be evaluated statistically. Evaluation of the proposed runoff coefficient in ARR87 should be carried out based on flood frequency analysis and comparison of the results with ARR87 partial duration series of rainfall. Chapter 5 Statistical Evaluation of The Rational Method 5-2 Due to the short length of available flood peak records, partial duration series was considered in flood frequency analysis. Furthermore, ARR87 uses partial duration series for design rainfall, so using partial for flood peak is consistent with rainfall. The flood flows for different return periods were estimated according to Log Pearson Type IH theoretical distribution which is the proposed distribution in Australian practice (ARR87). 5.1. Partial Duration Series Specifications The nature of runoff from urban catchments is different from that in rural catchments. In rural catchments the extent of impervious man-made areas is low and negligible. However, in urban catchments runoff may generate from impervious areas in case of low rainfall intensity and dry soil or from both impervious and pervious areas when rainfall intensity is high and/or catchment soil/grassland are partly or entirely saturated. Because of the existence of impervious areas, the frequency of small floods in urban catchments increases. The dual nature of land use in urban catchments, impervious/pervious, causes difficulty in the decision making about a base discharge required to build a partial duration series for flood frequency analysis. The recommended criteria in ARR87 to build a partial series are as follows: a. Selection of base discharge b. Checking the independence of the selected consecutive floods c. Number of floods ( K) should be at least 2 to 3 times of number of years of record (N) In applying partial duration series, care should be taken regarding the independence of the selected flood peaks. There are many criteria for identifying independent floods. Some of these are used in large catchments ( Jayasuria and Mein 1985). Regarding small catchments, Potter and Pilgrim (1971) used 3 calendar days as an index to separate floods. This criteria was used in conjunction with base discharge and also checking the suitable number of floods in this study as well. In the case of consecutive floods which satisfied the base discharge, but violated the independence criteria (less than three days) the larger flood was selected. Chapter 5 Statistical Evaluation of The Rational Method 5-3 In Australian practice, the number floods, K, is recommended to be kept low compared with the number of years of record, N, because of high range of flow and dry years. In ARR87 it is recommended that the base discharge be high enough to exclude the small events which cannot be considered as flood. This is specially true in urban catchments which are the concern of this study. Although there are many small floods because of directly connected impervious areas of urban catchments, a partial duration series of flood peaks should encompass both impervious area runoff and combined runoff from both impervious and pervious areas of catchments. In relation to the extent of impervious areas, in some urban catchments it is quite possible that low return period floods such as 1 and 2 year to be generated only from impervious areas of the catchments. This issue should be considered in selection of the base discharge to build a partial duration series of floods. 5.1.1. Base discharge selection in partial duration series Regarding the criterion (a) in Section 5.1, three indexes for recognition of base discharge were considered and tested in this study. Thefirst on e was taken as flood peak with observed runoff coefficient equal or greater than the percentage of directly connected impervious areas in each catchment. The flood peaks related to these runoff coefficients will be selected as partial duration series of floods. Using this index the number of floods was found to be large, and also in one of the catchments, Maroubra, the largest observed flood peak was omitted (Table 5.1). The second index was considered as average runoff coefficient for impervious area runoff events for the catchments. Again with this index number of floods was found to be very large and the ratio of K/N exceeded 3 (Table 5.1). CnapterS Statistical Evaluation of The Rational Method 5-4 Table 5.1. Comparison of Partial Duration Series Indexes Catch. D.CIMPV AVE. IMPV. No. of Index 1 Index 2 .(Index 1) R.O.C. Record (Index 2) (N) K* K/N K K/N MAR 0.19 0.181 9 25 2.8 27 3.0 JAM 0.32 0.203 6 22 3.7 47 7.8 STR - 0.227 11 - - 51 4.6 FGC 0.31 0.390 6 12 2.0 8 1.3 * No. of floods The third index was selected as the minimum observed flood peak in annual series of each catchment which gave satisfactory results. Using this index, there is less chance for small frequent floods to be selected but it does not reject the inclusion of events with impervious area runoff in the series. Application of this index, gave a reasonable number of flood peaks in Maroubra, Jamison Park and Fisher's Ghost Creek catchments, but not in Strathfield. In the Strathfield catchment using minimum flood peak in annual series (3.68 m3/s) a total of 52 floods were selected as partial duration series while the number of year of record was as equal to 11. In this series the ratio of K to N was found to be very high ( 4.7 ) which violated the criterion in small catchments in Australia. To consider this exception the base discharge was elevated to 7.5 m /s and consequently only 23 floods were selected as partial duration series in this catchment. It is worth mentioning that in this catchment most of events are impervious area runoff ( 63 out of 75 events), so the base discharge should be selected sufficiently high to exclude those frequent events from impervious areas and include the majority of combined area runoff. In Cranebrook catchment due to very short length of data available ( only 2 years) flood frequency (annual or partial duration series) was not feasible to be carried out. Number of floods (K) in the partial duration series and number of years of record for the catchments, N, are indicated in Table 5.2. The ratio of K/N for all the catchments is in the reasonable range of 1.7-2.8. According to the selected index the partial duration series of flood peaks are presented in Table 5.3. Average rainfall intensity during the time Chapter 5 Statistical Evaluation of The Rational Method 5-5 of concentration and runoff coefficients for the selected flood peaks are also included in Table 5.3 for performing frequency analysis on these values as well. These rainfall intensities and runoff coefficients were calculated from the deterministic application of the Rational method to recorded events in Chapter 4. Table 5.2. Number of floods in the partial duration series Catchment Maroubra Jamison Fisher's Strathfield Park Ghost Ck No. of Record (N) 9 6 7 11 No. of Floods (K) 21 10 11 23 K/N 2.3 1.7 1.6 2.1 Table 5.3. Partial duration series of flood peak, average rainfall intensity and runoff coefficient Jamison Park Fisher's Ghost Creek Date Qp, Rain, C Date Qp, Rain, c m3/s mm/hr m3/s mm/hr 210383 0.801 21.60 0.558 111184 4.31 24.53 0.267 270784 1.418 24.00 0.889 181186 5.45 29.05 0.285 071184 1.399 61.90 0.340 161087 5.49 26.53 0.314 111184 0.884 20.40 0.652 191081 4.75 13.37 0.514 131285 1.924 45.90 0.635 021181 5.96 - - 261186 0.759 16.30 0.701 200383 7.81 18.44 0.644 111187 0.765 25.20 0.456 081285 5.16 26.65 0.295 010188 1.139 - - 150186 11.85 34.44 0.524 030488 0.779 15.20 0.711 060886 8.49 18.36 0.704 290488 1.839 32.00 0.865 241087 9.15 22.27 0.625 050688 15.65 44.85 0.530 Chapter 5 _ Statistical Evaluation of The Rational Method 5-6 Table 5.4. Partial duration series of flood peak, average rainfall intensity and runoff coefficient Maroubra Strathfield Date Qp, Rain, C Date Qp, Rain, C 3 m /s mm/hr m3/s mm/hr 010377 1.03 29.90 0.217 210277 9.23 - - 030378 1.65 75.30 0.138 070477 9.65 - - 180378 1.55 59.42 0.165 121080 8.54 34.53 0.380 130678 1.08 32.27 0.211 291280 7.94 45.49 0.268 190679 1.41 53.46 0.166 020381 11.75 50.77 0.356 170383 2.12 110.3 0.121 22058 IB 7.88 38.33 0.316 051184 1.81 164.7 0.069 310182 8.78 33.71 0.400 111284 1.29 34.39 0.237 250382 15.20 27.66 0.845 010585 1.28 35.1 0.230 270982 8.57 37.61 0.350 081185 1.27 78.00 0.103 160383 16.61 130.8 0.195 271285 1.37 55.50 0.156 1901083 8.99 39.01 0.354 160186 1.32 65.61 0.127 131283 9.49 22.19 0.657 120486 1.61 57.04 0.178 080184 9.55 47.97 0.306 040187 1.21 - - 220384 10.44 30.74 0.522 030787 1.30 34.61 0.237 111184 10.31 39.04 0.406 231087 1.45 36.14 0.253 231085 11.15 39.87 0.430 250388 1.08 54.20 0.126 161285B 11.76 20.67 0.875 020488 1.15 50.68 0.143 090386 15.42 - - 070488 1.36 33.40 0.257 081184 16.0 58.96 0.417 280488 1.38 66.00 0.132 040886 11.91 - - 150688 1.29 42.00 0.194 030488 13.98 35.38 0.607 280488 12.30 34.88 0.542 040788 16.22 27.23 0.916 Chapter 5 Statistical Evaluation of The Rational Method 5-7 5.1.2. Frequency analysis of partial duration series of flood peaks Frequency analysis was preformed on the partial duration series of flood peak in Tables 5.3 and 5.4 to obtain the magnitude of floods with return period of 1. 2, 5 and 10 years. The flood series were ranked decreasingly and the N values of the largest were selected and after logarithmic transformation their statistics (mean, standard deviation and skewness) were calculated (Table 5.5). Log Pearson Type III frequency distribution was fitted to the partial duration series of flood peaks ( Figures 5.1 and 5.2). It should be noted while the selection of a suitable theoretical distribution is very controversial, practically most of the well known distributions, eg. Normal, Log Normal, Pearson, Log Pearson and Gumble, for return periods up to 10 years give similar results. Flood peak with return periods of 1,2, 5 .and 10 years were calculated using the adopted distribution (Table 5.6). Table 5.5. Statistics of partial duration series offlood peaks (logarithms) Catchment. Maroubra Jamison Fisher's Strathfield Park Ghost Ck X 0.198 0.141 0.938 1.137 S.D. 0.063 0.127 0.160 0.067 SKEWNESS 1.112 -0.467 0.412 -0.045 N 9 6 7 11 Table 5.6. Magnitude of flood peaks from frequency analysis-m3/s ARI,yr Maroubra Jamison Pk Fisher's Ghost Strathfield 1 1.270 0.634 3.860 9.541 2 1.538 1.418 8.453 13.716 5 1.758 1.780 11.697 15.592 10 1.916 1.979 14.072 16.660 Chapter 5 Statistical Evaluation of The Rational Method 5-8 - f——;——| rr—- - : -- • 1 ; . , !- --:-•:—- -" j •• --•-•-••: j-: i , A - :'-'ri'r"~~- '- ' — ' ::-—::-^T^Iirr^=-L^^^; - JJS^^gESESSESE=Ji : • msiSm Ji'Ai^ps^^ AA :—! ! T-A i: •——1 ! 1 )"• i rV.::l:;: ;-'—- : —\^zz=Bf.=4Ez\l ~~'-'JZ^=lp~^^^:^==z=~: • --^r-"T^j^?^Ar=^ur—:—1 ——: : .!...._„ . '.Z'.. .:'." .\ " A'iA •"•;;---;- ; A - i :'__;^i.:™:-j ; : '• : : z'il : :-'. -.:_ :-i-x^\=r.:_±=zr7=?:z:=-^r:=zi=~^~£ • z : : :J: : ': ~:; : ••:•:•: :-- - •—::! - -z ~l-~'.': :- - -~ z:i7~i- r£X* —t*-—*-*-—- : d • - i.i/—- Av.: - : ~ -.i---. —A A A-.: A "— i."-|"~"-! i •-!—1 f—|—[— AKKS: A =L^ : ;::~££iH£-rir:ij—{==&;l^-> .^•^'••-H^-- :i":ii r^-ng^^-feg^Hg-jizlzE^^ ; ::.:: | : t:::.j :.'..:-:_:..',:•:.;-.: i '.'.]'.. i . ~: T . . : ! . ... 1 ' iiii ij':''ZZZ--i I A A" T f - , • • • (a): Maroubra .01 3 oj c i •) : :fEA • -. r TH~F ! IgAAif^ FAALAtA : !~.-:--:Lft-pT-:r~!-rt ::-r[-_— zf=z±-Z +44-J r T. :: .. - ,WR.;7 A — • - pr.::: r :: ri :: Fr.: ::: :; ,=rr •— — r: f—:::::L.::— ZZZZZ :— f~*~| " - _ - - - .._..._ . —.— i—. E3r; r!:: i~— -AA=A k ... .L._j—^_ I—l_r:: __i : 1 ___—1 j . . . . i i — • — ..., ".:_ I • • •, AAi ...-— — -.. .:. ~r-- i ' i !" ..!....]...... :._:....!_- (. y--, - r " " " " •' ,: "• " " ''• '" AEP, <* (b): Strathfield Fig. 5.1. Frequency Distribution Fitted to the Partial Duration Series of Flood Peaks Chapter 5 Statistical Evaluation of The Rational Method 5-9 \u>. "r (a) : Jamison Park c ,n c os : i .': • : > : • •- :* n • ' •' ft tc *" ** "* ••» -— i- -i — ! . L. |-~ -.—1 ( i.. .1....I...... _. . , ; j . r ...... : . _j._"-. - —'. i—1 — — --1- - ...— I— -1--: -- I-- '— I'- ._.[.. --. 1— -,—,— j _j—1 ,~r~r ~r r-^r ' : ——: -•- — 1 .TrrAHi •~a sLIrhslilfssKS: H~~= •— .. i 1_—.,—. . . , .... — ._...._...... , — . , . ... __ 1— — . T: =ii^=S - ~. rL-i ~ M 1 rl^r!™ A-i-M-^-^- — t _.. _ t - • i • —.-—-: — T* l :._^_! _.—_IJ_« r ....1. ',: ._. ..;.._. .: _i . [.. -. u -:.:• ". " : : - ": [r~-i\x= • EI-I.|~=:|=^~==|=i= —- i— -- 7-: i.:.:_.~:Zl: i 1Z.ZZZ1 -, l ::: A^fiAAAA ^zAA »f-;f FJf.LflA' i' r^zTz\^.\^^:V~r^:^\^^\:^.\-~=~. ~.~~£ j::~=r=£{?: ; =-- : \=2 Jlil ErL "ii 1::L: : :--t- •"" 1 "-" : -'—: = ^A'iAiin-A^IA-r- 'TTE-' —-^- A~-[:Ap^-- - :.A>^ AAAA. :.A: . \=z; ;AA A 1 , ^^_-^T | L. r i h :-±-:|i—!— .._i^e : -j | ' - — i \ •! 1 — : :; • i i i" r" — i A 'A | r -1 r-Jr r-ri- ^ , |j.::|..... j^_ • -H-t-;--i--H | -;•-- __j M ^^ 1 L_ . f"f==E^^-=-j^f5=a=S=S=t==^=fi=pB£g= 1 ' f j -J—. ;,,.. ,. ,„,,, „i — ' .=^- j ::L-r-[^)r-r I-_pr^^lrp7=:M-^g^%=i*:zr:ri^=r--= = -—- '-•i-^<- --:rr=.-:tr-:t:-—;::r. . 1—, t- , A . \ - .—-r—j _ .- =-tH" j ____. ,—.— —. , «.«-« •»~ 1 M:M AA A-AM::::| — — ;— •s-xrj- 1 ^ 1 1 f :— ARIW7 A | A~A A~-~=^^:^=-=-::^==£^r^ ••=-•-- L-—-==-2EF: .-_—!-: -A -;— ~.-L:.l:;; : EM 1——! — -• —j— \— • .: :_~ -_-i r .| -. r ~' i • , • • = ; • ;'•; AMTTi -| ;• . . - ^ | AEP. ^ ( b): Fisher's Ghost Creek Fig. 5.2. Frequency Distribution Fitted to the Partial Duration Series of Flood Peaks Chapter 5 Statistical Evaluation of The Rational Method 5-10 5.2. Design Rainfall ARR87 provides design rainfall to be used in conjunction with the Rational formula. ( Chapter 2, Vol. 1 & Vol. 2, ARR87). To develop IFD for a specific site, the rainfall magnitudes of three basic durations of 1, 12 and 72 hrs for both return periods of 2 and 50 years should be scaled off the maps provided in ARR87. Short duration rainfall. 6 minutes, is calculated using intensities for 2 and 50 years return periods along with two factors, F2 and F50, which are obtained from maps provided in ARR87 Vol. 2. The other return periods (1,5, 10, 20 and 100 years) are obtained using algebraic equations provided by ARR87. The IFD for the catchments under study were prepared by Bufill (1989) which after cross-examination of their accuracy were used in the present study to estimate average rainfall intensities during the time of concentration of catchments (Table 5.7). Table 5.7. Magnitude of average rainfall intensities during time of concentration-mm/hr ARI,yr Maroubra Jamison Pk Fisher's Ghost Strathfield 1 65 36 30 36 2 81 48 40 48 5 110 62 48 62 10 120 70 55 70 5.3. Statistical Runoff Coefficient The results of flood peak frequency analysis and design rainfalls with the same return periods were substituted in the Rational formula and it was solved for runoff coefficient. The results for return periods of 1 to 10 years are presented in Table 5.8. The runoff coefficients for Maroubra catchments are roughly constant with return period. The reason for this uniformity is the high infiltration capacity of the catchment deep sandy soil. Furthermore, the extent of impervious areas is constant ,so the trend of increase in flood peak and rainfall is linear. The results in the Strathfield catchment are Chapter 5 Statistical Evaluation of The Rational Method 5-11 almost the same as in Maroubra, however, the soil texture is heavier than that in Maroubra. As it was already mentioned 63 out of 75 events in this catchment belong to the impervious areas. In Fisher's Ghost Creek runoff coefficient magnitudes increase as the return period increases which shows the contribution of more areas in runoff generation. In Jamison Park there is significant difference between runoff coefficient of 1 year and the other return periods which shows the occurrence of combined runoff from both impervious and pervious areas. The soil type of catchments is clay and combined runoff is quite common. Table 5.8. Statistical runoff coefficients ARI,yr Maroubra Strathfield Jamison Pk Fisher's Ghost 1 0.123 0.408 0.288 0.216 2 0.120 0.440 0.483 0.355 5 0.101 0.387 0.470 0.410 10 0.101 0.366 0.463 0.430 5.4. Comparison of Statistical Runoff Coefficient and ARR87 Method Calculated runoff coefficients using ARR87 method ( refer to Section 4.3. Chapter 4) and the results of frequency analysis are presented in Tables 5.9 and 5.10 and Figures 5.3 and 5.4. The general evaluation of the results indicates overestimation by ARR87. The trend of overestimation for light soil type catchments (Maroubra and Strathfield in Table 5.9) is increasing with return period, while this trend is opposite for heavy soil type catchments (Jamison Park and Fisher's Ghost Ck in Table 5.10). The highest average overestimate (113%) belongs to very light soil, deep sand, in Maroubra and the lowest overestimate (18.7%) belongs to very heavy soil, red clay, in Jamison Park. It is concluded that application of ARR87 in catchments with heavy soil type is more accurate than catchments with light soils. On average, ARR87 overestimate Chapter 5 Statistical Evaluation of The Rational Method 5-12 is 84% and 31% when applied to catchments with light soils and heavy soils respectively. Flood peaks estimated by both ARR87 method and frequency analysis are presented in Table 5.11. Table 5.9. Runoff coefficient from ARR87 and Frequency Analysis ARI,yr Maroubra Strathfield ARR87 F.A.* R.E.#, ARR87 F.A. R.E.. % % 1 0.207 0.123 68.3 0.540 0.408 32.4 2 0.220 0.120 83.8 0.580 0.440 31.8 5 0.246 0.101 143.6 0.650 0.387 68.0 10 0.259 0.101 156.4 0.680 0.366 85.8 Soil Type Deep Sand Fine Sand/Sandy loam AVE. RE 113.0% 54.5% * Frequency Analysis # Relative Error = (ARR87-F.A.)/F.A. Table 5.10. Runoff coefficient from ARR87 and Frequency Analysis ARI,yr Jamison Park Fisher's Ghost Creek ARR87 F.A.* R.E.#, ARR87 F.A. R.E., % % 1 0.436 0.288 51.4 0.425 0.216 96.8 2 0.463 0.483 -4.1 0.451 0.355 27.0 5 0.517 0.470 10.0 0.504 0.410 22.9 10 0.545 0.463 17.7 0.531 0.430 23.5 Soil Type Red Clay Medium Clay AVE. R.E 18.7% 42.5% * Frequency Analysis # Relative Error= (ARR87-F.A.)/F.A. Table 5.11. Magnitude of flood peaks from ARR87 and frequency analysis-m3/s ARI,yr Maroubra Jamison Pk Fisher's Ghost Strathfield 1 1.270/2.13* 0.634/0.960 3.860/7.58 9.541/12.64 2 1.538/2.82 1.418/1.36 8.453/10.72 13.716/18.10 5 1.758/4.28 1.780/1.96 11.697/14.38 15.592/26.20 10 1.916/4.92 1.979/2.33 14.072/17.36 16.660/30.94 * Frequency Analysis/ARR87 Method Chapter 5 Statistical Evaluation of The Rational Methn,. • ARR87I FA 0.8 : • - I 0.6 o 0.4 • <> 0.2 • • • • • II 0 0123456789 10 ARI, yr (a) : Maroubra • ARR87 0.8 • F.A. o • • 0.6 • o • 0.4 • • II 0.2 n . i i ,iii » : 01 23456789 10 ARI.yr (b): Strathfield Fig. 5.3. Runoff Coefficient by ARR87 and Frequency Analysis Chapter 5 Statistical Evaluation of The Rational Method 5-15 !*ARR87i 0.8 I.F.A. ' : 0.6 < o • • 1 • 1 0.4 • 0.2 ' ' 01 23456789 10 ARI.V (a): Jamison Park • ARR87] • F.A. | 0.8 0.6 A o • » 0.4 • • . • 0.2 • 0 123456789 10 ARI, yr (b): Fisher's Ghost Creek Fig. 5.4. Runoff Coefficient by ARR87 and Frequency Analysis Chapter 5 Statistical Evaluation of The Rational Method 5-16 The magnitude of overestimation by ARR87 is higher for 1 year return period than the other return periods particularly in the Jamison Park and Fisher's Ghost Creek catchments. This difference was investigated in relation to the so-called frequency factor used in ARR87 (refer to Table 4.35, Chapter 4). It should be noted that the frequency factor used in ARR87 is quite different from that of used in theoretical distributions ( eg in Log Pearson Type III). The frequency factor used in ARR87 for 1 year return period is equal to 0.8 * Cio, while it is equal to 0.62*C10 and 0.5*Ci0 for Jamison Park and Fisher's Ghost Creek respectively ( Refer to Table 5.8). The explanation for this difference is considered as the relation between ARI and AEP for 1 year return period. While the relation between ARI and AEP is unique for 2, 5, 10 and higher return periods, it is rather subjective for 1 year return period. For 1 year return period ARI can be considered as 1.00, 1.11, 1.01 and 1.001 which correspond to AEP of 1.00, 0.90, 0.99 and 0.999 respectively. Reference to Fig. 5.2(b), plotting Q value for 1 year return period from ARR87 on 0.90 AEP instead of 0.99 AEP brings this value in the same trend of Q values for the other return periods. Considering the difficulty of making decision on 1 year return period, if it is excluded from the evaluation the average overestimation by ARR87 for heavy soil type catchments will reduce to 16% which is almost half of those presented in Table 5.10. 5.5. Comparison of Statistical and Deterministic Runoff Coefficient Reference to Table 4.49 in Chapter 4, the 2-yr runoff coefficients calculated from observed data are compared with statistical runoff coefficient in Table 5.12. It is concluded that in catchments with light soils ( Maroubra and Strathfield) including pervious areas in flood peak calculation causes overestimation because statistical runoff coefficients are less than average observed runoff coefficients for impervious areas. On the other hand, in catchments with medium to heavy soils ( Fisher's Ghost Creek and Jamison Park ) only 1-yr statistical runoff coefficient is less than 2-year impervious area runoff coefficient. In other words, 2-yr flows and more come from both impervious and pervious areas, so they both should be considered in flood peak computation. In the Jamison Park catchment with heavy soil type even 1-year flow runoff coefficient is greater than 2-year deterministic impervious area runoff coefficient which shows the Chapter 5 Statistical Evaluation of The Rational Method 5-/7 contribution of pervious areas in 1-year flow return period, however the difference is very small. In the Fisher's Ghost Creek catchment thisfigure is less because the soil type is lighter than that of the Jamison park. Table 5.12. Runoff coefficient by Statistical and Deterministic Approaches Method Maroubra Strathfield Jamison Park Fisher's Ghost IMPV Comb. IMPV. Comb. IMPV. Comb. IMPV. Comb. Deterministic, 2-yr 0.248 - 0.505 0.824 0.273 0.415 0.303 0.482 Statistical, 1-yr 0.123 o.<10 8 0.288 0.216 Statistical, 2-yr 0.120 0.440 0.483 0.355 Statistical, 5-yr 0.101 0.387 0.470 0.410 Statistical, 10-yr 0.101 0.366 0.463 0.430 5.6. Summary In design situation statistical runoff coefficient is required because it includes the effects of all parameters contributing in flood peak generation and are not easily measurable. Runoff coefficient was studied from the view point of statistics in this chapter. Due to the short record of data available and also to be consistent with ARR87 design rainfall, partial duration series of flood peaks was used in frequency analysis. Among three different indexes considered to define a base discharge for building a partial duration series of flood peaks, the minimum in annual series was found to give very reasonable number of floods when compared with number of years of record, so it was adopted. Log Pearson Type III was fitted to the partial duration series of flood peaks and for return periods of 1,2,5 and 10 years flood peaks were calculated using the distribution. Design rainfalls were scaled off the IFD curves resulting from ARR87 partial duration series design rainfall for the catchments time of concentration. Statistical runoff Chapter 5 Statistical Evaluation of The Rational Method 5-18 coefficients were calculated based on frequency analysis of flood peaks and design rainfalls. For different return periods, it was concluded that statistical runoff coefficient remains constant in the catchments with light soils and has an increasing trend in the catchments with heavy soils. Generally speaking ARR87 overestimates runoff coefficient. However, for catchments with light soil type the overestimate is higher than that in the catchments with heavy soil type. On average, the magnitude of overestimation is 84% and 31% for catchments with light and heavy soil types respectively. The range of overestimates of 1 year runoff coefficients in ARR87 method is higher than those of the other return periods. The overestimation can be due to the subjective relation between ARI and AEP for 1 year return period. Comparison of deterministic runoff coefficient with statistical showed that in catchments with light soils estimation of flood peaks with return period up to 10 years can be performed considering only impervious area of catchments. In catchments with medium soils only 1-yr flow can be assumed to generate from impervious areas and for higher return periods the incorporation of pervious areas is necessary. Both pervious and impervious areas should be considered for flood computation of 1 year return period and higher in catchments with heavy soil type. CHAPTER SIX THE MOUSE MODEL Chapter 6 . The MOUSE Model 6-1 CHAPTER SEX 6. THE MOUSE MODEL 6.1. Introduction Reference to Chapter 2 the MOUSE model was adopted as an appropriate urban model to incorporate runoff coefficient of pervious areas in and to simulate design flood peak using temporal patterns. Bearing in mind that the present study is thefirst application of the model on separated urban stormwater drainage in Australia, it is required to examine the input data of the model and to test the internal structure of the model through a comprehensive sensitivity analysis. The sensitivity analysis of the runoff and pipe flow models of MOUSE is performed using Maroubra catchment data. This catchment has been studied by many researchers to evaluate urban models such as SWMM and ILSAX (Vale et al., 1986; Bufill, 1989; Boyd and Bufill, 1990; O'Loughlin et al. 1991). The soil type of the catchment is deep sand which covers the whole catchment. The study by Bufill (1989) showed that during the years of recorded data for 39 storms, ranging in size from 3.8 to 227.1 mm, there was no runoff from pervious areas of the catchment. Vale et al (1986) used SWMM and ILSAX to simulate both pervious and impervious area runoff of the catchment and found that the results overestimated recorded floods. This will be discussed in Chapter 7. In this study the first assumption made was based on the results of Bufill(1989). MOUSE was set up for simulation of runoff from impervious areas of the catchment only. The only areas which contribute to runoff are roads and pathways. In other words only directly connected impervious areas were assumed to produce runoff. Furthermore, the interaction of pervious and impervious area runoff is avoided by selection of Maroubra data for sensitivity analysis purposes. Chapter 6 _ The MOUSE Model 6 -2 6.2. Catchment and Pipe Data File The physical characteristics of subcatchments, required to simulate surface runoff using level A, are entered in afile whic h is used for all events. For each subcatchment this file includes: node number of outlet, total area and fraction of impervious area (Table 6.1). Coordinates, top and bottom level, shape of outlet and diameter of manholes are introduced to the model in thisfile a s well (Table 6.2). Equivalent circular manholes are used to represent square gully pits (Sec. 6.3.2.1). Pipe diameter, roughness coefficient, upstream and downstream bottom levels of pipes and their layout are entered in another section of the catchment and pipe datafile an d at other places (Table 6.3). Also the geometric data of trapezoidal channel at the bottom of the Maroubra catchment are put in other sections of thisfile whic h includes upstream and downstream bottom levels of nodal points, bottom width, angle of channel sidewalls and height. To create the catchment and pipe data file, the data presented in the ELSAX user manual (O'Loughlin 1988) are used which are originally taken from the cadastral map presented in Chapter 3. The ground surface level of each node, pit, and for some parts of the main line, 33 to outlet (Fig. 6.2) was found from the water board government contract map (Chapter 3) and for other lines by visiting the catchment and measuring the depth of each pit. In Fig. 6.1 the existing stormwater drainage network of Maroubra catchment created by MOUSE is demonstrated. This is a completely sewered catchment except a short trapezoidal open channel between node 2 and the outlet. The longitudinal profile of the main line of 33 to outlet, the steepest part of the network and council lines are illustrated in Figs. 6.2 to 6.5 respectively. Chapter 6 The MOUSE Model 6 -3 • 16 ••I.I : 33 H • A 15 I32 —-* 31: 131 12 'fZ9 ': 11 ' A 9 m : - 4 in • •~A '» 21 1? -An >3 la \AA -~-^4 : i" ~JK :— l 1 OUTPUT /l 3 PARAMETERS • l 4 NODES OUTLET, • •m 11M S CONDUITS 6 PROFILE DATAFILE : M1.SWF 1 7 ZOOM EDITED : 3-JAN-1994 1624 MUUiifc Fig. 6.1. Layout of pipe drainage network - Maroubra Table 6.1. Physical characteristics of sub catchments- Maroubra D:CATCHMENTS FILE:M1.SWF PAGE 1 CREATED: 29-JUL-1993 01:35:55 EDITED: 3-JAN-1994 16:24:17 Surface Distribution (total area): Impervious : 1: steep roof 2: flat roof 3: asfalt/concrete Semi pervious : 4: large- 5: small spacing between tiles Pervious : 6: planted 7: unplanted CT = Catchm.type(l-7) HL = Hydrologic Level (1-2) SP=Soil Parameter (1-3) Pet Imp= Total Impervious Areas (Directly Connectec or Disconnected) Row Nodal Total Slope Catch C Person Add. H Pet SP SurfaceDistribution Point Area prm length T eqvlt. flow L Imp 12 34567 No. ha m Pe/ha m3/s pet pet. 1 33 2.991 33 0 4 0 0.000 1 35 2 32 2.600 33 0 4 0 0.000 1 L 23 3 31 1.823 73 0 6 0 0.000 1 23 4 29 2.473 10 0 4 0 0.000 1 23 5 28 1.982 149 0 4 0 0.000 1 23 6 27 2.436 137 0 6 0 0.000 1 23 7 24 0.555 104 0 6 0 0.000 1 65 8 22 0.920 115 0 6 0 0.000 1 40 9 23 0.620 10 0 3 0 0.000 1 40 Chapter 6 The MOUSE Model 6 -4 10 21 1.215 152 0 6 0 0.000 1 65 11 26 1.300 10 0 6 0 0.000 1 25 12 25 2.325 357 0 6 0 0.000 1 23 13 19 0.618 423 0 6 0 0.000 1 80 14 18 4.880 10 0 6 0 0.000 1 40 15 17 0.539 10 0 6 0 0.000 1 45 16 16 4.384 10 0 6 0 0.000 1 23 17 15 0.300 192 0 6 0 0.000 1 90 18 14 1.859 192 0 6 0 0.000 1 23 19 13 1.527 500 0 6 0 0.000 1 23 20 12 1.268 126 0 6 0 0.000 1 23 21 11 1.205 126 0 6 0 0.000 1 23 22 9 1.110 500 0 6 0 0.000 1 45 23 8 1.205 500 0 6 0 0.000 1 45 24 7 3.847 10 0 6 0 0.000 1 23 25 6 2.104 10 0 6 0 0.000 1 23 26 5 1.443 10 0 6 0 0.000 1 30 27 4 1.065 10 0 6 0 0.000 1 23 28 3 5.327 10 0 6 0 0.000 1 23 29 2 3.942 10 0 3 0 0.000 1 23 Table 6.2. Characteristics of manholes- Maroubra KG1: Circular Manholes FTLErMl.SWF PAGE 1 CREATED: 29-JUL-1993 01:35:55 EDITED: 3-JAN-1994 16:24:17 Row Nodal Coordinates Levels, m Shape Dia. Point of m No. OuUet X, m. Y, m. Top Bottom 1-4 1 33 703.6 893.9 25.36 26.36 4 0.64 2 32 685.9 794.3 24.46 25.46 4 0.64 3 31 836.3 752.3 22.88 25.50 4 0.64 4 29 827.5 690.3 22.64 25.02 4 0.64 5 28 798.7 601.8 22.38 25.03 4 0.64 6 27 787.7 517.7 22.20 25.49 4 0.64 7 24 836.3 340.7 28.48 29.48 4 0.64 8 23 708.0 362.9 23.71 24.71 4 0.64 9 22 716.9 444.7 22.01 24.74 4 0.64 10 21 623.9 458.0 21.80 25.82 4 0.64 11 26 1106.3 287.6 46.53 47.53 4 0.64 12 25 823.1 327.5 34.60 36.60 4 0.64 13 19 588.5 458.0 21.63 23.63 4 0.64 16 16 480.1 920.4 24.23 25.23 4 0.64 17 15 495.6 825.3 22.88 24.88 4 0.64 18 14 455.8 829.7 22.64 24.64 4 0.64 19 13 446.9 739.0 22.08 23.48 4 0.64 20 12 473.5 725.7 21.94 23.34 4 0.64 21 11 486.8 641.6 21.61 23.11 4 0.64 22 9 508.9 562.0 21.27 24.27 4 0.64 23 8 493.4 473.5 20.93 22.93 4 0.64 24 7 265.5 681.5 22.76 23.76 4 0.64 25 6 303.1 593.0 21.74 22.74 4 0.64 26 5 320.8 500.0 20.83 22.13 4 0.64 27 4 393.8 486.8 20.63 23.13 4 0.64 Chapter 6 The MOUSE Model 6 -5 28 3 380.6 380.6 20.08 22.08 4 0.64 29 2 190.3 230.1 18.96 20.96 4 0.64 Table 6.3. Pipes and conduits characteristics - Maroubra LI: Conduits (Pipes) FILE:M1.SWF PAGE 1 CREATED: 29-JUL-199 3 01:35:.5 5 EDITED: 3-JAN-1994 16:24:17 Nodal Points Mat Alt Bottom Level Alt Infiltration Alt Pipe Row Sect. Dia..m Upstr. Dwstr. Upstr. Dwstr. Flow GW No. No. m. m. m3/s/m level, m 1 33 32 3 25.36 24.46 0.000 0.450 2 32 31 3 24.46 22.88 0.000 0.450 3 31 29 3 22.88 22.64 0.000 1.220 4 29 28 3 22.64 22.38 0.000 1.220 5 28 27 3 22.38 22.20 0.000 1.220 6 27 22 3 22.20 22.01 0.000 1.220 7 22 21 3 22.01 21.80 0.000 1.372 8 21 17 3 21.80 21.63 0.000 1.372 9 19 18 3 24.79 22.67 0.000 0.457 10 18 17 3 22.67 21.63 0.000 0.914 11 17 8 3 21.63 20.93 0.000 1.372 12 9 8 3 21.27 20.93 0.000 0.610 13 11 9 3 21.61 21.27 0.000 0.610 14 12 11 3 21.94 21.61 0.000 0.610 15 13 12 3 22.08 21.94 0.000 0.610 16 14 13 3 22.64 22.08 0.000 0.457 17 15 14 3 22.88 22.64 0.000 0.457 18 16 15 3 24.23 22.88 0.000 0.305 19 8 4 3 20.93 20.63 0.000 1.372 20 5 4 3 20.83 20.63 0.000 0.914 21 6 5 3 21.74 20.83 0.000 0.457 22 7 6 3 22.76 21.74 0.000 0.457 23 4 3 3 20.63 20.08 0.000 1.524 24 3 2 3 20.08 18.96 0.000 1.372 25 25 18 3 34.60 22.67 0.000 0.525 26 26 25 3 46.53 34.60 0.000 0.450 27 23 22 3 23.71 22.01 0.000 0.450 28 24 23 3 28.48 23.71 0.000 0.450 Chapter 6 The MOUSE Model 6-6 26.00 2S.0O- 24.00 23.00 21.00 19.00 TOP LEVEL m 2636 2S.46 2SSO2S.02 2S03 2S.49 24.74 2S.23.63 2ZS3 23.13 22.06 202131 BOTTOM LEU. m. 2S.36 24.46 22882264 2238 2220 2201 21.21.63 2033 20.63 20.08 181876 LENGTH m 1012 1S62 626 93.1 844 101.7 939 3S4 964 1005 107X1 2426 3IJ3 DIAMETER rrc 0.4S0 04S0 1220 1220 1220 1220 137213721372 1372 1524 1372 CANAL SLOPE oloo: S3 10.1 3.8 28 21 13 22 43 73 30 S 1 46 64 I PRINTER DATAFILE Ml.SWF 2 PLOTTER EDITED 3-JAN-1994 1624 3 METAFILE SCALE LEN3TH . V.S967 Fig. 6.2. The longitudinal profile of the main pipe line -Maroubra Fig. 6.3. The longitudinal profile of the steepest pipe line of the catchment - Maroubra Chapter 6 The MOUSE Model 6 -7 21.00- TOP LEVEL ire 2S.23 BOTTOM LEV. rrc 2423 LENGTH m 96.4 DIAMETER m. 0.30S SLOPE o/oo: 14.0 1 PRINTER DATAFILE M1.SWF 2 PLOTTER EDITED 3-JAN-1994 1624 3 METAFILE SCALE LENGTH : 123S1 Fig. 6.4. The longitudinal profile of the Council line - Maroubra 23S0- 23.00" 22S0- 22.00' 2150- 21.00- TOP LEVEL ire 23.76 22.74 2213 23.13 BOTTOM LEV. ire 2276 21.74 20.83 2QB3 LENGTH ire 962 947 742 DIAMETER ire 0.4S7 0.4S7 0.914 SLOPE o/oo: ia6 &6 2.7 1 PRINTER DATAFl£ :M1.SWF T 2 PLOTTER EDITED 3-JAN-1994 1624 3METAFLE SCALE LENGTH : 1:1210 MOUSE Fig. 6.5. The longitudinal profile of the Council line - Maroubra Chapter 6 The MOUSE Model 6 -8 MY-M/S RANFALL. 60X100 - 40,000- 30X100 - 20.000- 10.000- 0.000-1 ' ' 1 ' • ' * T"—' ! 1 ' ' ; Tr^ OOO 0 15 0:30 04S ISO IIS 130 1983 17/3 Tine Houn: Kocla "~ BOUNDARY D«T» •- MOUSE | | -f Fig. 6.6. Sample Rainfall hyetograph - Maroubra, 17.03.1983 6.2.1. Rainfall data MOUSE accepts rainfall intensity from several raingauge stations provided that subcatchments under the effect of each raingauge are assigned to the model. However, introducing the data of one station or average rainfall of several stations to the model to be used for the whole catchment is possible. For Maroubra and other catchments in this study the average rainfall by the Theisen method is used (Bufill 1989). The dimension of rainfall in MOUSE is in micron meters per second, so firstly the rainfall intensity data from mm/hr was transferred to ^im/sec and secondly they were used to create a data base file, and finally, using Boundary data they were saved based on a system file, in binary code, which is acceptable to MOUSE. The rainfall format was KMD which is suitable for historical rainfall (refer to MOUSE user manual). In MOUSE this file is named Boundary Data File because it accepts observed discharge and water level as well. In Fig. 6.6 a sample hyetograph of the rainfall dated 17.03.83 with a total depth of 35.5 mm and the main burst duration of 35 Minutes in Maroubra is presented. This event is used in a sample rainfall data file in the sensitivity analysis. Chapter 6 The MOUSE Model 6-9 6.2.2. Hydrology data The datafile i s prepared for simulation of runoff with level A or impervious area runoff only. The parameters in this level are initial loss, time area diagram (TAD), Hydrological Reduction Factor (HRF), and time of concentration of each sub catchment. Time area diagrams 1,2 or 3 are used which simulate rectangular, convergent or divergent catchment isochrones. Initial loss for urban catchments is proposed between 0.5 - 1.0 mm and for this case the upper limit was adopted. HRF shows the ratio of impervious areas within each subcatchment which contribute to surface runoff. The magnitude of HRF based on the ratio of directly connected impervious areas to total impervious areas is presented in Table 6.4. This parameter, based on the land use pattern of each subcathment, could be variable for different subcatchments. However when the percentage of directly connected impervious areas is not available, a similar HRF would be assumed for all subcatchments. This factor is proposed to be between 0.8-0.95 (MOUSE user manual). Table 6.4 shows a sample hydrologyfile. Th e base value of time of concentration is adopted as Chow's formula for gutter travel time. (Sec. 6.3.1.1.). 6.2.3. Hydraulics data Boundary conditions at the outlet, manhole head loss, friction loss for pipes and channels are entered into thefiles of hydraulic data (Table 6.5). If users do not define specific head losses or pipe friction coefficient, the model will utilise global values. The Manning number in Table 6.5 is simply the inverse of Manning's roughness coefficient (n). Boundary conditions at the catchment outlet could be either time function discharge or water level, or constant discharge or water level. For time function discharge the observed hydrograph could be given to the model directly. In the case of time function water level, the observed hydrograph must be transformed to water level using the rating curve at the outlet. Chapter 6 The MOUSE Model 6-10 Table 6.4. Hydrology datafile - Maroubra HYDROLOGICAL DATA MOUSE-SYSTEM Filename VER.ROF Edited 31-DEC-1993 15:42 Created 29-JUL-1993 03:47 Level (A/B) A Global values Not specified No. of nodal points 29 SPECIFIC PARAMETERVALUES LEVE LA Row Sub- Hydrological Initial Loss Time Area Time of Catchment Reduction Factor m Diagram No. Concentration Minutes 1 33 0.61 0.0010 10.00 2 32 0.46 0.0010 16.60 3 31 0.46 0.0010 5.00 4 29 0.46 0.0010 8.00 5 28 0.46 0.0010 6.80 6 27 0.46 0.0010 12.30 7 24 0.75 0.0010 5.00 8 22 0.65 0.0010 4.00 9 23 0.65 0.0010 5.00 10 21 0.65 0.0010 10.20 11 26 0.50 0.0010 5.00 12 25 0.46 0.0010 12.90 13 19 0.77 0.0010 3.10 14 18 0.65 0.0010 13.40 15 17 0.68 0.0010 2.00 16 16 0.46 0.0010 10.30 17 15 0.79 0.0010 2.00 18 14 0.46 0.0010 5.00 19 13 0.46 0.0010 5.00 20 12 0.46 0.0010 5.00 21 11 0.46 0.0010 5.00 22 9 0.68 0.0010 2.00 23 8 0.68 0.0010 5.00 24 7 0.46 0.0010 2.00 25 6 0.46 0.0010 5.00 26 5 0.57 0.0010 5.00 27 4 0.46 0.0010 5.00 28 3 0.46 0.0010 5.00 29 2 0.46 0.0010 10.60 Chapter 6 The MOUSE Model 6-11 Table 6.5. Hydraulics datafile - Maroubra HYDRAULIC DATA - Global headlosses in basins Shape of outlet (code) Headloss coefficient 1. Round edged outlet 0.25 2. Sharp edged outlet 0.50 3. Orificing pipe outlet 0.50 4. No cross section changes 0.00 HYDRAULIC DATA - Global friction loss in pipes Material (codeno.) Manningno. (ml/3s-l) 1. Smooth concrete 84.75 2. Normal concrete 75.19 3. Rough concrete 58.82 4. Plastic 80.00 5. Iron 69.93 6. Ceramics 69.93 7. Stone 80.00 8. Others 20.00 HYDRAULIC AND BOUNDARY DATA MOUSE-SYSTEM Filename COCOl.PWF Edited 31-DEC-1993 09:27 Created 13-SEP-1993 17:38 Form Number Constant discharge 0 Timevarying discharge 0 Constant waterlevel 0 Timevarying waterlevel 1 Specific headloss coefficients 0 Specific manning numbers 0 Control functions 0 Hydraulic data - Time varying water level 1 NODE NUMBER ....: OUTLET No. of dataset.: 14 t (min): 0.00 24.00 27.00 30.00 39.00 45.00 H (m): 18.76 18.97 19.22 19.32 19.27 19.23 t(min): 51.00 57.00 63.00 69.00 75.00 81.00 H (m): 19.10 19.02 18.98 18.95 18.90 18.86 t(min): 87.00 117.00 H (m): 18.86 18.83 Chapter 6 The MOUSE Model 6-12 6.3. Sensitivity Analysis of MOUSE Sensitivity analysis is a useful tool in modelling regardless of the nature and type of phenomenon which is being modelled. There are different hydrologic models in the field of surface water and flood peak estimation, ranging from simple to complex. For a simple formula such as the Rational method performance of sensitivity analysis is easy and straightforward. The sensitivity function is derived using partial derivatives of discharge to rainfall intensity and area. Sensitivity analysis of a complex model such as MOUSE due to the inclusion of many parameters, is very difficult when using the sensitivity function. In this case a suitable alternative is the study of the results while all parameters are kept constant except one. Sensitivity analysis gives the user the guidance to select parameters much closer to reality and prevents interaction to some degree. Sensitivity analysis should be carried out regardless of the type of calibration which is used in the model. In other words in both manual and automatic calibration, sensitivity analysis helps the user to achieve a clear and precise result which is close to reality and free of interaction. Bearing in mind that the results of the runoff model in MOUSE are completely independent of those of the pipe flow model, these two sections are studied for sensitivity of parameters separately. 6.3.1. Runoff model level A MOUSE simulates the surface runoff hydrograph from the impervious area of each subcatchment using the computation level A. In this level required parameters are initial loss, hydrologic reduction factor, time of concentration, time area diagram and area for each subcatchment. Surface runoff hydrographs are computed at the outlet of subcatchments which are normally pits or manholes. If observed hydrographs are available at the outlet of a subcatchment, sensitivity studies of the variations in parameters could be properly performed and a quantitative result would be obtained. However, due to the lack of observed hydrographs at the subcatchment outlets, sensitivity results for subcatchments are mostly qualitative. The comparisons are made Chapter 6 The MOUSE Model 6-13 for the variation of subcatchment output and also for the whole catchment outlet when all subcatchments are contributing to production of surface runoff. 6.3.1.1. Time of concentration When using level A for surface runoff calculation, time of concentration of a subcatchment is the travel time of flow over connected impervious areas, mainly along gutters.This is the time of entry for subcatchment flow to reach the pipe system. To evaluate the sensitivity of the model to the time of concentration, four different values of Tc are tested including; constant values for all subcatchments and different values for each subcatchment. In the first and second tests Tc was assumed to be equal to 5 and 10 Minutes for all subcatchments. As it is expected for 5 Minutes time to peak, Tp, of hydrographs are shorter and the flood peaks are larger than those for 10 Minutes. These effects are transferred to the catchment outlet via the pipe flow model. When Tc is 10 Minutes for each subcatchments a longer Tp of total hydrograph and smaller flood peak will be concluded when the results are compared with the 5 Minutes Tc (Table 6.6). Table 6.6. Sensitivity test of time of concentration (Maroubra, 170383) Tc, Minutes 5 10 % Change Total Peak, m3/s 2.468 2.298 -7.00 Total Tp, Minutes 28 32 + 14.00 Subcath. 33 Peak 0.25 0.198 -20.80 flow, m3/s Subcath. 33, Tp, 23 26 +13.04 Minutes Subcath. 18 Peak 0.501 0.398 -20.60 flow, m3/s Subcath. 18, Tp, 23 26 +13.04 Minutes As presented in Table 6.6 a 100% increase in time of concentration of each subcatchment decreases the flood peak by nearly 21% and increases Tp equal to 13%. The decrease of flood peak at the outlet of the catchment equals 7% which is much less than those for Chapter 6 The MOUSE Model 6-14 subcatchments. This difference is because of the pipe system effect on the flow. The increase in Tp of flood peak at the outlet is nearly the same as those for subcatchments (14%). In Fig. 6.7 the simulated hydrographs at the outlet overlaid on the observed one are illustrated. The rapid Tp of total hydrograph of 5 Minutes Tc when compared to the 10 Minutes Tc and the observed hydrographs shows that the assumption of 5 Minutes Tc for all subcatchments is too short. Furthermore, the assumption of equal Tc for all subcatchments regardless of the Tc values is unrealistic, because all of them reach to peak simultaneously and a large or small peak will be concluded at the outlet. In real cases, Tc for every subcatchment should be calculated. Assuming one temporal pattern of rainfall over the catchment, Tc for each subcatchment depends on the physical characteristics of the subcatchment waterway (gutter) including; length, slope, roughness coefficient and depth. Referring to Chapter 4 this assumption is tested using Chow's formula (Chow et al. 1988) and the ARR87 method (Fig. 14.9 ARR1987) to estimate gutter flow travel time. The results of the application of these two formulas are presented in Table 6.7 for two subcatchments and the outlet. The results show that the application of both ARR87 and Chow's formula have produced the same Tp at the outlet, but different flood peaks. An increase of 65% in Tc has decreased 9p by 11% and 4% and has increased Tp by 8 and 14% at two sample subcatchments. It is concluded that for each subcatchment the increase in Tc reduces ^p at the outlet, but the variations of Tp at the catchment outlet is mainly due to travel time in the pipe system. Table 6.7 Comparison the effect of ARR87 and Chow's Formula for Gutter flow time of concentration Subcatchment No. 33 Subcatchment No. 32 Outlet Method Tc, The simulated hydrographs using ARR87 and Chow's formula are compared with the observed one in Fig. 6.8. The simulated hydrographs when Chow's formula is used for Tc estimation for subcatchments agrees better with the observed one than that of the ARR87 method. ARR87 uses an average velocity at 60% of the length of the gutters down the subcatchments, and so gives a larger peak; however, Chow's formula is more realistic. Chow's formula was adopted for Maroubra because most of travel path is gutter flow. CD rt LT MRROUBRR DfiTE=l"7Q383 o BBS. To 5 Min cr To 10 Min - - or i CD °°. _ 120 160 200 Min Fig. 6.7. Superimposed hydrographs for testing the effect of Tc Chapter 6. The MOUSE Model 6 -16 CD nl MRROUBRR Lr DOTE:170383 o DBS. Ch o UJ e q ri az RRR07 eqn - - - cn c_> CD _ i 40 80 120 160 200 TIME Min Fig. 6.8. Superimposed hydrographs with two methods of Tc estimate 6.3.1.2. Time area diagram (TAD) MOUSE employs time area diagram (TAD) and time of concentration to transform surface runoff to a hydrograph at the outlet of each subcatchment. Three options are provided in MOUSE for TAD selection including; rectangular, divergent and convergent (Fig. 6.9). Users can choose a suitable TAD depending on the shape of the subcatchment, provided a measured hydrograph is available at the subcatchment outlet for checking. Considering that measured hydrographs are only available at the outlet of catchment, the effect of the type of TAD is studied at the measuring station quantitatively. The effect of the TAD selection is evaluated at the subcatchment outlet comparatively. Table 6.8 shows the results of 4 subcatchments. Generally speaking TAD No. 3 (convergent) increases both time to peak and flood peak; however, for short time of concentration these effects are negligible. Characteristics of the simulated hydrographs with different TADs at the outlet of the catchment are presented in Table 6.9 and Figs. 6.10 and 6.11. ( liapte/O The MOUSE Model 6 -17 Fig. 6.9. TAD options in MOUSE Table 6.8. TAD effect on the subcatchments hydrograph - Maroubra Sub- TAD No. catch. Tc 1* 2 3 HRF qP Tp qP Tp qp Tp 24 5 0.109 23 0.108 22 0.114 23 0.67 32 16.6 0.069 33 0.067 25 0.078 35 0.38 2 10.6 0.117 27 0.129 24 0.137 29 0.38 7 2.0 0.163 20 0.163 20 0.163 20 0.38 * 1 : Rectangular 2: Divergent 3: Convergent, qp, m/s, Tp, Minute Table 6.9. TAD effects on flood peak and time to peak of hydrograph at the outlet- Maroubra Hydrograph TAD No. Observed Specifications 1* 2 3 Hydrograph Qp, m3/s 2.089 2.230 2.040 2.115 Tp, Minutes 29.00 28.00 36.00 30.00 * 1 : Rectangular 2: Divergent 3: Convergent Note that hydrographs at subcatchment outlets are calculated using only the TAD and runoff model, whereas hydrographs at the catchment outlet use both the runoff model plus pipe model. Chapter 6 The MOUSE Model 6 -18 Evaluation of the results in Table 6.9 denotes that the application of TAD No. 1 (rectangular) in the Maroubra catchment could give better correspondence of flood peak and time to peak compared with the observed. CD o -C o T. r MRROUBRR DOTE:1703S3 ^^ C\J QBS. TOD N01 CC TRD N02 - CC =f cv "7 < CM S ij I [ - > I 1. 6 i o. a 0\ FLO W ca '•A 1 1 40 ^T" 0 120 160 2C TIM80E - Min Fig. 6.10. The effect of TAD selection on hydrograph shape CD r MRROUBRR DP.TE:17Q3B3 TR0BSD. NOl az THD N03 0C =* cn C_5 CO I I 40 80 120 160 200 TIME - Min Fig. 6.11. The effect of TAD selection on hydrograph shape Chapter 6 The MOUSE Model 6-19 6.3.1.3. Hydrologic reduction factor (HRF) Hydrologic reduction factor, HRF, determines the percentage of impervious areas which contribute to surface runoff production. This is an important parameter in urban catchment hydrologic studies. Directly connected impervious areas or hydraulically effective areas in urban catchments produce runoff during the majority of events. In some events indirectly connected impervious areas or supplementary impervious areas become active and the generated runoff, after passing over pervious areas or through pipes and pathways, joins the main system network. Table 6.10 depicts typical calculation of HRF for two sub catchments. Table 6.10. Calculation of HRF Sub Total % of Total % Directly % HRF = Catchment Area, Impervious Connected supplementary FIC/FI hec. Area, (FI) Impervious Impervious Area Area,( FIC) 33 2.991 35 25 10 0.71 32 2.600 23 13 10 0.56 Due to the importance of this parameterfive event s are simulated using two different HRF. In thefirst tria l both directly and indirectly impervious areas contribute to surface runoff and HRF would simply be the ratio of FI/FI or equal to one. In the second trial, only directly connected impervious areas, FIC, were assumed to produce runoff, so HRF in this case is the ratio of FIC/FI. The results are illustrated in Fig. 6.12 to Fig. 6.16. For all the simulated events volumes and peaks of runoff are much higher than those of the observed when total impervious area, FI, is used in calculation of HRF. However, when directly connected impervious areas, FIC, is used to calculate HRF, volumes and peaks are still higher than those of the observed, except for the event dated 210578. These figures denote that for the Maroubra urban catchment HRF is less than the ratio of FIC/FI. Directly connected impervious areas are measured using streets and sidewalks which still include some grassed areas between sidewalks and streets. To study the sensitivity of flood peak and runoff volume to HRF, FIC/FI is taken as an index and its effect is evaluated in the range of (FIC/FI) +10% and (FIC/FI)-40% as HRF for each subcatchment. For example, if FIC/FI = 80% these adjustments would be Chapter 6 The MOUSE Model 6 -20 80+10 =90% and 80-40= 40%. Table 6.11 shows the results at the catchment outlet. The figures in Table 6.11 are plotted to study their trend (Figs. 6.17 and 6.18). In Figs 6.17 and 6.18, the FIC/FI values shown are after adjustment by FIC/FI +10% and FIC/FI - 40% etc, where the base value of FIC/FI is 50%. There is a direct linear relationship between HRF, Qp and volume of runoff. For instance, FIC/FI - 14% can nearly reproduce the observed Qp and volume. There is no effect on Tp in the range of (FIC/FI)-5% to (FIC/FI)-20%. Table 6.11. Sensitivity of directly connected impervious areas in simulation of event 170383-Maroubra Active Impervious Area, % Qp, m3/s Tp, Min Volume, mm FIC*/FP* 2.696 30 6.49 (FIC/FI) + 10% 2.968 33 7.49 (FIC/FI) + 5% 2.845 30 6.99 (FIC/FI)- 5% 2.504 30 5.99 (FIC/FI) -10% 2.309 30 5.49 (FIC/FI) -20% 1.894 30 4.49 (FIC/FI) -30% 1.467 36 3.49 (FIC/FI) -40% 1.044 31 2.49 Observed 2.115 30 5.18 * FIC: Directly connected impervious areas of each subcatchment ** FI: Total impervious area of each subcatchment Chapter 6 The MOUSE Model 6-21 -CZ co E o 1 OD IJ CD MRROUBRR CC DOTE:030378 OBS. Cd HRF - FIC/Ft =* HRF = FI/Ft CSi cn C_5 CO F—» °^ 160 200 TIME - Fig. 6.12. Sensitivity of flood hydrograph to HRF magnitude- 030378 CD O -r-1 L. -C o L| z n MRROUBRR 1 <=> — 1 ' DPITE: 170383 . c3 OBS. 1 ' HRF - FlC/ft CC •/•« ' HRF = FI/pj* CC =K CM cn C_J> CO _ W A 1 it v, 1 <=> Ll_ •i t ^ •Y O o 1 1 1 80 0 40 120 160 2C TIME •- Min Fig. 6.13. Sensitivity of flood hydrograph to HRF magnitude- 170383 Chapter 6 The MOUSE Model 6-22 ^3?" £ CD I C\J J MRROUBRR •—• = DPTE:21Q578 az a~\ HRDBSF. - FIC/PT HRF = FI/pj cn f 1 oo CD =: _ I O 50 100 150 200 250 TIME Min Fig. 6.14. Sensitivity of flood hydrograph to HRF magnitude- 210578 -C7- o MRR0UBRR DP.TE: 190679 az o - HROBSF. - FlCfle; HRF = FI/pr cn i I CD T 240 320 400 TIME - Min Fig. 6.15. Sensitivity of flood hydrograph to HRF magnitude- 190679 Chapter 6 The MOUSE Model 6 -23 L. o X r^~ CD e=s — OJ ^ f \ MRROUBRR — DP.TE: 200679 az Cd-3 BBS. 3- HRF - FICteJ CD HRF = FI/pi o =r _ I C=3 A •}i7 \V \- \\*> if <--/ N. % iJ >wM (=1 yf x^^^ CD I T 1 1 40 80 120 160 200 TIME Min Fig. 6.16. Sensitivity of flood hydrograph to HRF magnitude- 200679 VOL., mm IO e — 3D 40 SO BO 70 80 SO IOO FIC/FI. "K> Fig. 6.17. Correlation of FIC/FI and flood volume - Maroubra Chapter 6 The MOUSE Model 6 -24 Op. CI i ' i ' i f I < i ; :—'—i— 10 20 30 40 60 SO 70 eo IOO FIC/FI. 9fc Fig. 6.18. Correlation of FIC/FI and flood peak - Maroubra 6.3.2. Pipe flow model Hydrographs generated by the runoff model enter the pipe system via pits or manholes. Characteristics of the simulated hydrograph at the outlet, water levels in pits/manholes and generally depth and velocity of the flow within the system depend on the physical characteristics of the pipe/waterway network. The continuity and momentum equations are solved simultaneously by an implicit numerical scheme built in MOUSE (Chapter 2). Generally speaking there are two main groups of parameters which affect the output of the pipe flow model. Thefirst group is related to the physical characteristics, geometry and layout of the pipe system including: roughness coefficient, head loss in pits/manholes and grate entry limitation. The second group is mostly related to the solution method, boundary conditions effect, time step of the simulation and kinematic or dynamic wave approximation to model the pipe flow. In this section the ultimate effects of the change in the above parameters on the catchment outlet hydrograph and the local effects within the system are discussed. Chapter 6 The MOUSE Model 6 -25 6.3.2.1. Introducing circular manhole instead of square pit As mentioned in Chapter 2, generally MOUSE is developed for combined systems which carry both domestic sewage and surface runoff together. Surface runoff is admitted into the pipe system via circular manholes. In separate systems (e.g. in Australia) stormwater drainage networks are separate from sewage networks. The surface runoff enters the system mainly through different types of pits. Generally pits are square in shape and accept flow via their openings. The pit entrances depend on the surface slope and include sag inlet, on-grade grated inlet, or a combination of these. An option is provided with MOUSE called STRUCTURE. To model square pits, they could be introduced to the model as STRUCTURE and the model takes them as basins. In order to include the entry loss due to the installed grates over the pits, the top surface of the pits could be taken as smaller than their body cross section which limits the rate of entering hydrographs (Fig. 6.19). Q,time function ..hydrograph effective entrance area •> Fig. 6.19. Modified pit to consider grate entry loss The problem when introducing pits as STRUCTURE to the model is that when the water level exceeds the top of the pits during flooding the calculation by the model stops. The program asks about increasing the top level of pits which is not realistic, because the top level given to the model is ground level. Chapter 6 The MOUSE Model 6 -26 To overcome the above problem another alternative to introduce pits to the model is a modified manhole. The top area of manholes could be equal to the effective area of grates. Normally grate dimensions are 90 cm by 60 cm and 60% of this area is effective in admitting hydrographs into the pits. If pits have side entry inlets, by including the side opening area in the effective area they could be modelled as well. In this case flooding could be simulated because MOUSE creates a fictitious basin over the manholes and stores overflow in it. The stored water is released into the system when flow decreases in the pipe system. In real catchments the bypass flow of a manhole/pit moves downwards via gutters and enters the system through another pit. MOUSE does not have this option, but ELSAX has. The bypass flow, after some travel time, enters downstream pits in the ELSAX model. This problem does not seem to be a weak point in MOUSE, because the bypass flow travel time is represented in the storage time in the fictitious basins over the manholes. To test the effect of pits and manholes on the flow specifications, the model was run twice. One datafile contained square pits with contracted top openings to simulate the effect of the grate loss entry (e.g. Fig. 6.19) and the other file included modified cylindrical manholes with the top and base area equal to the effective area of the grates. The other necessary datafiles were the same for both cases. It should be noted that the square pit shown in Fig. 6.19 is probably more like the prototype than the cylindrical manhole. Although for both of them the top openings are the same, cylindrical manhole volume is less than that of square one. For a manhole with different top and base areas STRUCTURE should be used, the limitations of which have already been mentioned. Table 6.12 shows the results of the two cases for maximum discharge and elevation within the branches and at the outlet of the catchment. The difference between maximum discharges and elevations when using the pits or manholes are very small. The discharge at the outlet for pits is smaller than that for manholes but it is negligible (0.4%). The water level for the pit condition at the outlet is slightly greater than that of the manhole condition (0.6%) because of the small difference between vertical velocities as the water level rises and falls in the pits and manholes. Chapter 6 The MOUSE Model 6-27 Table 6.12. Test of the effect of pits/manholes on maximum discharge and elevation - Maroubra (Hmax : m, Qmax: m3/s, Time: Minutes) Pits Manholes Pits Man ides Branch Hmax Time Hmax Time Qmax Time Qmax Time 33 32 25.61 22:00 25.61 22:00 0.120 22:00 0.120 22:00 32 31 24.76 23:00 24.76 23:00 0.175 23:00 0.175 23:00 31 29 23.19 23:00 23.19 23:00 0.222 24:00 0.222 24:00 29 28 23.02 24:00 23.02 24:00 0.297 24:00 0.297 24:00 28 27 22.82 24:00 22.82 24:00 0.346 25:00 0.347 25:00 27 22 22.68 25:00 22.68 25:00 0.425 26:00 0.426 26:00 22 21 22.52 24:00 22.52 24:00 0.557 24:00 0.559 24:00 21 17 22.34 24:00 22.34 24:00 0.700 25:00 0.702 24:00 19 18 25.01 21:00 25.01 21:00 0.146 21:00 0.146 21:00 18 17 23.08 22:00 23.08 22:00 0.572 22:00 0.572 22:00 17 8 22.24 24:00 22.25 24:00 1.257 24:00 1.261 24:00 9 8 22.33 22:00 22.35 22:00 0.417 22:00 0.419 22:00 11 9 22.67 22:00 22.68 22:00 0.328 22:00 0.331 22:00 12 11 22.94 22:00 22.96 22:00 0.284 22:00 0.286 22:00 13 12 23.01 22:00 23.03 22:00 0.238 22:00 0.240 22:00 14 13 23.59 22:00 23.62 22:00 0.191 21:00 0.191 21:00 15 14 23.71 22:00 23.74 22:00 0.125 20:00 0.125 20:00 16 15 24.46 22:00 24.46 22:00 0.077 23:00 0.077 23:00 8 4 21.76 23:00 21.76 23:00 1.653 24:00 1.656 24:00 5 4 21.42 22:00 21.42 22:00 0.296 21:00 0.296 21:00 6 5 22.36 22:00 22.36 22:00 0.232 21:00 0.233 21:00 7 6 23.06 21:00 23.06 21:00 0.171 21:00 0.171 21:00 4 3 21.39 24:00 21.39 23:00 1.927 24:00 1.933 24:00 3 2 20.93 24:00 20.93 24:00 2.051 24:00 2.059 24:00 25 18 34.74 23:00 34.74 23:00 0.127 23:00 0.127 23:00 26 25 46.64 21:00 46.64 21:00 0.062 21:00 0.062 21:00 23 22 23.93 21:00 23.93 21:00 0.154 21:00 0.154 21:00 24 23 28.63 21:00 28.63 21:00 0.098 21:00 0.098 21:00 2- OUTLET 19.60 25:00 19.49 25:00 2.135 25:00 2.143 25:00 Chapter 6 The MOUSE Model 6 -28 6.3.2.2. Manning's roughness coefficient Pipe friction in MOUSE is considered using Manning's roughness coefficient (n). Seven default values of ' n ' are provided with MOUSE; however, the user can change the coefficients when required. Two runs were conducted with rough concrete ( n = 0.017) and smooth concrete (n = 0.012) for the pipe system. A decrease in ' n ' has a serious effect on the water level at nodes (manholes) and branches (pipes). For example, when ' n ' is equal to 0.017 node No. 16 and branch 16 - 15 are under surcharge and flooding, while when n is equal to 0.012 they are only under small surcharge (Figs. 6.20 and 6.21). The variations in the maximum peak discharges and elevations are presented in Table 6.13 for all the branches within the system. The decrease in ' n ' causes shorter time to peak, larger peak flow and earlier recession at the outlet (Fig. 6.22), so calibration of ' n ' can give a good match of the simulated hydrograph with the observed. Chapter 6 The MOUSE Model 6 -29 METER WATER LEVEL BRANCHES 16 - IS 0 METER METER WATER LEVEL BRANCHE S 16 -IS 48 METER 25.00 METER WATER LEVEL BRANCHES 16 — IS 36 METER 23.00 H 0 20 40 60 80 100 DATAFILE :M1.SWF PIPE MODEL DYNWAVE MOUSE RESULT FILE :R0UGH1PRF CALCULATED : 29-DEC-1993.09S1 •JT" '•••' Fig. 6.20. Flooding and surcharge - n = 0.017 METER WATER LEVEL BRANCHES 16 -IS 0 METER 24.60 - METER WATER LEVEL BRANCHES 16 - 1S 48 METER 23.30 - 2i80 - 23.70- 23,60- i r 20 40 60 30 100 WATER LEVEL BRANCHES 16 - 15 S*>MLILH 23.30 23.20- 23.10- 23.00- 22.90- i '• I i : I 0 /20 40 V 60 80 100 DATAFILE :M1.SWF PPE MODEL DYNWAVE MOU5E RESULT FLE: ROUQH2PRF CALCULATED: 29-DEC-1933. 10:12 •*' Fig .6.21. Small surcharge - n = 0.012 Chapter 6 The MOUSE Model 6 -30 Table 6.13. Sensitivity of roughness coefficient (Hmax : m, Qmax: m3/s, Time: Minutes) n= 0.017 n= 0.012 n= 0.017 n= 0.012 Branch Hmax Time Hmax Time Qmax Time Qmax Time 33 32 25.72 26:00 25.63 26:00 0.197 26:00 0.197 26:00 32 31 24.98 29:00 24.76 27:00 0.236 28:00 0.247 27:00 31 29 23.21 27:00 23.16 27:00 0.269 27:00 0.287 27:00 29 28 23.04 27:00 22.98 27:00 0.337 27:00 0.361 27:00 28 27 22.84 28:00 22.77 27:00 0.381 28:00 0.412 27:00 27 22 22.69 29:00 22.62 28:00 0.448 30:00 0.483 28:00 22 21 22.53 28:00 22.45 27:00 0.576 29:00 0.631 27:00 21 17 22.34 28:00 22.25 27:00 0.719 29:00 0.785 28:00 19 18 25.02 21:00 24.98 21:00 0.161 22:00 0.161 22:00 18 17 23.07 26:00 22.99 25:00 0.535 26:00 0.537 25:00 17 8 22.25 28:00 22.15 27:00 1.266 29:00 1.327 28:00 9 8 22.39 25:00 21.97 25:00 0.452 24:00 0.447 25:00 11 9 22.78 25:00 22.18 26:00 0.350 25:00 0.369 26:00 12 11 23.10 25:00 22.38 25:00 0.319 23:00 0.349 24:00 13 12 23.19 25:00 22.48 25:00 0.296 23:00 0.307 24:00 14 13 24.03 25:00 23.02 25:00 0.247 23:00 0.257 24:00 15 14 24.22 25:00 23.21 24:00 0.177 23:00 0.192 24:00 16 15 25.73 29:00 24.54 27:00 0.121 30:00 0.134 26:00 8 4 21.75 28:00 21.64 27:00 1.665 28:00 1.820 27:00 5 4 21.40 26:00 21.30 25:00 0.280 23:00 0.299 23:00 6 5 22.18 24:00 22.03 22:00 0.226 22:00 0.235 22:00 7 6 23.04 21:00 22.98 21:00 0.163 21:00 0.163 21:00 4 3 21.38 27:00 21.27 27:00 1.884 27:00 2.043 27:00 3 2 20.91 28:00 20.78 27:00 1.992 28:00 2.168 27:00 25 18 34.74 25:00 34.71 25:00 0.116 26:00 0.115 26:00 26 25 46.64 23:00 46.62 23:00 0.061 24:00 0.061 24:00 23 22 23.95 24:00 23.90 24:00 0.170 24:00 0.170 24:00 24 23 28.64 23:00 28.61 23:00 0.108 24:00 0.108 24:00 2 OUTLET 19.49 30:00 19.51 28:00 2.089 29:00 2.262 28:00 Chapter 6 The MOUSE Model 6 -31 CD rt if MRROUBRR CD DRTE:170383 CNJ OBS. p. - 0.017 n = 0.012 - - - Od CO CLJJ CO I o T 120 160 200 TIME - Min Fig. 6.22. The effect of roughness coefficient on simulated outlet hydrograph 6.3.2.3. Simulation of grate entry limitation/manhole opening Generally speaking, efficiency of an underground drainage system mainly depends on the measures which guide surface runoff into it. Pits/manholes are the most common kinds of measures. An urban hydrologic model should be able to reproduce the function of these structures as close to reality as possible. The entry loss of flow while passing through the installed grates over the pits is an important factor in stormwater drainage systems. As mentioned previously (Section 6.3.2.1) simulation of the effective area of grates is possible in MOUSE. In the first trial manhole diameter was assumed equal to 0.64 m for all nodes. The water levels in branch 16-15 are shown in Fig. 6.23. In the second trial the manhole diameters for nodes 16, 32 and 31 was taken equal to 1 m. The results are shown in Fig. 6.24. The water levels in this case are less than those of the first test. To check whether it is manhole opening or pipe capacity limitation which causes surcharge and flooding in the model, two runs were conducted. In the first run manhole diameters were set equal to 0.64 m, but the pipe diameter between node 16 and 15 was taken as lm instead of 0.305 m. The enlargement of the pipe diameter was to make sure that there is no pipe capacity limitation. No flooding occurred on node 16 during the run Chapter 6 The MOUSE Model 6 -32 (Fig. 6.25). In the second run the diameter of the manhole of node 16 was reduced to 0.30 m, but pipe diameter was kept the same (1 m ). In this case the model failed to simulate flow because of the out of range accumulation of water above node 16. These two tests show that inflow to manhole/pit via grates could be simulated by MOUSE fairly well and it is the manhole entrance which restricts inflow not the pipe diameter. However, in some cases when pipe capacity is limited, surcharge/flooding over the manhole could occur. In this test pipe diameter was assumed to be large (lm) deliberately to investigate the effect of manhole opening size. METER WATER LEVEL BRANCHES IS 0 METER 2S.S0 -. METER WATER LEVEL BRANCHES 16 - IS 48 METER 2S.00 ~ A 24.50 -E l\ 24.00 -E j \ . A| , '—i- .M ""• 1 20 40 60 80 100 METER WATER LEVEL BRANCHES IS 36 METER 23.00 -Ei 1 ' 1 1 i : : • • i 0 20 40 60 80 100 DATAFILE :M1.SWF PIPE MODEL DYNWAVE MOUSE RESULT FILE: GRATE 1PRF CALCULATED :30-OEC-1993. 15:40 J"™ Fig. 6.23. Flooding and surcharge in the pipe 16-15 - manhole diameters equal to 0.64 m and pipe diameter 0.305 m Chapter 6 The MOUSE Model 6 rt (WATER LEVEL BRANCHES 16 - 15 0 METER 2S.S0 z / "\ 2S.00 z / \ 24.50 - ^y —^— i • i • ' " - 20 40 60 80 100 M WATER LEVEL BRANCHES 16 - IS 48 METER 24.50 "E 24.00 ~ - r A\ \ A \ > —-i — i ' 60 80 100 METER WATER LEVEL BRANCHES IS 96 METER 24.00 2350 23.00 d 0 20 40 60 80 100 DATAFILE .M1.SWF PIPE MODEL DYNWAVE MOUSE RESULT FILE:GRATE2J>RF CALCULATED .-30-DEC-1993. 1S:48 •^~ Fig. 6.24. Flooding and surcharge in the pipe 16-15 - manhole diameters equal to 1.00 m and pipe diameter 0.305 m Chapter 6 The MOUSE Model 6 -34 METER WATER LEVEL BRANCHES 16 IS 0 METER 24.50" 24.40 24.30 ~ 60 80 100 METER WATER LEVEL BRANCHES 16 -15 48&CTER 23.90 "J R WATER LEVEL BRANCHES 16 - IS 96 METER 23.60 - /A^ 23.40 - / \ 23.20 - / \^ 23.00 - _y l i ^-^^_i ^ 0 20 40 60 80 100 DATAFILE :M1.SWF PIPE MODEL DYNWAVE MOUSE RESULT FILE: GRATE3.PRF CALCULATED : 30-DEC-1993. 16:11 •* Fig. 6.25. Free surface flow in the pipe 16-15, manhole diameters equal to 0.64 m and pipe diameter 1.00 m 6.3.2.4. Head loss in manholes Head loss in manholes and junctions is calculated in MOUSE for the following features of inlet and outlet pipes. a. head loss due to the change in flow direction b. head loss due to the change in elevation of pipe invert c. head loss due to the contraction d. head loss due to the outlet shape In the Maroubra pipe network the head loss of type ' b ' is not considered because there was no data available for pipe elevation changes at manhole sites. However, for most of them the manholes base has the same elevations as the inlet and outlet pipe inverts. This was true for the other catchments as well. Assuming no change in inlet and outlet pipe inverts elevation, type ' b ' head loss is ignored in this study. Type 'a ' head loss is considered by the model based on the angle of inlet and outlet pipes. The head loss type ' Chapter 6 The MOUSE Model 6 -35 c ' is due to the contraction of flow when it leaves the manhole towards the outlet and will be calculated by the model. In Fig. 6.26. a typical manhole used in simulation of the Maroubra and other catchments is illustrated. The head loss of type ' d ' is tested to see the effect on water level at nodes and branches. This head loss is related to the outlet pipe edge shape including, rounded, sharp, orificing and no cross section change. In thefirst tes t no cross section change, no head loss, was assumed and the results are presented in Fig. 6.27. In the second test orificing outlet which exerts the largest head loss was introduced to the model and the results are illustrated in Fig. 6.28. for the same branch as in the first test. Normally when the orificing edge is used the water level in thefirst pit of every branch decreases while for the middle and bottom branch it increases. An orificing outlet edge is shown in Fig. 6.29. Q, Time Function, Hydrography :sz: D2. Dl •> •> Fig. 6.26. Schematic receiving manhole in Maroubra network Chapter 6 The MOUSE Model 6 -36 METER WATER LEVEL BRANCHES 16 -IS OfcCTER METER WATER LEVEL BRANCHES 16 --IS 48 METER 2S.00 -zt METER WATER LEVEL BRANCHES 16 -IS 98NCTER 24.00 z 0 20 40 60 80 100 DATAFILE :M1.SWF PPE MODEL DYNWAVE MOUSE RESULT FILE :MHEAD1.PRF CALCULATED: 29-DEC-1393. 12*3 Fig. 6.27. Water level in pipe 16-15, no outlet head loss METER WATER LEVEL BRANCHES 16 -IS 0 METER 2SS0-EJ 2S.00 -q 24S0 3 i 20 40 60 SO 100 METER WATER LEVEL BRANCHES 16 -IS 48 METER 2S.00 -EJ METER WATER LEVEL BRANCHES 16 -1S 96t DATAFILE :M1.SWF PPE MODEL DYNWAVE MOUSE RESULT RLE: (*EAD2J»RF CALCULATED: 29-DEC-1993. 12*6 ** Fig. 6.28. Water level in pipe 16-15, orificing outlet Chapter 6 The MOUSE Model 6-37 Q, Time Function, Hydrography, sz: D2 ^ •> DI Fig. 6.29. Orificing type of manhole outlet 6.3.2.5. Boundary conditions effects (fixed/time function) MOUSE accepts fixed or time function external Boundary Conditions, B.C., at the outlet nodes and inflow nodes of catchments. Inflow hydrographs computed by the runoff model at nodes are samples of B.C. There is no limitation on the introduction of more than one inflow hydrograph, constant or time function, at the inflow nodes, but for outlet nodes one and only one B.C. must be specified (MOUSE user manual 1988). The B.C. at inflow nodes is defined as a time function discharge (Q(t), t) while B.C. at the outlet nodes is time function water level (H(t), t). The variations of H as a function of time could be introduced to the model using the observed hydrograph and rating curve of outlet for the event which needs to be simulated. The flow depth at the initial condition of computation, t = 0, is assumed by the model automatically equal to 10% of the pipe diameter and calculation of discharge proceeds using Manning's formula. The model was run twice with the time function and with fixed B.C. at the outlet. In the first run the water level was given to the model using the rating curve and hydrograph of the observed events. In the second run the bottom level of the channel at the measuring Chapter 6 The MOUSE Model 6-38 station was introduced to the model as fixed B.C. The results of these two tests are presented in Figs. 6.30 to 6.31 for selected branches. Water levels for these branches are the same regardless of fixed/time function B.C. Maximum water levels and discharges for all branches within the system for both runs are presented in Table 6.14 The superimposed hydrographs of fixed, time function and the observed at the outlet are illustrated in Fig. 6.32 which show no difference at all. It is concluded that in urban catchments like Maroubra which are not under tidal effect, introducing fixed or time function B.C. has no serious effect on the model computation of discharge and water level. Presumably the pipe slopes are steep enough, so that downstream boundary conditions have no effect. METER WATER LEVEL BRANCHES 16 -15 0 METER 2550^3 25.00 •=! 2450 z! I 60 80 100 METER WATER LEVEL BRANCHES 16 -IS 48 METER METER WATER LEVEL BRANCHES 16 -15 96 METER 24.00 q 2350 zt 23.00 -d. 0 20 40 60 80 100 DATAFILE :M1.SWF PIPE MODEL DYNWAVE MOUSE RESULT FILE :FTBC1PRF CALCULATED: 29-DEC-1993. 17:33 •rf" Fig. 6.30. Water level in pipe 16-15, time function B.C. Chapter 6 The MOUSE Model 6 A METER WATER LEVEL BRANCHES 16 - 1S 0 METER 24.50 -^ METER WATER LEVEL BRANCHES 15 48KCTER 25.00 zt 24.50 tj 24.00 " METER WATER LEVEL BRANCHES 16 -15 36 METER 24.00 d 2350 q 23.00 i 0 20 40 60 80 100 DATAFILE :M1.SWF PPE MODEL DYNWAVE MOUSE RESULT FILE :FIXBCPRF CALCULATED : 29-DEC-1993. 17:40 -* Fig. 6.31. Water level in pipe 16-15, fixed B.C. Cliapter 6 The MOUSE Model 6 -40 Table 6.14. Boundary conditions effects (Hmax : m, Qmax: nr/s, Time: Minutes) F( I ) B.C. Fixed B.C. F(t)B.C. Fixed B.C. Branch Hmax Time Hmax Time Qmax Time Qmax Time 33 32 25.72 26:00 25.72 26:00 0.197 26:00 0.197 26:00 32 31 24.98 29:00 24.98 29:00 0.236 28:00 0.236 28:00 31 29 23.21 27:00 23.21 27:00 0.269 27:00 0.269 27:00 29 28 23.04 27:00 23.04 27:00 0.337 27:00 0.337 27:00 28 27 22.84 28:00 22.84 28:00 0.381 28:00 0.381 28:00 27 22 22.69 29:00 22.69 29:00 0.448 30:00 0.448 30:00 22 21 22.53 28:00 22.53 28:00 0.576 29:00 0.576 29:00 21 17 22.34 28:00 22.34 28:00 0.719 29:00 0.719 29:00 19 18 25.02 21:00 25.02 21:00 0.161 22:00 0.161 22:00 18 17 23.07 26:00 23.07 26:00 0.535 26:00 0.535 26:00 17 8 22.25 28:00 22.25 28:00 1.266 29:00 1.266 29:00 9 8 22.39 25:00 22.39 25:00 0.452 24:00 0.452 24:00 11 9 22.78 25:00 22.78 25:00 0.350 25:00 0.350 25:00 12 11 23.10 25:00 23.10 25:00 0.319 23:00 0.319 23:00 13 12 23.19 25:00 23.19 25:00 0.296 23:00 0.296 23:00 14 13 24.03 25:00 24.03 25:00 0.247 23:00 0.247 23:00 15 14 24.22 25:00 24.22 25:00 0.177 23:00 0.177 23:00 16 15 25.73 29:00 25.73 29:00 0.121 30:00 0.121 30:00 8 4 21.75 28:00 21.75 28:00 1.665 28:00 1.665 28:00 5 4 21.40 26:00 21.40 26:00 0.280 23:00 0.280 23:00 6 5 22.18 24:00 22.18 24:00 0.226 22:00 0.226 22:00 7 6 23.04 21:00 23.04 21:00 0.163 21:00 0.163 21:00 4 3 21.38 27:00 21.38 27:00 1.884 27:00 1.884 27:00 3 2 20.91 28:00 20.91 28:00 1.992 28:00 1.992 28:00 25 18 34.74 25:00 34.74 25:00 0.116 26:00 0.116 26:00 26 25 46.64 23:00 46.64 23:00 0.061 24:00 0.061 24:00 23 22 23.95 24:00 23.95 24:00 0.170 24:00 0.170 24:00 24 23 28.64 23:00 28.64 23:00 0.108 24:00 0.108 24:00 2 OUTLET 19.49 30:00 19.48 29:00 2.089 29:00 2.090 29:00 nJ V MflROUBRR ORTE;l-7D383 OBS. FIX B.C. VRR. B.C. - - C H CO —I i ?n ?nn TIME - Min Fig. 6.32. Superimposed hydrographs simulated with fixed/lime function B.C. Chapter 6 The MOUSE Model 6 -41 6.3.2.6. Time step in simulation Computational grid for finite difference solution of flow equations in MOUSE is performed based on the following condition: V. At < AX Where V: velocity, m/s At: time step, s AX : distance between computational nodes in the pipe, m Although an implicit finite difference scheme (the 6-point Abbott-Scheme) is used in the model which remains stable at any selected time step, the length of some pipes may be too short to satisfy the above condition. The time step should be small enough to make sure of the accuracy of the results. For DYN.W 5 to 30 seconds is recommended (DHI 1988). Several runs were investigated to see the effect of the time step on the simulation of water level. Time steps of 5, 10, 20, 30, 60 and 120 seconds were used in simulation trials. Whereas the selected time step mostly affects short pipe lengths between nodes, the water levels are presented for the tributary 7-2 for different time steps at the same time (Fig. 6.33). Although for time steps 5 to 60 seconds the water level is nearly the same, for 120 seconds it is fluctuating between nodes 7, 6 and 5 which shows that 120 seconds is not a suitable time step for these short distances. During urban catchment flash floods, selection of suitable time step is very important in simulation of flow when inflow to the system suddenly increases to a high level. A suitable time step of simulation should be found by trial and error while checking the consistency and stability of the solution. The model gives warning when the selected time step does not satisfy the stability of solution within the system. Chapter 6 The MOUSE Model 6-42 (a) 5 Sec. (b) 10 Sec. (OX— I •?«)!•*« I <0*0-*1l7flO)MtfH aoo xx tec *CTW IX so (c) 20 Sec. (d) 30 Sec. 2ND (Qm-HI7»)Mvtll am (ooo-> iiTtfiiMi! (e) 60 Sec. (f) 120 Sec. nm Fig. 6.33. The effect of time step on simulation Chapter 6 The MOUSE Model 6 -43 6.3.2.7. Kinematic and dynamic wave approximation Generally speaking, KW cannot simulate pipe flow properly when some parts of the system or the whole are under pressure. In partly full condition the results of the KW and DYN.W are mostly the same and KW is a good approximation for the pipe flow solution. However, in pressurised pipe flow KW is not an accurate solution because it does not consider the terms of pressure and inertia in the general form of momentum equation. To understand what happens in the systems, Dynamic Wave is the best and the safest solution because surcharge may happen in some parts of the system which is not predictable. To compare performance of KW and DYN.W in simulation pipe flow, they have been tested using small, medium and large events. The model was run for event 210578 (small event ) with the same runoff model but different pipe flow models (KW and DYN.W). The results of maximum water level for two cases are presented in Table 6.15. The maximum water levels and discharges for the pipes between nodes are very close together. As mentioned above this is a small event and pipes are running partly full, so KW could fairly accurately predict the flow characteristics. Time steps of the above two tests were the same, i.e., equal to 15 Seconds. The runoff resulting from the heaviest observed rainfall, event 051184, was simulated successfully using DYN.W (Table 6.16), but KW failed to simulate flow from beginning to the end of the storm because node 3 overflowed more than the accepted value. This case is an absolutely pressurised pipe flow that KW is not able to handle. The comparison of the ability of two solution levels for a medium event, 170383, showed that both KW and DYN.W were successful in simulation of flow but with different results. The maximum water levels for both cases are presented in Table 6.17. In this event some of the system pipes were under surcharge. In spite of successful simulation using KW, in that there was no error in execution of the model, the results of maximum water level or pressure line are not correct. The calculated pressure lines for both cases over the longest tributary of the system, 16-9, at the same time during the rainfall are illustrated in Fig. 6.34. The pressure line is not stable when KW is used and it is discontinuous over the manholes, while for DYN.W it is smooth and the connection Chapter 6 The MOUSE Model 6-44 between manhole heads and pressure head in the pipes are kept quite reasonable. (Fig. 6.34). The water level in the pipes is affected by the KW solution level as well. Fig. 6.35 shows the variation of water level for the whole event in the beginning and near end of the pipe between nodes 15 and 14. Using DYN.W this pipe is under surcharge and flooding from the beginning to the end while KW does not show the flooding in the beginning and the flooding in the end is much worse than that of DYN.W. Furthermore, there is discontinuity in the water level which is not realistic. Table 6.15. The results of KW and DYN.W for a small event simulation (Hmax : m, Qmax: m3/s, Time: Minutes) KW DY]vi. W KW DY1M. W Branch Hmax Time Hmax Time Qmax Time Qmax Time 33 32 25.55 47:00 25.55 46:00 0.066 46:00 0.067 47:00 32 31 24.67 48:00 24.68 48:00 0.089 49:00 0.088 49:00 31 29 23.08 49:00 23.09 49:00 0.103 51:00 0.103 49:00 29 28 22.88 49:00 22.89 50:00 0.124 49:00 0.125 50:00 28 27 22.66 50:00 22.66 51:00 0.134 49:00 0.139 51:00 27 22 22.50 51:00 22.51 51:00 0.160 51:00 0.160 52:00 22 21 22.32 52:00 22.32 51:00 0.188 51:00 0.198 52:00 21 17 22.08 51:00 22.11 51:00 0.241 52:00 0.239 52:00 19 18 24.91 40:00 24.91 45:00 0.040 45:00 0.040 40:00 18 17 22.91 48:00 22.92 49:00 0.196 48:00 0.196 49:00 17 8 21.97 50:00 21.99 51:00 0.407 49:00 0.429 51:00 9 8 21.61 48:00 21.63 49:00 0.154 48:00 0.154 48:00 11 9 21.90 49:00 21.91 48:00 0.128 47:00 0.131 49:00 12 11 22.22 48:00 22.23 48:00 0.119 48:00 0.120 48:00 13 12 22.33 48:00 22.35 47:00 0.106 48:00 0.106 48:00 14 13 22.89 47:00 22.89 47:00 0.090 47:00 0.090 47:00 15 14 23.09 46:00 23.10 46:00 0.069 46:00 0.069 47:00 16 15 24.41 47:00 24.41 46:00 0.051 47:00 0.051 47:00 8 4 21.44 50:00 21.43 51:00 0.588 50:00 0.594 51:00 5 4 21.08 51:00 21.13 48:00 0.097 45:00 0.093 48:00 6 5 21.93 45:00 21.93 42:00 0.072 45:00 0.072 43:00 7 6 22.91 44:00 22.91 39:00 0.047 45:00 0.047 39:00 4 3 21.08 51:00 21.08 51:00 0.672 51:00 0.668 51:00 3 2 20.57 51:00 20.58 51:00 0.699 51:00 0.698 52:00 25 18 34.69 49:00 34.69 48:00 0.042 49:00 0.042 49:00 26 25 46.60 45:00 46.60 42:00 0.019 46:00 0.018 42:00 23 22 23.83 45:00 23.84 43:00 0.046 45:00 0.046 44:00 24 23 28.57 45:00 28.57 43:00 0.029 42:00 0.029 43:00 2 OUTLET 19.24 52:00 19.24 52:00 0.711 52:00 0.723 53:00 Chapter 6 The MOUSE Model 6 -45 Table 6.16. The results of DYN.W simulation of a large event and pressurised flow (Hmax : m, Qmax: m3/s, Time: Minutes) Branch Hmax Time Hmax/D Qmax Time Qmax/Qf Qacc 33 32 27.30 51:00 4.30 0.193 48:00 1.08 1068.4 32 31 26.27 56:00 4.03 0.262 57:00 1.38 1519.3 31 29 23.36 46:00 0.51 0.347 42:00 0.21 1848.2 29 28 23.27 46:00 0.67 0.462 47:00 0.32 2289.7 28 27 23.19 47:00 0.78 0.569 49:00 0.46 2643.3 27 22 23.15 47:00 0.90 0.764 50:00 0.65 3077.2 22 21 23.11 46:00 0.92 1.013 50:00 0.58 4210.3 21 17 23.06 46:00 1.03 1.265 50:00 0.49 5075.2 19 18 25.08 42:00 2.13 0.190 42:00 0.69 642.9 18 17 23.64 45:00 1.54 0.949 44:00 0.77 3473.4 17 8 23.04 45:00 1.32 2.117 48:00 0.67 8826.2 9 8 23.54 45:00 3.72 0.458 30:00 1.75 2728.5 11 9 23.83 47:00 3.72 0.390 58:00 1.43 2157.1 12 11 24.13 51:00 3.64 0.365 65:00 1.38 1946.1 13 12 24.22 51:00 3.59 0.251 60:00 0.86 1731.7 14 13 25.27 47:00 5.75 0.213 43:00 1.38 1465.2 15 14 25.47 46:00 5.75 0.151 22:00 0.99 1139.3 16 15 26.50 59:00 8.50 0.129 69:00 1.63 781.1 8 4 22.74 45:00 1.32 2.627 46:00 1.30 12174.1 5 4 22.29 45:00 1.76 0.350 33:00 0.54 1478.3 6 5 23.48 47:00 3.81 0.245 30:00 1.27 1059.5 7 6 24.26 45:00 3.81 0.182 42:00 0.90 685.2 4 3 22.24 45:00 1.16 3.006 46:00 0.86 13839.9 3 2 21.84 45:00 1.28 3.267 45:00 1.30 14788.8 25 18 34.79 44:00 1.86 0.193 44:00 0.29 692.0 26 25 46.67 42:00 0.43 0.081 42:00 0.21 276.6 23 22 24.06 44:00 2.44 0.210 43:00 0.77 729.6 24 23 28.67 42:00 0.77 0.134 42:00 0.37 458.4 2 OUTLET 19.72 45:00 0.24 3.453 45:00 - 15499.0 Chapter 6 The MOUSE Model 6 -46 Table 6.17. The results of KW and DYN.W for simulation of a medium event (Hmax : m, Qmax: m3/s, Time: Minutes) KW DY M.W KW DY]M. W Branch Hmax Time Hmax Time Qmax Time Qmax Time 33 32 26.67 28:00 26.78 29:00 0.203 28:00 0.204 25:00 32 31 26.05 31:00 25.84 33:00 0.217 31:00 0.249 25:00 31 29 23.20 34:00 23.26 27:00 0.264 33:00 0.282 27:00 29 28 23.04 26:00 23.09 28:00 0.337 26:00 0.356 27:00 28 27 22.85 28:00 22.90 29:00 0.385 27:00 0.399 28:00 27 22 22.73 29:00 22.76 29:00 0.465 29:00 0.474 30:00 22 21 22.56 29:00 22.60 29:00 0.590 28:00 0.604 30:00 21 17 22.31 28:00 22.43 29:00 0.750 28:00 0.755 29:00 19 18 25.05 21:00 25.06 21:00 0.171 22:00 0.171 22:00 18 17 23.11 27:00 23.12 26:00 0.576 25:00 0.586 26:00 17 8 22.25 29:00 22.34 29:00 1.358 29:00 1.352 29:00 9 8 24.47 25:00 22.73 25:00 0.466 25:00 0.458 24:00 11 9 24.47 25:00 23.23 25:00 0.371 25:00 0.341 23:00 12 11 23.47 24:00 23.64 25:00 0.341 24:00 0.313 22:00 13 12 23.47 24:00 23.75 25:00 0.292 24:00 0.293 22:00 14 13 25.10 25:00 24.75 25:00 0.227 25:00 0.242 22:00 15 14 25.10 25:00 24.94 24:00 0.207 27:00 0.166 22:00 16 15 25.87 33:00 25.93 34:00 0.098 33:00 0.110 37:00 8 4 21.94 28:00 21.87 28:00 1.805 28:00 1.746 28:00 5 4 21.47 27:00 21.49 34:00 0.325 24:00 0.312 23:00 6 5 23.19 25:00 22.87 24:00 0.224 25:00 0.243 23:00 7 6 23.19 25:00 23.47 24:00 0.183 21:00 0.183 21:00 4 3 21.47 27:00 21.48 28:00 2.063 27:00 1.965 28:00 3 2 21.06 28:00 21.04 29:00 2.175 28:00 2.065 29:00 25 18 34.76 25:00 34.76 26:00 0.128 26:00 0.130 26:00 26 25 46.66 24:00 46.66 23:00 0.069 24:00 0.068 24:00 23 22 23.98 24:00 23.99 24:00 0.181 24:00 0.183 24:00 24 23 28.65 23:00 28.66 23:00 0.116 23:00 0.116 24:00 2 OUTLET 19.51 31:00 19.50 31:00 2.232 29:00 2.167 30:00 Chapter 6 The MOUSE Model 6-4' (a) DYN.W 24:00) (ttOO — > 117:00 ) MM:SS METER 2S.S0 2S.00 [itii 3S0 400 NCTER (b)KW 24:00) ( 0:00 —> 1 17:00 ) MNtSS METER 2S.S0 200 Fig. 6.34. DYN.W and KW solution of water surface profile Chapter 6 The MOUSE Model 6 -48 (a) DYN.W ME TER WA TER LE VEL BRANCHE S IS - 14 0 METER 25.00 tJ 24.00 -i 23.00 ME TER WA TER LE VEL BRANCHE S IS - 14 40&CTER 24.00 23.00 20 40 BO 80 IOO (b)KW — -> n^> WW WA 1£R LEVEL BRANCF)LS> -rs — 14 UMtlLH 24.00 - 2350- ME TER w A TER LE VEL BRANCHE S IS — 14 40 METER 24.O0 -3 23.00 - DATAFILE : Ml.SWF PtPE. MODEL KIN.WAVE RESULT FILE : I70383K.PRF CALCULATED : S-FEB- 1994. 20:44 «f»A«E UR>/ tPRGE DOWN> / Fig. 6.35. DYN.W and KW solution Chapter 6 The MOUSE Model 6 -49 6.4. Summary Sensitivity analysis of MOUSE was performed using Maroubra catchment. Catchment data, network, rainfall and boundary conditions files were set up for Maroubra to carry out the analysis on both hydrologic and hydraulic parts of the model. 6.4.1. Hydrologic parameters sensitivity study results Runoff model level A was tested to recognise the most sensitive parameters in surface runoff production. Due to the lack of observed hydrograph at the subcatchment outlets, the study in this part was mostly qualitative and comparative. The effective components of the runoff model level A consist of time of concentration, TAD and HRF which are incorporated in each subcatchment individually. 6.4.1.1. Time of concentration Time of concentration variations were investigated by studying magnitudes of flood peak and time to peak of hydrographs at the subcatchments and at the whole catchment outlets. An increase of 100% in time of concentration of each subcatchment caused a decrease of 21% in flood peak and an increase of 13% in time to peak of their hydrographs. However, the decrease in flood peak at the catchment outlet was less than those of subcatchments and equalled 7%. The increase in time to peak of the hydrograph at the catchment outlet was nearly the same (14%). ARR87 uses average velocity at 60% of the length of the gutters down the subcatchments, and so gives a larger peak; however, Chow's formula is more realistic. For this catchment Chow's formula will be used for gutter flow travel time of subcatchments. The results of the proposed formula in ARR87 for gutter flow time were compared with those of Chow's formula for triangular channels. An increase of 65% in time of concentration of each subcatchment, when Chow's formula was used, lowered the whole catchment flood peak equal to 7%, but had no effect on the time to peak of hydrograph. Chapter 6 The MOUSE Model 6 -50 6.4.1.2. Time Area Diagram MOUSE employs three different TADs to route the surface runoff through subcatchments. Among three available TAD shapes including rectangular, divergent and convergent, the first one was found suitable to simulate hydrograph at the catchment outlet and will be used for all catchments. However, depending on the special shape of subcatchments any one of them could be selected. 6.4.1.3. Hydrologic Reduction Factor Hydrologic reduction factor, HRF, was found to be the most sensitive parameter in simulating flood peak and runoff volume. Total and directly connected impervious areas were used to compute HRF in runoff simulation. Compared with the observed, both runoff volume and flood peak were highly overestimated when total impervious areas were taken to estimate HRF. Although replacement of total impervious area with directiy connected impervious areas reduced the problem significantiy, it still needed adjustment to match the observed and simulated. Therefore, this parameter will be calibrated on all catchments. 6.4.2. Hydraulic parameters sensitivity study results Pipe flow modelling in MOUSE integrates network characteristics, magnitude of flow and boundary conditions. Unsteady pipe flow is simulated numerically while different surface hydrographs of subcatchments enter the network via pits or manholes flowing down to the outlet. The effect of the physical shape of pits, flow resistance, grate entry loss, boundary conditions type, time step of simulation and solution level were studied through several runs of the model and results were compared with the observed. In Australian practice surface runoff is admitted into the underground urban stormwater network via square pits. However, MOUSE accepts surface runoff into the system through circular manholes. Simulations of maximum flow peak and water level, using square pits and circular manhole as flow conveyor to the system separately, were found to be in close agreement. An equivalent circular manhole will be used in modelling of catchments instead of square pits. Chapter 6 The MOUSE Model 6-51 6.4.2.1. Manning's Roughness Coefficient Pipe flow resistance in MOUSE is considered using Manning's roughness coefficient. The model was found to be quite sensitive to the variations of roughness coefficient. The decrease in the roughness coefficient caused shorter time to peak, larger peak flow and earlier recession at the outlet, so in calibration of the hydrograph's overall shape it should be considered carefully. Considering the age of networks, the standard value of the roughness coefficient will be 0.017, but the exact value will be calibrated. 6.4.2.2. Grate entry hydraulic loss Despite the lack of grate entry loss in MOUSE, this phenomena could be simulated by using the equivalent top surface area of manhole instead of the grate's effective area. Two trials of models without pipe capacity limitation showed that when manhole diameter is selected, small surcharge/flooding resulted, so it was concluded that grate entry loss could be simulated using modified manholes. Modified circular manholes with the top cross section equal to the effective grate area will be used in this study. 6.4.2.3. Manhole head loss Head loss in manholes was tested for two types of outlet shapes including, no head loss and orificing shape. When orificing outlet shape is used water level in thefirst pit of every branch decreases while for the middle and the bottom branch it increases. In this study no head loss option for the outlet shape will be used. Note that MOUSE automatically calculates losses for pipe bends and different diameters. 6.4.2.4. Boundary Conditions effects The study of flood peak and maximum water level at the outlet and nodes showed that assuming fixed or time function B.C. has no significant effect on them. The results of two runs with fixed B.C., the bottom elevation of measuring station at the outlet, and time function B.C., H(t) versus t for the observed hydrograph, were found to be the same. It is concluded that when there is no tidal effect and also supercritical conditions at the catchment outlet, introducing fixed or time function B.C. gives the same results. For Chapter 6 The MOUSE Model 6 -52 all the catchments, fixed boundary conditions, set at the bottom elevation of the measuring station, will be used in this study. 6.4.2.5. Time step in simulation The computational grid for the numerical solution is constructed by the model based on the simulation time step that the user introduces. A suitable time step could be found by trial and error while checking the consistency and stability of the solution. During urban catchment flash floods, selection of shorter time steps is preferred because of sudden increases of flow levels in the system. 6.4.2.6. Simulation methods For small events (no pressurised flow) both KW and DYN.W produced the same results. Although both methods were able to simulate a medium flood hydrograph, KW produced unreasonable and unstable pressure lines over the manholes. The runoff resulting from the heaviest observed rainfall was simulated successfully using DW, but KW failed during the simulation because of overflowing in the system. Generally DYN.W is the preferred solution because of the ability of handling back water and surcharge everywhere in the system, so this approach will be used in all catchments and for simulation of each event. CHAPTER SEVEN SIMULATION OF IMPERVIOUS AREA RUNOFF AND URBANISATION EFFECTS USING MOUSE Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-1 CHAPTER SEVEN 7. SIMULATION OF IMPERVIOUS AREA RUNOFF AND URBANISATION EFFECTS USING MOUSE Increase in the occurrence of high frequency floods such as 1-2 yr return periods due to urbanisation causes problems in cities. Generally there is no difference for low frequency flood such as 100 yrs or more between rural and urban areas because during these kinds of events the pervious areas of urban catchments will be saturated and act almost like impervious areas. A proper system should be able to cope with frequent events as well as for maximum design return periods. The frequent events normally generate runoff only from hydraulically effective impervious areas of urban catchments. Simulation of high frequency events, consisting entirely of impervious area runoff, is considered in this chapter for three reasons: thefirst i s the evaluation of MOUSE in simulation of these kinds of events; the second is the acquisition of a better estimate of directly connected impervious areas of catchments; and the third objective is the necessity for some parameters; eg, HRF and Tc, to be derived from this calibration so that they can later be used for simulation of combined runoff events (Chapter 8). If the model is set up for the impervious part of a catchment, the effects of any modifications or developments in the catchment on runoff volume and peak flow will be distinguishable. As an example of application of the calibrated model on the catchments and also testing the capability of MOUSE in prediction of the increase in flood peak due to land use changes, the effects of urbanisation in the form of new developments are investigated at the end of this chapter. 7.1. Calibration of The Model The MOUSE model hardly needs calibration, because it is a distributed model and users provide the model with the measured physical characteristics of the subcatchments. In spite of the above, some parameters especially in the hydrologic section of the model require calibration to some degree. For instance, the correct measurement of directly Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Usine MOUSE 7-2 connected impervious areas of a catchment is very difficult, so calibration of the model for some events could give us good knowledge of this. The roughness coefficient in the pipe flow model is the other important parameter which needs calibration. In some cases the method of solution (kinematic or dynamic wave) of pipe flow and also time step of simulation may affect the output hydrographs. The most reliable solution method can be achieved during the calibration. Despite the availability of many automatic optimisation procedures, MOUSE does not use them. The model should be calibrated simply by trial and error. The trial and error method, despite being time consuming and frustrating, has the advantage of simplicity and prevents interaction between parameters. The knowledge of the modeller regarding catchment characteristics, physical interpretation of the optimised parameters and sensitivity analysis results are important in order to get a reliable calibration with the trial and error method. 7.1.1. Hydrologic Module To simulate impervious area runoff, MOUSE uses level A to compute hydrographs at the outlets of subcatchments. These hydrographs are used as internal boundary conditions at manholes or nodes (Chapter 2). The summation of the volume of the hydrographs was compared with the observed volume at the catchment outlet. An adjustment of Hydrologic Reduction Factor, HRF, in level A of the model reproduces the observed volume. HRF has a smaller effect on the flood peak discharge. Initial loss, IL, is a parameter involved in both level A and B hydrology. The magnitude of IL for impervious areas of urban catchments ranges from 0.5 to 1 mm (DHI 1988). Although it could be significant in simulation of small storms, it is negligible for large storms. In the model IL could have different values for impervious and pervious areas. It has an interaction with HRF for small storms; however, when the size of storms become larger this interaction diminishes. IL for all the catchments under study was taken as being 1 mm. Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-3 The method of calculating Tc for subcatchments is outlined in the section for each of the four catchments. 7.1.2. Hydraulics Module To achieve a correct answer and stable solution, hydraulic calculations in MOUSE need special attention. Hydrographs simulated by the hydrologic module enter the pipe/channel system and are routed as unsteady flow making use of numerical solutions for continuity and momentum equations. Three options are available with the model to solve pipe flow including, Kinematic, Diffusive and Dynamic wave solutions. Dynamic Wave is the best and the safest solution because surcharge may happen in some parts of the system which is not predictable (Refer to Chapter 6, Section 6.3.2.7.). Dynamic wave with a suitable time step of simulation is used for simulation of pipe flow in the catchments under study. Time step of simulation was set as the minimum time to make sure every computational node, created by the model automatically, is considered in the calculation. The model, based on the full running pipe flow velocity and the time step introduced by the user, At, computes the velocity criteria and compares it with Ax. If the multiplication of velocity by A t is less than or equal to Ax the model proceeds with the calculation, otherwise it gives the user a warning and suggests a suitable time step for the pipe under consideration to get a stable solution. Considering the variety of pipe slope and diameter in a catchment, different suitable time steps are suggested by the model. In this study the minimum suggested time step was used in pipe flow simulation to make sure of the stability of the numerical solution. Boundary conditions at the catchment outlet are another important factor which might affect the water level within pipes, channels and pits. When there are special boundary conditions such astidal effects, the water level variations with time should be introduced to the model as B.C. In the catchments under study the outlets are not affected by tides, so the bottom levels of channels/pipes or control sections, such as weirs or flumes are introduced to the model as a fixed B.C. In Chapter 6, it was concluded that there is no Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-4 difference between the outlet hydrographs and water levels within the whole system when simulated by the use of time function andfixed wate r levels as B.C. During the sensitivity analysis, Chapter 6, it was concluded that the pipe roughness coefficient' n ' has a major effect on the time to peak of hydrograph at the outlet. It has a minor effect on the flood peak. TAD selection can change the shape of the hydrograph and time to peak as well. To prevent interaction between TAD and ' n ', considering the rectangular shape of streets, it was decided to select TAD No. 1 for all subcatchments and by varying ' n ' simulated hydrographs and the observed ones were matched. The roughness coefficient for pipes and channels should be calibrated to match time to peak of both the observed and simulated hydrographs. Thefirst estimat e of ' n ' was made by considering the type of pipe material (concrete) and the age of the system. 7.1.3. Comparison of Simulated and Observed Volumes and Peaks To keep computation stable and continuous, MOUSE generates water during dry periods within a multiple storm. Furthermore, the model assumes 2% partial filling of the network in the beginning of the computation and also retains some water in pipes, manholes and structures at the end of simulation. Thefinal outputs of the runoff model and the pipe model are called inflowing and diverted volumes respectively. These assumptions in the model make the inflowing and diverting volumes different, and these are summarised by a continuity balance equation (Table 7.1). For a single peak and short storm the continuity balance is small. For a multi peak storm with frequent dry durations the difference becomes bigger, but generally it is in the range of 5-10% of inflowing volume which is precise enough for most computation purposes ( MOUSE user manual 1988). Comparison of the simulated volume with the observed is made based on the magnitude of the inflowing volumes in this study and the water generated by the pipe model is ignored. To compare flood peaks, making use of simulated hydrographs is inevitable; however, MOUSE does not add water to the system during wet periods and flood peaks are less affected. Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Usin? MOUSE 7-5 Table 7.1. Continuity balance of inflowing and diverted hydrographs in MOUSE (a): single peak and short duration storm MOUSE - PIPE FLOW MODEL - DYNAMIC WAVE DATA FILES CALCULATION PARAMETERS SEWER SYSTEM FILE : MAROUBRA.SWF CALCULATION TIME T : 117 RAIN DATA FILE : 170383M. CALCULATION TIME STEP DT : 4 RUNOFF HYDROGRAPH FILE : 170383M.RRF NO. TIME STEPS BETWEEN SAVE: 45 SUPPLEMENTARY FILE 170383M.PWF RESULT FILE 170383M.PRF CALCULATION START :21-MAY-1994 - 15:57 NODES 30.0 CALCULATION END :21-MAY-1994 - 16:00 BRANCHES .... 29.0 CALCULATION SIZE INDEX : 36.7 GRID POINTS 145.0 1 - STARTVOLUME IN PIPES, MANHOLES AND STRUCTURES .... 7.0 M3 2 - STOPVOLUME IN PIPES, MANHOLES AND STRUCTURES 14.3 M3 3 - INFLOWING VOLUME (HYDROGRAPHS AND INFLOWS).... 3012.7 M3 4 - DIVERTED VOLUME (WEIRS, PUMPS AND OUTLETS) 3019.2 M3 5 - CONTINUITY BALANCE (2-1)- (3-4 ) 13.8 M3 ************************************************************** (b): multi peak and long duration storm MOUSE - PIPE FLOW MODEL - DYNAMIC WAVE DATA FILES CALCULATION PARAMETERS SEWER SYSTEM FILE ..: MAROUBRA.SWF CALCULATION TIME T : 2268 RAIN DATA FILE : 150688M. CALCULATION TIME STEP DT : 4 RUNOFF HYDROGRAPH FILE : 150688M.RRF NO. TIME STEPS BETWEEN SAVE: 45 SUPPLEMENTARY FILE....: MF.PWF RESULT FILE : 150688M.PRF CALCULATION START : 2-JUN-1994 - 03:40 NODES : 30.0 CALCULATION END : 2-JUN-1994 - 04:33 BRANCHES ....: 29.0 CALCULATION SIZE INDEX : 694.9 GRID POINTS .: 145.0 1 - STARTVOLUME IN PIPES, MANHOLES AND STRUCTURES : 7.2 M3 2 - STOPVOLUME IN PIPES, MANHOLES AND STRUCTURES .: 24.3 M3 3 - INFLOWING VOLUME (HYDROGRAPHS AND INFLOWS)...: 6823.7 M3 4 - DIVERTED VOLUME (WEIRS, PUMPS AND OUTLETS)..: 6985.7 M3 5 - CONTINUITY BALANCE (2-1) - (3-4) 179.0 M3 Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-6 12. Verification of The Parameters Verification of the accuracy of calibrated parameters can be performed by applying them to another set of storms and inspecting the adequacy of the results. Normally in rainfall- runoff modelling the available data is split up-thefirst par t is used in calibration and the second in verification of parameters. However, this is a usual practice in rural catchments because of the high variability and the large number of parameters. In urban catchment simulation, especially for impervious runoff events, the parameters involved are small in number and low in variability, so calibration could be performed using only a few events. The average parameters from the calibration can then be applied to the remaining events for verification. 7.3. Catchments Simulated Simulation of impervious runoff events was carried out in four catchments out of five including, Maroubra, Jamison Park, Fisher's Ghost Creek and Cranebrook. The detailed physical characteristics of thefifth catchment, Strathfield, were not available at the time of this study. 7.3.1. Maroubra In this catchmentfive events including, small, medium and large events were used in the calibration process. As an initial estimate, HRF was assumed to be equal to the average ratio of directly connected impervious area to the total impervious area of the subcatchments. For calibrating the model on the five selected events the time to peak was adjusted by variation of' n '. By changing the HRF, the volume and flood peak were simulated as close as possible to the observed values. Most emphasis was placed on simulating the volume. It should be noted that there is still a small interaction between ' n ' and HRF in simulating flood peak which is unavoidable. The results of calibration including the flood peaks, volume of runoff and time to peak of hydrographs are presented in Table 7.2. For these events B.C. at the outlet was taken as Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-7 time function water level corresponding to each event by using the rating curve. Dynamic wave with a time step of 4 seconds was adopted as a pipe flow simulator to make sure the solution remained stable and consistent. The average value of ' n ' is estimated as equal to 0.022 to match the computed and observed hydrographs time to peak. However, the calibrated 'n' value is higher than usual for old pipes ( eg. 0.017 in Chow 1957 ) but the system is very old ( 50 years). The average of HRF is 59% which means that the total impervious area does not contribute to the surface flow completely. Computed and observed volumes, flood peaks and time to peaks are presented in Fig. 7.1. Table 7.2. The calibration results of MOUSE on Maroubra catchment Date Qp, m3/s Vol, mm Tp, Min. HRF n Time*, Obs. Com. Obs. Com. Obs. Com. % Min. 030378 1.647 1.587 6.47 6.54 27 30 65 0.030 165 170383 2.115 2.101 5.18 5.18 30 30 52 0.020 117 190679 1.405 1.289 5.67 6.42 27 27 56 0.022 336 200679 0.435 0.434 1.38 1.37 42 45 45 0.020 111 210578 0.834 0.801 2.57 2.45 51 54 75 0.020 231 Average 59 0.022 * Time base of hydrograph or simulation time Simulation of Impervious Area Runoff And Urbanisation Effects Usui?. MOi S m Com. Vat, mm (a) volume K> Com. Qp, m*/a (b) flood peak m Tp. Cam.. Min- (c) time to peak Fig. 7.1. MOUSE calibration results on Maroubra - (a) volume, (b) flood peaks (c) time to peak Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-9 7.3.1.1. Verification of the Calibrated Parameters The average values of' n ' and HRF from the calibration were used to verify the accuracy of predicted peak flow and runoff volume of 34 events other than those calibrated. The results are presented in Table 7.3. Event 051184 was the heaviest rainfall that occurred during the record of and will be discussed later in this chapter. 7.3.1.1.1. Volume of runoff The verified simulated and the observed runoff depths, over the whole catchment, are plotted in Fig. 7.2. The scatter of points around the line of equal value shows that the volume of runoff is estimated fairly well by the model. There is no obvious systematic error in simulation and the random error is mainly related to the random nature of rainfall . It is concluded that HRF was well calibrated. 7.3.1.1.2. Flood peak The computed and observed flood peaks are plotted in Fig. 7.3. The scatter in flood peak is expected with a hydrologic-hydraulic model like MOUSE-firstly because of the same temporal pattern of rainfall which is assumed over the whole catchment and secondly because of the variation of roughness coefficient within the network. Regarding the high correlation between the simulated volume with the observed, it is concluded that depth of rainfall is fairly constant over the catchment, so the simulated flood peak differences should be mostly related to the assumption of the same temporal pattern over all the subcatchments. It should be noted that Tc for subcatchments is assumed constant for all storms, so it may be too long for some events which results in an underestimated, delayed peak and vice versa. Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-in Table 7.3. Verification results of calibration- Maroubra Date Qp, m3/s Vol, mm Tp, Min. HRF n Time*. Obs. Com. Obs. Com. Obs. Com. % Min. 010377 1.033 0.696 8.20 8.22 45 60 59 0.022 435 050377 0.566 0.330 1.43 1.37 24 42 n ti 147 170378 0.234 0.220 0.67 1.01 102 90 " " 174 180378 1.553 1.391 8.18 7.26 354 345 • " 474 190378 0.842 0.366 2.94 1.79 147 150 • •• 243 270378 0.620 0.376 0.85 0.81 9 21 • " 69 080478 0.664 0.615 5.94 6.26 126 192 • " 480 130478 0.658 0.427 3.74 3.12 222 177 • II 240 180578 0.939 0.833 1.70 1.79 15 24 • ti 93 210578B 0.865 0.774 2.33 2.02 48 54 rt n 333 290578 0.903 0.467 19.20 22.15 1626 1461 • •• 5583 130678 1.204 0.834 12.13 13.23 588 513 i n 2190 180683 0.610 0.270 1.00 0.83 105 108 • " 150 051184 ------• " 570 061184 0.309 0.157 0.71 0.49 129 153 i •• 249 061184B 0.350 0.319 2.84 3.03 120 120 • •• 444 081184 1.703 2.038 13.58 16.17 123 120 • II 321 it 111184 1.156 1.042 5.48 5.62 45 45 • 318 111284 1.295 0.781 4.57 3.38 174 171 • ri 330 010585 1.276 0.792 1.99 1.63 141 168 • •• 177 081185 1.274 1.904 2.76 3.83 27 24 • •• 117 271285 1.369 1.444 5.13 4.58 39 48 • " 144 160186 1.317 1.632 18.45 19.84 1380 1419 " 2484 120486 1.613 1.410 3.86 4.52 57 48 " 102 040187 1.211 1.016 3.86 3.08 75 45 " 210 030787 1.290 0.684 3.22 2.54 15 24 " 111 201087 1.130 0.712 7.34 6.50 363 372 M 564 231087 1.451 0.914 4.25 3.27 102 102 It 192 130288 0.942 0.886 12.66 14.27 1155 663 " 1836 250388 1.082 1.283 3.61 4.41 30 33 It 252 020488 1.278 1.240 23.35 23.62 1848 1557 " 3099 070488 1.358 0.838 4.94 4.69 39 42 " 765 280488 1.410 1.766 46.72 39.42 2913 2925 It 3093 150688 1.293 1.018 10.31 11.79 102 108 " 2268 Time base of hydrograph or simulation time 7.3.1.1.3. Time to peak of hydrograph To evaluate the verification simulated hydrograph's shape, time to peak of the simulated and observed hydrographs are plotted against each other in Fig. 7.4. Six long events, with time to peak greater than 600 minutes, are not included in this figure. For some of the long events like 280488 and 160186 the time of peak occurrence is estimated to be very close to the observed, but for some of them like 290578 and 020488 the differences are too great. For some shorter events like 050377 the difference to time to peaks is too Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-JI large to be justified. The computed time to peak for event 050377 is 40 minutes while the observed is 24. The inspection of the hydrograph and hyetograph of this event showed that the starting time of runoff is earlier than the rainfall, probably due to malfunction of pluviograph (Fig. 7.5 (a)). Opposite to the this event for 020488 reported starting time of rainfall is much earlier than the beginning of runoff so there is a difference of 274 minutes between the peak of the observed and simulated. ( Fig. 7.5 (b)). Two samples of calibrated and verified hydrographs are shown in Fig. 7.6. The catchment network and the main pipe longitudinal cross section are shown in Appendix A, Figs. A-l and A-2. The catchment and hydrology data are presented in Appendix B, Tables B-l and B-2. Superimposed hydrographs of simulated and observed are presented in Appendix C. Chapter 7 Simulation of Impervious Area Runoff And Urbanisation E A : r_\jr.-; MC>1 SE A. * oem, vol, mm Fig. 7.2. Verification of flood volume- Maroubra '-' Com. Qp. mS/a Fig. 7.3. Verification of flood peak - Maroubra <*- Tp, Com.. Min Fig. 7.4. Verification of time to peak- Maroubra Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects LZin? MOUSE ~-13 I § ll MRROUBRR i , o DRTE:CEQ37"7 cr o- OBS. n— =3 COM. s_ co s: _ <—i S i ° ED OJ _ 1— 120 160 200 TIME - Min (a) advancement of runoff C3 CD 1 1| TT ('[' E CD rn I MflROUBRR DOTE-.0201X88 CE g- OBS. COM. S_ CO I ED CD CD 80 160 240 320 1400 TIME - Min SKID1 (b) retardance of runoff Fig. 7.5. Typical malfunction of recorded data in Maroubra catchment- (a) advancement of runoff, (b) retardance of runoff Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-14 CD CD L. -d CD 1 s MRROUBRR 1 <= r DOTE:170383 CD ~~ OBS. az COM. e\j C=O 3_ \1 I 3 CO , j] 1 — I] if —^- Q ^D OO _ CZ> 1 1 '1 1 HO 80 120 160 200 TIME Min (a) calibrated c r • -»-•» v"wy—fi—if r r» w—" |w T' ' l in MRROUBRR DOTE-.150888 az OBS. Cd COM. ffl —. CO CJ) C3 i ~- ED LT) _ ^A ^ >1 JK fa \A/A; J\AAAJV ^ 50 100 150 200 250 TIME - Min aelO1 (b) verified Fig. 7.6. Samples of calibrated and verified hydrographs- (a) calibrated, (b) verified Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-15 7.3.1.2. Evaluation of surcharge and flooding for the heaviest observed rainfall The problem of surcharging and flooding within urban catchments causes possible errors in the measurement of the water level and consequently discharge. In Maroubra catchment during a rainfall (event 051184) which is believed to have a return period of 200 years (Vale et al. 1986) surcharge and flooding happened and the measured hydrograph seemed to be in error. Bufill (1989) stated that during this rainfall flooding occurred and either the majority of flow passed over the catchment boundaries or the measuring device at the outlet was not able to cope with the magnitude of the peak of this flood. Using the calibrated model for Maroubra this event was simulated ( Table 7.4.). The results of simulation are compared with those of the previous research on this catchment using ILSAX and SWMM by Vale et al. (1986). Table 7.4. Comparison of the results of MOUSE simulation with those of the other models for event 051184- Maroubra Model Qp, m3/s VOL, mm Tp, Min Pipe flow simulation OBS. COM. OBS. COM. OBS COM method ILSAX 1.809 4.58 25.4 95 24 - steady state SWMM 1.809 4.69 25.4 94.3 24 - kw- runoff + Transport MOUSE 1.809 3.35 25.4 28.9 24 45 DYN. Wave unsteady The dynamic wave model with a short time step, 4 seconds, was applied and the result was successful. The volume of runoff is in accordance with the observed. The ratio of the runoff volume to rainfall volume is equal to 0.17 which is exactly the same as the ratio of the directly connected impervious area of the catchment (0.17). The multiplication of HRF equal to 0.59 by the fraction of impervious areas, 0.29, gives 0.17 which is the same as the runoff ratio for this event. The similarity of the simulated and observed runoff volume shows that in contrast to Bufill's idea, all runoff passed through Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-16 the gauging station and not over the boundaries. There was probably a malfunction in the water level recorder which could not register the water level for the magnitude of peak around 3.35 m3/S which is estimated by MOUSE. Overall shape of the observed hydrograph is simulated by the model very accurately except for the highest peak. The goodness offit between the two hydrographs denotes the accuracy of rainfall/runoff synchronisation (Fig. 7.7). It is worth noting that volume overestimation by both ILSAX and SWMM is because of the incorporation of pervious areas in runoff generation which is not the case in this catchment which has deep sandy soils. !* rw~ MRROUBRR O •0TE!D5118U CNJ OBS. cr COM. cd B co L J CO I ""• ED OO . 1 T~ 60 80 100 TIME - Min aao1 Fig. 7.7. Superimposed simulated and observed hydrographs of the heaviest observed rainfall - Maroubra Another conclusion from this simulation is that for the highest rainfall intensity equal to 165.5 mm/hr, average rainfall intensity equal to 82.1 mm/hr and total depth of 169.5 mm, the pervious area of the catchment did not contribute to runoff generation. In this event some pipes in the system were running to full capacity and pressurised flow occurred, so some branches could not cope with the flow and surcharged from the pipe into the pit. Some other points the surcharging flow exceeded ground level and flooding occurred. The pressure lines calculated by model, were studied for event 051184. Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-/7 Surcharged/flooded nodes of the catchment are depicted in Fig. 7.8. A study of the pressure profile of the longest tributary (16-8 ) shows that surcharge and flooding occurred over the nodes 16,15,14,13,12 and 11. Nodes 9 and 8 were under surcharge from the pipe into the pit only. Nodes 7, 6 and 5 from tributary line 7-4 were under both surcharge and flooding while nodes 4 and 3 were only under surcharge. These two lines lie in the commercial area of the catchment. Line 16-8 extends along Anzac Parade and the line 7-4 in the middle and downstream end lies in the commercial area next to a shopping centre. Line 33-outlet, the main line and collector of the catchment, is located in a residential area at both the upstream end and in the middle, but the downstream end lies in a commercial area. Flooding over this line happened at the upstream end, nodes 33 and 32, because of the large size of the subcatchments. No flooding was observed in lines 26-18 and 19-17 because of the steep slope of the pipes due to the steep topography, but surcharge was estimated in nodes 18 and 17. In Fig. 7.9 the longitudinal profiles of the above lines associated with the maximum water surface profile are illustrated. Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Usim MOUSE ! '16K • ! 1 , : 14 i. : 33 F ; fi 15 F £32 F s nk 12F "^31 F •- 7 \ H F j 'fa \6F \ /28S - :\ 9sl / A 1? 4 / ''111 S - ^ T~-AJ5 F "'21 s " ' / Vhr~-^J21s 3S \ '• 23 : J lis^J^i^ x 1I9 . • ~~?5 1 OUTPUT Ai 3 PARAMETERS 1 4 NODES OUTLETJ. • i ' i ' :• MM S CONDUITS 6 PROFILE DATAFILE Ml SWF F; FLOODED 7 ZOOM 4 MOUSE Fig. 7.8. Surcharged and flooded pits on 051184 - Maroubra Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE A^ «*ooi ( ooo—.tTOODMtii SCO fc*TE« 48:00 ( 0:00 —> S70:00 3 MM: SS 1200 METER Fig. 7.9. Longitudinal profile of Maroubra network along with simulated maximum water surface profile of event 051184 Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-20 7.3.2. Jamison Park The physical characteristics of this catchment are presented in Chapter 3. Reference to chapter 4 shows that combined runoff events make up almost 50% of all recorded events. MOUSE is calibrated on 10 impervious area runoff events in order to estimate parameters such as HRF and 'n'. Assuming that these parameters remain constant during combined events, they will be used in calibration of combined events for this catchment in Chapter 8. The catchment network and main pipe longitudinal cross section created by the model is presented in Appendix A, Figs. A-3 and A-4. 7.3.2.1. Input Data A total of 47 subcatchments, discharging into pits, are considered to simulate the runoff hydrographs of impervious areas of the catchment. Input data required for MOUSE are presented in Appendix B. Table B.3 shows the total area, length, surface slope and total fraction of impervious area of each subcatchment. The coordinates of nodal points, top and bottom level, shape of outlet and diameter of manholes are presented in Table B.3 as well. The top level of each manhole was measured directly by visiting the catchment. In chapter 6 it was concluded that replacing the modified circular manholes with square pits has no serious effect on the calculated hydrograph and water level. Diameters of manholes are based on the opening of the pit inlet including, grated, sag inlet or a combination of both. Pipe network data of the catchment is depicted in Table B-3 and Fig. A-3. Pipe diameters were checked directly byfield inspection. The main pipe longitudinal profile based on the data of Table B-3 is produced by the model and illustrated in Fig. A-4. The hydrological data file used for verification of the model is presented in Table B-4. As already mentioned in Chapter 4, in this catchment the percentage of directly connected impervious areas is close to the total percentage of the impervious area, so it should be considered in the subcatchment time of concentration estimate. In Maroubra, subcatchment times of concentration were estimated based on the length and slope of gutters because the only active impervious areas during the rainfall were roads and sidewalks. In contrast to Maroubra, directly connected impervious areas in Jamison Park Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-21 include roofs, pathways and yards, as well as roads and sidewalks. However, sidewalks in this catchments are mostly unpaved and covered with grass. To consider travel path of flow through subcatchments the representative length of each subcatchment, mainly diagonal, was measured from the map. The surface slope of subcatchments was estimated along these lengths. Kinematic wave formula for overland flow travel time developed by Ragan and Duru (1972) presented in ARR87 was used to estimate time of concentration of subcatchments. This formula needs rainfall intensity as input. Rainfall intensity with a return period of 2 years and duration of 25 minutes was used as representative rainfall duration and intensity. The average roughness coefficient for concrete-asphalt surface (0.011-0.013, ARR87) was used in the formula. The 25-minute-duration rainfall, according to the Tc of the catchment for combined events, is adopted for calculating overland flow travel time for both impervious and combined runoff simulation. The reason for this selection is the relative scatter of impervious areas all over subcatchments which makes the extension of impervious areas unknown. To consider this scatter diagonal of each subcatchment was taken as the path length in the Ragan and Duru formula (Chapter 4, Section 4.1.1.1 ) for both impervious and combined runoff simulation. A rating curve for the gauging station of the catchment was not available to be considered as an external boundary condition, so the bottom elevation of the flume was given to the model as afixed boundar y condition at the outlet. A dynamic wave solution level with a time step of 2 seconds was used in simulation. Time steps greater than 2 seconds cause instability in the solution of some pipes in the system. Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-22 7.3.2.2. Calibration of The Model A total of 10 impervious area runoff events were selected for calibration of the model. As afirst estimat e the roughness coefficient of pipe was assumed equal to 0.012 and initial loss was set at 1 mm. In the first trial HRF for every subcatchment was set at 0.8 and 10 events were simulated. Although the volume was duplicated fairly well in this trial, hydrograph shape was poor especially in the beginning of events (eg 170383 and 280488 ). To get a better match of both hydrographs and volumes, another trial was accomplished. In the second trial for thefirst 5 events HRF was adjusted separately and then averaged. The averaged HRF equal to 0.76 was used in the next 5 events for verification. To match the beginning of the hydrographs the initial loss was set equal to 2 mm in this trial. Table 7.5. shows the results of two trials for volume of runoff. Both the volumes and absolute error are given in mm. Summation of absolute error for the calibrated events (thefirst five events) in the second trial is much smaller than thefirst one , but on verification events (the next five events ) total absolute error of thefirst trial is smaller than the second one. It is concluded that despite the attempt to get the better presentation by the model, verification of the parameters failed in the second trial. The first trial was selected as representative of catchment impervious area runoff (Table 7.6.). The illustrative comparison of flood peaks, volumes and time to peaks is made in Fig. 7.10. Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-23 Table 7.5. Comparison of volumes resulting from two sets of modelling, mm- Jamison Park First trial Second trial Date Obs. Com. Abs. Obs. Com. Abs. Error Error 150383 2.0 1.96 0.04 2.0 1.96 0.04 170383 12.43 12.27 0.16 12.43 12.27 0.16 170284 4.03 3.25 0.78 4.03 3.94 0.09 251185 7.86 9.41 1.55 7.86 7.65 0.21 010187 5.35 7.68 2.33 5.35 5.28 0.07 Total 4.86 0.57 180887 14.48 13.05 1.43 14.48 12.34 2.14 240188 1.401 1.35 0.051 1.40 1.28 0.12 230388 1.05 1.15 0.10 1.05 1.10 0.05 010488 2.38 2.47 0.09 2.38 2.34 0.04 280488 6.65 6.11 0.54 6.65 5.80 0.85 Total 2.211 3.20 IL= 1mm, HRF = 0.80 EL = 2.0, HRF = 0.76 Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-24 Table 7.6. The results of thefirst trial calibration - Jamison Park Date Qp, m3/s Vol, mm Tp, Min. HRF n Time*, Obs. Com. Obs. Com. Obs. Com. Min. 150383 0.328 0.462 2.00 1.96 30 30 0.80 0.012 90 170383 0.710 0.645 12.43 12.27 135 135 0.012 240 170284 0.738 0.618 4.03 3.25 30 15 0.012 120 251185 0.609 0.552 7.86 9.41 180 180 0.012 440 010187 0.535 0.570 5.35 7.68 310 300 0.012 430 180887 0.398 0.328 14.48 13.05 1150 1165 0.012 1180 240188 0.268 0.371 1.40 1.35 15 15 0.012 135 230388 0.472 0.473 1.05 1.15 8 10 0.012 74 010488 0.202 0.216 2.38 2.47 45 40 0.012 265 280488 0.251 0.272 6.65 6.11 115 110 0.012 555 Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-25 Obs. Vol, mm (a)volume '•* Com. Op, ma/a Obi. Op. m9/t (b) flood peak BU Com. Tp. Min. 4DO A son 200 - *fc 100 VM • 1 1 1 Oba. Tp. Mil (c) time to peak Fig. 7.10. Comparison of observed and computed events for Jamison Park-(a) volume, (b) flood peak and (c) time to peak Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-26 The closeness of the simulated and observed time to peak of hydrographs shows that the magnitude of roughness coefficient and Tc of subcatchments are mirrored by the model (Fig. 7.10(c)). Comparison of the simulated and the observed volumes shows an unbiased scatter on or around the line of equal value (Fig. 7.10 (a)). Flood peak is overestimated by the model for small and medium sized floods; however the trend is approaching the line of equal value when the size of flood increases (Fig. 7.10 (b)). Normally in combined events catchments receive a lot of rain which causes large flood peaks from the impervious areas, so correct estimation of large flood peak on impervious areas is important. Two samples of simulated and observed hydrographs are illustrated in Fig. 7.11. All the simulated events are presented in Appendix C. Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-27 £ a I =* JRMISON PRRK cn d DRTE:1"70383 OBS. COM. H cn l CD O 250 CD I lUIIULJ 1=3 ir X JRMISON PRRK DRTE-.2aousa a: OBS. CC R_ COM. CO 1=3 CM _ 1 CD E3> —i 1 1 1 i 20 40 60 80 100 TIME - Min aao1 Fig. 7.11. Superimposed simulated hydrographs by MOUSE and the observed- Jamison Park Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-2S 7.3.3. Fisher's Ghost Creek Among the four catchments analysed, this catchment has the largest area (214.0 hectares) with a low percentage of total impervious area (about 27%). The network of the catchment consists of pipes and open natural channels, so simulation of the hydraulics part should be considered carefully. Almost 50% of the observed events in this catchment are combined runoff with a flood peak range from 3.6 to 15.65 m3/s, showing the importance of pervious area in runoff production in this catchment. Flood peak discharges of impervious runoff events range between 2.89 and 5.45 m3/s. These events were separated as impervious or combined runoff events by comparison of runoff ratio with the percentage of imperviousness (Bufill 1989). 7.3.3.1. Input Data A total of 12 events were used to simulate impervious area runoff of catchments. The pipe/channel data file was set up using the SWMM datafile prepare d by Vale(1986) and also the catchment was visited to check the accuracy of the network. Generally 48 Subcatchments were considered to simulate runoff at the main outlet. Subcatchment times of concentration were estimated using the Ragan and Duru formula(1972). Regarding the discussion in Section 7.3.2.1. for the Jamison Park catchment, a 2-year rainfall with a duration of 44.2 minutes and intensity of 39 mm/hr was used as typical rainfall to estimate Tc by the formula. Input datafiles for Fisher's Ghost Creek are presented in Tables B-5 and B-6 in Appendix B. Reference to Chapter 3, discharge is measured at the outlet using a weir and water level recorder. The top elevation of the weir was introduced to the model as fixed B.C. The catchment network and longitudinal cross section of main waterway are presented in Appendix A, Figs. A-5 and A-6. 7.3.3.2. Calibration of The Model Volume of runoff was the first variable to be simulated in this catchment. While trying to calibrate Volume of runoff, it was concluded that for 6 events out of 12, HRF approached one or greater. When all the impervious areas of a catchment contribute to runoff production, HRF will be equal to one. Table 7.7 shows that for some events, even with a HRF of one, the simulated volume is less than the observed. Initial loss was Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-29 assumed equal to 1 mm for all events. Bufill (1989) stated that some marginal areas of natural waterways were saturated during these events and discharged into the stream. Considering 1309 m of open natural channels along the main waterway of this catchment, this statement seems reasonable. However, the volume of runoff from marginal areas of streams is small and has no serious effect on the flood peak resulting from impervious areas of the catchment, because of the pervious flow delay time. During a combined event runoff, the contribution of the stream margins is obvious, so HRF equal to one was considered for impervious area runoff simulation when combined events are to be simulated. For the impervious runoff events with HRF less than 1.0 the average value is equal to 0.82 (events 170383, 070284, 161087 and 131283, but excluding 050383). The HRF for event 050383 is equal 0.31 which implies the contribution of only a small part of the impervious areas. This small part could be street and sidewalk areas which generate runoff during small events. The runoff volume for this event is the smallest among those observed. If the HRF equal to 0.82 is applied to simulate the rest of events, the estimated volume will be underestimated. For seven events including thefive larges t events the HRF was found equal 1.0. Because of the difference between HRF for large and small events, in this catchment verification was not done. However, in case of combined event simulation HRF will be taken as equal one. Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-30 Table 7.7. The results of runoff volume simulation - FGC Date Vol, mm HRF Obs. Com. 040181 4.1 3.12 1.00 050383 2.12 2.09 0.31 170383 4.32 4.32 0.88 271183 6.79 6.69 1.00 070284 3.20 3.20 0.63 111184 7.03 5.63 1.00 181186 4.24 3.76 1.00 161087 5.09 5.08 0.84 251281 4.77 3.41 1.00 131283 3.62 3.61 0.91 260184 6.90 6.50 1.00 240588 10.19 10.18 0.98 Calibration of flood peak and time to peak of runoff regarding the combination of the network of pipes and natural channels, needs specific attention. In Chapter 4 section 4.2.4. it was concluded that the roughness coefficient for open channels of main waterways had been underestimated, so the results of the velocity method and lag method for Tc were not consistent. The catchment impervious area Tc, using lag method, was estimated as 26.5 minutes versus 15.0 minutes by the ARR87 method (Table 4.34), so an increase of 50% in ' n ' seems necessary to bring them together. The magnitude of ' n ' for pipes was selected as 0.014 which is almost equal to the value selected by Vale (1986) with SWMM modelling. The open channel roughness coefficients used in this simulation were 1.5 times larger than those used by Vale (1986). Incorporating the original values of' n ', Chapter 3 Table 3.9, caused overestimation of flood peaks and underestimation of time to peak. However, for some events like 050383, using the original n values gives the best simulation results, but with the others these values seem low. For example, simulation of event 271183 using the original values of n overestimated peak flow and underestimated time to peak, while increasing n by 50% gave better agreement of flood peaks and time to peak. Table 7.8. shows the results for the two events above. Scatter diagrams of volume, flood peak and time to peak for Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-31 simulated and observed events are illustrated in Fig. 7.12. Two samples of overlaid hydrographs of the simulated and the observed are shown in Fig. 7.13. All the simulated events are presented in Appendix C. Table 7.8. Effects of varying open natural channel roughness coefficient - Fisher's Ghost Creek Date Qp, m3/s Vol, mm Tp, Min. HRF n Time*, Obs. Com. Obs. Com. Obs? Com. Min. 050383 3.68 2.52 2.12 2.09 33 45 0.31 2n* 147 050383 3.68 2.948 2.12 2.09 33 39 0.31 1.5n* 147 050383 3.68 3.538 2.12 2.09 33 33 0.31 n* 147 271183 3.80 3.69 6.79 6.69 69 78 1.0 2n* 1104 271183 3.80 3.94 6.79 6.69 69 72 1.0 1.5n* 1104 * The original values of open channel roughness coefficients, Vale(1986) The overall results of simulation for all the events with suitable HRF for every event, but n values of 1.5 times as the original are presented in Table 7.9. Dynamic wave with 4 seconds time step was applied to all events. Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-32 Table 7.9. The overall results of calibration - Fisher's Ghost Creek Date Qp, m3/s Vol, mm Tp, Min. HRF n* Time*, Obs. Com. Obs. Com. Obs. Com. Min. 040181 3.47 1.911 4.1 3.12 75 84 1.00 1.5n* 273 050383 3.68 2.828 2.12 2.09 33 39 0.31 •• 147 170383 3.68 3.685 4.32 4.32 93 108 0.88 n 219 271183 3.80 3.815 6.79 6.69 69 72 1.00 1104 070284 4.18 4.390 3.20 3.20 24 36 0.63 357 111184 4.31 4.382 7.03 5.63 237 231 1.00 531 181186 5.45 5.400 4.24 3.76 81 81 1.00 234 161087 5.49 3.739 5.09 5.08 993 987 0.84 1497 251281 3.00 3.031 4.77 3.41 42 39 1.00 261 131283 3.30 4.011 3.62 3.61 27 33 0.91 201 260184 2.89 3.535 6.90 6.50 66 81 1.00 504 240588 3.94 3.275 10.19 10.18 162 192 0.98 657 * The original values of open channel roughness coefficients, Vale(1986) Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-33 '• Com. Vo (a) volume twi Com. Qp. ma/a B 6 A - «TJ yS IE - 9 ^ IK 9 — SL 1 - ' 1 1 1 ' I 1 1 2 3 4 Ob*. Qp. m3/» (b) flood peak s: Com. Tp. Mir 4DO SOO 1QO 1000 Obi. Tp. Min. (c) time to peak Fig. 7.12. Comparison of observed and computed events for Fisher's Ghost Creek-(a) volume, (b) flood peak and (c) time to peak Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-34 £ <=>. FISHERS CK. DOTE: 2S12S1 OBS. s_ COM. —I— BO 160 2U0 320 400 TIME Min rr FISHERS CK, I | CD az <=3 DRTE:27Ll83 eD OBS. cc COM. CO CO <•—' «=3 _ I = ii 80 120 160 200 TIME - Min *10 Fig. 7.13. Superimposed simulated hydrographs by MOUSE and the observed- Fisher's Ghost Creek Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-35 7.3.4. Cranebrook This is the smallest catchment among those which have been modelled in this study. The majority of recorded events are impervious area runoff events. However, the clay soil type of the catchment has enough potential to generate runoff during medium to heavy rainfall. This is a totally sewered catchment and discharge at the outlet is measured by means of a flume and water recording level. 7.3.4.1. Input Data Unfortunately the time increment of recorded rainfall and runoff is 5 minutes and in rare cases 3 minutes which is not suitable for modelling within a small catchment like Cranebrook with an area of 11.5 hectare. Regarding this problem, 10 impervious area runoff events, stage versus time, were selected, and using the rating curve of the flume were transformed to discharge (Gallen 1990). The pipe datafile prepare d for ILSAX modelling by Gallen (1990) was used to create a catchment and pipe data file for MOUSE. The bottom level of the flume at the outlet was introduced to the model as fixed B.C. The input datafiles are presented in Tables B-7 to B-8 in Appendix B. The catchment network and longitudinal cross section of main pipe are presented in Appendix A, Figs. A-7 and A-8. A roughness coefficient equal to 0.014 was found adequate to simulate flood peak and time to peak. This catchment was simulated using 44 small subcatchments. Due to the small size of subcatchments and short length of overland flow Tc of subcatchments was estimated to be very short, so 2.0 minutes was adopted for all the subcatchments. 7.3.4.1. Calibration of Model To estimate an average value of HRF for impervious area of the catchment, two methods were tested. In thefirst approach an attempt was made to calibrate volume for thefirst 5 events and the average of HRF was used to simulate the next 5 events. In the second approach volume was calibrated for each event individually and HRFs were averaged for 10 events. Table 7.10 shows the results for the two methods. In both methods HRF tends to increase as the event size increases. This trend shows that when the event size increases the contributing impervious areas increase. It means that during heavy rainfall Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-36 and combined events, besides directly connected impervious areas, disconnected impervious area runoff, after passing over pervious areas, has reached the network. It is worth noting that many parameters affect HRF in a catchment; for instance, spatial variability of rainfall and the adopted initial loss in case of impervious runoff events. Regardless of these, according to the variations of HRF in the catchments under study the trend of runoff generation could be categorised in three parts- In thefirst ste p directly connected impervious areas-Secondly disconnected impervious areas and thirdly pervious areas. In Table 7.10 the events are all small, and the average HRF wasfind to be 0.45. This should apply to impervious runoff only. This was rounded to 0.50. For larger combined events in this catchment, it is expected that HRF will be equal to 1.0. Table 7.10. Comparison of simulated volumes - Cranebrook Date Obs. Vol, Com. HRF Vol.,mm mm 1st 2nd 1st 2nd 040788 0.94 0.92 0.92 0.33 0.33 151188 6.27 6.21 6.21 0.56 0.56 111189 0.96 0.96 0.96 0.32 0.32 210489 3.52 3.51 3.51 0.56 0.56 200689 2.72 2.70 2.70 0.46 0.46 260989 1.02 1.22 1.00 0.45 0.37 110389 0.80 1.32 0.79 0.45 0.27 200388 0.74 0.77 0.73 0.45 0.43 211087 0.39 0.41 0.39 0.45 0.43 040488 0.53 0.32 0.53 0.45 0.75 Average 0.45 0.45 The results of dynamic wave simulation with a time step of 2 seconds, HRF of 0.5 and pipe roughness coefficient of 0.014 are depicted in Table 7.11. Comparison of volume, flood peak and time to peak of simulated and observed hydrographs is demonstrated in Fig. 7.14. Two typical simulated hydrographs are shown in Fig. 7.15. All the simulated events are presented in Appendix C. Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-37 There is an outlier in Fig. 7.14(b), event 210489, which needs comment. For this event, the volume of runoff is simulated very well, but the peak is poorly simulated (0.124 m3/S simulated versus 0.703 m3/S). The simulated and observed hydrographs along with the hyetograph are presented in Fig. 7.16. Assuming the accuracy of recorded hydrograph ordinates, the main reason for this mismatch is hyetograph ordinates. A fairly uniform hyetograph is reported for this event which cannot represent this sharp hydrograph properly. Table 7.11. The results of calibration - Cranebrook Date Qp, m3/s Vol, mm Tp, Min. HRF n Time*, Obs. Com. Obs. Com. Obs. Com. Min. 040788 0.051 0.07 0.94 1.40 195 192 0.5 0.014 350 151188 0.11 0.093 6.27 5.55 105 45 830 111189 0.192 0.186 0.96 1.51 50 51 85 210489 0.703 0.124 3.52 3.13 30 39 110 200689 0.121 0.067 2.72 2.94 150 147 305 260989 0.073 0.071 1.02 1.36 75 48 145 110389 0.134 0.082 0.80 1.47 45 45 220 200388 0.309 0.142 0.74 0.85 6 12 69 211087 0.076 0.072 0.39 0.45 6 21 70 040488 0.313 0.083 0.53 0.35 6 12 n 70 * Time base of hydrograph or simulation time Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-18 m Com. Vol, mm (a) volume an Com. Qp, ml/i 0.2 O.I Obs. Qp, mS/i (b) flood peak » Com. Tp. Min. SO 120 IOO zoo Obi. Tp, Min. (c) time to peak Fig. 7.14. Illustration of MOUSE calibration on Cranebrook -(a) volume-(b) flood Peak (c) time to peak Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-39 CRHNEBROOK DP.TE-. lit 189 Cd =• OBS. COM. S!_ cn CO CD CD I 1 20 40 60 BO 100 TIME - Min l^jiwr'^y111" CRRNEBRO0K *—k DRTEilSLlSrsr S cn co - OBS. Cd ~"' COM. CM "I "I— I 1 20 40 60 80 100 TIME - Min HelD1 Fig. 7.15. Superimposed simulated hydrographs by MOUSE and the observed- Cranebrook Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-40 CD L J O \ J ir i •——'* L\ J"H" ^ ^ CRRNEBROOK o ~ 1—1™" DflTE-21QU89 az c©s. — Cd CNJ COM. S_ "" cn 1=3 ao CD n c=> i =r _ o - ': r-5 i i 1 ! ' 1 40 80 120 160 200 TIME Min Fig. 7.16. Possible misrepresentation of hyetograph to MOUSE - Cranebrook 7.4. Investigation of Urbanisation Effect on Flood Peak Using MOUSE Landuse change including urbanisation has the potential to adversely impact on runoff quality and quantity. Increasingly there is a need for urban planners to consider different landuse patterns to minimise impacts on runoff. Numerical models are more frequently being used to ascertain the impact of landuse change during the planning phase. In planning for new urban areas or urban consolidation within established areas, urban planners need to recognise that different patterns of development can vary the impact of development/consolidation on runoff quantity and quality. One of the major impacts of urbanisation is rapid increase in frequent runoff events in comparison with undeveloped catchments. These frequent runoff events, which are generated from impervious areas within urban catchments, can impact on existing creek and stream ecosystems and destabilise creek banks and lead to increased erosion and sediment exports (Hammer (1972), Nanson and Young (1981) and Ruthefurd & Ducatel (1994)). When planning new urban areas or area for consolidation, the likely impacts on runoff need to be quantified in order to identify landuse pattern which minimise the impacts of increased impervious areas. The effect of new development and/or consolidation on runoff peaks also varies spatially in relation to the scale of development in comparison Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-41 with existing development and the distance from the area of landuse change. The spatial distribution of impervious areas has the greatest effect on runoff peaks at a local scale. The effects of spatial distribution of urban development on runoff peaks was investigated using the calibrated MOUSE model. 7.4.1. Spatial distribution effects of new development on flood peak The effects on flood peak of different spatial developments were tested using the four gauged urban catchments. Although patterns of development in existing urban areas can be numerous, three scenarios were investigated for each catchment as follows: Case 1 : Uniform Development ( UD)- 15% of the catchment area was uniformly converted into new urban areas. (Fig. 7.17-a). Case 2 : Separate Concentrated Developments- Five separate concentrated developments, each equivalent to 3% of the catchment area were located at the " top", " middle", " bottom", " left" and "right" of the catchment.(Fig. 7.17-b). Case 3 : Concentrated Development - Three percent of total area of each catchment was considered as a Concentrated Development ( CD ). This development was incorporated into the catchments, each time at one spot only. A total of 7 different spots were tested. (CD1 to CD7). In thefirst trial, the 3% development was placed at CD1 at the top of the catchments. In the second trial, the 3% development was placed closer to the outlet, and so on, until the last trial which placed the 3% near the outlet. For example, CD1 means 3% of total area of each catchment as new development placed on top of the catchment while CD7 is the same development near the catchment outlet. Using this scenario, the effect of the distance of new development on the flood peak at the point of concern was studied, while the volume of runoff was the same for all trials. The point of concern in this study was considered to be the measuring station (Fig. 7.17-c). Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-47 (a): Uniformly Distributed Development Case 1) (b): Separate Concentrated Developments(Case 2) (c): Concentrated Development Case 3) Fig. 7.17. Land use Development Scenarios Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-43 7.4.2. Spatial distribution and event size The effect of event size was investigated for the case of Maroubra catchment. Three individual storm events including large (Date 170383), medium ( Date 111184) and small (Date 200679) were selected. The model was run using the pattern and arrangement introduced in Case 3 for the three events. The results are presented in Table 7.12. Comparison of the average increase in flood peak does not show any significant correlation with event size. It is concluded that the effect of spatial distribution of new developments using the MOUSE model can be investigated using a typical storm for four catchments, however, for an accurate comparison the rainfall amount and temporal pattern should be the same for four catchments. Table 7.12. The effects of event size and spatial distribution of new developments- Maroubra Pattern Large event Medium event Small event (170383) (111184) (200679) Qp, m3/s (Qp-Qb)/Qb, Qp, m3/s (Qp-Qb)/Qb, Qp, m3/s (Qp-Qb)/Qb, % % % BASE 2.101* 0.0 1.025 0.0 0.434 0.0 CD1 2.138 2.0 1.09 6.0 0.457 5.0 CD2 2.089 -1.0 1.053 3.0 0.471 9.0 CD3 2.171 3.0 1.152 12.0 0.482 11.0 CD4 2.247 7.0 1.12 9.0 0.475 9.0 CD5 2.238 7.0 1.122 9.0 0.475 9.0 CD6 2.22 6.0 1.12 9.0 0.473 9.0 CD7 2.416 15.0 1.158 13.0 0.484 12.0 AVE 5.6 8.7 9.1 *Qb : The original flood peak The effect of catchment size and development patterns was also investigated for all four catchments using a typical large event. Event 170383 on Maroubra was adopted for this and applied to all four catchments. The results of simulations are presented in Table 7.13. Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-44 Table 7.13. The effects of catchment size and spatial distribution of new developments Trial Cranebrook, Jamison Park, Maroubra, Fisher's Ghost Creek, 11.5 ha 22.0 ha 57.8 ha 214.3 ha Qp, (Qp-QbVQb, Qp, (Qp-Qb)/Qb, QP, (Qp-Qb)/Qb, Qp, (Qp-Qb)/Qb, m3/s % m3/s % m3/s % m3/s % BASE 0.990* 0.0 2.328* 0.0 2.010* 0.0 13.036* 0.0 CDl 1.066 7.7 2.537 9.0 2.138 6.4 13.410 2.9 CD2 1.066 7.7 2.412 3.6 2.089 3.9 13.906 6.7 CD3 1.060 7.1 2.491 7.0 2.171 8.0 13.960 7.1 CD4 1.057 6.8 2.526 8.5 2.247 11.8 13.432 3.0 CD5 1.059 7.0 2.523 8.4 2.238 11.3 13.728 5.3 CD6 1.062 7.3 2.514 8.0 2.220 10.4 14.407 10.5 CD7 1.058 6.9 2.486 6.8 2.416 20.2 15.933 22.2 Mean 7.214 7.329 10.286 8.24 SD 0.367 1.828 5.208 6.687 CV,% 5.1 24.9 50.6 81.1 *Qb : The original flood peak Comparison of Concentrated Developments effect on flood peak (CDl to CD7 in Table 7.13 ) with the BASE development for smaller catchments of Cranebrook and Jamison Park does not show any trends when new development approaches the measuring station at the outlets. However, in larger catchments of Maroubra and Fisher's Ghost Creek there is an increasing trend in flood peak when new development approaches the outlet. Regardless of catchment size, the average increase in flood peaks is 8.25% for 3% increase in urbanisation in the catchments. However, the variations of the increase in flood peaks, CV. values in Table 7.13, in larger catchments ( Fisher's Ghost Ck and Maroubra) are much greater than those in smaller catchments (Cranebrook and Jamison Park) which imply the scale-dependent of the effect of spatial distribution of new urban development on flood peak. The effect of both Separated Concentrated and Uniform Developments are shown in Table 7.14. Despite the equal percentage of urbanisation for two patterns, 15% of total catchments area, the average increase in flood peaks for SCD is 33%, while for UD is 38%. There is no specific trend between the increase in flood peak and catchment size. Separated Concentrated Development is the pattern that usually happens in urban development/consolidation. As a rule of thumb the increase in percentage of flood peak is twice as much as that of the increase in urbanisation. It should be noted that the urbanisation effects on flood peak is a very complex phenomenon and much more Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-45 investigations and data are needed to elucidate the process. However, using mathematical rainfall/runoff models like MOUSE looks promising in this avenue. Table 7.14. The effect of overall urbanisation in catchments on flood peak. Trial Cranebrook, Jamison Park, Maroubra, Fisher's Ghost Creek. 11.5 ha 22.0 ha 57.8 ha 214. 3 ha Qp, (Qp-Qb)/Qb, Qp, (Qp-QbVQb, Qp, (Qp-Qb)/Qb, QP, (Qp-Qb)/Qb, m3/s % m3/s m3/s % m3/s % BASE 0.990 0.0 2.328 0.0 2.01 0.0 13.036 0.0 SCD 1.349 36.26 3.139 34.84 2.662 26.70 17.846 36.90 UD 1.372 38.59 3.189 36.98 2.965 41.10 18.898 44.97 7.5. Summary Despite MOUSE being a distributed model and hardly needing calibration, the acquisition of detailed data necessary for application of the model is difficult. Calibration is an alternative which can be used to achieve some knowledge about the catchment parameters. MOUSE was calibrated for four catchments on impervious area runoff events. Three indices were considered for calibration of the model including; volume, flood peak and time to peak. Volume and flood peak were simulated by adjustment of HRF while for hydrograph shape, the Manning roughness coefficient was adjusted. Simulation of volume was thefirst concern in this study because of runoff coefficient estimation. However, flood peaks and time to peak were simulated as well. To prevent interaction among four model parameters including; IL, TAD, HRF and n, thefirst two were kept constant for all events and in all the catchments. IL was set equal to 1 mm which is the norm for urban areas, and TAD No. 1 which assumes a linear relationship between isochrones and area of catchment was used. HRF and n were considered for calibration of hydrographs. Table 7.15 shows the summary results of HRF and 'n 'for all the catchments. Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-46 Table 7.15. The summary results of calibration of MOUSE - Level A Catchment Area, IMPV D.CIMPV HRF, HRF, npipes D.C.IMPV/IMPV Km2 IMPV COMB. Maroubra 0.57 0.29 0.19 0.59 1.0 0.022 0.66 Jamison Pk 0.22 0.35 0.32 0.80 1.0 0.012 0.91 FGC 2.14 0.27 0.31* 0.85 1.0 0.014 1.00 Cranebk 0.115 0.38 0.21 0.50 1.0 0.014 0.57 * Estimated by rainfall runoff analysis by Bufill(1989) Comparison of HRF with the percentage of directly connected impervious areas of catchments shows a relation between them. For example, in Jamison Park and Fisher's Ghost Creek where directly connected impervious area percentages are close to the total imperviousnesses (IMPV), HRF's approach one. Bearing in mind that in Fisher's Ghost Creek with HRF equal 1.00, there is still some runoff resulting from stream bank margins. In Maroubra total imperviousness and the directly connected impervious areas equal to 0.29 and 0.19 are calculated based on the input data files of MOUSE. According to these percentages the HRF for Maroubra is in accordance with ratio of the directly connected areas to the total imperviousness. Directly connected impervious areas of the other catchments are taken from previous research which was measured by using small scale maps (Chapter 3). A general conclusion of the HRF study could be stated as within the limitations of the data, the best estimate of HRF is percentage of directiy connected impervious areas to the total impervious areas as follows: HRF = 0.875 *(DCIMP/IMP) Total impervious areas ( IMP) include both connected and disconnected impervious areas in urban catchments. The roughness coefficient was selected based on system age. The oldest and youngest network systems are Maroubra and Cranebrook respectively. The variations of 'n' for the pipe systems from 0.022 to 0.014 reflects the age and materials of the systems. The time step of Dynamic Wave solution was found in the range 2-4 seconds for the catchments. According to the built in stability criteria in MOUSE, Courant Condition, a Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-47 time step greater than 4 seconds caused instability of solution in some parts of the systems. MOUSE automatically creates computational nodal points according to the full running velocity of pipe/channel flow, and users should provide the model with a suitable time step of simulation to achieve a stable solution. However, the above time step increases the computation time considerably. Accuracy of rainfall/runoff data, especially synchronisation, is an important issue in modelling. During simulation a few faulty cases were diagnosed with synchronisation problems, eg. wrong and inconsistent peak flows. Time increment of rainfall is another factor which affects simulation results. Discretization of rainfall should be consistent with the size and correspondingly the time of concentration of catchments. In Cranebrook catchment the time increment of rainfall was in the order of 3-5 minutes which is large for this catchment of 11.5 hectares and Tc of 6.0 minutes. The relation of computed and observed volume, flood peak and time to peak was studied using simple regression equations (Table 7.16 to 7.18). Comparison of the simulated and the observed volumes showed an unbiased scatter on or around the line of equal value. The coefficient of determinations denotes that at least 94% of the volume variations could be explained by the model. The closeness of the simulated and the observed time to peak of hydrographs shows that the magnitude of roughness coefficient is reflected correctly by the model. Generally results for large floods were found to be more satisfactory than small or medium ones. Normally during combined events catchments receive a lot of rain which cause large flood peaks over impervious areas, so accurate estimation of large flood peaks on impervious areas is an important point to be considered. The capability of MOUSE model in prediction of land use changes and its effect on flood peak was investigated. The calibrated model on catchments was used to predict the increase in flood peak due to the incorporation of three different patterns of new developments in existing urban areas. The results achieved looked promising, however, more investigations are needed in this regard. Chapter 7 Simulation of Impervious Area Runoff And Urbanisation Effects Using MOUSE 7-48 Table 7.16. Results of volume simulation Catchment Equation R2 No. of events Maroubra Y=0.49+0.93X 0.97 33 Jamison Park Y=0.42+0.94X 0.95 10 Fisher's Ghost Ck Y=-0.28+O.98X 0.94 12 Cranebrook Y=0.42+0.83X 0.97 10 Table 7.17. Results of flood peak simulation Catchment Equation R2 No. of events Maroubra Y=-0.22+1.07X 0.66 33 Jamison Park Y=0.13+0.7 IX 0.85 10 Fisher's Ghost Ck Y= 1.25+0.6 IX 0.34 12 Cranebrook Y=0.08+0.90X 0.19 10 Table 7.18. Results of time to peak simulation Catchment Equation R2 No. of events Maroubra Y=13.08+0.90X 0.97 28 Jamison Park Y=-2.28+0.99X 1.00 9 Fisher's Ghost Ck Y=9.04+0.99X 1.00 12 Cranebrook Y=3.41+0.87X 0.88 10 CHAPTER EIGHT MODIFICATION OF MOUSE MODEL IN COMBINED RUNOFF SIMULATION Chapter 8 Modification of MOUSE Model in Combined Runoff Simulation 8-1 CHAPTER EIGHT 8. MODIFICATION OF MOUSE MODEL IN COMBINED RUNOFF SIMULATION A combined event is formed when runoff is generated on both impervious areas, eg roads, and pervious areas, eg grass land. The resulting hydrograph is complex, because of the unknown percentage of pervious area runoff and also different lag times for runoff from these two sources. Although urban catchment flooding is largely due to the existence of impervious areas, there are many situations where the contribution of pervious areas is considerable. Soil type is a typical parameter which should be considered in runoff generation in urban areas. The percentage of urbanisation could affect the inclusion of pervious areas in runoff estimates. In congested urbanised regions, eg commercial areas, the pervious areas are scarce, so they can be excluded from the runoff estimate. The design return period of the events may also affect the inclusion or exclusion of pervious areas; for instance, for frequent events in the range of 1 to 2 years, impervious areas are much more active than pervious areas in runoff production. Vital infra-structures like highway culverts, runway drainage systems and trunk drainage are designed based on 100 year return period storm and total catchment area, so a combination of pervious and impervious areas within urban catchments should be considered in these designs. Climatological conditions are another factor which affect the involvement of pervious areas in the runoff generation process. At the beginning of the investigation into urban area flooding in Australia, two major characteristics of these regions were emphasised- variability of soil type and climatological conditions (Aitken 1975). With reference to Chapter 2, a few attempts have been made to include pervious areas of urban catchments in the rainfall-runoff models (OLoughlin 1988). Most of the models including RAFTS, ILSAX, SWMM and MOUSE employ infiltration equations to estimate rainfall excess over pervious areas of urban catchments. RORB and WBNM use the method of initial loss and loss rate. Chapter 8 Modification of MOUSE Model in Combined Runoff Simulation 8-2 8.1. MOUSE Runoff Module- Level B Combined event simulation by MOUSE should in theory be carried out using Level B of the runoff module ( Chapter 2 section 2.7.3.1 ). Besides evaporation data, the estimates of wetting storage, depression storage, are required for impervious, semipervious and pervious areas. With both semipervious and pervious areas input of initial and ultimate infiltration magnitude are required. The roughness coefficient or Manning number should be introduced for each area. Table 8.1. shows the global values of required input data at this level. Application of Level B is very rare because of the high volume of required data (Harremoes et al. 1993). Table 8.1. Global input values of hydrologic level B in MOUSE (DHI 1988) Impervious area Semipervious area Pervious area Parameter Sloping Flat roof Spreaded Dense Pervious area roof with/without vegetation Evap (m/s) 2.00E-8 2.00E-8 2.00E-8 2.00E-8 2.00E-8 Wetting(m) 5.00E-5 5.00E-5 5.00E-5 5.0OE-5 5.00E-5 Storage(m) _ 1.00E-3 1.50E-3 1.50E-3 5.00E-3 Init. Inf (m/s) - - 8.00E-7 8.00E-7 2.00E-5 Ultim. Inf(m/s) _ - 8.00E-7 8.00E-7 3.00E-6 Exponent (l/s) _ _ 0 0 1.50E-3 Manning No. 75 75 75 75 75 (mJ/3 s-!),(l/n) Assuming the availability of data, the calibration of Level-B in MOUSE model is an absolutely difficult task. If the magnitude of evaporation, wetting and Manning No. are assumed constant in Table 8.1, there are still 9 parameters remained to be calibrated. These parameters are storages, start and end infiltration (Init. Inf. and Ultim. Inf.) and exponent of infiltration equation. The storage parameters should be calibrated for impervious, semi-impervious and pervious areas while the rest of parameters belong to both semi-impervious and pervious areas only. If extent of semi-impervious areas is assumed zero and also storage of impervious areas is assumed negligible, there are still 4 parameters to be calibrated for pervious areas. These parameters are storage, start and end infiltration and exponent of infiltration equation ( Horton's equation). These Chapter 8 Modification of MOUSE Model in Combined Runoff Simulation 8-3 parameters have interaction and the real magnitude of them is not easily determined. Different combination of these parameters can produce the same results at the catchment's outlet, however, there is no guarantee for physical meaning of the parameter values. For example the calibrated parameter's magnitude in most applications of rainfall runoff models has no physical interpretation, and finding optimum values of parameters for catchments are found to be very difficult (Johnston and Pilgrim 1976). 8.1.1. Limitation of Parameters Range in MOUSE Regarding Australian conditions the magnitude of runoff from pervious areas is significant. The maximum allowed pervious area storage in MOUSE model is 10 mm which is very low in Australian catchments conditions. Resulting from calibration of a rainfall-runoff model called SFB, surface storage capacity for grassland cover is estimated equal to 70 mm in Australian rural catchments ( Boughton 1984). Another limitation is related to the exponent of Horton's equation ( k ). The range of this parameter is bound between 0-0.01. The real exponent of this equation for catchment soil type is not known, but a common figure of 2 which is recommended in ILSAX model manual for all soil types ( O'Loughlin 1988). Although the magnitude of ' k ' depends on the unit used for infiltration rate (mm/hr in ILSAX and mm/s in MOUSE), its upper value (0.01) seems very small. 8.1.2. Associated Land use with Level-B in MOUSE Besides the limitation in the range of parameters and interaction between their magnitudes, the land use classification in Level-B is too detailed to be practical. Although the classification attempts to present a real picture of urban catchments physics, it is almost impractical to be used by designers or even researchers. In the Level- B seven different land use categories are definable including: steep roof, flat roof, concrete/asphalt, semi-impervious with large and small infiltration, pervious planted and finally pervious unplanted. This comprehensive land use is introduced in this model while many models are still seeking suitable storage levels/delay time on impervious and pervious areas of urban catchments. Chapter 8 Modification of MOUSE Model in Combined Runoff Simulation 8-4 8.1.3. Simulation of combined hydrograph in MOUSE Level-B After calculation of excess rainfall ( using water balance model ), the MOUSE model at level B lumps the runoff responses of pervious and impervious areas. The lumped runoff will be transformed to a hydrograph using kinematic wave ( Chapter 2, Sec. 2.7.3). The separate simulation of pervious and impervious areas of urban catchments as two parallel storages have already been discussed and emphasised by Wittenberg (1975), Diskin et al. (1978), Diskin (1980) and Bufill (1989). The separate simulation is used in ILSAX model as well ( Chapter 2, Sec. 2.6.2.1.1). 8.1.4. Application of Level-B Bearing in mind parameters limitations, interactions and scarcity of application of the Level-B model (Harremoes et al. 1993), the performance of the model was examined in a real situation. Regarding the necessity of pervious area runoff simulation in Australian conditions ( Aitken 1975, O'Loughlin & Goyen 1990 ), the model was applied to the Jamison Park catchment using Level-B. Assuming semi-impervious areas of the catchment are negligible, two types of land use including steep roof and asphalt/concrete for impervious part and one type of land use for pervious part were considered. To obtain the extent of steep roofs, the area of streets was scaled off the catchment map and was subtracted from total impervious area of each subcatchment. To minimise the interaction between parameters, the magnitude of storage was fixed at 10 mm. The exponent of infiltration equation was set at the highest allowed in the model equal to 0.01. The wetting parameters for both impervious and pervious were considered equal to 1 mm. Although this value is reasonable for impervious area, it is low for pervious area. Evaporation was set equal 2 mm/day which is not very important during a rainfall event. The remaining parameters, initial and ultimate infiltration, should be calibrated by matching observed and simulated volume of runoff. To calibrate the runoff model, a wide range of initial infiltration ( 3 to 210 mm/day) and ultimate infiltration ( 1 to 70 mm/day) was considered. The events were split up and the Runoff-Module was run using the above range for thefirst part of events ( Table 8.2 ). Chapter 8 Modification of MOUSE Model in Combined Runoff Simulation 8-5 To select the best match between the observed and simulated runoff volumes,the sum of squares index ( SSQ) was calculated for each trial using the following formula: SSQ=I (VOL^. - VOL ^ 2 For the trial with the initial infiltration of 45mm/day and ultimate infiltration of 15 mm/day the SSQ was minimum, so it was selected as the optimum for this catchment (Table 8.2). It should be mentioned that optimisation of parameters could be achieved using different sets of initial and ultimate infiltration values and the real value of these parameters are not really known. This is a common problem in rainfall-runoff modelling with large number of parameters (Johnston and Pilgrim 1976, Boughton 1984 ). Table 8.2. Calibration of Runoff Module Level B on Jamison Park Catchment Date Obs Tl T2 T3 T4* T5 T6 T7 T8 T9 T10 Til T12 T13 210383 29.5 38.9 38.3 37.7 37.0 36.2 35.5 34.8 34.2 33.5 32.8 32.1 30.9 29.6 140284 6.44 7.69 7.49 7.28 7.06 6.84 6.65 6.48 6.36 6.24 6.13 6.04 5.91 5.81 150284 8.12 3.43 3.43 3.43 3.43 3.43 3.43 3.43 3.43 3.43 3.43 3.43 3.43 3.43 270784 64.23 64.5 63.0 61.3 59.3 57.4 55.4 53.7 52.3 50.7 49.1 47.5 45.0 42.7 071184 15.3 23.7 23.5 23.2 22.9 22.6 22.3 22.0 21.7 21.4 21.1 20.9 20.4 19.9 131285 20.19 25.7 25.3 25.0 24.5 24.1 23.7 23.3 23.0 22.7 22.3 22.0 21.4 20.8 SSQ - 212. 195. 184. 178 183. 197. 215. 234. 264. 300. 342. 423. 508. * Selected (Initial infiltration 45 mm/day , ultimate infiltration, 15 mm/day) The results of the calibrated runoff model were introduced to the hydraulic module of the model and simulated hydrographs were obtained at the outlet. Table 8.3 indicates the results for volume, flood peak and time to peak of both simulated and observed events. The results of simulation are presented in Fig. 8.1 for both flood peak and volume respectively. Linear equations, forced to the origin, are fitted to the computed and observed values for both flood peak and volume. The line of equal values, dashed lines, are also drawn to compare the fitted linear lines with them. The comparison of fitted line with the line of equal value shows underestimation by MOUSE for flood peaks (Fig. 8.1.-a). The same comparison for volume indicates overestimation by MOUSE ( Fig. 8.1.-b). Chapter 8 Modification of MOUSE Model in Combined Runoff Simulation 8-6 Table 8.3. Calibration results of combined events using MOUSE Level B - Jamison Park Date Qp, m3/s Vol, mm Tp, Min. Time* Obs. Com. Obs. Com. Obs. Com. Min. 210383 1.023 1.039 29.50 36.96 305 318 410 140284 0.688 0.689 6.44 7.06 20 21 230 150284 0.484 0.175 8.12 3.43 260 267 410 270784 j 1.544 1.113 64.23 59.32 730 735 1080 071184 1.399 1.822 15.30 22.88 21 18 234 131285 1.920 1.433 20.19 24.53 70 84 330 141285 0.793 0.58 9.13 4.69 30 42 250 121186 0.349 0.54 30.63 37.97 1870 1863 2400 111187 0.765 0.834 14.83 27.81 300 306 690 010188 1.139 0.763 5.62 5.75 10 15 190 030488 0.779 0.529 9.26 11.90 490 483 660 080488 0.507 0.465 14.93 14.49 100 147 790 290488 1.839 1.22 69.64 83.61 1280 1323 1540 * Simulation time Modification of MOUSE Model in Combined Runoff Simulation 8-7 y = 0.8215x tf = 0.654 0.5 1 1.5 Obs. Qp, CMS (a): Flood Peak • MOUSE y = 1.1185x R2 = 0.9332 Linear uu / / 80 - A / 60 / / o A* > 40 // E o 20 o n -j^ 1—I 1 1 1 0 20 40 60 80 100 Obs. Vol., mm (b) : Volume Fig. 8.1. The results of simulation using MOUSE Level-B in Jamison Park Chapter 8 Modification of MOUSE Model in Combined Runoff Simulation 8-8 8.2. Investigation of Alternative Solutions For Combined Events Regarding the limitations of the MOUSE model in simulation of combined events and also to avoid the interaction of parameters an attempt was made to modify the runoff model for simulation of pervious area runoff. The main objective of this modification is to estimate excess rainfall without using many parameters like soil storage, initial and final infiltration etc. This method should be easy to use and applicable in ungauged catchments. Besides infiltration curve method, four methods are presented in ARR1987 for excess rainfall calculation ( Fig. 8.2). Methods (ii) and (iii) are most appropriate in case of large storms when Horton process is dominant. Method (iv) (infiltration curve, eg Horton's equation in MOUSE model) is the common method used in rainfall-runoff modelling. The associated interaction with infiltration equation parameters were already discussed in this chapter. Method (v) is used in the USA according to the hydrological conditions and soil groups, however the results are very sensitive to the selection of curve number (ARR1987). Method (iii) has been applied with reasonable success in some Australian urban/rural hydrologic models like WBNM, RORB and RAFTS. Over the time, application of this method has provided useful information about some aspects of runoff and excess rainfall in Australian catchments. According to the application of this method ARR87 provides a guide to the designer in selecting Initial Loss and Continuing Loss based on soil type and the geographic location of the catchments analysed (ARR1987, Chapter 6). In case of gauged catchments these parameters, IL and LR, can be calculated using the recorded hydrograph-hyetograph. The median values of losses are used in design situation ( ARR1987). Implementation of this method in the model was not possible because the lack of source code. However, the main theme of this research is focusing on runoff coefficient. Among loss models presented in Fig. 8.2, method (i), constant fraction, is recommended by ARR87 to be used in case of saturated overland flow from part of a catchment. The proposed method of excess rainfall calculation in the present study is a combination of methods (i) and (iii) which is shown in Fig. 8.3. The constant fraction in this method has the same concept of volumetric runoff coefficient or HRF in MOUSE model. Chapter 8 Modification of MOUSE Model in Combined Runoff Simulnrin., X.Q (i) Constant fraction ' (jj) Constant loss rate ' Curves tor different catchment conditions Rainfall (v) U.S. SCS. relation Fig. 8.2. Loss models used lo estimate rainfall excess ( From ARR1987) c ' (vi) Initial loss-Constant fraction Fig. 8.3. The proposed loss model used in MMOUSE Chapter 8 Modification of MOUSE Model in Combined Runoff Simulation 8-10 Using this concepts corresponds the main theme of the present research which is the study of runoff coefficient in urban catchments. It is worth noting that in case of modelling and estimation of excess rainfall using the rate runoff coefficient is not correct. As it was investigated both deterministically and statistically in chapters 4 and 5, rate runoff coefficient is only used for estimate of flood peaks. Volumetric runoff coefficient should be used to estimate excess rainfall. Refer to Chapter 4, section 4.3.2, volumetric runoff coefficient is calculated based on runoff and rainfall depths and only includes the effect of API. In case of wet conditions this coefficient is high and approaches one and in case of dry condition it is close to zero when there is negligible runoff from pervious areas. If this concept is implemented in the model, the extent of contribution of pervious areas of urban catchments in every climatological conditions will be recognisable regarding rainfall characteristics and soil types of the catchments. The conjunction of a simple concept like Volumetric Runoff Coefficient with a distributed hydraulic unsteady routine model like MOUSE provides the practitioners with the opportunity to design/analyse urban drainage systems with minimum hydrologic data available. Furthermore, the application of the method in urban catchments can assist in achievement of global values of volumetric runoff coefficient in urban catchments with different soil types. Incorporation of the Volumetric Runoff Coefficient was found possible by using the same concept used in Level A in the MOUSE model ( Chapter 7). However, some modifications are required to cater for the extent and time of entry of pervious areas. 8.2.1. Proposed Solution For Combined Events In the present study the MOUSE model is modified ( MMOUSE) for both excess rainfall calculation and separate simulation of pervious and impervious areas as two independent storages in case of combined runoff. The calculated runoff hydrographs from two storages are added at the subcatchments outlet manhole. Simulation of combined events could be performed much more simply by introducing a Hydrological Reduction Factor for pervious areas of the catchment. In the case of Chapter 8 Modification of MOUSE Model in Combined Runoff Simulation 8-11 combined events, the total impervious areas will contribute regardless of whether they are directly or indirectly connected. As a result, the HRF of impervious areas would be taken as equal to one and the percentage of pervious land would be calculated by subtraction of the total imperviousness fraction from 100 for each subcatchment. Initial loss can be considered in this approach as different from that of impervious areas. A value of 3 mm was adopted for EL on pervious surfaces. HRFPER is the only parameter which needs to be calibrated. This parameter could be interpreted physically in two ways: firstly, all of the pervious areas contribute runoff and represents the percentage of excess rainfall, and secondly a percentage of the pervious areas become saturated and transform all rainfall to runoff, and HRFPER represents that percentage. According to the recently introduced concept of source areas in catchments and their role in runoff production (Mein and O'Loughlin 1991) the probability of occurrence of the second phenomenon is high; however till now there is no evidence to support this idea in urban catchments. Formulation of the above process for combined events runoff could be made as follows: Total Runoff = Impervious Area Runoff + Pervious Area Runoff Impervious Area Runoff = A* IMP/100 * HRF,MP * (Rain-ILiMp) Pervious Area Runoff = A * (100-IMP)/100* HRFPER *(Rain - ELPER) Where: A : total area of subcatchment IMP: imperviousness percentage, total HRFTMP : HRF of impervious areas = 1.00 HRFPER : HRF of pervious areas ELiMP : initial loss of impervious areas ELPER : initial loss of pervious areas Another factor which should be accounted for is related to the delay time of impervious and pervious area runoff. Generally impervious areas deplete their runoff more rapidly than pervious areas. During combined events runoff from both impervious and pervious surfaces occur together to produce the total hydrograph. The time difference could be Chapter 8 Modification of MOUSE Model in Combined Runoff Simulation 8-12 introduced by considering different times of concentrations for impervious and pervious areas, which is discussed in the next section. 8.2.2. Implementation of the Proposed Method To implement the proposed method, Level A of the hydrologic module of MOUSE is used as follows: a. Introducing a fictitious manhole right over the impervious area's real manhole. The real manhole is used as the outlet of the impervious part of the catchment which receives runoff according to the HRF, EL and time of concentration of the impervious area. The fictitious manhole acts as the outlet of the pervious part of the subcatchment which receives runoff based on the area, EL and time of concentration of this part. In other words for every subcatchment we have two separate parts, pervious and impervious, which generate runoff differently. b. Introducing a fictitious conduit for the pervious part of each subcatchment. The beginning of this conduit is the dummy manhole and the ending is the real manhole. Bearing in mind that the dummy manhole is placed right over the real one, the length of this conduit is practically zero. However, MOUSE extends the length of pipes less than 15 metres to 15 to carry out the numerical computation. To minimise the effect of flow routing inside the dummy conduit the diameter selected should be large; however, the length is too short to have a significant effect on flow attenuation. c. The pervious part of each subcatchment is introduced to the model as a separate fictitious subcatchment along with the relevant IL, HRF and time of concentration. Pervious percentage magnitudes are entered into the same column as impervious percentages in the catchment datafile. HRF in the hydrologicfile wil l be calibrated for pervious areas using the observed combined hydrographs. d. Time of concentration is estimated using overland flow time formula of Ragan and Duru (1972). The roughness coefficient in this formula is selected based on lawn or bare soil depending on subcatchment land use. Chapter 8 Modification of MOUSE Model in Combined Runoff Simulation 8-13 8.3. Catchments Analysed Obviously urban catchments with observed combined events data could be used in calibration of HRFPER with the proposed method. Among the 4 catchments under study, Maroubra had no recorded combined events and the Cranebrook data of combined events were found to be unreliable. The method was applied to two catchments, Jamison Park and Fisher's Ghost Creek, in which combined events make up almost 50% of those recorded (Chapter 3). 8.3.1. Jamison Park Among combined events of this catchment, 13 events in which the contribution of pervious areas was clear were selected for simulation. Time of concentration of pervious subcatchments was estimated as in Chapter 7, section 7.3.2.1., but with a different magnitude of roughness coefficient. The range of the roughness coefficient associated with Ragan and Duru's (1972) formula for overland flow travel time over lawns is given as equal to 0.17-0.48 (ARR87). The lower boundary of the surface roughness was used for pervious areas time of concentration estimates. Data files were prepared according to the outline presented in the model implementation in section 8.2.2. Catchment, manholes, conduits and hydrology files are presented in Appendix B.fTables B-9 and B-10). Reference to Table B-10, times of concentration of pervious areas is about 5 times as much as those of impervious areas. EL for pervious areas is assumed to be equal to 3 mm compared with 1 mm for impervious areas. For impervious subcatchments thefinal result s of calibration of the model, presented in Chapter 7, were used including pipe roughness coefficient and IL. Although HRFIMP was found to be equal to 0.80 to simulate impervious areas runoff, in combined events simulation it was taken to be equal 1.00. The reason for this increase is that the indirectiy connected impervious areas produce runoff during combined events. Calibration of the model using the above data file was carried out by trying to produce simulated volume as close as possible to the observed by varying HRFPER while HRFIMP was kept constant and equal to 1.00. After duplicating volume by the runoff module the results were introduced to the pipe flow model. The solution method, time step and Chapter 8 Modification of MOUSE Model in Combined Runoff Simulation 8-14 Boundary Conditions, B.C., were the same as those for impervious runoff events simulation. 8.3.1.1. Comparison of MOUSE and the proposed method To compare the results of the proposed method ( Modified MOUSE) with the results of the original model for simulation of combined events the proposed method was calibrated on thefirst si x events of Table 8.3 and the average of HRFPER was used to verify the remaining seven events (Table 8.4). For each event, HRFPER was calibrated individually to match the simulated and observed volumes of runoff. After duplicating the observed volume by the runoff module, the simulated hydrographs were introduced to the pipe flow model. The results are presented in Table 8.4. for volume, flood peak and time to peak. For all events HRFiMP was kept constant and equal to 1.00. Using the modified MOUSE ( MMOUSE), comparison of simulated flood peaks and volumes with the observeds are presented in Fig. 8.4. Similar to the comparison made for MOUSE model in section 8.1.4., the MMOUSE indicates better results in simulation of both flood peaks and volumes. Furthermore, MMOUSE uses just one parameter of HRFPER and the calibration of model is very time efficient when compared with the operations indicated in Table 8.1 for MOUSE model. Chapter 8 Modification of MOUSE Model in Combined Runoff Simulation 8-15 Table 8.4. Calibration results of combined events using MMOUSE - Jamison Park 3 Date Qp, m /s Vol, mm Tp, Min. HRFPER Time*, Obs. Com. Obs. Com. Obs. Com. Min. 210383 1.023 0.965 29.50 29.40 305 315 0.33 410 140284 0.688 0.962 6.44 6.41 20 15 0.08 230 150284 0.484 0.508 8.12 8.09 260 265 0.85 410 270784 1.544 1.230 64.23 63.40 730 735 0.74 1080 071184 1.399 2.140 15.30 15.34 21 18 0.17 234 131285 1.920 1 1.649 20.19 20.20 70 85 0.36 330 141285 0.793 1.00 9.13 7.45 30 43 0.42 250 121186 0.349 0.53 30.63 37.92 1870 1856 0.42 2400 111187 0.765 1.232 14.83 27.75 300 304 0.42 690 010188 1.139 1.399 5.62 8.94 10 13 0.42 190 030488 0.779 0.791 9.26 14.68 490 483 0.42 660 080488 0.507 0.778 14.93 16.32 100 93 0.42 790 290488 1.839 1.502 69.64 64.50 1280 1323 0.42 1540 Chapter 8 Modification of MOUSE Model in Combined Runoff Simulation fi-1' y = 1.0348x • MMOUSE R2 = 0.4849 - Linear 2.5 - 7 • / • / 2 y yy CO vyv p yV • 1.5 /y • o„ • A On • • E 1 oo *v 0.5 *>A • • 0 —i i •I—— -i , - 0.5 1 1.5 2 2.5 Obs. Qp, CMS (a): Flood Peak y = 1.0591X R2 = 0.92 100 • E E o > E o O 0 20 40 60 80 100 Obs. Vol., mm (b): Volume Fig. 8.4. The results of simulation using MMOUSE in Jamison Park Chapter 8 Modification of MOUSE Model in Combined Runoff Simulation 8-17 8.3.1.2. Simulation results The comparison of simulated volume, flood peak and time to peak with those observed is presented in both Table 8.5 and Fig. 8.5. The scatter of points around the line of equal value for flood peak is random. The coefficient of determination shows that almost 70% of variations of flood peak could be explained by the proposed method. The high correlation between simulated time to peak of the hydrograph and the observed (Fig. 8.5.c) denotes that the estimate of the subcatchment's time of concentration is fairly reasonable and furthermore the calibrated roughness coefficient of the pipe system is still valid for combined events. The other reason for the close agreement of the simulated time to peak with the observed is the unsteady pipe flow simulation which is much closer to reality than steady flow assumption. The HRFPER value varies from 0.02 to 0.85 in this catchment for simulated events. The latter value shows that 85% of pervious areas are active during the rainfall, or alternatively that 85% of incoming rainfall changes to runoff. The relation of HRFPER and rainfall, volumetric runoff coefficient and API is discussed in Chapter 9. Two samples of simulated and observed hydrographs are shown in Fig. 8.6. The rest of the hydrographs are presented in Appendix C. Chapter 8 Modification of MOUSE Model in Combined Runoff Simulation 8-18 Table 8.5. Simulation results of combined events using MMOUSE- Jamison Park 3 Date Qp, m /s Vol, mm Tp, Min. HRFPER Time*. Obs. Com. Obs. Com. Obs. Com. Min. 210383 1.023 0.965 29.50 29.40 305 315 0.33 410 140284 0.688 0.962 6.44 6.41 20 15 0.08 230 150284 0.484 0.508 8.12 8.09 260 265 0.85 410 270784 1.544 1.230 64.23 63.40 730 735 0.74 1080 071184 1.399 2.140 15.30 15.34 21 18 0.17 234 131285 1.920 1.649 20.19 20.20 70 85 0.36 330 141285 0.793 1.095 9.13 9.11 30 45 0.66 250 121186 0.349 0.453 30.63 30.46 1870 1855 0.23 2400 111187 0.765 0.829 14.83 14.69 300 305 0.02 690 010188 1.139 1.083 5.62 5.56 10 15 0.03 190 030488 0.779 0.537 9.26 9.27 490 485 0.05 660 080488 0.507 0.708 14.93 14.97 100 95 0.34 790 290488 1.839 1.536 69.64 69.60 1280 1325 0.50 1540 * Simulation time Chapter 8 Modification of MOUSE Model in Combined Runoff Simulation s/y (a) volume y - o a».o ;•• r"2 - o.sa Wl com. Op, mS/. s.o 2.6 _ - „ / 2.0 / y m 1.6 y m m LO m 0.6 ^^ m my 0.0 1 1 ' 1 ' 1 ' 1 ' 1 O.O 0.0 LO 1.6 2.0 2.6 3.0 Ob*. Op, md/a (b) flood peak (c) time to peak Fig. 8.5. Comparison of observed and computed combined events for Jamison Park using MMOUSE-(a) volume, (b) flood peak and (c) time to peak 8 Modification of MOUSE Model in Combined Runoff Simulation 8-2<'> 200 *10' "vV"^w~JLv-'Yl-rr' £ <=> T JAMISON PARK DRTE:290US8 az CD OO OBS. Cd COM. B CM CO C3 CO i —' ED CD _ CD o 40 80 120 160 200 TIME - Min *1DJ Fig. 8.6. Combined events simulation using MMOUSE -Jamison Park Chapter 8 Modification of MOUSE Model in Combined Runoff Simulation 8-21 8.3.2. Fisher's Ghost Creek All of the recorded combined events of this catchment, 11 events, were used in the simulation. Time of concentration of pervious subcatchments was estimated as for Jamison Park. Datafiles wer e prepared according to the outline presented in the model implementation section. Catchment, manholes, conduits and hydrology files are presented in Appendix B. (Tables B-ll and B-12). Initial loss for pervious areas is assumed equal to 3 mm compared with 1 mm for impervious areas. For impervious subcatchments the final results of calibration of the model, presented in Chapter 7, were used including HRFIMP and the pipe and open channel roughness coefficient. HRFIMP equal to 1.00 along with a pipe roughness coefficient of 0.014 were found reasonable to simulate impervious runoff events. Calibration was carried out in the same way as the Jamison Park catchment. 8.3.2.1. Simulation results The HRFpER was calibrated on each event individually. After duplicating the observed volume by the runoff module, the simulated hydrographs were introduced to the pipe flow model. The results are presented in Table 8.6. for volume, flood peak and time to peak. The calibrated HRFpgR and simulation time are included in the table as well. For all events HRFJMP was kept constant and equal to 1.00. Simulated volume, flood peak and time to peak with those observed are presented in Fig. 8.7. The scatter of points around the line of equal value for flood peak is random. The coefficient of determination shows that 81% of variation of flood peak could be explained by the proposed model. Similar to those of impervious areas runoff simulation in this catchment, Chapter 7, time to peak are simulated very well (Fig. 8.7.b) which shows the validity of the estimate of subcatchment time of concentration and calibrated roughness coefficient of the pipe/channel system. The HRFP£R value varies from 0.06 to 0.55 in this catchment for simulated events. Two samples of simulated and observed hydrographs are shown in Fig. 8.8. The rest of the hydrographs are presented in Appendix C. Chapter 8 Modification of MOUSE Model in Combined Runoff Simulation 8-22 Table 8.6. Simulation results of combined events using MMOUSE-Fisher's Ghost Creek 3 Date Qp, m /s Vol, mm Tp, Min. HRFPER Time*, Obs. Com. Obs. Com. Obs. Com. Min. 191081 4.75 3.76 7.83 7.84 126 129 0.25 414 021181 8.51 8.54 17.59 17.69 258 258 0.27 780 200383 9.06 7.42 40.57 41.07 414 426 0.50 1020 081184 3.60 4.94 10.77 10.76 174 180 0.40 666 091184 3.99 6.13 10.93 10.92 90 78 0.51 645 081285 5.21 5.90 5.80 5.92 39 51 0.06 210 150186 12.64 9.07 21.84 22.02 177 186 0.20 486 060886 8.68 7.89 57.32 56.74 111 132 0.55 918 241087 9.15 6.39 37.43 37.61 720 699 0.25 1503 280488 8.57 6.68 91.96 91.96 2094 2070 0.37 2640 050688 15.65 13.74 57.58 57.28 1137 1155 0.31 2343 * Simulation time Chapter 8 Modification of MOUSE Model in Combined Runoff Simulation 8-23 I M ' I ' I ' I ' I 0 10 20 30 40 to ao 70 ao ao no Ob. Vol. mm (a) volume I ' I ' I ' I ' I ' I ' I ' I ' ! ' I ' I ' I ' I ' O 1 3 3 . 0 8 7 B B IO 11 12 13 14 16 Ob.. Op. ma/a (b): flood peak y - 7.a8 •* O.BO* r"2 - IOO 3000 — A 2000 — A - y lOOO — Xy s*/ - y o — \*r I'l' 10OO 2DDO Obi. Tp. Mtn. (c): time to peak Fig. 8.7. Comparison of observed and computed combined events for Fisher's Ghost Creek using MMOUSE-(a) volume, (b) flood peak and (c) time to peak Chapter 8 Modification of MOUSE Model in Combined Runoff Simulation 8-24 W FISHERS CK. CC g DATE:021181 Cd -H OBS. COM. CM CO 5 g I => ED CD EZ! - o CJ CD 80 100 TIME - Min *1D* p—pnn^ST^ £ O era I LO FISHERS CK, DOTE-.050688 cr g-i OBS. rr- i=J COH. CO 50 100 150 200 250 TIME - Min aslD1 Fig. 8.8. Combined events simulation using MMOUSE- Fisher's Ghost Chapter 8 Modification of MOUSE Model in Combined Runoff Simulation 8-25 8.4. Summary Combined runoff events are important in Australian urban catchments and should be considered in network design. Theoretically, simulation of combined runoff events in MOUSE is performed by using infiltration equations along with evaporation and storage data; however, this practice is very data intensive. A method was proposed according to the concept of the runoff coefficient of pervious areas of urban catchments. In this method the time factor is considered by time of concentration and excess rainfall by HRF and IL for pervious areas. The pervious part of each subcatchment has the potential to be fully or partly saturated. Calibration of HRF for the pervious part of each subcatchment shows either the percentage of saturated land or the percentage of excess rainfall with respect to the total. Implementation of the proposed method is performed by use of the executive file and modification of input datafiles; however, if the source code of the model was available, programming of this part as Simplified Level B of the model could be performed. In this method fictitious manholes and conduits are introduced to the model. Calculated pervious areas, Tc and an estimate of EL are given to the model. Calibration proceeds by adjusting HRF for pervious areas. HRFQ^P is set as 1.00 and the calibrated pipe roughness coefficient of impervious areas is used in the simulation of combined events. Despite using only one parameter to calibrate runoff model, the modified model (MMOUSE) indicated better results for flood peak and volume compared with the original model ( MOUSE). There is no interaction between parameters and the calibration process is very time efficient with the proposed method. This method gives the practitioners the opportunity to use a complex model like MOUSE and a simple concept like runoff coefficient in urban drainage practice. Using this method the model can be calibrated on few observed events and be applied in design situation ( Chapter 9). The proposed method was tested on two catchments where combined runoff was frequently observed. The results for flood peak were satisfactory. Time to peak and overall hydrographs shape were simulated very well. HRFPER was calibrated individually Chapter 8 Modification of MOUSE Model in Combined Runoff Simulation 8-26 for each event, so it varies for different events which should be investigated in relation to rainfall intensity, depth and API (Chapter 9). CHAPTER NINE PERVIOUS AREA RUNOFF AND SIMULATION OF DESIGN FLOODS USING MODIFIED MOUSE Chapter 9 Pervious Area Runoff And Simulation of Design Floods Using Modified MOUSE 9-1 CHAPTER NINE 9. PERVIOUS AREA RUNOFF AND SIMULATION OF DESIGN FLOODS USING MODIFIED MOUSE In urban catchments, two types of runoff are common: impervious area runoff and combined area runoff. The former comes from the directly connected impervious surfaces of the catchment, while the latter comes from both impervious and pervious surfaces. In the latter, both directly and indirectly connected impervious areas are incorporated. The runoff ratio is an index to determine the nature of runoff; eg, how much of it is generated from impervious/pervious areas. If the runoff ratio is less than total impervious area, the runoff will be classified as impervious runoff. If the runoff ratio is greater than total impervious areas, the runoff is combined impervious and pervious. Direct observation of pervious area runoff in urban catchments is almost impossible because runoff from both pervious and impervious areas is mixed. Furthermore, the delay in commencement of runoff from these two different surfaces adds complexity to the analysis. Separate modelling of the two surfaces could shed light on the pervious area characteristics of urban catchments. The recorded rainfall of combined events can be used to calculate runoff from the impervious areas. This could be done by using the hydrologic module of MOUSE or simply by multiplication of rainfall by the total impervious area fraction. HRF in this case will be taken as 1.00. In this chapter, pervious area runoff, resulting from the subtraction of impervious area runoff from observed runoff data, is considered. The variations in the runoff coefficient and HRF of pervious areas are discussed and effective parameters are highlighted. The data for two catchments, Jamison Park and Fisher's Ghost Creek, are analysed and discussed. Design flood peaks are estimated using both MOUSE and modified MOUSE. Temporal patterns from ARR87 Vol. 2 and design rainfalls with durations equal to time of concentrations of catchments are used along with HRF for impervious and pervious areas in design flood calculation. Chapter 9 Pervious Area Runoff And Simulation of Design Floods Using Modified MOUSE 9-2 9.1. Pervious Area Runoff The hydrologic module of MOUSE model was used to calculate the impervious area runoff of combined events. In Jamison Park, the HRF of impervious area runoff during combined events was taken as being equal to 1.00 and at this stage an IL equal to 1.0 mm, as for impervious runoff simulation (Chapter 7) was adopted. The HRF for impervious area runoff simulation was calibrated as equal to 0.80 in Chapter 7, but for combined event simulation it should be increased to 1.00. The reason for this increase is the definite contribution of indirectly connected impervious area to runoff generation during combined events. The total fraction of impervious areas in this catchment is equal to 0.35 which is used in simulation. In Fisher's Ghost Creek, an EL of 1.0 mm and HRF equal to 1.00 were used to simulate the impervious area runoff incorporated in combined events. The fraction of total impervious areas in this catchment is equal 0.27 (Vale 1986), which was used in simulation. For the two catchments, Fisher's Ghost Creek and Jamison Park, the rainfall of combined events were fed into the model and the resulting calculated runoff was simply taken away from the observed. In this case the total impervious area of each subcatchment was considered as the rainfall receiving surface (HRF of impervious area equal 1.00). This practice has two advantages-firstly it uses only the impervious areas of the subcatchment which produce runoff simply; and secondly the observed combined runoff data are used to calculate pervious area runoff, which include the real effect of pervious surfaces of the catchments. It is worth noting that in this case impervious runoff volume could be simply calculated by using a fraction of the impervious area, rainfall and initial loss. The hydrologic module of MOUSE model is only useful when spatial variations of runoff within catchments are of concern. Total rainfall, runoff, pervious area and impervious area runoff are presented in Tables 9.1. and 9.2. for the catchments analysed. Chapter 9 Pervious Area Runoff And Simulation of Design Floods Using Modified MOUSE 9-3 Table 9.1. Separated impervious and pervious runoff, Jamison Park Date Total Rainfall, mm Runoff, mm PER./Total Total IMP. PER. 210383 53.8 29.5 18.55 10.95 0.37 140284 17.2 6.44 5.69 0.75 0.12 150284 11.2 8.12 3.58 4.54 0.56 270784 79.8 64.23 27.25 36.98 0.58 071184 34.8 15.30 11.87 3.43 0.22 131285 36.4 20.19 12.43 7.76 0.38 141285 13.8 9.13 4.49 4.64 0.51 121186 62.6 30.63 21.63 9.00 0.29 111187 41.6 14.83 14.24 0.59 0.04 010188 16.2 5.62 5.34 0.28 0.05 030488 25.4 9.26 8.57 0.69 ' 0.07 080488 28.0 14.93 9.48 5.45 0.37 290488 105.2 69.64 36.59 33.05 0.47 Table 9.2. Separated impervious and pervious runoff, Fisher's Ghost Creek Date Total Rainfall, mm Runoff, mm PER./Total Total IMP. PER. 191081 19.05 7.83 4.91 2.92 0.37 021181 39.57 17.59 10.49 7.10 0.40 200383 66.69 40.57 17.86 22.71 0.56 081184 21.71 10.77 5.47 5.30 0.49 091184 19.94 10.93 4.92 6.01 0.55 081285 20.02 5.80 5.17 0.63 0.11 150186 54.41 21.84 14.53 7.31 0.33 060886 86.53 57.32 23.26 34.06 0.59 241087 84.62 37.43 22.74 14.69 0.39 280488 170.55 91.96 46.46 45.50 0.49 050688 116.99 57.58 31.54 26.04 0.45 Chapter 9 Pervious Area Runoff And Simulation of Design Floods Using Modified MOUS £A -' BD Total Runoff, mm •" P»r. Area Runoff, mm * Imp. Area Runoff, mm BO 70 SO 50 40 30 20 an BE !»; 10 W\ T ' FA A A A A A A A o 10 20 30 40 so eo 70 so eo 100 110 120 Rainfall, mm (a) Jamison Park aa Total Runoff, mm •• Per. Area Runoff, mm • Imp. Area Runoff, mm I A A r O 20 4.0 SO 80 IOO 120 140 1SO ISO 2O0 Rainfall, mm (b) Fisher's Ghost Creek Fig. 9.1. Comparison of rainfall with runoff from pervious and impervious areas- (a): Jamison Park, (b): Fisher's Ghost Creek Chapter 9 Pervious Area Runoff And Simulation of Design Floods Using Modified MOUSE 9-5 A comparison of runoff from pervious/impervious areas with rainfall denotes that pervious area runoff sometimes is less, equal or even greater than impervious area runoff .(Fig. 9.1) A greater percentage of pervious area runoff emphasises the importance of these areas in fully urbanised catchments like Jamison Park and Fisher's Ghost Creek and also the necessity of incorporating them in network design in Australian urban catchments. The scatter of pervious areas runoff is considerable. This scatter could be related to rainfall intensity and Antecedent Precipitation Index (API). The variation in total runoff of combined events in urban catchments is mostly related to runoff from the pervious part. The relationship of pervious area runoff to rainfall is illustrated in Figs. 9.2 and 9.3 for the catchments. The regression equations of rainfall-runoff for the catchments are summarised in Table 9.3. The coefficients of determination show that 75% and 86% of pervious area runoff variation could be explained in terms of the recorded rainfall for Jamison Park and Fisher's Ghost Creek respectively. The rest of the runoff variation is related to other factors such as API, intensity and the temporal pattern of rainfall. The initial losses are estimated from the intersection of regression lines and rainfall axis (Table 9.3). The magnitude of initial losses in this case is equal to 15.9 mm and 7.3 mm for Jamison Park and Fisher's Ghost Creek respectively. A smaller magnitude of IL was expected for Jamison Park, because the soil type of the catchment has less infiltration capacity than that of Fisher's Ghost Creek. Furthermore, the average slope of the Jamison Park catchment is steeper than that of Fisher's Ghost Creek. These initial losses are inconsistent with the magnitude which was adopted for the pervious area runoff simulation equal to 3 mm in Chapter 8. The magnitude of regression coefficients, b values in Table 9.3, shows the percentage of pervious runoff which includes initial loss. This figure for Jamison Park is 0.37 which is bigger than that of Fisher's Ghost Creek which is equal to 0.28. The explanation for the relatively high initial loss in Jamison Park is that after 15.9 mm rainfall, pervious areas should generate runoff and a combined event will occur. However, many impervious area runoff events have occurred in this catchment with total rainfall of two to three times as much as the estimated initial loss (events dated 170383 Chapter 9 Pervious Area Runoff And Simulation of Design Floods Using Modified MOUSE 9-6 and 180887 in Table 3.7). This discussion is true for Fisher's Ghost Creek as well (See events 050383 and 240588 in Table 3.10). It is concluded that the satisfaction of initial loss is not the only factor to be considered in pervious area runoff occurrence. Factors like API and rainfall intensity are the most likely to be incorporated in the process. In other words, soil storage capacity should be satisfiedfirst an d rainfall intensity must exceed final infiltration capacity of the soil to have pervious area runoff. Noting that there is considerable scatter in Figs. 9.2 and 9.3, so the difference in IL on Jamison Park and Fisher's Ghost Creek may be a result of the scatter. ac Per. Area Runoff, mr BO y — -B.B1 *• D.37x r"2 — Q.76 40 — SO — 20 IO T^ I A A A A A A A O IO 20 30 AO SO SO 70 SO SO IOO 110 120 Rainfall, mm Fig. 9.2. Pervious area runoff and rainfall relationship- Jamison Park Chapter 9 Pervious Area Runoff And Simulation of Design Floods Using Modified MOUSE SB Per. Area Runoff, 50 y - -2.04 •*• 0.2Sx r"2 - 0.S6 40 30 20 — 10 50 100 150 200 Rainfall, Fig. 9.3. Pervious area runoff and rainfall relationship- Fisher's Ghost Creek Table 9.3. Regression equations of rainfall-runoff of the catchments Catchment Jamison Park Fisher's Ghost Creek a* b R2 EL, mm a b R2 IL, mm Per. Area -5.91 0.37 0.75 15.90 -2.04 0.28 0.86 7.30 * yV =- a + bx 9.1.1. Relation of pervious area runoff and total runoff To study the relation of pervious area runoff and total runoff during combined events, they have been plotted against each other for the catchments (Fig. 9.4). There is a good correlation between pervious areas runoff and total runoff. Reference to Tables 9.1 and 9.2. the percentage and variations of pervious area runoff are presented. In the Jamison Park catchment the pervious area runoff ratio is between 0.04 and 0.58 with an average of 0.31 (Table 9.1). This ratio in the Fisher's Ghost Creek is between 0.11 and 0.59 with an average of 0.43 (Table 9.2). On average the ratio in these two catchments with clay soil type is 37% which shows the significant contribution of pervious area runoff in total runoff. This fact should be useful in urban water quality and sediment transport studies in urban areas with heavy soil type. ChqpteJL'i- Pervious Area Runoff And Simulation of Design Floods Using Modified MOl SF 9- s ac Per. Area Runoff, mm 50 40 — 30 20 — 10 ~!~I 'I'l r~l r , 0 10 20 30 40 SO 60 70 80 Total Runoff, mm (a): Jamison Park IB Per. Area Runoff, mm 60 40 — 30 - 20 10 — I A A A ' I ' I ' I ' i ' I ' 0 10 20 30 40 SO 60 70 BO 90 100 Total Runoff, mm (b): Fisher's Ghost Creek Fig. 9.4. Relation of pervious area runoff and total runoff Chapter 9 Pervious Area Runoff And Simulation of Design Floods Using Modified MOUSE 9-9 9.1.2. Pervious area runoff coefficient Generation of runoff on pervious areas of urban catchments is a complex process which needs careful consideration. Rainfall intensity, API and soil type are the most significant parameters which are taken into account in most rainfall-runoff models. In the present study the main concern is the runoff evaluation according to the different concepts of runoff coefficient including rate and volumetric coefficient. The rate runoff coefficient was discussed in Chapter 4 for impervious and combined events. In this section the volumetric runoff coefficient is evaluated for both combined and pervious areas runoff to find out its relationship with rainfall depth and P5. The P5 is the sum of rainfall for five days before the occurrence of the event which is an index of API. In Tables 9.4 and 9.5 combined and pervious area runoff coefficients, along with calibrated HRF for pervious areas, rainfall duration and intensity and also P5 are presented for the catchments. The rainfall intensity was averaged over the whole storm duration in both tables. A pervious area IL of 3.0 mm was adopted for these catchments as recommended in the MOUSE user manual (see Chapter 8, Section 8.3.2). Table 9.4. Pervious area runoff coefficient, HRF and P5-Jamison Park Date Combined Pervious Area HRFPER Rainfall Rainfall P5, mm R.O. Coef. R.O. Coef. Duration, Intensity, minutes mm/hr 210383 0.55 0.20 0.33 410 7.88 66.0 140284 0.37 0.04 0.08 230 4.59 4.0 150284 0.73 0.41 0.85 410 1.64 21.2 270784 0.80 0.46 0.74 1080 4.43 15.6 071184 0.44 0.10 0.17 234 8.93 79.2 21 131285 0.55 L °- 0.36 330 6.62 6.8 141285 0.66 0.34 0.66 250 3.31 43.2 121186 0.49 0.14 0.23 2400 1.57 22.0 111187 0.36 0.01 0.02 690 3.62 9.0 010188 0.35 0.02 0.03 190 5.12 17.4 030488 0.36 0.03 0.05 660 2.31 16.4 080488 0.53 0.19 0.34 790 2.13 76.4 290488 0.66 0.31 0.50 1540 4.10 0.4 Chapter 9 Pervious Area Runoff And Simulation of Design Floods Using Modified MOUSE 9-10 Table 9.5. Pervious area runoff coefficient, HRF and P5-Fisher's Ghost Creek Date Combined Pervious Area HRFpHR Rainfall Rainfall P5, mm R.O. Coef. R.O. Coef. Duration, Intensity, minutes mm/hr 191081 0.41 0.15 0.25 414 2.76 22.3 021181 0.44 0.18 0.27 780 3.04 16.2 200383 0.61 0.34 0.50 1020 3.92 61.5 081184 0.50 0.24 0.40 666 1.96 88.6 091184 0.55 0.30 0.51 645 1.86 144.3 081285 0.29 0.03 0.06 210 5.72 14.8 150186 0.40 0.13 0.20 486 6.72 0 060886 0.66 0.39 0.55 918 5.66 196.2 241087 0.44 0.17 0.25 1503 3.38 70.5 280488 0.54 0.27 0.37 2640 3.88 0.4 050688 0.49 0.22 0.31 2343 3.00 2.2 The pervious area runoff coefficient is investigated in relation to rainfall depth, rainfall depth plus P5 and also rainfall intensity for the catchments. Rainfall intensity was calculated using total rainfall depth over the period of rainfall. The results are illustrated in Fig. 9.5 and 9.6. For both catchments there is no significant relation between the pervious area runoff coefficient and rainfall depth, nor rainfall intensity. Introducing P5 had no effect in Jamison Park, but increased the coefficient of determination in Fisher's Ghost Creek from 0.117 to 0.694 which shows the effect of catchment wetness on the runoff coefficient. In Jamison Park the same improvement was expected, but daily rainfall data for calculation of P5 were not available for the measuring station within the catchment. The P5 in this catchment was calculated using a nearby rainfall station in Penrith. Soil type of both catchments is clay with low infiltration capacity, so the reason for the inconsistency could be a result of data suitability. Chapter 9 Pervious Area Runoff And Simulation of Design Floods Using Modified MOl SE 9-11 3D P»r. R.O.Oo»T. I.OO _ o oo y - o.iie •. o.oozx r a - 0.112 • .eo O.70 o.eo 0.6O 0.40 0.30 0.20 • .IO O.OO (a) rainfall Kl Per H.O.Coatf. 1—'—1—'—1—'—1—'—r-'—r 0.0 20.0 40.o eo.o ao.o 100.0 120.0 140.• API-*.RalnlBll. mm (b): sum of rainfall and P5 S3 P»r. BO.CO.I 1.00 O.BO y - 0.230 - O.OIOX r"2 - O.Ola O.BO o.ro O.BO O.BO 0.40 O.BO 0.20 0.10 O.OO I ' I ' I ' ' I ' I ' I ' I ' I ' I ' Ol 2S46B7BB10 R«lr>r»H, mm/hr (c): Rainfall intensity Fig. 9.5. The relation of pervious area runoff coefficient with rainfall and P5 - Jamison Park Chapter 9 Pervious Area Runoff And Simulation of Design Floods Using ModineJ MO! SE A J ffl Parvlou. Aria R.O.C. 1.00 y - 0.17B •» O.OOIx r"2 - 0.117 O.BO O.BO 0.70 O.BO 0.60 0.40 0.30 0.20 O.IO O.OO BO IOO Rainfall, mm (a): rainfall i—'—i—'—r 100 160 2O0 260 30O APKRalnfall. mm (b): sum of rainfall and P5 IB Pvrvlou* Ara. R.O.C l.OO y - 0.276 - 0.014X 1-2 - 0.O47 o.oo D ao 0.70 o.ao O.BO 0.40 o.ao 0.20 O.IO D.OO 1 I ' ! ' I ' I ' I ' i ' I DI 2 3 4 6 B 7 Rainfall, mm/hr (c): rainfall intensity Fig. 9.6. The relation of pervious area runoff coefficient with rainfall and PS- Fisher's Ghost Creek Chapter 9 Pervious Area Runoff And Simulation of Design Floods Using Modified MOUSE 9-13 9.2. Inter-relation of HRF and Runoff Coefficient The Hydrologic Reduction Factor of pervious areas, HRFPER, calculated from the application of MOUSE to the combined events of the catchments was studied to establish a possible relation between it and the runoff coefficient. Combined and pervious area runoff coefficients were plotted against HRF (Fig. 9.7 and 9.8). A high correlation is expected between volumetric runoff coefficient resulting from rainfall runoff analysis and HRFpER from MOUSE calibration. For the pervious area runoff coefficient, the regression coefficients remain fairly constant while intercepts approach zero. For these two catchments, on average the magnitude of HRF is 1.6 times as much as the runoff coefficient of pervious areas. It should be noted that HRF and C are just slightiy different ways of expressing the same thing. The reason for high ratio of HRF to C is because C is calculated based on the ratio of pervious runoff to total catchment area while HRF is calculated by ratio of pervious runoff to pervious area of the catchment. Reference is made to Chapter 8 runoff volume calculated as follows: Pervious Area Runoff = APER * HRFPER*(Rain - ILPER) While pervious area runoff by volumetric runoff coefficient, C, is simply calculated as Pervious Area Runoff = A*C * Rain So the relation of HRF and C will be HRFPER= (A/ APER)[Rain / (Rain - ILPER) ]*C If IL is assumed zero and C is calculated based on the pervious area, the calibrated values of HRFPER will be the same as runoff coefficients. Chapter 9 Pervious Area Runoff And Simulation of Design Floods Using Modified MOUSE 9-14 S P*r. Area HHF 1.00 y - -0.82 •*• 1.81X r"2 - 0.95 0.80 — is / o.eo — / 0.40 — 0.20 /& / 0.00 l , ! , ! , ( . 1 0.00 0.20 0.40 0.80 0.80 1.00 Combined R.O.Coaf. (a) combined runoff coefficient IS Par. Aran HRF I.OO / y - -0.01 i- 1.81x r~2 - 0.87 / / 0.80 EB / /-/ / O.SO / / / BE/ / / /m 0.40 A / 0.20 / * O.OO i j | i I I O.OO 0.20 0.40 o.eo O.SO 1.00 Par. R.O.Coaf. (b): pervious area runoff coefficient Fig. 9.7. The correlation of HRF and runoff coefficient- Jamison Park Chapter 9 Pervious Area Runoff And Simulation of Design Floods Using Modified MOUSE 9-15 32 Par. Area HRF 1.00 y - -0.34 +• 1.39x r"2 - 0.93 A 0.80 O.BO — 0.40 0.20 — O.OO —| 1 1 1 1 1 1 1 p O.OO 0.20 0.40 O.BO 0.80 1 OO Combinad R.O. Coaf. (a): combined runoff coefficient i- Par. Araa HRF I.OO / y - 0.02 •+• 143x r"2 - O.Gfl 0.80 — o.eo 0.4O — 0.20 — O.OO (b): pervious area runoff coefficient Fig. 9.8. The correlation of HRF and runoff coefficient- Fisher's Ghost Creek Chapter 9 Pervious Area Runoff And Simulation of Design Floods Using Modified MOUSE 9-16 9.3. Application of Modified MOUSE in Design Flood Estimation The present study is seeking simple methods which can be used with short records to calibrate models and make them applicable in design situation. The modified MOUSE model proposed in this study has the potential to be calibrated using a few recorded events and to be applied in design flood estimation using design rainfalls and partial series temporal patterns from ARR87 Vol. 2. The approach provides practitioners with the benefits of both the simplicity of runoff coefficient and the complexity of a hydraulic model like MOUSE. 9.3.1. Temporal pattern The temporal patterns are provided in ARR87 Vol. 2 for all over Australia which is divided to eight Zones. The temporal patterns are given for return periods of less or greater than 30 years. The durations of these patterns range from 10 minutes to 72 hours. The time steps of hyetographs resulting from the temporal patterns include 5, 15, 30 minutes for short duration design rainfalls and also 1 and 2 hours for long durations. The duration of design rainfalls for the catchments was taken equal to their time of concentrations from Chapter 4. The temporal patterns relevant to the time of concentration were selected from Zone 1 and for return period of less than 30 years. (Fig. 9.9). Design rainfalls were distributed according to the temporal patterns relevant to the time of concentrations and were entered into the calibrated MMOUSE model for each catchment for design flood estimation. Chapter 9 Pervious Area Runoff And Simulation of Design Floods Using Modified MOUSE 9-1 1 2 Time Interval - S Min. (a): 10-minute 1 2 3 Tim e Interval - 5 M in. (b): 15-minute 39 40 • 28 . » 30 17 .E 20 9 n ;Jif?iV:V;'- 7 OC 10 0 •r — -'— 2 3 4 5 Time Interval- 5Min. (c): 25-minute 24.7 25 .... .18. 3 20 •\ A <-> * 15 qc H-6 c y.o 7-5 6.1 1 0 "4.8 3.3 1 5 .," . A 0 V .-A-' • .. 123456789 Time Interval - 5 Min. (d): 45-minute Fig. 9.9. Temporal patterns of design rainfalls for ARI < 30 years, Zone 1, (ARR87 Vol. 2) Chapter 9 Pervious Area Runoff And Simulation of Design Floods Using Modified MOUSE 9-18 9.3.2. Land use and simulation of design flood Reference to Chapter 5, section 5.5, it was concluded that in design flood estimation (up to 10 years return period) in the catchments with light soil type, only impervious areas of the catchments should be considered. The incorporation of pervious areas in calculation makes the flood peak overestimated in these catchments. Regarding soil type in the Maroubra catchment ( deep sandy soil), only the impervious areas were considered to be effective in producing runoff. On the other hand, for the Jamison Park and Fisher's Ghost creek catchments both pervious and impervious areas were considered in the model when the design rainfalls with return periods of more than 2 years are used. For 1- year return period rainfall, only impervious areas are involved in modelling. 9.3.3. Excess Rainfall Estimation of rainfall excess is very basic when deterministic models are used in design flood calculation. Both the overestimation or underestimation of the excess rainfall directly affect the magnitudes of design flood. Some methods for excess rainfall estimation were explained in Chapter 8. With the method of Initial Loss - Loss Rate, ARR87 recommends median of the observed values when this approach is used for estimation of design flood rainfall excess. In Section 9.2 of this chapter it was indicated that the Hydrological Reduction Factor for pervious areas is firmly correlated with runoff coefficient. The median and mean values of HRF are presented in Table 9.6 for the catchments. To be consistent with the previous research concerning rainfall excess and design floods, the median value of HRFs along with design rainfall were used in estimation of rainfall excess and simulation of design floods in the present study. Table 9.6. Median and mean values of HRFPER used in design floods simulation Catchment No. of Mean Median Land Use in Design Flood Events Simulation Maroubra 39 0.59 0.59 Impervious Areas Jamison Park 13 0.34 0.33 Impervious and Pervious areas Fisher's Ghost Ck 11 0.33 0.31 Impervious and Pervious areas Cranebrook 10 0.50 0.50 Impervious Areas Chapter 9 Pervious Area Runoff And Simulation of Design Floods Using Modified MOUSE 9-19 9.3.4. Simulation of Design Floods Design floods for return periods of 1,2,5 and 10 years were simulated using MOUSE and MMOUSE. In Maroubra catchment the original Level -A in MOUSE was used for impervious runoff simulation ( Chapter 7 ). The modified MOUSE (MMOUSE) model was used in simulation of design floods in both the Jamison Park and Fisher's Ghost Creek catchments. The results of simulation for design flood peaks are presented in Table 9.7. The flood peaks for combined events in the Cranebrook catchment are derived using the median HRFPER calculated for the Jamison Park. In Cranebrook, there was no enough data available either to calibrate HRFPER for pervious areas or to carry out flood frequency analysis. This catchment is close to the Jamison Park catchment and has the same soil type (red clay). Table 9.7. Design floods simulated using modified MOUSE model, m3/s Catchment ARLyr 1 2 5 10 Maroubra 1.614 2.043 2.865 3.123 Jamison Park 1.052/1.489* 2.013 2.592 2.894 Fisher's Ghost Ck 8.213/9.75* 13.232 15.830 17.836 Cranebrook 0.385/1.248* 1.634 1.977 2.100 * The smaller flood peaks are simulated using impervious areas of the catchments and design rainfalls and temporal patterns related to their time of concentrations. The larger flood peaks are for runoff from pervious and impervious areas and a larger time of concentrations. Design flood magnitudes resulting from flood frequency analysis, ARR87 method and modified MOUSE are presented in Tables 9.8 and 9.9 and also Figures 9.10 and 9.11. Generally speaking the proposed method (MMOUSE) has produced similar/related flows compared with the ARR87 and Frequency Analysis methods. However, this method has a deterministic base while the other methods have statistical. The advantage of modelling is that hydrographs of design floods and water levels in the drainage system are available at the catchment outlet or any other points of concern within the study area. Furthermore, with this method the effects of future land use changes on design floods Chapter 9 Pervious Area Runoff And Simulation of Design Floods Using Modified MOUSE 9-20 can be investigated and necessary measures can be taken into consideration to control flood liable areas. Table 9.8. Design flood magnitudes from frequency analysis, ARR87 and MMOUSE, m3/s ARLyr Maroubra Fisher's Ghost Creek F.A. ARR87 MOUSE F.A. ARR87 MMOUSE 1 1.270 2.130 1.614 3.860 7.580 8.213 2 1.538 2.820 2.043 8.453 10.720 13.232 5 1.758 4.280 2.865 11.697 14.380 15.830 10 1.916 4.920 3.123 14.072 17.360 17.836 Table 9.9. Design flood magnitudes from frequency analysis, ARR87 and MMOUSE, m3/s ARLyr Jamison Park Cranebrook F.A. ARR87 MMOUSE F.A. ARR87 MMOUSE 1 0.634 0.960 1.052 - 0.647 0.385 2 1.418 1.360 2.013 - 1.036 1.634 5 1.780 1.960 2.592 - 1.464 1.977 10 1.979 2.330 2.894 - 1.756 2.100 Chapter 9 Pervious Area Runoff And Simulation of Design Floods Using Modified MOUSE 9-21 - • F.A. A ARR87 • MMOUSE J—1 . t/3 6 1. . « 4 A I- I a A m u ° 2 1 : n 0 j—i—i— i _.. i. i i 01 23456789 10 ARI,yr (a): Maroubra • F.A. AARR87 • MMOUSE c« 6 « 4 a • 1 • * 1 ° 2 1 • 0 1 i i -1- 1 L„ 1 0123456789 10 ARI,yr (b): Jamison Park zu : • F.A. A ARR87 • MMOUSE i ' 15 • t/2 A n • • us 10 A a 1 • O 5 : • n i i i i i 1 | 0 1 4 5 6 8 9 10 ARI,yr (c): Fisher's Ghost Creek Fig. 9.10. Design floods usingfrequency analysis , ARR87 and MMOUSE Chapter 9 Pervious Area Runoff And Simulation of Design Floods Using Modified MOUSE 9-22 A ARR87 • MMOUSE 1 - • II • A A A • i i i | 1 i • 01 2345678 9 10 ARI,yr Fig. 9.11. The results of application of Modified MOUSE model in the Cranebrook catchment 9.3.5. Comparison of the methods of design flood estimation Both frequency analysis and ARR87 methods give flood peak only and prediction of land use change effects on design flood peak is not possible using these methods. However, modelling gives flood hydrograph and any land use changes in future can be taken into consideration. The former methods are quick in application and are used for sizing pipes and channels while the latter method needs catchment physical data and rainfall/runoff observed events. The required expertise and thetime consumed for calibration and data preparation are the other associated issues with the modelling. Neither statistical analysis nor deterministic modelling inactivate each other, because each method can be used in a certain phase of study. For example, statistical methods are used for reconnaissance study for initial cost estimates in projects while deterministic modelling can be used for evaluation of the designed systems and their operational management. The relation of the three methods applied on the catchments in the present study was investigated by calculation of the ratios of 10 and 5 year flood peaks to 2-year flood peak (Table 9.10). Comparison of the ratios of the three methods show that the frequency analysis and the MMOUSE results are close together and different from those of ARR87. The closeness of the ratios of design floods in modelling with frequency analysis indicates that the probabilistic concept of the frequency analysis is represented in the modelling by both median Hydrological Reduction Factor (or approximately runoff coefficient) and temporal pattern of design rainfall. Chapter 9 Pervious Area Runoff And Simulation of Design Floods Using Modified MOUSE 9-23 Reference to Table 9.8, the results of MOUSE for design floods he between the frequency analysis and ARR87 method results in Maroubra catchment. In this catchment only impervious areas were considered in design flood estimation. The results of MMOUSE in the remaining three catchments shows overestimation by this model when compared with the frequency analysis. If the results of statistical are assumed accurate, the reason for overestimation of MMOUSE is either the high value of median HRFPER or temporal pattern.. Table 9.10. Comparison of design flood ratios Catchment Frequency Modified ARR87 Analysis. MOUSE Q10/Q2 Q5'Q2 Q.0/Q2 Q5/Q2 Q10/Q2 Q5/Q2 Maroubra 1.246 1.114 1.529 1.402 1.745 1.518 Jamison Park 1.396 1.255 1.438 1.288 1.713 1.440 Fisher's Ghost Ck 1.665 1.384 1.348 1.196 1.619 1.341 Strathfield 1.215 1.137 - - 1.709 1.448 Cranebrook 1.285 1.210 1.695 1.413 Average 1.381 1.223 1.400 1.274 1.696 1.432 Table 9.11. Comparison MOUSE, MMOUSE with frequency analysis ARLyr Maroubra Fisher's Ghost Creek F.A. R.E.*, % MOUSE F.A. R.E.#, % MMOUSE 1 1.270 27.1 1.614 3.860 112.8 8.213 2 1.538 32.8 2.043 8.453 56.5 13.232 5 1.758 63.0 2.865 11.697 35.3 15.830 10 1.916 63.0 3.123 14.072 26.7 17.836 * R.E. = (MOUSE-F.A.)/F.A # R.E. = (MMOUSE-F.A.)/F.A. Chapter 9 Pervious Area Runoff And Simulation of Design Floods Using Modified MOUSE 9-24 Table 9.12. Comparison MMOUSE with frequency analysis ARLyr Jamison Park Cranebrook F.A. R.E.#, % MMOUSE F.A. R.E., % MMOUSE 1 0.634 65.9 1.052 . - 0.385 2 1.418 42.0 2.013 _ _ 1.634 5 1.780 45.6 2.592 . - 1.977 10 1.979 46.2 2.894 - - 2.100 # R.E. = ( MMOUSE-F.A.)/F.A. 9.3.6. Investigation of pervious areas contribution in design flood peaks As mentioned above the reason for overestimation by MMOUSE could be either high value of HRFPER or inappropriateness of temporal patterns. While trying tofind a suitable HRFPER to reproduce closer flood peaks to the frequency analysis results, it was concluded that even though with HRFPER equal to zero still flood peaks for 2,5 and 10 year return periods in Jamison park are greater than those of frequency analysis. It means that flood peaks for these ARIs are being generated from impervious areas only. Although it seems to be true for 2-year flow it is doubtful for higher return periods. In the Jamison Park catchment comparison of runoff volumes resulting from the simulation of design flood peaks using HRFPER equal to 0.33 and 0.0 shows that runoff volume is decreased by 36% when HRFPER is reduced to zero. However, the reduction in flood peaks for the same HRFs is equal to 16.3% for 2,5 and 10 years return periods ( Table 9.13). In the Fisher's Ghost Creek catchment the reductions in runoff volume and design flood peaks are 44% and 24% respectively (Table 9.14). A practical conclusion obtained from this test is the adequacy of impervious areas of urban catchments to simulate design flood peaks using design rainfall and temporal patterns. However, for simulation of runoff volume pervious areas should be incorporated. It should be noted that for generation of a combined event antecedent moisture conditions should be high enough, otherwise no runoff will generate from these areas. Chapter 9 Pervious Area Runoff And Simulation of Design Floods Using Modified MOUSE 9-25 Table 9.13. Adjusting F.A. and MMOUSE Results- Jamison Park ARI F.A., MMOUSE, m3/s VOL., mm m3/s HRFPER - median 0.58*(median) 0.00 0.00 median 2 1.418 2.013 1.870 1.679 6.672 10.325 5 1.780 2.592 2.407 2.154 8.72 13.62 10 1.979 2.894 2.695 2.42 9.89 15.51 Table 9.14: Adjusting F.A. and MMOUSE Results- Fisher's Ghost Creek ARI F.A., MMOUSE, m3/s VOL., mm m3/s HRFPER - median 0.58*(median) 0.00 0.00 median 2 8.453 13.232 11.792 9.926 7.90 14.02 5 11.697 15.830 14.114 11.888 9.531 17.00 10 14.072 17.836 16.054 13.510 10.96 19.62 • F.A. • HRFPm=0.33 AHRFPB3=0.19 • HRFPBfc=0.00 (a): Jamison Park 20 15 + A • F.A. £ A • • HRFPB=fc=0.33 y 104 Ar«FFER=0.19 d • HRFFER=0.00 O 5 + -r- 4 6 10 A«,YR (b): Fisher's Ghost Creek Fig. 9.12. Contribution of pervious areas in design flood peak Chapter 9 Pervious Area Runoff And Simulation of Design Floods Using Modified MOUSE 9-26 9.4. Summary Using the runoff volume resulting from the simulation of impervious areas of the catchments, and the observed runoff volume, runoff from pervious areas of the catchments was calculated. The derived runoff volume for pervious areas showed that these kinds of land use contribute significantly to runoff generation in Australian urban catchments. On average the ratio of pervious areas runoff to total runoff in two catchments with clay soil type was calculated equal to 37%. This fact should be useful in urban water quality and sediment transport studies in urban areas with heavy soil type. The pervious area runoff coefficient was investigated in relation to rainfall depth, rainfall intensity and sum of rainfall forfive days before the occurrence of the event (P5)which is an index of API No significant correlation was found between the pervious area runoff coefficient and rainfall depth or intensity or P5. When sum of the P5 and event rainfall was used as API, the correlation coefficient changed very significantly for one catchment while for the other it did not change at all. The HRF of the pervious area ( HRFPER), calibrated by the Modified MOUSE, was studied in relation to the pervious area runoff coefficient. They are just slightiy different ways of expressing the same things. Runoff coefficient was calculated based on the ratio of pervious runoff to total catchment area, but HRFPER was calculated by the ratio of pervious runoff to the pervious area of the catchment. Deterministic simulation of design flood peaks using design rainfalls and temporal patterns was accomplished using both MOUSE and the modified MOUSE in four catchments. Design rainfalls were considered during the time of concentration of catchments and were distributed over the time using temporal patterns of ARR87, Vol. 2. For simulation of design floods in the light soil type catchments MOUSE Level-A was used while for the heavy soil type catchments MMOUSE which incorporates pervious areas runoff coefficient was used. The median values of Hydrologic Reduction Factor for pervious areas (HRFPER) of the catchments were used for excess rainfall calculations. Chapter 9 Pervious Area Runoff And Simulation of Design Floods Using Modified MOUSE 9-27 Comparison of the ratios of 10 and 5 years flood to 2 years flood for three methods of ARR87, MMOUSE and frequency analysis showed that the frequency analysis and the MMOUSE results are close together and different from those of ARR87. It is concluded that deterministic simulation of design floods ( MMOUSE + Design Rainfall + Temporal Patterns) can produce similar/related values as frequency analysis of flood peaks. However, the results of deterministic simulation were generally overestimated compared with those of frequency analysis. Apart from the different basis of two methods the reasons for overestimation of deterministic simulation can be either high value of HRFPER or inappropriateness of temporal patterns. Using MMOUSE two other HRFPER values were examined to obtain closer design floods to the frequency analysis. It was concluded that even though with HRFPER equal zero the design flood magnitudes resulting from simulation are different from those of frequency analysis. In other words in deterministic simulation the incorporation of pervious areas in estimation of design flood is not required, however, for runoff volume simulation they should be considered. It is concluded that the MOUSE model at Level-A is sufficient to estimate design flood peaks which are closely similar to the frequency analysis results. The application of the model at this level is very simple and time efficient ( Refer to Chapter 7). CHAPTER TEN SUMMARY AND CONCLUSIONS Chapter 10 ___ Summan and Conclusions 10-1 CHAPTER TEN 10. SUMMARY AND CONCLUSIONS Rapid growth of urban areas causes variations in nature of stormwater runoff. Accurate estimates of flood peak and volume are important in water quality studies and hydraulic design of urban stormwater networks, including pipe diameter, bridge, culvert and detention basins. This study follows two major hydrologic design indices in urban catchments including, flood peak and flood hydrograph using both deterministic and statistical analysis. Deterministic analysis of runoff in urban catchments paves the way for a better understanding of the runoff formation and sources where runoff originates from. The deterministic analysis of runoff will help in achievement of more accurate design parameters in the statistical approach. The study sheds light on the hydrologic and hydraulic parameters effects of two main land uses of urban catchments known as impervious and pervious areas. The role of these land uses in urban catchments flood estimation in conjunction with soil type and also frequency of events are the issues which are highlighted and investigated in the present study by using analysis of rainfall-runoff, frequency analysis of flood peaks and deterministic simulation of events. The study follows specifically three broad objectives of flood peak estimation using the Rational formula, hydrograph simulation using MOUSE and design flood using both temporal patterns and design rainfall considering the urban catchments land use and soil type. To achieve more insights in the proportion of rainfall which transforms to runoff, the main theme of the study is concentration on the rate or volumetric runoff coefficient which runs through the thesis from both point of views of deterministic and statistical. The most widely used formula, the Rational formula, is evaluated from both deterministic and statistical point of views. The study seeks the possible relation between statistical and deterministic runoff coefficients in the Rational method. Two important parameters of this formula, the time of concentration and the runoff coefficient, are derived by use of observed data from five gauged urban catchments in Sydney. The variations in runoff coefficient on pervious and impervious areas of urban catchments are studied, and effective parameters such as average rainfall intensity during the time of concentration Chapter 10 Summary and Conclusions 10-2 and during the burst duration are discussed. The effect of Antecedent Moisture Conditions, AMC, of the catchments on runoff coefficient variations is also considered. The statistical interpretation of runoff coefficient is investigated through partial duration series of flood peaks and design rainfalls using Intensity-Frequency-Duration curves from ARR87. The method presented in ARR87 for urban catchments runoff coefficient determination is evaluated using the results of both deterministic and statistical studies. Modelling of hydrograph and peak flow was performed by use of the MOUSE package for both pervious and impervious areas of the catchments. Runoff from pervious areas of some of the catchments was analysed to study the dependence of runoff coefficient on this kind of land use in urban catchments. In Australian conditions occurrence of combined runoff from both pervious and impervious areas of urban catchments is very common. On the other hand the data from gauged catchments are insufficient to generalise the MOUSE model parameters for application in design situations. The present study seeks an innovative and simple approach for calculation of excess rainfall to be used in complex hydraulic models such as MOUSE. The study concentrates on reducing the number parameters of excess rainfall calculation. Providing practitioners to benefit from both simplicity of runoff coefficient concept for excess rainfall calculation and the complexity of hydrodynamic MOUSE model for drainage system design and management is one of the goals of the present study. Application of design rainfalls and temporal patterns in conjunction with deterministic models is a common practice in hydrologic investigations and studies. The present study aims to determine whether the application of a deterministic model such as MOUSE along with design rainfalls and temporal patterns from ARR87 Vol. 2 can produce similar results with frequency analysis of flood peaks. The results of simulation are compared with the frequency analysis results for flood peaks and the sufficiency of the model in producing similar flood peaks and also urban catchments land use contribution in design flood peak are evaluated. The literature review of urban flood studies denotes the inadequacy and difficulty of data acquisition in these catchments which causes uncertainty in developing design criteria for them. Considering urban growth and perpetual changes in cities, the Chapter 10 . Summary and Conclusions 10-3 measured data are non-stationary as well. This instability causes uncertainty in design criteria, even in the statistical interpretation of runoff coefficient in the Rational method. However, a deterministic approach has been considered by some researchers to account for the variation in land use and rainfall pattern. When the record lengths of gauged urban catchments are short, it is difficult to apply the statistical method unless regional values of runoff coefficient and rainfall intensities are available. However, recorded events of rainfall-runoff are available and can test validity of the Rational method as a deterministic formula, which may assist in statistical application. Deterministic modelling of urban hydrology and hydraulics is considered to cover all the possible land uses and rainfall patterns or antecedent moisture conditions. Pervious and impervious parts of the urban catchments are simulated by these models separately. In some lumped models the weighted average parameters, eg runoff coefficient or infiltration, are used for the pervious and impervious areas. Besides the hydrologic and hydraulic effects of man-made structures which distinguishes urban catchments from rural, almost all of the hydrological processes are involved in urban catchments runoff generation. One unique feature in urban catchment stormwater drainage design is a great number of locations which design peak flow is required, while for the same size rural catchment estimation at one or two locations would be sufficient. Computation of volume and flood peak for detention basins, gully pits, bridges and culverts requires both a simple formula for an initially rapid estimate, and a spatially distributed model for making final design decision. Compared with rural watersheds urban catchments are very complex in physical characteristics and runoff production. The existence of considerable impervious areas and many man-made features make the study of the runoff generation mechanism more complicated than that of rural catchments. Study of urban catchment hydrology by employing mathematical models could make some presently vague aspects of runoff generation and flooding clear. Chapter 10 Summary and Conclusions 10-4 To simulate flood hydrographs, two of the most well-known models, including: DLSAX, MOUSE were considered carefully. The hydrologic and hydraulic structures of the models were studied and compared. A qualitative comparison of ILSAX and MOUSE showed that in some cases both models are similar, but some strong and weak points can be found in each model. Hydrologically speaking, both models are fairly similar, except that MOUSE uses KW approximation to route excess rainfall through subcatchments in Level B while ILSAX uses TAD and lag time for runoff from both pervious and impervious areas. The TAD in ELSAX is linear while in MOUSE it could be linear, concave or convex. Both models use the same infiltration equation. ILSAX considers API and catchment soil moisture conditions, while this is not incorporated in MOUSE. In the hydraulic section of the models, unsteady flow simulation of branched/looped systems makes MOUSE closer to reality than ILSAX. A considerable head loss in urban drainage systems occurs in junctions, gully pits and manholes, which are not incorporated in ILSAX, but are in MOUSE. In ILSAX bypass flow to pits or manholes is diverted to the downstream pits via gutters while in MOUSE the bypass flow is accumulated in a fictitious storage and is released later into the same pits/manholes. This process in ILSAX is more realistic than in MOUSE. In ILSAX two drainage systems including above ground and below ground can be defined while in MOUSE only the below ground system is available. However, flooding and surcharge can be easily detected and managed in MOUSE by manipulating pipe characteristics or routing the flow downstream via gutters. Introducing urban networks to MOUSE is much easier than in ILSAX. In MOUSE every junction, pit or manhole has definite coordinates by which it is easily located. Tidal or any other downstream conditions could be investigated using MOUSE. In the present study MOUSE was selected as the model to simulate the runoff hydrograph. Due to the complexities and interactions of rainfall-runoff models parameters, attempts are made to reduce number of required parameters. This study is mostly concerned with simulation of runoff by use of runoff coefficient. This parameter is Chapter 10 Summary and Conclusions 10-5 introduced in MOUSE by HRF for impervious areas. It was shown that runoff from pervious areas of an urban catchment could be simulated using HRF for those areas. The HRF concept is close to the runoff coefficient definition, so with some modifications on time of concentration and initial loss can be applied to pervious areas of urban catchments. Implementation of the proposed method was only possible using the MOUSE model. However, unsteady pipe flow simulation was another main reason for the adoption of MOUSE. Data of five urban catchments, ranging from 11.5 to 234 hectares, were used in this study. The time increment of rainfall and runoff was found inadequate in some cases. Some discrepancies due to either malfunction or inadequacy of measuring devices in data were inspected and discussed. In order to evaluate the adequacy of the current method of flood peak estimation in urban catchments ( ARR87), a deterministic approach to the runoff coefficient for the Rational method was applied to the catchments under study. Two important parameters of the Rational formula including; time of concentration and runoff coefficient were investigated using observed rainfall and streamflow. Time of concentration of the catchments was estimated by using three common methods consisting of a velocity method, a typical minimum time ofrise, and lag time. Among these three methods, lag time was found to be the best and the most consistent. The assumption of linear reservoir system was in agreement with recession analysis of hydrographs to compute lag time for the impervious areas of the catchments. Comparison of the results of minimum time ofrise metho d with the Tc derived by lag time (lag time* 1.417) showed that the minimum time of rise underestimates for both impervious and combined events. Average time ofrise showed a good agreement with Tc from the lag method for both impervious and combined events. However, time of rise was found to be dependent on the temporal pattern of rainfall. The velocity method could be considered suitable in ungauged catchments. The velocity of half full pipe conditions can give reasonable results for average travel time estimate. Chapter JO Summon- and Conclusions 10-6 The magnitudes of time of concentration by the lag time method were adopted for further study on the catchments. There are two reasons for this adoption. Firstly, the derivation of lag times is based on observed data, and secondly total time of concentration of catchments is achievable with this method through lag analysis for combined events. Rate runoff coefficient was selected as a suitable surrogate for all the effective abstractions and attenuations of the flood peak. This coefficient was calculated using the average rainfall intensities and flood peaks by substituting them in the Rational formula. The average rainfall intensity was calculated both during the bursts and during the catchment time of concentration. The regression study of flood peak showed higher correlation with average rainfall intensity during the catchment time of concentration than with that during the observed rainfall bursts. It was concluded that when burst duration is greater than the time of concentration of the catchment, the flood peak magnitude is more related to the maximum average rainfall intensity during the Tc rather than to the average rainfall intensity during the burst duration. This emphasises the fact that the time of concentration is a crucial parameter in the Rational formula. The integration of deterministic values with statistical values was performed by relating the average observed values of runoff coefficient during the catchment's times of concentration and 2-yr return period runoff coefficient from ARR87. It was concluded that ARR87 estimates for 2-yr return period are correlated very well with the observed values. The integration of deterministic values of runoff coefficients with a statistical method can be useful because it incorporates the effects of both soil type and time of concentration of catchments in the statistical method of ARR87. The established relationship between statistical and deterministic values can compensate for deficiencies with the ARR87 regarding catchment's soil type and the relation of time of concentration of catchments and runoff coefficient. In design situation statistical runoff coefficient is required because it includes the effects of all parameters contributing in flood peak generation and are not easily measurable. Chapter 10 Summan and Conclusions 10-7 Besides deterministic evaluation, runoff coefficient was studied from the view point of statistics as well. Due to the short record of data available and also to be consistent with ARR87 design rainfall, partial duration series of flood peaks was used in frequency analysis. Among three different indexes considered to define a base discharge for building a partial duration series of flood peaks, the minimum in annual series was found to give very reasonable number of floods when compared with number of years of record, so it was adopted. Log Pearson Type III was fitted to the partial duration series of flood peaks and for return periods of 1,2,5 and 10 years flood peaks were calculated using the distribution. Design rainfalls were scaled off the IFD curves resulting from ARR87 partial duration series of design rainfall for the catchments time of concentration. Statistical runoff coefficients were calculated based on frequency analysis of flood peaks and design rainfalls. For different return periods, it was concluded that statistical runoff coefficient remains constant in the catchments with light soils and has an increasing trend in the catchments with heavy soils. Generally speaking ARR87 overestimates runoff coefficient. However, for catchments with light soil type the overestimate is higher than that in the catchments with heavy soil type. On average, the magnitude of overestimation is 84% and 31 % for catchments with light and heavy soil types respectively. The range of overestimates of 1 year runoff coefficients in ARR87 method is higher than those of the other return periods. The overestimation can be due to the subjective relation between ARI and AEP for 1 year return period. Comparison of deterministic runoff coefficient with statistical showed that in catchments with light soils estimation of flood peaks with return period up to 10 years Chapter 10 Summon and Conclusions 10-8 can be performed considering only impervious area of catchments. In catchments with medium soils only 1-yr flow can be assumed to generate from impervious areas and for higher return periods the incorporation of pervious areas is necessary. Both pervious and impervious areas should be considered for flood computation of 1 year return period and higher in catchments with heavy soil type. Sensitivity analysis of MOUSE was performed using the Maroubra catchment data. Catchment data, drainage network, rainfall and boundary conditionsfiles were set up for the Maroubra catchment to carry out the analysis of sensitivity on both hydrologic and hydraulic parts of the model. Runoff model level A was tested to recognise the most sensitive parameters in surface runoff production. Due to the lack of observed hydrographs at the subcatchment outlets, the study in this part was mostly qualitative and comparative. The effective components of the runoff model level A consist of time of concentration, TAD and HRF which are incorporated in each subcatchment individually. Time of concentration variations were investigated by studying magnitudes of flood peak and time to peak of hydrographs at the subcatchment and the whole catchment outlets. An increase of 100% in the time of concentration of each subcatchment caused a decrease of 21% in flood peak and an increase of 13% in time to peak of their hydrographs. However, the decrease in flood peak at the catchment outlet was less than those of subcatchments and equal to 7%, but the increase in time to peak of the outlet hydrograph was nearly the same (14%). ARR87 uses the average velocity at 60% of the length along the gutter of the subcatchments, and so gives a larger peak; however, Chow's formula is more realistic. For this catchment Chow's formula was used for gutter flow travel time of subcatchments. The results of the formula in ARR87, Izzard's formula, for gutter flow travel time were compared with those of Chow's formula which is for triangular channels. When Chow's formula was used, an increase of 65% in time of concentration of each subcatchment lowered the whole catchment flood peak by 7%, but had no effect on the time to peak of the hydrograph. It is concluded that for each subcatchment the increase in Tc reduces at the outlet, but the variations of Tp at the catchment outlet is mainly due to travel time in the pipe system. MOUSE employs three different TADs to route the surface runoff through subcatchments. Among the three available TAD shapes, including rectangular, divergent and convergent, the first one was found suitable to simulate hydrographs at the catchment outlet and was used for all catchments. However, depending on the special shape of subcatchments any one of them could be selected. Hydrologic reduction factor, HRF, was found to be the most sensitive parameter in simulating flood peak and runoff volume. Total and directly connected impervious areas were used to compute HRF in runoff simulation. Compared with the observed, both runoff volume and flood peak were highly overestimated when total impervious areas were taken to estimate HRF. Although replacement of total impervious areas by directly connected impervious area reduced the problem significantly, it still needed adjustment to match the observed and simulated. Therefore, this parameter was calibrated on all catchments. Pipe flow modelling in MOUSE integrates network characteristics, magnitude of flow and boundary conditions. Unsteady pipe flow is simulated numerically while different surface hydrographs of subcatchments enter the network via pits or manholes, flowing down to the outlet. The effect of the physical shape of pits, flow resistance, grate entry loss, boundary condition type, time step of simulation and solution level were studied through several runs of the model, and results were compared with observed. In Australian practice surface runoff is admitted into the underground urban stormwater network via square pits. However, MOUSE accepts surface runoff into the system through circular manholes. Simulations of maximum flow peak and water level, using square pits and circular manholes as flow conveyers to the system separately, were found to be in close agreement. An equivalent circular manhole was used instead of square pits in modelling of catchments . Chapter 10 Summon and Conclusions 10-10 Pipe flow resistance in MOUSE is considered using Manning's roughness coefficient. The model was found quite sensitive to variations of roughness coefficient. A decrease in the roughness coefficient caused shorter time to peak, larger peak flow and earlier recession at the outlet, so in the calibration of the hydrograph's overall shape, it should be considered precisely. Considering the age of networks, the standard value of the roughness coefficient was considered to be equal to 0.017, but the suitable value was calibrated. Despite the lack of grate entry loss in MOUSE, this phenomena could be simulated by using the equivalent top surface area of manhole instead of the grate's effective area. Two trials of models without pipe capacity limitation showed that when a small manhole diameter is selected, surcharge/flooding occurred, so it was concluded that grate entry loss could be simulated using modified manholes. Modified circular manholes with the top cross section equal to the effective grate area were used in this study. Head loss in manholes was tested for two types of outlet shapes including, no head loss and orificing shape. When orificing outlet shape is used water level in thefirst pit of every branch decreases, while for the middle and the bottom branch it increases. In this study no head loss option for the outlet shape was used. MOUSE automatically calculates losses for pipe bends and different diameters of inlet and outlet pipes. The study of flood peak and maximum water level at the outlet and nodes showed that assuming fixed or time function B.C. has no significant effect on results. The results of two runs with fixed B.C., the bottom elevation of measuring station at the outlet, and time function B.C., H(t) versus t for the observed hydrograph, were found to be the same. It was concluded that when there is no tidal effect and also in supercritical conditions at the catchment outlet, introducing fixed or time function B.C. gives the same results. For all the catchments, fixed boundary conditions, the bottom elevation of measuring station, was used in this study. The computational grid for the numerical solution is constructed by the model based on the simulation time step provided by the user. A suitable time step can be found by trial and error while checking the consistency and stability of the solution. During urban Chapter 10 Summon and Conclusions 10-11 catchment flash floods, selection of shorter time steps is preferred because of sudden increases of flow levels in the system. For small events (no pressurised flow) both KW and DYN.W produced the same results. Although both methods were able to simulate a medium flood hydrograph, KW produced unreasonable and unstable pressure lines over the manholes. The runoff resulting from the heaviest observed rainfall was simulated successfully using DYN.W, but KW failed during the simulation because of overflowing in the system. Generally DYN.W is the preferred solution because of the ability of handling back water and surcharge everywhere in the system, so this approach was used in all catchments and for simulation of each event. Despite MOUSE being a distributed model and hardly needing calibration, the acquisition of detailed necessary data for application of the model is difficult. Calibration is an alternative which can be used to achieve some knowledge about the catchment parameters. MOUSE was calibrated for four catchments on impervious area runoff events. Three indices were considered for calibration of the model including; volume, flood peak and time to peak. Volume and flood peak were simulated by adjustment of HRF while for time to peak and hydrograph shape, roughness coefficient calibration was used. Simulation of volume was the first concern in this study because of runoff coefficient estimation. However, flood peaks and time to peak were simulated as well. To prevent interaction among the four parameters including; IL, TAD, HRF and n, the first two were kept constant for all events and in all catchments. IL was set equal to 1 mm which is the norm for urban areas and TAD No. 1 which assumes a linear relationship between isochrones and area of catchment was used. HRF and n were considered for calibration of hydrographs. Comparison of HRF with the percentage of directly connected impervious areas of the catchments showed a relation between them. For example, in Jamison Park and Fisher's Ghost Creek where directly connected impervious area percentages were close to the total imperviousnesses (IMPV), HRF's approached one. In Maroubra total imperviousness and the directly connected impervious areas are equal to 0.29 and 0.19 Chapter 10 Summon and Conclusions 10-12 respectively. According to these percentages, the HRF for Maroubra (0.59) is in accordance with ratio of the directly connected areas to the total imperviousness. A general conclusion of the HRF study could be stated as "within the limitations of the data, the best estimate of HRF is the ratio of directly connected impervious area to the total impervious area multiply by 0.875". Total impervious areas include both connected and indirectly connected impervious areas in urban catchments. The roughness coefficient was selected based on system age. The oldest and youngest network systems are Maroubra and Cranebrook respectively. The variations of 'n' for the pipe systems from 0.022 to 0.014 reflects the age and material of the systems. The time step of the Dynamic Wave solution was found to be in the range 2-4 seconds for the catchments. According to the built in stability criteria in MOUSE, Courant Condition, a time step greater than 4 seconds caused instability of solution in some parts of the systems. MOUSE automatically creates computational nodal points according to the full running velocity of pipe/channel flow and users should provide the model with a suitable time step of simulation to achieve a stable solution. However, the above time step increases the computation time very much. The accuracy of rainfall/runoff data, especially synchronisation, is an important issue in modelling. During the simulation a few erroneous cases were diagnosed with synchronisation problems, eg. wrong and inconsistent peak flows. The time increment of rainfall is another factor which affects simulation results. Discretization of rainfall should be consistent with size and correspondingly with the time of concentration of the catchments. In Cranebrook catchment the time increment of the recorded rainfall/runoff data was in the order of 3-5 minutes which is large for this catchment with a size of 11.5 hectares and a Tc of 6.0 minutes. The relation of computed and observed volume, flood peak and time to peak was studied using simple regression equations. Comparison of the simulated and observed volumes showed an unbiased scatter on or around the line of equal value. The coefficients of determination denoted that at least 94% of the volume variations could be explained by Chapter 10 Summon and Conclusions 10-13 the model. The closeness of the simulated and the observed time to peak of hydrographs showed that the magnitude of roughness coefficient is reflected correctly by the model. Generally the flood peak simulation results for large floods were found more satisfactory than small or medium ones. Normally during combined events, catchments receive a lot of rain which causes large flood peaks over impervious areas, so accurate estimation of large flood peaks on impervious areas is an important point to be considered. The capability of MOUSE model in prediction of land use changes and its effect on flood peak was investigated. The calibrated model on catchments was used to predict the increase in flood peak due to the incorporation of three different patterns of new developments in existing urban areas. Separated Concentrated Development is the pattern that usually happens in urban development/consolidation. As a rule of thumb the increase in percentage of flood peak is twice as much as that of the increase in urbanisation. The results achieved looked promising, however, more investigations are needed in this regard. Combined runoff from both pervious and impervious areas are important in Australian urban catchments and should be considered in network design and stormwater management. Generally, simulation of combined runoff events in the MOUSE model is performed using Level B Module. In this module excess rainfall is calculated by a water balance equation which incorporates infiltration equation, evaporation and storage data. The excess rainfall is transformed to a hydrograph at each subcatchment outlet by using Kinematic wave equation. The MOUSE model at this level averages the effects of pervious and impervious areas of each subcatchment, however these areas have different responses in producing runoff. Besides the combination of pervious and impervious areas, the process of rainfall excess is very data intensive and the involved parameters have interactions which makes the calibration time consuming and unstable. Because of interactions, the magnitudes of the calibrated parameters have no physical interpretation. In the present study the MOUSE model was modified ( MMOUSE) regarding excess rainfall calculation and separated storages for impervious and pervious areas. In MMOUSE a fictitious manhole and a zero length conduit were introduced to the model Chapter 10 Summon and Conclusions 10-14 for pervious area of each subcatchment. The fictitious manhole was used to admit pervious runoff to the network and the zero length conduit was used to join the fictitious manhole to the real network. Calculated pervious areas Tc and an estimate of IL were given to the model. In MMOUSE the excess rainfall of pervious areas is calculated according to the concept of the runoff coefficient for pervious areas and different initial losses for pervious and impervious areas. In this method runoff is calculated from two parallel storages of impervious and pervious areas separately and is added at manholes. The delay between response time of pervious and impervious areas is considered by different times of concentration. Implementation of the proposed method is performed by use of an executive code of the model and modification of input datafiles; however , if the source code was available, incorporation of the modification could have been performed. Despite using only one parameter to calibrate runoff model, the modified model (MMOUSE) indicated better results for flood peak and volume compared with the original model (MOUSE). With the proposed method there is no interaction between parameters and the calibration process is very time efficient. This method gives the practitioners the opportunity to use a complex model like MOUSE and a simple concept like runoff coefficient in urban drainage practice. Using this method the model can be calibrated on few observed events and be applied in design situation. The pervious part of each subcatchment has the potential to be fully or partly saturated. Calibration of HRF for pervious part of each subcatchment shows either the percentage of saturated land or the percentage of excess rainfall with respect to the total rainfall. The proposed method was tested on two catchments where combined runoff was frequently observed. The results for flood peak simulation were satisfactory. Time to peak and overall hydrograph shape were simulated very well. Calibration proceeded by adjusting HRF for pervious areas. HRFj^p was set as 1.00 and the calibrated pipe roughness coefficient of impervious areas was used in the simulation of combined events. Chapter JO Summan and Conclusions 10-15 Using the runoff volume resulting from simulation of impervious areas of the catchments, and the observed total runoff, runoff from pervious areas of the catchments was calculated. The derived runoff volume for pervious areas showed significant contribution of these kinds of land uses in runoff generation in Australian urban catchments. On average the ratio of pervious areas runoff to total runoff in two catchments with clay soil type was calculated equal to 37%. This fact should be useful in urban water quality and sediment transport studies in urban areas with heavy soil type. The pervious area runoff coefficient was investigated in relation to rainfall depth, rainfall intensity and sum of rainfall forfive days before the occurrence of the event (P5)which is an index of API. No significant correlation was found between the pervious area runoff coefficient and rainfall depth or intensity or P5. When sum of the P5 and event rainfall was used as API, the correlation coefficient changed very significantly for one catchment while for the other it did not change at all. The HRF of the pervious area ( HRFPER), calibrated by the Modified MOUSE, was studied in relation to the pervious area runoff coefficient. They are just slightly different ways of expressing the same things. Runoff coefficient was calculated based on the ratio of pervious runoff to total catchment area, but HRFPER was calculated by the ratio of pervious runoff to the pervious area of the catchment. Deterministic simulation of design flood peaks using design rainfalls and temporal patterns was accomplished using both MOUSE and the modified MOUSE in four catchments. Design rainfalls were considered during the time of concentration of catchments and were distributed over the time using temporal patterns of ARR87 Vol. 2. For simulation of design floods in the light soil type catchments MOUSE Level-A was used while for the heavy soil type catchments MMOUSE which incorporates pervious areas runoff coefficient was used. The median values of Hydrologic Reduction Factor for pervious areas ( HRFPER) of the catchments were used for excess rainfall calculations. Comparison of the ratios of 10 and 5 years flood to 2 years flood for three methods of ARR87, MMOUSE and frequency analysis showed that the frequency analysis and the Chapter 10 Summan and Conclusions 10-16 MMOUSE results are close together and different from those of ARR87. It is concluded that deterministic simulation of design floods ( MMOUSE + Design Rainfall + Temporal Patterns) can produce similar/related values as frequency analysis of flood peaks. However, the results of deterministic simulation were generally overestimated compared with those of frequency analysis. Apart from the different basis of two methods the reasons for overestimation of deterministic simulation can be either high value of HRFPER or inappropriate temporal patterns. Using MMOUSE two other HRFPER values were examined to obtain closer design floods to the frequency analysis. 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Proceedings of the National Symposium on Urban Hydrology and Sediment Control, University of Kentucky, Lexington, KY, pp 161-167. WP-Software (1989). " EXTRAN-XP User Manual *'. WP-Software (1991). " RAFTS-XP User Manual". APPENDICES APPENDIX A STORMWATER DRAINAGE NETWORKS Appendix A Stormwater Drainage \envorks APPENDIX A DATAFILE MAROUBRA. SWF EDITED 1-JUN-1994 1S:S7 SCALE 1:4S32 Fig. A-1. Stormwater drainage network - Maroubra 26.00 25.00- 22.00 21.00- 20.00- iaoo- TOP LEVEL m. 26.36 2S.46 2S.S02S.02 2S.03 2S.49 24.74 2-23.63 22.93 23.13 22.08 20.18.76 BOTTOM LEV rre 2S 36 24.46 22.8822.64 22.38 22^0 22.01 21.21.63 20.93 20.63 20.08 18.18.76 LENGTH m- 101.2 1S6.2 62.6 93.1 84.8 101.7 33.9 3S.4 36.4 100_ 107_ 2426 31.3 DIAMETER ire 0.4S0 0.4S0 1.220 1.220 1.220 1.220 1.3721.3721.372 1.372 1524 1.372 CANAL SLOPE o/oo: 8.9 iai a8 28 21 1.9 22 4.8 73 3.0 S.1 4.6 6.4 1 PRINTER DATAFILE : MAROUBRA SWF 2 PLOTTER EDITED : 1-JUN-1994 1S_7 3 METAFILE SCALE : LENGTH : 1S967 Fig. A-3. Stormwater drainage network - Jamison Park 40.00- 38.00- 30.00- TOP LEVEL m: 4357414241.97 33.7038.23 36.83S.4G4.47 31.94 31.0330.830.96 30.80 303C0.S7 3021 28.16 BOTTOM LEV. m; 42.00404C40.07 382737.17 3SSO4.SC33.09 30.84 30-2CZ9.629S4 29.00 282J28S7 2831 28.16 LENGTH m: 4aS1616.146.8 41_> 4S.4 3S.7 36.1 S9.1 SS.3 38228.6 78.4 61.8 1612.1 103.3 SSI DIAMETER rrc a4SaO.4S0.4S0a4S00.6000.60_.600 0.900 0.900 a90H-200 1200 1.3S01.:1S40 1S40 1S40 SLOPE o*oo: 2S.S163S.S38_; 26.7 3S.9 29.1 39.0 2S.3 11.6 14.9 3.1 6.9 4.9 3.6.8 2S 2.7 1 PRINTER DATAFILE ; JAMPK.SWF 2 PLOTTER EDITED :28-MAR-1994 13:11 3 METAFILE SCALE [LENGTH : 1:3733 stc DATAFILE FGC. SWF EDITED 17-JUN-1994 1S:16 SCALE 1:9SS4 Fig. A-5. Stormwater drainage network - Fisher's Ghost Creek iso.oo- • 14a oo- 130.00 120.00- ilaoo- loaoo- TOP LEVEL m: 1S4.01S1.H47.146.144.00 13T13S.00 13aU29.(12S.00 118.(1111«1 laOO 1 iaOC107.00 102010 -100-0C BOTTOM LEV. n* 1S3.1150:146.14S. 143.03 13E13423 129.128.124.86 117.(1111:11252 109.7E106.39 101.810C-10O0C LENGTH m: 10S291286.483.4 219.6 67.1 2342 33232.7 2S6.7 93.C49JB12 176.7 131.3 190.1 1O8.0K13.81 DIAMETER m: 0S3CD.68a9CANALCANAL 1.3S0 1.3S0 1.3SCANAL CANAL CAN2.CANACANAL CANAL CANAL 2SCJCANAL SLOPE o/oo: 27.8 37218.626.4 29.1 3S.0 21.8 1093S.7 28.0 40.99.U42 1S.4 2S.9 23.7 16.3 1XJ.7 1 PRINTER DATAFLE FGCSVWF 2 PLOTTER EDITED 17-JUN-1994 IS 16 3 METAFILE SCALE LENGTH : 1:10178 J 1 1 1 1 1 1 1 1 1 1 1 1 • iii • iii • » * » t * i t * i t % t i IIII IIIII . . . , 1 ._ 400"" t - : - IIII I ; - • • - ; - ; - • - "III | • i i i | i i i I | IIII | I I I i | i i i i | IIII [III • | i •~ri 1 | IIII | 1 ( I 1 | 1 73 125 175 225 275 325 375 425 475 525 575 DATAFILE : CRN.SWF EDITED : 11-MAY-1994 09:40 MOUSE SCALE : 1:2580 Fig. A-7. Stormwater drainage network - Cranebrook i2aoo- 1 15.00- I iaoo- 105.00- loaoo- TOPLEVEL m: 121212021 118.0(11S.S11S2S 1111114S8 110.8)108^4 10SS8 1045:10426 103S0 101.S 101.099.67 BOTTOM LEV. m 121111921 117.0(114.(1142S 1111113.S8 109.8H07.S4 104S8 103S.-103.2S 10220 10OS 10O.O99.67 LENGTH m: 1414.137.7 33.6 2S.7 4tt1 1214.S 621 31.6 602 S0.8 30.9 S92 66.0 29.7 26S DIAMETER m: 0.30.3730.375 0.4S00.4SO O.4SCOO.4S0 0.4S0 a4S0 0.4S0 0.600 a67S 0.67S 0.7SO 0.8233.900 SLOPE o/oo: 3338.3 S7.4 62.6 272 ia7 11.6.9 S9.9 73.4 492 19.7 10.4 17.9 2S3 17 8 124 1 PRINTER DATAFILE : CRN.SWF 2 PLOTTER EDITED : 11-MAY-1934 09:40 3 METAFILE SCALE : LENGTH : 1:2799 MOUSE AND MMOUSE MODELLING DATA Appendix B MOUSE And MMOUSE Modelling Paid APPENDIX B Table B-l. Physical characteristics of sub catchments- Maroubra D:CATCHMENTS FILE.MAROUBRA.SWF PAGE 1 CREATED: 28-OCT-1993 15:36:44 EDITED: l-JUN-1994 15:57:35 Surface Distribution (total area): Impervious : 1: steep roof 2: flat roof 3: asfalt/concrete Semi pervious : 4: large- 5: small spacing between tiles Pervious : 6: planted 7: unplanted CT = Catchm.type(l-7) HL = Hydrologic Level (1-2) S *=Soil Parameter (1-3) Row Nodal Total Slope Catch c Person Add. H Pet SP Surface Distribution Point Area lgth. T eqvlt. flow L Imp. 12 3 4 5 6 7 No. ha prm m Pe/ha m3/s Pet pet. 1 33 2.991 33 0 4 0 0.000 1 35 2 32 2.600 33 0 4 0.000 1 23 3 31 1.823 73 0 6 0 0.000 1 23 4 29 2.473 10 0 4 0 0.000 1 23 5 28 1.982 149 0 4 0 0.000 1 23 6 27 2.436 137 0 6 0 0.000 1 23 7 24 0.555 104 0 6 0 0.000 1 65 8 22 0.920 115 0 6 0 0.000 1 40 9 23 0.620 10 0 3 0 0.000 1 40 10 21 1.215 152 0 6 0 0.000 1 65 11 26 1.300 10 0 6 0 0.000 1 25 12 25 2.325 357 0 6 0 0.000 1 23 13 19 0.618 423 0 6 0 0.000 1 80 14 18 4.880 10 0 6 0 0.000 1 40 15 17 0.539 10 0 6 0 0.000 1 45 16 16 4.384 10 0 6 0 0.000 1 23 17 15 0.300 192 0 6 0 0.000 1 90 18 14 1.859 192 0 6 0 0.000 1 23 19 13 1.527 500 0 6 0 0.000 1 23 20 12 1.268 126 0 6 0 0.000 1 23 21 11 1.205 126 0 6 0 0.000 1 23 22 9 1.110 500 0 6 0 0.000 1 45 23 8 1.205 500 0 6 0 0.000 1 45 24 7 3.847 10 0 6 0 0.000 1 23 25 6 2.104 10 0 6 0 0.000 1 23 26 5 1.443 10 0 6 0 0.000 1 30 27 4 1.065 10 0 6 0 0.000 1 23 28 3 5.327 10 0 6 0 0.000 1 23 29 2 3.942 10 0 3 0 0.000 1 23 Cont. Table B-l. Characteristics of manholes- Maroubra KG1: Circular Manholes FILE.MAROUBRA.SWF PAGE CREATED: 28-OCT-1993 15:36:44 EDITED: l-JUN-1994 15:51 Row Nodal Coordinates Levels, m Shape Diameter, m Point of No. outlet X,m Y,m Top Bottom 1-4 1 33 703.6 893.9 25.36 26.36 4 0.64 2 32 685.9 794.3 24.46 25.46 4 0.64 Appendix B MOUSE And MMOUSE Modelling Data 3 31 836.3 752.3 22.88 25.50 4 0.64 4 29 827.5 690.3 22.64 25.02 4 0.64 5 28 798.7 601.8 22.38 25.03 4 0.64 6 27 787.7 517.7 22.20 25.49 4 0.64 7 24 836.3 340.7 28.48 29.48 4 0.64 8 23 708.0 362.9 23.71 24.71 4 0.64 9 22 716.9 444.7 22.01 24.74 4 0.64 10 21 623.9 458.0 21.80 25.82 4 0.64 11 26 1106.3 287.6 46.53 47.53 4 0.64 12 25 823.1 327.5 34.60 36.60 4 0.64 13 19 623.9 243.4 24.79 26.79 4 0.64 14 18 606.2 351.8 22.67 24.67 4 0.64 15 17 588.5 458.0 21.63 23.63 4 0.64 16 16 480.1 920.4 24.23 25.23 4 0.64 17 15 495.6 825.3 22.88 24.88 4 0.64 18 14 455.8 829.7 22.64 24.64 4 0.64 19 13 446.9 739.0 22.08 23.48 4 0.64 20 12 473.5 725.7 21.94 23.34 4 0.64 21 11 486.8 641.6 21.61 23.11 4 0.64 22 9 508.9 562.0 21.27 24.27 4 0.64 23 8 493.4 473.5 20.93 22.93 4 0.64 24 7 265.5 681.5 22.76 23.76 4 0.64 25 6 303.1 593.0 21.74 22.74 4 0.64 26 5 320.8 500.0 20.83 22.13 4 0.64 27 4 393.8 486.8 20.63 23.13 4 0.64 28 3 380.6 380.6 20.08 22.08 4 0.64 29 2 190.3 230.1 18.96 20.96 4 0.64 Cont. Table B-l. Outlet characteristics -Maroubra KU: Outlets FILE: MAROUBRA.SWF PA 5 CREATED: 28-OCT-1993 15:36:44 EDITED: l-JUN-1994 15:51 Row Nodal Coordinates Levels, m Point No. X,m Y,m Top Bottom 1 OUTLET 168.2 208.0 18.76 18.76 Appendix B . MOUSE And MMOUSE Modelling Data Cont. Table B-l. Pipes and conduites characteristics - Maroubra LI: Conduits (Pipes) FILE:MAROUBRA.SWF PAGE 6 CREATED: 28-OCT-199315:36:44 EDITED: l-JUN-1994 15:57:35 Nodal Points Mat Alt Bottom Level Alt Infiltration Alt Pipe Row Sect. Dia.,m Upstr. Dwstr. Upstr. Dwstr. Flow GW No. No. m. m. m3/s/m level, m 1 33 32 3 25.36 24.46 0.000 0.450 2 32 31 3 24.46 22.88 0.000 0.450 3 31 29 3 22.88 22.64 0.000 1.220 4 29 28 3 22.64 22.38 0.000 1.220 5 28 27 3 22.38 22.20 0.000 1.220 6 27 22 3 22.20 22.01 0.000 1.220 7 22 21 3 22.01 21.80 0.000 1.372 8 21 17 3 21.80 21.63 0.000 1.372 9 19 18 3 24.79 22.67 0.000 0.457 10 18 17 3 22.67 21.63 0.000 0.914 11 17 8 3 21.63 20.93 0.000 1.372 12 9 8 3 21.27 20.93 0.000 0.610 13 11 9 3 21.61 21.27 0.000 0.610 14 12 11 3 21.94 21.61 0.000 0.610 15 13 12 3 22.08 21.94 0.000 0.610 16 14 13 3 22.64 22.08 0.000 0.457 17 15 14 3 22.88 22.64 0.000 0.457 18 16 15 3 24.23 22.88 0.000 0.305 19 8 4 3 20.93 20.63 0.000 1.372 20 5 4 3 20.83 20.63 0.000 0.914 21 6 5 3 21.74 20.83 0.000 0.457 22 7 6 3 22.76 21.74 0.000 0.457 23 4 3 3 20.63 20.08 0.000 1.524 24 3 2 3 20.08 18.96 0.000 1.372 25 25 18 3 34.60 22.67 0.000 0.525 26 26 25 3 46.53 34.60 0.000 0.450 27 23 22 3 23.71 22.01 0.000 0.450 28 24 23 3 28.48 23.71 0.000 0.450 Cont. Table B-l Pipes and conduites characteristics - Maroubra LI: Conduits (Trapezoidal Canals) FILE:FGC.SWF PAGE 7 CREATED: 28-OCT-199315:36:44 EDITED: l-JUN-1994 15:57:35 Nodal Points Mat Alt Bottom Level Alt Infiltration Bot. Angle Max Row Wdth. Heght, m m Upstr. Dwstr. Upstr. Dwstr. Flow GW No. No. m. m. m3/s/m leveLm 1 2 oun^r 7 1 18.96 18.76 1 0.000 3.2 0.3 1.4 Appendix B MOUSE And MMOUSE Modelling Data Table B-2. Hydrology data file - Maroubra HYDROLOGICAL DATA MOUSE-SYSTEM Filename MARCAL.ROF Edited 26-MAY-1994 14:22 Created 29-JUL-1993 03:47 Level (A/B) A Global values Not specified No. of nodal points 29 SPECIFIC PARAN[ETERVALUE S LEVEL A Row Sub- Hydrological Initial Loss Time Area Time of Catchment Reduction m Diagram No. Concentration Factor Minutes 1 33 0.59 0.0010 10.00 2 32 0.59 0.0010 16.60 3 31 0.59 0.0010 5.00 4 29 0.59 0.0010 8.00 5 28 0.59 0.0010 6.80 6 27 0.59 0.0010 12.30 7 24 0.59 0.0010 5.00 8 22 0.59 0.0010 4.00 9 23 0.59 0.0010 5.00 10 21 0.59 0.0010 10.20 11 26 0.59 0.0010 5.00 12 25 0.59 0.0010 12.90 13 19 0.59 0.0010 3.10 14 18 0.59 0.0010 13.40 15 17 0.59 0.0010 2.00 16 16 0.59 0.0010 10.30 17 15 0.59 0.0010 2.00 18 14 0.59 0.0010 5.00 19 13 0.59 0.0010 5.00 20 12 0.59 0.0010 5.00 21 11 0.59 0.0010 5.00 22 9 0.59 0.0010 2.00 23 8 0.59 0.0010 5.00 24 7 0.59 0.0010 2.00 25 6 0.59 0.0010 5.00 26 5 0.59 0.0010 5.00 27 4 0.59 0.0010 5.00 28 3 0.59 0.0010 5.00 29 2 0.59 0.0010 10.60 Appendix B MOUSE And MMOUSE Modelling Data Table B-3. Physical characteristics of sub catchments- Jamison Park D:CATCHMENTS FILE:JAMPK.SWF PAGE 1 CREATED: 28-OCT-1993 15:36:44 EDITED: 28-MAR-1994 13:11:19 Surface Distribution (total area): Impervious : 1: steep roof 2: flat roof 3: asfalt/concrete Semi pervious : 4: large- 5: small spacing between tiles Pervious : 6: planted 7: unplanted CT = Catchm.type(l-7) HL = H:^drologi c Level (1-2) SI3 =Soil Parameter (1-3) Row Nodal Total Slope Catch C Person Add. H Pet SP Surface Distribution Point Area lgth. T eqvlt. flow L Imp. 12 3 4 5 6 7 No. ha prm m Pe/ha m3/s Pet pet. 1 DI 0.990 6 116 2 0 0.000 40 2 E1E2 0.685 4 98 6 0 0.000 57 3 D2 1.010 6 102 2 0 0.000 27 4 D3D4 0.096 1 62 6 0 0.000 60 5 FI 0.456 5 109 3 0 0.000 36 6 D5 0.209 4 62 3 0 0.000 35 7 D6 0.338 6 105 3 0 0.000 26 8 G1G2 0.870 5 129 3 0 0.000 39 9 D7 0.279 5 99 3 0 0.000 32 10 HI 0.200 3 138 3 0 0.000 100 11 11 0.162 6 52 3 0 0.000 40 12 12 0.132 6 39 3 0 0.000 44 13 D8 0.298 4 102 3 0 0.000 24 14 Kl 0.285 4 77 3 0 0.000 42 15 K2 0.211 1 55 3 0 0.000 46 16 D9J1 0.276 4 83 3 0 0.000 41 17 LI 0.245 4 58 3 0 0.000 35 18 D10 0.447 3 99 3 0 0.000 L35 19 Ml 0.274 4 80 3 0 0.000 45 20 Dll 0.275 1 72 3 0 0.000 35 21 Ol 0.568 2 124 3 0 0.000 53 22 02 1.204 3 195 3 0 0.000 18 23 Ql 0.090 30 3 0 0.000 47 24 PI 0.180 55 3 0 0.000 45 . 25 03 0.127 55 3 0 0.000 43 26 05 0.214 55 3 0 0.000 42 27 R1R2 0.323 3 151 3 0 0.000 60 28 R3 0.176 55 3 0 0.000 43 29 R4 0.711 3 157 3 0 0.000 37 30 SI 0.596 132 3 0 0.000 45 31 07 0.194 55 3 0 0.000 40 32 NI 0.187 2 55 3 0 0.000 48 33 D12 0.178 61 3 0 0.000 42 34 D13 0.444 94 3 0 0.000 44 35 Tl 0.575 99 3 0 0.000 42 36 Ul 0.069 39 3 0 0.000 42 37 T2 0.430 96 3 0 0.000 38 38 T3 0.349 83 3 0 0.000 47 39 VI 0.438 220 3 0 0.000 55 40 D15 0.631 160 3 0 0.000 46 41 Wl 3.704 5 250 3 0 0.000 8 Appendix B MOUSE And MMOUSE Modelling. Data 42 W2 1.119 4 412 3 0 0.000 1 46 43 XI 0.282 63 3 0 0.000 1 47 44 X2 0.311 77 3 0 0.000 1 43 45 D16 0.267 55 3 0 0.000 1 47 4_6J YI 0.932 110 3 0 0.000 1 33 47 D18 0.030 55 3 0 0.000 1 20 Cont. Table B-3. Characteristics of manholes- Jamison Park KG 1: Circular Manholes FTLEJAMPK.SWF PAGE 3 CREATED: 28-OCT-1993 15:36:44 EDITED: 28-MAR-1994 13:11:19 Row Nodal Coordinates Levels, m Shape Diameter Point of No. outlet X,m Y,m Top Bottom 1-4 1 DI 297.5 533.5 42.00 43.27 4 0.73 2 D2 259.0 547.3 40.90 41.97 4 0.73 3 D3 253.5 532.1 40.64 42.04 4 0.73 4 D3D4 239.7 523.9 40.07 41.97 4 0.73 5 D5 239.7 477.1 38.27 39.70 4 0.73 6 D6 239.7 435.9 37.17 38.23 4 0.73 7 D7 239.7 390.5 35.54 36.82 4 0.73 8 D8 239.7 354.8 34.50 35.46 4 0.73 9 D9J1 228.7 320.4 33.09 34.47 4 0.73 10 D10 228.7 231.3 30.84 31.94 4 0.50 11 Dll 228.7 176.0 30.20 31.03 4 0.50 12 D12 228.7 137.8 29.63 30.83 4 0.50 13 D13 217.7 111.4 29.54 30.96 4 0.73 14 D14 217.7 33.0 29.00 30.80 4 1.00 15 D15 155.9 33.0 28.69 30.99 4 0.50 16 D16 146.8 46.8 28.64 30.74 4 0.50 17 D17 134.7 46.8 28.57 30.57 4 0.73 18 D18 30.8 46.8 28.31 30.21 4 0.73 19 El 255.7 596.8 43.61 44.61 4 0.50 20 E1E2 239.7 556.9 41.36 42.48 4 0.73 21 FI 228.7 484.0 39.25 39.99 4 0.73 22 Gl 186.1 402.9 37.62 38.62 4 0.50 23 G1G2 228.7 402.9 36.02 36.96 4 0.73 24 11 197.1 369.0 35.75 36.51 4 0.73 25 12 224.6 369.0 34.83 35.75 4 0.73 26 Jl 232.2 327.3 33.29 34.29 4 0.50 27 HI 275.5 363.0 35.61 36.61 4 0.50 28 Kl 254.8 327.3 34.14 34.98 4 0.73 29 K2 254.8 319.0 33.78 34.54 4 0.73 30 LI 238.4 240.9 31.27 32.07 4 0.50 31 Ml 238.4 181.5 30.29 30.95 4 0.50 32 NI 238.4 137.8 29.64 30.25 4 0.50 33 RI 356.6 176.0 30.91 31.91 4 0.50 34 R1R2 322.2 176.0 30.48 31.04 4 0.73 35 R3 322.2 137.5 30.26 31.26 4 0.50 36 R4 315.4 137.5 30.18 30.74 4 0.73 37 01 402.0 176.0 30.82 31.52 4 0.73 Appendix B MOUSE And MMOUSE Modellm g Data 38 02 393.7 170.5 30.75 31.45 4 0.73 39 03 393.7 129.3 30.65 31.31 4 0.73 40 04 385.8 121.0 30.60 31.21 4 0.73 41 05 326.4 121.0 30.23 31.00 4 0.73 42 06 304.4 121.0 30.12 30.78 4 0.50 43 07 245.8 121.0 29.72 31.12 4 0.73 44 Tl 462.5 33.0 30.25 30.86 4 0.50 45 T2 385.7 33.0 29.81 30.42 4 0.50 46 T3 314.0 33.0 29.47 30.27 4 0.50 47 XI 155.9 121.0 29.49 29.95 4 0.50 48 X2 155.9 111.4 29.25 29.95 4 0.50 49 Wl 134.7 152.6 29.34 30.10 4 0.73 50 W2 146.8 152.6 29.27 30.03 4 0.50 51 W3 146.8 105.9 29.03 30.03 4 0.50 52 YI 44.5 66.0 28.43 29.01 4 0.50 53 Qi 407.8 121.0 30.74 31.50 4 0.73 54 PI 402.0 129.0 30.70 31.46 4 0.50 55 si 304.3 111.4 30.13 30.84 4 0.50 56 Ul 388.5 27.5 29.95 30.78 4 0.50 57 VI 173.8 27.5 28.89 29.55 4 0.73 Cont. Table B-3. Outlet characteristics - Jamison Park KU: Outlets FILE: JAMPK.SWF PAGE: 1 CREATED: 28-OCT-1993 15:36:44 EDITED: 28-MAR-1994 13:11:19 Row Nodal Coordinates Levels, m Point No. X,m Y,m Top Bottom 1 END -24.3 46.8 28.16 28.16 Cont. Table B-3. Pipes and conduites characteristics - Jamison Park LI: Conduits (Pipes) FILE:JAMPK.SWF PAGE 6 CREATED: 28-OCT-199315:36:44 EDITED: 28-MAR-1994 13:11:19 Nodal Points Mat Alt Bottom Level Alt Infiltration Alt Pipe Row Sect. Dia.,m Upstr. Dwstr. Upstr. Dwstr. Flow GW No. No. m. m. m3/s/m level m 1 DI D2 42.00 40.90 1 0.000 0.450 2 D2 D3 40.90 40.64 1 0.000 0.450 3 D3 D3D4 40.64 40.07 1 0.000 0.450 4 D3D4 D5 40.07 38.27 1 0.000 0.450 5 D5 D6 38.27 37.17 1 0.000 0.450 6 D6 D7 37.17 35.54 1 0.000 0.600 7 D7 D8 35.54 34.50 1 0.000 0.600 8 D8 D9J1 34.50 33.09 1 0.000 0.600 9 D9J1 D10 33.09 30.84 1 0.000 0.900 10 D10 Dll 30.84 30.20 1 0.000 0.900 H Dll D12 30.20 29.63 1 0.000 0.900 Appendix B MOUSE And MMOUSE Modelling Data 12 D12 D13 29.63 29.54 0.000 1.200 J 13 D13 D14 29.54 29.00 0.000 1.200 14 D14 D15 29.00 28.70 0.000 1.350 15 D15 D16 28.69 28.64 0.000 1.350 16 D16 D17 28.64 28.57 0.000 1.540 17 D17 D18 28.57 28.31 0.000 1.540 18 D18 END 28.31 28.16 0.000 1.540 19 Gl G1G2 37.62 36.02 0.000 0.225 20 G1G2 D7 36.02 35.54 0.000 0.375 21 11 12 35.75 34.83 0.000 0.450 22 12 D8 34.83 34.50 0.000 0.525 23 HI D8 35.61 34.50 0.000 0.225 24 Kl K2 34.14 33.78 0.000 0.375 25 K2 D9J1 33.78 33.09 0.000 0.375 26 Jl D9J1 33.29 33.09 0.000 0.150 27 01 02 30.82 30.75 0.000 0.375 28 02 03 30.75 30.65 0.000 0.375 29 03 04 30.65 30.60 0.000 0.375 30 04 05 30.60 30.23 0.000 0.600 31 05 06 30.23 30.12 0.000 0.675 32 06 07 30.12 29.72 0.000 0.900 33 07 D13 29.72 29.54 0.000 0.900 34 RI R1R2 30.91 30.48 0.000 0.150 35 R1R2 R3 30.48 30.26 0.000 0.375 36 R3 R4 30.26j 30.18 0.000 0.450 37 R4 06 30.18j 30.12 0.000 0.600 38 Tl T2 30.25 29.81 0.000 0.375 39 T2 T3 29.81 29.74 0.000 0.375 40 T3 D14 29.47 29.00 0.000 0.450 41 Wl W2 29.34 29.27 0.000 0.450 42 W2 W3 29.27 29.03 0.000 0.450 43 W3 D16 29.03 28.64 0.000 0.600 44 El E1E2 43.61 41.36 0.000 0.225 45 E1E2 D2 41.36 40.90 0.000 0.375 46 YI D18 28.43 28.31 0.000 0.375 47 PI 03 30.70 30.65 0.000 0.375 48 Ql 04 30.74 30.60 0.000 0.375 49 SI 06 30.13 30.12 0.000 0.375 50 Ul T2 29.95 29.81 0.000 0.375 51 VI D15 28.89 28.69 0.000 0.375 52 FI D5 39.25 38.27 0.000 0.375 53 LI D10 31.27 30.84 0.000 0.375 54 Ml Dll 30.29 30.20 0.000 0.375 55 NI D12 29.64 29.63 0.000 0.375 56 XI X2 29.49 29.25 0.000 0.375 57 X2 W3 29.25 29.03 0.000 0.375 Appendix B MOUSE And MMOUSE Modelling Data Table B-4. Hydrology data file - Jamison Park HYDROLOGICAL DATA MOUSE-SYSTEM Filename JAMISON.ROF Edited 5-MAY-1994 16:47 Created 24-MAR-1994 11:07 Level (A/B) A Global values Not specified No. of nodal points 47 SPECIFIC PARAM ETERVALUES LEVEL A Row Sub- Hydrological Initial Loss Time Area Time of Catchment Reduction m Diagram No. Concentration Factor Minutes 1 DI 0.80 0.0010 4.20 2 E1E2 0.80 0.0010 4.30 3 D2 0.80 0.0010 3.90 4 D3D4 0.80 0.0010 4.90 5 FI 0.80 0.0010 4.30 6 D5 0.80 0.0010 3.20 7 D6 0.80 0.0010 3.90 8 G1G2 0.80 0.0010 4.70 9 D7 0.80 0.0010 4.00 10 HI 0.80 0.0010 5.70 11 11 0.80 0.0010 2.60 12 12 0.80 0.0010 2.20 13 D8 0.80 0.0010 4.40 14 Kl 0.80 0.0010 3.70 15 K2 0.80 0.0010 4.60 16 D9J1 0.80 0.0010 3.90 17 LI 0.80 0.0010 3.10 18 D10 0.80 0.0010 4.70 19 Ml 0.80 0.0010 3.80 20 Dll 0.80 0.0010 5.40 21 01 0.80 0.0010 6.10 22 02 0.80 0.0010 7.00 23 01 0.80 0.0010 3.20 24 PI 0.80 0.0010 4.60 25 03 0.80 0.0010 4.60 26 05 0.80 0.0010 4.60 27 R1R2 0.80 0.0010 6.00 28 R3 0.80 0.0010 4.60 29 R4 0.80 0.0010 6.20 30 SI 0.80 0.0010 7.70 31 07 0.80 0.0010 4.60 32 NI 0.80 0.0010 3.70 33 D12 0.80 0.0010 4.90 34 D13 0.80 0.0010 6.30 35 Tl 0.80 0.0010 6.50 36 Ul 0.80 0.0010 3.70 37 T2 0.80 0.0010 6.40 38 T3 0.80 0.0010 5.90 39 VI 0.80 0.0010 10.50 40 D15 0.80 0.0010 8.70 41 Wl 0.80 0.0010 7.00 Appendix B MOUSE And MMOUSE Modelling Data 42 W2 0.80 0.0010 10.10 43 XI 0.80 0.0010 5.00 44 X2 0.80 0.0010 5.60 45 D16 0.80 0.0010 4.60 46 YI 0.80 0.0010 6.90 47 D18 0.80 0.0010 4.60 Table B-5. Physical characteristics of sub catchments- Fisher's Ghost Creek D:CATCHMENTS FILEJAMPK.SWF PAGE 1 CREATED: 28-OCT-1993 15:36:44 EDITED: 17-JUN-1994 15:16:12 Surface Distribution (total area): Impervious : 1: steep roof 2: flat roof 3: asfalt/concrete Semi pervious : 4: large- 5: small spacing between tiles Pervious : 6: planted 7: unplanted CT = Catchm.type(l-7) HL = Hydrologic Level (1-2) S 5=Soil Parameter (1-3) Row Nodal Total Slope Catch C Person Add. H Pet SP Surface Distribution Point Area lgth. T eqvlt. flow L Imp. 12 3 4 5 6 7 No. ha prm m Pe/ha m3/s Pet pet. 1 480 4.540 10 193 2 0 0.000 30 2 470 3.780 9 228 6 0 0.000 30 3 460 5.640 10 249 2 0 0.000 27 4 450 2.920 8 276 6 0 0.000 30 5 440 4.120 10 276 3 0 0.000 30 6 430 4.200 10 359 3 0 0.000 30 7 420 0.700 9 138 3 0 0.000 30 8 410 5.730 11 352 3 0 0.000 30 9 400 1.260 7 104 3 0 0.000 15 10 390 6.560 8 276 3 0 0.000 15 11 380 6.910 8 345 3 0 0.000 30 12 370 5.950 8 297 3 0 0.000 28 13 360 6.750 7 276 3 0 0.000 30 14 350 4.410 9 332 3 0 0.000 29 15 340 3.690 10 235 3 0 0.000 28 16 330 4.980 9 249 3 0 0.000 30 17 320 3.060 10 180 3 0 0.000 25 18 310 5.570 10 228 3 0 0.000 26 19 300 7.300 9 228 3 0 0.000 29 20 290 4.990 5 339 3 0 0.000 29 21 280 3.380 7 173 3 0 0.000 20 22 270 6.660 8 325 3 0 0.000 23 23 260 6.520 11 394 3 0 0.000 30 24 250 4.710 10 435 3 0 0.000 30 25 240 4.980 15 249 3 0 0.000 30 26 230 9.810 8 359 3 0 0.000 30 27 220 1.880 7 193 3 0 0.000 30 28 210 4.080 9 228 3 0 0.000 15 29 200 0.820 7 138 3 0 0.000 15 30 190 2.660 8 297 3 0 0.000 30 31 180170 6.480 9 808 3 0 0.000 29 32 160 6.840 10 352 3 0 0.000 20 33 150 2.250 8 366 3 0 0.000 29 Appendix B MOUSE And MMOUSE Modelling Data 34 140 0.750 6 124 3 0 0.000 15 35 130 8.130 6 297 3 0 0.000 25 36 120 6.690 11 332 3 0 0.000 30 37 110 5.880 8 497 3 0 0.000 30 38 100 7.150 14 366 3 0 0.000 30 39 90 3.050 11 491 3 0 0.000 29 40 80 4.800 8 359 3 0 0.000 30 41 70 4.960 6 325 3 0 0.000 30 42 60 2.280 3 152 3 0 0.000 15 43 50 3.040 6 187 3 0 0.000 10 44 40 6.320 9 449 3 0 0.000 30 45 30 2.440 6 228 3 0 0.000 30 46 20 3.620 4 297 3 0 0.000 28 47 10 1.070 3 152 3 0 0.000 25 Cont. Table B-5. Characteristics of manholes- Fisher's Ghost Creek KG 1: Circular Manholes FILE:JAMPK.SWF PAGE 3 CREATED: 28-OCT-1993 15:36:44 EDITED: 28-MAR-1994 13:11:19 Row Nodal Coordinates Levels, m Shape Diameter, m Point of No. outlet X,m Y,m Top Bottom 1-4 1 480 2106.9 1271.1 153.15 154.00 4 1.00 2 470 2065.5 1174.4 150.23 151.00 4 1.00 3 460 2051.7 1088.0 150.70 151.00 4 1.00 4 450 1993.0 1119.1 146.84 147.00 4 1.00 5 440 1920.4 1088.0 145.95 146.00 4 1.00 6 430 1920.0 1181.3 148.80 149.00 4 1.00 7 420 1906.6 1119.1 145.23 146.00 4 1.00 8 410 1851.3 1226.2 150.23 151.00 4 1.00 9 400 1824.4 1132.9 143.03 144.00 4 1.00 10 390 1651.0 998.2 136.64 137.00 4 1.00 11 380 1592.3 1160.5 142.29 143.00 4 1.00 12 370 1595.7 960.2 134.29 135.00 4 1.00 13 360 1409.2 818.6 129.19 130.00 4 1.00 14 350 2037.9 587.2 146.05 147.00 4 1.00 15 340 1955.0 542.3 144.25 145.00 4 1.00 16 330 1868.6 621.7 145.00 146.00 4 1.00 17 320 1851.3 531.9 142.48 143.00 4 1.00 18 310 1682.1 573.4 136.30 137.00 4 1.00 19 300 1533.6 601.0 134.50 135.00 4 1.00 20 290 1316.0 818.6 128.17 129.00 4 1.00 21 280 1281.4 884.2 125.67 126.00 4 1.00 22 270 1223.4 822.1 124.86 125.00 4 1.00 23 260 1260.7 1063.8 134.53 135.00 4 1.00 24 250 1184.7 1063.8 133.20 134.00 4 1.00 25 240 1018.9 1367.8 141.22 142.00 4 1.00 26 230 984.4 1036.2 126.63 127.00 4 1.00 27 220 960.2 871.1 118.52 119.00 4 1.00 28 80170 832.4 829.0 115.50 116.00 4 1.00 29 210 967.1 808.2 117.68 118.00 4 1.00 30 200 832.4 763.3 113.39 114.00 4 1.00 31 201 877.3 784.1 113.88 114.00 2 1.00 32 190 887.7 718.4 116.81 117.00 4 1.00 33 160 832.4 594.1 119.81 120.00 4 1.00 Appendix B MOUSE And MMOUSE Modelling Data 34 150 832.4 663.2 115.09 116.00 4 1.00 35 140 773.7 746.1 112.52 113.00 4 1.00 36 130 597.5 732.2 109.79 110.00 4 1.00 37 120 504.3 604.5 110.56 111.00 4 1.00 38 110 594.1 967.1 116.13 117.00 2 1.00 39 100 659.7 1523.2 131.51 132.00 4 1.00 40 90 574.7 ^1257.3 j 120.03 121.00 4 1.00 41 80 490.5 967.1 111.58 112.00 4 1.00 42 70 452.5 808.2 107.52 108.00 4 1.00 43 60 480.1 673.5 106.39 107.00 4 1.00 44 50 307.4 594.1 101.89 102.00 4 1.00 45 20 200.3 607.9 100.13 101.00 4 1.00 46 30 217.6 728.8 105.88 106.00 4 1.00 47 40 226.6 884.2 110.45 111.00 4 1.00 48 10 152.7 566.5 100.01 101.00 2 1.00 Cont. Table B-5. Outlet characteristics - Fisher's Ghost Creek KU: Outlets FILE: FGC.SWF PAGE: 5 CREATED: 28-OCT-1993 15:36:44 EDITED: 17-JUN-1994 15:161:12 Row Nodal Coordinates Levels, m Point No. X,m Y,m Top Bottom 1 WEI 145.0 555.0 100.00 100.00 R Cont. Table B-5. Pipes and conduites characteristics - Fisher's Ghost Creek LI: Conduits (Pipes) FILE:FGC.SWF PAGE 7 CREATED: 28-OCT-199315:36:44 EDITED: 17-JUN-1994 15:16: 12 Nodal Points Mat Alt Bottom Level Alt Infiltration Alt Pipe Row Sect. Dia.,m Upstr. Dwstr. Upstr. Dwstr. Flow GW No. No. m. m. m3/s/m level, m 1 480 470 3 153.15 150.23 0.000 0.530 2 470 450 3 150.23 146.84 0.000 0.680 3 460 450 3 150.70 146.84 0.000 0.450 4 430 420 3 148.80 145.23 0.000 0.530 5 450 420 3 146.84 145.23 0.000 0.900 6 440 420 3 145.95 145.23 0.000 0.530 7 410 400 3 150.23 143.03 0.000 0.600 8 390 370 3 136.64 134.29 0.000 1.350 9 380 370 3 142.29 134.29 0.000 0.530 10 370 360 3 134.29 129.19 0.000 1.350 11 360 290 3 129.19 128.17 0.000 1.350 12 350 340 3 146.05 144.25 0.000 0.530 13 340 320 3 144.25 142.48 0.000 0.750 14 330 320 3 145.00 142.48 0.000 0.530 15 320 310 3 142.48 136.30 0.000 0.900 16 310 300 3 136.30 134.50 0.000 1.050 17 300 290 3 134.50 128.17 0.000 1.050 Appendix B _ _ MOUSE And MMOUSE Modelling Data 18 280 270 3 125.67 124.86 0.000 1 0.900 19 190 200 3 116.81 113.39 0.000 1 0.450 20 160 150 3 119.81 115.09 0.000 1 0.450 21 150 140 3 115.09 112.52 0.000 1 0.600 22 120 60 3 110.56 106.39 0.000 1 0.530 23 110 80 3 116.13 111.58 0.000 1 0.600 24 100 90 3 131.51 120.03 0.000 1 0.680 25 90 80 3 120.03 111.58 0.000 1 0.900 26 80 70 3 111.58 107.52 0.000 1 1.050 27 70 60 A _j 107.52 106.39 0.000 1 1.050 28 40 30 3 110.45 105.88 0.000 1 0.680 29 30 20 3 105.88 100.13 0.000 1 0.750 30 50 20 3 101.89 100.13 0.000 1 2.500 31 260 250 3 A34.53 133.20 0.000 1 0.680 32 250 230 3 133.20 126.63 0.000 1 0.750 33 240 230 3 141.22 126.63 0.000 1 0.680 34 220 210 3 118.52 117.68 0.000 1 0.900 35 180170 200 3 115.50 113.39 0.000 1 0.530 36 201 200 3 113.88 113.39 0.000 1 2.300 Cont. Table B-5. Pipes and conduites characteristics - Fisher's Ghost Creek LI: Conduits (Trapezoidal Canals) FILE:FGC.SWF PAGE 7 CREATED: 28-OCT-199315:36:44 EDITED: 17-JUN-1994 15:16: 2 Nodal Points Mat Alt Bottom Level Alt Infiltration Bot. Angle Max Row Wdth, Heght, m m Upstr. Dwstr. Upstr. Dwstr. Flow GW No. No. m. m. m3/s/m level, m 1 420 400 8 145.23 143.03 0.000 1.0 6.0 1.5 2 400 390 8 143.03 136.64 0.000 1.0 10.0 2.0 3 290 270 8 128.17 124.86 0.000 1.5 2.0 4.0 4 270 210 8 124.86 117.68 0.000 2.5 2.0 4.0 5 210 201 8 117.68 113.88 0.000 2.0 2.0 5.0 6 200 140 8 113.39 112.52 0.000 2.0 2.0 5.0 7 140 130 8 112.52 109.79 0.000 2.5 4.0 5.0 8 130 60 8 109.79 106.39 0.000 1.0 3.0 6.0 9 60 50 8 106.39 101.89 0.000 2.0 1.5 6.0 10 20 10 8 100.13 100.01 0.000 2.0 3.0 6.0 11 10 WEIR 8 100.01 100.00 0.000 3.0 4.0 6.0 12 230 220 8 126.63 118.52 0.000 1.0 1.5 1.0 Appendix B MOUSE And MMOUSE Modelling Data Table B-6. Hydrology data file - Fisher's Ghost Creek HYDROLOGICAL DATA MOUSE-SYSTEM Filename FGC.ROF Edited 6-MAY-1994 19:09 Created 25-APR-1994 15:32 Level (A/B) A Global values Not specified No. of nodal points 47 SPECIFIC PARAM ETERVALUES LEVEL A Row Sub- Hydrological Initial Loss Time Area Time of Catchment Reduction m Diagram No. Concentration Factor Minutes 1 480 1.00 0.0010 5.30 2 470 1.00 0.0010 6.00 3 460 1.00 0.0010 6.20 4 450 1.00 0.0010 7.00 5 440 1.00 0.0010 6.60 6 430 1.00 0.0010 7.70 7 420 1.00 0.0010 4.50 8 410 1.00 0.0010 7.40 9 400 1.00 0.0010 4.10 10 390 1.00 0.0010 7.00 11 380 1.00 0.0010 8.00 12 370 1.00 0.0010 7.30 13 360 1.00 0.0010 7.30 14 350 1.00 0.0010 7.60 15 340 1.00 0.0010 6.00 16 330 1.00 0.0010 6.40 17 320 1.00 0.0010 5.10 18 310 1.00 0.0010 5.80 19 300 1.00 0.0010 6.00 20 290 1.00 0.0010 9.10 21 280 1.00 0.0010 5.50 22 270 1.00 0.0010 7.70 23 260 1.00 0.0010 7.90 24 250 1.00 0.0010 8.60 25 240 1.00 0.0010 5.50 26 230 1.00 0.0010 8.20 27 220 1.00 0.0010 5.90 28 210 1.00 0.0010 6.00 29 200 1.00 0.0010 4.80 30 190 1.00 0.0010 7.30 31 180170 1.00 0.0010 12.90 32 160 1.00 0.0010 7.60 33 150 1.00 0.0010 8.30 34 140 1.00 0.0010 4.70 35 130 1.00 0.0010 8.00 36 120 1.00 0.0010 7.10 37 110 1.00 0.0010 10.00 38 100 1.00 0.0010 7.00 39 90 1.00 0.0010 9.00 40 80 1.00 0.0010 8.20 Appendix B MOUSE And MMOUSE Modelling Data 41 70 1.00 0.0010 8.40 42 60 1.00 0.0010 6.60 43 50 1.00 0.0010 6.10 44 40 1.00 0.0010 9.10 45 30 1.00 0.0010 6.80 46 20 1.00 0.0010 9.00 47 10 1.00 0.0010 6.60 Table B-7 Physical characteristics of sub catchments- Cranebrook DCATCHMENTS FILE:CRN.SWF PAGE 1 CREATED: 28-OCT-1993 15:36:44EDITED: 11-MAY-1994 09:40:07 Surface Distribution (total area): Impervious : 1: steep roof 2: flat roof 3: asfalt/concrete Semi pervious : 4: large- 5: small spacing between tiles Pervious : 6: planted 7: unplanted CT = Catchm.type(l-7) HL = Hydrologic Level (1-2) SI?=Soi l Parameter (1-3) Row Nodal Total Slope Catch C Person Add. H Pet SP Surface Distribution Point Area lgth. T eqvlt. flow L Imp. 12 3 4 5 6 7 No. ha prm m Pe/ha m3/s Pet pet. 1 1 0.350 10 193 2 0 0.000 75 2 2 0.350 9 228 6 0 0.000 60 3 3 0.080 10 249 2 0 0.000 100 4 4 0.120 8 276 6 0 0.000 85 5 5 0.400 10 276 3 0 0.000 35 6 6 0.200 10 359 3 0 0.000 10 7 8 0.400 9 138 3 0 0.000 45 8 9 0.050 11 352 3 0 0.000 100 9 10 0.010 7 104 3 0 0.000 100 10 11 0.170 8 276 3 0 0.000 30 11 7 0.350 8 345 3 0 0.000 10 12 12 0.150 8 297 3 0 0.000 100 13 14 0.070 7 276 3 0 0.000 100 14 15 0.020 9 332 3 0 0.000 100 15 13 0.020 10 235 3 0 0.000 100 16 16 0.040 9 249 3 0 0.000 100 17 18 0.450 10 180 3 0 0.000 16 18 17 0.020 10 228 3 0 0.000 100 19 20 0.550 9 228 3 0 0.000 23 20 19 1.630 5 339 3 0 0.000 30 21 22 0.430 7 173 3 0 0.000 29 22 21 0.380 8 325 3 0 0.000 30 23 24 0.450 11 394 3 0 0.000 24 24 25 0.070 10 435 3 0 0.000 100 25 23 0.010 15 249 3 0 0.000 100 26 27 0.500 8 359 3 0 0.000 35 27 29 0.430 7 193 3 0 0.000 40 28 28 0.300 9 228 3 0 0.000 32 29 30 0.120 7 138 3 0 0.000 100 30 31 0.070 8 297 3 0 0.000 100 31 32 0.050 9 808 3 0 0.000 100 32 33 0.420 10 352 3 0 0.000 21 33 34 0.350 8 366 3 0 0.000 37 Appendix B MOUSE And MMOUSE Modelling Data 34 36 0.700 6 124 3 0 0.000 15 35 35 0.080 6 297 3 0 0.000 100 36 37 0.010 11 332 3 0 0.000 100 37 38 0.010 8 497 3 0 0.000 100 38 39 0.040 14 366 3 0 0.000 40 39 41 0.430 11 491 3 0 0.000 26 40 42 0.100 8 359 3 0 0.000 100 41 40 0.120 6 325 3 0 0.000 43 42 26 0.390 3 152 3 0 0.000 52 43 43 0.380 6 187 3 0 0.000 47 44 45 0.950 9 449 3 0 0.000 23 45 44 0.080 6 228 3 0 0.000 100 Cont. Table B-7. Characteristics of manholes- Cranebrook KGl: Circular ManholesFILE:CRN.SWF PAGE 4 CREATED: 28-OCT-1993 15:36:44EDITED: ll-MAY-1994 09:40:07 Row Nodal Coordinates Levels, m Shape Diameter, m Point of No. outlet X,m Y,m Top Bottom 1-4 1 45 448.2 33.5 100.08 101.00 4 1.00 2 44 448.0 26.4 100.00 101.00 4 1.00 3 43 427.3 47.7 100.53 101.53 4 1.00 4 26 401.9 108.6 102.20 103.50 4 1.00 5 23 379.6 163.4 103.26 104.26 4 1.00 6 25 363.4 167.5 103.37 104.37 4 1.00 7 24 357.3 178.6 103.45 104.45 4 1.00 8 21 365.4 190.8 103.58 104.58 4 1.00 9 22 372.5 198.9 103.73 104.73 4 1.00 10 19 343.3 236.5 104.58 105.58 4 1.00 11 20 353.4 247.7 105.13 106.13 4 1.00 12 17 330.9 295.4 107.54 108.54 4 1.00 13 18 343.1 311.6 108.29 109.29 4 1.00 14 16 325.8 326.6 109.86 110.86 4 1.00 15 13 314.7 387.7 113.58 114.58 4 1.00 16 15 324.4 398.8 113.68 114.68 4 1.00 17 14 334.1 401.9 113.98 114.98 4 1.00 18 12 302.7 395.9 113.68 114.68 4 1.00 19 7 300.4 408.0 113.82 114.82 4 1.00 20 11 309.6 410.1 113.97 114.97 4 1.00 21 10 337.0 415.1 115.22 116.22 4 1.00 22 9 341.0 424.3 116.39 117.39 4 1.00 23 8 352.8 427.3 116.71 117.39 4 1.00 24 6 260.9 400.9 114.25 115.25 4 1.00 25 5 235.5 396.9 114.95 115.95 4 1.00 26 4 202.0 398.8 117.05 118.05 4 1.00 27 3 167.5 383.7 119.21 120.21 4 1.00 28 2 154.3 388.7 119.75 120.75 4 1.00 29 1 145.1 377.6 120.23 121.23 4 1.00 30 41 401.9 132.0 102.90 103.90 4 1.00 31 40 415.1 124.8 102.44 103.44 2 1.00 32 42 419.2 118.8 102.52 103.52 4 1.00 Appendix B . MOUSE And MMOUSE Modelling Da:e. 33 39 443.6 138.0 103.15 104.15 4 1.00 34 38 458.4 142.1 103.98 104.98 4 1.00 35 37 468.9 149.2 105.00 106.00 4 1.00 36 35 473.0 161.4 105.38 106.38 4 1.00 37 36 488.2 154.3 105.51 106.51 4 1.00 38 34 471.2 184.7 105.91 106.91 2 1.00 39 33 443.6 253.8 107.45 108.45 4 1.00 40 32 434.4 260.9 108.32 109.32 4 1.00 41 31 425.3 304.5 112.30 113.30 4 1.00 42 30 434.4 317.7 112.66 113.66 4 1.00 43 28 431.4 322.8 112.76 113.76 4 1.00 44 29 422.2 329.9 113.52 114.52 4 1.00 45 27 456.5 327.8 114.76 115.76 4 1.00 Cont. Table B-7. Outlet characteristics - Cranebrook KU: Outlets FILE: CRN.SWF PAGE: 5 CREATED: 28-OCT-1993 15:36:44 EDITED: 28-MAR-1994 13:11:19 Row Nodal Coordinates Levels, m Point No. X, m Y, m Top Bottom 1 FLUME 450.7 0.0 99.67 99.67 Cont. Table B-7. Pipes and conduites characteristics - Cranebrook LI: Conduits (Pipes) FILE:CRN.SWF PAGE 6 CREATED: 28-OCT-1993 15:36:44 EDITE D: ll-MAY-1994 09:40:07 Nodal Points Mat Alt Bottom Level Alt Infiltration Alt Pipe Row Sect Dia.,m Upstr. Dwstr. Upstr. Dwstr. Flow GW No. No. m. m. m3/s/m level, m 1 1 2 3 120.23 119.75 0.000 0.375 2 2 3 3 119.75 119.21 0.000 0.375 3 3 4 3 119.21 117.05 0.000 0.375 4 4 5 3 117.05 114.95 0.000 0.450 5 5 6 3 114.95 114.25 0.000 0.450 6 6 7 3 114.25 113.82 0.000 0.450 7 8 9 3 116.71 116.39 0.000 0.375 8 9 10 3 116.39 115.22 0.000 0.375 9 10 11 3 115.22 113.97 0.000 0.375 10 11 7 3 113.97 113.82 0.000 0.375 11 7 12 3 113.82 113.68 0.000 0.450 12 12 13 3 113.68 113.58 0.000 0.450 13 14 15 3 113.98 113.68 0.000 0.375 14 15 13 3 113.68 113.58 0.000 0.375 15 13 16 3 113.58 109.86 0.000 0.450 16 16 17 3 109.86 107.54 0.000 0.450 17 18 17 3 108.29 107.54 0.000 0.375 18 17 19 3 107.54 104.58 0.000 0.450 19 20 19 3 105.13 104.58 0.000 0.375 20 19 21 3 104.58 103.58 0.000 0.600 21 22 21 3 103.73 103.58 0.000 0.375 22 21 23 3 103.58 103.26 0.000 0.675 23 24 25 3 103.45 103.37 0.000 0.375 24 25 23 3 103.37 103.26 0.000 0.375 25 23 26 3 103.26 102.20 0.000 0.675 26 29 28 3 113.52 112.76 0.000 0.375 27 27 28 3 114.76 112.76 0.000 0.375 28 28 30 3 112.76 112.66 0.000 0.375 29 30 31 3 112.66 112.30 0.000 0.375 30 31 32 3 112.30 108.32 0.000 0.375 31 32 33 3 108.32 107.45 0.000 0.375 32 33 34 3 107.45 105.91 0.000 0.450 33 34 35 3 105.91 105.38 0.000 0.450 34 36 35 3 105.51 105.38 0.000 0.375 35 35 37 3 105.38 105.00 0.000 0.450 36 37 38 3 105.00 103.98 0.000 0.525 37 38 39 1 103.98 103.15 0.000 0.525 38 39 40 1 103.15 102.44 0.000 0.525 39 41 40 1 102.90 102.44 0.000 0.375 40 42 40 1 102.52 102.44 0.000 0.375 41 40 26 1 102.44 102.20 0.000 0.600 42 26 43 1 102.20 100.53 0.000 0.750 43 43 44 1 100.53 100.00 0.000 0.825 44 45 44 1 100.08 100.00 0.000 0.375 45 44 FLUME 1 100.00 0.000 0.900 99.67 Table B-8. Hydrology data file - Cranebrook HYDROLOGICAL DATA MOUSE-SYSTEM Filename CRN.ROF Edited 8-JUN-1994 10:13 Created 24-MAR-1994 11:07 Level (A/B) A Global values Not specified No. of nodal points 45 SPECIFIC PARAM1ETERVALUE S LEVEL A Row Sub- Hydrological Initial Loss Time Area Time of Catchment Reduction m Diagram No. Concentration Factor Minutes 1 1 0.50 0.0010 2.00 2 2 0.50 0.0010 2.00 3 3 0.50 0.0010 2.00 4 4 0.50 0.0010 2.00 5 5 0.50 0.0010 2.00 6 6 0.50 0.0010 2.00 7 8 0.50 0.0010 2.00 8 9 0.50 0.0010 2.00 Appendix B MOUSE And MMOUSE Modelling Data 9 10 0.50 0.0010 2.00 10 11 0.50 0.0010 2.00 11 7 0.50 0.0010 2.00 12 12 0.50 0.0010 2.00 13 14 0.50 0.0010 2.00 14 15 0.50 0.0010 2.00 15 13 0.50 0.0010 2.00 16 16 0.50 0.0010 2.00 17 18 0.50 0.0010 2.00 18 17 0.50 0.0010 2.00 19 20 0.50 0.0010 2.00 20 19 0.50 0.0010 2.00 21 22 0.50 0.0010 2.00 22 21 0.50 0.0010 2.00 23 24 0.50 0.0010 2.00 24 25 0.50 0.0010 2.00 25 23 0.50 0.0010 2.00 26 27 0.50 0.0010 2.00 27 29 0.50 0.0010 2.00 28 28 0.50 0.0010 2.00 29 30 0.50 0.0010 2.00 30 31 0.50 0.0010 2.00 31 32 0.50 0.0010 2.00 32 33 0.50 0.0010 2.00 33 34 0.50 0.0010 2.00 34 36 0.50 0.0010 2.00 35 35 0.50 0.0010 2.00 36 37 0.50 0.0010 2.00 37 38 0.50 0.0010 2.00 38 39 0.50 0.0010 2.00 39 41 0.50 0.0010 2.00 40 42 0.50 0.0010 2.00 41 40 0.50 0.0010 2.00 42 26 0.50 0.0010 2.00 43 43 0.50 0.0010 2.00 44 45 0.50 0.0010 2.00 45 44 0.50 0.0010 2.00 Table B-9. Physical characteristics of sub catchments- Jamison Park, Combined events D:CATCHMENTS FILE:JAMCOMBT.SWF PAGE 1 CREATED: 28-OCT-1993 15:36:44 EDITED: 18-MAY-1994 17:09:46 Surface Distribution (total area): Impervious : 1: steep roof 2: flat roof 3: asfalt/concrete Semi pervious : 4: large- 5: small spacing between tiles Pervious : 6: planted 7: unplanted CT = Catchm.tvDefl-7) HL = Hydrologic Level (1-2) SI3 =Soil Parameter (1-3) Row Nodal Total Slope Catch C Person Add. H Pet SP Surface Distribution Point Area Igth. T eqvlt. flow L Imp. 12 3 4 5 6 7 No. ha prm m Pe/ha m3/s Pet pet. 1 DI 0.990 6 116 2 0 0.000 1 40 2 FD1 0.990 10 0 3 0 0.000 1 60 3 E1E2 0.685 4 98 6 0 0.000 1 57 Appendix P MOUSE And MMOUSE Modelling Data 4 FE1E2 0.685 10 0 3 0 0.000 43 A 5 D2 1.010 6 102 2 0 0.000 27 6 FD2 1.010 10 0 3 0 0.000 73 7 D3D4 0.096 1 62 6 0 0.000 60 8 FD3D4 0.096 10 0 3 0 0.000 40 9 FI 0.456 5 109 3 0 0.000 36 10 FF1 0.456 10 0 3 0 0.000 64 11 D5 0.209 4 62 3 0 0.000 35 12 FD5 0.209 10 0 3 0 0.000 65 13 D6 0.338 6 105 3 0 0.000 26 14 FD6 0.338 10 0 3 0 0.000 74 15 G1G2 0.870 5 129 3 0 0.000 39 16 FG1G2 0.870 10 0 3 0 0.000 61 17 D7 0.279 5 99 3 0 0.000 32 18 FD7 0.279 10 0 3 0 0.000 68 19 HI 0.200 3 138 3 0 0.000 100 20 11 0.162 6 52 3 0 0.000 40 21 HI 0.162 10 0 3 0 0.000 60 22 12 0.132 6 39 3 0 0.000 44 23 FI2 0.132 10 0 3 0 0.000 56 24 D8 0.298 4 102 3 0 0.000 24 25 FD8 0.298 10 0 3 0 0.000 76 26 Kl 0.285 4 77 3 0 0.000 42 27 FK1 0.285 10 0 3 0 0.000 58 28 K2 0.211 1 55 3 0 0.000 46 29 FK2 0.211 10 0 3 0 0.000 54 30 D9J1 0.276 4 83 3 0 0.000 41 31 FD9J1 0.276 10 0 3 0 0.000 59 32 LI 0.245 4 58 3 0 0.000 35 33 FL1 0.245 10 0 3 0 0.000 65 34 D10 0.447 3 99 3 0 0.000 35 35 FD10 0.447 10 0 3 0 0.000 65 36 Ml 0.274 4 80 3 0 0.000 45 37 FM1 0.274 10 0 3 0 0.000 55 38 Dll 0.275 1 72 3 0 0.000 35 39 FD11 0.250 10 0 3 0 0.000 65 40 01 0.568 2 124 3 0 0.000 53 41 F01 0.568 10 0 3 0 0.000 47 42 02 1.204 3 195 3 0 0.000 18 43 F02 1.204 10 0 3 0 0.000 82 44 01 0.090 1 30 3 0 0.000 47 45 FQ1 0.090 10 0 3 0 0.000 53 46 PI 0.180 1 55 3 0 0.000 45 47 FP1 0.180 10 0 3 0 0.000 55 48 03 0.127 1 55 3 0 0.000 43 49 F03 0.127 10 0 3 0 0.000 57 50 05 0.214 1 55 3 0 0.000 42 51 F05 0.214 10 0 3 0 0.000 58 52 R1R2 0.323 3 151 3 0 0.000 60 53 FR1R2 0.323 10 0 3 0 0.000 40 54 R3 0.176 1 55 3 0 0.000 43 55 FR3 0.176 10 0 3 0 0.000 57 56 R4 0.711 3 157 3 0 0.000 37 57 FR4 0.711 10 0 3 0 0.000 63 58 SI 0.596 1 132 3 0 0.000 45 Appendix B MOUSE And MMOUSE Modelling Data 59 FS1 0.596 10 0 3 0 0.000 55 60 07 0.194 1 55 3 0 0.000 40 i 61 F07 0.194 10 0 3 0 0.000 60 62 NI 0.187 2 55 3 0 0.000 48 63 FN1 0.187 10 0 3 0 0.000 52 64 D12 0.178 1 61 3 0 0.000 42 65 FD12 0.178 10 0 3 0 0.000 58 66 D13 0.444 1 94 3 0 0.000 44 67 FD13 0.444 10 0 3 0 0.000 56 68 Tl 0.575 1 99 3 0 0.000 42 69 FT1 0.575 10 0 3 0 0.000 58 70 Ul 0.069 1 39 3 0 0.000 42 71 FU1 0.069 10 0 3 0 0.000 58 72 T2 0.430 1 96 3 0 0.000 38 73 FT2 0.430 10 0 3 0 0.000 62 74 T3 0.349 1 83 3 0 0.000 47 75 FT3 0.349 10 0 3 0 0.000 53 76 VI 0.438 1 220 3 0 0.000 55 77 FV1 0.438 10 0 3 0 0.000 45 78 D15 0.631 1 160 3 0 0.000 46 79 FD15 0.631 10 0 3 0 0.000 54 80 Wl 3.704 5 250 3 0 0.000 8 81 FW1 3.704 10 0 3 0 0.000 92 82 W2 1.119 4 412 3 0 0.000 46 83 FW2 1.119 10 0 3 0 0.000 54 84 XI 0.282 1 63 3 0 0.000 47 85 FX1 0.282 10 0 3 0 0.000 53 86 X2 0.311 1 77 3 0 0.000 43 87 FX2 0.311 10 0 3 0 0.000 57 88 D16 0.267 1 55 3 0 0.000 47 89 FD16 0.267 10 0 3 0 0.000 53 90 YI 0.932 1 110 3 0 0.000 33 91 FY1 0.932 10 0 3 0 0.000 67 92 D18 0.030 1 55 3 0 0.000 20 93 | FD18 0.030 10 0 3 0 0.000 80 Appendix B MOUSE And Cont. Table B-9. Characteristics of manholes- Jamison Park, Combined KG 1: Circular Manholes FILE.JAMCOMBT.SWF PAGE CREA TED: 28-OCT-1993 15:36:44 EDITED: 18-MAY-1994 17:09:^ 6 Row Nodal Coordinates Levels, m Shape Diameter.m Point of No. outlet X,m Y,m Top Bottom 1-4 1 DI 297.5 533.5 42.00 43.27 4 0.73 2 D2 259.0 547.3 40.90 41.97 4 0.73 3 D3 253.5 532.1 40.64 42.04 4 0.73 4 D3D4 239.7 523.9 40.07 41.97 4 0.73 5 D5 239.7 477.1 38.27 39.70 4 0.73 6 D6 239.7 435.9 37.17 38.23 4 0.73 7 D7 239.7 390.5 35.54 36.82 4 0.73 8 D8 239.7 354.8 34.50 35.46 4 0.73 9 D9J1 228.7 320.4 33.09 34.47 4 0.73 10 D10 228.7 231.3 30.84 31.94 4 0.50 11 Dll 228.7 176.0 30.20 31.03 4 0.50 12 D12 228.7 137.8 29.63 30.83 4 0.50 13 D13 217.7 111.4 29.54 30.96 4 0.73 14 D14 217.7 33.0 29.00 30.80 4 1.00 15 D15 155.9 33.0 28.69 30.99 4 0.50 16 D16 146.8 46.8 28.64 30.74 4 0.50 17 D17 134.7 46.8 28.57 30.57 4 0.73 18 D18 30.8 46.8 28.31 30.21 4 0.73 19 El 255.7 596.8 43.61 44.61 4 0.50 20 E1E2 239.7 556.9 41.36 42.48 4 0.73 21 FI 228.7 484.0 39.25 39.99 4 0.73 22 Gl 186.1 402.9 37.62 38.62 4 0.50 23 G1G2 228.7 402.9 36.02 36.96 4 0.73 24 11 197.1 369.0 35.75 36.51 4 0.73 25 12 224.6 369.0 34.83 35.75 4 0.73 26 Jl 232.2 327.3 33.29 34.29 4 0.50 27 HI 275.5 363.0 35.61 36.61 4 0.50 28 Kl 254.8 327.3 34.14 34.98 4 0.73 29 K2 254.8 319.0 33.78 34.54 4 0.73 30 LI 238.4 240.9 31.27 32.07 4 0.50 31 Ml 238.4 181.5 30.29 30.95 4 0.50 32 NI 238.4 137.8 29.64 30.25 4 0.50 33 RI 356.6 176.0 30.91 31.91 4 0.50 34 R1R2 322.2 176.0 30.48 31.04 4 0.73 35 R3 322.2 137.5 30.26 31.26 4 0.50 36 R4 315.4 137.5 30.18 30.74 4 0.73 37 Ol 402.0 176.0 30.82 31.52 4 0.73 38 02 393.7 170.5 30.75 31.45 4 0.73 39 03 393.7 129.3 30.65 31.31 4 0.73 40 04 385.8 121.0 30.60 31.21 4 0.73 41 05 326.4 121.0 30.23 31.00 4 0.73 42 06 304.4 121.0 30.12 30.78 4 0.50 43 07 245.8 121.0 29.72 31.12 4 0.73 44 Tl 462.5 33.0 30.25 30.86 4 0.50 45 T2 385.7 33.0 29.81 30.42 4 0.50 46 T3 314.0 33.0 29.47 30.27 4 0.50 Appendix B MOUSE And MMOUSE Modelling Data 47 XI 155.9 121.0 29.49 29.95 4 0.50 48 X2 155.9 111.4 29.25 29.95 4 0.50 49 Wl 134.7 152.6 29.34 30.10 4 0.73 50 W2 146.8 152.6 29.27 30.03 4 0.50 51 W3 146.8 105.9 29.03 30.03 4 0.50 52 YI 44.5 66.0 28.43 29.01 4 0.50 53 Qi 407.8 121.0 30.74 31.50 4 0.73 54 PI 402.0 129.0 30.70 31.46 4 0.50 55 SI 304.3 111.4 30.13 30.84 4 0.50 56 Ul 388.5 27.5 29.95 30.78 4 0.50 57 VI 173.8 27.5 28.89 29.55 4 0.73 58 FD1 297.5 533.5 42.00 43.27 4 0.73 59 FD2 259.0 547.3 40.90 41.97 4 0.73 60 FD3D4 239.7 523.9 40.07 41.97 4 0.73 61 FD5 239.7 477.1 38.27 39.70 4 0.73 62 FD6 239.7 435.9 37.17 38.23 4 0.73 63 FD7 239.7 390.5 35.54 36.82 4 0.73 64 FD8 239.7 354.8 34.50 35.46 4 0.73 65 FD9J1 228.7 320.4 33.09 34.47 4 0.73 66 FD10 228.7 231.3 30.84 31.94 4 0.50 67 FD11 228.7 176.0 30.20 31.03 4 0.50 68 FD12 228.7 137.8 29.63 30.83 4 0.50 69 FD13 217.7 111.4 29.54 30.96 4 0.73 70 FD15 155.9 33.0 28.69 30.99 4 0.50 71 FD16 146.8 46.8 28.64 30.74 4 0.50 72 FD18 30.8 46.8 28.31 30.21 4 0.73 73 FE1E2 239.7 556.9 41.36 42.48 4 0.73 74 FF1 228.7 484.0 39.25 39.99 4 0.73 75 FG1G2 228.7 402.9 36.02 36.96 4 0.73 76 HI 197.1 369.0 35.75 36.51 4 0.73 77 FI2 224.6 369.0 34.83 35.75 4 0.73 78 FK1 254.8 327.3 34.14 34.98 4 0.73 79 FK2 254.8 319.0 33.78 34.54 4 0.73 80 ELI 238.4 240.9 31.27 32.07 4 0.50 81 FM1 238.4 181.5 30.29 30.95 4 0.50 82 FN1 238.4 137.8 29.64 30.25 4 0.50 83 FR1R2 322.2 176.0 30.48 31.04 4 0.73 84 FR3 322.2 137.5 30.26 31.26 4 0.50 85 FR4 315.4 137.5 30.18 30.74 4 0.73 86 FOl 402.0 176.0 30.82 31.52 4 0.73 87 F02 393.7 170.5 30.75 31.45 4 0.73 88 F03 393.7 129.3 30.65 31.31 4 0.73 89 F05 326.4 121.0 30.23 31.00 4 0.73 90 F07 245.8 121.0 29.72 31.12 4 0.73 91 FT1 462.5 33.0 30.25 30.86 4 0.50 92 FT2 385.7 33.0 29.81 30.42 4 0.50 93 FT3 314.0 33.0 29.47 30.27 4 0.50 94 FX1 155.9 121.0 29.49 29.95 4 0.50 95 FX2 155.9 111.4 29.25 29.95 4 0.50 96 FW1 134.7 152.6 29.34 30.10 4 0.73 97 FW2 146.8 152.6 29.27 30.03 4 0.50 98 FY1 44.5 66.0 28.43 29.01 4 0.50 99 FQ1 407.8 121.0 30.74 31.50 4 0.73 100 FP1 402.0 129.0 30.70 31.46 4 0.50 101 FS1 304.3 111.4 30.13 30.84 4 0.50 Appendix B MOUSE And MMOUSE Modelling Data 102 FU1 388.5 27.5 29.95 30.78 4 0.50 103 FV1 173.8 27.5 28.89 29.55 4 0.73 Cont. Table B-9. Outlet characteristics - Jamison Park, Combined events KU: Outlets FILE: JAMCOMBT.SWF PA 5 CREATED: 28-OCT-1993 15:36:44 EDITED: 18-MAY-1994 17:09:46 Row Nodal Coordinates Levels, m Point No. X,m Y.m Top Bottom 1 END -24.3 46.8 28.16 28.16 Cont. Table B-9. Pipes and conduites characteristics - Jamison Park, Combined events LI: Conduits (Pipes) FILE:JAMCOMBT.SWF PAGE 6 CREATED: 28-OCT-199315:36:44 EDITED: 18-MAY-1994 17:09:46 Nodal Points Mat Alt Bottom Level Alt Infiltration Alt Pipe Row Sect. Dia.,m Upstr. Dwstr. Upstr. Dwstr. Flow GW No. No. m. m. m3/s/m level, m 1 DI D2 42.00 40.90 0.0000 0.450 2 D2 D3 40.90 40.64 0.0000 0.450 3 D3 D3D4 40.64 40.07 0.0000 0.450 4 D3D4 D5 40.07 38.27 0.0000 0.450 5 D5 D6 38.27 37.17 0.0000 0.450 6 D6 D7 37.17 35.54 0.0000 0.600 7 D7 D8 35.54 34.50 0.0000 0.600 8 D8 D9J1 34.50 33.09 0.0000 0.600 9 D9J1 D10 33.09 30.84 0.0000 0.900 10 D10 Dll 30.84 30.20 0.0000 0.900 11 Dll D12 30.20 29.63 0.0000 0.900 12 D12 D13 29.63 29.54 0.0000 1.200 13 D13 D14 29.54 29.00 0.0000 1.200 14 D14 D15 29.00 28.70 0.0000 1.350 15 D15 D16 28.69 28.64 0.0000 1.350 16 D16 D17 28.64 28.57 0.0000 1.540 17 D17 D18 28.57 28.31 0.0000 1.540 18 D18 END 28.31 28.16 0.0000 1.540 19 Gl G1G2 37.62 36.02 0.0000 0.225 20 G1G2 D7 36.02 35.54 .. 0.0000 0.375 21 11 12 35.75 34.83 0.0000 0.450 22 12 D8 34.83 34.50 0.0000 0.525 23 HI D8 35.61 34.50 0.0000 0.225 24 Kl K2 34.14 33.78 0.0000 0.375 25 K2 D9J1 33.78 33.09 0.0000 0.375 26 Jl D9J1 33.29 33.09 0.0000 0.150 27 01 02 30.82 30.75 0.0000 0.375 28 02 03 30.75 30.65 0.0000 0.375 29 03 04 30.65 30.60 0.0000 0.375 Appendix B MOUSE And MMOUSE Modelling Data 30 04 05 30.60 30.23 0.0000 0.600 31 05 06 30.23 30.12 0.0000 0.675 32 06 07 30.12 29.72 0.0000 0.900 33 07 D13 29.72 29.54 0.0000 0.900 34 RI R1R2 30.91 30.48 0.0000 0.150 35 R1R2 R3 30.48 30.26 0.0000 0.375 36 R3 R4 30.26 30.18 0.0000 0.450 37 R4 06 30.18 30.12 0.0000 0.600 38 Tl T2 30.25 29.81 . 0.0000 0.375 39 T2 T3 29.81 29.74 0.0000 0.375 40 T3 D14 29.47 29.00 0.0000 0.450 41 Wl W2 29.34 29.27 0.0000 0.450 42 W2 W3 29.27 29.03 0.0000 0.450 43 W3 D16 29.03 28.64 0.0000 0.600 44 El E1E2 43.61 41.36 0.0000 0.225 45 E1E2 D2 41.36 40.90 0.0000 0.375 46 YI D18 28.43 28.31 0.0000 0.375 47 PI 03 30.70 30.65 0.0000 0.375 48 01 04 30.74 30.60 0.0000 0.375 49 SI 06 30.13 30.12 0.0000 0.375 50 Ul T2 29.95 29.81 0.0000 0.375 51 VI D15 28.89 28.69 0.0000 0.375 52 FI D5 39.25 38.27 0.0000 0.375 53 LI D10 31.27 30.84 0.0000 0.375 54 Ml Dll 30.29 30.20 0.0000 0.375 55 NI D12 29.64 29.63 0.0000 0.375 56 XI X2 29.49 29.25 0.0000 0.375 57 X2 W3 29.25 29.03 0.0000 0.375 58 FD1 DI 42.00 42.00 0.0000 1.000 59 FD2 D2 40.90 40.90 0.0000 1.000 60 FD3D4 D3D4 40.07 40.07 0.0000 1.000 61 FD5 D5 38.27 38.27 0.0000 1.000 62 FD6 D6 37.17 37.17 0.0000 1.000 63 FD7 D7 35.54 35.54 0.0000 1.000 64 FD8 D8 34.50 34.50 0.0000 1.000 65 FD9J1 D9J1 33.09 33.09 0.0000 1.000 66 FD10 D10 30.84 30.84 0.0000 1.000 67 FD11 Dll 30.20 30.20 0.0000 1.000 68 FD12 D12 29.63 29.63 0.0000 1.500 69 FD13 D13 29.54 29.54 0.0000 1.500 70 FD15 D15 28.69 28.69 0.0000 1.500 71 FD16 D16 28.64 28.64 0.0000 1.540 72 FD18 D18 28.31 28.31 0.0000 1.540 73 FG1G2 G1G2 36.02 36.02 0.0000 1.000 74 FI1 11 35.75 35.75 0.0000 1.000 75 ¥12 12 34.83 34.83 0.0000 1.000 76 FK1 Kl 34.14 34.14 0.0000 1.000 77 FK2 K2 33.78 33.78 0.0000 1.000 78 FOl 01 30.82 30.82 0.0000 1.000 79 F02 02 30.75 30.75 0.0000 1.000 80 F03 03 30.65 30.65 0.0000 1.000 81 F05 05 30.23 30.23 0.0000 1.000 82 F07 07 29.72 29.72 0.0000 1.000 83 FR1R2 R1R2 30.48 30.48 0.0000 1.000 84 FR3 R3 30.26 30.26 0.0000 1.000 Appendix B MOUSE And MMOUSE Modelling Data 85 FR4 R4 30.18 30.18 0.0000 1.000 86 FT1 Tl 30.25 30.25 0.0000 1.000 87 FT2 T2 29.81 29.81 0.0000 1.000 88 FT3 T3 29.47 29.47 0.0000 1.000 89 FW1 Wl 29.34 29.34 0.0000 1.000 90 FW2 W2 29.27 29.27 0.0000 1.000 91 FE1E2 E1E2 41.36 41.36 0.0000 1.000 92 FY1 YI 28.43 28.43 0.0000 1.000 93 FP1 PI 30.70 30.70 0.0000 1.000 94 FQ1 Qi 30.74 30.74 0.0000 1.000 95 FS1 SI 30.13 30.13 0.0000 1.000 96 FU1 Ul 29.95 29.95 0.0000 1.000 97 FV1 VI 28.89 28.89 0.0000 1.000 98 FF1 FI 39.25 39.25 0.0000 1.000 99 FL1 LI 31.27 31.27 0.0000 1.000 100 FM1 Ml 30.29 30.29 0.0000 1.000 101 FN1 NI 29.64 29.64 0.0000 1.000 102 FX1 XI 29.49 29.49 0.0000 1.000 103 FX2 X2 29.25 29.25 0.0000 1.000 Table B-10. Hydrology data file - Jamison Park, Combined events HYDROLOGICAL DATA MOUSE-SYSTEM Filename JAMCOMB.ROF Edited 4-JUL-1994 11:25 Created 24-MAR-1994 11:07 Level (A/B) A Global values Not specified No. of nodal points 93 SPECIFIC PARAM]ETERVALUE S LEVEL A Row Sub- Hydrological Initial Loss Time Area Time of Catchment Reduction m Diagram No. Concentration Factor Minutes 1 DI 1.00 0.0010 4.20 2 E1E2 1.00 0.0010 4.30 3 D2 1.00 0.0010 3.90 4 D3D4 1.00 0.0010 4.90 5 FI 1.00 0.0010 4.30 6 D5 1.00 0.0010 3.20 7 D6 1.00 0.0010 3.90 8 G1G2 1.00 0.0010 4.70 9 D7 1.00 0.0010 4.00 10 HI 1.00 0.0010 5.70 11 11 1.00 0.0010 2.60 12 12 1.00 0.0010 2.20 13 D8 1.00 0.0010 4.40 14 Kl 1.00 0.0010 3.70 15 K2 1.00 0.0010 4.60 16 D9J1 1.00 0.0010 3.90 17 LI 1.00 0.0010 3.10 18 D10 1.00 0.0010 4.70 19 Ml 1.00 0.0010 3.80 20 Dll 1.00 0.0010 5.40 MOUSE And MMOUSE Modellm 21 01 1.00 0.0010 6.10 22 02 1.00 0.0010 7.00 23 01 1.00 0.0010 3.20 24 PI 1.00 0.0010 4.60 25 03 1.00 0.0010 4.60 26 05 1.00 0.0010 4.60 27 R1R2 1.00 0.0010 6.00 28 R3 1.00 0.0010 4.60 29 R4 1.00 0.0010 6.20 30 SI 1.00 0.0010 7.70 31 07 1.00 0.0010 4.60 32 NI 1.00 0.0010 3.70 33 D12 1.00 0.0010 4.90 34 D13 1.00 0.0010 6.30 35 Tl 1.00 0.0010 6.50 36 Ul 1.00 0.0010 3.70 37 T2 1.00 0.0010 6.40 38 T3 1.00 0.0010 5.90 39 VI 1.00 0.0010 10.50 40 D15 1.00 0.0010 8.70 41 Wl 1.00 0.0010 7.00 42 W2 1.00 0.0010 10.10 43 XI 1.00 0.0010 5.00 44 X2 1.00 0.0010 5.60 45 D16 1.00 0.0010 4.60 46 YI 1.00 0.0010 6.90 47 D18 1.00 0.0010 4.60 48 FD1* 0.66 0.0030 20.50 49 FE1E2 0.66 0.0030 21.00 50 FD2 0.66 0.0030 19.00 51 FD3D4 0.66 0.0030 24.10 52 FF1 0.66 0.0030 20.90 53 FD5 0.66 0.0030 15.90 54 FD6 0.66 0.0030 19.30 55 FG1G2 0.66 0.0030 23.10 56 FD7 0.66 0.0030 19.70 57 FI1 0.66 0.0030 12.70 58 ¥12 0.66 0.0030 10.70 59 FD8 0.66 0.0030 21.50 60 FK1 0.66 0.0030 18.10 61 FK2 0.66 0.0030 22.50 62 FD9J1 0.66 0.0030 19.00 63 FL1 0.66 0.0030 15.30 64 FD10 0.66 0.0030 23.00 65 FM1 0.66 0.0030 18.60 66 FD11 0.66 0.0030 26.40 67 F01 0.66 0.0030 29.70 68 F02 0.66 0.0030 34.50 69 FQ1 0.66 0.0030 15.60 70 FP1 0.66 0.0030 22.50 71 F03 0.66 0.0030 22.50 72 F05 0.66 0.0030 22.50 73 FR1R2 0.66 0.0030 29.60 74 FR3 0.66 0.0030 22.50 75 FR4 0.66 0.0030 30.30 Appendix B . MOUSE And MMOUSE Modelling Data 76 FS1 0.66 0.0030 38.00 77 F07 0.66 0.0030 22.50 78 FN1 0.66 0.0030 18.20 79 FD12 0.66 0.0030 23.90 80 FD13 0.66 0.0030 31.00 81 FT1 0.66 0.0030 32.00 82 FU1 0.66 0.0030 18.30 83^ FT2 0.66 0.0030 31.40 84 FT3 0.66 0.0030 28.80 85 FV1 0.66 0.0030 51.60 86 FD15 0.66 0.0030 42.60 87 FW1 0.66 0.0030 34.40 88 FW2 0.66 0.0030 46.60 89 FX1 0.66 0.0030 24.40 90 FX2 0.66 0.0030 27.50 91 FD16 0.66 0.0030 22.50 92 FY1 0.66 0.0030 34.00 93 FD18 0.66 0.0030 22.50 * F in front of subcatchment names stands for fictitious which is used for pervious area runoff simulation, e.g. DI is impervious part and FD1 is the pervious part of subcatchment DI, etc. Table B-l 1. Physical characteristics of sub catchments- Fisher's Ghost Creek, Combined events D:CATCHMENTS FILE:FGCOMB.SWF PAGE 1 CREATED: 28-OCT-1993 15:36:44 EDITED: 24-JUN-1994 17:36:56 Surface Distribution (total area): Impervious : 1: steep roof 2: flat roof 3: asfalt/concrete Semi pervious : 4: large- 5: small spacing between tiles Pervious : 6: planted 7: unplanted CT = Catchm.type(l-7) HL = H vdrologic Level (1-2) SIP=Soi l Parameter (1-3) Row Nodal Total Slope Catch C Person Add. H Pet SP Surface Distribution Point Area Igth. T eqvlt. flow L Imp. 12 3 4 5 6 7 No. ha prm m Pe/ha m3/s Pet pet. 1 480 4.540 10 193 2 0 0.000 1 30 2 470 3.780 9 228 6 0 0.000 1 30 3 460 5.640 10 249 2 0 0.000 1 27 4 450 2.920 8 276 6 0 0.000 1 30 5 440 4.120 10 276 3 0 0.000 1 30 6 430 4.200 10 359 3 0 0.000 1 30 7 420 0.700 9 138 3 0 0.000 1 30 8 410 5.730 11 352 3 0 0.000 1 30 9 400 1.260 7 104 3 0 0.000 1 15 10 390 6.560 8 276 3 0 0.000 1 15 11 380 6.910 8 345 3 0 0.000 1 30 12 370 5.950 8 297 3 0 0.000 1 28 13 360 6.750 7 276 3 0 0.000 1 30 14 350 4.410 9 332 3 0 0.000 1 29 15 340 3.690 10 235 3 0 0.000 1 28 16 330 4.980 9 249 3 0 0.000 1 30 17 320 3.060 10 180 3 0 0.000 1 25 18 310 5.570 10 228 3 0 0.000 1 26 19 300 7.300 9 228 3 0 0.000 1 29 Appendix B . . MOUSE And MMOUSE Modelling Data 20 290 4.990 5 339 3 0 0.000 29 21 280 3.380 7 173 3 0 0.000 20 22 270 6.660 8 325 3 0 0.000 23 23 260 6.520 11 394 3 0 0.000 30 24 250 4.710 10 435 3 0 0.000 30 25 240 4.980 15 249 3 0 0.000 30 26 230 9.810 8 359 3 0 0.000 30 27 220 1.880 7 193 3 0 0.000 30 28 210 4.080 9 228 3 0 0.000 15 29 200 0.820 7 138 3 0 0.000 15 30 190 2.660 8 297 3 0 0.000 30 31 180170 6.480 9 808 3 0 0.000 29 32 160 6.840 10 352 3 0 0.000 20 33 150 2.250 8 366 3 0 0.000 29 34 140 0.750 6 124 3 0 0.000 15 35 130 8.130 6 297 3 0 0.000 25 36 120 6.690 11 332 3 0 0.000 30 37 110 5.880 8 497 3 0 0.000 30 38 100 7.150 14 366 3 0 0.000 30 39 90 3.050 11 491 3 0 0.000 29 40 80 4.800 8 359 3 0 0.000 30 41 70 4.960 6 325 3 0 0.000 30 42 60 2.280 3 152 3 0 0.000 15 43 50 3.040 6 187 3 0 0.000 10 44 40 6.320 9 449 3 0 0.000 30 45 30 2.440 6 228 3 0 0.000 30 46 20 3.620 4 297 3 0 0.000 28 47 10 1.070 3 152 3 0 0.000 25 48 F480 4.540 10 193 2 0 0.000 70 49 F470 3.780 9 228 6 0 0.000 70 50 F460 5.640 10 249 2 0 0.000 73 51 F450 2.920 8 276 6 0 0.000 70 52 F440 4.120 10 276 3 0 0.000 70 53 F430 4.200 10 359 3 0 0.000 70 54 F420 0.700 9 138 3 0 0.000 70 55 F410 5.730 11 352 3 0 0.000 70 56 F400 1.260 7 104 3 0 0.000 85 57 F390 6.560 8 276 3 0 0.000 85 58 F380 6.910 8 345 3 0 0.000 70 59 F370 5.950 8 297 3 0 0.000 72 60 F360 6.750 7 276 3 0 0.000 70 61 F350 4.410 9 332 3 0 0.000 71 62 F340 3.690 10 235 3 0 0.000 72 63 F330 4.980 9 249 3 0 0.000 70 64 F320 3.060 10 180 3 0 0.000 75 65 F310 5.570 10 228 3 0 0.000 74 66 F300 7.300 9 228 3 0 0.000 71 67 F290 4.990 5 339 3 0 0.000 71 68 F280 3.380 7 173 3 0 0.000 80 69 F270 6.660 8 325 3 0 0.000 77 70 F260 6.520 11 394 3 0 0.000 70 71 F250 4.710 10 435 3 0 0.000 70 72 F240 4.980 15 249 3 0 0.000 70 73 F230 9.810 8 359 3 0 0.000 70 74 F220 1.880 7 193 3 0 0.000 70 J Appendix B MOUSE And MMOUSE Modelling Data 75 F210 4.080 9 228 3 0 0.000 85 76 F200 0.820 7 138 3 0 0.000 85 77 F190 2.660 8 297 3 0 0.000 70 78 F180170 6.480 9 808 3 0 0.000 71 79 F160 6.840 10 L 352 3 0 0.000 80 80 F150 2.250 8 366 3 0 0.000 71 81 F140 0.750 6 124 3 0 0.000 85 82 F130 8.130 6 297 3 0 0.000 75 83 F120 6.690 11 332 3 0 0.000 70 84 F110 5.880 8 497 3 0 0.000 70 85 F100 7.150 14 366 3 0 0.000 70 86 F90 3.050 11 491 3 0 0.000 71 87 F80 4.800 8 359 3 0 0.000 70 88 F70 4.960 6 325 3 0 0.000 70 89 F60 2.280 3 152 3 0 0.000 85 90 F50 3.040 6 187 3 0 0.000 90 91 F40 6.320 9 449 3 0 0.000 70 92 F30 2.440 6 228 3 0 0.000 70 93 F20 3.620 4 297 3 0 0.000 72 94 F10 1.070 3 152 3 0 0.000 75 Cont. Table B-l 1. Characteristics of manholes- Fisher's Ghost Creek, Combined events KG1: Circular Manholes FILE:FGCOMB.SWF PAGE 3 CREATED: 28-OCT-1993 15:36:44 EDITED: 24-JUN-1994 17:36:56 Row Nodal Coordinates Levels, m Shape Diameter, m Point of No. outlet X,m Y,m Top Bottom 1-4 1 480 2106.9 1271.1 153.15 154.00 4 1.00 2 470 2065.5 1174.4 150.23 151.00 4 1.00 3 460 2051.7 1088.0 150.70 151.00 4 1.00 4 450 1993.0 1119.1 146.84 147.00 4 1.00 5 440 1920.4 1088.0 145.95 146.00 4 1.00 6 430 1920.0 1181.3 148.80 149.00 4 1.00 7 420 1906.6 1119.1 145.23 146.00 4 1.00 8 410 1851.3 1226.2 150.23 151.00 4 1.00 9 400 1824.4 1132.9 143.03 144.00 4 1.00 10 390 1651.0 998.2 136.64 137.00 4 1.00 11 380 1592.3 1160.5 142.29 143.00 4 1.00 12 370 1595.7 960.2 j 134.29 135.00 4 1.00 13 360 1409.2 818.6 j 129.19 130.00 4 1.00 14 350 2037.9 587.2 146.05 147.00 4 1.00 15 340 1955.0 542.3 144.25 145.00 4 1.00 16 330 1868.6 621.7 145.00 146.00 4 1.00 17 320 1851.3 531.9 142.48 143.00 4 1.00 18 310 1682.1 573.4 136.30 137.00 4 1.00 19 300 1533.6 601.0 134.50 135.00 4 1.00 20 290 1316.0 818.6 128.17 129.00 4 1.00 21 280 1281.4 884.2 125.67 126.00 4 1.00 22 270 1223.4 822.1 124.86 125.00 4 1.00 23 260 1260.7 1063.8 134.53 135.00 4 1.00 24 250 1184.7 1063.8 133.20 134.00 4 1.00 MOUSE And MMOUSE Modelling Data 25 240 1018.9 1367.8 141.22 142.00 4 1.00 26 230 984.4 1036.2 126.63 127.00 4 1.00 27 220 960.2 871.1 118.52 119.00 4 1.00 80170 28 832.4 829.0 115.50 116.00 4 1.00 29 210 967.1 808.2 117.68 118.00 4 1.00 30 200 832.4 763.3 113.39 114.00 4 1.00 31 201 877.3 784.1 113.88 114.00 2 1.00 32 190 887.7 718.4 116.81 117.00 4 1.00 33 160 832.4 594.1 119.81 120.00 4 1.00 34 150 832.4 663.2 115.09 116.00 4 1.00 35 140 773.7 746.1 112.52 113.00 4 1.00 36 130 597.5 732.2 109.79 110.00 4 1.00 37 120 504.3 604.5 110.56 111.00 4 1.00 38 110 594.1 967.1 116.13 117.00 2 1.00 39 100 659.7 1523.2 131.51 132.00 4 1.00 40 90 574.7 1257.3 120.03 121.00 4 1.00 41 80 490.5 967.1 111.58 112.00 4 1.00 42 70 452.5 808.2 107.52 108.00 4 1.00 43 60 480.1 673.5 106.39 107.00 4 1.00 44 50 307.4 594.1 101.89 102.00 4 1.00 45 20 200.3 607.9 100.13 101.00 4 1.00 46 30 217.6 728.8 105.88 106.00 4 1.00 47 40 226.6 884.2 110.45 111.00 4 1.00 48 10 152.7 566.5 100.01 101.00 2 1.00 49 F480 2106.9 1271.1 153.15 154.00 4 1.00 50 F470 2065.5 1174.4 150.23 151.00 4 1.00 51 F460 2051.7 1088.0 150.70 151.00 4 1.00 52 F450 1993.0 1119.1 146.84 147.00 4 1.00 53 F440 1920.4 1088.0 145.95 146.00 4 1.00 54 F430 1920.0 1181.3 148.80 149.00 4 1.00 55 F420 1906.6 1119.1 145.23 146.00 4 1.00 56 F410 1851.3 1226.2 150.23 151.00 4 1.00 57 F400 1824.4 1132.9 143.03 144.00 4 1.00 58 F390 1651.0 998.2 136.64 137.00 4 1.00 59 F380 1592.3 1160.5 142.29 143.00 4 1.00 60 F370 1595.7 960.2 134.29 135.00 4 1.00 61 F360 1409.2 818.6 129.19 130.00 4 1.00 62 F350 2037.9 587.2 146.05 147.00 4 1.00 63 F340 1955.0 542.3 144.25 145.00 4 1.00 64 F330 1868.6 621.7 145.00 146.00 4 1.00 65 F320 1851.3 531.9 142.48 143.00 4 1.00 66 F310 1682.1 573.4 136.30 137.00 4 1.00 67 F300 1533.6 601.0 134.50 135.00 4 1.00 68 F290 1316.0 818.6 128.17 129.00 4 1.00 69 F280 1281.4 884.2 125.67 126.00 4 1.00 70 F270 1223.4 822.1 124.86 125.00 4 1.00 71 F260 1260.7 1063.8 134.53 135.00 4 1.00 72 F250 1184.7 1063.8 133.20 134.00 4 1.00 73 F240 1018.9 1367.8 141.22 142.00 4 1.00 74 F230 984.4 1036.2 126.63 127.00 4 1.00 75 F220 960.2 871.1 118.52 119.00 4 1.00 76 180170 832.4 829.0 115.50 116.00 4 1.00 77 F210 967.1 808.2 117.68 118.00 4 1.00 78 F200 832.4 763.3 113.39 114.00 4 1.00 79 F190 887.7 718.4 116.81 117.00 4 1.00 Appendix $ . MOUSE And MMOUSE Modelling Data 80 F160 832.4 594.1 119.81 120.00 4 1.00 81 F150 832.4 663.2 115.09 116.00 4 1.00 82 F140 773.7 746.1 112.52 113.00 4 1.00 83 F130 597.5 732.2 109.79 110.00 4 1.00 84 F120 504.3 604.5 110.56 111.00 4 1.00 85 F110 594.1 967.1 116.13 117.00 2 1.00 86 F100 659.7 1523.2 131.51 132.00 4 1.00 87 F90 574.7 1257.3 120.03 121.00 4 1.00 88 F80 490.5 967.1 111.58 112.00 4 1.00 89 F70 452.5 808.2 107.52 108.00 4 1.00 90 F60 480.1 673.5 106.39 107.00 4 1.00 91 F50 307.4 594.1 101.89 102.00 4 1.00 92 F20 200.3 607.9 100.13 101.00 4 1.00 93 F30 217.6 728.8 105.88 106.00 4 1.00 94 F40 226.6 884.2 110.45 111.00 4 1.00 95 F10 152.7 566.5 100.01 101.00 2 1.00 Cont. Table B-l 1. Outlet characteristics - Fisher's Ghost Creek, Combined events KU: Outlets FILE: FGCOMB.SWF PAGE CREATED: 28-OCT-1993 15:36:44 EDITED: 24-JUN-1994 15:161:12 Row Nodal Coordinates Levels, m Point No. X,m Y,m Top Bottom 1 WEI 145.0 555.0 100.00 100.00 R Cont. Table B-l 1. Pipes and conduites characteristics - Fisher's Ghost Creek, Combined events LI: Conduits (Pipes) FILE:FGCOMB.SWF PAGE 6 CREATED: 28-OCT-199315:36:44 EDITED: 24-JUN-1994 17:36:56 Nodal Points Mat Alt Bottom Level Alt Infiltration Alt Pipe Row Sect. Dia.,m Upstr. Dwstr. Upstr. Dwstr. Flow GW No. No. m. m. m3/s/m level, m 1 480 470 3 153.15 150.23 1 0.000 0.530 2 470 450 3 150.23 146.84 1 0.000 0.680 3 460 450 3 150.70 146.84 1 0.000 0.450 4 430 420 3 148.80 145.23 1 0.000 0.530 5 450 420 3 146.84 145.23 1 0.000 0.900 6 440 420 3 145.95 145.23 1 0.000 0.530 7 410 400 3 150.23 143.03 1 0.000 0.600 8 390 370 3 136.64 134.29 1 0.000 1.350 9 380 370 3 142.29 134.29 1 0.000 0.530 10 370 360 3 134.29 129.19 1 0.000 1.350 Appendix B MOUSE And MMOUSE Modelling Data 11 360 290 3 129.19 128.17 0.000 1.350 12 350 340 3 146.05 144.25 0.000 0.530 13 340 L320 3 144.25 142.48 0.000 0.750 14 330 320 3 145.00 142.48 0.000 0.530 15 320 310 3 142.48 136.30 0.000 0.900 16 310 300 3 136.30 134.50 0.000 1.050 17 300 290 3 134.50 128.17 0.000 1.050 18 280 270 3 125.67 124.86 0.000 0.900 19 190 200 3 116.81 113.39 0.000 0.450 20 160 150 3 119.81 115.09 0.000 0.450 21 150 140 3 115.09 112.52 0.000 0.600 22 120 60 3 110.56 106.39 0.000 0.530 23 110 80 3 116.13 111.58 0.000 0.600 24 100 90 3 131.51 120.03 0.000 0.680 25 90 80 3 120.03 111.58 0.000 0.900 26 80 70 3 111.58 107.52 0.000 1.050 27 70 60 3 107.52 106.39 0.000 1.050 28 40 30 3 110.45 105.88 0.000 0.680 29 30 20 3 105.88 100.13 0.000 0.750 30 50 20 3 101.89 100.13 0.000 2.500 31 260 250 3 134.53 133.20 0.000 0.680 32 250 230 3 133.20 126.63 0.000 0.750 33 240 230 3 141.22 126.63 0.000 0.680 34 220 210 3 118.52 117.68 0.000 0.900 35 180170 200 3 115.50 113.39 0.000 0.530 36 201 200 3 113.88 113.39 0.000 2.300 37 F480 480 3 153.15 153.15 0.000 2.000 38 F470 470 3 150.23 150.23 0.000 2.000 39 F460 460 3 150.70 150.70 0.000 2.000 40 F430 430 3 148.80 148.80 0.000 2.000 41 F450 450 3 146.84 146.84 0.000 2.000 42 F440 440 3 145.95 145.95 0.000 2.000 43 F410 410 3 150.23 150.23 0.000 2.000 44 F390 390 3 136.64 136.64 0.000 2.000 45 F380 380 3 142.29 142.29 0.000 2.000 46 F370 370 3 134.29 134.29 0.000 2.000 47 F360 360 3 1 129.19 129.19 0.000 2.000 48 F350 350 3 1 146.05 146.05 0.000 2.000 49 F340 340 3 144.25 144.25 0.000 2.000 50 F330 330 3 145.00 145.00 0.000 2.000 51 F320 320 3 142.48 142.48 0.000 2.000 52 F310 310 3 136.30 136.30 0.000 2.000 53 F300 300 3 134.50 134.50 0.000 2.000 54 F280 280 3 125.67 125.67 0.000 2.000 55 F190 190 3 116.81 116.81 0.000 2.000 56 F160 160 3 119.81 119.81 0.000 2.000 57 F150 150 3 115.09 115.09 0.000 2.000 58 F120 120 3 110.56 110.56 0.000 2.000 59 F110 110 3 116.13 116.13 0.000 2.000 60 F100 100 3 131.51 131.51 0.000 2.000 61 F90 90 3 120.03 120.03 0.000 2.000 62 F80 80 3 111.58 111.58 0.000 2.000 63 F70 70 3 107.52 107.52 0.000 2.000 64 F40 40 3 110.45 110.45 0.000 2.000 65 F30 30 3 105.88 105.88 0.000 2.000 Appendix B MOUSE And MMOUSE Modelling Data 66 F50 50 3 101.89 101.89 0.000 5.000 67 F260 260 3 ^34.53 134.53 0.000 2.000 68 F250 250 3 133.20 133.20 0.000 2.000 69 F240 240 3 141.22 141.22 0.000 2.000 70 F220 220 3 118.52 118.52 0.000 2.000 71 F180170 180170 3 115.50 115.50 0.000 2.000 72 F420 420 3 145.23 145.23 0.000 2.000 73 F400 400 3 143.03 143.03 0.000 2.000 74 F290 290 3 128.17 128.17 0.000 2.000 75 F270 270 3 124.86 124.86 0.000 2.000 76 F230 230 3 126.63 126.63 0.000 2.000 77 F210 210 3 117.68 117.68 0.000 2.000 78 F200 200 3 113.39 113.39 0.000 2.000 79 F140 140 3 112.52 112.52 0.000 2.000 80 F130 130 3 109.79 109.79 0.000 2.000 81 F60 60 3 106.39 106.39 0.000 2.000 82 F20 20 3 100.13 100.13 0.000 2.000 83 F10 10 3 100.01 100.01 0.000 2.000 Cont. Table B-l 1. Pipes and conduites characteristics - Fisher's Ghost Creek, Combined events LI: Conduits (Trapezoidal Canals) FILE:FGCSWF PAGE 7 CREATED: 28-OCT-199315:36:44 EDITED: 17-JUN-1994 15:16: 2 Nodal Points Mat Alt Bottom Level Alt Infiltration Bot. Angle Max Row Wdth, Heght, m m Upstr. Dwstr. Upstr. Dwstr. Flow GW No. No. m. m. m3/s/m level, m 1 420 400 8 145.23 143.03 0.0000 1.0 6.0 1.5 2 400 390 8 143.03 136.64 0.0000 1.0 10.0 2.0 3 290 270 8 128.17 124.86 0.0000 1.5 2.0 4.0 4 270 210 8 124.86 117.68 0.0000 2.5 2.0 4.0 5 210 201 8 117.68 113.88 0.0000 2.0 2.0 5.0 6 200 140 8 113.39 112.52 0.0000 2.0 2.0 5.0 7 140 130 8 112.52 109.79 0.0000 2.5 4.0 5.0 8 130 60 8 109.79 106.39 0.0000 1.0 3.0 6.0 9 60 50 8 106.39 101.89 0.0000 2.0 1.5 6.0 10 20 10 8 100.13 100.01 0.0000 2.0 3.0 6.0 11 10 WEIR 8 100.01 100.00 0.0000 3.0 4.0 6.0 12 230 220 8 126.63 118.52 0.0000 1.0 1.5 1.0 Appendix B MOUSE And MMOUSE Modelling Data Table B-l2. Hydrology data file - Fisher's Ghost Creek, Combined events HYDROLOGICAL DATA MOUSE-SYSTEM Filename FGCOMB.ROF Edited 19 - JUL - 1994 17:41 Created 25-APR-1994 15:32 Level (A/B) A Global values Not specified No. of nodal points 94 SPECIFIC PARAM ETERVALUES LEVEL A Row Sub- Hydrological Initial Loss Time Area Time of Catchment Reduction m Diagram No. Concentration Factor Minutes 1 480 1.00 0.0010 5.30 2 470 1.00 0.0010 6.00 3 460 1.00 0.0010 6.20 4 450 1.00 0.0010 7.00 5 440 1.00 0.0010 6.60 6 430 1.00 0.0010 7.70 7 420 1.00 0.0010 4.50 8 410 1.00 0.0010 7.40 9 400 1.00 0.0010 4.10 10 390 1.00 0.0010 7.00 11 380 1.00 0.0010 8.00 12 370 1.00 0.0010 7.30 13 360 1.00 0.0010 7.30 14 350 1.00 0.0010 7.60 15 340 1.00 0.0010 6.00 16 330 1.00 0.0010 6.40 17 320 1.00 0.0010 5.10 18 310 1.00 0.0010 5.80 19 300 1.00 0.0010 6.00 20 290 1.00 0.0010 9.10 21 280 1.00 0.0010 5.50 22 270 1.00 0.0010 7.70 23 260 1.00 0.0010 7.90 24 250 1.00 0.0010 8.60 25 240 1.00 0.0010 5.50 26 230 1.00 0.0010 8.20 27 220 1.00 0.0010 5.90 28 210 1.00 0.0010 6.00 29 200 1.00 0.0010 4.80 30 190 1.00 0.0010 7.30 31 180170 1.00 0.0010 12.90 32 160 1.00 0.0010 7.60 33 150 1.00 0.0010 8.30 34 140 1.00 0.0010 4.70 35 130 1.00 0.0010 8.00 36 120 1.00 0.0010 7.10 37 110 1.00 0.0010 10.00 38 100 1.00 0.0010 7.00 39 90 1.00 0.0010 9.00 40 80 1.00 0.0010 8.20 41 70 1.00 0.0010 8.40 Appendix^ -— . MOUSE And MMOUSE Modelling Data 42 60 1.00 0.0010 6.60 43 50 1.00 0.0010 6.10 44 40 1.00 0.0010 9.10 45 30 1.00 0.0010 6.80 46 20 1.00 0.0010 9.00 47 10 1.00 0.0010 6.60 48 F480* 0.06 0.0030 26.00 49 F470 0.06 0.0030 29.60 50 F460 0.06 0.0030 30.20 51 F450 0.06 0.0030 34.40 52 F440 0.06 0.0030 32.20 53 F430 0.06 0.0030 37.70 54 F420 0.06 0.0030 21.90 55 F410 0.06 0.0030 36.20 56 F400 0.06 0.0030 19.90 57 F390 0.06 0.0030 34.40 58 F380 0.06 0.0030 39.40 59 F370 0.06 0.0030 36.00 60 F360 0.06 0.0030 35.90 61 F350 0.06 0.0030 37.10 62 F340 0.06 0.0030 29.20 63 F330 0.06 0.0000 31.20 64 F320 0.06 0.0030 24.90 65 F310 0.06 0.0030 28.70 66 F300 0.06 0.0030 29.60 67 F290 0.06 0.0030 44.80 68 F280 0.06 0.0030 27.00 69 F270 0.06 ' 0.0030 37.90 70 F260 0.06 0.0030 38.70 71 F250 0.06 0.0030 42.30 72 F240 0.06 0.0030 26.80 73 F230 0.06 0.0030 40.30 74 F220 0.06 0.0030 30.00 75 F210 0.06 0.0030 29.60 76 F200 0.06 0.0030 23.70 77 F190 0.06 0.0030 36.00 78 FI 80170 0.06 0.0030 63.30 79 F160 0.06 0.0030 28.50 80 F150 0.06 0.0030 37.30 81 F140 0.06 0.0030 40.80 82 F130 0.06 0.0030 23.30 83 F120 0.06 0.0030 39.20 84 F110 0.06 0.0030 34.90 85 F100 0.06 0.0030 49.00 86 F90 0.06 0.0030 34.50 87 F80 0.06 0.0030 44.20 88 F70 0.06 0.0030 40.30 89 F60 0.06 0.0030 41.40 90 F50 0.06 0.0030 32.30 91 F40 0.06 0.0030 29.70 92 F30 0.06 0.0030 44.50 93 F20 0.06 0.0030 33.50 94 F10 0.06 0.0030 44.30 * F in front of subcatchment names stands for fictitious which is used for pervious area runoff simulation, e.g. 480 is impervious part and F480 is the pervious part of subcatchment 480, etc. APPENDIX C COMPUTED AND OBSERVED HYDROGRAPHS Appendix C Computed and Observed Hydrographs APPENDIX C C.l. 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