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1996 Catchment rainfall-runoff computer modelling Forood Sharifi University of Wollongong
Recommended Citation Sharifi, Forood, Catchment rainfall-runoff omputc er modelling, Doctor of Philosophy thesis, Department of Civil and Mining Engineering, University of Wollongong, 1996. http://ro.uow.edu.au/theses/1230
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CATCHMENT RAINFALL-RUNOFF COMPUTER MODELLING
1 UNIVERSITY W j
A thesis submitted in fulfilment of the requirements for the award of the degree of
Doctor of Philosophy (PhD)
from
THE UNIVERSITY OF WOLLONGONG
by
Forood Sharifi B.E., Tabriz University - Iran P.G. Diploma (Hydrau. Eng.), IHE - Delft, The Netherlands M.E. (Hons), University of Wollongong
DEPARTMENT OF CIVIL AND MINING ENGINEERING
1996 du/um 4/.'Ju 1
ii DECLARATION
This is to certify that the work presented in this thesis was carried out by the author in the Department of Civil and Mining Engineering at the University of Wollongong and has not been submitted for a degree to any other University or Institution.
/i
(Forood Sharifi)
iii ABSTRACT
Rapid population growth has caused an increasing demand for water in both agricultural and industrial sectors. With wastage of water, deterioration of water sources through pollution and the impact of humans on the water cycle, further water shortages are expected. An increasing demand for water dictates the necessity for on-going research into the assessment and modelling of surface and subsurface water resources. As water resources have become scarcer, the trend in water resources development has shifted from large to small catchments, many of which are ungauged.
In water resources design a long record of runoff is desirable, but this is not usually available in small catchments. Rainfall records are more readily available than runoff records in most situations. This emphasises the need for better and more consistent rainfall-runoff modelling.
The primary objectives of this research are the assessment of currently available rainfall- runoff models. An investigation of suitably complex rainfall-runoff models, and an evaluation of the physical interpretation of the parameter values and their interactions has been carried out in order to achieve a better understanding of the rainfall-runoff processes in natural catchments.
The secondary objectives involved the development of a methodology to estimate both the catchment runoff and the parameter values of the rainfall-runoff models for catchments with short records, with a view to extending the use of rainfall-runoff models to ungauged catchments.
The first part of the study consists of a literature review which includes the hydrological processes, current rainfall-runoff models, and basic issues on rainfall-runoff modelling. Different models are reviewed in relation to their selection, calibration, parameter estimation, and optimisation techniques.
Evaporation and transpiration, which are the most important losses in rainfall-mnoff modelling, are investigated in the next part of the study. Water balance studies in five catchments were carried out which showed that evapotranspiration accounts for more
iv than 40% of total rainfall. Surface runoff, subsurface runoff and other losses are calculated as equal to 32, 16, and 12% of total rainfall respectively. Several approaches to estimating evapotranspiration are reviewed and criteria for selection of a method to be used in modelling have been investigated. Selection of an appropriate technique for the estimation of PET and evaluation of its effects in rainfall runoff-modelling has been discussed. Pan evaporation measurements and the complementary approach recommended by Morton are the most widely accepted methods for the calculation of evaporation in rainfall-runoff modelling. However, the limitations of these methods were highlighted and a methodology for converting pan evaporation to PET has been proposed.
Rainfall-runoff modelling was carried out using data from eight catchments located in Australia. A description of the catchments' physical and climatic characteristics and a summary of the results is also presented.
Existing models range from the simple runoff coefficient model to more complex rainfall-runoff models. Some complex models failed to recognise the spatial variability of the hydrological processes. In many cases these models estimated total streamflow values in agreement with the total recorded streamflow, but incorrectly predicted the different streamflow components. Consequently, in this study attention was focussed on the estimation of both baseflow and surface runoff. In addition, the source areas of storm runoff was investigated.
Several methods for partitioning the total streamflow into baseflow and surface runoff were investigated and a number of improvements to established techniques were made. These improvements included the evaluation and modification of two methods of streamflow partitioning and the development of a new model for the separation of the baseflow from the total runoff. The model was based on the use of a streamflow recession and recharge parameter. The storage-discharge relation of the catchment was analysed by studying catchment lag parameters and their relationship to catchment characteristics. The study was conducted on the most appropriate technique for calculating streamflow recession and recharge parameters.
v Two rainfall runoff models (the SFB and AWBM model) with physically-based parameters were adopted. Sensitivity analysis of the SFB and AWBM models was carried out in order to test the relative importance of parameter values and their interactions. In addition, problems associated with the models' optimisation have been highlighted and a new approach for the parameter estimation of rainfall runoff models based on separated surface runoff and baseflow was proposed.
The effect of using pan evaporation data and pan evaporation data multiplied by conversion factors in model calibration was investigated. It was found that the use of conversion factors improved the accuracy of model simulation in most catchments.
In most of the catchments, the physically realistic parameters of the SFB model were unable to achieve accurate predictions of runoff. Conversely, whilst the optimised parameter values gave a reasonable runoff prediction they did not have physically realistic values. The optimum values of parameters described data characteristics rather than physical characteristics. It was concluded that the optimised parameter values of this model did not truly represent the movement of water in heterogeneous catchments and could not be related to the physical characteristics of the catchments with any degree of reliability.
In order to ascertain the effects of complexity in rainfall-runoff modelling the results of the SFB and AWBM models were compared with the more complex SDI model.
If the models had been assessed on the basis of a comparison between recorded and predicted runoff over the calibration period, the best model would have been SDI followed by the AWBM and SFB models respectively. This demonstrates that accuracy during the calibration period increases as the number of parameters (or model complexity) increases.
It was concluded that if the number of parameters are increased, the differences between predicted and actual total runoff (surface runoff plus baseflow) can easily be minimised.
vi However, the error in data propagates into the estimated parameters and increases the uncertainty in optimised parameter values.
Parameter estimation based solely on the total streamflow may present misleading results. In this case errors in predicted baseflow and surface runoff cancel each other and the model estimates approximately correct total flows. To avoid this problem in modelling, parameter estimation procedures utilising a separated continuous streamflow and separated individual events were proposed and shown to be promising.
Contrary to the results obtained from predicting total flow, when the models were assessed on the basis of baseflow and surface runoff prediction, the AWBM model gave a significantly better performance, while the two other models gave extremely poor results. The poor performance of the SDI and the SFB models is probably due to a combination of model errors, parameter errors and input errors. Among the factors which seem to be more important is the failure of these models to consider the spatial variability in soil storage capacity of catchments. As a result, these models failed to simulate the movement of water in the catchment correctly. Consequently, the optimised parameter values of these models cannot be regarded as having a physical significance.
The number of parameters is not the only requirement for modelling accuracy and it is unlikely that all of the processes incorporated in complex models can be supported by the limited information obtained from rainfall and streamflow measurement. The degree of complexity does not play a significant role in the correct calibration of the model, unless the formulation of models is based on correct assumptions.
Relating model parameters to physically measurable catchment characteristics has been an important aim of many researchers over the past three decades, but has met with little success. This study showed that the AWBM model, with efficient parameterisation and a good physical basis, performed better than the more complex 11-parameter SDI model. The primary results showed that the AWBM model has potential for application on ungauged catchments.
vii Since the accuracy of model parameters can be adversely influenced by erroneous input data, any attempt to relate model parameters to catchment characteristics will need to account for the uncertainties resulting from rainfall and streamflow data errors.
In order to ascertain the effect of data errors on parameter values, AWBM was calibrated using error free data. All daily values of the rainfall, streamflow and evaporation data were then varied by ±10 and ±20 and the model parameters were optimised using different combinations of correct and erroneous data. It was concluded that errors in the input data seriously affected the parameter values of the model without introducing significant changes in the model output.
At the present stage it seems unlikely that the parameters of any model, when estimated solely from rainfall and streamflow data, will lead to values that have a reliable relationship to the physical characteristics of the catchments. However, the AWBM model, with some refinements, has the potential to elucidate the physical basis of the parameters and may eventually be utilised for ungauged catchments.
viii ACKNOWLEDGMENT
Praise be to God, the Cherisher and the Sustainer of the world. Without the strengths and blessings from God, I simply could not come to this stage. However, whoever is not thankful to people is not thankful to God. Therefore, some valuable contributions must be acknowledged here.
Financial and moral support for my program of study was provided by the Ministries of Culture and Higher Education, and the Construction Jehad of the Islamic Republic of Iran. Their aid is heartily appreciated.
I would like to thank my supervisor, Professor M. J. Boyd; his guidance, encouragement and assistance throughout the period of my work was greatly appreciated.
The author would like to thank Prof. W. C. Boughton, for providing computer programs for two models, and also for the encouragement and advice he gave throughout my PhD program.
This work was carried out in the Department of Civil and Mining Engineering, the provision of facilities greatly expedited my work.
I would like to express my gratitude to Professor R. N. Chowdhury, Head of Department of Civil and Mining Engineering.
I have benefited from the cooperation of many of my colleagues including Dr. Amin Baki and Dr. M. Tavasoli, former Ph.D. students of this University.
I would also like to thank the following people for their help: Mr Derek Henry, Dr. F. Chiew, Dr. R. Nathan, Mr. S. Morton, Mr. P. Flenji, Mr. M. Chinnaiyan, and Mr Y.
Rana.
Finally, I would like to express my deepest thanks and love to my Parents, my Wife and children. Their moral support during the difficult period of my study, their encouragement, and my children's patience contributed to the success of this thesis. I am proud of them.
JmA UJWVRJ i& amknkAj to- Jmam KUMOMI rna/ituAy ol m/mmri a/nxl hMkjb.
ix PAPERS PUBLISHED AND IN PREPARATION FROM THE MATERIAL PRESENTED IN THIS THESIS
1- Sharifi F & Boyd M.J (1994), A Comparison of the SFB and AWBM Rainfall-Runoff Models. 25th
Congress of The International Association of Hydrologeologists/ International Hydrology & Water Resources Symposium of The Institution of Engineers, Australia. ADELAIDE 21-25 November.
The Institution of Engineers, Australia, pp. 491-494 ( National Conference Publication No. 94/15)
2- Boyd M.J & Sharifi F (1996), Estimation of Rainfall-Runoff Model Parameters Using Separated Surface Runoff and Baseflow (in preparation).
x TABLE OF CONTENTS
TITLE PAGE I DECLARATION HI ABSTRACT IV ACKNOWLEDGMENT IX LIST OF PUBLICATIONS X TABLE OF CONTENTS XI LIST OF FIGURES XVIII LIST OF TABLES XXIII NOTATIONS XXVII
CHAPTER ONE INTRODUCTION AND OVERVIEW 1.1
1.1 SIGNIFICANCE OF THE RESEARCH 1.1 1.2 AIM OF THE RESEARCH 1.2 1.3 SCOPE OF THE THESIS 1.3
CHAPTER TWO HYDROLOGICAL PROCESSES IN CATCHMENTS .2.1
2.1 INTRODUCTION 2.1 2.2 HYDROMETEOROLOGICAL FACTORS 2.2 2.2.1 Precipitation 2.2 2.2.1.1 Introduction 2.2 2.2.1.2 Point Rainfall 2.2 2.2.1.3 Areal Rainfall 2.3 2.2.2 Evapotranspiration 2.4 2.2.2.1 Introduction 2.4 2.2.2.2 Factors Affecting Evapotranspiration 2.5 2.2.2.3 Methods of Estimating Evapotranspiration 2.6 2.2.2.4 Actual Evapotranspiration 2.14 2.2.2.5 Selection of Method for Estimating Potential Evapotranspiration 2.16 2.2.2.6 Discussion 2.22 2.2.3 Runoff 2.23 2.2.3.1 Factors Affecting Runoff 2.24
xi 2.2.3.2 Runoff Losses 2.24 2.2.3.3 The Process of Surface Runoff Generation 2.25 2.3 PROCESS FACTORS 2.26 2.3.1 Introduction 2.26 2.3.2 Interception 2.27 2.3.3 Infiltration 2.27 2.3.4 Soil Moisture Storage 2.31 2.3.5 Subsurface Runoff 2.32 2.3.6 Channel Processes 2.33 2.3.7 Transmission Losses 2.33 2.4 PHYSICAL FACTORS 2.34 2.5 SUMMARY 2.34
CHAPTER THREE MODELLING THE RAINFALL-RUNOFF PROCESS 3.1
3.1 INTRODUCTION 3.1 3.2 A REVIEW OF METHODS USED AT PRESENT 3.1 3.2.1 Empirical Methods 3.4 3.2.2 Runoff Determination by Statistical or Probability Methods 3.5 3.2.3 Simulation Methods 3.7 3.2.3.1 Introduction 3.7 3.2.3.2 Stanford Watershed Model 3.8 3.2.3.3 Sacramento Model (SMA) 3.10 3.2.3.4 European Hydrological Model (SHE) 3.12 3.2.3.5 API Type Models 3.13 3.2.3.6 Soil Dryness Index Types Models (SDI) 3.14 3.2.3.7 SCS Methods 3.18 3.2.3.8 Semi-Arid-Zone Model 3.20 3.2.3.9 Boughton Model 3.21 3.2.3.10 SFB Model 3.24 3.2.3.11 AWBM Model 3.26 3.3 SELECTION OF MODELS 3.27 3.4 CALIBRATION OF MODELS 3.29 3.4.1.1 Parameter Optimisation (Automatic search techniques) 3.30
xii 3.4.1.2 Alternatives to Automatic Optimisation 3.34 3.5 TEST OF MODELS 3.35 3.6 SUMMARY 3.35
CHAPTER FOUR DESCRIPTION OF CATCHMENTS AND
PRELIMINARY ANALYSES OF CLIMATIC INPUTS 4.1
4.1 INTRODUCTION 4.1 4.2 STUDY AREA 4.1 4.3 GENERAL CHARACTERISTICS OF CATCHMENTS 4.3 4.3.1 Sandy Creek Catchment 4.3 4.3.2 Kangaroo Valley Catchment 4.4 4.3.3 Macquarie Rivulet Catchment. 4.6 4.3.4 Bungonia Creek Catchment 4.6 4.3.5 Mongarlowe River Catchment at Mongarlowe 4.7 4.3.6 Endrick River Catchment 4.8 4.3.7 Corang River Catchment 4.9 4.3.8 Shoalhaven River Catchment 4.10 4.4 GEOLOGY 4.12 4.5 SOIL DESCRIPTION 4.12 4.6 VEGETATION 4.14 4.7 PRELIMINARY ANALYSES OF DATA 4.16 4.7.1 Introduction 4.16 4.7.2 Data Collection 4.17 4.7.3 Data Availability 4.18 4.7.4 Filling the Missing Records 4.18 4.7.5 Data Characteristics of the Region 4.21 4.7.5.1 Double Mass Curve Analysis 4.22 4.7.5.2 Time Series Plot and Statistical Analysis of Data 4.23 4.7.6 Variability of the Data in the Study Area 4.30 4.8 SUMMARY 4.32
xiii CHAPTER FIVE BASEFLOW RECHARGE AND DISCHARGE ANALYSES WITH A VIEW TOWARDS RAINFALL-RUNOFF MODELLING 5.1
5.1 INTRODUCTION.... 5.1 5.2 CLASSIFICATION OF STREAMS AND STREAM RISES 5.2 5.3 BASEFLOW SEPARATION 5.3 5.3.1 General 5.3 5.3.2 Separation of Streamflow Using Recursive Digital Filter, Method 1 5.7 5.3.3 Automated Technique Adopted by Boughton, Method 2 5.7 5.3.4 Improved Frequency-Domain Filter Technique, Method 3 5.8 5.3.5 Modified Automated Technique, Method 4 5.12 5.3.6 Proposed Method Based on Travel Time of Runoff, Method 5 5.12 5.3.6.1 Background 5.12 5.3.6.2 Use of Method 5 in Catchments with Linear Recession Characteristics 5.15 5.3.6.3 Use of Method 5 in Catchments with Nonlinear Recession Characteristics 5.16 5.3.7 Results and Discussions 5.18 5.4 BASEFLOW INDEX (BFI) 5.28 5.4.1 General 5.28 5.4.2 Results and Discussions 5.29 5.5 BASEFLOW RECESSION EQUATIONS AND TECHNIQUES 5.40 5.5.1 General 5.40 5.5.2 The Master Recession Curve (MRC) 5.42 5.5.2.1 The Matching Strip Method 5.42 5.5.2.2 The Tabulating Method 5.44 5.5.2.3 The Correlation Method 5.44 5.5.2.4 Analytical Method 5.44 5.5.2.5 Results and Discussions 5.46 5.6 INVESTIGATING THE POSSIBILITY OF DETERMINING THE MASTER RECHARGE CURVE 5.51 5.6.1 General 5.51 5.6.2 Results and Discussions 5.51 5.7 SUMMARY AND CONCLUSIONS 5.54
xiv CHAPTER SIX THE SFB MODEL 6.1
6.1 INTRODUCTION 6.1 6.2 DESCRIPTION OF THE SFB MODEL 6.2 6.3 OPERATION OF THE SFB MODEL 6.4 6.4 METHODS OF EVALUATING PARAMETERS 6.4 6.4.1 Initial Estimates of Parameters and Adequacy of Warm-up Period 6.5 6.4.2 Measure of Goodness of Fit 6.7 6.4.3 Parameter Optimisation Using Total Streamflow 6.10 6.4.3.1 Optimisation Technique Used 6.10 6.4.3.2 Optimisation Procedure 6.14 6.4.4 Parameter Optimisation Using Separated Baseflow and Surface Runoff. 6.15 6.4.5 Parameter Optimisation Using Water Balance of Individual Storm Events...6.17 6.4.6 Parameter Optimisation Using a Split Sample 6.20 6.4.7 Parameter Optimisation after Excluding Inconsistent Data 6.26 6.5 APPLICATION OF THE MODEL TO ALL CATCHMENTS 6.27 6.5.1 Kangaroo Valley Catchment 6.27 6.5.2 Macquarie Rivulet Catchment. 6.28 6.5.3 Bungonia Creek Catchment 6.30 6.5.4 Mongarlowe River Catchment. 6.31 6.5.5 Endrick River Catchment 6.34 6.5.6 Corang River Catchment 6.36 6.5.7 Shoalhaven River Catchment at Kadoona 6.37 6.6 DISCUSSION OF RESULTS 6.39 6.6.1 General Results 6.39 6.6.2 Adequacy of The SFB Model 6.41 6.7 SUMMARY 6.42
CHAPTER SEVEN THE AWBM MODEL 7.1
7.1 INTRODUCTION 7.1 7.2 DESCRIPTION OF THE AWBM MODEL 7.1 7.3 THEORETICAL BEHAVIOUR OF SURFACE STORAGE IN THE AWBM MODEL 7.2
XV 7.4 OPERATION OF THE MODEL 7.6 7.5 METHODS OF EVALUATING PARAMETERS 7.8 7.5.1 General 7.8 7.5.2 Parameter Estimation Using Direct Storm Analysis 7.8 7.5.3 Parameter Estimation Using a Split Sample 7.72 7.5.4 Parameter Estimation Using Automatic Multiple Regression Technique 7.13 7.5.5 Application of The Model to all Catchments Using the Automatic Multiple Regression Technique 7.75 7.5.5.1 Kangaroo Valley Catchment 7.15 7.5.5.2 Macquarie Rivulet Catchment 7.17 7.5.5.3 Bungonia Creek Catchment 7.19 7.5.5.4 Mongarlowe River Catchment 7.22 7.5.5.5 Endrick River Catchment 7.25 7.5.5.6 Corang River Catchment 7.28 7.5.5.7 Shoalhaven River Catchment at Kadoona 7.31 7.6 MODEL SENSITIVITY ANALYSIS 7.33 7.7 SUMMARY 7.34
CHAPTER EIGHT INVESTIGATION OF MODEL COMPLEXITY AND DATA ERRORS IN RAINFALL-RUNOFF MODELLING 8.1
8.1 INTRODUCTION 8.1 8.2 THE SDI MODEL 8.2 8.3 MODEL COMPARISONS 8.7 8.3.1 Sandy Creek Catchment 8.7 8.3.2 Kangaroo Valley Catchment 8.8 8.3.3 Macquarie Rivulet Catchment. 8.10 8.3.4 Bungonia Creek Catchment S.77 8.3.5 Mongarlowe River Catchment. &72 8.3.6 Endrick River Catchment 8.14 8.3.7 Corang River Catchment 8.15 8.3.8 Shoalhaven River Catchment at Kadoona &77 8.3.9 Summary 8.18 8.4 ERROR ANALYSIS 8.19
xvi 8.4.1 Error in Evaporation Data 8.19 8.4.2 Error in Rainfall and Streamflow Data 8.20 8.5 SUMMARY AND CONCLUSION 8.24
CHAPTER NINE SUMMARY, CONCLUSIONS AND RECOMMENDATIONS FOR FUTURE RESEARCH 9.1
9.1 SUMMARY AND CONCLUSIONS 9.1 9.7.7 Hydrological Processes In Catchments 9.1 9.1.2 Modelling The Rainfall-Runoff Processes 9.3 9.1.3 Catchments And Data 9.5 9.1.4 Baseflow Recharge And Discharge 9.6 9.1.5 The SFB Model 9.9 9.1.6 The AWBM Model 9.70 9.1.7 Effects of Complexity and Data Errors In Rainfall-Runoff Modelling 9.77 9.2 RECOMMENDATIONS FOR FUTURE RESEARCH 9.15
REFERENCES R.l
APPENDICES A.1 A: PHOTOGRAPHS OF THE STUDY AREA A.2 B: DETAILS OF RECORDING STATIONS AND SUMMARY OF DATA CHECKING AND ANALYSIS B.l C: LIST OF PROGRAMS USED FOR DATA ANALYSIS AND MODELLING C.l D: DETAILS OF RECESSION ANALYSIS IN ALL CATCHMENTS D.l
xvii LIST OF FIGURES
No. Description Pages
Figure 2.1 Schematic Representation of the Complementary Relationship 2.13
Figure 2 Plot of Monthly Correlations Between Pan Evaporation and Morton's Wet Environment Evapotranspiration (St. No. 215004) 2.19
Figure 2.3 Time Series Plot between Monthly Morton's ETW and Pan Evaporation Data (St. No. 215004) 2.19
Figure 2.4 Cumulative Regression Plot between Morton's ETW and Pan Evaporation Data (St. No. 215004) 2.20
Figure 2.5 Cumulative Regression Plot between Morton's ETW and Pan Evaporation Data in Five Catchments 2.20
Figure 2.6 Proposed Influences of Climate, Soils, Vegetation and Topography on Runoff Generating Processes (after Boughton, 1988; Dunne, 1983) 2.26
Figure 2.7 Rainfall and Ponded Infiltration 2.28
Figure 3.1 An Overall Perspective of some Possible Approaches in the Analysis of Water Resources 3.2 Figure 3.2 Flow Chart of the Stanford Watershed Model 3.10
Figure 3.3 Schematic Representation of the Sacramento Model 3.12
Figure 3.4 Structure of the SDI Model 3.17
Figure 3.5 Schematic Diagram of the Modified Semi-Arid-Zone Model 3.21
Figure 3.6 Computation of AET in Boughton's Model 3.22
Figure 3.7 Structure of the Modified Boughton Model 3.24
Figure 3.8 Schematic Diagram of the SFB Model 3.25
Figure 3.9 AWBM Structure 3.27
Figure 4.1 Location of Catchments in the Region 4.2
Figure 4.2 Map of Sandy Creek 4.4
Figure 4.3 Profile of Sandy Creek 4.4
Figure 4.4 Map of Kangaroo Valley Catchment 4.5
Figure 4.5 Profile of Kangaroo River 4.5
Figure 4.6 Map of Macquarie Rivulet 4.6
xviii Figure 4.7 Profile of Macquarie Rivulet 4.6
Figure 4.8 Map of Bungonia Creek 4.7
Figure 4.9 Profile of Bungonia Creek 4.7
Figure 4.10 Map of Mongarlowe River 4.8
Figure 4.11 Profile of Mongarlowe River 4.8 Figure 4.12 Map of Endrick River 4.9
Figure 4.13 Profile of Endrick River 4.9
Figure 4.14 Map of Corang River 4.10
Figure 4.15 Profile of Corang River 4.10
Figure 4.16 Profile of Shoalhaven River 4.11
Figure 4.17 Profile of Shoalhaven River 4.11
Figure 4.18 Plots of Average Monthly Runoff (Q), Rainfall (P), and Pan Evaporation (EP) Records Used in the Study 4.24 Figure 4.19 Plots of Annual Runoff (Q) and Rainfall (P) Records Used in the Study.. 4.25 Figure 4.20 Residual Mass Curve of Annual Data Used in the Study 4.31
Figure 5.1 Hydrograph Illustrating Methods of Baseflow Separation 5.5
Figure 5.2 The Frequency Characteristic of a Typical Lowpass Filter 5.9
Figure 5.3 Recommended Filtering Procedures (a) and (b) 5.11
Figure 5.4 An Example of the Typical and Proposed Baseflow Separation 5.15
Figure 5.5 (a,b,c) Examples of Continuous Baseflow Separation Using Methods 1 to 5 5.21 Figure 5.6 Plots of Annual BFI Determined Using Methods 1 to 5 Against Time (Catchment 206026) 5.29
Figure 5.7 Plots of Annual BFI Determined Using Methods 1 to 5 Against Time (Catchment 215220) 5.29
Figure 5.8 Time Series Plots of Baseflow Residuals (relative to Method 5) 5.30
Figure 5.9 Correlation Between Annual BFI's, from 8 Studied Catchments (137 Years), Derived Using Method 5 and Methods 1 to 4 5.31
Figure 5.10 Correlation Between Annual Baseflow Using Method 5 and Methods 1,2,3 & 4 5.31
Figure 5.11 Plot of the Mean Annual BFI Ratios as a Function of Size of Catchments 5.32
xix Figure 5.12 Plot of the Coefficient of Variation (C.V.) of the BFI Ratios, QB, QS and Q for the Studied Catchments 5.33
Figure 5.13 Plots of Events BFI Determined Using Methods 5 Against Time (Catchment 215220) 5.33
Figure 5.14 Frequency Distribution of Event Baseflow Index (BFI) 5.34 Figure 5.15 Frequency Distribution of Event Baseflow 5.34
Figure 5.16 Example of Master Recession Curve Derived by Matching Strip Method 5.43
Figure 5.17 Example of The Master Recession Curve Derived by Analytical Method: dQB/dt versus QB 5.48
Figure 5.18 Comparison of the Daily Flow Duration Curves of the Studied Catchments 5.50
Figure 5.19 Correlation Between Recession Constants Derived Using Matching Strip and Analytical Method versus Q90/Q50 Ratio 5.50
Figure 5.20 Example of The Master Recharge Curve Derived by Analytical Method: dQB/dt versus QB 5.52
Figure 5.21 Frequency Distribution of Event Recharge Constant 5.54
Figure 6.1 Schematic Diagram of SFB Model for Calculating Values of AET 6.3
Figure 6.2 Plot of US Against Time (after Baki, 1993) 6.7
Figure 6.3 Plot of SS Against Time (after Baki, 1993) 6.7
Figure 6.4 Flow Chart of Pattern-Search Optimisation Technique 6.12
Figure 6.5 The 3-Dimensional Response Surface of SFB model using SSQ (B=0.2) ..6.14
Figure 6.6 Plot of SSQ, SAD, & PSAD against B for Optimised value of S & F (Period 1, B=0.95) 6.22
Figure 6.7 The 3-Dimensional Response Surface of SFB model using SSQ (Period 1,B=0.95) 6.23
Figure 6.8 Contours of SSQ for Variation of Parameters S and F (Period 1, B=0.95) 6.23
Figure 6.9 The 3-Dimensional Response Surface of SFB model using SSQ (Period 2,B=1) 6.24 Figure 6.10 Contours of SSQ for Variation of Parameters S and F (Period 2, B=l) ...6.24
Figure 6.11 The 3-Dimensional Response Surface of SFB model using SSQ (Period 3, B=0.2) 6.25
Figure 6.12 Contours of SSQ for Variation of Parameters S and F (Period 3, B=0.2) 6.25
XX Figure 6.13 Mass Curve of Actual and Simulated Q, QB and QS for the SFB Model Applied to Kangaroo Valley Catchment 6.28
Figure 6.14 Mass Curve of Actual and Simulated Q, QB and QS for the SFB Model Applied to Macquarie Rivulet Catchment 6.30
Figure 6.15 Mass Curve of Actual and Simulated Q, QB and QS for the SFB Model Applied to Bungonia Creek Catchment 6.31 Figure 6.16 Mass Curve of Actual and Simulated Q, QB and QS for the SFB Model Applied to Mongarlowe River Catchment 6.33
Figure 6.17 Mass Curve of Actual and Simulated Q, QB and QS for the SFB Model Applied to Endrick River Catchment 6.36
Figure 6.18 Mass Curve of Actual and Simulated Q, QB and QS for the SFB Model Applied to Corang River Catchment 6.37
Figure 6.19 Mass Curves of Actual and Simulated Q, QB and QS for the SFB Model Applied to Shoalhaven River Catchment at Kadoona 6.39
Figure 7.1 Rainfall-Runoff Relationship for Catchment with Single Uniform Surface Storage Capacity 7.3
Figure 7.2 Rainfall-Runoff Relationship for Catchment with Two Surface Storage Capacities 7.3 Figure 7.3 Rainfall-Runoff Relationship Variability in Surface Storage Capacity 7.4
Figure 7.4 AWBM Model Combined with Flood Hydrograph Model 7.5
Figure 7.5 Actual Runoff Compared with Result from AWBM-URBS System (after Boughton and Carroll, 1993) 7.6
Figure 7.6 Estimated Runoff for Cl=10-mm Compared with Actual Runoff 7.10
Figure 7.7 Estimated Runoff from Area of C2=15 mm Compared with Actual Runoff Minus Runoff from First Source Area 7.11
Figure 7.8 Mass Curves of Actual and Simulated Q, QB and QS for the AWBM Model Applied to Kangaroo Valley Catchment 7.16
Figure 7.9 Mass Curves of Actual and Simulated Q, QB and QS for the AWBM Model Applied to Macquarie Rivulet Catchment 7.18
Figure 7.10 Mass Curves of Actual and Simulated Q, QB and QS for the AWBM Model Applied to Bungonia Creek Catchment 7.21
Figure 7.11 Mass Curves of Actual and Simulated Q, QB and QS for the AWBM Model Applied to Mongarlowe River Catchment 7.24
Figure 7.12 Mass Curves of Actual and Simulated Q, QB and QS for the AWBM Model Applied to Endrick River Catchment 7.27
xxi Figure 7.13 Mass Curves of Actual and Simulated Q, QB and QS for the AWBM Model Applied to Corang River Catchment 7.30
Figure 7.14 Mass Curves of Actual and Simulated Q, QB and QS for the AWBM Model Applied to Shoalhaven River Catchment at Kadoona 7.32
Figure 8.1 Plot of Actual and Simulated Monthly Streamflow Using the SDI Model 8.6
Figure 8.2 Mass Curves of Actual and Simulated Q, QB and QS for the SDI Model Applied to Sandy Creek Catchment 8.8 Figure 8.3 Mass Curves of Actual and Simulated Q, QB and QS for the SDI Model Applied to Kangaroo Valley Catchment 8.9
Figure 8.4 Mass Curves of Actual and Simulated Q, QB and QS for the SDI Model Applied to Macquarie Rivulet Catchment 8.10
Figure 8.5 Mass Curves of Actual and Simulated Q, QB and QS for the SDI Model Applied to Bungonia Creek Catchment 8.12
Figure 8.6 Mass Curves of Actual and Simulated Q, QB and QS for the SDI Model Applied to Mongarlowe River Catchment 8.13
Figure 8.7 Mass Curves of Actual and Simulated Q, QB and QS for the SDI Model Applied to Endrick River Catchment 8.14
Figure 8.8 Mass Curves of Actual and Simulated Q, QB and QS for the SDI Model Applied to Corang River Catchment 8.16
Figure 8.9 Mass Curves of Actual and Simulated Q, QB and QS for the SDI Model Applied to Shoalhaven River Catchment at Kadoona 8.17
xxii LIST OF TABLES
No. Description Pages
Table 2.1 Estimated Potential and Actual Evapotranspiration, Surface Runoff and Subsurface Runoff Using Water Balance Approach 2.5
Table 2.2 Comparison between Water Balance and Morton Methods for Calculating Pan Evapotranspiration Conversion Factors (St. No. 215004) 2.21
Table 2.3 Daily Infiltration Capacity for Various Soil Texture and Moisture Content 2.30
Table 3.1 Estimation of Parameter Values for Use of the SFB Model on Ungauged Catchments (Boughton, 1984) 3.27
Table 4.1 Summary of the General Characteristics 4.3 Table 4.2 Soil Types, Soil Stores and Infiltration Characteristics 4.14
Table 4.3 Main Type of Vegetation in all Catchments 4.16
Table 4.4 Period of Available Data 4.18
Table 4.5 Summary Statistics of Monthly Data in Sandy Creek Catchment (Station No. 206026) 4.27
Table 4.6 Summary Statistics of Monthly Data in Kangaroo Valley Catchments(Station No. 215220) 4.27
Table 4.7 Summary Statistics of Monthly Data in Macquarie Rivulet Catchment (Station No. 214003) 4.28
Table 4.8 Summary Statistics of Monthly Data in Bungonia Creek Catchment (Station No. 215014) 4.28
Table 4.9 Summary Statistics of Monthly Data in Mongarlowe River Catchment (Station No. 215006) 4.28
Table 4.10 Summary Statistics of Monthly Data in Endrick River Catchment (Station No. 215009) 4.29
Table 4.11 Summary Statistics of Monthly Data in Corang River Catchment (Station No. 215004) 4.29
Table 4.12 Summary Statistics of Monthly Data in Shoalhaven River Catchment (Station No. 215008) 4.29
Table 4.13 Linear Regression between Rainfall and Surface runoff 4.30
xxiii Table 5.1 Correlation Between Annual Baseflow and BFI's derived from Method 5 and Methods 1 to 4 5.32
Table 5.2 Annual BFI for Sandy Creek Catchment (206026) Using Methods 1 to 5...5.36
Table 5.3 Annual BFI for Corang River Catchment (215004) Using Methods 1 to 5 5.36
Table 5.4 Annual BFI for Endrick River Catchment (215009) Using Methods 1 to 5 5.36
Table 5.5 Annual BFI for Bungonia River Catchment (215014) Using Methods 1 to 5 5.37
Table 5.6 Annual BFI for Mongarlowe River Catchment (215006) Using Methods 1 to 5 5.37
Table 5.7 Annual BFI for Shoalhaven River Catchment (215008) Using Methods 1 to 5 5.38
Table 5.8 Annual BFI for Kangaroo River Catchment (215220) Using Methods 1 to 5 5.38
Table 5.9 Annual BFI for Macquarie Rivulet Catchment (214003) Using Methods 1 to 5 5.39
Table 5.10 Comparison Between K Values Obtained by Matching Strip and Analytical Method 5.49
Table 5.11 Estimation of Recharge Rates in the Studied Catchments Using Analytical Method 5.53
Table 6.1 Model Sensitivity to Parameter Variations for SFB 6.16
Table 6.2 Water Balance for Storm Event 9-16 Feb., 1976 6.18
Table 6.3 Parameter Estimates and Model Predictions (Period 3 from 1976-1987) ....6.20
Table 6.4 Optimum Set of Parameters for the SFB Model using different Calibration Periods 6.26
Table 6.5 Parameters of the SFB Model for Kangaroo Valley Catchment 6.27
Table 6.6 Results of SFB Model for Kangaroo Valley Catchment 6.28
Table 6.7 Parameters of the SFB Model for Macquarie Rivulet Catchment 6.28
Table 6.8 Results of SFB Model for Macquarie Rivulet Catchment 6.29
Table 6.9 Parameters of the SFB Model for Bungonia Creek Catchment 6.30
Table 6.10 Results of SFB Model for Bungonia Creek Catchment 6.31
Table 6.11 Parameters of the SFB Model for Mongarlowe River Catchment 6.32
Table 6.12 Results of SFB Model for Mongarlowe River Catchment 6.32
xxiv Table 6.13 Parameters of the SFB Model for Endrick River Catchment 6.34
Table 6.14 Results of SFB Model for Endrick River Catchment 6.35
Table 6.15 Parameters of the SFB Model for Corang River Catchment 6.36
Table 6.16 Results of SFB Model for Corang River Catchment 6.37 Table 6.17 Parameters of the SFB Model for Shoalhaven River Catchment at Kadoona 6.38
Table 6.18 Results of SFB Model for Shoalhaven River Catchment at Kadoona 6.38
Table 7.1 Errors of Estimating No. of Runoff Events for First Surface Storage Capacity 7.10
Table 7.2 Errors of Estimating No. of Runoff Events for Second Surface Storage Capacity 7.11
Table 7.3 Summary of Parameter Values and Statistics of Results for Sandy Creek Catchment 7.12
Table 7.4 Summary of Parameter Values and Statistics of Results for Sandy Creek Catchment 7.13 Table 7.5 Parameters of the AWBM Model for Kangaroo Valley Catchment 7.15 Table 7.6 Results of AWBM Model for Kangaroo Valley Catchment 7.15
Table 7.7 Various Combinations of Surface Storage Capacities and Source Area Fractions Giving Similar Q and SSQ in Kangaroo Valley Catchment 7.17
Table 7.8 Parameters of the AWBM Model for Macquarie Rivulet Catchment 7.17
Table 7.9 Results of AWBM Model for Macquarie Rivulet Catchment 7.18
Table 7.10 Various Combinations of Surface Storage Capacities and Source Area Fractions Giving Similar Q and SSQ in Macquarie Rivulet Catchment 7.19
Table 7.11 Parameters of the AWBM Model for Bungonia Creek Catchment 7.20
Table 7.12 Results of AWBM Model for Bungonia Creek Catchment 7.20
Table 7.13 Various Combinations of Surface Storage Capacities and Source Area Fractions Giving Similar Q and SSQ in Bungonia Creek Catchment 7.22
Table 7.14 Parameters of the AWBM Model for Mongarlowe River Catchment 7.23
Table 7.15 Results of AWBM Model for Mongarlowe River Catchment 7.23
Table 7.16 Various Combinations of Surface Storage Capacities and Source Area Fractions Giving Similar Q and SSQ in Mongarlowe River Catchment ....7.25
Table 7.17 Parameters of the AWBM Model for Endrick River Catchment 7.26
Table 7.18 Results of AWBM Model for Endrick River Catchment 7.26
XXV Table 7.19 Various Combinations of Surface Storage Capacities and Source Area Fractions Giving Similar Q and SSQ in Endrick River Catchment 7.28 Table 7.20 Parameters of the AWBM Model for Corang River Catchment 7.29
Table 7.21 Results of AWBM Model for Corang River Catchment 7.29 Table 7.22 Various Combinations of Surface Storage Capacities and Source Area Fractions Giving Similar Q and SSQ in Corang River Catchment 7.30
Table 7.23 Parameters of the AWBM Model for Shoalhaven River Catchment at Kadoona 7.31
Table 7.24 Results of AWBM Model for Shoalhaven River Catchment at Kadoona...7.31
Table 7.25 Various Combinations of Surface Storage Capacities and Source Area Fractions Giving Similar Q and SSQ in Shoalhaven River Catchment 7.33
Table 7.26 Model Sensitivity to Parameter Variations for AWBM Model 7.33
Table 7.27 Parameters of the AWBM Model for all Catchment 7.34
Table 8.1 Parameters and Results of the SDI Model for All Catchments 8.3
Table 8.2 Results of all Models for Sandy Creek Catchment 8.8 Table 8.3 Results of Models for Kangaroo Valley Catchment 8.10 Table 8.4 Results of Models for Macquarie Rivulet Catchment 8.11
Table 8.5 Results of Models for Bungonia Creek Catchment 8.12
Table 8.6 Results of Models for Mongarlowe River Catchment 8.14 Table 8.7 Results of the Models for Endrick River Catchment 8.15
Table 8.8 Results of all Models Model for Corang River Catchment 8.16
Table 8.9 Results of all Models for Shoalhaven River Catchment at Kadoona 8.18 Table 8.10 Ranking of the Models in all Catchments 8.19
Table 8.11 Sensitivity of the SFB and AWBM Models to the Change in the Evaporation Input 8.20
Table 8.12 Result of Fitting the AWBM Model with Errors in Rainfall, Streamflow and Evaporation Input Data 8.22
Table 8.13 Percentage Error of Parameters and Predicted Values of the AWBM Model due to the Different Combinations of Error-free and Error- contaminated Data 8.24
xxvi NOTATIONS
Abbreviations: API the Antecedent Precipitation Index (API) Model CBM Commonwealth of Australia, Bureau of Meteorology ML Megalitres Mod. Btn the Modified Boughton Model MOSAZ Modified Semi-Arid Zone Model NLFIT Optimisation Program developed by Kuczera using correction of least squares violation technique R.A.N. Royal Australian Navy Rec Recorded Runoff Data (mm) SHE European Hydrological System (Systeme Hydrologique Europeen) USWB United States Weather Bureau WRC Australian Water Resources Commission
Data Analysis and Catchment Description: s slope of catchment in %
Iv index of runoff variability Pav spatial average daily rainfall (mm)
Ps recorded rainfall for station s (mm)
Rx linear correlation coefficient between rainfall and runoff a , b regression coefficients
Px data at station with the missing record
Pi , P2, ,and P3 data at Index stations during period of study
Ni , N2, ,and N3 the annual data of the different stations
Xt the substituted daily data for station X (station considered) for day t
Yt the recorded daily data for station Y (adjacent station) for day t C.V. coefficient of variation S.D. Standard Deviation
For hydrological processes: JJ> the sum of precipitation XET the sum of evapotranspiration £QB the sum of baseflow SQS the sum of surface runoff SQUG the sum of groundwater contribution (the sum of groundwater flowing out of the catchment less the sum flowing into the catchment)
xxvii AS the change in soil moisture storage (mm)
Interception processes: IL interception loss (mm) V water film stored on the surface of vegetation (mm)
Ev evaporation loss from surface of vegetation during the storm (mm)
Horton's infiltration formula: F daily infiltration capacity (mm/day)
Fc daily infiltration capacity when subsoil is saturated (in mm/day)
F0 daily infiltration capacity when subsoil is dry (in mm/day) k empirical constant in Horton's daily infiltration equation
Philip infiltration formula: f the infiltration rate per unit time t
Ps the saturated permeability
S0 the sorptivity t time
Holtan infiltration formula: fh the infiltration capacity (inch per hour) GI the Growth Index of vegetation fa the infiltration capacity (inches per hour per inch 1 -4) of available storage, which is an index of surface-connected-porosity (a function of roots density) fk the constant rate of infiltration (inches per unit time)
Sa the available storage in surface layer of soil (inches)
Mein and Larson infiltration formula: f infiltration rate (mm) Fcum tne cumulative infiltration I the rainfall intensity (cm s" *) INRATE the infiltration rate (cm s" *)
Ks the saturated conductivity (cm s~l) Md the moisture deficit
Sav the average capillary suction at wetting front (cm)
xxviii Evapotranspiration Process: ET Evapotranspiration AET Actual evapotranspiration, which is the evapotranspiration that actually occurs when the supply of moisture is limited PET Potential Evapotranspiration, which is the maximum rate of water leaving the land surface at the given time assuming unlimited supply of water. ETOP Evapotranspiration opportunity, which is the maximum available moisture for evapotranspiration per unit time at a point in a catchment. EP the Pan evaporation (mm) fp pan factor for estimation of potential evapotranspiration from EP
Penman's formula using meteorological data:
E0 daily water evaporation rate for open water surface (mm) fp the pan factor, which is defined as ratio of lake evaporation to pan evaporation (E\JEp) EL lake evaporation (mm) Ep pan evaporation (mm) 2 Rn energy budget or net radiation; daily heat budget in cal/cm /day (Rn = Rc-Rb)
RD reflected radiation RQ incoming solar radiation
Ea a vapour flow parameter (mm) A slope of the vapour pressure curve of air at mean air temperature (°F/mm)
Ra total possible radiation for the period of estimation ra albedo of surfaces n duration of bright sunshine per day (hours) N maximum possible duration of bright sunshine (hours) a?b constants used in Penman's equations: a = 0.18; b = 0.55 4 2 sTa black body radiation at mean air temperature Ta (in °K) in cal/cm /day ea saturation vapour pressure at mean air temperature (mm Hg) e(l saturation vapour pressure at dew point (mm Hg) W"2 mean wind velocity at 2 meters above the ground (miles per day)
Ea a vapour flow parameter (mm)
Thorntwaite formula: PETmonthly monthly potential evapotranspiration (mm) T the mean monthly air temperature (°C) ae an exponent with value of 0 to 4.25 for Ie in the range 0 to 160
xxix Ie the annual heat index
Blaney and Criddle: Kc the seasonal consumptive use coefficient for corresponding months Dh the percentage day-time hours in a year ran the number of months
Holtan's evapotranspiration formula: km the (PET/EP) ratio at crop maturity EP the pan evaporation(mm) Sp the total porosity ASp the available porosity
Complementary formula: E'TP the Net Energy (insolation absorbed by the region) ETW =Ewet the wet environment areal evapotranspiration that would occur if there is no limitation on the availability of moisture XEEN the sum of energy lost (or gained) in different ways to or from sky, water and soil SOR the incident solar radiation ar the albedo of the basin ep the saturation vapour pressure at equilibrium temperature TP ed the saturation vapour pressure at dew point temperature fT the vapour transfer coefficient
TP equilibrium temperature
Ta air temperature a the Stefan-Boltzmann constant y the psychrometric constant ea 5.22* 10'8 Win2 K4 e emissivity which is equal to 0.92 bi and b2 empirical constants equal to 28 and 2.4 respectively
A the slope of the saturation vapour pressure curve at TP
Stanford Watershed Model: ft the segment mean infiltration capacity at time t (mm) INF the infiltration index level (mm) r maximum evaporation opportunity k3 index to vegetation drawing moisture from the lower zone
XXX LZS current level of lower zone soil moisture storage LZSN field capacity of lower zone soil moisture storage
Evapotranspiration in Boughton Model: AET = actual evapotranspiration H = the potential evaporation rate LZS = current level of primary soil moisture storage B = portion of the residual potential evapotranspiration applied to the soil storage
Soil Moisture Storages: AWC available water capacity NCP non-capillary porosity
Interflow formula: Qo flowrate at time to
Qt flow rate at any time t after the initial time t0 K a recession constant
Runoff Coefficient Model: Q the predicted runoff (mm)
Cr runoff coefficient
Soil Conservation Services Model (SCS):
Ia the initial abstraction (combination of interception, depression and initial infiltration) CN Curve Number F daily infiltration rate (inches per day) S the potential maximum retention (inches per day) Q the runoff (inches) P the precipitation (inches)
Thiessen's formula: p or P spatial average rainfall (mm) A total catchment area (km^) Ai sub-area associated with station i (km^) pi rainfall data for station i (mm)
xxxi Baseflow separation, recession and recharge a the filter parameter
QSt the quick response of the filter at the ith sampling instant <2, streamflow rate at time i (mm)
Q0 streamflow rate at time 0 (mm) QBi and QBM baseflow at times i and i-1 f a fraction which can be in the range of 0.01 to 0.05 k the lag time between centroids of the inflow and outflow K recession parameter QBfirst ,Q^iast baseflow at the start and end of the event n the event duration a a variable for the nonlinearity P a coefficient which was found to be 0.007 for the catchments studied I is inflow from rainfall, (mm/day) S volume of water in storage, (mm) k magnitude of storage-discharge relation m non-linearity of storage-discharge relation which varies with the range of Oto 1 MRC master recession curve
SFB and Boughton's model: AET(us) & AET(ss) the evapotranspiration loss from the upper and lower soil zones PET the maximum evapotranspiration rate PCEN the percentage of evapotranspiration loss occurring from the upper zone B the baseflow contribution (range: 0 -1) CEP interception storage (mm) CEPMAX interception storage capacity (mm) DEP percentage of sub-soil store loss to deep groundwater percolation DR drainage component of upper soil store (mm) DRMAX drainage storage capacity (mm) ET calculated daily evapotranspiration (mm) F daily infiltration capacity (mm/day)
Fc daily infiltration capacity when subsoil is saturated (in mm/day)
F0 daily infiltration capacity when subsoil is dry (in mm/day) H maximum evapotranspiration at field capacity (about 8.89 mm for most types of vegetation) k an empirical constant P recorded rainfall (mm)
xxxii PCUS percentage of evapotranspiration demand which is satisfied by the upper soil store (%) Pexcess rainfall excess (mm) Q predicted runoff (mm) QB baseflow (mm) QS surface runoff (mm) S surface soil moisture storage capacity (mm) SS subsoil store (mm) SSMAX subsoil storage capacity (mm) US non-drainage component of upper soil store (mm) USMAX upper soil storage capacity (mm) S1 and S2 the soil moisture level of each section of the lower soil store SDRMX and SSMAX maximum capacities of the lower soil store sections. KBB and K2 the baseflow recession for the lower soil store sections determined from the streamflow data
SDI model: ATHRU throughfall threshold value for SDI Model BEP vapour pressure deficit stress coefficient for SDI Model BFD baseflow coefficient BTHRU throughfall coefficient for SDI Model CANCAP canopy capacity (mm) CEP soil moisture stress coefficient for SDI Model CEPF fraction of rainfall intercepted by the interception store DEEP portion of seepage loss from baseflow DSSQ daily value of sum of squared differences (mm^) EET reduction factor for evapotranspiration ESW soil moisture stress EVPD vapour pressure deficit stress FR fraction of rainfall discharged as flash runoff
HSt saturated soil store for day t (mm) KG baseflow response parameter KI interflow response parameter P the recorded rainfall (mm) Peff effective or net rainfall (mm) PF linear rainfall factor Pt rainfall for day t (mm) Q the predicted runoff (mm)
xxxiii QBt baseflow during day t QF flash runoff (mm) QFt flash runoff for day t (mm) QO soil capacity overflow (mm) QOt interflow for day t (mm) RGEt recharge from soil store to the saturated soil store during day t (mm) SCOF soil capacity overflow storage depletion constant SCOST soil capacity overflow storage (mm) SDI Soil Dryness Index (mm) SDIAM soil dryness index in the morning SDH initial soil dryness index (mm) SDIPM soil dryness index in the afternoon SEEPt seepage loss during day t (mm) SMAX threshold for interflow (mm) THt throughfall for day t (mm) W evaporation from the interception stores (mm) WETFRAC fraction of the catchment from which throughfall becomes the flash runoff WET, WETS and WETH are the model parameters representing the effects of antecedent conditions on the value of WETFRAC
Antecedent Precipitation Index (API) Model: API Antecedent Precipitation Index APIi the API value at time i k the recession constant for API a, b, c, d coefficients derived empirically to represent the graphical API Model (for the Analytical API Model) bi coefficients for API, which are function of time (t) inc, si coefficients in the API Model, which are found by linear regression N curvature parameter for the Analytical API Model n parameter which correlates P and RI to QS for the Analytical API Model
PAt the precipitation (rainfall) during time (At)
Optimisation and Measures of Accuracy: SAPS Semi-Automatic Pattern Search method of parameter optimisation qt and f(xt,b) observed and simulated response of the catchments respectively (for time interval t) xt vector of inputs such as rainfall and evaporation b a parameter vector
xxxiv OLS ordinary least square Q predicted runoff (mm) Qa recorded runoff (mm) SSQ sum of squared differences (mm^) SAD sum of absolute differences (mm) PS AD sum of the percentage of the absolute differences PSSQ sum of the percentage of the squared differences et residuals (differences between predicted and recorded runoff) E coefficient of efficiency D coefficient of determination
sa the standard deviation of the recorded data S standard error of estimate (mm) WLS weighted least squares AQ percentage difference in mean runoff (between predicted and recorded) s the estimate of the standard deviation of observation errors N the number of observations
AWBM model: Al the first source area fraction of the catchment A2 the second source area fraction of the catchment A3 the third source area fraction of the catchment CI surface storage capacity of the first source area C2 surface storage capacity of the second source area C3 surface storage capacity of the third source area
Cav average surface storage capacity BFI baseflow storage K daily recession constant BS total runoff (mm) Q total runoff (mm) QB the daily discharge from baseflow storage (mm) QS surface runoff (mm)
XXXV CHAPTER ONE
INTRODUCTION AND OVERVIEW CHAPTERONE
INTRODUCTION AND OVERVIEW
1.1 SIGNIFICANCE OF THE RESEARCH
Water has always been essential to the environment. It is one of the most important natural resources which sustains all living organisms. Water is so vital that wars have been fought over it. Human beings utilize water in many different ways.
Although water is the most abundant resource on earth, only a very small proportion, about one-hundredth of one percent, occurs in rivers and lakes in a form suitable for human consumption. Another problem is the uneven distribution of water resources compared with the concentration of population. This problem and the irregular nature of rainfall can result in drought during one season and flooding in another season of the same year.
With rapid population growth and increasing demand from agricultural and industrial sectors, overuse and wastage of water, degradation of water sources through pollution, and the impact of people on the water cycle, further water shortages as well as flooding can be expected. This dictates a corresponding increase in the levels of research in assessment and modelling of both surface and subsurface water resources.
New water resource augmentation schemes must often be built in small ungauged catchments, rather than large basins. In water resources design, a long record of runoff is required. However, long and accurate records of runoff are rarely available. On the other hand there are usually plenty of rainfall records available in most situations. This emphasizes the need for better and more consistent rainfall-runoff models to be applied to estimating runoff from catchments. Chapter One Introduction and Overview. 1.3
1.3 SCOPE OF THE THESIS
In accordance with the outlined aims, this thesis includes the following nine chapters.
In Chapter two a review of the hydrological processes in a catchment, including hydrometeorological factors, process factors and physical factors is carried out. Evaporation and transpiration which are the most important losses in rainfall-runoff modelling are investigated. Water balance studies are carried out using data from five catchments in Australia. Several approaches to estimating evapotranspiration are reviewed and criteria for selection of a method are investigated. The limitations of the different methods are highlighted and a methodology for converting pan evaporation to PET is proposed. Two main theories for explaining surface runoff generation, the Hortonian theory and the variable source area concept are discussed together with those factors affecting runoff rates and losses.
Chapter three includes a study of hydrological modelling. In this chapter some well- known models are discussed and issues related to model selection, calibration, parameter estimation, and optimisation (for finding optimum parameter values) are investigated.
Chapter four describes the catchments used in this study. A description of the physical and climatic characteristics of the catchments and a summary of the data analysis procedure are given. The quality of the data is checked and missing data are infilled. Statistical analyses of monthly values are carried out. Different time series plots of rainfall and streamflow are used to compare the recorded rainfall and runoff data.
The behavior of the baseflow recharge and discharge of eight catchments has been studied using daily runoff and reported in Chapter five. Various techniques that have been used for baseflow and recession analysis are discussed and a new model for baseflow separation from total runoff is proposed in this chapter. A study is conducted into the selection of a technique for calculating streamflow recession and recharge parameters. The storage-discharge relation of the catchment and its relationship with catchment characteristics is analyzed.
In Chapter six the results of the simple three-parameter SFB model applied to eight catchments are presented. The analysis is carried out initially using data from one small catchment in the New England region of Australia. Parameters are estimated by Chapter One Introduction and Overview. 1.4
optimisation, using the total streamflow and on separated baseflow and surface runoff, as well as directly from individual events and from split samples. The model is applied to seven other catchments in the Shoalhaven area and its performance is checked. The importance of calibrating models by considering both baseflow and surface runoff is discussed in this chapter. Some problems associated with parameter estimation using optimisation of model parameters are discussed and model sensitivity to parameter variations is evaluated.
In Chapter seven the use of the variable source area AWBM model is investigated. Particular attention is given to the way that saturation overland flow occurs on a catchment. This model is applied to the same catchments used in Chapter six, with the same sets of data. Model parameters are estimated directly from the recorded rainfall and streamflow events using an automated technique. Also, a split sample test is used to check the effects of the data sequence on the obtained parameter values. A sensitivity analysis of the models is given in the final section of this chapter.
In Chapter eight, the potential for improving rainfall-runoff modelling by applying a more complex model is investigated. A more complex model (SDI) is applied to the same data set and the results are compared with the results obtained from the SFB and AWBM models. The limitations of modelling when only total streamflow is used in calibration is discussed. In addition, the effects of data quality on the parameter values are discussed and a study of error analysis using the AWBM model is carried out.
The conclusions of this study are presented in Chapter nine. Suggestions for future work are also discussed.
Appendix A includes the photographs of the study area. Details of the recording stations and a summary of analyzed data is presented in Appendix B. A list of computer programs is given in Appendix C. Results of recession analysis for all catchments are presented in Appendix D. Finally, the programs and models used for data analysis and modelling, a full list of the daily average streamflow, rainfall and evaporation data which were used and a summary of models output are given in the enclosed floppy disks in IBM format, suitable for further study. CHAPTER TWO
HYDROLOGICAL PROCESSES IN CATCHMENTS CHAPTER TWO
HYDROLOGICAL PROCESSES IN CATCHMENTS
2.1 INTRODUCTION
The hydrologic cycle is a concept that considers the processes of motion, loss and recharge of the catchment's water resources. The four significant phases of this cycle are precipitation, runoff, evapotranspiration and groundwater.
The variables used in relating rainfall to runoff are classified as: hydrometeorological factors, process factors, and physical factors.
Hydrometeorological factors consist of measurements of the mass and energy transfer to and from the land surface. In hydrology, mass consists of water in its different forms and the transfer of mass occurs by precipitation or evapotranspiration.
Process factors involve information on processes that affect the movement and distribution of water in the land phase of the hydrologic cycle. Interception by vegetation and infiltration of water into the soil profiles are examples of such data.
Physical factors consist of information representing the physical conditions of the river basin which can be defined analytically or in geometric terms. For example the stream channel network can be defined geometrically by measurement of length, slope, and cross section. Chapter Two Hydrologic Processes in Catchments. 2.2
2.2 HYDROMETEOROLOGICAL FACTORS
2.2.1 Precipitation
2.2.1.1 Introduction
Precipitation includes all forms of moisture falling from the atmosphere to the earth's surface. Precipitation occurs under certain sets of conditions. There are three methods whereby an air mass can be lifted to cause cooling and condensation to droplets. These types of rainfall are classified as cyclonic, convective, and orographic.
In many cases there are abundant of rainfall records, while the more elaborate and expensive streamflow measurements are often limited and rarely available. Accordingly, the input of most hydrologic models is precipitation. Considerable use is made of rainfall data for environment related research as well as for the practical purpose of assessing runoff and water resources. The hydrologist's interest in precipitation data relates to engineering design, water supply and river forecasting, watershed management and research. The data are usually subjected to analysis of frequency characteristics, extremes, regression and physical relationships. Because the records of rainfall data are more available than streamflow data, estimation of runoff from these records is desirable for design of environment related projects.
2.2.1.2 Point Rainfall
Rainfall is measured by collecting the rain falling at a point in a space by using two types of gauges; a storage gauge and a recording or autographic gauge. The spatial and temporal distribution of rainfall is highly variable. Even closely spaced gauges can collect different amounts of rainfall. The differences in amounts of rain caught in a single rainstorm between identical raingauges installed within a few meters of each other can be up to 10 percent (Landsberg, 1983).
There are various sources of errors in the recorded data. These can be systematic and non-systematic. Systematic errors refer to persistent instrument errors and poor site location. Non-systematic errors include observation errors and random errors. Also, the Chapter Two Hydrologic Processes in Catchments. 2.3
height difference between the catchment and the raingauge, and the wind direction and spatial distributions of rainfall are sources of error in recorded rainfall.
2.2.1.3 Area! Rainfall
Due to the variability of precipitation, it is unrealistic to assume that a point measurement of rainfall is applicable to the entire catchment area. It is desirable to obtain an estimate of average rainfall over an area by using the data from several point gauges. The extrapolation process of point rainfall to areal rainfall or averaging several raingauge records introduces some errors in the rainfall data which should be taken into account in modelling.
Areal reduction factors are available for relating values of point rainfall intensity to catchment area. Since the available factors are mostly site specific and depend on rainfall duration or its return period, they are not suitable for application to a continuous rainfall series. The following methods can be used to estimate the average rainfall over an area:
• arithmetic mean • Thiessen method • isohyetal method • interpolation to a regular grid • trend surface analysis • multiple regression and correlation
Areal estimates of rainfall based on point measurements should be regarded as an index of the true mean rainfall over a catchment and errors between 10 to 20 percent can be regarded as normal. Where strong wind effects or mountainous catchments are being considered, errors up to 60 percent can be experienced (Hall and Barclay, 1975).
There are two main sources of possible error in the estimation of areal mean catchment rainfall, the measurement of point samples and extrapolation from point to areal rainfall. Rainfall variability is a major factor limiting the accuracy of rainfall runoff models. Errors may be additive because of the compounding effects of measurement deficiencies due to wind, and biased because of location of gauges at lower elevations leading to underestimating catchment rainfall in hilly or mountainous terrain (Boughton, 1981). Chapter Two Hydrolosic Processes in Catchments. 2.4
2.2.2 Evapotranspiration
2.2.2.1 Introduction
The transfer of water from the liquid and plant to the vapour state is known as evaporation and transpiration, respectively. Evaporation is the conversion of liquid water to vapour from the catchment surface (from wet surface). Transpiration refers to that part of water which is transpired by plants. Evapotranspiration (ET) corresponds to the combination of evaporation and transpiration of moisture from the land phase to the atmospheric phase. Several terms are used to describe the evapotranspiration processes. Potential Evapotranspiration (PET) refers to the maximum rate at which water leaves the land surface assuming an unlimited supply of available moisture. Where moisture supply is limited, the Potential Evapotranspiration will not be achieved and the quantity of the moisture removed from surfaces is termed Actual Evapotranspiration (AET).
Evapotranspiration opportunity (ETOP) is defined as the maximum available moisture
for evapotranspiration per unit time at a point in a catchment.
Evaporation and transpiration are the most important losses in the hydrologic cycle. These are also significant factors in rainfall-runoff modelling, soil moisture modelling and crop yield studies. The importance of catchment evapotranspiration can be observed by inspection of a water balance equation.
P-QS-QB-AET± AS = 0.0 2.1
where P represents rainfall, QS surface runoff, QB subsurface runoff, and AS change in catchment storage and other losses such as deep percolation losses.
Water balance studies were carried out for this thesis using data from five catchments in Australia. The results showed that, on average, evapotranspiration accounts for more than 40 percent of rainfall while surface runoff, subsurface runoff and other losses form respectively, 32, 16, and 12 percent of total rainfall.
Table 2.1 represents estimated potential and actual evapotranspiration, surface runoff and subsurface runoff using a water balance approach for these catchments. In this table columns 3, 4, and 5 are recorded data. Column 6 is potential evapotranspiration (PET) which is estimated by a coefficient fp (in this case 0.7) multiplied by pan evaporation Chapter Two Hydrologic Processes in Catchments. 2.5
(EP). Column 7 is actual evapotranspiration which is assumed to occur at the potential rate during rainy days and two days after rainfall ceases and with zero AET at all other times. This is an approximation and the values in Table 2.1 should be taken as a guide. Columns 8 and 9 are separated surface runoff and baseflow. Details of hydrograph separation to baseflow and surface runoff will be given in Chapter 5.
Table 2.1 Estimated Potential and Actual Evapotranspiration, Surface Runoff and Subsurface Runoff Using Water Balance Approach Station No. Rainfall Streamflow EP (Pan) PET AET QS QB other No. of Years (mm) (mm) (mm) (mm) (%) % % losses % 1 2 3 4 5 6 7 8 9 10 215008 15 13057 5213 26292 18404 52 24 16 8 215004 8 5229 1816 13919 9743 47 23 12 18 215220 21 35002 18925 36765 25735 31 37 17 14 214003 40 67964 22817 71047 49733 45 19 14 21 215009 10 8822 6546 17869 12508 28 56 16 0
Evapotranspiration varies spatially as a result of variations in climate, vegetation or soil, and therefore predicting ET requires the consideration of many of these variables. Techniques for estimating PET are based on measurement or calculation from formulae using meteorological data. The following section discusses well known methods used for calculating evapotranspiration in rainfall runoff modelling.
2.2.2.2 Factors Affecting Evapotranspiration
As pointed out previously, evapotranspiration includes the sum of water used by both transpiration and evaporation processes. It is obvious that many of the primary climatic factors that influence the amount of evaporation from a free water surface also affect the amount of evapotranspiration. Some of these factors include solar radiation intensity and duration, wind conditions, relative humidity, cloud cover, and atmospheric pressure. In addition to the above factors, both soil and plant factors govern evapotranspiration from
an area.
Plants control a large number of processes that determine AET rates, either by their use of radiant energy, stomatal control of leaf transpiration, or root interaction with available soil water. This is particularly important for hydrologic predictions because catchment vegetation in many climates seldom transpires at a potential rate. In general, plant related factors which affect ET can be divided into the main categories of: (a) canopy, (b) phonology (plant's ability to transpire), (c) root distribution, (d) water stress. Chapter Two Hydrologic Processes in Catchments, 2.6
When the surface layer of a soil is wet, evaporation is governed primarily by atmospheric conditions. However as the surface layer dries out, the rate of evaporation decreases and the relative humidity of the air close to the soil surface, the diffusion coefficient, the capillary rise and hydraulic conductivity of the soil layer govern the rate of evaporation. Evaporation of water from a soil surface is very similar to transpiration from a plant. Soil evaporation is often described as occurring in three separate stages. In thefirst stag e the drying rate is limited by and equals the evaporative demand (available energy). During the second stage, water availability progressively becomes more limiting. The third stage is described as an extension of the second but is limited to a more constant rate.
2.2.2.3 Methods of Estimating Evapotranspiration
Several techniques have been developed for estimating the amount of evapotranspiration. In general these methods may be grouped into the following categories.
• pan evaporation • tank and lysimeter • energy budget methods • temperature based methods • mass transfer methods • empirical formula (combination methods) • soil moisture sampling • study of groundwater fluctuations • inflow outflow measurement • direct micrometeorological methods • remote sensing • complementary approach
These methods are explained briefly in the following section.
• Pan Evaporation
Potential evaporation measurements are made by observing the loss from the free surface of a known volume of water contained within a standard size evaporation pan. The evaporation meter can be located either on the land surface or on a large body of water. The pan evaporation measurements are usually higher than lake evaporation measurements, and require adjustments to represent potential evaporation. Correlations have been developed between pan evaporation, EP, and PET and are explained by a simple equation, (PET= fp*EP), where fp is a coefficient that varies usually from 0.7 to 0.8. Several methods have also been developed to calculate pan evaporation from meteorological data (Penman 1948, Kohler et al. 1955, Christiansen 1966-1968). Chapter Two Hydrologic Processes in Catchments. 2.1
• Tank And Lysimeter
Evapotranspiration rates are calculated by measuring water loss from a lysimeter (container) on which plants are grown. By using soil moisture neutron sampling tubes situated throughout the soil profile or weighing the Lysimeter, the soil moisture changes at different time intervals can be measured. The result of the lysimeter data may be relatively accurate if the conditions in the tank are maintained the same as field conditions.
• Energy Budget Method
The energy balance or energy budget method deals with the conservation of flow of energy. Methods of estimating PET based on the energy budget of a vegetated surface have a physical basis, because energy limits evapotranspiration where moisture is readily
available and the necessary vapour transport occurs. Correlation of the net radiation (Rn)
with PET is the basis for several estimation techniques. Besides, the relationship of Rn to pan evaporation (EP) indicates that from both of these variables PET can be estimated.
• Temperature Based Methods
The measurement of water vapour, as it is transported away from an evaporation surface, is another direct measurement of PET. The approach usually involves measuring temperature and vapour pressure of the air at two or more heights above the evaporating crop and a profile of wind velocities, to define moisture and temperature gradients and wind transport. Alternatively, fluctuations of vertical velocity and humidity can be
measured at a single height (Gupta, 1968).
Several methods have been developed for predicting PET based on average temperature or accumulated degree-days. The Blaney-Criddle (1950, 1966) method was developed for predicting PET for conditions where the soil moisture is not limited. Also Thornthwaite (1944) studied the correlation between temperature and evapotranspiration. The method is a useful complement to the Penman approach and gives reasonably good results. However, temperature-based methods should be used only
in cases where the available data is limited. Chapter Two Hydrologic Processes in Catchments, 2.8
• Mass Transfer Method
The mass transfer method for calculating evaporation from a water surface or a catchment surface is based on the concepts of discontinuous and continuous mixing applied to mass transfer in the boundary layer. When air passes over catchment surfaces, the lower atmosphere may be divided into three layers including laminar, turbulent and an outer layer of frictional influence. The temperature, humidity and wind velocity vary almost linearly with height in the laminar layer which is only a few millimetres in thickness. Besides the transfer of heat, the transfer of water vapour and momentum are essentially molecular processes. In the turbulent layer which is several meters in thickness, the humidity and wind velocity vary approximately linearly with the logarithm of height, and the transfer of heat, vapour and momentum through the layer are turbulent processes. Two of the most widely used mass transfer equations are those given by Sverdrup (1946) and Thornthwaite and Holzman (1939).
• Empirical Formulae (Combination Methods)
Neither the energy balance nor the aerodynamic theory methods are capable of predicting PET without assumptions and limitations. Difficulties in measurement of the required field data and lack of basic data result in the development of evapotranspiration equations that can relate the evapotranspiration to some readily available climatic data. Some well-known formulae in assessing potential evapotranspiration are those of Penman, Modified Penman, Blaney-Criddle, Thornthwaite, Turce, and Slatyer-Mellory
(Jenson, 1973).
Many equations have been developed for estimating evaporation using either the energy balance or mass transfer method. Probably the greatest advantage of empirical formulae is their simplicity and the fact that they allow estimates of evaporation to be made from standard meteorological data. Penman (1948, 1956) was thefirst to combine the energy balance equation with a mass transfer equation. This formula is widely used with
considerable success.
PET = fp Eo 2.2
where E = daily rate of evaporation for open water surface (mm) Chapter Two Hydrologic Processes in
E_ARn+ 0.27 Ea A + 0.27 2-3
A = slope of the vapour pressure curve of air at mean air temperature (in °F/mm Hg)
Rn = energy budget or net radiation (daily heat budget in cal/cm /day), and is given by:
Rn = Rc A 2.4 where R. = incoming solar radiation
Rc = Ra(l-ra)(a + ^) 2.5
Ra = total possible radiation radiation for the period of estimation r = albedo of surfaces a n = duration of bright sunshine per day in hours N = maximum possible duration of bright sunshine in hours a,b = constants: a = 0.18; b = 0.55
RD = reflected radiation
4 Rb = oTa (0.56 - 0.09 Je~ ) (0.1 + 0.9—) 2.6 v N where
oTa4 = black body radiation at mean air temperature Ta (in °K) in cal/cm^/day
Ea = a vapour flow parameter in mm
Ea = 0.35 (ea -ed) (1 + 0.0098W2) 2.7 where e = saturation vapour pressure at mean air temperature (mm Hg) ed = saturation vapour pressure at dew point (mm Hg)
W"2 = mean wind velocity at 2 meters above the ground (miles per day )
Thornthwaite (1944) proposed a relationship to estimate PET based on temperature:
ior PETm0nthly= 1.6 2.8 where T = the mean monthly air temperature ( C ae = an exponent with value of 0 to 4.25 for I in the range 0 to 160 I = the annual heat index, which given by:
IC=XT 2.9 i LJJ The heat index is also a function of mean monthly temperature (T). Chapter Two Hydrologic Processes in Catchments. 210
Blaney and Criddle (1950) proposed the following relationship, which was also based on temperature:
PET = Kc£z),r 2.10 i where
Dh = the percentage day-time hours in a year T = the mean monthly air temperature (°F)
Kc = the seasonal consumptive use coefficient for corresponding months nm = the number of months
Holtan (1961) proposed a relationship based on agricultural application.
o.i PET = GI km EP 2.11 where GI = the Growth Index EP = the pan evaporation (mm) km = the (PET/EP) ratio at crop maturity Sp = the total porosity As = the available porosity.
• Soil Moisture Sampling
This method is appropriate forfield plot s where the groundwater does not influence soil moisture fluctuations within the root zone. The amount of water added to the plot by precipitation and irrigation is measured, as well as surface runoff. Consumptive use is calculated as the difference between the input and output, and adjusted for changes in the soil moisture storage (determined by direct measurement).
• Study of Groundwater Fluctuations
This method determines evapotranspiration by measurement of the changes in the water table. In areas where vegetation obtains most of its water requirements from the groundwater, the daily fall and rise of the water table can give an estimation of evapotranspiration losses.
• Inflow Outflow Measurement
This method uses the principle of water balance. The difference between the amount of water entering (inflow) and leaving (outflow) from the area, adjusted by the change in Chapter Two Hydrologic Processes in Catchments. 2.11
groundwater storage, during the same period will be considered as the loss by evapotranspiration for the period. This method assumes that the subsurface inflow is equal to the subsurface outflow and the difference in the storage change over a long period and is considered to be negligible.
• Direct Micrometeorological Methods
The aerodynamic, energy balance and eddy correlation approaches are the most direct micrometeorologically based methods. Some of the semi-empirical combination approaches can also be categorised in this group. These methods need information on the gradient of temperature, humidity above the surface, variation in vertical wind speed and net radiation above the crop. According to Moran (1982) the main usefulness of these methods is in promoting an understanding of the factors controlling the evapotranspiration process. It is not realistic to expect catchments to be extensively monitored with the instruments needed to collect micrometeorological data, because the data collection scheme requires careful installation of complex instruments, frequent maintenance of the instruments and constant supervision of the data collection systems. Furthermore, the results of AET values using the above methods are restricted by the types of crops grown in the surrounding area. Consequently, these methods cannot be used to estimate AET for use in the rainfall-runoff models.
Regional Evapotranspiration
• Remote Sensing
With the aid of remotely sensed data, recently developed complex models are capable of estimating evapotranspiration from different types of vegetative cover. With rapid advances in technology these methods may play an important role in the assessment of the different water balance components in the near future. According to Oliver (1983), using radar estimation of rainfall and satellite estimation of cloud data, a number of remote sensing methods are being developed to estimate evapotranspiration on a regional scale.
• Complementary Approach
Bouchet (1963), using an analysis based on the energy balance proposed that for a large uniform and closed system on a regional scale, actual and potential rates of Chapter Two Hydrologic Processes in Catchments, 2.12
evapotranspiration are complementary quantities. This means that a decrease in actual evapotranspiration caused by a reduction in the availability of water will increase the temperature and decrease the humidity of the overpassing air, and this in turn will increase the potential evapotranspiration. Conversely, an increase in actual evapotranspiration causes the opposite atmospheric effects, and hence will induce a negative response in the potential rate.
Morton (1969, 1971, 1978, 1983, 1984) carried out extensive work on the complementary method. This approach was also adopted by Solomon (1966, 1967) in his analysis of wet tropical catchments, together with Kovacs (1982), Byrne, et al., 1988, Granger (1989b), Nash (1989), Jayasuriya (1991), and Nathan and McMahon (1990).
The complementary approach states that AET (actual areal evapotranspiration) is the complementary value of PET (the potential evapotranspiration rate that would occur from a continuously moist surface in which the effects on evapotranspiration from the overpassing air are negligible) to obtain the net energy, which represents the total insolation absorbed by the region. When there is no water to evaporate, AET is zero and under humid conditions when availability of water is not a limiting factor,
AET=PET=ETW- The relationship may be represented by:
AET + PET = 2ETW =E'TP 2-u
where = the wet environment areal evapotranspiration that would occur if there is no limitation ETW on the availability of moisture = the Net Energy (insolation absorbed by the region), and given by: ETP
E'TP = (1 - ar) SOR - ZEEN 2.13
where theSO Rincident solar radiation ar the albedo of the basin SEEN the sum of energy lost (or gained) in different ways to or from sky, water and soil
The schematic representation of the complementary equation is given in Figure 2.1. Chapter Two Hydrologic Processes in Catchments. 2.13
1 2Ew=Dry environment potential evapotranspiration
s o PET =PotentiaI evapotranspiration
B K) o ex ETW = Wet environment areal ea > evapotranspiration w o B AET = Actual evapotranspiration
Water Supply to the Soil and Plant System
Figure 2.1 Schematic Representation of the Complementary Relationship
Bouchet (1963), Brutsaert and Strieker (1979) and Morton (1983) used the concept of the complementary relationship to develop models to calculate AET.
Morton (1975,1983) adopted the complementary equation based on a solution of the vapour transfer and energy balance equations. This provides the basis from which actual areal evapotranspiration can be estimated from climatological observations without having to consider the explicit interactions between the soil-plant-atmosphere system.
The potential evapotranspiration term is estimated by finding the equilibrium temperature at which the energy balance equation and the vapour transfer equation for a moist surface give the same result. The vapour transfer equation used by Morton (1983) is similar to that used in Penman's (1948) combination approach. Morton (1983) used an iterative technique to determine the value of equilibrium temperature that satisfies both energy balance and vapour pressure equations as presented below:
PET = fT (ep-ed) 2.14
3 PET = RT-[ Tfx + 4 e a (TP +273) ] (TP-Ta) 2.15 where PET = the potential evapotranspiration in the units of latent heat ep = the saturation vapour pressure at equilibrium temperature TP = the saturation vapour pressure at dew point temperature ed fr = the vapour transfer coefficient = equilibrium temperature TP Chapter Two Hydrologic Processes in Catchments. 2.14
Ta = air temperature a = the Stefan-Boltzmann constant Y = the psychrometric constant ea =5.22*10"8Wm"2K"4 e = emissivity which is equal to 0.92
Morton used an iterative technique to determine the value of equilibrium temperature that satisfies both Equations (2.14 & 2.15). He substituted the Priestly-Taylor formulation for 2Exw with the potential evapotranspiration from a completely arid region
(E'TP). For a completely arid area, AET is equal to zero. Thus the potential
evapotranspiration ETP or E'Tp is estimated by the relationship:
A, E'TP =2 ETW = bi+b2 Rnp 2.16 in which t>i and b2 = empirical constants equal to 28 and 2.4 respectively
Ap = the slope of the saturation vapour pressure curve at TP
Morton (1983) showed that the equilibrium temperature (TP) should not change in response to a change in the availability of water for evapotranspiration from the surrounding area, and thus the use of equilibrium temperature in the latter equation makes the result independent of the availability of water. The two constants are estimated from an iterative solution of the energy balance and vapour transfer equations in the model. They were obtained by calibrating the model on climatic data from extreme arid and humid areas. The constant bi represents advection and is particularly important when net radiation is low.
2.2.2.4 Actual Evapotranspiration
The assessment of potential evapotranspiration is only one part in calculating the actual loss of moisture from the catchment surface to the atmosphere. Variability in moisture supply limits the evapotranspiration processes and introduces the need for the concept of evapotranspiration opportunity.
The concept of PET was first introduced by Thornthwaite (1944). Since then researchers have defined the term PET in different ways. Penman (1948) was one of the first researchers to propose a physically based model for calculating PET. He described PET as "the quantity of water evaporated per unit area and unit time from an idealised, Chapter Two Hydrologic Processes in Catchments. 215
extensive free water surface under prevailing atmospheric conditions." The above definition implies that the PET is limited by the available energy only, and as a result, it can be calculated from the meteorological variables. In a review by Stewart (1983), it was stated that surface factors can be considered of secondary importance only for short crops. Gash and Stewart (1975) and Calder (1976) stated that for tall crops, the rate of evapotranspiration can be much less than the potential rate; even when soil water is in plentiful supply. Subsequently Doorenbos and Pruitt (1977) defined the definition of PET with reference to crop evapotranspiration which refers to the rate of evapotranspiration from surface of 8 to 15 cm tall green grass. This cover should have uniform height, actively growing, completely shielding the ground and should not be in shortage of water.
Actual evapotranspiration loss from the catchment surface will always be less than or equal to the PET amount. The various sources of water supply for possible evapotranspiration in a catchment surface are losses from water surfaces, interception storage and the saturated surface soil zone. The other sources of possible evapotranspiration are losses from; snow surfaces, the subsurface soil zone and groundwater within reach of the surface. Losses of moisture from soil storage due to combination of direct evaporation and removal by vegetation are losses which are controlled by the soil moisture level at a given time and location.
In most of the rainfall runoff models, PET is converted to AET using empirical factors. Techniques used in rainfall-runoff models differ substantially from one another.
The Crawford and Linsley (Equation 2.17) model relates actual evapotranspiration to soil moisture deficit. At the surface soil zone the potential rate is applied when the soil moisture storage is more than field capacity. As the moisture supply in the surface soil zone reduces below this value, the evapotranspiration rate reduces and will be less than the potential rate.
0.2 LZS r= * 2.17 l-k3 LZSN where r = maximum evaporation opportunity k =index to vegetation drawing moisture from the lower zone 3 LZS = current level of lower zone soil moisture storage pgr WO —— Hydrologic Processes in Catchments. 2.16
LZSN = field capacity of lower zone soil moisture storage
Boughton included in his model a function (Equation 2.18) similar to one used in the Stanford model.
LZS AET = H*B 2 18 LZSN ZA* where AET = actual evapotranspiration H =the potential evaporation rate LZS =current level of primary soil moisture storage B = portion of the residual potential evapotranspiration applied to the soil storage
In this equation the actual evapotranspiration rate varies from zero at the wilting point to the maximum value at field capacity.
2.2.2.5 Selection of Method for Estimating Potential Evapotranspiration
The selection of a method for PET estimation depends on several criteria. Data availability often dominates. Accuracy required and time available to develop accurate estimates from available data are important. The comparison of values by some researchers showed that data inputs and the locations at which the equations were developed account for much of the difference in various methods (see Jenson, 1973) . Blaney and Criddle (1950) used only mean daily air temperature, Jensen and Haise (1963) used solar radiation and air temperature, and Christiansen (1966) used all available data. The Penman (1948, 1956), and Van Bavel (1966), Weather Bureau methods are related developments and require air temperature, air humidity, wind and radiation (Haan, 1982). The Mustonen-McGuinness method (Mustonen and McGuinness, 1968) adds soil moisture data to the Weather Bureau methods. The first three methods were developed for the irrigated areas of Western United State. The more simple and direct methods that use only net radiation and pan evaporation provide almost equally accurate estimates as those requiring more data.
In a review of 15 methods for estimating PET, Jenson (1973) showed that only the combination equations of Penman or its modification by Van Bavel (Van Bavel, 1962) and others, and the Rn based method of Jensen-Haise (Jensen and Haise, 1963) would be recommended for periods of 5 days or less. Despite this progress, still there is a very strong need for local calculation of all equations (Haan, 1982). Chapter Two Hydrologic Processes in Catchments. 2.17
Moran (1982), compared pan evaporation with Morton's potential evapotranspiration. The comparison shows that potential evapotranspiration was greater than pan evaporation, particularly during summer. Since evapotranspiration rate is governed solely by the available energy, this does not seem to be rational and might be due to the overestimation of potential evapotranspiration. In another study Sukvanachikul (1983) compared the calculated evapotranspiration from Morton's method, the Brutsaert and Strieker (1979) and Bouchet (1963) methods with Lysimeter evapotranspiration data. His results indicated that Morton's method gives better results than the other two methods, although it overestimated the areal evapotranspiration in summer.
Chiew and Jayasuria (1990) investigated the applicability of Morton's ETW as an estimate of daily PET in rainfall runoff modelling. They concluded that ETW could be used successfully as the input for PET in rainfall runoff models operating on a daily basis. Nathan (1990) compared ETW with PET from Penman-Monteith for seven catchments in Australia and obtained similar results to Chiew and Jayasuriya (1990) from both models. Nathan also compared the ETW estimates with pan evaporation data and concluded that the pan data were unsatisfactory.
Actual rates of evapotranspiration are generally computed internally in the rainfall runoff models. It is necessary to select an appropriate method to estimate the maximum rate of evapotranspiration that would occur if soil moisture was not limited. Apart from the accuracy of any selected method, selection mainly depends on the availability of climatic data. Morton's method only needs wet and dry bulb temperature and sunshine duration data. This method has been tested by a number of researchers (Jayasuriya, 1991 and Nathan, 1990) with acceptable results.
The evaporation of water from a pan is governed by solar radiation, air temperature, humidity, and wind. These are the factors that determine the rate of evapotranspiration from catchment surfaces. The measurement of pan evaporation provides an index of actual evapotranspiration when moisture is not a limiting factor. Pan evaporation data provides the most preferred and simple direct measurement used in rainfall runoff models. Various researchers have carried out rainfall-runoff modelling using this approach, for example, Baki (1993), Boughton (1965, 1966, 1968, 1984, 1991), Mount (1972), Langford et al. (1978a, 1978b), Crawford and Linsley (1966) and Bell (1966). As measured evaporation by pan is greater than evapotranspiration from the land, pan Chapter Two Hydrologic Processes in Catchments. 2.18
factors are usually used to convert pan evaporation to potential evapotranspiration. Although many problems regarding the use of pan data have been reported, it is still the mostly widely used method of calculating PET in rainfall-runoff models due to its simplicity, availability and physical importance.
A check for practical similarities between pan evaporation and Morton's Ewet (ETW) was undertaken by comparing two sets of estimates for five catchments. Daily estimates of pan evaporation and ETW were correlated with one another. Figure 2.2 gives a regression plot obtained for station number 215004. The complete lack of correlation (R2=0.011) is surprising. This indicates that either pan evaporation is not a good indicator of potential evapotranspiration or that estimates of ETW are not correct. The lack of correlation can be largely due to the lag times associated with the change in storage of heat in Morton's evapotranspiration values. Plots of the monthly values of pan and Morton's data are presented in Figure 2.3, indicating this problem. To eliminate the time shift between these methods the plot of cumulative values is shown in Figure 2.4. The resulting correlation is very good and the slope of the regression line is 0.55 indicating a reasonable factor which relates pan evaporation to potential evapotranspiration. Examination of all five catchments revealed a similar relation between pan evaporation and Morton's Ewet with a mean value of 0.66 as a conversion factor for relating pan evaporation to potential evaporation (see Figure 2.5). Chapter Two Hydrologic Processes in Catchments. 2.19
O o ° 8 o 0 o o 0 y = -0.0587x +2.8574 » 0 Q O o ° 00 e 2 00 o o„o°° o ° o 0 R = 0.0114 "0 " o o°< 0 >oO0 „0° or °o °8 o° o ages w- w -0 00 Station No. 215004 8 o |o°o |8 oB ogg | e °o°. 8;o.igo"o|Jj° o aslf Voo 0 § E 5 888O00B§i§oo|:0o o RooO. _0„g 90.o ° 0a| 8«o °P a ° 8 000 8 E Oo o 8S°o„QB.80 §§| 80 0 ° o«° 8 g „ 0 o * e e w 4 $- e s *£JIWJ°1 ft g: 88o 8§o .So. ee9o o s- W0ifas .:°i • •'..si o S3
8 10 12 14 16 18 Class A pan (mm) Figure 2.2 Plot of Daily Correlation between Pan Evaporation and Morton's Wet Environment Evapotranspiration (St. No. 215004)
300
-Class A Pan Morton Ewet 215004 200
250
200 E £ 1150
100
50
20 40 60 80 100 120 Time (Month)
Figure 2.3 Time Series Plots of Monthly Morton's Em and Pan Evaporation Data (St. No. 215004) Chapter Two Hydrologic Processes in Catchments, 2.20
7000
y = 0.5386x R2 = 0.9966 6000
5000
S 4000
W c 1 3000
2000
215004 1000 1980-1986
2000 4000 6000 8000 10000 12000 14000 Class A Pan (mm)
Figure 2.4 Cumulative Regression Plot between Morton's Em and Pan Evaporation Data (St. No. 215004)
16000 Station No. a R2 2 14000 - . 1-215004 0.545 0.999 5 JT x 2- 420003 0.626 0.999 12000 A 3- 206001 0.662 0.998
o 4-210022 0.705 0.999 _ 10000 - s +5-412093 0.772 0.998 s W 8000 - E»a =a.EPa„ s o J- o ^ 6000 ^4* 4000
2000 -
5000 10000 15000 20000 25000 Class A Pan (mm)
Figure 2.5 Cumulative Regression Plots between Morton's Em and Pan Evaporation Data for Five Catchments Chapter Two Hydrologic Processes in Catchments. 2.21
A comparison between conversion factors obtained using the water balance and Morton's method is also made in this study. Monthly EP conversion coefficients have been calculated using the following equation:
Monthly EP conversion coefficient = (P - QS)/EP 2.19
where P = precipitation (mm) QS = surface runoff (mm) EP = pan evaporation (mm)
The analysed data was obtained for Corang River catchment at Hockey's (National Index No. 215004) over a period of 10 years (1980-89). The evapotranspiration from this catchment is similar to the other catchments in the study area.
The conversion factors obtained from water balance and Morton's data are presented in Table 2.2. The conversion factors for April, May, June and July using the water balance method are considerably larger in comparison to the Morton values. The average yearly values for the Water Balance and Morton methods are 0.73 and 0.61 respectively.
Table 2.2 Comparison between Water Balance and Morton Methods for Calculating Pan Evapotranspiration Conversion Factors (St. No. 215004 ) Month Water Balance Morton Jan 0.84 1.13 Feb 1.17 1.22 Mar 1.34 1.16 Apr 1.10 0.75 May 0.86 0.32 Jun 0.64 0.17 Jul 0.40 0.16 Aug 0.23 0.26 Sep 0.41 0.25 Oct 0.48 0.36 Nov 0.57 0.59 Dec 0.71 0.86 Average 0.73 0.60
This comparison indicates that the two estimates show similar trend and are essentially similar in annual scales. Considering that AET values should always be less than or equal to the PET values, results can be considered to be reasonable. Chapter Two Hydrologic Processes in Catchments. 2.22
2.2.2.6 Discussion
There are a number of problems associated with the use of the complementary approach. This method is developed by neglecting the change in subsurface stored heat. Therefore, the complementary approach should not be used for short time intervals because of sub surface storage changes, and because of the lag times associated with the change in storage of heat and water vapour in the atmospheric boundary layer after changes in surface conditions or the passage of the frontal systems. The other limitation is that this method cannot be used near sharp environmental discontinuities, such as a high altitude coastline or the edge of an oasis, because of advection effects in the lower layers of the atmosphere (Morton, 1983).
Evaporation from an evaporation pan and evaporation from an open water or land surface are different in many aspects. The energy storage of water is greater than that of vegetation. The surface temperature of the water is lower during the day and higher at night. Radiation and sensible heat exchanges at the sides and surfaces are not the same. Also air flow over the pan does not approximate that of a crop or even an extensive water surface. An evaporation pan has considerable heat storage capacity while a plant leaf has very little and consequently the temperature of a plant leaf responds more quickly to changes in air temperature and radiation. The reliability of pan data is dependent upon the skill of the operator and the quality of maintenance of the instrument.
An evaporation pan is a reasonable physical realisation of the potential evaporation if the effects of evapotranspiration on the overpassing air are negligible. The rate of water loss from an evaporation pan is dependent upon the humidity of the overlying air or, in turn, the moisture availability of the surrounding area.
The complementary relationship was developed based on energy considerations. However, the traditional approach was based on definitions of the physical process of evapotranspiration. Various researchers have developed their rainfall-runoff models using the traditional approach, for example, Boughton (1965, 1966, 1968, 1984, 1991), Mount (1972), Langford et al. (1978a, 1978b), Crawford and Linsley (1966) and Bell (1966). The conceptual basis of the traditional approach is more acceptable since it relates evaporation to the catchment physical properties. Therefore, in order to avoid Chapter Two Hydrologic Processes in Catchments. 2.23
introducing additional errors into the rainfall runoff models, pan evaporation with conversion coefficients was adopted by the author as a basis for estimating PET.
The results of this section showed that evapotranspiration is responsible for the most important losses in the hydrologic cycle. On average it accounted for the loss of more
than 40 percent of total rainfall. The correlation of pan evaporation and Morton's Ewet indicates that from both of these approaches PET can be estimated for long durations.
Developed correlations between pan evaporation, EP, and Morton's Ewet can be
explained by a simple equation, (PET= fp*EP). The monthly coefficient (fp) varied from 0.16 to 1.22 while the overall variation was between 0.55 and 0.77. The coefficient should always be less than or equal to one. A value greater than one does not seem to be rational and might occur due to the overestimation of potential evapotranspiration. However, according to Jenson (1973), values of pan factor can usually range from 0.3 to 1 due to the relative humidity, wind speed and pan surroundings changes. In many hydrologic applications a mean value of 0.6 to 0.8 can be used (Jenson, 1973).
If Morton's Ewet is used alone, additional errors may be introduced into the rainfall runoff models with time intervals of one day and less. If pan evaporation data is used alone, poor data quality and the conversion factor can introduce error in the rainfall-runoff models. By taking advantage of the merits of both methods, the problems of each method applied alone may be reduced.
As a result of this investigation, it was decided to adopt pan evaporation with conversion coefficient for the next stage of the study. It is clear from this research that further work is needed to clarify evapotranspiration loss in catchments.
2.2.3 Runoff
When rain falls on the ground, a portion of it infiltrates the ground, while the remainder flows across the surface to rivers and streams. A portion of the water that infiltrates into the ground comes out in the form of subsurface runoff under favourable geological conditions and joins the streams. In this way water reaches the stream from surface and subsurface runoff. The total quantity of water that reaches streams or rivers from surface flow as well as subsurface flow is known as runoff. Chapter Two Hydrologic Processes in Catchments. 2,24
2.2.3.1 Factors Affecting Runoff
Factors affecting runoff from any catchment area may be grouped into meteorological conditions, physical characteristics, and man made factors.
Meteorological factors that affect runoff can be grouped into the following groups:
• type of precipitation • intensity of rainfall over the catchment area • duration of rainfall • distribution of storm intensity on the basin • direction of storm movement • other climatic factors which affect evaporation and transpiration
The physical characteristics of a catchment that affect runoff can be classified as follows:
• area of the catchment • slope and shape of the catchment area • the degree of porosity of the soil of the catchment area. • soil moisture (initial state of the catchment area with respect the wetness) • Geology, vegetation, and land use factors
Perhaps the most significant impact upon the runoff process has been caused by human beings. The most important human influences in the process of runoff can be grouped as follows:
• construction of hydraulic structures such as dams and reservoirs • agricultural techniques and practices such as ploughing, irrigation, afforestation, deforestation and artificial drainage • increased impervious areas with increase of population densities, increased concentration of residential, industrial and commercial building
2.2.3.2 Runoff Losses
Runoff losses refer to the total losses due to evapotranspiration, absorption, and infiltration. It is very difficult to estimate these losses individually or collectively. These losses vary greatly in different regions, depending on the physical and geological conditions of the ground and atmosphere.
When rain falls on dry land, it is absorbed firstly by the ground and vegetation. If the rain continues to fall the ground becomes saturated. A portion of it collects in ponds, lakes and along certain well-defined channels, and ultimately streams are formed.
Some water is evaporated by the heat of the sun or drying winds, and some infiltrates the sub-layers. Different losses can be classified as follows: Chapter Two Hydrologic Processes in Catchments. 2.25
• evaporation losses from water surface • absorption losses • infiltration losses
Vegetation and trees always reduce runoff by absorption and transpiration. The losses due to transpiration mainly depend on the plant species, velocity of wind, atmospheric temperature and humidity.
2.2.3.3 The Process of Surface Runoff Generation
Surface runoff is the result of a complex process taking place between rainfall and land. The conventional theory of runoff generation assumes average conditions over the entire catchment. Horton (1933) developed the concept that surface runoff occurred when rainfall intensity exceeded the infiltration capacity of the ground. However, it is now believed that typical rainfall intensities are less than the infiltration capacities of many soils (Hewlett and Hibbert, 1967) and normal storm durations are usually shorter than the time it takes for most soils to become saturated at the surface (Ward, 1975).
The terms variable source area and channel expansion were first introduced by Hewlett and Hibbert (1967). They considered runoff as the response of the catchment channel system to rainfall. In this theory, overland flow is considered as an extension of the perennial channel system into zones of low soil storage capacity which quickly become saturated as a result of infiltration into the lower valley sides. This interflow feeds the expanding channel from below while rainfall feeds it from above. As the storm continues, surface runoff occurs from the expanding saturated area. The concept that different runoff processes are dominant in different areas is presented in Figure 2.6. More detailed analyses of runoff components will be given in Chapter five. Chapter Two Hydrologic Processes in Catchments^ 2.26
Thin Soils A L Saturation Flatter Slopes Overland Flow Dominant Soils Hortonian Overland and Flow Dominant Topography
Subsurface Flow Dominant Permeable Soils Steep Slopes r
Arid, Sparse Temperate Dense Vegetation Vegetation 4 Climate and Vegetation Figure 2.6 Proposed Influences of Climate, Soils, Vegetation and Topography on Runoff Generating Processes (after Boughton, 1988; Dunne, 1983)
2.3 PROCESS FACTORS
2.3.1 Introduction
Process factors include interception, infiltration, soil moisture storage, subsurface runoff, channel processes and transmission losses. Various theories and models (abstract systems) have been used to represent the real system (actual processes). The assessment of the process factors that reproduce the catchment response is the objective of model calibration.
In a process model, process factors are determined by a combination of direct measurement and indirect assessment during model calibration. This difference in parameter assessment is due to the difficulty that arises in measuring some hydrologic processes in the field. For example, quantitative measurement of soil moisture storage, infiltration rates, percolation rates, interflow and groundwater flow are difficult, if not practically impossible.
A brief review of several of these factors is given in the following sub-sections. Chapter Two Hydrologic Processes in Catchments. 2.27
2.3.2 Interception
Interceptive processes by vegetation and depression storage on the catchment surface are represented by the following equation (Jones, 1970):
IL = V + Ev 2.20 where IL = the interception loss V = the water film stored on the surface of vegetation
Ev = the evaporation loss from surface of vegetation during the storm
The value of V is the physical equivalent of the interception storage capacity in the model. The value of Ev is provided by the model operations. The interceptive processes have been represented as a storage component by many researchers (Crawford and Linsley, 1966, Boughton, 1966, 1984, and Fleming, 1975).
2.3.3 Infiltration
Infiltration is defined as the entry of water from the surface into the soil profile. The infiltration rate is the actual rate at which water enters the soil strata. It is an important hydrologic process that must be carefully considered in models or procedures for describing the hydrology of a catchment. Admittedly, an understanding of infiltration and the factors affecting it is essential for the determination of surface as well as subsurface movement of water. Factors affecting infiltration include soil properties, initial water content, rainfall rate and duration and surface sealing and crusting.
Bodman and Coleman (1943) showed that the profile of a relatively dry soil could be divided into four zones (shown schematically in Figure 2.7). The saturated zone extends from the surface to a maximum depth of approximately 1.5 cm. The transition zone, a region of rapid decrease of soil water content, extends from the zone of saturation to the transmission zone, a zone of nearly constant water content that lengthens as infiltration proceeds. The wetting front maintains a fairly constant shape during infiltration and culminates in the wetting front that forms the visible limit of water penetration into the soil. The results of Bodman and Coleman have been generally confirmed by other investigators (Shaw, 1972). Chapter Two Hydrologic Processes in Catchments. 2.28
Water Content
Water Content Saturated Zone Transition Zone a -;-• c o Application Rate R (constant) Transmission a. «3 Zone Q
Wetting Zone/
Wetting Front
T tp Time
Figure 2.7 Rainfall and Ponded Infiltration
The infiltration rate depends upon initial water content and soil type. Sealing of the soil surface due to raindrop impact, poor stability of soil aggregates and colloidal dispersion tends to enhance runoff but produces turbid and inferior quality water. The soil survey therefore should look at the structure of the surface soil and its stability.
Horton (1933, 1937, 1939, 1940) proposed an exponential relationship to represent the infiltration process:
•kt F = Fc + (Fo-Fc)e 2.21
where F = the daily infiltration capacity (mm/day) = the maximum (or initial) daily infiltration capacity which corresponds to dry soil moisture condition (mm/day) = the minimum (or steady state) daily infiltration capacity which corresponds to saturated soil (mm/day) k = an empirical constant t = time (day)
Horton developed this equation to account for time variability of the infiltration capacity during a storm. This relationship has been adopted by Boughton (1965, 1966) in his daily rainfall-runoff model. Chapter Two Hydrologic Processes in Catchments. 2.29
Philip (1954) proposed a physically based empirical relationship. This equation was derived to solve the diffusion equation for one-dimensional vertical infiltration into a uniform semi-infinite medium with a constant initial moisture content.
0.55 f=P- +—4~t^ 2.22
where f = the infiltration rate per unit time t
Ps = the saturated permeability
So = the sorptivity t = time
Holtan (1961) proposed another equation, based on assumptions that the dominant factors which influence the infiltration rate are soil moisture storage, surface-connected porosity and the effects of root paths.
f = GIf ^4- h aa
where
fh = the infiltration capacity (inch per hour) GI = the Growth Index of vegetation S = the available storage in surface layer of soil (inches)
fk = the constant rate of infiltration (inches per unit time 1.4 f = the infiltration capacity (inches per hour per inch ) of available storage, which is an index of surface-connected-porosity (a function of root density)
Crawford and Linsley (1966) proposed an infiltration formula based on the moisture status of the catchment. This equation is used in the Stanford Watershed Model to calculate the infiltration between the upper and lower soil zones.
INF 2.24
L LZSN J where f = the segment mean infiltration capacity at time t (mm) INF = the infiltration index level (mm) LZ = the lower zone soil moisture storage at time t-1 (mm) LZSN = the nominal value of soil moisture storage in lower soil zone = field capacity (mm) The exponent 2 is found by trial and error
Aston and Dunin (1979) developed an infiltration model using a small experimental catchment in New South Wales. The empirical relationships used in this model were Chapter Two Hydrologic Processes in Catchments. 2.30
developed by Mein and Larson (1973), based on assumptions that infiltration is a one- dimensional process, the soil medium is uniform and homogeneous, and the effect of raindrop compaction is minimal. For constant rainfall intensity:
™SavMd f = 2.25
where f = the infiltration (mm) S = the average capillary suction at wetting front (cm) av = the moisture deficit -1\ I = the rainfall intensity (cm s > K -l = the saturated conductivity (cm s ) When surface saturation is reached, the following equation that operates on an hourly basis and requires hourly rainfall and runoff data is used:
SavMd INRATE = Ks 1 + 2.26 where INRATE = the infiltration rate (cm s" *) F = the cumulative infiltration (mm) cum An earlier study of the physical interpretation of the infiltration capacity in Australia was made by Turner (1963) and the suggested values of infiltration capacity based on surface soil are shown Table 2.3.
Table 2.3 Daily Infiltration Capacity for Various Soil Texture and Moisture Content
Surface Soil Daily Infiltration Capacity (mm)
Aitken clay loam 360
Buell clay loam 17.5 Vernon fine sandy loam type: (1) 3.3 Vernon fine sandy loam type: (2) 1.5 Vernon fine sandy loam type: (3) 50 Extracted from Turner (1963) Chapter Two Hydrologic Processes in Catchments. 2.31
2.3.4 Soil Moisture Storage
Soil moisture storage is a very important component in the hydrologic cycle and in process models. It controls input water via precipitation and output water due to evapotranspiration and the drainage process.
Soil moisture storage represents the moisture holding capacity of the soil layers. The 'wilting point' is defined as the level at which vegetation cannot draw water from the soil, where the negative moisture pressure is about 15 atmospheres. 'Field capacity' is defined as the moisture content after gravity drainage is completed. Percolation (vertical movement of moisture in the soil) occurs when soil moisture exceeds the field capacity. 'Saturation' is defined when the moisture fills all the pores in the soil. 'Soil water zone' is defined as the depth of soil where water can return to the surface via capillary or vegetation. In the actual catchment, depths of these zones will vary with time. By the definition of the soil moisture capacity, the capacity of the soil store is given as the product of the depth of soil store with the available water capacity (AWC) of the soil.
The structure of the upper soil layer can be classified into two zones, unsaturated and saturated. The unsaturated zone usually covers the root zone and the zone between the root zone and the water table or capillary fringe. The soil water storage in the capillary fringe is almost saturated all the time but soil water storage in the root zone varies.
Soil moisture storage in the root zone varies due to additional water from rain and removal via root extraction and evaporation. The depth of the root zone depends on soil, climatic conditions and the type of vegetation. It may range from 0.30 to 3.0 meters. In humid regions the amount of rainfall is high and occurs almost throughout the whole year, hence the depth of the root zone tends to be small.
The process of water movement in the unsaturated zone is a complex phenomenon and depends on atmospheric conditions, soil type and land cover. Due to the complexities of the dynamic process of flow in the unsaturated zone, researchers have developed a number of different models. Some of these models are briefly discussed below.
The soil moisture model deals with water flowing through porous media. The model parameters are obtained from measurable fluid and soil properties. These models can be Chapter Two Hydrologic Processes in Catchments. 2.32
one to three dimensional and use numerical analysis. This approach is usually applied in small areas for research purposes.
The second type of model is based on water balance analysis. Two approaches in water balance analysis are hydrological and agricultural models. They have usually been selected for hydrologic modeling. The problem with this approach is the difficulty of establishing data requirements for calibration.
The third type of model, representing the process of flow in the unsaturated zone, is derived from a combination of field experiments and water balance analysis. These models are adequate for developing formulae and for research purposes, however, there are still some problems in establishing data requirements due to the expense and time involved in data retrieval.
Selection of an appropriate model depends on computational efficiency, calibration requirements, objectives of the study and the degree of accuracy required. In the case of rainfall runoff modelling and ungauged catchments, models of type two have often been used.
Several approaches that can be used to estimate the values of moisture storage capacity are field and laboratory experiments, regionalization of parameters to catchment characteristics, calibration of the model or combinations of those approaches. Many researchers have tried to determine the value of soil water storage capacity or soil holding capacity by using field experiment or calibration processes. Because of the heterogeneous soil types and properties in many catchments, field investigations experience some difficulties in establishing an average storage capacity. In rainfall-runoff models, with many parameters, an actual value for storage capacity is also difficult to find. Therefore, it has always been desirable to estimate the storage capacity of catchments using measurable catchment characteristics.
2.3.5 Subsurface Runoff
Subsurface runoff is the part of the streamflow which originates in, and travels slowly from, a lower soil store towards stream networks. Subsurface runoff consists of interflow, bankstorage flow and groundwater flow. Interflow is the flow occurring immediately beneath the soil surface. This flow can be observed in the slow recession of Chapter Two Hydrologic Processes in Catchments. 2.33
the hydrograph after the peak has been reached. Various relationships have been used by modellers to represent interflow. Most models include this component of runoff together with baseflow representing them as the recession from a linear reservoir (Boughton, 1984), Langford et al. (1978a, 1978b). Groundwater represents moisture storage below the water table. Input to this storage takes place via percolation. Outputs from groundwater occurs as baseflow (groundwater flow back into the streams) and water loss via percolation to deep groundwater storage. The baseflow and recession are described in detail in Chapter 5.
2.3.6 Channel Processes
Channel flow can be mathematically represented by differential equations of continuity and momentum, and using Chezy or Manning equations. Other approaches include; Unit- hydrograph, cascade of linear reservoirs (Nash, 1957, 60), Muskingum routing and channel time delay (Chow, 1964). The channel process is important for hydrograph
modelling.
2.3.7 Transmission Losses
Transmission losses represent the amount of moisture from the streams lost via infiltration into the bed and banks, and depression storage in the stream channel or flood plain. This component of losses is very significant in ephemeral streams (Keppel and Renard, 1962). Transmission losses are influenced by peak discharge, the duration of flow, the width and length of the channel, and the quantity and texture of the channel alluvium (Keppel and Renard, 1962). Sharp and Saxton (1962) found that transmission losses account for about 40% of the flow between two stations 53 miles apart. They also found transmission losses of 200 acre-ft /mile (which was about 75% of the flood
volume).
The effects of transmission losses on ephemeral streams were also considered by Boughton (1966) when he analysed some catchments in the semi-arid region of New South Wales. He found that the transmission losses caused the apparent values of soil
moisture capacity to become very high. Chapter Two Hydrologic Processes in Catchments. 2.34
Most of the streams in the current study are perennial streams. Therefore, the effect of transmission losses are not very critical when a daily time step is used.
2.4 PHYSICAL FACTORS
Once the precipitation is on the catchment, the physical characteristics of the basin directly impact on the runoff response characteristics (volume, magnitude and timing) of that catchment. The physical characteristics of the catchment can be identified as follows:
• land use • type of soil and vegetation • area • shape • elevation • slope • orientation • type of drainage network • extent of indirect drainage • artificial drainage • urbanisation
These factors are required by models to define the retention and release characteristics of a catchment and for calculating the catchment response.
2.5 SUMMARY
In this chapter, a review of the hydrological processes in catchments was made. They included: hydrometeorological factors, process factors, and physical factors.
Hydrometeorological factors include precipitation, runoff and evapotranspiration. Process factors refer to interception, infiltration, soil moisture storage, interflow, subsurface flow, channel processes, and transmission losses. Finally, the physical factors refer to the features of a basin which directly impact on the runoff response characteristics (volume, magnitude and timing) of a catchment.
The Hydrologist's interest in precipitation data relates to engineering design, water supply and river forecasting, watershed management and research. The data are usually subjected to analysis of frequency characteristics, extremes, regressions and physical relationships. Chapter Two Hydrologic Processes in Catchments. 2.35
Although precipitation records have been kept for a long period, and obviously considerable use has to be made of rainfall data for environment related research as well as for the practical purpose of assessing runoff and water resources, much accuracy is lost due to errors in the recorded data, extrapolation from point to areal rainfall and the averaging of several rain gauge records.
Areal reduction factors are available for relating values of point rainfall intensity to the catchment area. Since the available factors are mostly site specific and dependent upon the duration or return periods, they are not suitable for application to a continuous rainfall series. The following methods can be used to estimate the average rainfall on an
area:
• arithmetic mean • Thiessen method • isohyetal method • interpolation to a regular grid • trend surface analysis • multiple regression and correlation
Areal estimates of rainfall based on point measurements should be regarded as an index of the true mean rainfall over a catchment, and errors between 10 to 20 percent can be
regarded as normal.
Evaporation and transpiration are the most important loss factors in the hydrologic cycle. Water balance studies carried out for this thesis, using data from five catchments in Australia, showed that AET ranged from 28% to 52% with an average which accounts for more than 40 percent of total rainfall. Surface runoff, subsurface runoff and other
losses are, respectively, 32, 16, and 12 percent of total rainfall. Estimated PET, AET,
QS, and QB using water balance approach are presented in Table 2.1.
Several techniques are available for estimating the amount of evapotranspiration. These
approaches may be grouped under the following headings.
• pan evaporation • tank and lysimeter • energy budget method • temperature based methods • mass transfer method • empirical formula (combination methods) • soil moisture sampling • study of groundwater fluctuations • inflow outflow measurement • direct micrometeorological methods Chapter Two Hydrologic Processes in Catchments. 2.36
• remote sensing • complementary approach
The selection of a method for PET estimates depends on several criteria. Data availability is often a dominant concern. The degree of accuracy required and the time available to develop estimates from available data are important considerations. The actual rates of evapotranspiration are generally computed internally in the rainfall runoff models. It is necessary to select an appropriate method for estimating the maximum rate of evapotranspiration that would occur if soil moisture was not limited. Among various methods, pan evaporation and Morton's method are most widely accepted for the estimation of potential evapotranspiration. Pan evaporation presents a reasonable physical realisation of the potential evapotranspiration, and Morton's method only needs wet and dry bulb temperature, and sunshine duration data. These methods have been
tested by different researchers with acceptable results.
An evaporation pan has considerable heat storage capacity while, in a catchment, a plant leaf has very little. The reliability of the pan data is dependent upon the skill of the operator and quality of instrument maintenance. Morton's method is developed by neglecting the change in subsurface stored heat. Sub surface storage changes and the lag times associated with the change in storage of heat and water vapour in the atmospheric boundary layer after changes in surface conditions are main sources of errors in the values obtained by Morton's method. This approach does not give reliable results for short time intervals. The other limitation is that this method cannot be used near sharp environmental discontinuities, such as a high altitude coastline or the edge of an oasis,
because of advection effects in the lower layers of the atmosphere.
The comparison made in this research indicates that the two estimates are essentially similar in monthly and annual scales if the quality of data is good, but one should be cautious in using Morton's method on a daily basis. Also, before using pan evaporation the reliability of the data should be tested by comparing measured data with adjacent stations. The correlation between pan evaporation and Morton's estimates in five catchments resulted in constants in the range of 0.55 to 0.77 with a mean value of 0.66.
These constants compared well with conversion factors obtained from the water balance method and showed promise as a conversion factor for relating pan evaporation to Chapter Two Hydrologic Processes in Catchments. 2.37
potential evapotranspiration. As a result of this investigation, it was decided to adopt pan evaporation with a conversion coefficient for the next stage of the study.
The runoff rate mainly depends on the type, intensity, duration, direction, distribution of storm intensity over the basin, and other climatic and geological human factors.
Runoff losses refer to total losses due to evaporation, absorption, and percolation It is very difficult to estimate these losses individually or collectively. These losses vary greatly in different regions, depending on the physical and geological conditions of the
ground and atmosphere.
There are two main theories which explain the process of surface runoff generation. A significant group of researchers supports the Hortonian theory. They believe that surface runoff occurs when rainfall intensity exceeds the infiltration capacity of the ground. Another group is in favour of the variable source area concept. This group argues that typical rainfall intensities are less than the infiltration capacities of many soils and normal storm duration is usually shorter than the time required for most soils to become saturated at the surface, hence the Hortonian theory is not always valid. They consider runoff to be the response of the catchment channel system to rainfall. In this theory, overland flow is considered to be an extension of the perennial channel system into zones of low soil storage capacity which quickly become saturated as a result of infiltration into the lower valley sides. This interflow feeds the expanding channel from below while rainfall feeds it from above. As the storm continues, surface runoff occurs from an
expanding saturated area.
In summary, both rainfall and catchment characteristics are important in generating runoff. A light gentle rain may be intercepted by vegetation, or it may be absorbed and stored in the soil. The same rain, of long duration, may result in a considerable amount of runoff. Also, a sharp and intense rainfall of short duration may result in large amounts of runoff. The catchment surface is not uniform and it does not react to the rainfall
uniformly.
The basic runoff processes observed in catchments are Hortonian overland flow, saturation overland flow and subsurface or groundwater flow. Hortonian overland flow occurs when the rainfall intensity exceeds the soil infiltration rate. This kind of runoff process mostly occurs in arid or semi-arid regions where infiltration is low because Chapter Two Hydrologic Processes in Catchments. 2.38
vegetation is sparse. The heavy vegetation and well-drained soil of some areas usually have an infiltration rate far greater than 60 mm/hr; an intensity which ordinary rainfall would seldom exceed (Hewlett and Hibbert, 1967). Other types of surface flow are saturation overland and return flows. If rain falls on a saturated soil surface, saturation overland flow occurs. Some of the water that previously infiltrated into the top soil may now return to the surface as overland flow, known as return flow. Surface flow is often small but interflow, the flow that occurs close to the ground surface, may be large. Such surface flows and interflow are slower than the Hortonian overland flow and so their hydrographs are usually flatter. During a storm the saturated area, which produces surface flow, expands and contracts dynamically. With nonuniform distribution of soil capacity and soil moisture deficit over the catchment, the distribution of runoff processes
over the basin is uneven.
As explained by Dunne (1983) and Boughton (1988), different runoff processes can be dominant in different areas and none of these theories can precisely explain the process of runoff generation. However establishment of the runoff producing area is very important, both for continuous rainfall-runoff modelling as well as for flood studies and it
is one of the objectives of this study.
Process factors include interception, infiltration, soil moisture storage, interflow, subsurface flow, channel processes, and transmission losses. In a process model the process parameters are determined by a combination of direct measurement and indirect
assessment during model calibration.
Infiltration is defined as the entry of water from the surface into the soil profile. The infiltration rate is the actual rate at which water enters the soil strata. It is an important hydrologic process that must be carefully considered in models or procedures for
defining the hydrology of a catchment.
Soil moisture storage is a very important component of the hydrologic cycle as well as of process models. It controls the water input from precipitation and the water output due to evaporation and the drainage process. Soil moisture storages represent the moisture holding capacity of the soil layers. The process of water movement in the unsaturated zone is a complex phenomenon and depends on atmospheric conditions, soil type and land cover. Several methods have been developed to represent the process of flow in the Chapter Two Hydrologic Processes in Catchments. 2.39
unsaturated zone. Some of these are; models which deal with flow through porous media, models based on water balance analysis (both hydrological and agricultural approaches), and models with a combination offield experiments and a water balance analysis. The selection of an appropriate model depends on the computational efficiency, calibration requirements, objectives of the study and the degree of accuracy required. In the case of rainfall-runoff modelling and for ungauged catchments, models of the second type have often been used. In the rainfall-runoff models with many parameters, an actual value for storage capacity is also difficult tofind. Therefore , it has always been desirable to estimate the storage capacity of catchments using measurable catchment characteristics.
The physical parameters of a catchment refer to the existing features of the catchment surface which directly impact on the runoff response. These parameters are needed in models in order to define the retention and release characteristics of a catchment for estimating the catchment yield. The physical characteristics of a catchment that affect the runoff can be classified as follows:
• type of soil and vegetation • area • shape • elevation • slope • orientation • type of drainage network • extent of indirect drainage • artificial drainage • urbanisation CHAPTER THREE
MODELLING THE RAINFALL-RUNOFF PROCESSES CHAPTER THREE
MODELLING THE RAINFALL-RUNOFF PROCESS
3.1 INTRODUCTION
The estimation of flow characteristics of catchments can be carried out in several ways, ranging from simple runoff coefficient methods to a flow frequency analysis and estimation of the probability of occurrence of a specific flow. All these analyses need streamflow records of sufficient length to represent the long term variation of catchment runoff. Only in rare cases are sufficient measurements of streamflow available, because the majority of catchments are ungauged. For these catchments there are a number of possible solutions. Using rainfall records can be one of the best ways to assess runoff. For example short term streamflow can be used to calibrate a rainfall-runoff model and then the streamflow can be extended as long as the rain record is available. If the length of either rainfall or runoff is not sufficient, they can both be extended using stochastic models. Another alternative involves transposing the results of a gauged catchment with similar characteristics to an ungauged catchment. Also, data from a number of regional catchments can be used to obtain regional prediction equations and these can be applied to the catchment of interest.
Figure 3.1 illustrates schematically a number of possible approaches to the analysis of water resources and estimation of flow characteristics of the catchments.
3.2 A REVIEW OF METHODS USED AT PRESENT
A rainfall runoff model is a simplified representation of a complex catchment system. The field of rainfall-runoff modelling is not new. For a century, sciences have been developing mathematical formulae to describe various processes in catchment dynamics. Since the advent of computers and particularly with the availability of personal computers, it has become possible to use models with millions of computations. Chapter Three Modelling the Rainfall-Runoff Process. 3.2
Long Streamflow Records input No Streamflow Records
Short Streamflow Records
T 1 Regression Methodr s Stochastic Methods <. J Rainfall-Runoff analysis Empirical Equations Statistical Methods Models 1 4
Historical/Estimated Data Catchment and Climatic Data Regional Caracteristics
I 1 Catchments Reservoirs
Prameter Analysis Critical Period Methods .application Flow duration Analysis Stochastic Data Generation Recession Analysis Probability Matrix Methods Spell Duration Analysis Frequency Analysis Sedementation Transport Water Quality Analysis
Regional Equations , analysis
i Low Flow Charachteristics Storage Techniques Parameters of Rainfall-Runoff Design Flow for Environment Related Models Projects
i i i .application 4, i i i Low Flow Characteristics Relationship between Estimate of Streamflow at Design Flow for Environment Inflow Characteristics, Ungauged Catchments Related Projects Storage Environment Related Projects Draught Reliability Water Resources Development Water Resources Converting the Flow to Water Development Level Sedimentation
Figure 3.1 An Overall Perspective of some Possible Approaches in the Analysis of Water Resources Chapter Three Modelling the Rainfall-Runoff Process. 3.3
The field of rainfall-runoff modelling can be divided into two main categories including stochastic and deterministic models. Stochastic catchment models are those which aim to produce an output with certain statistical properties or certain probabilities of occurrence. Deterministic models have no stochastic components and thus for a given input the output can be accurately predicted.
Deterministic models can be subdivided into conceptual and process models. Although there is not a clear difference between these groups, the conceptual models are those whose structures make little attempt to represent the movement of water through the catchment (black box types). The process models are those where some effort is made to simulate the hydrologic components of a catchment. There is no known model which is entirely free from empiricism. Though they contain some empirical features, deterministic models such as soil moisture accounting models, to a certain extent are able to account for changes in catchment conditions. Physically based models, though more complex than simpler models, still cannot adequately describe the dynamic mechanisms of nature.
A deterministic model can be either a continuous or an event model. A continuous model predicts runoff as a sequential quantity by simulating catchment behaviour. Event based models concentrate more on modelling the streamflow hydrograph as a response to a particular rainfall excess hyetograph.
The input data for rainfall-runoff models vary. Some models are defined as one dimensional or lumped parameter models which assume a uniformity of processes over the entire catchment. Other models cater for the spatial variability of parameters and accept the concept of saturation overland flow, or subdivide the catchment into a number of small sub-catchments (distributed models). The internal time step of the models depends on the accuracy of the output required. In some cases, the continuous simulation of streamflow over a period is desirable, while for other applications accurate prediction of hydrographs will be required.
Daily rainfall and runoff data are more readily available than data of shorter time steps. The Australian Water Resources Council (1984) and the Commonwealth of Australia, Bureau of Meteorology (1963b, 1972) have shown that the majority of stations record daily values rather than shorter time step values. The record lengths of shorter time step data (eg. hourly rainfall) are much shorter than daily rainfall records. Chapter Three Modelling the Rainfall-Runoff Process. 3.4
In this study, daily time steps are preferred in order to obtain readily available data of long record length. Long record length data are very important, specially in computing the statistics of the data. This kind of modelling has better applicability for ungauged catchments, therefore current research will be limited to deterministic process models using daily time step.
Subsequent sub-sections will give brief descriptions of some of the models that are widely used. Selection and calibration of models will be made as well as selection of the models to be used in this study.
3.2.1 Empirical Methods
Empirical methods may be classified as a direct approach involving some mathematical equations. These equations, when given a certain input, yield an output. Minimal consideration is given to the relationship of parameters in the equation and to the processes being considered. Regression type analyses are examples of empirical models which have been used for a long time in thisfield. B y using different regression analysis (linear, non-linear, simple or multivariate) the relationships between variables governing the catchment rainfall-runoff are determined by different research workers. For example, Chapman (1963) developed multivariate regression relationships for the Upper Goulburn River. Diskin (1970) used a linear regression model correlating annual rainfall and runoff using the least squares method. The Average Runoff Coefficient Method is another empirical technique recommended by Burton (1965). This will be discussed briefly next.
• Average Runoff Coefficient Method
The Average Annual Runoff Coefficient Method, recommended by Burton (1965), is an empirical technique to relate rainfall to runoff with a certain probability. It might, for instance, be used for one or two years estimate with probability of 0.8 or 0.9, respectively (ie correct estimates in four years or more out of five on average). The runoff coefficient method is a simple rainfall-runoff model (Laurenson and Jones, 1968). The simplified rainfall-runoff relationship is given by:
Q = P-AET±AS 3.1 where Chapter Three Modelling the Rainfall-Runoff Process, 3.5
Q = the runoff (mm), P = the rainfall (mm), AET = the actual evapotranspiration losses (mm) AS = the change in soil moisture storage.
The runoff is estimated by applying a runoff coefficient (Cr) to rainfall, to account for AET and AS:
Q = Cr*p 3.2
These methods are quite crude, consisting simply of multiplying the average annual rainfall by a runoff coefficient. Consequently, considerable judgment must be exercised in selection of the runoff coefficient. This coefficient varies with average annual rainfall, nature of the soil, catchment slope, and required reliability. The runoff coefficient can be derived using the observed plot of Q against P.
Most of the empirical methods attempt to lump several parameters into a single factor that is too gross an approximation to reality, and should be avoided where possible (Laurenson and Jones, 1968). This model is popular in predicting runoff from ungauged catchments due to its simplicity (Jayasuriya, 1991).
3.2.2 Runoff Determination by Statistical or Probability Methods
Prediction of runoff and future floods are made by these methods on the basis of the past records. The data obtained can be safely used for determining the average or maximum runoff on a river with a given frequency. If sufficient past records are available and there are no appreciable change in the regime of the river during and after the collection of the records, this method can be considered as an accurate method of runoff determination.
• The Statistical Methods
The statistical methods include two subdivisions, probabilistic methods (frequency analysis), and stochastic approaches. Probabilistic methods introduce the concept of frequency. The objective in frequency analysis is to assess the number of years within which an event will occur at least once (Fleming, 1975). The usual procedure followed in frequency analysis is to assume the specific frequency distribution that the event is likely to follow, and to evaluate the parameters of the equation from experimental observations. In principle the probabilistic method is a good one, but unfortunately Chapter Three Modelling the Rainfall-Runoff Process. 3.6
records of direct measurement if available at all, are almost invariably too short to obtain an adequate definition of reliable estimate (Raudkivi, 1979). Both of these methods will be discussed briefly below.
a) Rainfall-Runoff Correlation
Regression and correlation techniques essentially determine the functional relationship between rainfall and runoff. Analysis is generally made on the average or total value of a variable between selected time intervals. The relationships obtained are characterised in statistical terms by the correlation coefficient, standard deviation, confidence limits and tests of significance.
If long term rainfall records are available, and the parameters of rainfall runoff correlation can be determined for the catchment, then the rainfall-runoff correlation will be valuable for ungauged catchments. Generally, the rainfall-runoff correlation can be used on a yearly, monthly and daily basis. The best application of this method appears to be for annual records, but for a shorter period, the scatter arising from errors in the measured values and variation in the volume of water stored on the surface becomes too great and some kind of parametric correlation becomes necessary. These models provide an appropriate tool for prediction of annual runoff and may be useful in cases where the use of more complicated model is not justified. This method gives the annual runoff volume of a catchment as a function of annual rainfall and catchment area without considering differences in rainfall pattern, soil, slope, vegetation and land-use of the different catchments. The runoff coefficient is to be selected from a nearby gauged catchment.
b) The Stochastic Approach
The stochastic approach is designed in hydrology to extend hydrologic forecasts and improve decision making ability. The design and operation of many water resource projects require long records of stream flow and rainfall data. However, in many cases these long records are not available, but may be extended using stochastic models. By using values of \i, a and p (population mean, standard deviation, and lag-one serial correlation determined from the historic record) and a selected random generator, the stream flow in the next time period can be estimated. The Markov type is one of several techniques in stochastic hydrology concerned with non pure random data; ie, data Chapter Three Modelling the Rainfall-Runoff Process. 3.7
composed of both causal and random elements. Another of these methods, the Monte Carlo model considers the data to be totally independent, ie., purely random.
Stochastic modelling uses the stochastic properties of observed time series in order to generate a synthetic long term time series. The statistical and stochastic properties of the observed time series are assumed to represent the population properties, and the synthetic long term time series are assumed to come from the same population. In fact these models try to model the hydrologic time series (such as rainfall or runoff) using two contributing factors; persistence (stochastically deterministic factors) and random factors.
3.2.3 Simulation Methods
3.2.3.1 Introduction
The abstract system is an attempt to represent the real system by a structure, device, scheme or procedure. The conceptual model may therefore be defined as an abstract system interrelating, in a given time reference, a sample of input and a sample of output without introducing any physical relevance into the equations and parameters used in the model, e.g., the Black-Box approach. If some physical relevance is incorporated in the equations and parameters of the model then it is called a Gray-Box approach.
Simulation is used to define the conceptual approach to physical hydrology, and is the representation of time variant interaction of physical processes. These techniques include; direct simulation using physical models, semi-direct simulation using analogue models and indirect simulation using digital models.
Most models of catchment response are quite diverse in their structure and operation, and are effective under a variety of different conditions. With the appearance of high speed digital computers, and as a consequence of numerous advances in scientific hydrology it has been possible to develop models that attempt to simulate on a very comprehensive scale the complicated response of a catchment to various natural and man-induced phenomena.
Boughton (1988a) reviewed catchment scale process models developed in Australia and commented on the selection and use of rainfall-runoff models. The Boughton model Chapter Three Modelling the Rainfall-Runoff Process. 3.8
(Boughton, 1966), SFB model (Boughton, 1984), AWBM model (Boughton, 1993), Hydrolog model (Porter and McMahon, 1971), Semi-arid-zone model (Sukvanachaikul and Laurenson, 1983) and the Australian Representative Basin Model (ARBM) (Chapman, 1970) are examples of some of the rainfall-runoff process models developed in Australia. The Sacramento (Burnash et al., 1973), the Stanford Watershed (Crawford and Linsley, 1966), and the USDA SCS curve number models are widely used rainfall- runoff models developed in the United States.
Such models are normally used together with rainfall and potential evaporation as an input to estimate the runoff and actual evaporation as output. Differences between the various water balance models arise from differences in the lumping of continuous processes into discrete elements and the restriction of them into a portion of the runoff process.
Methods of estimating runoff on small catchments are further limited by the lack of suitable data. In many cases there are usually plenty of rainfall records but the more elaborate and expensive stream flow measurements are often limited and rarely available for a specific catchment. Frequency analysis of precipitation data is usually more reliable because precipitation records have been kept for a longer period, but much of this gain is lost after the rainfall is related to the catchment's characteristics and expressed in terms of runoff.
The processes by which rainfall is converted into runoff are complex and variable both in time and space, and the devising of algorithms for modelling the rainfall-runoff phenomenon on a continuous, as opposed to an event type, basis has been a research topic for several decades. Many hydrologic models have been developed that use rainfall data as input. Several of the most widely used models will be described in the following sections.
3.2.3.2 Stanford Watershed Model
The Stanford Watershed Model (Crawford and Linsley, 1966), was developed to simulate closely the physical phenomena of rainfall-runoff processes. It utilises hourly rainfall and daily potential evapotranspiration as inputs to produce the runoff hydrograph, as well as other data on simulated catchment behaviour. The model has Chapter Three Modelling the Rainfall-Runoff Process. 3.9
many applications and has been used to produce continuous hydrographs, evaluate runoff coefficients and evaluate the effects of urbanisation on flood peak and volumes. The model, illustrated diagrammatically in Figure 3.2, operates by means of three moisture stores. Allowance is made for precipitation on impervious areas of the catchments and the surface of the lakes and streams, by directing a percentage of the rainfall to surface runoff. The upper zone storage represents the catchment surface and depression storage. Most of the water involved in the early infiltration is controlled by this soil layer. The lower zone store forms a significant part of the soil profile above the water table and is the main means of control on long term evaporation and infiltration. The ground water store controls baseflow. Water in the interception and upper soil storage is lost to evapotranspiration at a potential rate, and then from the lower zone at a rate which decreases with decreasing soil moisture. Evapotranspiration is also subtracted from the groundwater store at a relatively low rate.
The Stanford watershed model is a digital computer program used to synthesise continuous streamflow hydrographs. Input to the model includes hourly rainfall and average daily potential evapotranspiration at selected points over the catchment. The model represents all the physical processes involved in converting the hourly rainfall hyetograph to an hourly runoff hydrograph. This model has been considered as the most complete conceptual model by many researchers (Bell, 1966, Fleming and Black, 1974, Fleming, 1975). However, due to its extensive data requirements, which are not usually available and the duration of available data is also limited, it can not be considered as a practical design tool.
The Stanford Watershed Model was developed by Crawford and Linsley (1960), and was then modified and improved by Crawford and Linsley (1962, 1963, 1966). It has been adopted as the rainfall-runoff sub-model of the HYDROCOMP water resources simulation model (currently known as the HSP Model). However, the model is not adopted in this study due to its extensive data requirements. Hourly rainfall and runoff records are required to test this model. These data are not extensively available and the record period of available data is also limited. Chapter Three Modelling the Rainfall-Runoff Process. 3.10
Precipitation KEY , 'Actual Potential "* _ Evapotranspiration Evapotranspiration Input Storage Temperature \ Radiation /
Output Snow-melt
Function
Interception Evapotranspiration 'Interception Storage I Impervious Area Channel A inflow
Evapotranspiration /infiltration /Surface Runoff / Upper Overland /Channel / Interflow 7 Zone Flow / Inflow
Evapotranspiration
Evapotranspiration Lower Zone Lower Zone or Storage GroundwaterStorage
/Active or Deep ~~7 GroundwaterStorage/
Evapotranspiration Groundwater Channel J Channel Time 4----; Storage Inflow / Delay & Routine
Deep or Inactive or , Simulated Groundwater Storage . Streamflow
Figure 3.2 Flow Chart of the Stanford Watershed Model
3.2.3.3 Sacramento Model (SMA)
The Sacramento Model is one of the most widely used conceptual rainfall runoff models. This model was developed by Burnash et al. (1973) and has become the US National Weather Service's basic catchment hydrologic response model for operational forecasting. It is a deterministic model with a variable impervious area. The original model simulated runoff using daily precipitation input but was later adapted for versions of finer time increments (6-hour, 1-hour or less).
Input to the model includes precipitation and pan evaporation data. In general, model parameters are calibrated manually or automatically or by a combination of both methods Chapter Three Modelling the Rainfall-Runoff Process, 3 It
by miiiimising the differences between simulated and observed runoff. By treating a catchment as a closed system whereby all rainfall input returns as either storm runoff or AET, initial values for some of the basin parameters can be determined from the inspection and analysis of observed runoff hydrographs.
For the framework of the Sacramento model rain, occurring over a catchment, is considered to fall on two types of areas, permeable soil, and impervious areas (lakes, channel networks). Rain falling on impervious areas becomes direct runoff, whereas that which falls on the permeable soil undergoes a complicated sequence of water movements. Below the permeable soil surface, the soil moisture storage is made up, conceptually, of upper and lower zones. Each zone stores moisture in two forms, 'tension moisture' and 'free moisture'. 'Tension moisture' denotes water closely bound to the soil particles while 'free moisture' is the moisture thatfills u p the interstitial soil pores.
The upper zone represents topsoils and the catchment interception layer. Its tension water, bound closely to the soil particles, must befilled befor e moisture can be stored as free water in the upper zone and lateral drainage (interflow) to the channel can occur. If the precipitation rate exceeds the sum of lateral and vertical drainage rates, and the upper zone free water capacity is completely filled, excess surface runoff will occur. The actual percolation rate to the lower zone is governed by the interrelationship between soil drainage characteristics and the relative soil moisture conditions between the two zones.
The lower zone, which represents a groundwater reservoir, has a tension water storage and two free water storages (called primary and secondary). Water goes to the tension water zone first and then to the two free water zones, which generate primary and secondary baseflows. The reason for using three water zones is to allow a wide range of groundwater recession rates in an attempt to model baseflow accurately.
Moisture from the upper and lower tension zones and free water surfaces evaporates by means of evapotranspiration. Actual evapotranspiration occurs either at the potential rate, for areas covered by surface water, or at a daily mean adjusted by seasonal adjustment coefficients. Other factors that influence the basin's AET include the hierarchy of moisture extraction priority and the water available in each zone. Chapter Three Modelling the Rainfall-Runoff Process. 3.12
The model produces five runoff components, namely: direct runoff from impervious and water surfaces, surface runoff and interflow from the upper zone of free water, and primary and secondary baseflow from the lower zone of free waters. Runoff volumes from these five components feed to a channel and are routed down the channel by one of several available techniques. As there are more than 25 parameters, it is extremely difficult to find the physical equivalent of the parameters and hence, this model was not adopted for this study. A schematic representation of the model is given in Figure 3.3.
Figure 3.3 Schematic Representation of the Sacramento Model
3.2.3.4 European Hydrological Model (SHE)
The SHE is a generalised mathematical modelling originally developed by a consortium of European agencies in the 1980's as a research project (Abbott et al., 1986). The SHE is a conceptual and distributed physically based model. The catchment in this model is descretised by a number of rectangular grid systems. A similar multi-layer mesh is considered for the ground water system of the basin. Non-linear partial differential equations are used to simulate the flow processes. The equations used in the model are well-known and tested. Chapter Three Modelling the Rainfall-Runoff Process. 3.13
The Danish Hydraulic Institute (DHI) has further developed and enhanced the model and named it the MIKE-SHE model. The SHE represents the deterministic view of catchment hydrology containing no empirically-based process descriptions. It has an ability to take into account a large range of spatial and temporal data.
SHE, as a distributed model, has very demanding data requirements but is capable of defining the impact of specific changes within the simulated catchment. Also most of the parameters in the model can be interpreted physically and field measurements can be used to obtain the values of these parameters. The results obtained in Europe and Australia using SHE and MIKE-SHE models have been encouraging (Bathurst, 1986, Carr et al., 1993).
The components of the SHE are described by Morris and Godfrey (1979), Abbott et al. (1982), Morris (1982) and Jensen and Jonch-Clausen (1982). Details of the whole system have been described by Abbott et al. (1986a, 1986b), and applications and analysis of the whole system were made by Bathurst (1986a, 1986b).
Being a distributed model, it has a very demanding data requirement as well as computation time. This study is interested in the models using readily available data. Therefore, this model is not suitable for the purpose of this study.
3.2.3.5 API Type Models
Antecedent precipitation index (API) is an index of the moisture status of the catchment. It is defined by Equation 3.3 (Linsley et al., 1949). The moisture status is assumed to decrease exponentially during periods of no rain, and to be recharged by the amount of rain.
APIt = API,Atk + PAt 3.3 where API. = the API value at time i P = the precipitation (rainfall) during time (At) k = the recession constant
Runoff predictions using API werefirst introduced by Kohler and Linsley (1951). They proposed a coaxial graphical relationship between storm rainfall, storm duration, API, season (represented by week number in the year) and storm runoff. This is an empirical Chapter Three Modelling the Rainfall-Runoff Process. 3.14
model where the relationships are derived from graphical fittings. The model is an event type model rather than one which makes continuous predictions.
The Commonwealth of Australia, Bureau of Meteorology adopted the API principle (Bell, 1966) for estimating initial loss and continuing loss of rainfall during storms. This gives the value of excess rainfall, which is then routed via a unit hydrograph to produce storm runoff.
Betson et al. (1969) developed an analytical version of the API Model. This was based on the concepts used in the coaxial graphical API model of Kohler and Linsley (1951), but mathematical equations were used instead of a graphical relationship.
Sittner et al. (1969) also used API in their model to predict a continuous surface runoff hydrograph for a short time step. The model also utilised unit hydrograph, groundwater recession and groundwater flow relations, in predicting the total storm hydrograph. The model operated on an hourly basis. Nemec and Sittner (1982) used a similar API type model to predict the hydrograph using storm rainfall in the Indus River System in Pakistan. This model also operated on an hourly basis.
Fedora and Beschta (1989) developed a runoff prediction model based on the API principle, which operated on an hourly basis. The contribution of rainfall is not considered directly by this model, rather it is included in the value of API (Equation 3.3). The value of k is estimated from the slope of the bestfit line of the plot of Qt against Qt-
At during dry periods, based on the assumption that the influence of API on Q, decays at the same rate as the recession limb of the hydrograph during dry periods. The value of runoff (Q) is given by:
5 Qt°' = inc + si (API)t 3.4 where inc and si are obtained from linear regression.
3.2.3.6 Soil Dryness Index Types Models (SDI)
SDI models are conceptual models that use moisture accounting principles on a daily basis. These models were developed for the initial purpose of forecasting catchment dryness as a means of bushfire prediction. The original SDI Model was developed by Mount (1972) based on the Drought Index Model of Keetch and Byram (1968). Chapter Three Modelling the Rainfall-Runoff Process. 3.15
Langford et al. (1978a, 1978b) modified this model to predict streamflow in the Melbourne water supply systems. Kuczera (1983) used the model in his studies of parameters optimisation. Kuczera (1988) then modified the SDI model for further applications in runoff predictions.
Kuczera's SDI Model requires daily rainfall (P in mm), daily pan evaporation (EP in mm) and daily runoff (Qa in mm per unit area) as input data. P and EP are used directly in the model to produced values of predicted daily runoff, while Qa is used to calibrate the model parameters and measure the accuracy of the model. EP can be recorded as daily pan evaporation or average daily pan evaporation for each month.
The rainfall reaching the ground (throughfall) is defined as net rainfall after considering the interception losses by vegetation. Kuczera (1988) defined the throughfall of day t, by a piecewise linear relationship:
THt = min ( Pt, max (0, BTHRU*Pt - ATHRU)) 3.5 where
THt = the throughfall for day t P = the rainfall depth for day t ATHRU and BTHRU = model parameters, which can be optimised or inferred from field measurements
The flash runoff for day t (QFt) is the surface runoff that enters the stream network directly without flowing through the sub-surface. It is defined by Kuczera (1988) as a fraction of the throughfall of day t (THt):
QFt = THt * WETFRAC 3.6
Where WETFRAC = defined as the fraction of the catchment from which throughfall becomes the flash runoff (surface runoff) It is given by:
WETFRAC=max (0,min (l,WET-WETS*SDItl+WETH*Ht_1)) 3.7
Where WET, WETS and WETH are the model parameters representing the effects of antecedent conditions on the value of WETFRAC.
The evapotranspiration loss is related to PET by a reduction factor (between 0 and 1). This factor is given by:
EET = ESW * EVPD 3.8 Chapter Three Modelling the Rainfall-Runoff Process. 3.16
Where ESW = the soil moisture stress and defined by:
SDI 2 ifSDItl>0:ESW = max(0,min(l,(l-CEP*( —) )) 39 t-i ySMAXJ n otherwise :ESW =1 where CEP and SMAX = model parameters
SDIt. l = the Soil Dryness Index value at the end of day t-1 EVPD = the vapour pressure deficit stress, which is defined by:
EVPD = max (0, min (1, 1-BEP*EP)) 3.10 where BEP = the model parameter.
Interflow of day t (QOt) is defined as the flow through the soil store into the streams. This component of flow is modelled as being stored temporarily in the soil store and delayed by infiltration and sub-surface flow processes before reaching the streams. It is given by:
if SDIM < SMAX: QOt = KI * (SMAX - SDIJ 3.11
otherwise: QOt = 0 where SMAX = the model parameter defining the interflow threshold value KI = the response parameter.
The recharge from the soil store to the saturated soil store during day t (RGEt) is assumed to occur rapidly when the field capacity is exceeded. It is given by:
if SDIM < 0 :RGEt = - SDI^ 3.12 otherwise : RGEt = 0
The baseflow during day t (QBt) is assumed to be a discharge from a linear reservoir with a response parameter KG. It is given by:
l-exp(-is:G) QB = HS * (1- exp(-KG)) + RGE .,* 3.13 t M t KG
The seepage loss during day t (SEEPt) is defined as the amount of baseflow loss to deep percolation to deep groundwater, given by: Chapter Three Modelling: the Rainfall-Runoff Process. 3.17
SEEPt = (l-DEEP)*Qbt 3.14 where DEEP is the model parameter.
The total flow in the stream on day t (Qt) is the summation of all the flows less seepage loss, given by:
Qt = QFt + QOt + QBt - SEEPt 3.15
The soil stores at the end of day t are given by:
SDIt = SDI^j - THt + RGEt + QOt + QFt + ETt 3.16
HSt = HS^ + RGEt - QBt 3.17
The Soil Dryness Index (SDI) is reduced by the amount of throughfall and increased by the recharge, the interflow, the flash runoff and the actual daily evapotranspiration. The saturated soil store HS is recharged from the soil store and reduced by the baseflow.
The latter version of this model was applied with some six other models (included different types of SDI's model) by Baki (1993) to seven catchments in the Illawarra region of Australia. He concluded that Kuczera's SDI model performs the best. An examination of this model will be made in Chapter 8. The schematic representation of
Kuczera's SDI Model is shown in Figure 3.4.
Evapotranspiration, AET
Rainfall P Evapotranspiration EP Interception I J I Throughfall, TH ©- Flash Runoff, QF Soi1l Streamflow Store, S Interflow, QO Q — • Recharge RGE Baseflow, QB Saturated Soil Store, HS -©—' Seepage Loss, SEEP Figure 3.4 Structure of the SDI Model Chapter Three Modelling the Rainfall-Runoff Process, 3 tfi
3.2.3.7 SCS Methods
The United States Soil Conservation Service (SCS) has developed two methods for estimating the volume and rate of runoff from agricultural catchments in the US (US SCS National Engineering Handbook, 1972). The models are fairly complex in their requirements, involving estimates of catchment characteristics including slope, area, soil type and cover, but are simple to use. Both methods use 24-hr rainfall as the basis for calculating catchment input. The SCS model was developed from agricultural land data. This technique uses a simple curve to relate rainfall to runoff. Because of its simplicity, and in spite of its limitations, the method is widely used in ungauged basins for predicting runoff from rainfall data. It has also been applied to large catchments with varying degree of success. The method can be regarded as being most appropriate for small catchments. Moreover it has been modified for composite land and has been applied to different catchments from steep toflat, and from forested to desert areas. The methodology of the SCS curve number method namely, NEH-4, TR-20, and TR-55 are described in McCuen (1982).
The development of the SCS method is based on the following assumed rainfall-runoff relationship.
Q 3.18 S P-I a from the water balance equation
F = (P-Ia)-Q 3.19 and finally
Q = — a-L— 3.20 ^ (P-Ia) + S where F = the daily infiltration rate (inches per day) S = the potential maximum retention (inches per day) Q = the runoff (inches) P = the precipitation (inches)
Ia (inches) = the initial abstraction (combination of interception, depression and initial infiltration).
Usingfield data, SCS developed the following empirical relationship between S and Ia:
L = 0.2S 3.21 Chapter Three Modelling the Rainfall-Runoff Process. 3.19
where S is a function of land use treatment and soil condition Substituting Equation 3.21 into 3.20 and rearranging gives:
(P-0.2S)2 Q= (P + 0.SS) whe*P>°-2S 3.22
Q = 0 when P < 0.2 S 3.23
Because it has only one parameter S, the last equation has the advantage over most of the other simplified rainfall-runoff relationships. The SCS relates parameter S to a runoff , ,_ . . , 1000 curve number by an empirical relationship given as equation CN = . This factor can be assessed from soil surveys, site investigations and land use maps. CN represents the hydrologic effects of soil, land use, agricultural land treatment, hydrologic condition and antecedent soil moisture. The SCS developed a soil and vegetative cover classification system which gives a corresponding value for the curve number. By considering the total rainfall for the previous five days, the change in curve number is made accordingly to account for the antecedent moisture content. When the rainfall and CN are established from the above mathematical formula or graphs, the volume of runoff can be estimated.
As the SCS method predicts only direct runoff, it is unsuitable for use where baseflow forms a significant portion of runoff. Since the development of this method, researchers have tried to improve it by incorporating subroutines to account for evapotranspiration, drainage and baseflow with varying degree of success (Aitken, 1969, Williams and La Seur, 1976, and Chen, 1982). The antecedent moisture condition is a significant factor in the process and plays an important role in determining the pattern of daily runoff. A review of the SCS method was made by Boughton (1989).
The SCS method has achieved world-wide acceptance as it uses only one parameter S or CN-a constant which is evaluated from the soils, land use and vegetation of the catchment. However, compared with other popular models, it does not treat the antecedent moisture condition adequately. Even so, it is one of the most widely used methods for estimating runoff from rainfall. Its main shortcoming is that much work is necessary to verify the form of rainfall-runoff curves and the parameters for use in the method under regional conditions. The method is subject to large errors but these tend to Chapter Three Modelling the Rainfall-Runoff Process. 3.20
be compensated for by changing the parameters and the effect on the estimation of storage requirements is not as great as would be expected. The results are very sensitive to the value of the curve number.
As stated by Boughton (1989), if the model is expressed as an infiltration equation, the infiltration rate is dependent on both total storm rainfall and rainfall rate, regardless of antecedent moisture. If the method is expressed as a spatially varied saturation overland flow model of runoff, it implies that some part of any catchment will have an infinite surface storage capacity and never produce runoff. In spite of the good documentation on this model mainly in USA and some in other parts of the world, the lack of any physical reality in the model's formulation is the major weakness of the model and therefore it is not compatible with the objectives of this study.
3.2.3.8 Semi-Arid-Zone Model
The semi-arid zone model was initially developed by Sukvanachaikul (1983). This four parameter model (Sukvanachikul and Laurenson, 1983) is developed for semi-arid catchments where the baseflow is not significant in the catchment's response. The input data to the model are daily rainfall and Morton's wet environment evapotranspiration. The semi-arid zone model was simplified by Jayasuriya (1991) to include a baseflow component. This simplified model is the Modified Semi-Arid Zone or (MOSAZ) model. In the modified model, the traditional method of calculating actual evapotranspiration is replaced by the complementary theory (Bouchett, 1963, and Morton, 1971). The schematic diagram of the modified semi-arid-zone model is given in Figure 3.5. Chapter Three Modelling the Rainfall-Runoff Process. 3.21
Rainfall and wet Environment Evapotranspiration
^Actual Evapotranspiration
Surface Runoff, Rl Upper Soil Storage, US •Interflow, R2
the average soil Lower Soil moisture level, St Storage, LS
-• R3 Sub Soil Baseflow Storage, SDR -• R4
Figure 3.5 Schematic Diagram of the Modified Semi-Arid-Zone Model
3.2.3.9 Boughton Model
Boughton (1965, 1966) developed a daily rainfall-runoff model to predict the surface runoff from small ungauged ephemeral catchments. It is a conceptual model which models physical processes between rainfall, infiltration, evapotranspiration and runoff. This model was developed using Australian data. Pattison (1966) has discussed the application of this model. Murray (1970) modified this model for application to the Brenig catchments. Other applications are listed in Pattison and McMahon (1973).
One disadvantage of this model is that it does not account for baseflow contributions, which may be significant in perennial streams.
The model consists of four surface moisture storages including the:
• interception store which represents intercepted rainfall on the vegetation (CEPMX) • upper soil store which computes the moisture capacity between field capacity and wilting point of the upper soil zone (USMAX) • drainage store which represents the amount of water temporarily held in the topsoil (ie the moisture contents of the top soil between field capacity and saturation, DRMAX) • lower soil store; water remaining in drainage store drains in this part and ultimately deposited of by drainage to groundwater (SSMAX)
Precipitation first enters the interception store. When the interception store is full, water enters the upper soil zone store. After filling this part (betweenfield capacit y and wilting Chapter Three Modelling the Rainfall-Runoff Process. 3.22
point), the excess infiltration is temporarily held in the topsoil (between field capacity and saturation). Once these three storages are filled, runoff commences.
In the interception store, water is lost due to evapotranspiration at the potential rate, until it is empty. When the interception store is empty, evaporation loss occurs from the soil storages at a rate proportional to the moisture content of the soil. When the soil is saturated, evaporative loss occurs at the potential rate. The evaporative losses from the soil stores are given by the following equations when the soil is saturated.
AET(us)= PCEN*PET 3.24
AET(ss)=(l-PCEN)*PET 3.25 where AET(us) and AET(ss) = the evapotranspiration loss from the upper and lower soil zones PET = the maximum evapotranspiration rate PCEN = the percentage of evapotranspiration loss occurring from the upper zone, expressed as a fraction of PET
When either or both these moisture stores are at less than field capacity, evaporation losses are calculated using the relationship given in Figure 3.6.
Soil Moisture Capacity
e o c8 u. O a, £ X W a Sea •I 23 <* Wilting Point Field Capacity Saturation e X Figure 3.6 Computation of AET in Boughton's ModelS3
The maximum possible rate of evapotranspiration depends on the maximum rate at which evapotranspiration can occur (at field capacity) from the vegetation of the catchment (H), the moisture content in the upper soil and subsoil, and the percentage of evapotranspiration that comes from each of the two zones. Hence, for the upper soil, the maximum possible rate of loss is given by:
H * US PCUS 3.26 EP = * USMAX 100 Chapter Three Modelling the Rainfall-Runoff Process. 3.23
where EP = the maximum possible rate of loss (mm) H = the maximum evapotranspiration atfield capacit y = 8.89 mm PCUS = the percentage of evapotranspiration from the upper soil
As for the subsoil, the maximum possible rate of loss is given by:
^ H*SS ,(100- PCUS) EP = * 3 97 SSMAX 100 Water entering the lower soil zone drains to groundwater. The rate of transfer of water from drainage to the lower soil store is determined by the following equation.
kS F = F+(Fo-F)*e 3.28 where
F0 and Fc = the daily infiltration rates when the soil store moisture level is zero or saturated k = an empirical factor s = the volume of water in lower soil store F = daily infiltration capacity (mm/day) SS = subsoil store (mm)
Finally the volume of surface runoff (Q) will be calculated by using Equation 3.29.
P QS = P - F.tanh (—) 3.29 F where P = the excess rain after filling the first three storages
Deep percolation from subsoil storage to deep groundwater is represented by the depletion constant, DEP. Normally, the depletion is assumed to be 0.1% of the subsoil level, which gives: DEP = 0.999. This deep percolation only occurs if the subsoil moisture level is greater than 25.4 mm (1 inch).
There are several versions of this model. McMahon and Mein (1973) introduced a baseflow component into the model to make the model more generally applicable. The following equations explain the baseflow subroutine of the modified Boughton model.
QB = K2S2 if SS QB=K2 (SDRMX)+KBBSi if SDRMX QB=Spill+K2(SDRMX)+KBB(SSMX-SDRMX) ifSS>SSMX 3.32 Chapter Three Modelling the Rainfall-Runoff Process. 3.24 where QB = the daily baseflow Si and S2 = the soil moisture level of each section of the lower soil store SDRMX and SSMAX = maximum capacities of the lower soil store sections. S = the lower soil moisture status KBB and K2 = the baseflow recession for the lower soil store sections determined from the streamflow data The model has been written in FORTRAN IV and has 10 parameters requiring optimisation. The model is shown diagrammatically in Figure 3.7. Evapo transpiratiorI ]_x>sse s Rainfall ET P i 1 CEPMAX Interception • USMAX Upper Soil DRMAX Drainage r ^. Surface Runoff QS i ' SSMAX Subsoil Depletion from Subsoil Store • Baseflow DEP QB < Drainag eto Ground water Figure 3.7 Structure of the Modified Boughton Model 3.2.3.10 SFB Model The SFB water balance model was developed by Boughton (1984), as a simplification of his earlier 10 parameter model (Boughton, 1965). It is a simple three parameter model that operates on a daily basis with daily rainfall and potential evapotranspiration. To estimate the water yield from ungauged catchments, the model parameters; surface storage capacity (S), infiltration from surface storage to groundwater storage (F), and Chapter Three Modelling the Rainfall-Runoff Process, 3.25 baseflow (B), should be estimated from field observations of vegetation and soil type when runoff data for calibration are not available. The model is suitable for catchments where baseflow forms a significant part of runoff and for small catchments where only surface runoff occurs. The intended application of this model is to predict runoff from ungauged catchments using daily rainfall. The model is shown diagrammatically in Figure 3.8. k ET -*— QS Drainage "T Surface 0.5S Store Surface Runoff Non 13 U =Ex-Ftanh(EX/F) Drainage f 0.5S —*- F Infiltratiot n Baseflow Groundwater =0.005*B*(SS-25.4) Store z»— QB 0 The surface storage capacity of the catchments (S) is divided into two parts representing the drainage and non-drainage components. This parameter is of substantial significance in the simulation of the catchment's response. Drainage from the surface store to the lower store is always at a fixed rate, represented by F. The evapotranspiration loss is at the potential rate, while the storage of water in the surface store equals or exceeds 0.5S. But after the storage falls below 0.5S, actual evapotranspiration occurs at a rate less than the potential rate. Excess rainfall will flow as surface runoff onfilling th e surface stores. Water entering the lower soil store is disposed of by drainage to groundwater each day by a fixed value of 0.005 times the water remaining in the store. The baseflow parameter, B, determines how much of this water appears as baseflow in runoff and how much of it is lost to deep percolation. If B=1.0, all water depleted from the lower soil store becomes baseflow. The three parameters, S, F, and B need optimisation, if recorded Chapter Three Modelling the Rainfall-Runoff Process. 3.26 rainfall and streamflow data are available. Estimates of the parameter values, for use of the model on ungauged catchments, are given in Table 3.1. Table 3.1 Estimation of Parameter Values for Use of the SFB Model on Ungauged Catchments (Boughton, 1984) (a) (b) (c) Surface Storage Daily Infiltration Baseflow Factor Capacity S - mm Capacity F-mm/day B Catchment Cover S Soil Type F Flow Characteristics B mm mm/day 1. Extremely dense forest, 140 1. Deep permeable soils 7.0 1. Perennial streams, 1.0 cover, deep litter of uniform texture flow > 75% time 2. Predominantly forest 100 2. Sandy loams 5.0 2. Flow 55-75 % time 0.75 small areas of grass-land or cropland 3. Grassland, or cropland 70 3. Clay loams, cracking 3.0 3. Flow 40-55 % time 0.50 with mature crops clay soils 4. Cropland in fallow 50 4. Shallow dense 1.0 4. Flow 20-40 % time 0.25 clay soils 5. Arid lands, less than 10 5. Bare soils subject 0.5 5. Ephemeral streams 0.0 10% cover to surface sealing The model has been extensively used by many researchers with satisfactory results (Boyd et al., 1986, Baki, 1993, Nathan and McMahon, 1990). This model was adopted by the author because it is a simple model using the least number of physically based parameters while maintaining the accuracy of the original 10 parameter Boughton model. 3.2.3.11 AWBM Model The AWBM Model is a water balance model developed by Boughton (1993). It is a saturation overland flow model which uses daily (or hourly) rainfall and average monthly estimates of the catchment's evapotranspiration, to calculate daily values of runoff. This model has several advantages over many rainfall-runoff models. Its data requirements are readily available. The model requires evaluation of only three main parameters and can also be used as a one parameter model on ephemeral streams. When concurrent rainfall and streamflow data are available, the parameters of the model can be directly evaluated without any need for trial and error optimisation. This model calculates runoff from different source areas of surface runoff generation and allows for the different subareas to begin generating runoff at different times. The model is relatively simple in structure but simulates the pattern of runoff very well. As the model can calculate the start and Chapter Three Modelling the Rainfall-Runoff Process. 3.27 patterns of rainfall excess with sufficient accuracy, it can be used for flood forecasting research (see Boughton and Carroll, 1993). Figure 3.9 illustrates the structure of the model. Figure 3.9 AWBM Structure The saturation overland flow model approach (AWBM) provides a realistic estimate of the runoff for the catchments. As indicated by Boughton (1993) the AWBM model is a significant departure from the models which consider average conditions over the entire catchment. The sensitivity of the model's prediction can be reduced by the acceptance of the source area and storage capacity variation in the catchment. This model was selected to evaluate the source areas of streamflow generation in this study. 3.3 SELECTION OF MODELS In most situations there will be several different models that could be applied. For ungauged catchments, models with many parameters are not suitable. Obviously in these catchments, due to the shortage of runoff records and the absence of suitable methods for parameter evaluation, estimation of parameters from a knowledge of the physical characteristics of the catchments is very difficult. Choosing the best model depends on the specific problem. The main criteria that can be used in choosing most appropriate models can be categorised as follows. Chapter Three Modelling the Rainfall-Runoff Process. 3.28 • accuracy of prediction and the detail with which the hydrologic processes are to be modelled • simplicity of the model in application, as it is not practicable to devote a large amount of time to the problem in any one case • data availability • consistency of parameter estimates • sensitivity of results to changes in parameter values • the number of hydrologic processes that are active in generating runoff (groundwater flow or surface runoff, or both) • the variability of the hydrologic processes to be modelled (eg if the variability of streamflow is high a more complex model is needed) • if the time step is short the physical processes must be modelled fully and a more complex model needed Unfortunately, some of these criteria can be mutually exclusive so some compromise may be necessary. Although it is possible to refine a model to represent the physical processes more closely, this will involve greater complexity, an increase in the number of parameters and more stringent requirements on the data eg. reading at more frequent time intervals and on a more dense areal network. Data availability and the need for practical usefulness of results often restrict the choice of a model to one using rainfall- runoff data at time intervals of one day. If all other factors are equal, the simplest model is preferred. The SFB model has several attractive features for the purpose of this study. Its data requirements, daily rainfall and estimate of pan evaporation data, are readily available. It has a simple model structure and requires the evaluation of only three parameters. Despite this simplicity, the SFB model retains the conceptual basis of the original 10 parameter Boughton model (Boughton, 1966) and parameters can be identified with particular properties of the catchment. This model has been extensively used by many researchers with satisfactory results (Boyd et al., 1986, Baki, 1993 , Nathan and McMahon, 1990). The saturation overland flow model approach (AWBM) provides a realistic estimate of the runoff for catchments. As indicated by Boughton (1993) the AWBM model is a significant departure from the models which consider average conditions over the entire catchment. The sensitivity of the model's prediction is reduced by the acceptance of the source area and storage capacity variations in the catchment. The parameters for AWBM are the baseflow index, BFI, and baseflow recession K, the capacity C and area fraction Chapter Three Modelling the Rainfall-Runoff Process. 3.29 A for each of the 3 source areas. Parameters BFI and K can be estimated directly from the recorded streamflow. There are two major differences between these models. Firstly, for AWBM, the spatial variability of the surface store capacity is modelled using 3 stores of varying capacity. Variable source areas are therefore modelled. Secondly, water infiltrates to the lower soil store only when surface runoff occurs, whereas in SFB, infiltration can occur at any time as long as there is sufficient water in the surface store. An objective of this research is investigating the possibility of transferring hydrologic parameters from gauged to ungauged catchments based on the catchment's physical or climatic properties. To achieve this objective, it is necessary to select models with good physical reality parameters and to obtain the optimum parameter values of the selected models. Many studies have utilised only the total streamflow without considering the relative contributions of baseflow and surface runoff in obtaining the parameter values of the models. In these cases it is possible to predict a total streamflow value in agreement with the recorded total streamflow, but with incorrect prediction of each streamflow component, resulting in the estimation of physically unrealistic parameter values. A major aim in the current study is to check the accuracy of models and optimised parameter values based on the prediction of both total streamflow and baseflow. The SFB and AWBM models were adopted by the author for the reasons described. The adopted models represent complex and heterogeneous physical processes, while maintaining minimal data requirements. 3.4 CALIBRATION OF MODELS The different phases of modelling are: formulation, calibration, verification, and application. Each of these phases is important. A poorly formulated model will not be capable of accurate prediction, regardless of the effort expended on calibration. Also, a well-formulated model will not provide good results without adequate calibration. Each process model has a number of parameters whose values need to be determined for the particular catchment being modelled. In the ideal case, with well-formulated models, each parameter can be determined by direct measurements. Chapter Three Modelling the Rainfall-Runoff Process. 3.30 To begin calibration, it is necessary to make initial estimates of parameters. Starting values for initial conditions can be assigned numerical values based on observable phenomena. For example, where there is a period of concurrent rainfall, runoff and evaporation records, the usual method offinding the appropriate parameter value is to operate the model with estimates of those values. After comparing the model and observed runoff and making changes to the parameter values, the best agreement between computed and observed runoff records can be determined. The values that give this agreement are defined as optimum parameter values. The strategy used for parameter optimisation varies widely from model to model; ranging from different automatic search techniques which attempt to find the minimum value of the response surface of the output, to manual methods changing one parameter at a time, and parameter estimation using the water balance of individual storm events. A brief discussion of parameter evaluation is presented in the following subsections. 3.4.1.1 Parameter Optimisation (Automatic search techniques) Some constants in the functions used to represent the physical processes, and the capacities of the stores are the parameters of the model. They can be estimated by assigning numerical values before the model is used to predict the runoff for any particular catchment. The numerical values vary for different catchments because of different slope, vegetation, soil types and soil depths. For models that truly represent the physical process these values would ideally be estimated from measurements of the appropriate physical variables. However, it is necessary to obtain accurate estimates of the model parameters in order to fulfil the potential usefulness of the rainfall-runoff models. Furthermore, the accurate estimation of the runoff model parameters is a prerequisite where the regionalisation approach is to be applied. The values of optimised parameters depend on various factors. They include: catchment characteristics, length of input data, (Sorooshian et al. and Pilgrim, 1983), an estimation of initial parameter values and the optimisation techniques used (Johnston and Pilgrim, 1976), the selected objective function (Kuczera, 1983) and the interpretation by the modeller. Chapter Three Modelling the Rainfall-Runoff Process. 3.31 Before considering the evaluation of model parameters, it is necessary to express a catchment model within a statistical framework. Typically, a catchment model can be formulated as Equation 3.33 (Sorooshian and Dracup, 1980). qt= f(xt ,p )+£r t=l, ,n 3.33 where qt and f(x„P) = observed and simulated response of the catchments respectively (for time interval t) xt = vector of inputs such as rainfall and evaporation P = a parameter vector fct = the difference between the observed and predicted response that may either be due to measurement or model structural error n = the number of observations The parameter vector is determined by minimising et. The 'goodness offit' measure s determines the closeness of the recorded data and that predicted by the model. Although formulation of the catchment model is accompanied by a number of questionable assumptions (such as considering zero systematic errors for the input and additive stochastic error), it offers a powerful tool for studying parameter uncertainty. Optimisation methods have received much attention from research workers in thefield o f applied mathematics over the last few decades. Most of these methods maximise or minimise an objective function to determine optimum parameter values. In most cases the simple least square (OLS) procedure forms the objective function. Regression analysis is a simple method of optimisation, so that its solution is usually reproducible. The search for a set of optimum parameter values may be regarded as a search on the response surface for its lowest point. Techniques have been developed to conduct the search in a systematic way and these are known as optimisation methods (Rajendran et al., 1982). A good reference on this work is Kowalik and Osborne (1968). Optimisation techniques have been developed for finding the values of the model parameters. Objective Function In order to evaluate the agreement between simulated and observed runoff records in a quantitative way, it is necessary to select some feature of the observed runoff record (such as the runoff volume or peak) which is to be reproduced by the model. The numerical measure of the fit between simulated and observed runoff is usually estimated Chapter Three Modelling the Rainfall-Runoff Process. 3.32 by using the ordinary least square method (OLS) which involves solving the minimisation problem. 2 OLSof =£( The measure of 'goodness-of-fit' is known as an objective function and the optimum parameter values of this function are those which give a minimum value of this function. For a given catchment, the value of the objective function is dependent merely on the values assigned to the parameters. If there are n parameters represented by n of the coordinates of an (n+1) dimensional coordinate system, and the other coordinate represents the objective function, then this function forms a surface in (n+1) dimensional space known as a response surface. The lowest point on the surface is where the objective function is a minimum and the corresponding parameter values are regarded as the optimum parameter values (Rajendran et al., 1982). The automatic search techniques may be divided into two categories, direct search methods and descent methods. The general strategy behind these methods is outlined in the following sections. • Direct Search Methods The direct search methods proceed from a starting point (or for the simplex method, a group of points) on the response surface. Then, it evaluates function f (P) at a sequence of discrete points <|) ,ty , ..()) and compares values to reach the optimal point. The steps 1 2 n (iteration) will continue as long as the function continues to decrease. These methods may be further subdivided into methods forfinding the minimum of function of a single variable and method for functions of more than one variable. In general, these methods are used in different conditions for example: • when the computer facilities are not adequate for doing complicated algorithms and approximate solution may be required at an intermediate state df • when the function I (P) is not differentiable or the derivatives . are discontinuous In general, the direct search methods merely require the ability to compare the values of the objective function at different points on the response surface. They give an initial rapid reduction in the objective function but are slow at final convergence. Chapter Three Modelling the Rainfall-Runoff Process, 3.33 Methods for functions of a single variable deal with the most elementary type of optimisation problem. Since single variable optimisation often arises as a subproblem within the iterative procedures of solving multivariable optimisation problems, it is of central importance to both optimisation theory and practice. These methods may be used to find miiiimum values in a particular direction of a multidimensional space. After choosing equal sized steps and moving in that direction, the objective function is evaluated. The steps will continue as long as the function continues to decrease. Some examples of these methods are those of region elimination methods, polynomial approximation methods and methods requiring derivatives. Examples of the various direct search methods for functions of more than one variable are the pattern search method (Hooke and Jeeves, 1961) and various simplex methods (Spendley et al., 1962 and Nelder and Mead, 1965). The various methods differ in the way in which they generate new trial points on the response surface. These methods were developed for solving general optimisation problems and require only objective function values to proceed toward the values. These direct methods are important, because very often in practical engineering problems they present the only reliable information. • Descent Methods The methods discussed in the previous section for solving the general optimisation problem use objective function values only to proceed toward the solution. It is commonly considered that these methods appear to be successful in giving an initially rapid reduction in the objective function, but their final rate of convergence is often disappointing. Since most of the descent methods are devised by utilising additional information about the function to be optimised, it is reasonable to expect more efficient and more rapid ultimate convergence. In the univariate methods (Relaxation), the search directions for the descent iteration are the coordinate vectors, which are searched repeatedly in cyclic order as long as the function continues to decrease and no further improvement can be made. This method has been widely applied in the iterative solution of sets of linear equations with positive definite matrixes (Kowalik and Osborne, 1968). In general, the method is sometimes very slow and in the case of choosing a large step size it would stop at a non-optimum point. Chapter Three Modelling the Rainfall-Runoff Process. 3.34 The next method is the steepest descent method. In this method the search direction at each iteration is the direction of steepest slope from the current point. This direction is defined by the vector of partial derivatives and gives a rapid local reduction in OF as it cuts the adjacent level surfaces at a right angle. This is probably the most efficient direction in searching for an optimum when the optimum response is far from the current surface. However, when the current response surface is in an elongated valley, the convergence becomes slow and sometimes stops at a non-optimal point. The method of steepest descent ultimately approaches a minimum in a two-dimensional subsurface. This is likely to be the reason for the generally poor performance of the methods. This indicates that a good method will have the characteristic of any (t) consecutive search directions (t < n) being linearly independent. The significance of conjugate direction methods is that, if the function is of this form, the minimum will be found in (n) iterations. Conjugate direction provides a method for solving a set of linear equations in a finite number of steps. In this case the (n) searches chosen are linearly independent, and cycling through a small number of directions cannot occur. Because of the finite nature of the iteration, when these models apply to a quadratic form, it can be expected to have a fast rate of ultimate convergence. Where the function is not of the above form, more iterations are required. As the minimum is approached the quadratic approximation improves, so that the ultimate rate of convergence should be good (Johnston and Pilgrim, 1973). 3.4.1.2 Alternatives to Automatic Optimisation In the previous section the different methods of automatic optimisation were discussed. Some alternatives to these automatic optimisation techniques are manual, semi-automatic optimisation techniques, parameter estimation using separated surface runoff and baseflow, and evaluating parameters by applying a water balance to individual storm events. Manual optimisation is a trial and error search. In this approach, instead of finding derivatives analytically, the user who is knowledgeable about the sensitivity of the model parameters makes changes to one or two parameters at a time (somewhat subjectively) on the basis of a comparison of the simulated and observed values of variables. In some instances it may be possible to optimise some of the parameters separately. For example Chapter Three Modelling the Rainfall-Runoff Process. 3.35 parameters can be estimated using separated surface runoff and baseflow, depending on the sensitivity of estimated QB and QS to changes in parameters (Sharifi and Boyd, 1994). More details of these methods will be given in Chapter five. 3.5 TEST OF MODELS The previous section discussed different methods of parameter estimation for rainfall- runoff models. In this section an evaluation of the model's ability to simulate the catchment response to rainfall will be discussed briefly. A rainfall-runoff model is usually tested with independent data which was not used in the calibration of the model parameters. This test is actually a test of the model itself rather than a test of the correctness of parameter values or the so called global optimum set of parameters. A recommended procedure is to split the record,first fit th e parameters on one half and test on the other using parameters obtained fromfirst hal f of the data; then fit on the second half and test on the first. If the model is judged to be performing satisfactorily, the model parameters can be obtained using whole data set (Mein, 1977). An alternative to this method is to optimise the model parameters based on thefirst half of data, on the second half of the data and finally on the whole data set. If the model is formulated with a good physical reality, it should give similar parameters for the same catchment using different data sets (ideally for good quality and error free data). If the model is judged to be performing unsatisfactorily, this indicates a lack of physical reality in the model formulation. In this case, the parameters of the model and the model itself are site specific and data specific and its use should be limited to an empirical tool, and results should be treated cautiously. 3.6 SUMMARY In this chapter methods for estimating the flow characteristics of catchments are highlighted. Different rainfall runoff models are reviewed. Among these models the SFB model and AWBM model are emphasised. The selection and calibration of models are discussed. Chapter Three Modelling the Rainfall-Runoff Process. 3.36 Different approaches for describing the various processes in catchments include statistical, empirical and conceptual. The statistical methods have two subdivisions including probabilistic methods (frequency analysis), and stochastic approaches. Also, with the appearance of high speed digital computers, and as a consequence of numerous advances in scientific hydrology it has been possible to develop models that attempt to simulate on a very comprehensive scale the complex response of a catchment. In addition to these methods, a great number of empirical formula has been developed which relate rainfall to runoff. If sufficient past records are available and if there is no appreciable change in the catchment regime during and after the collection of the records, probabilistic methods can be considered appropriate for runoff determination. In using the stochastic approaches to extend hydrologic forecasts, care should be taken in using the accuracy of different assumptions in long term hydrologic predictions. Only in rare cases are streamflow records adequate for complete analysis and design, particularly in small catchments. In this case regional analyses and empirical equations are often used for determining the peak flow rate as well as the streamflow volume and timing at ungauged catchments. Conceptual models are normally used with rainfall and estimated potential evaporation as inputs to estimate the runoff and actual evaporation as outputs. The Sacramento (Burnash et al., 1973), the Stanford Watershed (Crawford and Linsley, 1966), and the USDA SCS curve number models (USDA, 1972) are widely used rainfall-runoff models. The Boughton model (Boughton, 1966), SFB model (Boughton, 1984), Hydrolog model (Porter and McMahon, 1971), Semi-arid-zone model (Sukvanachikul and Laurenson, 1983), the Australian research basin model (ARBM), and AWBM model are examples of some of the rainfall-runoff process models developed in Australia. Choosing the best model for application depends on the specific problem. The general criteria in choosing the most appropriate model can be; accuracy of prediction and required time interval of prediction, simplicity of the model in application and its data requirements, consistency of parameter estimates, and sensitivity of results to changes in parameter values. Also, the number of hydrologic processes that are active in generating Chapter Three Modelling the Rainfall-Runoff Process. 3,37 runoff and the variability of the hydrologic processes to be modelled are other important factors in the selection of rainfall-runoff models. After choosing a well-formulated model, for getting the best results, it is necessary to calibrate it by using observed rainfall and streamflow data. For example, after comparing the modelled and observed runoff and making changes to the parameter values, the best agreement between computed and observed runoff records can be determined. In the calibration phase, the values that give the best agreement between simulated and observed runoff are defined as the optimum parameter values. Optimisation techniques are used to find the values of the model parameters. There are several types of optimisation techniques including manual, automatic, and semi automatic. Three main categories of automatic optimisation procedures include; stochastic methods, descent methods (methods for functions of one variable or more) and direct search methods (various simplex methods, steepest descent and conjugate direction methods). The optimised parameter values of most models obtained using optimisation techniques involve substantial errors. Sources of the errors can be data errors, inconsistency and variation in the input data, the spatial and temporal distribution of catchment properties including parameters and variables, variation of the surface stores, lack of an accurate relationship to convert potential evaporation to actual evapotranspiration, interdependence of the parameter values, the method of optimisation used and the objective function adopted. These errors can not be detected while using automatic techniques which results in the estimation of misleading parameter values. Research has been carried out on replacing automatic optimisation techniques with manual or semi automatic optimisation of model parameters and producing models whose parameters can be determined from recorded rainfall and streamflow, or from catchment characteristics. The combination of manual and automatic procedures often performs better than either of these used separately. If the manual approach alone is adopted, it will be tedious. Although automatic methods are relatively simple to use, they rely heavily on one pre- specified objective function. If poor data is used or poor initial parameter values are selected, automatic methods may fail to search for parameters that are physically Chapter Three Modelling the Rainfall-Runoff Process. 3.38 meaningful. By taking advantage of the merits of both methods together, the problems of each method applied alone can be avoided. As a result of this survey, it was decided to select the SFB and the AWBM as main models and evaluate model parameters by combining SAPS optimisation technique With manual procedures. CHAPTER FOUR DESCRIPTION OF CATCHMENTS AND PRELIMINARY ANALYSIS OF CLIMATIC INPUTS CHAPTER FOUR DESCRIPTION OF CATCHMENTS AND PRELIMINARY ANALYSIS OF CLIMATIC INPUTS 4.1 INTRODUCTION In this chapter some of the physical characteristics of the catchments and a preliminary analysis of the available data are presented. The 1/25000 to 1/100000 scale maps were used to check the boundaries, identify vegetation and soil, and to compute land surface and stream longitudinal slopes. The accuracy of the available data is checked by analysis and comparison of rainfall, streamflow and pan evaporation data at different sites. 4.2 STUDY AREA For the purpose of this study, eight catchments were selected. The catchments are located in the Mawarra Region and the northern part of New South Wales. The first catchment under investigation was located in the northern part of New South Wales. This catchment was used as a basic template for drawing up the strategy of modelling in this study. In the next phase seven other catchments were selected from the Illawarra Region. The locations of the selected catchments are shown in Figure 4.1. An overall description of the catchments and some summary information about the climatic inputs is given in the following section. Two of the main drainage basins in the Illawarra Region are the Shoalhaven River Basin and the Wollongong Coast Basin. Six catchments were selected from the Shoalhaven River Basin. They were Kangaroo Valley (at Hampden Bridge), Shoalhaven River (at Kadoona), Bungonia Creek (at Bungonia), Endrick River (at Nowra Road), Corang River (at Hockeys) and Mongarlowe River (at Mongarlowe). Macquarie Rivulet (at Albion Park) was selected from the Wollongong Coast Basin and Sandy Creek (at Newholme) was selected from northern New South Wales. Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs, 4 2 Wollongong Lake Illawarra Moss Vale Roberson •! Macquari&^Rivulet Kangaroo River Kiama Kangaroo Valley Shoalhaven River • Tolwong Nowra Endrick River Lake George Jervis Bay Corang River Mongarlowe Shoalhaven River River Mongarlowe Monga H , Oranmeir Batemans By Kadoona Scale 1:1000 000 8 Moruya Figure 4.1 Location of Catchments in the Region Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs, 4.3 4.3 GENERAL CHARACTERISTICS OF CATCHMENTS Basic information on all the catchments is given in Table 4.1. Some of these characteristics were extracted from Baki (1993) and Morton and Boyd (1994), while some were estimated from topographic maps of the area. Table 4.1 Summary of General Characteristics No. Catchments Nat. Area Main Stream Stream Slope Index (km2) Length (km) (m/km) 1 Sandy Creek (Newholme) 206026 8.0 4.3 26.7 2 Kangaroo Valley 215220 330.0 34.5 13.5 3 Macquarie Rivulet (Albion Park) 214003 34.6 12.7 38.4 4 Bungonia Creek (Bungonia) 215014 164.0 24.5 7.3 5 Mongarlowe River (Mongarlowe) 215006 130.0 37.2 3.0 6 Endrick River (Nowra Road) 215009 210.0 27.3 4.9 7 Corang River (Hockeys) 215004 166.0 30.1 4.0 8 Shoalhaven River (Kadoona) 215008 280.0 31.2 13.8 4.3.1 Sandy Creek Catchment The Sandy Creek Catchment is located in the New England region of New South Wales and has an area of 8 km2. It is a small experimental catchment located in the upper MacLeay River Basin and managed by the University of New England. This catchment is located at latitude of 30° 25' S, and longitude 151° 39' E about 6 km north of Armidale. Synchronised measurements of rainfall and runoff data by a pluviometer located at the catchment outlet are available from 1976-1987. Pan evaporation was recorded at the Glen Innes Agricultural Research Station (St. No. 56013). The catchment boundary and drainage network are shown in Figure 4.2. The profile of Sandy Creek is shown in Figure 4.3. As previously stated, this was thefirst catchment chosen for this study, and was used for preliminary testing of the models. Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs. 4.4 Pluviometer • 0 1 2 km Figure 4.2 Map of Sandy Creek 1140 1120 ~ 1100 •£" 1080 o ^ 1060 S 1040 1020 1000 Distance Upstream (Km) Figure 4.3 Profile of Sandy Creek 4.3.2 Kangaroo Valley Catchment Kangaroo Valley catchment with an area of 330 km2, lies between the latitude of 34° 35' S and 34° 45' S, and between the longitude of 150° 30' E and 150° 45' E. It is located in the south-eastern region of New South Wales, about 150 kilometres south of Sydney and 50 kilometres from the coast. It is bounded by the Illawarra Range in the east and south, Robertson escarpment in the north and Fitzroy Falls Reservoir in the west. The outlet of the catchment is at Hampden Bridge in the Kangaroo Valley township. Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs, 4.5 The main stream in this catchment is the Kangaroo River which starts in the Illawarra Range in the north eastern region of the catchment and flows to the catchment outlet in the south. The main tributaries and the boundary of this catchment are shown in Figure 4.4. In the upper reaches, the stream is narrow and rocky but increases in size in the lower reaches. The profile of Kangaroo River is shown in Figure 4.5. Robertson Kangaroo N River \ \ J Upper . J j Kangaroo / \ \ Barren Grounds / \ River j \ 1 ®\ Budderoo ® J® ^~— Hampden Bridge f Brogers Creek tS 1 © Kangaroo ( Brogers K Valley J Creek i § Rain Gauge Scale 1:100 000 Figure 4.4 Map of Kangaroo Valley Catchment 800 Plateau on 700 215220 escarpment ^ 600 r 1 500 I 400 Carrington 1 300 t Falls S 200 100 0 10 20 30 Distance Upstream (Km) Figure 4.5 Profile of Kangaroo River Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs, 4.6 4.3.3 Macquarie Rivulet Catchment The Macquarie Rivulet catchment lies to the north-east of Kangaroo Valley catchment. It is bounded by the Illawarra Range in the north, Robertson escarpment in the west and Budderro escarpment in the south. The outlet of the catchment is at Albion Park. The main stream in this catchment is Macquarie Rivulet which starts in the Illawarra Range in the north western region of the catchment and flows to the catchment outlet in the east. It finally drains into Lake Illawarra. This catchment has an area of 34.6 km2. The Macquarie Rivulet catchment boundary and drainage network are shown in Figures 4.6 and 4.7. Robertson Scale: 1:25 000 Figure 4.6 Map of Macquarie Rivulet 700 s o te 5 10 Distance Upstream (Km) Figure 4.7 Profile of Macquarie Rivulet 4.3.4 Bungonia Creek Catchment Bungonia Creek catchment lies in the north-western part of the Shoalhaven River Basin and has an area of 164 km2. The main stream is Bungonia Creek, which starts on the Great Dividing Range in the west and flows in a north-easterly direction to the outlet at Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs. 4.7 Bungonia. The boundary, drainage network and profile of Bungonia Creek are shown in Figures 4.8 and 4.9. Bungonia Bungonia / I Creek I ^\_^ Inverary Scale: 1:100 000 Figure 4.8 Map of Bungonia Creek °nn 750 [ 215014 1 700 | 650 jg 600 550 500 - , 5 10 15 20 25 Distance Upstream (Km) Figure 4.9 Profile of Bungonia Creek 4.3.5 Mongarlowe River Catchment at Mongarlowe Mongarlowe River catchment at Mongarlowe is located to the south of the Corang River catchment and has an area of 130 km2. The main stream is the Mongarlowe River, which starts in the south and flows through a sinuous course to the outlet at Mongarlowe in the north of the catchment. Figures 4.10 and 4.11 show the boundary, drainage network and the profile of the Mongarlowe River. Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs. 4.8 Mongarlowe Scale: 1:100 000 Figure 4.10 Map of Mongarlowe River 900 850 | 800 | 750 j£ 700 « 650 600 10 20 30 40 Distance Upstream (Km) Figure 4.11 Profile of Mongarlowe River 4.3.6 Endrick River Catchment Endrick River catchment at Nowra Road lies in the south-eastern part of the Shoalhaven River Basin and has an area of 210 km2. The main stream is the Endrick River, which starts in the south, flows through a sinuous course on a plateau followed by a steep Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs. 4.9 gorge, to the outlet at Nowra Road in the north-west. The boundary map and profile of Endrick River are shown in Figures 4.12 and 4.13. Nowra Road Scale: 1:100 000 Figure 4.12 Map of Endrick River 5 10 15 20 25 Distance Upstream (Km) Figure 4.13 Profile of Endrick River 4.3.7 Corang River Catchment Corang River catchment at Hockeys lies adjacent to the Endrick River catchment. The main stream is the Corang River, which starts in the south-eastern part of the catchment and flows at an average mainstream slope of 4 m/km to the outlet at Hockeys in the north-west. This catchment has an area of 166 km2. The catchment boundary, drainage network and profile of Corang River are shown in Figures 4.14 and 4.15. Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs, 4 w Hockeys Scale: 1:100 000 Figure 4.14 Map of Corang River 800 750 E 700 | 650 « 600 550 500 0 5 10 15 20 25 30 Distance Upstream (Km) Figure 4.15 Profile of Corang River 4.3.8 Shoalhaven River Catchment The Shoalhaven River catchment at Kadoona lies in the south-western region of the Shoalhaven River Basin and has an area of 280 km2. It is bounded by the Great Dividing Range to the west and south. The main stream in this catchment is the Shoalhaven River, which starts in the south of the catchment in the Great Dividing Range and flows to the catchment outlet at Kadoona in the north. It is also drained by Jinden Creek in the western part of the catchment before it joins the Shoalhaven River near Yarra Glen. Figure 4.16 illustrates the catchment boundary and its tributaries. The profile of Shoalhaven River is shown in Figure 4.17. Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs. 4. J J Scale: 1:100 000 Figure 4.16 Profile of Shoalhaven River 1200 1100 J" 1000 « 900 63 800 700 0 5 10 15 20 25 30 35 Distance Upstream (Km) Figure 4.17 Profile of Shoalhaven River 4.4 GEOLOGY The Geological Survey of NSW (1986) illustrates the geological map of the region. The Sandy Creek catchment is comprised of around 95% Duval Adamellite (type of granite) parent material. A small area in the southwest comer of the catchment comprises of a tertiary basalt parent material. The Illawarra Plateau which is part of the study area occupies a vast sedimentary trough dated back to the Permian and Triassic era (Paix, Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs. 4.12 1968). The outcrops of this area are mainly siltstone and sandstone, with some isolated basalts on the high areas and some granite in the upper reaches of the Shoalhaven River (Herbert and Helby, 1980). The Wandrawandian Siltstones and the Snapper Point Formation are probably the most important strata involved in the hydrological process near Kangaroo River. They make up the upper soil layers which are actively involved in the hydrological cycle. Sandstones and siltstones make up most of the outcrops (about 90%) in the study areas. Sandstones have good infiltration properties due to existence of large pores and fissures, since hydraulic conductivity is a function of the size and continuity of pores. But, siltstones have lower values of porosity and sorption since it has fewer pores and fissures, which will give low infiltration. A medium range of infiltration values can be expected from these catchments through the combination of the two sedimentary rocks. 4.5 SOIL DESCRIPTION The soil types of the catchments are typically Podsolics and Krasnozems which have a slow infiltration rate. Charman (1978) described Podsolics as duplex soils which have a weak structure with a high sand content. This type of soil can have very high initial infiltration rates. The rate may be reduced due to surface sealing resulting in high values of surface runoff. The effective infiltration rate for this type of soil is generally low. Krasnozems are derived from Basalt and are generally stable with a consistently high infiltration rate. The main types of soils for each catchment are presented in Table 4.2. In the Sandy Creek catchment, the soils are derived from adamellite include grey brown earths on the rocky upper slopes which graduate to yellow earths and yellow podsolics on the mid slopes which graduate to lateritic and gleyed podsolics and yellow solodic soils on the lower slopes and drainage lines. Chocolate soils occur on the basalt which graduate to grey clays lower down on basalt colluvium. On the plateau of the Illawarra region, Hawkesbury Sandstone covers most of the areas. There are several types of soils available (Young, 1982b). Skeletal Soils contain small rock fragments, sand and some organic materials. They have a very high weathering resistance and they occur throughout the escarpment. On the cliffs, they appear as vertical and bare cliff faces. Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs. 4 73 Yellow Earths have a high clay content and break down more easily. Both types of soils, Podsolics and Krasnozems, support sedges, hedges and shrub vegetation, depending on the amount of water available. Therefore, on the plateau there are sedgelands and hedgelands, located near Budderoo and Barren Grounds. Occasionally, apart from those two types of soil, Podzols and Ferricrete are also found. According to the Department of Agriculture (1986), the land on the escarpment is not generally suitable for agriculture. The Soil Conservation Services of NSW (1984) shows that the lands are only suitable for grazing, and some limited cultivation in some areas. On the slopes of the escarpment and deep gorges, bouldery talus are formed from movement of debris down the slopes (Young, 1982b). The soils in the talus are mainly Podsolics and support vegetation growth. This can be observed with the existence of vegetation on the lower slope of the escarpment. Red, Yellow and Grey Podsolic Soils are the main types of soils in the shales and siltstones region, such as Berry Siltstone and Yarrunga Coal Measures (Young, 1982b). These types of soils can be found on the foot of the escarpment, in the valleys and gullies. Thick vegetation grows in this region, as observed on the foot of the escarpment. The depths of top-soil for all catchments are between 35 mm to 150 mm. The depths of top-soil are useful in the estimation of the soil moisture storage in the catchments. The estimated values of available water contents (AWC) were 0.158 mm/mm and 0.183 mm/mm for Podsolics and Krasnozems, respectively (Baki, 1993). Based on these figures, the estimated value of upper soil moisture storages are shown in Table 4.2. Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs. 4.14 Table 4.2 Soil Types, Soil Stores and Infiltration Characteristics Catchments Main Types of Soils Infiltration Soil Depth Soil Stores (mm) (mm) Sandy Creek Podsolics Low 150 24 Kangaroo Valley Podsolics Low 150 24 Macquarie Rivulet Krasnozems High 150 28 Shoalhaven River Podsolics & Krasnozems Moderate 50-75 8-12 Bungonia Creek Podsolics & Skeletal Low 35-75 6-12 Endrick River Podsolics Low 150 24 Corang River Podsolics Low 150 24 Mongarlowe River Podsolics Low 75 12 Table 4.2 shows that most of the catchments have low infiltration values. The only exceptions are the Macquarie Rivulet catchment which has high infiltration values, and the Shoalhaven River catchment which has moderate infiltration values. The estimated values of upper soil stores ranged from 6 mm (Bungonia Creek) to 28 mm (Macquarie Rivulet). 4.6 VEGETATION Vegetation affects the infiltration properties of the catchment. Rain water has to fall through vegetation before it reaches the soil. Vegetation also maintains the stability of the soil infiltration properties, since exposed soil can change its properties due to its delicate structure. Apart from affecting the soil infiltration properties, vegetation also affects the soil storage capacity. Vegetation provides a canopy to hold moisture. Vegetation is also involved in the evapotranspiration process as the transpiration allows moisture to travel through the roots, the stems and out to the atmosphere through the leaves. In the Sandy Creek catchment, native pastures are dominated by perennial grasses including Aristida, Eragrostis, Poa and Sporobalus spp. Tall woodlands and open forests are dominated by stringybarks (Eucalyptus laevopinea and E. caliginosa) occur on the steeper and upper slopes. Derived woodland and scattered trees occur in other locations (Yellow box, Blakelys Redgum and Rough-barked Apple on midslopes and Mountain Gum and New England Peppermint on lower slopes and along drainage lines). Hedgelands and sedgelands cover most of the plateau areas on top of the escarpment in the Illawarra region. The main vegetation types in these areas are Dry Sclerophyll Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs. 4.75 Woodlands, which consist of medium to tall, medium density, tablelands and plateau woodlands (Department of Environment and Planning, 1981). On the cliff faces, there is no vegetation. The cliffs are mainly bare showing sandstone features. These areas fall under category VIII of Land Capability (Soil Conservation Services of NSW, 1984), which is unsuitable for agricultural or pastoral use. On the slopes, however, thick vegetation grows. The main types of vegetation here are the Wet Sclerophyll Forest with Rainforest Pockets, which consist of tall, high density escarpment forest. The Illawarra Regional Environmental Plan No.l (1979), classified these areas as Lands Supporting Rainforest Vegetation Species. In the valleys, most of the original vegetation has been cleared to make way for dairy farms. Therefore, farmlands and grasslands provide the types of vegetation mainly found in the valleys. In the gullies and near the tributaries, there are some Highland Valley Forests, which consist of medium height, medium density, riverine-associated forests (Department of Environment and Planning, 1981). The lands in the valleys are mainly classified under Category IV, which are suitable for grazing with occasional cultivation, (Soil Conservation Services of NSW, 1984). Lands that are classified under Category II, which are suitable for regular cultivation, can be found in the valleys where the alluvial soils have been formed. Table 4.3 shows the main vegetation of all catchments. Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs. 4.16 Table 4.3: Main TypeS of Vegetation in all Catchments CATCHMENTS MAIN VEGETATION Sandy Creek About 39% of the area covered by forests, mainly eucalyptus, with low trees and native pasture covering the remainders. Kangaroo Valley About 50% of the area covered by scrub , pine (mainly eucalyptus, with low trees), 32% medium to scatter timber and 16% covered by grassland and shrubs, while wet swamp covers the 2% remainder of the catchment area. Macquarie Rivulet About 50% of the area covered by forests, mainly eucalyptus, with low trees and Bungonia Creek shrubs covering the remainders. Mongarlowe River Endrick River About 79% of the area covered by forests, mainly eucalyptus, with low trees and shrubs covering the remainders. Corang River About 84% of the area covered by forest, mainly eucalyptus, with grass covering the remainders. Shoalhaven River About 50% of the area covered by forests, mainly eucalyptus, with low trees and shrubs covering the remainders Table 4.3 shows that all catchments have more than one-half of the area covered by eucalyptus forests, while the other parts are covered by low trees and shrubs. The exceptions are Sandy Creek and Corang River catchments, where about 39% and 20% of the areas are covered by eucalyptus and grass covers the rest of the areas. From the aerial photograph of the region (Costin et al., 1984), the vegetal cover for all catchments is high (about 90%). This will provide a high canopy and interception storage values. High vegetal cover can provide stability to the values of infiltration since surface sealing and surface erosion can be minimised by the roots holding the soil together. Apart from the roots, the impact of raindrops on the soil surface can also be minimised by the canopy provided by the leaves. 4.7 PRELIMINARY ANALYSES OF DATA 4.7.1 Introduction This study requires daily streamflow, rainfall, and pan evaporation data. Streamflow is measured at the catchment outlet by using a water level recorder installed on the river or stream outlet. The recorded water levels are transformed to flow rate using the rating curve for the control section. Streamflow volumes are divided by catchment area to give the average runoff depth on each day. Rainfall is measured by means of pluviographs located in or around the catchment. The daily rainfall data is selected based on the availability of the daily streamflow data. The time step and duration of the available Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs. 4.17 rainfall data are the same as that of the water level records. The pan evaporation data is recorded as depth in millimetres (mm) per day, using a standard Type A pan. It is very important to examine the data collection procedure to see if the data are likely to be reliable. If not, there is little point in spending time on the data analysis. Perhaps the commonest failure in the data collection is a lack of uniformity and this can have an effect on all studies. Thefirst ste p in the preliminary analysis of the data was to assess the amount of variability and the length of records. The next step was to tabulate the data in a suitable format. The quality of the data was checked next and the numbers of missing observations were assessed. The summary statistics including the mean, standard deviation and correlation between each pair of data were calculated for the data as a whole. By comparing the summary of the data sets, wild observations or outliers which do not appear to be consistent with the rest of the data were detected. Summary statistics, box plots, scatter and linear plots for rainfall and runoff helped to spot obvious relationships between two variables, detect outliers and also any clusters of observations. 4.7.2 Data Collection The rainfall data were collected by the Commonwealth of Australia, Bureau of Meteorology (CBM) and the Water Board. CBM recorded most of the daily rainfall data, except for Budderoo rainfall station, which is controlled by the Water Board. Details of the stations are given in Baki (1993). The streamgauging stations are mostly controlled by the NSW Water Resources Commission (WRC), except for the Kangaroo Valley station at Hampden Bridge, which is controlled by the Water Board. The streamflow data was given in Megalitres per day (ML/day). Pan evaporation data was recorded as depth in millimetres (mm) per day, using a standard Type A Pan. The Royal Australian Navy has recorded daily pan evaporation values, which are kept by the Bureau of Meteorology. The pan evaporation data for this study was collected from the Bureau of Meteorology records. Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs. 4.18 4.7.3 Data Availability The period of record in which data was available varied from station to station. As stated before, the daily rainfall data was selected based on the availability of the daily streamflow data. The period of available data is presented in Table 4.4. Table 4.4 Period of Available Data No. Catchments Nat. Period of records Years Index 1 Sandy Creek (Newholme) 206026 1976-1988 12 2 Kangaroo Valley 215220 1970-1990 21 3 Macquarie Rivulet (Albion Park) 214003 1950-1989 40 4 Bungonia Creek (Bungonia) 215014 1981-1986 6 5 Mongarlowe River (Mongarlowe) 215006 1950-1970 23 6 Endrick River (Nowra Road) 215009 1970-1979 10 7 Corang River (Hockeys) 215004 1979-1986 8 8 Shoalhaven River (Kadoona) 215008 1972-1986 15 4.7.4 Filling the Missing Records Many climatological stations have short breaks in their records due to the absence of the observer or because of instrument failures. It is often necessary tofill in incomplete records by estimating values that are missing at one or more stations. In starting a project, the first step is usually the extension or adjustment of short records using the longer record of a selected base station in the region. The next is the completion of the gaps that are missing as a result of an interruption in a record. This section will only deal with the latter. Data for missing periods at each station can be estimated using different methods. Some of the methods are: a) Data for missing periods at each station can be estimated from observed data at several stations nearby and evenly spaced around the station with the missing record. If the average annual precipitation or streamflow per square kilometer at each of these stations is about equal (within 10 percent of that for the station with the missing record), a simple arithmetic average of the record at the three selected stations, provides the estimated value. If there is a significant difference in the average annual record of the stations, then the normal ratio method is used. In this method, the amounts at the index stations are weighted by the ratios of the normal annual data values. For example for 3 stations: Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs. 4.19 1 Nx Nx Nx where Px = data at station with the missing record Pi , P2, ,and P3 = data at Index stations during period of study Ni , N2, ,and N3 = the annual data of the different stations When there is a very high correlation between recorded data for the considered stations, the above procedure is satisfactory. b) The next method uses the mass curves of recorded data. Interpolation between the mass curves of recorded data will give a reasonable estimate of the missing data during short periods. A mass curve is equivalent to a recording-raingauge (pluviograph) record. Basically, curves arefirst plotted for continuous recording stations within or near the study area, and then by using the curve as a guide, a similar curve is plotted for non-recording or discontinuous stations in the same area. Stations should be grouped based on possible similarities of topographic influences and meteorological conditions. The mass curve for non-recording stations should be completed in accordance with the grouping by interpolating the curves between established points. Finally, incremental values can be extracted from the curves. c) Another method of estimating missing data is by means of regional analysis. This method of estimation determines, by multiple-linear regression, the correlation between the station with the missing data and all other nearby stations for the period when they have concurrent records. In this method different regional variables such as latitude, longitude and elevation can be used to obtain a regional equation. This is one of the most widely used techniques. Caution should be exercised in using these techniques because some regressions can give a goodfit fo r a given set of data but may have poor predictive performance for other data sets. Another possible danger in using this method is the temptation to include a large number of regression variables that appear to improve the fit for a given set of data but actually give a spuriousfit. I t is suggested that the number of variables should not exceed one quarter of the number of observations and should preferably not exceed four or five. Moreover the length of the data input should not be less than 30 observations (Chatfield, 1986). Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs, 4.20 d) Another method for filling missing periods is using correlation with recorded data at one other location in the region. The missing records can be estimated by establishing a correlation between the data for the station with the short break in its record, and concurrent data from a nearby station located in a hydrologically homogeneous region. Data for missing periods at each pertinent location were estimated by adopting the last method. It is more satisfactory to use a number of stations in the region that can contribute independent information on the missing data. However using all available stations was not practical and usually only one other location was used. From this basis the linear regression of daily stream flow, rainfall or pan evaporation was calculated. Missing streamflow data was estimated using streamflow and rainfall data recorded at the same station and the regression between the two sets of concurrent records. In this approach, a regression equation was fitted to a period of record (not less than 30 days) for which concurrent streamflow and rainfall records were available for both the variables. A simple regression equation was used in this analysis. 4 2 Xt = a*Yt + b - where Xt = the substituted daily data for station X (station considered) for day t Yt = the recorded daily data for station Y (adjacent station) for day t a and b = regression coefficients by the method of least squares Although the catchments used were often within a few kilometers of each other the correlations were sometimes poor and not directly useable. When annual data were correlated, better results were obtained, but the annual and monthly data for adjacent catchments sometimes occurred on different dates. There would therefore appear to be no physical justification for accepting such a correlation and they were not used. The poor correlation obtained between data on the same date was more acute on smaller than on larger catchments. Doubts were expressed on the merit of estimating values for short records where the quality of the data was suspect. Where some data were missed at the beginning or the end of the record, it was decided to shorten the length of the record. For all catchments except Sandy Creek, the catchment's average rainfall was calculated using the Theissen polygon method which was used by Baki in 1993. As stated by Baki, Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs. 4.21 when rainfall data was unavailable for a station, the polygons were redrawn using the remaining stations after checking for consistency of results between the various polygons. The method enabled the calculation of average daily rainfall for the catchments using available data. Since there were several stations used for the analysis, the voids did not create biased data. Daily evaporation data was taken from adjacent stations, and further checks were also made by comparing time series plots of pan evaporation records in the region. For the missing pan evaporation records, data was also replaced using the regression between adjacent stations. When adjacent stations did not have recorded data, the missing data was replaced by the average values for that particular month. This substitution was made based on the assumption that evaporation is dependent on the average daily temperatures, which remain fairly consistent within a season. The temperatures vary on a seasonal pattern. Therefore, the average values of daily pan evaporation for a particular month can be assumed to be the closest approximation of the daily pan evaporation for that month. Another point that requires mention is the method used to apportion accumulated measurements in the recorded data. Some of the recorded data contains two or three days periods where only the accumulated data is available. The procedure adopted was to apportion the total data according to the observed distribution of records at a nearby station. 4.7.5 Data Characteristics of the Region In hydrological analysis, it is vital to determine whether the data are homogeneous over time. This indicates that data samples from any period in the record belong to the same population or statistical distribution. Some of the causes for non-homogeneity are as follows: • relocating the gauging station or rain gauges • modifying the land use of the catchment • withdrawing water from the upstream part of the catchment or returning flow from irrigation areas • natural events such as earthquakes, hurricanes and floods Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs. 4.22 One of the methods for detecting inhomogeneities is the double mass curve analysis which will be discussed in this section. Another term, inconsistency in recorded data, refers to differences between the observed values and the true values due to systematic errors in the measurement process. The next term, non-stationarity implies variant probabilistic behaviour of a time series during its record. In addition to being homogeneous, consistent and stationary, data records should also be representative. This implies that the recorded data should not be drawn exclusively from unusually wet or dry periods. 4.7.5.1 Double Mass Curve Analysis This is a technique of adjusting meteorological records to take account of nonrepresentative factors such as changes in gauge location, instrumental exposure, or observational changes. The method is used as a check on the consistency of the record at a particular station by comparing its accumulated values with corresponding totals for a group of nearby stations. Obviously, the more homogeneous the records of surrounding stations, the more accurate the results will be. It is necessary to check the consistency of the record for each of the base stations and to delete the records showing inconsistency. The double mass curve is simply a comparison of accumulated values of two time series. Basically, the technique involves comparing accumulated annual or seasonal records at a station with the concurrent values from a graph of nearby stations. A change in slope of the curve indicates a change in the streamflow or rainfall regime of one of the stations. Stations should be grouped based on possible similarities of topographic influences and meteorological conditions. Searcy and Hardison (1960) suggest that any change in the slope of the curve that persists for less than 5 years is most probably due to chance and thus should be ignored. The double mass rainfall-runoff curve is another method for detecting changes in the time series of the streamflow over a period of time. Because the relationship between rainfall and runoff is nonlinear, the rainfall data should first be transformed into computed runoff via some rainfall-runoff equations. A double-mass curve is then constructed by plotting cumulative values of measured streamflow against computed runoff. Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs, 4.23 The consistency of the rainfall data compared to the spatial average over time were checked by Baki (1993) using a double mass curve analysis and were found to be acceptable. Details of double mass curve analysis of the data can be found in Baki (1993). 4.7.5.2 Time Series Plot and Statistical Analysis of Data Apart from the aforementioned analyses, the following tests were carried out to check the data. • Time Series Plot A time series is a collection of observations made sequentially in time. When the observations are dependent, several possible objectives can be achieved from the analysis. These objectives may be classified as description, explanation, prediction and control (Chatfield, 1989). Thefirst an d most important step in any time series analysis is to plot the observations against time. These graphs should show important features of the series, such as trend, seasonality, outliers and discontinuities. Box plots are also useful for comparing variability in different months of the year. The plot is also important to describe the data over the period of study. The aim of observing these variations is to detect if there are any anomalies in the data. These will also show if there are any seasonal variations in station data. Plots of the average monthly and annual values of the data are presented in Figure 4.18 and 4.19. The recorded streamflow data in all catchments were less than recorded rainfall data. However some anomalies were observed in Endrick River and Macquarie Rivulet catchments in which there were some years where runoff exceeded recorded rainfall. A summary of these analyses is given in Appendix B Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs. 4.24 -X •-- P --A-- EP •Q ---X --P --A-- EP 100 200 250 250 206026 A 215220 200 80 150 _ ^X } 200 E X \ A - A' S 60 -g- 150 * - -x 150 E, 100 ft. a. 40 100 100 x \ .* © =3 a. '-*•* A-* ,X.X'*1 50 50 ^^ 50 ' ••••-» .-»1 0 Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov Months Months • Q --.*--- P --A-- EP -Q -X- EP 250 A: 200 Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov Months Months -Q -X - P --A-- EP •Q -X - P --A-- EP Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov Months Months -Q ---X -- P --/ EP •Q -X •-- P --A-- EP 100 A 215004 200 215008 ^- . 200 X £ 150 x A 150 ,-. E A E * 100 \ •' £ 1 100 =8 w ° 50 50 n 0 Jan Mar May Jul Sep Nov Jan Mar May Jul Sep Nov Months Months Figure 4.18 Plots of Average Monthly Runoff(Q), Rainfall (P), and Pan Evaporation (EP) Records Used in the Study Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs, 4.25 Q- - 900 800 ; E 700 • F 600 • Is o 500 • 3 OS 400 - T) • B 300 ; 3a 200 ; ^= a 100 : 0 1974 1976 1978 1980 1982 1984 1986 1988 1970 1975 1980 1985 1990 Time (Year) Time (Year) 3500 1200 1000 • = £ E c S B OS 3 •a a s •a "2 1 1950 1960 1970 1980 1990 1981 1982 1983 1984 1985 1986 Time (Year) Time (Year) 1800 1600 \ i ; ; 215006 ? 1400 E 1200 • ' ' / * 1000 1 : ' ' ' ' i B 800 3 B " i t / * ' v 3 600 os •o •a 1C 400 E a 200 a "3 ^VY/^Vv^ I •a 1950 1955 1960 1965 1970 1975 1974 1976 1980 Time (Year) Time (Year) 1200 1600 E E E E fc fe o e B B 3 OS B •a a B a 1 OS •a 1979 1981 1983 1985 1972 1974 1976 1978 1980 1982 1984 1986 Time (Year) Time (Year) Figure 4.19 Plots of Annual Runoff (Q) and Rainfall (P) Records Used in the Study Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs. 4.26 • Statistical Analysis Statistical methods can be considered to be a useful tool in displaying the data so that variations can be quantified properly. Summary statistics is a term used to describe certain characteristics of a data set which give more exact measures. From this, a more precise understanding of the data can be gained than is obtained by all the tables and graphs. The statistical analyses of daily values involved: • computing the arithmetic mean, median and mode of the each measured data. • computing the variability of the data (ie., standard deviation, variance and coefficient of variation) • computing a third moment -the coefficient of skewness • computing correlation between each pair of variables The measurement of the variability of the data in the data set gives additional information about the reliability of the central tendency. Clearly the more variable the data is, the less the central location is representative of the whole data set. The first tool used for measuring the variability of the data is the "range" of the data set. The problem with using range as the measure of variability is that it only considers the highest and lowest values of the distribution and fails to take account of any other observation in the data set. As a result, it ignores the nature of variations among all other observations, and is heavily influenced by extreme values. The frequency distribution of the data can be obtained simply. According to rule of thumb, no matter what the shape of distribution is, at least 75% of the values will fall within plus and minus 2 standard deviations from the mean of distribution, and at least 89 percent of the values will he within plus and minus 3 standard deviation. However, if the curve is symmetrical and bell-shaped, an even greater percentage of the items will fall within these ranges. The next parameters that measure the variability of the data are standard deviation and variance (square of standard deviation). It cannot, however, be used as a basis for comparing two different data sets. This is due to different values of the mean for the different sets of data. For some purposes it is much more useful to measure the spread in relative terms as a coefficient of variation (C.V.) by dividing the standard deviation by the sample mean. It is therefore possible to compare the variability of two or more sets of data using their Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs. 4.27 coefficient of variation. Another advantage of the coefficient of variation is that it is independent of the units in which the variant is measured. For the study area, the coefficient of variation of monthly mnoff is between 1.12 to 1.95 and that of monthly rainfall is between 0.74 to 1.03. The values of median monthly runoff for the study area varies from 1 to 30 mm and between 40 to 140 mm for rainfall data. Summary statistics for all the catchments data are shown in Tables 4.5 to 4.12. Table 4.13 shows the results of a linear regression between monthly and annual surface runoff and rainfall in the studied catchments. Table 4.5 Summary Statistics of Monthly Data in Sandy Creek Catchment (Station No. 206026) 206026 o QB QS P EP Mean (mm) 6.0 1.2 4.8 42.6 114.3 Median (mm) 1.4 0.2 1.0 32.7 115.0 Mode (mm) 0.0 0.0 0.0 0.0 135.2 Standard Deviation 11.6 1.8 10.3 38.0 47.4 Skewness 3.5 2.0 3.8 1.6 0.2 Minimum (mm) 0.0 0.0 0.0 0.0 39.0 Maximum (mm) 77.2 7.9 73.0 212.8 220.8 C.V. 2.0 1.8 2.2 0.9 0.4 No. of Months 151 151 151 151 151 Table 4.6 Summary Statistics of Monthly Data in Kangaroo Valley Catchments (Station No. 215220) 215220 Q QB QS P EP Mean (mm) 75.3 23.6 51.7 138.9 145.8 Median (mm) 30.8 18.4 8.3 93.8 144.8 Mode (mm) 10.0 11.0 0.0 101.9 183.4 Standard Deviation 7.7 5.9 10.3 2.1 0.0 Skewness 2.6 2.0 3.0 1.6 0.5 Minimum (mm) 0.4 0.4 0.0 0.0 46.4 Maximum (mm) 623.4 138.5 604.6 662.3 302.2 C.V. 1.4 0.9 1.9 1.0 0.3 No. of Months 252 252 252 252 252 Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs. 4.2R Table 4.7 Summary Statistics of Monthly Data in Macquarie Rivulet Catchment (Station No. 214003) Station No. 214003 Q QB QS P EP Mean (mm) 47.5 20.8 26.7 141.6 148.0 Median (mm) 22.5 15.6 1.9 95.8 145.9 Mode (mm) 0.0 0.0 0.0 82.0 173.6 Standard Deviation 81.8 23.2 70.1 138.9 45.8 Skewness 4.7 6.2 5.2 1.8 0.4 Minimum (mm) 0.0 0.0 0.0 0.0 46.4 Maximum (mm) 739.5 320.7 653.9 823.5 302.2 C.V. 1.7 1.1 2.6 1.0 0.3 No. of Months 480 480 480 480 480 Table 4.8 Summary Statistics of Monthly Data in Bungonia Creek Catchment (Station No. 215014) 215014 Q QB QS P EP Mean (mm) 5.2 1.3 3.9 56.4 142.9 Median (mm) 1.2 0.7 0.4 50.9 149.4 Mode (mm) 0.0 0.0 0.0 75.2 154.6 Standard Deviation 9.3 1.9 7.7 41.8 45.7 Skewness 3.5 2.8 3.6 0.8 0.2 Minimum (mm) 0.0 0.0 0.0 0.0 66.1 Maximum (mm) 60.0 10.6 49.4 173.0 247.6 C.V. 1.8 1.4 2.0 0.7 0.3 No. of Months 72 72 72 72 72 Table 4.9 Summary Statistics of Monthly Data in Mongarlowe River Catchment (Station No. 215006) Station No. 215006 Q QB QS P EP Mean (mm) 24.1 13.9 10.2 79.3 151.6 Median (mm) 14.6 11.7 1.2 55.3 153.0 Mode (mm) 21.9 14.1 0.0 50.8 145.7 Standard Deviation 26.9 12.1 19.3 74.5 43.3 Skewness 2.0 1.4 2.5 1.5 0.4 Minimum (mm) 0.1 0.0 0.0 0.0 79.4 Maximum (mm) 157.1 59.3 112.5 383.7 291.7 C.V. 1.1 0.9 1.9 0.9 0.3 No. of Months 276 276 276 276 276 Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs. 4.29 Table 4.10 Summary Statistics of Monthly Data in Endrick River Catchment (Station No. 215009) 215009 Q QB QS P EP Mean (mm) 54.9 18.6 36.3 73.7 148.7 Median (mm) 13.4 7.3 6.9 53.5 140.6 Mode (mm) 3.1 3.3 0.0 0.0 183.4 Standard Deviation 101.6 40.3 69.3 71.1 50.3 Skewness 3.2 5.1 2.9 2.0 0.7 Minimum (mm) 0.5 0.3 0.0 0.0 67.0 Maximum (mm) 566.5 282.8 369.8 398.3 302.2 C.V. 1.9 2.2 1.9 1.0 0.3 No. of Months 120 120 120 120 120 Table 4.11 Summary Statistics of Monthly Data in Corang River Catchment (Station No. 215004) 215004 Q QB QS P EP Mean (mm) 19.1 6.5 12.6 55.0 145.1 Median (mm) 6.6 4.1 2.3 42.1 151.0 Mode (mm) 1.3 0.0 0.0 46.4 154.6 Standard Deviation 28.7 6.5 23.2 42.6 48.6 Skewness 2.7 1.6 2.9 1.2 0.2 Minimum (mm) 0.0 0.0 0.0 0.0 66.1 Maximum (mm) 164.4 32.5 131.9 200.6 247.6 C.V. 1.5 1.0 1.8 0.8 0.3 No. of Months 96 96 96 96 96 Table 4.12 Summary Statistics of Monthly Data in Shoalhaven River Catchment (Station No. 215008) 215008 Q QB QS R EP Mean (mm) 29.0 12.2 16.8 72.5 146.1 Median (mm) 9.5 7.1 1.5 49.4 146.3 Mode (mm) 5.6 3.7 0.0 66.1 86.4 Standard Deviation 53.8 14.5 44.3 75.0 49.5 Skewness 3.9 2.7 4.5 2.2 0.5 Minimum (mm) 0.0 0.0 0.0 0.8 66.1 Maximum (mm) 361.3 103.2 304.2 435.3 302.2 C.V. 1.9 1.2 2.6 1.0 0.3 No. of Months 180 180 180 180 180 Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs. 4.30 Table 4.13 Linear Regression between Rainfall and Surface runoff Catchments Nat. Monthly Annually Index a b R a b R Sandy Creek (Newholme) 206026 0.184 -3.05 0.68 0.223 54.70 0.83 Kangaroo Valley 215220 0.644 37.99 0.90 0.687 527.30 0.94 Macquarie Rivulet (Albion Park) 214003 0.311 17.32 0.62 0.358 288.20j 0.58 Bungonia Creek (Bungonia) 215014 0.102 1.901 0.55 0.163 64.19 0.91 Mongarlowe River (Mongarlowe) 215006 0.189 4.751 0.73 0.222 88.43 0.88 Endrick River (Nowra Road) 215009 0.852 26.90 0.87 1.03 476.36 0.93 Corang River (Hockeys) 215004 0.386 8.55 0.71 0.525 193.12 0.92 Shoalhaven River (Kadoona) 215008 0.493 18.99 0.84 0.530 260.00 0.90 QS = a.P±b 4.7.6 Variability of the Data in the Study Area To gain an appreciation of the longer term variability of recorded rainfall and runoff, the examination of the annual values of the rainfall and runoff was considered by computing the residual mass curves of data from all stations. The normalised residual mass curve can be defined as the accumulated difference between the actual annual rainfall or streamflow for each year and the mean annual rainfall or streamflow over all years of record, divided by that mean. The advantage of such a graph is that it clearly shows up sequences of wet or dry years. The residual mass curves for the study area are shown in Figure 4.20. Considerable variability is evident during this period. Comparison of the number of years above and below average in all stations shows that there is a fluctuation pattern with high and low flow years. It was found that there were considerable variations in streamflows in the study area. Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs. 4.31 100 _ a. e "3 o o -100 NO 00 O ON -* NO 00 t^ r- 00 00 00 00 oo ON ON o\ o o\ ON ON 1970 1975 1980 1985 1990 Time (Year) Time (Year) 450 100 300 •3? 300 i 214003 _ £ • • 3 j- 150 x 1 2 o N.I 1 A K * I 0 J L •. - "i fv fr o 5 £ I S -150 -50 E * E * p -300 - - Q m^ O Z -450 -100 o >o O lO o 150 150 60 100 215009 A 40 s 5; •a 3 2 ia 2 o 2 o 50 14 20 QS ^ JaSs *aj •a S" 0 "O w 0 S o / # N 0 —"^> \ w V" -20 — 2 m "-1 -50 es i * E * li -100 ...-Q - -p i" -40 s 3 *S -150 -60 * -150 I a z o t- 1950 1955 1960 1965 1970 s ON ON ON Time (Year) Time (Year) 300 ^ •a ? 150 2 o 5OS a;* •a w 0 V © iS o E* »- -150 *s -300 1979 1981 1983 1985 Time (Year) Time (Year) Figure 4.20 Residual Mass Curve of Annual Data Used in the Study Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs. 4.32 4.8 SUMMARY A description of catchments and a preliminary analysis of climatic inputs has been made in this chapter. Catchment descriptions which included identifying physical characteristics, geology, soil types and vegetation of the catchments were made. The soil types of the selected catchments vary from yellow and grey earth, red, grey podsolic soil, Krasnozems, solodised soil and black earth. Most of the soil textures of the catchments are fine to medium with relatively low infiltration rates. The geological characteristics of the catchments vary from siltstones and sandstones, with some isolated basalts on the high areas and some granite in the upper reaches. The catchments are mostly covered in eucalypts varying from low to high trees, low shrubs, and grasses. Based on physical characteristics and from the results obtained to this point, inferences were made of parameter values, which included interception storage, soil moisture storage and infiltration capacity. These values of parameters will be used as an initial estimate of parameters in rainfall runoff modelling. In the second part of this chapter, prehminary analyses of climatic inputs were carried out. Data which were used in this study consist of maps, rainfall, evaporation, and streamflow data. A daily time step has been adopted due to data availability. These data have been collected from the Commonwealth of Australia, the Bureau of Meteorology (CBM) and the Water Board. Topographic details, details of, soil, some general land cover, and geological maps were obtained from publications. Rainfall and evaporation data have been collected from the Bureau of Meteorology in Sydney and Melbourne. The quality of the data was checked. Different time series plots of rainfall and streamflow were used to compare the concurrent recorded data. A summary of these analyses will be given in Appendix B. In some cases the collected data did not cover the whole period of analysis and there were several periods where the data were not recorded. Missing data records were infilled using data from nearby stations and fitted a regression equation to a period of record for which concurrent data was available for both the station with missing data and a nearby station. Chapter Four Description of Catchments and Preliminary Analysis of Climatic Inputs. 4.33 The average catchment precipitation was calculated based on the Thiessen polygon method. Comparison between rainfall-runoff and their variabilities were made using average monthly and annual values. Statistical analyses of monthly values were carried out. This involved computing the arithmetic mean, median and mode of measured data, computing the parameters which measure the variability of the data (ie., standard deviation, variance and coefficient of variation), computing the correlation between each pair of variables, and computing a third moment - the coefficient of skewness. ilJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJJiJJJJJJIJJi To obtain an idea of the annual variation of streamflow and rainfall in different parts of the study area and to distinguish wet and dry years, the residual mass curves of data from all stations were calculated. Considerable variability was evident during the study period. Comparison of the number of years above and below average in all stations shows that there is a fluctuation pattern with high and low flow years. It was also found that there were considerable variations in streamflows in the study area. CHAPTER FIVE BASEFLOW RECHARGE AND DISCHARGE ANALYSES WITH A VIEW TOWARDS MODELLING CHAPTER FIVE BASEFLOW RECHARGE AND DISCHARGE ANALYSES WITH A VIEW TOWARDS RAINFALL-RUNOFF MODELLING 5.1 INTRODUCTION Streamflow can originate from surface flow, interflow, bank storage flow and groundwater flow. Streamflow has generally been grouped into two categories, surface runoff and baseflow. Surface runoff is that water which travels rapidly over the land surface, or laterally in the upper soil, to stream channels. Baseflow is that part of the streamflow which originates in a lower soil store and travels slowly towards stream channels, thus the baseflow does not fluctuate as rapidly as surface runoff. In some catchments a portion of runoff enters into the soil and percolates rapidly through macropores such as cracks, root holes and animal holes, then moving laterally in a temporarily saturated zone above a layer of low hydraulic conductivity. This flow rapidly reaches the stream channels. Another portion of the streamflow moves through the shallow top soil layers and has a travel time between that of surface runoff and groundwater flow. Baseflow recession analyses and the separation of the total flow into surface runoff and baseflow are important for several reasons. These include; flood analyses, and streamflow forecasting during dry periods, both for water volume and pollution loads. Also, these analyses can be useful in rainfall-runoff modelling, either for modelling the streamflow components separately or for checking the performance of the rainfall runoff models. They are particularly useful for unit hydrograph or runoff routing applications, which utilise surface runoff data only. Other uses could include the assessment of effects of agricultural practices and other basin treatments, investigating the relation between the Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.2 geologic and geomorphological characteristics of a catchment and its hydrologic characteristics; locating suitable areas for groundwater recharge and measurement of its effectiveness, and isolating the flood response of a catchment by separating the component flows of the flood hydrograph (Hall, 1971). If the baseflow and surface runoff are separated accurately, because there is little correlation between baseflow and surface runoff, their statistics or the model parameters relating to these components can be analysed individually. This will result in better estimates of rainfall-runoff model parameters and better estimates of the risk of low and high flows (Sharifi and Boyd 1994). For the purpose of the current study, two flow components need to be considered. Baseflow consists mainly of groundwater flow, but may include other components such as through flow and interflow, and surface runoff which enters the stream primarily by way of overland flow across the ground surface. Two aspects of groundwater discharge, the size of groundwater storage and the rate at which it discharges, also need to be investigated. In sections 5.3 and 5.4, the separation of total streamflow into surface runoff, baseflow and a baseflow index (BFI), all important for the accurate modelling of catchments, are discussed. In section 5.5 the analyses of hydrograph recessions and groundwater recharge, which have important implications for the baseflow parameters of the model to be evaluated, are carried out. 5.2 CLASSIFICATION OF STREAMS AND STREAM RISES Stream channels can be classified into three categories. In the case of ephemeral streams, that only flow when there is some rain over the catchment, the groundwater table is always below theriver bed. Then there are intermittent streams that flow during the wet season. And, finally, perennial streams that flow at all times and in which the groundwater table is never below theriver bed. In a stream, if the rainfall intensity is lower than the infiltration capacity and the depth of rainfall is not sufficient enough to satisfy the soil moisture deficit, there will be no recharge of groundwater and no surface runoff. If rainfall of higher intensity continues, after some time, the soil moisture capacity will be replenished and the recharge of groundwater will occur with a rise appearing in the groundwater outflow. Finally, if Chapter Five Baseflow recharge and discharge analyses with a view towards modelling, 5 3 rainfall intensity is greater than infiltration capacity, surface runoff will occur. In this case, if the total rainfall is large enough to satisfy the soil moisture deficit, the baseflow will contribute to the outflow hydrograph. Overland flow occurs when the net rate of rain exceeds the infiltration rate; but it may or may not reach the stream channels depending on the retention and detention capacities of the land surface over which it travels. If rain continues, the water table will rise and the groundwater contribution to the streamflow will increase. 5.3 BASEFLOW SEPARATION 5.3.1 General The total runoff can be divided into surface runoff and baseflow. In hydrograph analysis, the separation of these components is usually made in an arbitrary manner and both parts may contain a certain amount of the interflow. Different techniques for the separation of the baseflow from the total hydrograph have been developed. They can be grouped into non-analytical and analytical techniques. Non-analytical techniques include the use of chemical or radioactive tracers in order to determine the proportions of surface flow, sub surface flow and groundwater flow. Balek and Ralkova (1965) used comparative measurements of low level radioactivity in water for determining baseflow. Grouzet et al. (1970) analysed the relative proportions of tritium in rainwater, groundwater, and streamflow in order to determine the baseflow hydrograph. Similarly, Pilgrim et al. (1979) and Kobayashi (1986) studied the use of the specific conductance of runoff as an indicator of estimating the properties of different flow components. Also Kobayashi (1985) and Hina and Hasebe (1985) used water temperature and isotopes of oxygen as a means of separation. These techniques have generally been undertaken at the plot scale or on catchments less than 5 Km2 in area. Since these methods have some limitations for practical applications, they will not be discussed further in this study. The analytical techniques can be divided into two main categories based on different views about baseflow changes during the period of surface runoff. Some of these techniques assume that baseflow response starts at the same time that surface runoff starts. Other techniques account for the effects of bank storage assuming that baseflow recession continues even after surface runoff commencement. Many of the techniques Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.4 applied in the literature have been summarised by Dickinson et al. (1967). Some of these methods are presented in Figure 5.1. The precise estimation of the baseflow hydrograph is difficult, if not impossible. This difficulty can be observed from the different approaches expressed in Figure 5.1. The common feature of all of the methods is in identifying the start of surface runoff, the end of runoff and the time distribution of baseflow during the interval of surface runoff. The simplest method is to draw a straight line from the point of rise to an arbitrary point on the recession limb of the hydrograph. Determining the point of rise is not usually difficult, but the break between the baseflow recession and surface runoff may be difficult to define. Another method involves the extension of the baseflow recession curve prior to the stream rise to a point corresponding to peak runoff, and then extending a line to the point representing the end of the surface runoff. In this method, baseflow recession continues after therise of the total hydrograph. Due to the storage-routing effect of the subsurface stores, the baseflow peak will occur after the total hydrograph peak. The baseflow recession is most likely to follow an exponential decay function and it will rejoin the total hydrograph when surface runoff ceases. According to Hall (1971), this method bears a reasonable approximation to those occurring theoretically or experimentally Many empirical and subjective approaches have been used for identifying the time of baseflow peak. Some researchers used groundwater observation well data to determine the peak of baseflow (Hertzler, 1939) and some assumed that the peak occurs under the point of inflection of streamflow hydrograph (Chow, 1964). The end-point of surface runoff can be determined using various empirical formulae. The techniques discussed are generally aimed at separating the baseflow components for a given event, not on a continuous basis. Chapter Five Baseflow recharge and discharge analyses with a view towards modelling^ 5 S I », 1 •' • Figure 5.1 Hydrograph Illustrating Methods of Baseflow Separation Reviews of baseflow separation techniques from the view point of flood analysis are presented by Dickinson et al. (1967) and Hall (1971). Lyne and Hollick (1979) described a low-passfilter technique for modelling time varying and sluggish streamflow. Their procedure was slightly modified by O'Loughlin et al. (1982). The Institute of Hydrology (1980) applied simple smoothing and separation techniques to the total streamflow to separate the slow flow from surface runoff. Shirmohammadi et al. (1984) used rainfall data in conjunction with streamflow data to determine the period of surface runoff. Boughton (1988b) investigated two automated techniques for the continuous separation of baseflow. The first method uses a constant rate of baseflow increase at each time step, Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.6 and the second method increases the rate of baseflow by a fraction of the total runoff. Boughton found that the method based on the fraction of runoff performed as well as manual separation. Nathan and McMahon (1990a) applied two techniques for partitioning streamflow into baseflow and surface runoff. The first was a simple smoothing and separation technique developed by the Institute of Hydrology (1980), and the other was a recursive digital filter. They found that the simple digitalfilter (Equation 5.1) with a parameter set to 0.925 produced more accurate yields and repeatable results compared to the simple smoothing and separation rules. The results were found to be similar to those obtained by using traditional graphical techniques. They concluded that this approach is better suited to low baseflow conditions, is less variable and more strongly correlates with other low flow indicators. More complex methods require information that is not readily available in practice. Unfortunately, the application of more complex separation techniques does not necessarily result in more accurate separations. The selection of a continuous separation method is dependent upon a number of criteria; the method should be objective and suited to automated processing by computer; it should be based solely on streamflow data, and should, at least, be able to determine the start and end of surface runoff. For the current study, four models for computer partitioning of streamflow into baseflow and surface runoff were initially selected and applied to daily streamflow data from the Sandy Creek and Kangaroo Valley Catchments (Station Numbers 206026 and 215220). The results of these methods were compared with a graphical method of separating the baseflow from surface runoff. It was found that the graphical method gave the most reliable results with both low and high runoff rates. The graphical technique is tedious and impractical for separating a large number of events, and it is also significantly affected by subjective judgments. As a result, based on the results obtained from thefirst stag e of the study, a computer model was developed for partitioning total streamflow using a graphical approach. Then all of these techniques were applied to the daily streamflow of other catchments and a comparative analysis was made to check their performance in different situations. All the methods adopted in the study used daily streamflow data and results are expressed in daily time steps as well, but could also be used with other time steps. The methods used in this study have been programmed for operation on an IBM compatible personal computer (Appendix C). Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.7 The various adopted methods are presented in sections 5.3.2 to 5.3.6, and discussions of results are given in section 5.3.7. 5.3.2 Separation of Streamflow Using Recursive Digital Filter, Method 1 This baseflow separation technique is based on a procedure for modelling time varying data and is commonly used in signal analysis and processing (Lyne and Hollick, 1979). The following equation shows the simple form of the filter. (1 + a) QS, = a QSM + ^-^ (fi. - Q^) 5.1 where QSt = the quick response of the filter at the ith sampling instant Q{ = streamflow OC = the filter parameter (2,- - QS;) =thefiltered baseflow The filter was programmed using the Nathan and McMahon (1989,1990a) procedure in which the filter was applied first to the total hydrograph and then in the backwards direction to the separated surface runoff, then forward and backward again to the separated baseflow. The number of passes indicates the degree of smoothing and the backward filtering removes any phase distortion due to the forward pass of thefilter. As thefilter constan t a increases, the baseflow hydrograph becomes flatter and the degree of attenuation increases. The output of thefilter wa s constrained so that the separated baseflow was not negative or greater than the original input. The values of a that give the most acceptable separation results are in the range of 0.90 to 0.95, with the optimum found to be 0.925 which is consistent with the results obtained by other researchers (Nathan and McMahon, 1989 and 1990a). 5.3.3 Automated Technique Adopted by Boughton, Method 2 Boughton (1988) compared two simple models for the computer partitioning of streamflow into baseflow and surface runoff. He concluded that the results of a model which increases baseflow during periods of surface runoff by a fraction of the surface runoff (Equation 5.2), are accurate enough for flood and water balance studies. In the current study this model is used and the results are compared with the other techniques. Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.8 QB. = QBi_1+/.(Q.-QB.1) 5.2 where QB; and QBn = baseflow at times i and i-1 / = a fraction which can be in the range of 0.01 to 0.05 and should be calibrated for each catchment The operation of the model is based on the following assumptions: • baseflow increases whenever there is an increase in the rate of total runoff • the rate of increase in baseflow is calculated as a fraction of the difference between the total runoff and the baseflow on the previous day • the difference between the total flow and the new baseflow is surface runoff • surface runoff ends when the total runoff is less than the baseflow on the previous day 5.3.4 Improved Frequency-Domain Filter Technique, Method 3 Baseflow is generally defined as the flow from an aquifer towards a stream. This shows that baseflow cannot fluctuate rapidly since groundwater pore velocities are much smaller than flow velocities in natural streams. Hence, it can be assumed that the baseflow constitutes the low frequency components of total runoff. Method 3 uses a digitalfiltering approac h similar to method 1. A digitalfilter is commonly referred to as a frequency selective, time-invariant and linear system that passes the desired frequency component and rejects other unwanted components. For example, a lowpassfilter is capable of separating the low frequency components of the input data while a highpass filter lets only those components with higher frequencies pass through. The frequency characteristics of a typical lowpassfilter ar e shown in Figure 5.2. Chapter Five Baseflow recharge and discharge analyses with a view towards modeMr,*,, 5 9 H(eJ£°)| 1 +6i 1 -5, •a 3 Passband Transition Stopband oo p cos Frequency n co Figure 5.2 The Frequency Characteristic of a Typical Lowpass Filter The streamflow can be considered as a digital signal comprising of two components of high and low frequency bands. The rapid and slow responses of the streamflow correspond to the high and low frequency bands, respectively. This technique first employs a highpass filter to separate the quick component from the signal. Then the slow response is obtained by subtracting the quick response from the actual input data. In situations when one is dealing with a digitalfilter, two important issues must be considered. The first is the selectivity of thefilter, an d the second is the phase characteristics associated with thefilter. The selectivity of the filter refers to the ability of the filter to distinguish between specified wanted and unwanted signal components. This factor is commonly measured by the transition band of thefilter (see Figure 5.2). It is clearly seen that the narrower the transition band width, the better the selectivity of the filter. The phase characteristic of the filter is also important in the sense that it is an indication of the amount of distortion caused by the filter. It should be pointed out that the distortion can also be produced by the magnitude response of thefilter in the passband. In standard design techniques, the lowpass and passbandfilters are designed such that the magnitude distortion is minimised. However, the design procedure is usually performed without particular regard to the phase. In other words, the filter coefficients are derived from consideration of the magnitude characteristic only. In many applications it is necessary that the phase characteristic be linear or zero. It is well known that a linear phase characteristic is a mild form of distortion since its effect is only to shift Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.10 the input sequence in time (Oppenheim, 1989). The effect of phase distortion can be further minimised by using afilter with a zero phase characteristic. Causal filters cannot have zero phase. A causal filter is a system which has an impulse response equal to zero for n<0, otherwise it is non causal. In a causal system, the output sequence at every time instant depends only on current and previous input samples. In a non causal system, however, the output depends on the total input samples. This implies that a non causal system cannot be realised in real time. For the application in hand, the whole data is available in advance, and the processing will not be carried out in real time. In other words, the data to be filtered have a finite duration and are stored in computer memory beforehand. To obtain afilter with the zero phase characteristic, two methods have been proposed (Oppenheim, 1989). • Procedure (a): In this method, as depicted in Figure 5.3 (a),first th e data (Q(n)) is passed through the filter which has been designed by using one of the classical design techniques. The output of the filter (g(n)) then is processed by the same filter backward to get r(n). The desired signal will be equivalent to QS(n) = r(-n). • Procedure (b): As depicted in Figure 5.3 (b), the input sequence is processed through thefilter (h(n)) to get g(n). The same input sequence is again processed backward through h(n) to get r(n). The output is taken as the sum of g(n) and r(-n). Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.11 g(n)^ i Q(n) • h(n) f(n) g (-n)^_ . g(-n) r(n) r f-n)^_ h(n) f(n) QS(n) iii QB (n) = Q (n) - QS(n) (a) . Q(n) h(n) g(n) r. .. Q(-n) r(n) h(n) (b) iii QB (n) = g (n) +r (-n) Figure 5.3 Recommended Filtering Procedures (a) and (b) Note that thefilter, as characterised by h(n), is obtained by classical techniques in recursive or non recursive form. A simple recursive digital filter for baseflow separation was introduced for this field by Lyne and Hollick (1979) and later used by Nathan and McMahon (1990) and in the current study is referred to as method 1 (Equation 5.1). Because method 1 was found to underestimate the baseflow in all runoff events, by adopting procedure 'a' (shown in Figure 5.3), this method was slightly improved. This filter (method 3) is also afirst order highpass Butterworthfilter. Therefore, the output (QS(n)) corresponds to the rapid component of the streamflow. The values of a that give the most acceptable separation results are in the range 0.9 < a < 0.95, with the optimum found to be 0.925. In order to obtain the zero phase filter, one of the two methods previously described is performed several times in order to increase the degree of smoothing in the output data (procedure (a) depicted in Figure 5.3 with 3 passes was adopted). A rapid response is obtained with no phase distortion, therefore the low frequency component (baseflow component) is easily obtained by subtracting the rapid response from the input data (QB(n) = Q(n) - QS(n)). It should be pointed out that the selectivity of thefilter can be increased by using higher orderfilters. Unde r these circumstances, results generally show that the performance of the filtering marginally improves. The separation of streamflow using higher order filters, Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.12 however, is beyond the scope of this thesis and remains an issue for future study. A computer program was written based on method 3, using the matrix laboratory programming language (Matlab Package, 1994). This program and its Quick Basic version are presented in the attached floppy diskettes and their list is presented in Appendix C. 5.3.5 Modified Automated Technique, Method 4 The fourth method is based on a modification to method 2. Method 2 was found to overestimate baseflow for high runoff and underestimate it for low runoff events respectively. Considering the deficiency of method 2, a method which incorporates the antecedent moisture conditions was proposed (method 4). In this method, the increase in baseflow discharge depends on the rate of increase in total flow and in baseflow over the previous five days, ie, whenever there is an increase in the rate of total flow for the previous five days, baseflow increases as well. When the total flow is less than the baseflow of the previous day the surface runoff ends. In this model the baseflow recharge is dependent on the cumulative runoff volume rather than the daily runoff rate. J 1 5 3 QBi = QBM +/.- f - where QBi to QBi.5 = baseflow at times (i) to (i-5 ) / = a fraction which can be in the range of 0.01 to 0.05 and should be calibrated for each catchment. 5.3.6 Proposed Method Based on Travel Time of Runoff, Method 5 5.3.6.1 Background A method that is widely accepted is the conventional graphical method. In this method, as in most practical applications, the end of surface runoff is differentiated on the basis of travel times. For separating the total hydrograph into baseflow and surface runoff the following procedure was adopted. Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.13 1. all of the runoff hydrographs were plotted on semi-log paper (flow on log scale and time on natural scale). 2. The start and end of surface runoff were identified using change of slopes of the semi-logarithmic plots. This was complemented using the recorded rainfall data in conjunction with streamflow data, and by using empirical formulae. For example, according to one empirical formula, the end of surface runoff may be approximately taken as N = 0.83 A02 days after the peak at which surface runoff ends, where N is in days and A is the catchment area in square kilometres (Linsley et al, 1958). This was found to be a useful guide. 3. Different procedures can be adopted for determining the shape of baseflow hydrograph, as is shown in Figure 5.1. The separation of the baseflow from the surface runoff can be achieved by drawing tangents to the average recession curves at the points of start and finish of direct runoff (A and B in Figure 5.4a) and drawing a straight line between these tangent points. In many situations this straight line separation is quite acceptable since the maximum baseflow discharge is well below 10% of the maximum discharge. However if such a simple separation is unacceptable a more realistic separation may be obtained by the following method. Continue the average baseflow recession forward in time from point A on Figure 5.4a to a point approximately below the peak of the total hydrograph. Then join the end of this extended baseflow recession to point B by a smooth curve as shown in Figure 5.4a (Australian Rainfall and Runoff, 1987). In the case of complex hydrographs, the recession limb between the peaks can be extended to a point beneath the next peak of hydrograph or to 'N' days after the occurrence of the first peak, whichever is less. This point is the third point of the separation line and then by adopting the above procedure the baseflow hydrograph can be drawn between these three points. The difficulty associated with these procedures is their impracticality when applied to a large number of catchments, furthermore they cannot be formulated. In the current study, the start and end of surface runoff were identified using the change of slopes of the semi-logaritrimic plots as explained before. The procedure adopted for Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.14 determining the shape of the baseflow hydrograph has a conceptual background which is quite different from the traditional procedures adopted in the past. In this method it is assumed that the rate of recharge and depletion of groundwater are similar. Thus baseflow was separated from surface runoff using an exponential function line (Figure 5.4b) having the same K value as the hydrograph recessions. The method just described is also tedious and impractical for application to a large number of catchments. Accordingly, the procedure was programmed for semi-automated processing on a computer, leading to method 5. The justification for the use of this technique rests on the assumption that, theoretically, the recharge and depletion of groundwater in a catchment can exhibit very similar characteristics. The falling limb of the streamflow hydrograph during drought periods represents the depletion of the groundwater store in a catchment. This is mathematically known as a depletion curve having a characteristic exponential decay function. Recharge is caused by volume being added to the groundwater store. This should be related to the recharge from rainfall as indicated by the volume of surface runoff. The recharge curve can have a similar characteristic to the recession curve. It should be pointed out that there may be a hysteresis effect between recharge and decay to the filling and emptying of the soil voids which can be ignored for practical applications. Figure 5.4b depicts a typical picture of the recharge-recession of groundwater when recession and recharge are linear on a semi log graph. Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.15 •+ - Total Runoff • Method 5 ARR1 ARR2 ^ jp 20 Tangent at B \ (a) £ 15 i B 6 ia 10 o c 3 OS A Tangent at A 10 15 20 25 30 35 40 45 50 Time (day) • + - Total Runoff •Baseflow (Method5) 100 ++ + Recharge Period Discharge Period (b) + + >> + ea •a + B E 10 sa + o c + 3 QS 0 5 10 15 20 25 30 35 40 45 50 Time (day) Figure 5.4 An Example of the Typical and Proposed Baseflow Separation 5.3.6.2 Use of Method 5 in Catchments with Linear Recession Characteristics The first step for the separation of baseflow in catchments with linear recession characteristics is to determine an average value for the baseflow recession parameter K. The next step involves determining the start of the hydrograph rise for each event., Determining the start of the hydrograph rise is not usually difficult and the model simply identifies this point from the streamflow record. The next step involves determining the baseflow and surface runoff for the next day. The baseflow at time 'i' is calculated from Equation 5.4. This equation increases the baseflow by a fraction of the baseflow for the previous day. Surface runoff, at this time, can be calculated by the subtraction of the baseflow from the total runoff. The procedure continues until the baseflow is equal to or Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.16 greater than total mnoff. This point marks the end of surface runoff and from then on, the baseflow is set equal to the total runoff. L QBi=G*w.e*=^|= 5.4 where QBi and QBM are baseflow at times ( i ) and ( i-1 ), k is the lag time between centroids of the inflow and outflow and K is the recession parameter determined from semi-log plots of hydrograph recessions. Employing Equation 5.4 and using the average K value gives very quick and reasonable results. 5.3.6.3 Use of Method 5 in Catchments with Nonlinear Recession Characteristics Each hydrograph is a short-term event and its rate of recharge or recession varies from other events on account of variations in storage. Combining individual baseflow recessions gives an average curve. A more accurate estimation of the baseflow can be obtained by using different K values for different events in catchments. Many catchments produce non-linear master baseflow recession curves when plotted on semi-log paper. A non-linear recession curve may be matched by a non-linear equation or by several linear equations. In many catchments the K values vary with high and low baseflow, so for more accurate results it will be necessary to use a non-linear recession parameter. In this case K depends on values of Q, QB, and the event duration. Therefore, the value of non-linear K should be used in Equation 5.4. A general form of K for a non-linear recession can be considered to be K = 10 a±pesT. This describes several different recession equations used in the past. For partitioning streamflow in catchments with non-linear recession, a variable K value should be used. Equation 5.4 can be expressed in terms of QBfirst , QBlast and a non- linearity coefficient a as shown in Equation 5.5. From this equation the shape of the baseflow hydrograph can be determined. Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.17 QBI = ^ir = QBM .rtr > 5.5 * -QBM-(^ where QBfirst and QBlast are baseflow at the start and end of the event, and n is the event duration. The non-linearity coefficient a depends on the volume of surface runoff and can be calculated from Equation 5.6 (a, b & c). // (QBlast > QBfirst) Then o = 0 £ QS. 5.6a J i = l V {QBlast=QBfirst) Then o=0.0 5.6b // (QBlasf < QBfirst) Then QS{ = 0.0 5.6c When the baseflow at time (i) is calculated from Equation 5.5 and 5.6a, surface runoff at time (i) can be calculated by subtraction (Equation 5.6d). QS^Qt-^-f- 5.6d QSi is surface runoff in mm and P is a coefficient which was found to be 0.007 for the catchments studied and K is the recession parameter. In practice, and for the automatic partitioning of streamflow, when recessions show non- linearity it is necessary to determine the start and end of each runoff event and then employ equation 5.5. Two methods can be adopted tofind thefirst and last point of each runoff event. By assuming linear recession, an average K value can be calculated for the catchment of interest. Then by using Equation 5.4 the start and end of surface runoff for all events can be determined. An alternative method forfinding the start and end of surface runoff uses Equation 5.2. Before using this equation the fraction '/' should be calibrated for each catchment. For the studied catchments the value of '/ '= 0.02 was found to give the most reasonable results. The method described has been programmed for operation on an IBM PC. In order to compare the results of automatic technique with the manual separation of baseflow from Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.18 separated manually. It was found that the results of the automatic technique agreed well with those obtained by the manual approach. A comparison of the output of the manual and automatic separation is made and presented on an Excelfile calle d Fcompare.xls in the attached floppy disk. The model enables the user to exercise visual control over the selection of the start of surface runoff, the end of surface runoff as well as the shape of the line connecting these points, so that inappropriate results can be corrected in the separation procedure. In practice, the user identifies a point on the hydrograph which marks the end of a period of surface runoff. The model is then automatically calibrated so that the rate of increase of baseflow will make the surface runoff end at the chosen point. This interactive system generally simplifies baseflow separation while maintaining a similarity to established graphical procedures. 5.3.7 Results and Discussions Streamflow data from two catchments (Sandy Creek and Kangaroo Valley) were separated manually and the performance of the automatic version of method 5 was checked by comparing the results of automatic with manual separation. It was found that the results of the automatic technique agreed well with those obtained by the manual approach. Methods 1 to 5 were then applied to the streamflow data from eight catchments. Examples of continuous separation techniques using methods 1 to 5 are presented in Figure 5.5 (a to v) for catchment 206026. The relative performance and consistency of the techniques were evaluated using the results obtained from the daily streamflow records of the eight catchments. The investigations conducted in this section have resulted in the following conclusions. Analysis of the results showed that, method 1 gives a good estimate of the baseflow rise but it fails to predict the end of surface runoff. This problem is partly solved by method 3. Three filter parameter values (0.90, 0.925, 0.95) were tested. A value of a = 0.925 was found to be the most appropriate. In all of the studied catchments, method 1 estimated the lowest baseflow in all events. Chapter Five Baseflow recharge and discharge analyses with a view towards modelling, 5.19 Five values of '/' for method 2 were tested (1 to 5%). A value of/ =0.02 was found to give the most appropriate results. For events with high runoff, method 2 overestimates the baseflow which results in an underestimation of the surface runoff. This method estimates a very high baseflow in the troughs of multipeaked events and in the trough between two events which affect each other. This method also underestimates the baseflow for low flow events. Method 2 estimates some surface runoff for every rise in the hydrograph and when the peak is high, it overestimates the baseflow. When the streamflow is low, it does not show a reasonablerise, and it underestimates the baseflow. This method generates higher flows under flashy peaks which seems unlikely as baseflow discharge will not rise and fall as quickly as this in actual conditions. Three filter parameter values (0.90, 0.925, 0.95) were tested using method 3. A value of 0.925 was found to be most appropriate. Use of this digitalfilter was found to be fast for baseflow separation. In methods 1 and 3, many small rises increase the baseflow. These methods (1 and 3) recognise the start of the surface runoff but they fail to identify the point at which surface runoff ends. Initial success with thefiltering procedures (methods 1 and 3) and the attractive features of these methods, simplicity, robustness of the filter's performance and the consistency in the results from year to year, shows promise and should be investigated further. Method 4 shows promise in providing a good estimate for the end of surface runoff, and at the same time does not overestimate the baseflow in flashy peaks. Also method 4 appears to solve the problems associating with method 2. The short duration, high intensity rainfall gives rapid rise and fall of hydrograph. Because of this, method 2 calculates a rapidlyrising an d falling baseflow. In fact the baseflow onlyrises slowly , so that method 2 overestimates baseflow. Method 2 estimates more surface runoff in large runoff events and this is compensated for by lower estimates for small runoff events. Both methods 2 and 4 give a good estimation of the time when the surface runoff ceases. Five values of '/' for method 4 were tested (1 to 5%). A value of/ =0.02 was found to give the most appropriate results. Hydrograph separation methods based only on the analyses of streamflow hydrographs and without field observation data, do not determine the true baseflow contribution Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.20 confidently. However, most of the adopted methods (methods 2 to 4) could separate baseflow from surface runoff accurately enough for both flood studies and rainfall runoff modelling. The comparative analyses of method 5 with the other techniques can give some insight into the accuracy of each method in predicting the start and the end of surface runoff. Method 5 produced the most reasonable results; both for high and low runoff events. The results indicate that method 5 is better suited to the studied catchments. This method is based on the travel time of the catchment baseflow, and simulates the process of groundwater recharge based on its discharge characteristics. The analyses carried out in this section can be used to estimate the size of the groundwater store in each catchment. Also, it will be used to calculate the baseflow index (BFI) as the parameter required by the AWBM model. Also the results can be used; to check the performance of rainfall runoff models, and to improve rainfall runoff modelling. Chapter Five Baseflow recharge and discharge analyses with a view towards modelling, 5.2 J too 10 C o.i X-X-X-X-x-x X-X-X-X-X XX ,t^fe^\ o.oi -X Mtl • Mt2 » Mt3 MM " M1S o.ooi 36 44 52 60 68 Day Number 10 E J.0. 1 it o ••-•v.. E 0.01 - -X Mtl • Mt2 " " A" "MB MM MIS _l I L_ 0.001 _i i 1 u 169 177 185 193 201 209 Day Number 10 E B 0.1 t o *-**X -x _.X -X- 0.01 -X-. -X Mtl • Mt2 MB MM - Mt5 0.001 309 317 325 333 Day Number Figure 5.5 (a,b,c) Examples of Continuous Baseflow Separation Using Methods 1 to 5 Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.22 100 10 - E » E E 0.1 ^*^*X*^ft •t •••'S|i?.i A. 0.01 . g X Mfl . M12 _ _ A_ _ Mf3 MM _ - Mt5 0.001 423 431 439 447 Day Number 100 10 - 206026 E » E, / - •<< • •«. .At •"**.•»•*" •»• —A— A« v *•* • -%•«*•,. it v\—A- A-"S?5.^x -X- X- -x "*•=%-"Jx™** ^..'-:•«=> :~:> . •"*•••• o /. 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J.. .' o.ooi 1386 1394 1402 1410 1418 Day Number 206026 0.1 —x—-—T=X— > x- -x- ~x- 0.01 - X —-X X - MtS / / -X Mtl • Mt2 " *- " MB MM I' 0.001 2111 2119 Day Number 0.181 0.161 0.141 g 0.121 E * 0.101 0.081 0.061 0.041 - Mt>, • —• Mt2 - - »- - MB MM 0.021 /\ :'A /A o.ooi r-t: •4; 2597 2605 2613 2621 2629 2637 Day Number Figure 5.5 (m,n,o) Examples of Continuous Baseflow Separation Using Methods 1 to 5 Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.26 ;:'\/!\ I V;:VA „ l.\ 4UX L:;k fi %4- k-'!""* 0.01 «,y. 'i • Q X Mtl • Mt2 - - »- - MB MM - Mt5 0.001 ' ' ' I 1 I '''•''• 1 1 I I I 1 • t l l l f l t- l l I I ! 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A /.A'AAV . lisTa'A.IS*.*',* »'*'»»•• o ' ...... ri.-A*"^^—»; k«v-•'• ,* \£* ^•^•'••^/•^XXAX",.,, E •' .S.AA.TA-AWA»A3AV.-.-.V .<>r *•:•••:' *•:•*..•• *•%'• .'." ***.>-.• ".'.>'xxxxx'v V,»-.:.:c^ V.:,- 0.01 :W X - - Mtl • Mt2 " "•- " MB MM - - •- "MtS 0.001 •••'-'- _1 I I I—1—1—I—I—I—1—I—L- _J I I I I I I 1 1 1 L- 3474 3482 3490 3498 3506 3514 3522 3530 3538 3546 3554 3562 Day Number Figure 5.5 (s,t,u) Examples of Continuous Baseflow Separation Using Methods 1 to 5 Chapter Five Baseflow recharge and discharge analyses with a view towards modelling, 5.2H 10 E ~ ,;^ E 7 o.i LA, A X -x- I 0.01 •>—•>—•>- r -X -•- Mtl • Mt2 - -A- - Mt3 MM o.ooi 3569 3577 3585 3593 Day Number Figure 5.5 (v) Examples of Continuous Baseflow Separation Using Methods 1 to 5 5.4 BASEFLOW INDEX (BFI) 5.4.1 General The baseflow index (BFI) is a dimensionless ratio developed by Lvovich (1972) and the Institute of Hydrology (1980). This index can present some information about the proportion of the runoff that originates from stored sources. The index can be calculated from streamflow data or estimated from catchment geology (Institute of Hydrology, 1980). When streamflow is available, the basic step in calculation of BFI is separation of baseflow from surface runoff. BFI can then be calculated as the volume of baseflow divided by the volume of total runoff for each year or for total period of record. This index will be used as a parameter in rainfall-runoff modelling. Also it can be used as a catchment characteristic to compare the flow characteristic of different catchments. The primary aim of analyses earned out in this section is evaluation of different techniques aimed at calculating this index followed by a discussion about BFI. The BFI values for eight catchments using five methods were calculated. A discussion of results is given in the following section. Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.29 5.4.2 Results and Discussions Figures 5.6 and 5.7 compare the values of the baseflow index (BFI) obtained by methods 1 to 5 using daily su-eamflow on Catchments 206026 and 215220. These figures show that considerable variations occurred in the calculated values of BFI from year to year. With annual BFI (calculated BFI for each year of record), higher runoff years experience higher, and low runoff years experience lower BFI values than the average (see Figure 5.6 years 1978, 1979 and 1980, 1982). 0.50 0.45 206026 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 Time (Year) Figure 5.6 Plots of Annual BFI Determined Using Methods 1 to 5 Against Time (Catchment 206026) Time (Year) Figure 5.7 Plots of Annual BFI Determined Using Methods 1 to 5 Against Time (Catchment 215220) To test the relative performance of different methods, the residuals of the annual BFI between each method and method 5 for Sandy Creek catchment are calculated and shown in Figure 5.8. Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.30 15.00 1976 1978 1980 1982 1984 1986 1988 Time (year) Figure 5.8 Time Series Plots of Baseflow Residuals (relative to Method 5) Figures 5.9 and 5.10 show correlations between method 5 and the other techniques using annual baseflow and BFI. The results indicate that, except for method 1, there is a good agreement between the other methods and method 5. In overall performance and for larger and more sluggish catchments, there is little difference between method 5 in comparison with methods 2 and 4 (with fraction f= 0.02) as well as method 3 with a filter parameter of 0.925. However, there is also some scatter for values of BFI in the range of 0.3 to 0.6 which indicates that, for catchments in this range, the relationship between total hydrograph and baseflow hydrograph is more complicated. It is obvious that in these catchments, the baseflow depends on a very long past history of recharge by rainfall or lateral flux into the catchment from other sources. The baseflow from large catchments and perennialrivers were also found to be more easily separated than small catchments which usually have a faster response time. Chapter Five Baseflow recharge and discharge analyses with a view towards modelling, 5.31 1.00 0.90 n Mil O Mt2 •» MO • Ml4 * 0.80 .A* .A £ 0.70 >. * o-**v»- •3 0.60 -•t!i H.«P o A 18 0.50 4 a'^-A i *«•*]%^-a^-Q- " -°- "'- E 0.40 ea « 0.30 s o B % 0.20 ' % % 0.10 0.00 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 Annual BFI ("Methods) Figure 5.9 Correlation Between Annual BFl's, from 8 Studied Catchments (137 Years), Derived Using Method 5 and Methods 1 to 4 1200 i ° Mtl <> Mt2 * Mt3 • Mt4 c * 1000 A ri 800 o A C 5 600 A D E 400 , £ 1* cfl c A ea O 200 - 4$ 200 400 600 800 1000 1200 QB in mm (Mt 5) Figure 5.10 Correlation Between Annual Baseflow Using Method 5 and Methods 1,2,3 &4. Lines of best fit were obtained using the least squares method and the results are shown in Table 5.1. Method 1 stands out in all catchments because it consistently underestimates the baseflow. Methods 2 to 5 can explain up to 95% of the total variation in baseflow. Table 5.1 indicates that the values of partitioned baseflow using methods 2 to 4 had a AQB less than ±5% (slope of regression a, column 4) and an R2 greater than 0.90. The ability of method 5 to separate the baseflow from the surface runoff implies that the conceptual background of the method is accurate enough for most practical purposes. Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.32 Table 5.1 Correlation Between Annual Baseflow and BFI's derived from Method 5 and Methods 1 to 4 Y=a*X,N= 137 years Y X a Coeff. of Det. R2 Method 1 0.811 0.90 Method 2 QB 1.054 0.97 Method 3 1.057 0.93 Method 5 Method 4 0.954 0.97 Method 1 0.77 0.81 Method 2 BFI 0.97 0.90 Method 3 0.998 0.82 Method 4 0.862 0.86 Figure 5.11 represents a plot of the mean annual BFI ratio as a function of size of catchment area. For the catchments studied, there appear to be no definite relationship between the size of the catchment area and the baseflow index. 0.6 0.5 o 0.4 o o § 0.3 V 3 C B < 0.2 0.1 50 100 150 200 250 300 350 Catchment Area, K in 2 Figure 5.11 Plot of the Mean Annual BFI Ratios as a Function of Size of Catchments Figure 5.12 represents a plot of the coefficient of variation of BFI ratios, baseflow, surface runoff and total flow for all events in the studied catchments. For the catchments studied, a relatively low variation for BFI ratios and, as expected, high variations for surface runoff were apparent. The variation of the annual BFI index is lower than event BFI ratios. Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.33 o.oo 215006 215008 214003 215220 215004 215009 206026 215014 Catchment National Index Figure 5.12 Plot of the Coefficient of Variation (C. V.) of the BFI Ratios, QB, QS and Q for the Studied Catchments The variations of (event) BFI (the ratio of event baseflow volume to total streamflow volume for same event) are considerably high (Figure 5.13). As it is shown in this figure the event BFI is high for low flow and low for high flow events. 215220 •- _p o o 20 40 60 80 100 Q (mm) Figure 5.13 Plots of Events BFI Determined Using Methods 5 Against Time (Catchment 215220) Frequency distributions of event BFI as well as event baseflow depth for all of the studied catchments is presented in Figures 5.14 and 5.15, respectively. For all catchments the BFI values are symmetrically distributed. Chapter Five Baseflow recharge and discharge analyses with a view towards modelling, 5.34 Base Flow Inex (BFI) Figure 5.14 Frequency Distribution of Event Baseflow Index (BFI) H21 5220 50 CD2I 5004 H215009 40 • 214003 H21 5006 30 • 2 1 5 0 1 4 El 2 1 5 00 8 tifklOflln ,HL Jn JL ,n. 0.0 5.0 6.0 7 Base Flow mm/event Figure 5.15 Frequency Distribution of Event Baseflow As it is shown in Figure 5.15 the distribution of the event baseflow is positively skewed. The volumes of event baseflows vary from 0 to 16 mm with the most frequent values being around 1 mm/event. This indicates that, in all studied catchments, there are a greater number of events with a lower baseflow while events with a higher baseflow are those that exhibit a lower frequency of occurrence. It can be seen that events BFI ratios range from 0.1 to 0.5 with the most frequent value being around 0.3. The calculated annual BFI values for the eight catchments studied, based on different filter parameters, are presented in Tables 5.2 to 5.8. The calculated values of BFI from method 5 will be selected as the parameter required by the AWBM model. The baseflow index can be used as a relatively stable index representing catchment characteristics. Although a lengthy record of stream flow is not necessary for calculating BFI, since higher runoff years experienced higher, and low runoff years experienced lower values of Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.35 BFI, it is necessary to use a period of record which includes wet and dry sequences for calculating BFI. In smaller time scale (events), the BFI values are higher for low flows and lower for high flows, as presented in Figure 5.13. Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.36 Table 5.2 Annual BFI for Sandy Creek Catchment (206026) Using Methods 1 to 5 Year Mtl Mtl Mtl Mt2 Mt2 Mt2 Mt3 Mt3 Mt3 Mt4 Mt4 Mt4 MT5 Runoff 0.90 0.925 0.95 %4 %3 %2 0.95 0.925 0.90 %4 %3 %2 mm 1976 0.124 0.112 0.098 0.240 0.220 0.190 0.147 0.163 0.175 0.187 0.174 0.157 0.138 196.85 1977 0.140 0.114 0.109 0.280 0.250 0.220 0.186 0.213 0.236 0.232 0.216 0.197 0.194 152.22 1978 0.226 0.216 0.188 0.330 0.300 0.260 0.262 0.284 0.300 0.283 0.264 0.242 0.243 118.94 1979 0.197 0.183 0.166 0.320 0.300 0.270 0.22 0.249 0.270 0.278 0.262 0.241 0.227 29.53 1980 0.014 0.011 0.008 0.060 0.040 0.030 0.015 0.020 0.024 0.024 0.019 0.013 0.043 9.85 1981 0.052 0.043 0.031 0.160 0.130 0.100 0.06 0.080 0.100 0.114 0.095 0.070 0.115 10.46 1982 0.001 0.001 0.00 0.060 0.050 0.030 0.015 0.003 0.005 0.023 0.018 0.012 0.012 5.19 1983 0.168 0.159 0.145 0.250 0.230 0.200 0.194 0.204 0.220 0.206 0.191 0.174 0.160 106.74 1984 0.155 0.140 0.121 0.280 0.260 0.220 0.194 0.220 0.235 0.232 0.214 0.190 0.177 160.18 1985 0.129 0.120 0.106 0.240 0.210 0.180 0.153 0.169 0.180 0.190 0.174 0.152 0.134 39.80 1986 0.115 0.096 0.067 0.200 0.180 0.150 0.12 0.146 0.164 0.169 0.155 0.133 0.120 4.75 1987 0.173 0.145 0.107 0.330 0.300 0.270 0.22 0.258 0.280 0.284 0.265 0.242 0.206 6.26 Total 0.151 0.139 0.125 0.27 0.244 0.213 0.179 0.199 0.215 0.221 0.205 0.185 0.196 840.77 Table 5.3 Annual BFI for Corang River Catchment (215004) Using Methods 1 to 5 Year Mtl Mtl Mtl Mt2 Mt2 Mt2 Mt3 Mt3 Mt3 Mt4 Mt4 Mt4 Mt5 Runoff 0.90 0.925 0.95 %4 %3 %2 0.95 0.925 0.90 %4 %3 %2 mm 1979 0.666 0.611 0.595 0.777 0.766 0.750 0.726 0.740 0.750 0.704 0.682 0.648 0.77 57.44 1980 0.237 0.213 0.186 0.439 0.406 0.361 0.543 0.563 0.580 0.395 0.370 0.334 0.33 44.42 1981 0.223 0.196 0.160 0.388 0.357 0.312 0.377 0.397 0.416 0.343 0.320 0.287 0.26 192.21 1982 0.302 0.264 0.219 0.551 0.525 0.490 0.362 0.394 0.419 0.443 0.417 0.379 0.50 492.30 1983 0.253 0.239 0.221 0.378 0.354 0.322 0.326 0.353 0.375 0.333 0.317 0.296 0.30 244.00 1984 0.221 0.207 0.185 0.357 0.331 0.298 0.318 0.333 0.348 0.306 0.289 0.267 0.28 584.79 1985 0.263 0.243 0.212 0.408 0.382 0.345 0.298 0.322 0.341 0.348 0.330 0.301 0.33 418.21 1986 0.193 0.181 0.168 0.363 0.335 0.301 0.291 0.314 0.334 0.308 0.290 0.226 0.28 225.83 Total 0.257 0.243 0.225 0.395 0.368 0.333 0.291 0.314 0.334 0.348 0.330 0.305 0.35 2259.2 Table 5.4 Annual BFI for Endrick River Catchment (215009) Using Methods 1 to 5 Year Mtl Mtl Mtl Mt2 Mt2 Mt2 Mt3 Mt3 Mt3 Mt4 Mt4 Mt4 Mt5 Runoff 0.90 0.925 0.95 %4 %3 %2 0.95 0.925 0.90 %4 %3 %2 mm 1970 0.251 0.228 0.20 0.432 0.408 0.380 0.310 0.330 0.350 0.326 0.300 0.264 0.36 158.84 1971 0.213 0.189 0.156 0.344 0.316 0.284 0.257 0.287 0.310 0.293 0.274 0.250 0.27 281.55 1972 0.199 0.188 0.171 0.328 0.302 0.272 0.231 0.248 0.263 0.275 0.259 0.240 0.25 328.17 1973 0.291 0.276 0.259 0.405 0.377 0.343 0.312 0.340 0.363 0.361 0.343 0.318 0.32 220.33 1974 0.166 0.155 0.141 0.278 0.255 0.224 0.192 0.210 0.226 0.232 0.218 0.196 0.21 1469.5 1975 0.115 0.106 0.096 0.272 0.243 0.210 0.134 0.153 0.172 0.221 0.201 0.179 0.18 1009.0 1976 0.156 0.141 0.123 0.284 0.260 0.232 0.191 0.215 0.231 0.231 0.218 0.201 0.23 991.34 1977 0.192 0.173 0.148 0.334 0.310 0.282 0.212 0.243 0.267 0.283 0.269 0.252 0.27 355.56 1978 0.148 0.129 0.107 0.289 0.265 0.234 0.172 0.206 0.231 0.251 0.232 0.209 0.19 1517.8 1979 0.279 0.269 0.258 0.408 0.383 0.353 0.302 0.322 0.340 0.360 0.345 0.324 0.37 214.26 Total 0.201 0.185 0.166 0.337 0.312 0.281 0.231 0.255 0.275 0.283 0.266 0.243 0.265 6546.4 Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.37 Table 5.5 Annual BFI for Bungonia River Catchment (215014) Using Methods 1 to 5 Year Mtl Mtl Mtl Mt2 Mt2 Mt2 Mt3 Mt3 Mt3 Mt4 Mt4 Mt4 Mt5 Runoff 0.90 0.925 0.95 %4 %3 %2 0.95 0.925 0.90 %4 %3 %2 mm 1981 0.158 0.139 0.118 0.304 0.272 0.229 0.223 0.238 0.252 0.252 0.232 0.200 0.34 12.56 1982 0.02 0.011 0.004 0.297 0.266 0.234 0.161 0.177 0.201 0.160 0.139 0.118 0.16 1.08 1983 0.171 0.161 0.146 0.304 0.276 0.236 0.188 0.211 0.227 0.252 0.237 0.207 0.21 74.97 1984 0.175 0.163 0.147 0.294 0.269 0.237 0.203 0.220 0.232 0.239 0.225 0.204 0.22 175.90 1985 0.180 0.170 0.158 0.314 0.288 0.254 0.210 0.234 0.255 0.265 0.247 0.224 0.24 65.33 1986 0.143 0.131 0.112 0.300 0.270 0.230 0.174 0.200 0.223 0.247 0.224 0.196 0.23 43.36 Total 0.171 0.161 0.146 0.300 0.273 0.238 0.200 0.220 0.234 0.249 0.233 0.209 0.23 373.2 Table 5.6 Annual BFI for Mongarlowe River Catchment (215006) Using Methods 1 to 5 Year Mtl Mtl Mtl Mt2 Mt2 Mt2 Mt3 Mt3 Mt3 Mt4 Mt4 Mt4 Mt5 Runoff 0.90 0.925 0.95 %4 %3 %2 0.95 0.925 0.90 %4 %3 %2 mm 1950 0.574 0.545 0.506 0.638 0.625 0.610 0.602 0.625 0.640 0.613 0.604 0.590 0.66 622.83 1951 0.486 0.466 0.438 0.544 0.529 0.512 0.491 0.518 0.535 0.513 0.503 0.490 0.55 456.95 1952 0.485 0.467 0.444 0.531 0.513 0.490 0.507 0.523 0.538 0.496 0.482 0.460 0.56 484.07 1953 0.604 0.562 0.504 0.785 0.778 0.771 0.715 0.735 0.747 0.669 0.641 0.601 0.81 213.14 1954 0.342 0.327 0.303 0.412 0.398 0.384 0.370 0.380 0.385 0.363 0.352 0.339 0.39 117.58 1955 0.315 0.279 0.234 0.430 0.399 0.364 0.346 0.386 0.419 0.394 0.667 0.336 0.36 220.67 1956 0.464 0.431 0.383 0.546 0.515 0.479 0.471 0.514 0.550 0.521 0.495 0.463 0.46 531.00 1957 0.377 0.347 0.303 0.477 0.462 0.445 0.417 0.444 0.459 0.420 0.407 0.391 0.47 130.47 1958 0.537 0.505 0.462 0.630 0.613 0.593 0.581 0.612 0.632 0.600 0.587 0.570 0.64 153.57 1959 0.417 0.393 0.366 0.485 0.461 0.432 0.425 0.460 0.483 0.453 0.434 0.406 0.46 484.43 1960 0.550 0.521 0.484 0.616 0.593 0.557 0.587 0.610 0.620 0.558 0.528 0.479 0.62 239.62 1961 0.471 0.454 0.428 0.509 0.488 0.459 0.481 0.495 0.511 0.469 0.451 0.422 0.48 696.15 1962 0.576 0.547 0.505 0.697 0.690 0.683 0.661 0.668 0.674 0.614 0.600 0.570 0.70 451.31 1963 0.640 0.610 0.568 0.737 0.772 0.699 0.680 0.710 0.723 0.682 0.662 0.623 0.74 442.57 1964 0.626 0.603 0.558 0.717 0.709 0.701 0.674 0.686 0.696 0.658 0.646 0.621 0.72 253.58 1965 0.715 0.692 0.658 0.798 0.787 0.766 0.753 0.776 0.789 0.723 0.700 0.660 0.79 61.42 1966 0.401 0.382 0.356 0.473 0.456 0.433 0.430 0.446 0.457 0.432 0.418 0.396 0.46 156.33 1967 0.528 0.497 0.450 0.624 0.609 0.589 0.580 0.602 0.620 0.569 0.546 0.512 0.65 233.99 1968 0.513 0.446 0.348 0.771 0.760 0.748 0.678 0.705 0.724 0.630 0.605 0.569 0.76 30.60 1969 0.417 0.395 0.367 0.487 0.459 0.416 0.432 0.469 0.495 0.459 0.436 0.399 0.49 233.74 1970 0.689 0.657 0.610 0.796 0.787 0.777 0.769 0.777 0.785 0.714 0.687 0.644 0.63 224.40 1971 0.502 0.455 0.388 0.673 0.656 0.627 0.606 0.642 0.667 0.609 0.584 0.545 0.64 222.46 1972 0.196 0.158 0.116 0.373 0.347 0.317 0.242 0.281 0.314 0.283 0.258 0.228 0.36 59.37 6720.2 Total 0.520 0.496 0.464 0.590 0.573 0.549 0.556 0.579 0.596 0.565 0.551 0.531 0.58 Chapter Five Baseflow recharge and discharge analyses with a view towards modellinp. 5 38 Table 5.7 Annual BFI for Shoalhaven River Catchment (215008) Using Methods 1 to 5 Year Mtl Mtl Mtl Mt2 Mt2 Mt2 Mt3 Mt3 Mt3 Mt4 Mt4 Mt4 Mt5 Runoff 0.90 0.925 0.95 %4 %3 %2 0.95 0.925 0.90 %4 %3 %2 mm 1972 0.441 0.412 0.370 0.577 0.544 0.502 0.497 0.537 0.536 0.537 0.508 0.470 0.62 133.91 1973 0.473 0.456 0.429 0.570 0.537 0.495 0.522 0.553 0.573 0.533 0.505 0.467 0.62 128.11 1974 0.264 0.251 0.235 0.408 0.378 0.338 0.298 0.322 0.345 0.361 0.338 0.307 0.32 892.54 1975 0.214 0.196 0.175 0.400 0.369 0.331 0.244 0.280 0.310 0.352 0.329 0.300 0.30 903.76 1976 0.254 0.229 0.197 0.415 0.388 0.355 0.295 0.332 0.356 0.366 0.348 0.322 0.35 660.65 1977 0.394 0.365 0.330 0.588 0.552 0.509 0.444 0.484 0.518 0.545 0.510 0.466 0.50 218.28 1978 0.296 0.273 0.246 0.443 0.412 0.371 0.326 0.366 0.396 0.405 0.383 0.351 0.36 726.35 1979 0.366 0.340 0.300 0.530 0.508 0.480 0.420 0.447 0.466 0.475 0.458 0.435 0.50 191.06 1980 0.592 0.554 0.494 0.723 0.703 0.673 0.724 0.736 0.744 0.655 0.632 0.597 0.75 14.43 1981 0.319 0.290 0.253 0.488 0.459 0.421 0.394 0.416 0.437 0.446 0.425 0.395 0.48 102.45 1982 0.594 0.551 0.471 0.834 0.824 0.810 0.795 0.810 0.820 0.695 0.669 0.631 0.88 20.84 1983 0.377 0.358 0.332 0.489 0.463 0.427 0.394 0.424 0.444 0.448 0.428 0.401 0.47 232.17 1984 0.422 0.395 0.355 0.574 0.548 0.511 0.519 0.537 0.556 0.520 0.498 0.462 0.54 420.30 1985 0.341 0.316 0.276 0.489 0.457 0.406 0.354 0.402 0.437 0.447 0.422 0.379 0.39 419.74 1986 0.410 0.382 0.350 0.595 0.568 0.530 0.467 0.507 0.537 0.524 0.496 0.456 0.59 147.90 Total 0.315 0.296 0.271 0.464 0.434 0.395 0.380 0.412 0.437 0.426 0.404 0.374 0.40 5212.5 Table 5.8 Annual BFI for Kangaroo River Catchment (215220) Using Methods 1 to 5 Year Mtl Mtl Mtl Mt2 Mt2 Mt2 Mt3 Mt3 Mt3 Mt4 Mt4 Mt4 Mt5 Runoff 0.90 0.925 0.95 %4 %3 %2 0.95 0.925 0.90 %4 %3 %2 mm 1970 0.401 0.337 0.333 0.464 0.436 0.399 0.436 0.455 0.471 0.411 0.384 0.347 0.41 530.40 1971 0.267 0.235 0.188 0.413 0.389 0.357 0.331 0.369 0.396 0.364 0.345 0.315 0.35 767.80 1972 0.274 0.254 0.228 0.418 0.394 0.364 0.318 0.352 0.377 0.372 0.357 0.337 0.36 805.04 1973 0.382 0.362 0.339 0.485 0.453 0.413 0.412 0.451 0.479 0.465 0.438 0.404 0.45 519.70 1974 0.263 0.247 0.226 0.381 0.360 0.333 0.292 0.316 0.335 0.335 0.323 0.303 0.33 2142.5 1975 0.172 0.157 0.142 0.363 0.330 0.290 0.202 0.231 0.257 0.315 0.290 0.260 0.25 1537.0 1976 0.290 0.267 0.237 0.420 0.397 0.365 0.334 0.368 0.390 0.376 0.361 0.339 0.36 1355.9 1977 0.284 0.256 0.222 0.441 0.420 0.394 0.325 0.362 0.388 0.399 0.384 0.364 0.39 671.45 1978 0.209 0.197 0.183 0.347 0.322 0.287 0.239 0.262 0.282 0.302 0.284 0.259 0.20 1743.5 1979 0.352 0.334 0.306 0.512 0.489 0.459 0.404 0.430 0.453 0.461 0.443 0.419 0.42 416.04 1980 0.342 0.317 0.277 0.501 0.475 0.442 0.391 0.422 0.444 0.459 0.441 0.415 0.45 178.28 1981 0.254 0.229 0.195 0.415 0.388 0.351 0.323 0.344 0.365 0.370 0.351 0.324 0.30 586.93 1982 0.432 0.395 0.340 0.656 0.631 0.593 0.556 0.591 0.618 0.566 0.538 0.497 0.66 202.70 1983 0.284 0.269 0.247 0.408 0.385 0.352 0.308 0.335 0.355 0.367 0.352 0.328 0.32 584.20 1984 0.238 0.222 0.200 0.398 0.376 0.342 0.291 0.312 0.331 0.352 0.335 0.310 0.28 1171.0 1985 0.333 0.310 0.274 0.454 0.430 0.397 0.371 0.403 0.422 0.408 0.391 0.367 0.35 989.00 1986 0.229 0.218 0.206 0.390 0.362 0.329 0.258 0.286 0.310 0.335 0.315 0.291 0.26 732.50 1987 0.245 0.231 0.215 0.380 0.354 0.320 0.289 0.315 0.335 0.325 0.306 0.281 0.29 573.30 1988 0.234 0.220 0.198 0.378 0.353 0.319 0.285 0.303 0.321 0.327 0.310 0.285 0.25 1049.8 1989 0.412 0.392 0.356 0.521 0.497 0.465 0.453 0.476 0.493 0.477 0.460 0.432 0.43 926.61 1990 0.213 0.202 0.187 0.366 0.344 0.312 0.240 0.260 0.287 0.320 0.305 0.283 0.23 1440.6 Total 0.273 0.256 0.235 0.407 0.382 0.348 0.310 0.336 0.356 0.367 0.350 0.326 0.31 19825 Chapter Five Baseflow recharge and discharge analyses with a view towards modelling, 5 39 Table 5.9 Annual BFI for Macquarie Rivulet Catchment (214003) Using Methods 1 to 5 Year Mtl Mtl Mtl Mt2 Mt2 Mt2 Mt3 Mt3 Mt3 Mt4 Mt4 Mt4 Mt5 Runoff 0.90 0.925 0.95 %4 %3 %2 0.95 0.925 0.90 %4 %3 %2 mm 1950 0.378 0.363 0.340 0.467 0.422 0.364 0.426 0.454 0.477 0.398 0.360 0.305 0.26 1275.0 1951 0.430 0.406 0.377 0.467 0.422 0.364 0.463 0.498 0.526 0.434 0.393 0.338 0.31 943.63 1952 0.408 0.385 0.355 0.532 0.498 0.453 0.449 0.479 0.504 0.489 0.460 0.417 0.40 831.74 1953 0.644 0.615 0.572 0.748 0.722 0.679 0.684 0.715 0.737 0.703 0.674 0.614 0.66 195.34 1954 0.500 0.473 0.438 0.651 0.624 0.591 0.545 0.591 0.622 0.610 0.583 0.549 0.58 183.10 1955 0.573 0.524 0.453 0.682 0.649 0.596 0.637 0.681 0.706 0.660 0.628 0.579 0.59 277.20 1956 0.445 0.418 0.376 0.491 0.445 0.384 0.482 0.519 0.549 0.459 0.418 0.363 0.35 987.00 1957 0.411 0.378 0.330 0.491 0.451 0.394 0.492 0.534 0.558 0.454 0.423 0.373 0.38 83.52 1958 0.503 0.467 0.415 0.633 0.605 0.567 0.598 0.623 0.644 0.599 0.575 0.542 0.58 165.47 1959 0.297 0.272 0.248 0.416 0.387 0.350 0.341 0.374 0.399 0.372 0.348 0.319 0.36 1309.0 1960 0.496 0.484 0.464 0.576 0.553 0.524 0.542 0.561 0.583 0.507 0.485 0.453 0.59 421.80 1961 0.256 0.237 0.464 0.339 0.312 0.284 0.242 0.276 0.303 0.288 0.270 0.248 0.28 1960.5 1962 0.465 0.443 0.415 0.624 0.602 0.575 0.573 0.595 0.615 0.541 0.512 0.466 0.61 927.60 1963 0.453 0.435 0.409 0.464 0.413 0.345 0.499 0.517 0.536 0.411 0.365 0.302 0.27 1016.7 1964 0.558 0.523 0.467 0.743 0.723 0.695 0.657 0.682 0.701 0.671 0.646 0.609 0.71 447.20 1965 0.418 0.402 0.385 0.584 0.549 0.500 0.458 0.492 0.528 0.535 0.503 0.457 0.55 178.97 1966 0.353 0.323 0.290 0.559 0.533 0.498 0.482 0.497 0.518 0.509 0.488 0.457 0.53 308.40 1967 0.583 0.545 0.490 0.669 0.628 0.562 0.649 0.689 0.718 0.633 0.591 0.524 0.57 428.97 1968 0.579 0.550 0.513 0.730 0.706 0.671 0.639 0.677 0.706 0.654 0.623 0.577 0.80 61.54 1969 0.468 0.450 0.428 0.602 0.578 0.542 0.487 0.529 0.558 0.572 0.553 0.524 0.55 638.67 1970 0.616 0.551 0.451 0.835 0.820 0.801 0.779 0.784 0.792 0.691 0.650 0.589 0.88 144.10 1971 0.537 0.504 0.456 0.648 0.623 0.588 0.580 0.618 0.641 0.610 0.586 0.550 0.62 250.46 1972 0.265 0.246 0.219 0.421 0.402 0.371 0.304 0.336 0.361 0.380 0.369 0.348 0.67 212.50 1973 0.567 0.535 0.489 0.654 0.622 0.577 0.614 0.650 0.674 0.614 0.582 0.532 0.67 212.50 1974 0.258 0.230 0.199 0.435 0.411 0.381 0.290 0.342 0.383 0.392 0.376 0.354 0.35 2864.7 1975 0.192 0.183 0.172 0.307 0.285 0.257 0.212 0.226 0.236 0.258 0.243 0.224 0.22 984.39 1976 0.532 0.505 0.465 0.544 0.500 0.445 0.564 0.607 0.634 0.507 0.466 0.413 0.44 583.87 1977 0.519 0.488 0.451 0.638 0.615 0.586 0.562 0.600 0.627 0.594 0.573 0.544 0.60 444.05 1978 0.477 0.459 0.434 0.578 0.533 0.469 0.525 0.557 0.584 0.547 0.504 0.441 0.44 607.10 1979 0.561 0.528 0.475 0.719 0.696 0.663 0.638 0.670 0.693 0.660 0.634 0.596 0.60 444.00 1980 0.547 0.511 0.461 0.688 0.664 0.623 0.599 0.638 0.663 0.644 0.621 0.583 0.66 111.19 1981 0.415 0.381 0.337 0.599 0.527 0.479 0.447 0.496 0.535 0.534 0.506 0.462 0.52 190.71 1982 0.558 0.507 0.420 0.706 0.669 0.608 0.626 0.666 0.694 0.688 0.656 0.597 0.76 64.86 1983 0.483 0.419 0.328 0.631 0.585 0.520 0.558 0.615 0.653 0.610 0.566 0.505 0.45 216.29 1984 0.517 0.471 0.403 0.679 0.643 0.588 0.604 0.645 0.674 0.635 0.595 0.534 0.50 312.83 1985 0.566 0.537 0.491 0.648 0.619 0.574 0.593 0.636 0.661 0.607 0.583 0.539 0.62 434.77 1986 0.550 0.525 0.489 0.685 0.660 0.623 0.610 0.646 0.672 0.628 0.598 0.554 0.65 366.36 1987 0.523 0.499 0.464 0.673 0.652 0.622 0.598 0.624 0.643 0.607 0.584 0.548 0.69 223.76 1988 0.506 0.475 0.425 0.551 0.515 0.460 0.569 0.593 0.610 0.515 0.481 0.426 0.46 353.71 1989 0.511 0.480 0.425 0.531 0.486 0.431 0.592 0.619 0.634 0.478 0.433 0.373 0.39 491.87 Total 0.411 0.389 0.360 0.507 0.475 0.433 0.477 0.509 0.533 0.476 0.450 0.414 0.43 22155 Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.40 5.5 BASEFLOW RECESSION EQUATIONS AND TECHNIQUES 5.5.1 General When a hydrograph is plotted on semi-log paper, it can usually be represented by several lines. These lines classify the flow components on the basis of travel time or the storage coefficient. The recession curve used in most of studies is an exponential function as shown in Equation 5.7. t l -1 Qt = QoK =Qoe 5.7 where Qt is the flow rate at any time t after the initial time 0, Q0 is the flow rate at time 0, K is a recession parameter and k is the lag time between centroids of the inflow and outflow. This equation implies that the recession is linear on a semi-log graph. Hydrograph recessions can also be represented by the superposition of the exponential terms in Equation 5.7 to give Equation 5.8, where the different recession constants are related to two separate sources. klt k2t Qt = Q01 e +Q02 e 5.8 An alternative equation that describes recession flow is the hyperbolic function. According to Werner and Sundquist (1951) the hyperbolic function is an approximate solution to the differential equation governing transient flow in an unconfined condition. Qo Qt = 2 5.9 1 (1 + cct)2 As t increases, the exponential function decreases to zero more rapidly than the hyperbolic function. Accordingly, the latter equation should be more applicable to baseflow recession as it would be expected that baseflow contributions would mostly originate from unconfined aquifers. However, as stated by Hall (1968), baseflow recessions do not suit this equation unless a constant P is included. The extra (3 represents another source of baseflow. Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.47 Q, = M + P 510 1 (1 + at)2 K Some other measures such as the half flow-period (Martin, 1973) have been used. This measure is defined as the time required for flow in a stream to be halved (Equation 5.11). It can be related to the recession constant (K) by Equation 5.12. 5 log K 1 K=0.5t05 5.12 A number of recession analyses have been conducted (Laurenson, 1961; Singh and Stall, 1971; Klaassen and PUgrim, 1975) in which the recession constants relating to the major streamflow components have been determined. The ranges of daily K values have been found (typically) to be: 0.2 to 0.8 for surface runoff, 0.7 to 0.94 for interflow, and 0.93 to 0.995 for baseflow. The overlapping ranges reflect the difficulties in identifying a particular recession as being either surface runoff or interflow, or interflow or baseflow. For example, the distinctions between interflow and baseflow involve a certain degree of subjective discrimination. Both of these components may be composed of delayed flow from different sources. According to some researchers (Ineson and Downing, 1964), after detailed inspection of semi logarithmic plots, no single linear plot can be constructed for baseflow recession. This non-linearity can be a function of factors such as the carry over flow from a prior period of recharge, discharge from different aquifers, variations in areal patterns of recharge, bank and flood plain storages, and streamflow losses due to evapotranspiration and transmission loss. Another problem, particularly in humid and subhumid areas, is that recharge may occur frequently, depending on hydrologic and geologic conditions. The major consequence is that baseflow may be fed by pulses of recharge or by drainage of soil moisture. Hewlett (1961) suggests that the area supplying baseflow is not constant but is expanding and shrinking in response to the interactions between recharge, soil moisture and precipitation. Therefore baseflow, as commonly defined may occur, either in arid or semiarid areas, or where aquifers are relatively unaffected by precipitation. Chapter Five Baseflow recharge and discharge analyses with a view towards modelling, 5.42 Because of the variations in storage, an individual recession cannot be considered to be an indicator of catchment characteristics. It is necessary to combine individual baseflow recessions to get an average characterisation of the groundwater depletion of a catchment. This process results in what is called the master recession curve, MRC, which represents the most frequent depletion situation in a catchment. 5.5.2 The Master Recession Curve (MRC) A large number of techniques have been developed to provide an average characterisation of baseflow response (the so called master recession curve, MRC). The most widely used methods are the matching strip method (Snyder, 1939), the correlation method (Langbein, 1938) and analytical evaluation of MRC from log-log plots of dQB/dt versus QB for all events. These methods are described in the next three sections. Some other methods which have been developed include; the method of successive approximation applied to non-linear least squares (Snyder, 1962); ordination of discharges (Federer, 1973); and a stochastic model based on the correlation between successive river stages (Jones and McGilchrist, 1978). The latter methods are either not widely accepted, or else imply a more complex procedure than the one adopted. 5.5.2.1 The Matching Strip Method The matching strip method (Wisler and Brater, 1949, Toebes and Morrissey, 1962) consists of making transparent overlays for all recession periods and superimposing and adjusting them horizontally to obtain a mean line through the overlapping parts. This method is generally accurate because visual control allows omission of very high or very low recessions. Figure 5.16 illustrates a typical master recession curve derived using this method. The upper part of the curve shows higher baseflow and steeper depletion and the lower part indicates recessions with lower baseflow and a more sustained depletion rate. The steeper and flatter parts of the curve may indicate flows from different source areas, with recession constants Ki and K2 respectively. Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.43 10.0 r 0 20 40 60 80 100 120 Time (day) Figure 5.16 Example of Master Recession Curve Derived by Matching Strip Method The traditional matching strip method is quite tedious and impractical for application to a large number of catchments. The method of deriving a master baseflow recession has been automated by Boughton (Boughton, 1995). In the computerised approach, the recession constant is calculated by combining all of the periods of baseflow recessions into a master recession. The method consists of the following steps. a) The streamflow is partitioned into surface runoff and baseflow. The segments of baseflow between periods of surface runoff are used to form the MRC. Periods of baseflow recharge during surface runoff and short segments of recession (< 5 days) are ignored. b) The segments of baseflow from step (a) are sorted in order of magnitude of the starting value of the segment and the segment with the highest starting value becomes the start of the MRC. The segment with the second highest starting value is then combined with the master recession, and a new master recession is calculated. The segment with the third highest starting value is then added, and this continues until all segments have been added. After each segment is added, the MRC is sorted into descending order of magnitude to ensure that the flow on any day is equal to or less than the flow on the previous day. When all segments are ranked and have been combined, a value of daily recession constant (or several values of Ki, K2 and K3) can be calculated using K= QBt+i/QBt. The fit of the calculated linear regressions can be manually adjusted. The master recession Chapter Five Baseflow recharge and discharge analyses with a view towards modelling, 5.44 curve derived using this method depends mostly on the more frequent recessions in the catchment, which come from the source areas which become active after small events. 5.5.2.2 The Tabulating Method The tabulating method (Johnson and Dils, 1965) is similar to the matching strip method. It involves the tabulation of daily runoff for individual recessions in columns. The columns are adjusted vertically until the discharges agree horizontally. The mean discharges constitute the MRC. 5.5.2.3 The Correlation Method This method (Langbein, 1938) involves plotting Qt against Qt+n (Qt, N days later) on log- log paper and fitting a straight line through the data points. This method is based upon Equation 5.7, from which it is seen that the recession constant K is a function of the slope of the correlation line (Q/Q0) and the lag interval t. 1 O - K=e-k=(^y 5.13 The correlation method was recommended by Hall (1968) as potentially the most useful one. The Institute of Hydrology (1980) used this approach and noted that this method is less subjective in comparison with other methods. 5.5.2.4 Analytical Method The temporary storage of runoff on the surface or in the groundwater store of a catchment can be represented by a linear or non-linear storage reservoir. The groundwater outflow equation (Equation 5.14) can be derived from the continuity equation for the reservoir. Also the storage-flow relation can be represented by Equation 5.15. I-Q = dS/dt 5-14 S = k Q 5.15 Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.45 Where I (mm/day) is inflow from rainfall, Q (mm/day) flow rate at the stream gauging station, S (mm) volume of water in storage, t (day) time, k is magnitude of storage- discharge relation, and m is non-linearity of storage-discharge relation which varies with the range of 0 to 1 and is equal to 1 in case of a linear reservoir. For the linear recession curve, solving Equations 5.14 and 5.15 for the hydrograph recession where inflow from rainfall is 1= 0 and m=l gives: -Q = k.(dQ/dt) 5.16 Integrating Equation 5.16 gives Equation 5.17, which plots as a straight line on a log-log graph and k is given by the slope of straight line. t/k Qt = Q0e- 5.17 k = t/ln (Q(/Qt) 5.18 Solving Equations 5.14 and 5.15 for a non-linear reservoir gives Equation 5.19. -Q = imQm-1dQ/dt 5.19 Integrating Equation 5.19 gives Equation 5.20 for non-linear reservoir which plots as a curved line on semi-log graph. Comparison of Equations 5.16 and 5.19 shows that non linear reservoir can be considered to have k=imQm'1. am-1=o?-1+(- )t 5.20 km The parameters of Equation (5.20) can be determined from a plot of dQ/dt versus Q (Boyd and Bufill, 1989). In practice, this consists of plotting (Qt -Q^)/ At against (Qi + Qi-i) / 2, where Q{ is the flow rate at time t and Q^ is the discharge at t - At, and then fitting equation 5.16 or 5.19 (for linear and non-linear reservoir respectively) and forcing the best fit for each catchment through the origin using least square method. With I and m known, the groundwater storage S at any time can be calculated from the concurrent discharge using Equation 5.15. Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.46 The advantage of this method is that it utilises all points on the baseflow hydrographs, it eliminates the problem of determining the time reference t= 0 after each interruption of baseflow recession by rainfall, and the length of a given discharge or recharge period is not of primary importance. It also minimises the errors of recession perturbing by rainfall, evaporation or pumping. These methods allow identification of k (for linear recession when m =1) or k and m for non-linear recession from the hydrograph recession. Also, different recession constants which come from different source areas can be recognised using this procedure. Of these methods, correlation was recommended by Hall (1968) as potentially the most useful one. Nathan and McMahon (1990) used both matching strip and correlation methods in their study of low flow hydrology. The results of their study showed the fundamental advantage of the matching strip method over the correlation method. The tabulation method is essentially the same as the strip method. Therefore, based on the literature review, it was decided to adopt the matching strip approach and to compare the results with an analytical evaluation of MRC from log-log plots of dQB/dt versus QB for all events. In the following section application of the two methods to the daily streamflow from eight catchments are made and a discussion of the results is presented. 5.5.2.5 Results and Discussions Both the matching strip and analytical methods were applied to the daily streamflow of the eight catchments. Mean values of the daily baseflow recession constants for each of the catchments are listed in Table 5.10. For all eight catchments, some variations generally occurred in the recession constants derived from different years using the matching strip method. The variation of the event recession constants is expected to be much larger than that of average recessions; for each year and by using analytical or matching strip method. The variations might be due to the following factors; • spatial variations of recharge, rainfall, losses and moisture storagefrom event to event • rainfall during some recessions and variation in evapotranspiration and infiltration • seasonal and annual changes in the responses of the catchments • one day interval data cannot accurately define the falling lamb of the hydrograph. • channel transmission losses and high evapotranspiration from the proximity of the streams probably contribute to less sustained recessions and low values of K on the catchments • the size of source areas that expand and shrink during events. K values vary as the source area of catchment runoff varies Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.47 To analyse the variation of dQB/dt with QB, several plots of baseflow during discharge periods were plotted for each catchment. Figure 5.16 shows results for Kangaroo Valley Catchment. The plots display a considerable scatter indicating that, for a given value of QB, the value of dQB/dt is not unique. Part of this comes from noise in the flow data and the use of one day time step, but part of it may also be due to the variation of K due to the variation of source areas. Thefinal part s of the recession showed more non-linearity in some catchments. This part of the recession is usually more subject to inaccuracies due to measuring equipment inaccuracies, the accumulation of sediment at the gauging station, and effects of evaporation and transmission loss. The effective baseflow producing parts of the catchment expand and shrink during events, hence the catchment should not be treated as uniform, ignoring the variation of the recession parameter. The values of K vary from storm to storm. Analysis of K can help in the identification of the properties of a catchment that contribute both flow components at different times during a storm. Figure 5.17 shows an example of recessions for the Kangaroo River Catchment (215220). Three clusters of recessions can be observed. The upper line indicates a separate source of baseflow which depletes rapidly. The lower line represents the normal recession of this catchment. Different slopes and, in turn, different baseflow regimes can be identified using Figure 5.17. The upper line indicates a source of baseflow with fast depletion rates. This can signify the recession of the catchment when the whole area contributes baseflow. The recession parameter K reflects the depletion of different source areas (first, second and third sources). Analysis shows that about 75% of the surface runoff events on each catchment occurred from the first source area. The lower line K=0.975 represents the depletion of thefirst source area and is the normal (more frequent) recession of this catchment. This part of the catchment exhibits relatively lower rates of depletion and recharge. The middle line shows the depletion of baseflow from first and second source areas. For low flow forecasting purposes the lower line is more accurate while the upper line should give a more accurate estimate of flood potential contributed by the whole area. The resulting straight lines indicate that the runoff from this catchment can be modelled Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.48 by several linear reservoirs with the recession being represented by Equation 5.16. Other researchers (Bufill, 1989) are in agreement with this that the linear storage can indicate the outflow from one single linear storage or from a set of identical linear storages in parallel. Discharge mm/day Figure 5.17 Example of The Master Recession Curve Derived by Analytical Method: dQB/dt versus QB As would be expected, there appears to be a tendency for smaller catchments to have a lower K value (as the recession should be less sustained) and higher variations in the recession constant (see Table 5.10). For example, the smallest catchment, Sandy Creek, has a K=0.808 and CV = 0.0348 whereas the largest catchment of Kangaroo Valley has K=0.914 and CV = 0.0095. This is confirmed by the fact that the permanent water table in small catchments is generally situated beneath the stream beds. The analytical method has the advantage of using all points on the baseflow recession curve. Also, no particular assumptions regarding the linearity or non-linearity of the baseflow recession are needed. If the recession plots as a straight line on semi-log graph paper, then the storage-discharge relation is linear and K is determined from the slope of the line using Equation 5.16. If the recession plots as a curve, then it can be represented based on the a non-linear function (Equation 5.19). Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.49 Table 5.10 Comparison Between K Values Obtained by Matching Strip and Analytical Method Catchment Equation for Recession Recession Constant Recession Constant C.V. of Average K Values for National Index (Analytical Method) (K Analytical) (K Matching Strip) Each Year (matching Strip) 1 2 3 4 5 215004 y = 0.128x R=0.84 0.880 0.955 0.01071 n = 878 215009 y = 0.1308 x R= 0.84 0.877 0.943 0.0179 n = 1350 215014 y = 0.2164x n = 326 0.805 0.923 0.0312 R=0.89 215006 y = 0.09 x n=483 0.914 0.977 0.00977 R = 0.80 215008 y=0.106x R=0.85 0.899 0.957 0.0178 n = 1500 206026 y = 0.2135x R=0.80 0.808 0.966 0.0348 n = 532 215220 y = 0.0903x R=0.78 0.914 0.948 0.00952 n = 3763 214003 y = 0.1015 x R=0.49 0.903 0.970 0.0228 n = 2840 n= number of points on recession Variations of recession constants from catchment to catchment were not significantly different from the variations of individual values for a given large catchment. Comparing K analytical and K matching strip (columns 3 and 4 in Table 5.10), there appears to be a dissimilarity between the two sets of results. The line of bestfit between the two sets of results was obtained using the least squares method (K^,^ = 0.71 K + 92 * matching strip 0-1 )> where the coefficient of determination R was 0.36. In order to determine which of these methods was more reliable, both sets of results were compared with other variables relating to low flow characteristics. A comparison of the daily flow duration curves of these catchments is presented in Figure 5.18. A flow duration curve is a useful tool for illustrating the flow characteristics of a catchment. For example, when the slope of the curve in the low flow portion is steep, it indicates that groundwater contributions are poor. Accordingly, a comparison of an index obtained from the flow duration curve and these two methods was made (in Figure 5.19) in which Q90/Q50 ratios are plotted against recession constants. The correlation between the matching strip results and Q90/Q50 and BFI ratio were found to be negligible, with coefficient of determination being 0.013 and 0.29, respectively. However, the Chapter Five Baseflow recharge and discharge analyses with a view towards modelling, 5.50 corresponding coefficients of determination for the analytical results were 0.84 and 0.24 which indicates that the latter approach provides more reliable results for low flow studies. 1UU I! « !!!• + ' x «— - * x x O • D X •a V w : ' '*• 2 » % a X • • 214003 o «0 a X o 10 o © •a A 0 + • • * 4 215014 NV *!• » • °r. X 3 A ° * 215009 V I ! : :iSa • 215006 a 215220 Discha i '•>•%> E + 215008 0B,D H « *h^» CM »215004 o . « '^ > P 206026 u o ° • 0.01 0.1 10 100 1000 Catchment Runoff Depth in mm/day Figure 5.18 Comparison of the Daily Flow Duration Curves of the Studied Catchments O K (matching strip) • K (analytical method) 1.00 6 0.98 O o 0.96 o 0 0.94 o y = 0.0303X + 0.9563 0.92 (1 R2 = 0.0134 • * 0.90 • • ^--- 0.88 - • • y == 0.4629x +0.8188 0.86 R2 = 0.8383 0.84 . 0.82 o 0.80 0.00 0.05 0.10 0.15 0.20 0.25 Q90/Q50 Figure 5.19 Correlation Between Recession Constants Derived Using Matching Strip and Analytical Method versus Q90/Q50 Ratio As the results of this study reflect the advantages of the analytical method over the matching strip method, the recession results obtained from the analytical method will be adopted for use in the next stage of the research. Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.51 The master recession curves for all catchments, calculated from the matching strip method, will be presented in Appendix D. The calculated values of recession constants will be used for rainfall-runoff modelling. 5.6 INVESTIGATING THE POSSIBILITY OF DETERMINING THE MASTER RECHARGE CURVE 5.6.1 General After separating surface runoff from baseflow (discussed in section 5.3), Equation 5.16 was applied to the separated baseflow during storm events in order to test the possibility of calculating the dominant recharge behaviour for each catchment. The procedure used to calculate the master recharge curve is similar to that used for the master recession curve and is explained in section 5.5.2. Scatter plots of dQB/dt versus QB during storm events were made, and Equation 5.16 was applied to test the possibility of calculating the dominant master recharge behaviour for each catchment. The analyses showed that there have clearly been several different rates of recharge during the study period. The results are presented in the following section. 5.6.2 Results and Discussions Scatter plots of dQB/dt versus QB during storm events were made and Equation 5.16 was applied to test the possibility of calculating the dominant master recharge behaviour of- each catchment. The analysis showed that there have been several different rates of recharge during the study period. Mean values of the daily baseflow recharge constants for each of the catchments are listed in Table 5.11. In some of these catchments there have been several rates (at least three) of recharge. An example of master recharge curve identification using the analytical method is presented in Figure 5.20. From this figure different recharge regimes can be recognised. The upper and lower lines represent the maximum and minimum observed rates of groundwater recharge. For example the slopes of the upper envelopes are 0.35, 0.33 and 0.16 and those of the lower lines are 0.051, 0.040 and 0.036, for catchments 215006, 215008 and Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.52 215220 respectively. The estimation of recharge rates in the studied catchments using the analytical method is presented in Table 5.11. The value of QB depends mainly on the storage of water in the catchment. The upper line provides information on the recharge of groundwater when the whole area of each catchment is effective and contributes to recharge. Thus the rate of recharge is high. This indicates that, in some events, the catchments start with an initially high recharge which decreases rapidly as the flow continues. The lower line represents a regime of recharge (or the advanced states of recharge), which is more stable for all of the studied catchments. This is the normal recharge occurring from thefirst source area which has a lower infiltration capacity and lower recharge rate. 1.4 dQ/dt = 0.16Q 215220 k„P =6.26 days 1.2 K =0.852 /- . / • V s^ dQ/dt = 0.0791 Q - • k„ =12.64 days P 0.8 - /<• Kov =0.924 . // R2 =0.41 - - n =3180 $< 0.6 s"'' •o " -. ; -->-" " 0.4 - - '; •-' -"-„ ,- ZS''- • - -_" ._- - -_' _ -V^ - - -' ^-*T~" ^ :- ~- -. _-_-_ : \ _zs- "-.";" ^^^-^"~""- • ; =- 0.2 - .Cr-.=:~. -:— ^£f~ "-. V: i-*-" . - • • .- •z—s- " dQ/dt = 0.035 Q =28.57 days :> ^^•^.'[-.^s^^^Z- =- '.;__-=.--"l-^• — ..-"-•• - k_ =0.965 Illlljji^^^: ' 1 4 5 Recharge mm/day Figure 5.20 Example of The Master Recharge Curve Derived by Analytical Method: dQB/dt versus QB Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.53 Table 5.11 Estimation of Recharge Rates in the Studied Catchments Using Analytical Method Catchment Coefficient for recharge Equation (a) R Number of Data Recharge Constant National Index -dQB/dt=a.QB n (K) 215004 al =0.045 0.956 a2=0.020 0.79 933 0.912 a3=0.180 0.835 215009 al=0.056 0.945 a2=0.091 0.68 1067 0.874 a3=0.210 0.811 215014 a=0.1334 0.87 473 0.875 215006 al=0.051 0.950 a2=0.090 0.35 1093 0.914 a3=0.350 0.705 215008 al=0.040 0.961 a2=0.010 0.69 1720 0.905 a3=0.330 0.719 206026 a2=0.1443 0.62 527 0.866 215220 al=0.040 0.961 a2=0.079 0.64 3180 0.924 a3=0.150 0.861 214003 al=0.040 0.961 a2=0.069 0.72 4000 0.933 a3=0.120 0.877 The frequency distribution of (event) K values during recharge periods are presented in Figure 5.19. The distribution of the event recharge constant is negatively skewed. It can be seen that K (recharge) ranges from 0.65 to 0.995 with the most frequent values being around 0.960. This shows that there are a greater number of events with higher K values and more sustained recharge versus a smaller number of high events which have higher runoff rates, less sustained recession, and come from steeper parts of the catchments. The skewness indicates that more than 50% of the groundwater recharge events for each of the catchments occurred in thefirst sourc e area. Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.54 45 • 215220 40 • 215004 35 E215009 S214003 30 a 215006 0215014 25 3 0215008 O" to 20 m 206026 15 10 iMjn,.Jj.iI MH li|l am\mm HI •iMMiiiinna|llHlL1iy[LM |i _E_ 0.650 0.690 0.730 0.770 0.810 0.850 0.890 0.930 0.970 0.995 1.000 K Values (Recharge Periods) Figure 5.21 Frequency Distribution of Event Recharge Constant 5.7 SUMMARY AND CONCLUSIONS The total flow in a stream is made up of two main components: baseflow and surface runoff. These components have quite different properties. In rainfall-runoff modelling, it is possible to predict a total runoff value in agreement with the recorded total runoff, but with incorrect prediction of both flow components. When analysing the hydrology of a catchment it is useful to consider these two components separately by partitioning the streamflow into baseflow and surface runoff. This allows for the estimation of several other hydrologic properties of the catchment, including the groundwater store state, low flow prediction, soil store capacity, source area, and the rate of groundwater recharge from infiltration. Five streamflow partitioning methods were investigated in this chapter. They include: a) recursive digital filter, method 1 b) automated technique adopted by Boughton, method 2 c) improved frequency-domain filter technique, method 3 d) modified automated technique, method 4 e) proposed method based on travel time of runoff, method 5 Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.55 Methods 1 and 3 are based on a procedure for modelling time varying data and is commonly used in signal analysis and processing. Methods 2 and 4 are empirical methods which assume baseflow increases during periods of surface runoff by a fraction of the difference between the total runoff and the baseflow on the previous day, or the previous five days. Thefifth method separates the baseflow component of the hydrograph based on a relationship between the volume of water in storage and the baseflow discharge, which is similar to the graphical method and has a physical basis. Streamflow data from two catchments was separated manually, and the performance of the fifth method was checked by comparing its results with manual separation. It was found that the results of the automatic technique agree well with those obtained by the manual approach. The relative performance and consistency of all methods was evaluated using the daily streamflow records for the eight catchments. All of the methods investigated for separating the baseflow from the surface runoff have advantages and disadvantages and, at best, each method will produce an approximate estimate of the actual baseflow. At present there is no practical way of achieving a completely accurate baseflow separation. These techniques are basically analytical tools for determining the approximate separation between surface runoff and baseflow and they have an empirical rather than physical basis. These methods, which can potentially be a systematic and quick means of separation, should be used with caution. Method 1 underestimates the baseflow relative to the other methods. It gives a good estimate of the baseflow rise but it fails to predict the end of surface runoff. This problem is partly solved by method 3. For high events, method 2 overestimated the baseflow which resulted in an underestimation of the surface runoff. This method also estimated a very high baseflow in the troughs of multipeaked events and in the troughs between two events that affected each other. This method also underestimates the baseflow for low flow events. Method 4 appears to solve the problems occurring with method 2. Both methods 2 and 4 give a good estimate of the time when the surface runoff ceases. Initial success with thefiltering procedures (methods 1 and 3) and the attractive features of method 5's, simplicity, the robustness of the filter's performance and the consistency of results from year to year, shows promise and should be investigated further. The Chapter Five Baseflow recharge and discharge analyses with a view towards modelling 5 56 design of a filter with respect to the antecedent condition of the catchment and the incorporation of a routing function for the baseflow, and optimisation of the frequency cut off of the slow flow can be the subject of further research. Analysis of the five baseflow separation methods presents the following conclusions: • The contribution of the baseflow to the total streamflow represents a significant component of the hydrologic cycle and, consequently, should be investigated further. The validity of these methods could not easily be checked. More research is needed to bring more clarity in the problem of baseflow and recessions. • Method 1 underestimates the baseflow relative to the other methods. Methods 1 to 4 underestimates the storm runoff end time for all catchments. Method 5 can be used to determine this point more accurately based on the travel time of runoff. • Methods 2 to 5 can be used to obtain reasonable estimates of baseflow and surface runoff. The results of this study show that using methods 2 to 5 can be accurate enough for flood studies and rainfall runoff modelling. These methods can explain up to 95% of the total variations in baseflow and a AQB less than ±5%. Method 5 which is based on the travel time of runoff gives the most reliable and repeatable results. In section 5.3 of Chapter 5 the concept of a baseflow index (BFI), a dimensionless ratio which can give useful information about the proportion of the runoff that originates from stored sources was discussed. The BFI values for eight catchments usingfive method s were calculated and a discussion of results was presented. The results indicate that except for method 1 there is a good agreement between the results of method 5 and other methods. In overall performance and for larger and more sluggish river catchments, there is little difference between the BFI values estimated from method 5 in comparison with those estimated by methods 2 and 4. In general higher runoff years experienced higher, and low runoff years experienced lower BFI values than the average. For the catchments studied, there appears to be no definite relationship between the size of the catchment area and the baseflow index. A relatively low variation for BFI ratios and, as expected, high variations for surface runoff were apparent. The variation of the annual BFI index was found to be lower than BFI event ratios. The variations of BFI events (the ratio of baseflow event Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.57 volume to total streamflow volume for the same event) are considerably high. Calculated values of BFI from method 5 will be used as the parameter required by the AWBM model. The hydrology of a catchment can be more clearly understood if the discharge and recharge of baseflow storage in a catchment is studied in more detail. In the second part of Chapter 5, analyses of hydrograph recessions and groundwater recharge, which are important for rainfall-runoff modelling were carried out. Both the matching strip and analytical methods were applied to the eight catchments. The calculated values of daily recessions using both methods, for eight studied catchments are presented in Tables 5.10 of this chapter. These will be used as the parameter required by the AWBM model in the next stage of this study. The results indicate that the analytical approach has advantages over the matching strip method for low flow conditions. The main advantage of this method is that it utilises all points on the baseflow hydrographs and is not affected by the length of the given discharge period. Furthermore, it iriinimises recession errors caused by either rainfall, evaporation or pumping. However the automated version of the matching strip method (Boughton, 1995) is much easier to use and eliminates the subjectivity of manual methods. This method is a fast and practical tool and facilitates the estimation of baseflow recessions. Results from investigation of the Master Recession and Recharge Curves indicate that, for most of the studied catchments, several clusters of recession and recharge curves can be constructed. This can be a function of factors such as the recession and recharge of different aquifers, the expansion and shrinkage of source areas and seasonal variations in pattern of recession and recharge. Because higher runoff years experienced higher, and low runoff years experienced lower values of BFI, it is necessary to use a period of record which includes wet and dry sequences for calculating BFI. In smaller time scales (events), the value of (event) BFI is higher for low flow and lower for high flow. Partitioned streamflow from method 5 will be used to check the results of rainfall-runoff models and to estimate model parameters. Calculated annual BFI, based on different filter parameters, for eight studied catchments is presented in Tables 5.2 to 5.8 of this chapter. Calculated values of BFI from method 5 will be used as the parameter required Chapter Five Baseflow recharge and discharge analyses with a view towards modelling. 5.58 by the AWBM model. Also it is used as an index to compare the flow characteristics of different catchments. The calculated values of recession constants will be used for rainfall-runoff modelling. For most situations there are usually plenty of rainfall records but the streamflow data is more expensive to establish, is limited and is rarely available. Although precipitation records have been kept for a longer period, much of the information is unreliable because of errors in the recorded data, extrapolation from point to areal rainfall and the averaging of several rain gauge records. Apart from the systematic, random and observation errors which have a great impact on the measured values, the elevation difference between the catchment and the raingauge, the wind direction and spatial distributions of rainfall can all create rainfall recording errors. The data should be screened for errors before any modelling commences. Further research into appropriate screening procedures would be desirable. CHAPTER SIX THE SFB MODEL CHAPTER SIX THE SFB MODEL 6.1 INTRODUCTION The applicability of rainfall-runoff models relies on physical interpretations of the model parameters relating to the catchment characteristics. This allows model parameters to be estimated from the catchment properties even when hydrological data are not available. One of the important aims in the development of rainfall-runoff process models has always been in applying them to ungauged catchments by using the parameters derived from hydrologically similar catchments. Differences between various water balance models arise from differences in the lumping of continuous processes into discrete elements, and restricting them into a portion of the runoff process. The application of hourly rainfall runoff modelling is often limited by the lack of suitable data. However, daily data are usually available for many catchments, so daily rainfall-runoff models are preferred. The SFB model was selected for this study because of the daily data available for these catchments, its simple model structure and small number of parameters, and the good conceptual basis of the model. Moreover the model parameters can be identified with particular properties of the catchment. The performance of the simple three-parameter SFB model in eight catchments is investigated in this chapter. The analysis was carried out initially using data from the small catchment in the New England region of Australia. Parameters were estimated by an optimisation procedure using the total streamflow and on separated baseflow and surface runoff, as well as directly from individual events and split samples. The model was applied to seven other catchments in the Shoalhaven area and its performance was checked. An overall description of the catchments and some summary information about the climatic inputs used are presented in Chapter 4. Chapter Six The SFB Model 6 2 6.2 DESCRIPTION OF THE SFB MODEL The SFB model is a water balance model developed by Boughton (1984), as a simplification of his earlier 1965 model. His extensive experience in use of the original model enabled Boughton to develop a model which has a good physical basis, a small number of parameters and is capable of predicting runoff with an acceptable degree of accuracy. This model predicts daily runoff from a catchment for given daily rainfall, evaporation and catchment values. The intended application of this model is to predict runoff from ungauged catchments. The model has been extensively used by many researchers with satisfactory results (Boyd et al., 1986, Srikanthan et al., 1988, Nathan and McMahon, 1990, Baki, 1993). The model is based on the following assumptions: (1) The topsoil layer of the catchment surface is covered by porous materials with a high infiltration capacity. Upper soil moisture storage is divided into two equal parts, including moisture held between wilting point andfield capacity (non-drainage component), and moisture held betweenfield capacity and saturation point. (2) The subsoil moisture store represents all the soil below the topsoil layer, where the infiltration capacity is much less due to higher density. This store is depleted each day by the fixed fraction of 0.005 of water remaining in the store. (3) Infiltration is controlled by the percolation between upper and lower soil layers. The moisture in the subsoil controls the infiltration rate, with a higher infiltration at lower moisture content. (4) The baseflow parameter B determines how much of the water in the lower storage appears as baseflow in runoff and how much is lost in deep percolation. At least 25 mm of water has to be in the subsoil moisture before any baseflow occurs. The model's structure is discussed in Chapter 3. A brief discussion of the model will be given in the following subsection. Three parameters of the model require calibration. The most important parameter in this model is S, the surface storage capacity of the catchment. The daily infiltration capacity controlling infiltration from the surface store to the subsoil store is represented by F. The Chapter Six The SFB Model 6 3 baseflow factor B determines the portion of the daily depletion of subsoil store moisture which appears as baseflow runoff. The upper soil storage is depleted daily by evapotranspiration, AET. The amount of evapotranspiration depends on the upper soil storage. AET is equal to PET when the total amount of moisture in the upper soil storage is greater than 50% of S. The potential evapotranspiration is taken as a fraction of the daily pan evaporation value. If the moisture in the upper soil storage is less than 50% of the surface storage capacity, then evapotranspiration depends on the amount of moisture, as shown in Equation 6.1. AET = 8.9*(^|) 6.1 where 8.9 mm is the value of maximum potential evapotranspiration for most types of vegetation (Boughton, 1965). On the basis of the relationship used in the SFB model, AET decreases linearly from PET atfield capacit y to zero at wilting point. The drainage component (moisture held between field capacity and saturation) will satisfy the demand of evapotranspiration AET as long as it has sufficient moisture. Otherwise, the residual of the demand (AET-DR) will be satisfied by the non-drainage component (moisture held between field capacity and wilting point). A schematic illustration of the model for calculating values of actual evapotranspiration from the potential rate is given in Figure 6.1. PETma!I = 8.9mm C c O o Q. o. J-. s c a a u. u* O O c O. l k > ^ > c a o 3 CL, o < WP = 0 US FC = 0.5S Storage level in non-drainage component of the surface store Figure 6.1 Schematic Diagram of SFB Model for Calculating Values of AET Chapter Six The SFB Model 6 4 6.3 OPERATION OF THE SFB MODEL In the operation of the SFB model, several sequences can be recognised. First the upper soil store US is replenished with the daily rainfall P (Equation 6.2), if this US store exceeds half the surface storage S, the excess is put in the drainage component DR and US is set to 0.5*S. If DR exceeds 0.5*S, the excess is given by Equation 6.3 and surface runoff QS commences. This is calculated by Equation 6.4. If the subsoil store SS is greater than one inch (25.4 mm), it is depleted each day by afixed fractio n of 0.005 of water remaining in the store, and a portion of this depleted moisture flows as baseflow (Equation 6.5). The evapotranspiration losses AET from the soil storages will be calculated in the drying sequence; the drainage and then the non drainage component of the surface store is depleted each day by evapotranspiration. The drainage store will deplete at the rate of F mm per day infiltration until it becomes empty. US = US + P 6.2 Pexcess = DR-0.5*S 6.3 QS = Pexcess - F * tanh (^^pesjL) 6.4 QB = 0.005 * (SS - 25.4) * B 6.5 6.4 METHODS OF EVALUATING PARAMETERS As discussed earlier, some constants in the functions used to represent the physical processes and the capacities of the stores are parameters of the model, and numerical values can be assigned to them. The numerical values vary for individual catchments because of the catchments different physical characteristics. For models that represent the physical processes, these values would ideally be estimated from measurement of the appropriate physical variables. For example, infiltration F could be estimated from infiltration experiments in the field. In practice however, this is often difficult and does not give good results, and parameters need to be estimated from rainfall and streamflow data. Chapter Six The SFB Model. 6.5 Different techniques were used in this study to find the values of the parameters. This section presents methods for determining values of the model parameters. There are several approaches for determining model parameters. They include parameter optimisation using total streamflow, parameter optimisation using separated surface runoff and baseflow, and parameter estimation using water balance of individual storm events. There are also several types of optimisation techniques; manual, automatic and semi automatic. Manual optimisation is simply a trial and error search, which is very time consuming and difficult if there are large numbers of parameters. There are three main categories of automatic optimisation procedures; stochastic methods, descent methods and direct search methods. These methods have been discussed in Chapter 2. The approaches which were used in this study will be discussed in the following subsection. The methods used have the advantage of estimating model parameters (without the costs involved infield and laboratory investigation), reducing the number of fitting parameters, mirrimising error in calibration, enabling the model to be optimised (without the domination of high flows in the calibration process), and finally enabling the model to be applied to ungauged catchments in a realistic way. 6.4.1 Initial Estimates of Parameters and Adequacy of Warm-up Period To begin calibration of the SFB model, it is necessary to make initial estimates of the parameters. These values can be estimated based on recommendations made by Boughton (1984) and Nathan and McMahon (1990). Estimation of the parameter values, for application of the model on ungauged catchments was given in Chapter 3 (Table 3.1). The most significant parameter in the model is the surface storage S. For this, the vegetation of the catchment is the main determining factor. In all eight catchments, the cover ranges from dense forest at the foot of the escarpment, to grassland in the valley, to sedgeland on the plateau. The value of S=100 mm (for predominantly forest, small areas of grassland or crop land) is usually a good selection as the initial estimate of parameter S. For the daily infiltration capacity, F, the soil type is the dominant factor. The guidelines on F in Table 3.1 suggest that 7 and 0.5 mm per day are appropriate for deep permeable Chapter Six The SFB Model. 6.6 and low permeable soils respectively (Nathan and McMahon 1990, suggest a range of zero to 20 mm per day). The catchments were mainly covered by Podzolics type soil. The value of F=5.0 mm/day (for sandy loams) was selected as the initial estimate of parameter F. Flow characteristics are the dominant factors for the baseflow parameter B. For perennial streams which flow more than 75% of the time an initial estimate of B=1.0, and for ephemeral streams some value near zero would be appropriate. For thefirst ran, the initial values of soil stores were adopted: US=0 and SS=0 (assuming that the catchment is dry). As discussed in Chapter two, there are different methods for estimating the values of pan factor (fp): a fixed value (fp=0.7), seasonal values factor (fp=0.8 for summer, fp=0.6 for winter, and fn=0.7 for spring and autumn) or a monthly value of pan factor. Initially, a constant value of pan factor (fp=0.7) was used. This was followed using seasonal values of the pan factor. Since the values of the seasonal pan factor gave better results, they were adopted in modelling all catchments. The adequacy of the warm-up period for the model was discussed by Nathan (1990) and Baki (1993). The SFB model was applied using several sets of initial values of soil stores. The daily values of the soil stores were monitored and it was found that one year is enough for the sub soil store to converge to similar values regardless of the initial values. The upper soil stores dried up considerably quicker, in less than a month, indicating a smaller capacity and a bigger extraction rate. Thesefindings are consistent with those of Nathan (1990) and Baki (1993). Figure 6.2 shows the comparison of the upper soil stores US for the four different initial values. Figure 6.3 illustrates the comparison of the sub-soil stores SS, for the four different sets of initial values. Chapter Six The SFB Model. 6.7 V2 w (X VI -J i—i O VI (X w P-. PL. 50 60 TIME (days) S=100mm, F=5 mm/day, B=1.0 Figure 6.2 Plot of US Against Time (after Baki, 1993) 400 300 V2 9 w r^ O CO i % CO 15 20 25 30 35 40 Time (months) S=100 mm, F=5 mm/day, B=1.0 Figure 6.3 Plot ofSS Against Time (after Baki, 1993) 6.4.2 Measure of Goodness of Fit The accuracy of the predictions made by the models was measured by comparing the monthly predictions (Qe) to the monthly recorded values (Qa). As the routing of daily mnoff may cause misalignment of predicted and recorded daily values, calibration of the model against monthly runoff reduces the effect of possible inconsistencies and is usually recommended. There are many types of objective functions which can be used to measure the goodness offit o f a simulation. In the following subsection several objective functions are discussed and one of them which is more suitable for optimising model parameters will be selected. Chapter Six The SFB Model. 6.8 The most common objective function is the sum of squared differences (SSQ), which is also known as ordinary least squares. This measure is also minimised in a least squares regression. It is given by the following relationship: 2 SSQ = Z(Qe-Qa) 6.6 where Qe = the predicted runoff O = the recorded runoff Another measure is the sum of the absolute differences (SAD), which is given by: SAD = Z I Qe - Q a\ 6.7 This measure also considers the magnitude of the deviations. It is quite similar to SSQ, except that the weighting of large deviations is lessened by taking the absolute value rather than the squares. These two measures have been used by Johnston and Pilgrim (1973). Another measure is the sum of the squared percentage differences (PSSQ), which is given by: PSSQ=J,S^)2 6.8 This measure places the weighting on the percentage of the deviation of Qe from Qa, rather than the magnitude of the deviation. Another measure is the sum of the absolute percentage differences (PSAD), which is given by: PSAD=X 6.9 Qa The coefficient of determination (D) is also very commonly used for measuring the degree of association between the recorded and predicted runoff. Aitken (1973) described D in the following form: Chapter Six The SFB Model. 6.9 2 2 _ g a - ea ) - no, - &, ) D = ^oj where Qe = the predicted runoff Q = the recorded runoff _a Q = the mean recorded runoff ^•a Qest = estimated runoff obtained from the regression line of Qa on Qe The value of D will always be less than unity, and the closer it is to unity, the greater the degree of association between the predicted and the recorded runoff. Nash and Suttcliffe (1970) proposed a measure called the coefficient of efficiency (E). This coefficient is analogous to the coefficient of determination but is not identical. A coefficient of efficiency significantly different from 1 indicates that the simulated results do not plot about the regression line and there is bias in the simulatedflows. It is given by: 2 2 c nQa-Qa) -*Qa-Qe) ... E = =—;; 6.11 nQa-Qar If the correlation is high D and E are both have similar values. Another suitable measure is the standard error of estimate (S), which is a measure of the variation between the monthly predicted and recorded runoff and is given by: S = aaJ(l-D) 6.12 where aa is the standard deviation of the recorded runoff Aitken (1973) suggested the residual mass curve coefficient (MR) as a measure of accuracy, which measures the degree of association between the residual mass curves of the recorded and predicted runoff. The residual mass curve is given by the sequential summation of the difference between each month's runoff (Qa) and the corresponding average monthly runoff ( Qa). MR is then given by: X(A,-A,)2 Chapter Six The SFB Model. 6.10 where Da = the departure from the mean for the recorded residual mass curve Da = the mean of Da De = the departure from the mean for the predicted residual mass curve Johnston and Pilgrim (1973) found that the SSQ is more desirable than SAD, since SSQ had steeper slopes in the response surface. This makes it more suitable for automatic optimisation. In the study conducted by Baki (1993), thefirst fou r objective functions described above were used in the process of optimisation. The results of the study showed that the sum of squared differences (SSQ) between the monthly recorded and simulated runoff was the best objective function. Therefore, it was decided to adopt the sum of squared differences (SSQ) as the main performance criteria for this study. The secondary performance criteria used were: the differences in the mean runoff (%AQ), the coefficient of determination (D), the coefficient of efficiency (E), the standard error of estimate (S), comparison between the predicted and recorded flows based on the plots of these two values and their mass curves versus time. Finally the sign tests of the residuals (et = Qe -Qa) were made using the plots of et against time and et against Qa, to detect biased predictions. The tertiary performance criterion was a comparison between the predicted and partitioned baseflow and surface runoff. The results indicated the importance of checking the calibration of the model with some measure other than a comparison derived from the total recorded and simulated streamflow. It not only increases the accuracy of the model's prediction, but avoids parameter interaction, and detects the problem of error compensation with over or under-estimation of dependent components. Finally, the plots of Qe and Qa against time and the mass curve of Qe and Q were made to illustrate the visual comparison between the monthly Qe and Q . 6.4.3 Parameter Optimisation Using Total Streamflow 6.4.3.1 Optimisation Technique Used Optimisation techniques used in this section aim atfinding the values of the parameters using the total streamflow. Optimisation was carried out using automatic and semi automatic pattern search optimisation procedures, which involved searching the response surface of the objective function to find the global minimum by successive increments and decrements about each trial parameter value. Chapter Six The SFB Model 6 11 surface of the objective function to find the global minimum by successive increments and decrements about each trial parameter value. The pattern-search method is classified as a direct search optimisation procedure. These methods utilise the actual values of the objective function rather than the gradients. Some of the most popular direct search methods are the univariate search, the rotating coordinate search, the simplex method and the pattern-search. The pattern-search technique was developed by Hooke and Jeeves (1961). A comparison is made between the actual values of the objective function, however changes in the parameter values are made based on the direction of reduction in the objective function with each parameter. Hendrickson et al. (1988) found the pattern-search to be more robust than the gradient methods. Jayasuriya (1991) selected the pattern-search technique after it was compared with the simplex, the Gauss-Marquardt, the steepest descent and Kuczera's NLFIT method. There are several assumptions used in the descent techniques, which may not be valid for all the models, whereas by using direct search, a direct comparison is made between the values of objective functions. Therefore, the pattern-search technique was adopted as an optimisation technique for this study. The pattern-search method uses a pattern of coefficient adjustments that minimise the objective function. Two types of adjustments are LE (local excursion) and PM (pattern move). Thefirst determine s the probable direction of a successful move, and the next improves the value of the objective function (OF). It is this feature of the method that makes the technique superior to a pure trial and error search. The differences between the two types of adjustments is that LE is usually a small percentage (eg 10%) of the present parameter value and is arbitrarily selected by the user, however, in PM the size of the adjustment applied to each parameter is determined from the trend of its past local excursions. The flowchart of the pattern-search technique is shown in Figure 6.4. Chapter Six The SFB Model. 6.12 Pick Initial Values of: Parameters, Soil Stores & Local Excursion Search Evaluate Objective Function (O.F.) ©- Make Local Search Moving a Distance LE to Each Sides [ Parameter (I) ± LE(i) ] and Evaluate (O.F.) Figure 6.4 Flow Chart of Pattern-Search Optimisation Technique Chapter Six The SFB Model. 6.13 This technique uses the following steps: 1) The parameters are set to an initial value and the objective function OF is computed. 2) The increments for the parameter variations LE are specified by the user. 3) The parameter is increased by its increment and the objective function is computed. 4) If the objective function has been improved, the new value of the parameter is adopted and the next parameter is increased by its increment (step 7), otherwise go to step 5. 5) The parameter (before step 3) is reduced by its increment and the objective function is computed. 6) If the objective function has been improved, the new parameter value is adopted, otherwise keeps the previous value of the parameter. 7) Step 3 onwards is repeated for other parameters until all parameters are considered. 8) After all parameters are considered (completed one iteration), each parameter is changed corresponding to its improved pattern (increase or decrease), and the objective function for this set of parameters is computed. 9) If the objective function is improved, this new set of parameters is adopted, otherwise reset the parameters to the set before step 8. 10) If no improvement had been made in the previous iteration, all the increments is reduced by half 11) If the divisions of the increments are less than the desired fraction of the original increments, then the current minimum objective function is adopted as the optimum and the current set of parameters is adopted as the optimum set, otherwise, step 3 is repeated for another iteration. The semi-automatic method is a combination of manual and automatic techniques. The automatic technique is used to search for the optimum set using the initial values adopted. The use of an automatic technique by itself does not guarantee that the global optimum will be found (Jayasuriya, 1991). Therefore this optimum set was not directly adopted. A manual comparison with other sets of parameters was made to ensure that the optimum value obtained was the true optimum and not the local optimum. A manual method by itself would have been too time consuming and very difficult for a large number of parameters. A combination approach (a semi-automatic method) was more Chapter Six The SFB Model. 6.14 desirable. Therefore, it was decided to use the semi-automatic method (with the Pattern- Search as the automatic technique). 6.4.3.2 Optimisation Procedure There were three parameters to be optimised for this model. Thefirst case study was the 6 km2 experimental Sandy Creek catchment with a 12 year period of data. Parameters were estimated by optimisation to minimise SSQ over the period of available data. For a range of B, parameters S and F were optimised. The results of this catchment are given in Table 6.3, which summaries all techniques. The minimum SSQ was obtained for S=2.2, F=46.7 and B=0.2 presented in rows 2a-f, Table 6.3. These values are clearly unrealistic and result from the strong interaction between parameters S and F (Figures 6.5). Figure 6.5 The 3-Dimensional Response Surface of SFB model using SSQ ( B=0.2) Chapter Six The SFB Model 6 15 Table 6.3 also shows that while the total streamflow is estimated reasonably well, QB is overestimated and QS is underestimated. Note that QB can be overestimated even for small values of B if parameters S and F are poorly chosen (row 2e, Table 6.3). If a more realistic value of F is used together with a small value of B (row 2d, Table 6.3) better estimation of SUMQB is obtained. Values of S=21 and F=7 along the response surface valley of Figure 6.5 also give a much better estimation of SUMQ, SUMQB and SUMQS but with some increase in SSQ (row 2f, Table 6.3). This value of S however is lower than would be expected for a forested catchment. This part of the investigation showed that minimising SSQ based on the total streamflow alone can give quite poor modelling of the catchment hydrology. It can give poor parameter values, poor SUMQ, poor SUMQS and SUMQB. Even if SUMQ is predicted well, SUMQB and SUMQS can be in error. Hence, it was decided to investigate some alternative parameter estimation techniques. 6.4.4 Parameter Optimisation Using Separated Baseflow and Surface Runoff The optimised parameter values of most models on the total Q involve substantial errors. Sources of the errors can be; the spatial and temporal distribution of catchment properties including parameters and variables, variation of the surface stores, lack of an accurate relationship to convert potential evaporation to actual evapotranspiration, interdependence of the parameter values, data errors, inconsistency and variation in the input data, the method of optimisation used and the objective function adopted. Bearing the above problems in mind, model parameters were estimated using separated surface runoff and baseflow, depending on the sensitivity of estimated QB and QS to changes in parameters. Table 6.1 shows the sensitivity of estimated QB, QS and Q for changes of (±10-20% ) in parameters S, F and B using 12 years data from Sandy Creek catchment. These results are consistent with those of Boyd et al. (1986) and Boughton (1984). Chapter Six The SFB Model. 6.16 Table 6.1 Model Sensitivity to Parameter Variations for SFB Parameter Values are percentage changes in estimated flow for change in parameter SFB -20% -10% 10% 20% QS 34.4 16.0 -13.2 -26.3 S QB 10.0 5.7 -6.2 -9.5 Q 20.3 10.1 -9.3 -16.6 QS 3.5 1.7 -1.6 -3.4 F QB -13.9 -7.2 -5.9 11.1 Q -6.5 -3.4 2.7 5.0 QS 0.0 0.0 - - B QB -41.0 -23.3 - - Q -23.8 -13.4 - - In this part of the study the SAPS procedure was adopted to optimise the model parameters using surface runoff and baseflow separately. The procedure set out in the following steps was used to evaluate each of the model parameters independently of the adopted values of the other parameters. Some of the advantages of this procedure are reducing the effect of evaporation data errors (details will be discussed in Chapter 8) and reducing the effect of the domination of high flows against low flows on the optimised values of model parameters. (a) Using separated baseflow obtained by using method 5 discussed in Chapter 5, and fixing B (assigning a value of 1 or less depending on the % flow days) the parameters S and F can be optimised. As the baseflow is more sensitive to the parameter F (see Table 6.1), from this step the value of this parameter that is close to the true catchment value can be obtained. (b) The model was next optimised by comparing actual and estimated surface runoff. For this the model was set to calculate only surface runoff with B=0, F obtained from the first step and optimising S. Since the surface runoff is more sensitive to the parameter S (see Table 6.1) and this is less sensitive to the errors in the evaporation data, from this step the value of surface storage capacity can be determined close to the true surface storage capacity (average S) of the catchment. (c) With the parameters S and F determined, and using the total mnoff records, the parameter B can be optimised. Using the new B value, steps a to c can be repeated to obtain a more accurate set of all 3 parameter values. This method has several advantages compared to using total streamflow alone in the optimisation process. The period of low flow is longer than periods of high flow in the Chapter Six The SFB Model. 6.17 record. Also high and low flows have distinct statistical properties. Since the method allows the estimation of parameters representing the low flow characteristics of the catchment, the differences are taken into account. The small difference between observed and estimated low flow does not significantly affect the total runoff volumes. As a result, the parameters obtained from the baseflow alone have lower error. At the same time the error in the low flow can be detected. 6.4.5 Parameter Optimisation Using Water Balance of Individual Storm Events As the SFB model represents the physical structure of the catchment, it is possible to estimate the parameters of the model by applying a water balance to individual storm events. This method is based on the analysis of rainfall and runoff events to estimate the recharge, infiltration rate, and surface storage capacity of catchments. If recharge and F are derived from the direct analysis of storm events, the uncertainties in the rainfall data do not enter into the obtained values (because recharge and F are determined by applying a water balance to the subsoil store using baseflow discharge only). This method can result in consistent parameter estimates in some catchments and avoids the problem of parameter interaction which occurs with optimisation techniques. At thefirst step a water balance of the major events in which the whole catchment contributes runoff (these events occur at the end of long dry periods when the stores are empty, and the effect of antecedent wetness is minimum) should be carried out. The recharge of groundwater storage shows itself in the increase of baseflow during and after storm events. A relation between the volume of water present in groundwater storage and the concurrent baseflow, as discussed in Chapter 5, is used in the estimation of recharge during storm events. The total volume of recharge to ground water storage can be determined using a water balance for the lower soil store over storm duration. Recharge = ZQB- XQBB +As +Deep seepage 6.14 where £QB = the sum of baseflow during storm event £QBB = the sum of baseflow during storm event if recharge did not occur As = change in ground water store Deep seepage = deep percolation Chapter Six The SFB Model. 6.18 If we assume a linear recession and recharge relationship and no downward percolation to deep groundwater, then Equation 6.14 can be replaced by the following equation. -QB. Recharge = -j—|- (K n-Kn) 6.15 where QBo = the initial baseflow at time 0 K = recession and recharge constant Daily infiltration (F, mm/day) can be estimated by dividing total estimated recharge by storm duration (n, day): F=Recharge ^ The surface store capacity S can be estimated from the depth of rain needed to produce surface runoff after a dry period (when the surface store is empty). S = P - AET - QS - Recharge 6.17 The discharge-storage relationship for baseflow on the Sandy Creek catchment was found to be: 0.644 , , n s= 13.42 QB 6.18 t ^ t where s _ storage on day t, mm QB = baseflow discharge on day t, mm The basic method is presented in Table 6.2 which shows a water balance of the storm event of 9 to 16 February 1976. Total runoff for this period is 44.5 mm and the estimated increase in the ground water storage based on Equation (6.18) applied to the baseflow rates before and after the storm is 5.94 mm. Allowing for the flow from the baseflow storage generated by the storm (IQB- XQBB=1.57), as well as the increase in the storage from start to end of the storm (5.95), there was a total recharge of 7.5 mm from the rainfall into the baseflow storage. The infiltration rate of F=l.l (mm per day) can be obtained by dividing the total recharge by storm duration (7.7 mm during 7 days). Chapter Six The SFB Model 6.19 Table 6.2 Vfater Balance forStorm Event 9-16 Feb., 1976 date Q P AET QBB QB QS S mm mm mm mm mm mm mm 9 2 1976 0.06 0.60 2.10 0.06 0.06 0.00 2.19 10 2 1976 2.76 42.80 2.10 0.04 0.08 2.70 11 2 1976 27.61 52.60 1.82 0.03 0.11 27.50 12 2 1976 8.93 6.40 1.54 0.02 0.15 8.80 13 2 1976 2.70 0.20 1.40 0.02 0.21 2.50 14 2 1976 1.28 0.60 3.50 0.01 0.29 1.00 15 2 1976 0.70 0.00 4.48 0.01 0.40 0.30 16 2 1976 0.46 0.00 3.08 0.01 0.46 0.00 8.14 Totals 44.50 103.20 20.02 0.20 1.77 42.80 5.95 The volume of rainfall for 5 and 10 days prior to this event are 1.8 and 9.1 mm respectively. Subtracting the amount of the storm excess rainfall (P- AET), 83.18 mm, from the sum of surface runoff plus recharge during the event, 50.5 mm, gives an estimated capacity for the surface storage, equal to 32.7 mm. One of the problems with this method is the limited data available. Only five storm events were suitable to estimate parameters in this way. Average results for thefive events are shown in Table 6.3, row 4. To estimate more accurate values of surface storage capacity based on the analysis of a number of events, the sum of (QS + recharge) for all events can be plotted versus (P- AET). Although this method results in more consistent parameter estimates and also avoids the problem of parameters interaction, it has several major limitations. Firstly, the number of suitable events with negligible antecedent wetness is limited. Next, the method is subjective and results are dependent on the user's judgement. Finally, while analysing a number of storms, a large degree of scatter appears in the obtained surface storage capacities, which indicates the existence of different storage capacities in the catchment. The SFB model however assumes catchments as having a uniform storage capacity and this could be a major limitation of the model. Chapter Six The SFB Model. 6.20 Table 6.3 Parameter Estimates and Model Predictions (Period 3 from 1976-1987) Method S F B SSQ SUMQ SUMQS SUMQB 0 - - - - 841 668 173 1 80.0 3.0 1.0 11858 759 199 560 2a 123 1.50 1.0 10426 507 182 325 2b 70.5 2.30 0.8 11672 618 275 343 2c 70.0 2.27 0.6 11871 497 281 216 2d 68.0 2.10 0.4 12191 411 291 120 2e 2.25 46.7 0.2 9691 921 326 595 2f 21 7.0 0.2 16575 838 654 184 3 40.0 0.8 0.72 18138 838 669 169 4 45.0 1.0 0.78 15677 813 579 234 Method 0 Recorded values 1 SFB Recommended Values for Ungauged Catchment 2 SFB Optimisation using Total Streamflow 3 SFB Optimisation using Separated Surface Runoff and Baseflow 4 SFB Water Balance on Individual Events 6.4.6 Parameter Optimisation Using a Split Sample To check the model's ability to produce similar parameter values using different data sets from a single catchment, the split sample test was used. This is actually a test of the model itself rather than a test of the correctness of parameter values. As discussed earlier, the recommended procedure is to split the record,first fit th e parameters on one half and test on the second, thenfit on the second half and test on the first. If the model is judged to be performing satisfactorily, thefinal se t of model parameters can be obtained using the whole data set. If the model is formulated with a good physical reality, it should give similar parameters for the same catchment using different data sets (ideally for good quality and error free data). If the model is judged to be performing unsatisfactorily, this indicates a lack of physical reality in the model formulation. In this case, the parameters of the model and the model itself are site specific and data specific and its use should be limited to an empirical tool, and results should be treated cautiously. Daily input data were divided into three periods; period 1 from (1976-1981), period 2 from (1982-1987), and period 3 from (1976-1987). Parameters werefirst estimate d from the split data and using SAPS optimisation technique on total Q. For optimum values of S and F using thefirst period of data, a plot of various B values versus different objective functions is presented in Figure 6.6. This figure shows that the Chapter Six The SFB Model 6.21 best value of B for the catchment is 0.95, since for four different combinations of S and F values, B=0.95 gave the least value of SSQ for each combinations. Figures 6.7 and 6.8 are response surfaces of the model showing the 3 dimensional plots of SSQ and the contours of SSQ values (with optimised value of B=0.95) when thefirst perio d of data was used. In thefirst period, there are large differences between recorded rainfall and runoff. The response surface is shown in Figures 6.7 and 6.8. The response surface clearly indicates that the model is more sensitive to the value of S (while S is less than 100 mm) and is less sensitive to the values of the other two parameters F and B. Above this threshold the F parameter becomes more effective. Figures 6.10 and 6.11 represent the 3 dimensional plots of SSQ and the contours of SSQ values for various values of S and F when the second period of data was used. These figures clearly show the existence of different combination of parameters with similar SSQ. In the second period of data, there are small differences between rainfall and runoff. The response surface shows that the objective function is significantly sensitive to the values of S less than 40 mm. Large reduction in SSQ was achieved with a change in S value and bringing several searches into close proximity on the response surface. From this point the response surface is a flat-bottomed valley and at each iteration very small change in SSQ can be observed. At this part of the response surface the F parameter becomes more effective. More difficulties were observed in obtaining the optimum parameter values of the model when the whole period of data (period 3) was used. The whole period consisted of the event with large and small differences between their rainfall and runoff. For the model using period 3, a long curved-bottomed valley in the response surface resulted, as shown in Figures 6.11 and 6.12. The sides of the valley consist of very small slopes. A large number of combinations of parameter values of S and F will give similar low values of SSQ. The search is terminated in a point on the response surface that has the lowest value of SSQ ,however, the obtained parameters are not realistic. Parameters were then estimated from the split data and using different methods (Boughton recommended values, using separated QS and QB, using direct data analysis). Chapter Six The SFB Model 6 22 Parameters were then estimated from the split data and using different methods (Boughton recommended values, using separated QS and QB, using direct data analysis). Table 6.4 summaries the optimised parameter values using all methods. Different methods resulted in different values of model parameter. The rainfall-runoff process is very complex. There are always some small runoff events generated by large rainfall events and vis versa, due to catchment and data characteristics. A period of recorded data, available for calibration usually includes many events with large to small differences between their rainfall and runoff. The differences between rainfall and runoff determine the model parameters in the calibration period. Depending on the model structure, the differences are minimised by alteration of parameters and the parameters which give the lowest objective function can be obtained by optimisation or any other methods. In the SFB model, large values of storage capacity with small values of infiltration (or visa versa) interact when modelling the rainfall and runoff. This is evident from the response surfaces of the model using the three periods of input data. Different input data changes the configuration of the response surface. Deficiencies in model structure lead to obtain different sets of parameter values. Some other factors for achieving different sets of parameter values are probably a combination of model error, parameter error and input error. Among the factors which are important are the failure of the model to consider different soil storage capacities and parameter interactions. 7000 0.4 0.6 B Figure 6.6 Plot of SSQ, SAD, & PSAD against Bfor Optimised value ofS & F (Period 1, B=0.95) Chapter Six The SFB Model 6 21 Figure 6.7 The 3-Dimensional Response Surface of SFB model using SSQ (Period 1, B=0.95) Figure 6.8 Contours of SSQ for Variation of Parameters S and F (Period 1, B=0.95) Chapter Six The SFB Model 6 24 Figure 6.9 The 3-Dimensional Response Surface of SFB model using SSQ (Period 2, B=l) 0 20 40 60 (S) 80 100 120 Figure 6.10 Contours of SSQ for Variation of Parameters S and F (Period 2, B=l) Chapter Six The SFB Model. 6.25 Figure 6.11 The 3-Dimensional Response Surface of SFB model using SSQ (Period 3, B=0.2) 0 20 40 S(mm) 60 80 -|00 i2o Figure 6.12 Contours of SSQ for Variation of Parameters S and F (Period 3, B=0.2) Chapter Six The SFB Model, 6 76 6.4.7 Parameter Optimisation after Excluding Inconsistent Data Because of the inconsistency of results for the 3 periods, it was decided to check the amount of recorded rainfall and streamflow data in each event for possible inconsistencies and exclude them from further analysis. Identifying inconsistencies between the rainfall and runoff was carried out using the water balance method for each event discussed in section 6.4.5. Events where inconsistencies occur indicated errors in either rainfall or runoff data and were excluded from data (30 events out of 258 and 18 events out of 292 were excluded from data in Period 1 and 2). Table 6.4 (last row) summarises the parameters obtained using this procedure. The effect of the inconsistent data in the period of record on the optimised values of surface store capacity S is significant. As can be seen from Table 6.4, the problem of achieving different sets of parameters from different data set still occurs. Even with the adopted method discussed in section 6.4.4 the baseflow component was not well simulated. This indicates that structure of the SFB model is inadequate for simulating runoff in this catchment. Table 6.4 Optimum Set of Parameters for the SFB Model using different Calibration Periods Sandy Creek Period 1 Period2 Whole Period Catchment (1976-1981) (1982-1987) (1976-1987) 206026 Method S F i B S F B S F B (mm) mm/da> i (mm) i (mm) mm/day Boughton mm/day Recommended 80 3 1 80 3 1 80 3 1 values Calibrate 125.5 1.75 0.95 40.5 2.25 1.0 2.25 46.75 0.2 total Q Calibrate separated 115 1.8 0.975 40 1.5 1.0 40 0.8 0.72 QS and QB Direct Data 56 1.1 l 37 1.8 1 45 1 0.78 Analysis Calibrate total Q 56 1.5 1 30 1.5 0.985 49 1.4 1 after excluding inconsistent data Chapter Six The SFB Model 6 27 6.5 APPLICATION OF THE MODEL TO ALL CATCHMENTS In the previous section, the SFB model was applied to a small experimental catchment. It was found that the model gave poor parameter values, poor SUMQB, and poor SUMQS. In this section, the model will be applied on the seven catchments, followed by a comparison of results between the predicted and actual surface runoff and baseflow in order to examine the adequacy of the model in predicting SUMQ, SUMQB and SUMQS in other catchments. Parameters were estimated by optimisation of the total streamflow Q. Values of the objective function SSQ, plus other measures including AQ, E, D, SAD, S, and MR were also calculated. 6.5.1 Kangaroo Valley Catchment The whole period of analysis for the Kangaroo Valley catchment was 21 years which started from 1/1/70 and ended in 31/12/90. Optimised parameters of the model for Kangaroo Valley are presented in Table 6.5. Table 6.5 Parameters of the SFB Model for Kangaroo Valley Catchment Parameters S(mm) F (mm/day) B SSQ (mm2) Optimisation Process 113.25 2.475 1.0 277864 The values of parameters for the Kangaroo Valley catchment were found to be physically realistic. As discussed earlier, the value of 113.25 mm was a realistic value for forested catchments. The optimum value of daily infiltration capacity (F) was slightly less than the physically inferred value for sandy loams. The value obtained was close to the value for clay loams of 3.0 mm/day (Chapter two). For a perennial stream like the Kangaroo River, B=l was expected. Table 6.6 shows the measures of accuracy for the optimised parameter values. Considering all measures, the simulation results are satisfactory. Figure 6.13 shows that the predicted values of total streamflow, surface runoff and baseflow are very close to the actual values. The values of correlation coefficients (Rx) between rainfall and runoff for this catchment were: 0.74, 0.92 and 0.97 for daily, monthly and annual data respectively. As the correlations were quite high the rainfall-runoff relationship in this catchment was easily explained by the simple three parameter model. Chapter Six The SFB Model 6 2S Table 6.6 Results of SFB Model for Kangaroo Valley Catchment Q QB QS SSQ AQ E D SAD S MR 2 (mm) (mm) (mm) (mm ) (%) (mm) (mm) Recorded 18925 5948 12977 0 0.00 1.00 1.00 0 0.00 1.00 SFB 17066 5875 11191 347265 -9.81 0.88 0.89 5976 36.24 0.66 + Q A QB A QS x Qe x QBe o QSe 20000 215220 gco, 16000 x © 6 ±xx 5 E 12000 . +x A .++ + A 0 3 S xxx E g, 8000 +x xx r. 4000 3 < 0* 1970 1975 1980 1985 1990 Time (Year) Figure 6.13 Mass Curve of Actual and Simulated Q, QB and QSfor the SFB Model Applied to Kangaroo Valley Catchment 6.5.2 Macquarie Rivulet Catchment Forty years of daily rainfall and runoff data from this catchment were used to investigate the SFB model's accuracy. The period of analysis was from 1st January 1949 to 31st December 1989. The values of parameters for Macquarie Rivulet Catchment are presented in Table 6.7. These values are found to be physically unrealistic, even though the values of predicted runoff are not significantly different from the recorded runoff values. Table 6.7 Parameters of theSFB Model for Macquarie Rivulet Catchment Parameters S(mm) F (mm/day) B SSQ (mm2) Optimisation Process 0.1 79.25 0.316 2245093 Table 6.8 shows the measures of accuracy for the optimised parameter values for this catchment. Chapter Six The SFB Model. 6.29 Table 6.8 Results of SFB Model for Macquarie; Rivulet Catchment Q QB QS SSQ AQ E D SAD S MR 2 (mm) (mm) (mm) (mm ) (%) (mm) (mm) Recorded 22817 9693 13144 0 0.00 1.00 1.00 0 0.00 1.00 SFB 24252 13497 10755 2245093 6.30 0.30 0.31 15387 68.08 -0.52 The value of the soil moisture storage capacity (S) for the Macquarie Rivulet catchment was physically unrealistic. A very low value of S (S = 0.1 mm) indicates that the porous top-soil layer assumptions were not applicable to this catchment. The entrance of moisture into the sub-soil moisture storage (SS) was controlled by the infiltration capacity (F), which was found to be very high (79.25 mm) to compensate for the low value of S, so that surface runoff occurrences will not occur immediately after low values of rainfall (a high initial loss was retained). Low S and high F values meant that a large proportion of rainfall will infiltrate into sub-soil moisture storage. Even though a high value of infiltration was expected for Krasnozems soils, the value obtained for this catchment exceeded the range suggested by Boughton (1984). There were big differences between the average rainfall and runoff. In order to model these differences, a large value of F was obtained through optimisation, which allowed for the continuous infiltration of moisture into the soil store, ie. a high continuous loss. A low value of B (0.32) meant that almost 70% of the depletion from the sub-soil store is lost to deep percolation, even though the stream is a perennial one. A very low upper soil storage meant that one half of the evapotranspiration demand, which was supposed to be met by the upper soil storage, has to be replaced by another loss component. Therefore, moisture lost via deep percolation (by the reduction of B value), incorrectly represented the evapotranspiration losses. Figure 6.14 shows that even though physical characteristics were not represented by the values of parameters, data characteristics were well represented. This figure shows that the predicted value of total streamflow is close to the actual value. Considering all measures, the simulations of total streamflow results are satisfactory. However, baseflow is overestimated and surface runoff is underestimated. Chapter Six The SFB Model. 6.30 25000 xx 214003 + Q A QB A QS X Qe x QBe o QSe co 20000 + + ix x xx * o> x x * a xXxxxxx •o 9 15000 + + 3 .§ 10000 - *** $£xxxxx*J 9J E g 10000 x*? A- ll8^^^ « CO •a c s oyL*l*_!JLf , , , , , "3 1950 1955 1960 1965 1970 1975 1980 1985 1990 9 Time (Year) Figure 6.14 Mass Curve of Actual and Simulated Q, QB and QSfor the SFB Model Applied to Macquarie Rivulet Catchment This characteristic was also illustrated by low correlations between rainfall and runoff. The linear correlation coefficient (Rx) between daily, monthly and yearly rainfall and runoff data were 0.36, 0.72 and 0.81, respectively. This catchment is the second smallest catchment studied (36 km2) and has a very steep slope (38.4 m/km). 6.5.3 Bungonia Creek Catchment Six years of daily rainfall and runoff data were used. The period of analysis was from 1st January 1981 to 31st December 1986. This catchment was very dry with an average daily runoff of 0.17 mm and average monthly runoff of 5.64 mm. The average daily runoff values were consistently low regardless of the values of the average daily rainfall. The values of parameters for the Bungonia Creek Catchment are presented in Table 6.9. Based on guidelines recommended by Boughton (1986), these values were found to be physically realistic. The values of predicted total runoff were compatible with the recorded runoff. Table 6.9 Parameters of the SFB Model for Bungonia Creek Catchment 2 Parameters S(mm) F (mm/day) B SSQ (mm ) Optimisation Process 103.9 2.225 0.894 4034 Table 6.10 shows the measures of accuracy for the optimised parameter values for the catchment. Chapter Six The SFB Model. 6.31 Table 6.10 Results of SFB Model for Bunsonia Creek Catchment Q QB QS SSQ AQ E D SAD S MR 2 Jmm) (mm) (mm) (mm ) (%) (mm) (mm) Recorded 373 95 278 0 0.00 1.00 1.00 0 0.00 1.00 SFB 376 340 36 4034 0.49 0.34 0.35 322 7.53 0.90 Since the catchment has a mixture of forest and grassland as vegetal cover, the value of S (103.9 mm) is regarded as physically acceptable. A low value of infiltration was expected for this catchment. Therefore, the value of F (2.2 mm), which lay between the inferred value for clay loams and shallow dense clay, can be regarded as realistic. Bungonia Creek is a perennial stream and the value of B (0.89) was close to 1, as expected. Mass curves of simulated and actual Q, QB, and QS against time for the model are shown in Figure 6.15. This shows that the model with its corresponding optimum set of parameters was able to predict total runoff values very well. In this case the total predicted streamflow is in agreement with the recorded total streamflow with incorrect prediction of each streamflow component. A total recorded streamflow of 373 mm that consists of 95/278 mm of baseflow/surface runoff is predicted as 376 mm which consists of 340/36 mm baseflow/surface. Obviously, parameters of the model calibrated in this way do not have any physical meaning. AQB AQS =8 350 ; +Q CO : XQe XQBe OQSe x " O 300 X ??250 • 215014 X 3a -J, 200 A B ~N J a 150 • S 100 • * A 1a 50 • I X • o O <' 1 oi 1 • ' O ' 198I1—*- 1982 1983 1984 1985 1986 Time (Year) Figure 6.15 Mass Curve of Actual and Simulated Q, QB and QSfor the SFB Model Applied to Bungonia Creek Catchment 6.5.4 Mongarlowe River Catchment The next catchment analysed in this study was the Mongarlowe River (at Mongarlowe) Catchment. Twenty-three years of daily rainfall and runoff data were used. The period of analysis was from 1st January 1950 to 31st December 1972. The peak values of average Chapter Six The SFB Model 6 32 daily runoff occur about a month later than the peaks of the average daily rainfall. The values of average daily rainfall peaked in February, June and October, while runoff peaks occurred in March, July and November. The response of runoff values were also small compared to the increase in rainfall values. The value of daily Rx between rainfall and runoff of 0.199 was the lowest compared to the other catchments. However, the values of monthly and annual Rx were 0.833 and 0.962. The values of E and D for this catchment can be expected to be relatively high amongst the catchments analysed since these measures were calculated on monthly values. The weak response observed in this catchment may be due to the low value of daily Rx between actual rainfall and runoff. The average runoff values were always less than the average rainfall. The values of parameters for the Mongarlowe River Catchment are presented in Table 6.11. Based on guidelines recommended by Boughton (1986), these values were found to be physically unrealistic. Table 6.11 Parameters of theSFB Model for Mongarlowe River Catchment Parameters S(mm) F (mm/day) B SSQ (mm2) Optimisation Process 22.00 93.35 0.5875 99897 The optimum value of daily infiltration was much higher than the physically inferred value for this catchment. The value of surface storage capacity and the baseflow factor were much lower than the expected values. Table 6.12 shows the measures of accuracy for the optimised parameter values for this catchment. Table 6.12 Results of SFB Model for rMongarlowe River Catchmeni Q QB QS SSQ AQ E D SAD S MR 2 (mm) (mm) (mm) (mm ) (%) (mm) (mm) Recorded 6649 3825 2824 0 0.00 1.00 1.00 0 0.00 1.00 SFB 6839 6022 817 99897 2.85 0.50 0.51 3420 18.80 0.86 The values of predicted total runoff were compatible with the recorded runoff. The values of parameters for the Mongarlowe River catchment were found to be physically unrealistic, even though the values of predicted runofffitted wel l when compared to the recorded runoff. The value of F for this catchment was found to be very high (F=93.35 mm/day). The value of B was also not realistic for a perennial stream like the Chapter Six The SFB Model. 6.33 Mongarlowe River. Rainfall and runoff of the Mongarlowe River catchment behaved similarly to the rainfall and runoff of the Macquarie Rivulet. The values of parameters had similar characteristics as the parameters of the Macquarie Rivulet catchment (ie. low S, very high F and low B), but these values were not as low as those of this catchment). However, the differences between rainfall and runoff values were not as high, which is indicated by the values of S and B. This catchment exhibits high initial and continuous losses, which are indicated by the data characteristics. Mass curves of simulated and actual Q, QB, and QS against time for the model are shown in Figure 6.16. This shows that the model was able to predict total runoff values which were only 3% less than the recorded runoff. While the total streamflow was estimated accurately the results of QB and QS are clearly unrealistic. QB is overestimated and QS is underestimated. This is due to the strong interaction between parameters S and F. A total recorded streamflow of 6649 mm that consists of 3825/2824 mm of baseflow/surface runoff is predicted as 6839 mm which consists of 6022/817 mm baseflow/surface runoff. Obviously, parameters of the model calibrated in this way do not have any physical meaning and only represent the data characteristics of the catchment. This catchment can be regarded as a non-homogeneous catchment. + Q A QB A QS X Qe X QBe o QSe 7000 „- 6000 o Time (Year) Figure 6.16 Mass Curve of Actual and Simulated Q, QB and QSfor the SFB Model Applied to Mongarlowe River Catchment 6.5.5 Endrick River Catchment The Endrick River catchment was analysed using ten years of daily rainfall and runoff data. The period of analyses was from 1st January 1970 to 31st December 1979. The values of monthly runoff in this catchment followed the trend of the monthly simulated Chapter Six The SFB Model. 6.34 runoff. The values of Rx between rainfall and runoff were 0.76, 0.89 and 0.94 for daily, monthly and annual data respectively. High values of E and D can be expected for this catchment. However, there were two anomalies: in March, the value of average daily runoff is similar to the average daily rainfall; and in June, the value of the average daily runoff is greater than the average daily rainfall. There were some differences between rainfall and runoff values from September to February. The difference may be due to the amount of moisture retained by the catchment. When the amount of rainfall increased in March and June, the amount of moisture in the soil moisture storages exceeded the capacity quickly due to recharge by the rainfall plus the moisture stored in the storages. Therefore the moisture discharged as surface runoff occurred almost immediately after the rainfall. The surface runoff plus the baseflow from the soil moisture storages may equal or exceed the amount of rainfall for March and June. The values of parameters for the Endrick River Catchment are presented in Table 6.13. Based on guidelines recommended by Boughton (1986), some of these values were found to be physically unrealistic. The value of parameter S for the Endrick River Catchment was found to be physically unrealistic, but the value of F seems to be realistic for the optimised S value. Also the B value seems to be physically meaningful. The value of predicted runoff was not significantly different from the recorded runoff. Table 6.13 Parameters of the SFB Model for Endrick River Catchment 2 Parameters S(mm) F (mm/day) B SSQ (mm ) Optimisation Process 0.0300 0.9750 0.970 311244 Table 6.14 shows the measures of accuracy for the optimised parameter values for this catchment. Chapter Six The SFB Model 6.35 Table 6.14 Results of SFB Model for Endrick River Catchment Q QB QS SSQ AQ E D SAD S MR 2 (mm) (mm) (mm) (mm ) (%) (mm) (mm) Recorded 6546 1492 5054 0 0.00 1.00 1.00 0 0.00 1.00 SFB 7854 33 7821 311244 19.97 0.74 0.81 4072 45.10 0.13 The value of S (0.03 mm) for the Endrick River catchment was similar to that for the Macquarie Rivulet catchment (ie. non-porous top soil). The value of F (0.975 mm/day) for this catchment was close to the inferred value for shallow dense clay soils (ie. low infiltration). Quick runoff response was observed for this catchment, which indicated low values of initial and continuous losses. This quick response may be due to the apparent non-porous top-soil layer (very low S and F values). Surface runoff will occur almost immediately after rainfall. As discussed earlier, Podsolic soils could have surface sealing occurring very quickly, which would make the apparent value of infiltration very low. This surface sealing effect may cause low initial and continuous losses, which will cause the rapid occurrence of surface runoff as indicated by the low value of S. As for the Macquarie Rivulet catchment, the values of parameters for the Endrick River catchment represented data characteristics rather than physical characteristics. However, the value of B (0.89) was close to the value for a perennial stream. Mass curves of simulated and actual Q, QB, and QS against time (Figure 6.17) show that the model was able to predict total runoff values very well. The total predicted streamflow is in agreement with the recorded total streamflow, but with incorrect prediction of each streamflow component. The predicted baseflow (33 mm) is only 1.5% of the actual baseflow in this catchment which is 2231 mm. Parameters of the model calibrated in this way do not have any physical meaning. Chapter Six The SFB Model 6 36 + Q A QB A QS x Qe x QBe o QSe 8000 isr *J 7000 215009 CO + O 6000 SI H 5000 Si B + « J. 4000 + B « + .i= o 3000 + co ^ a •g 2000 ia ea « 1000 4-4 ' X ' 'X • X ' X1 • X X 5 o197 m0 1972 1974 1976 1978 1980 Time (Year) Figure 6.17 Mass Curve of Actual and Simulated Q, QB and QSfor the SFB Model Applied to Endrick River Catchment 6.5.6 Corang River Catchment For the Corang River catchment, eight years of daily rainfall and runoff data were used. The period of analysis covered from 1st January 1979 to 31st December 1986. For most of the months, the values of actual runoff followed the trend of the simulated runoff. The exceptions were in March, September and November, where increases in the values of average daily rainfall did not increase the values of runoff. Contrary to the Endrick River catchment, this catchment may have a higher soil moisture holding capacity. The increase in rainfall in the three months mentioned above may be insufficient to recharge the moisture storage. The values of parameters for Corang River Catchment are presented in Table 6.15. On consideration of the guidelines recommended by Boughton (1986), one of these values was found to be physically unrealistic. Table 6.15 Parameters of the SFB Mode I for CorangRiver Catchment Parameters S(mm) F (mm/day) B SSQ (mm2) Optimisation Process 17.1250 3.0825 1.00 45958 Table 6.16 shows the measures of accuracy for the optimised parameter values for this catchment. Chapter Six The SFB Model. 6.37 Table 6.16 Results of SFB Model for Corang River Catchment Model Q QB QS SSQ AQ E D SAD S MR 2 (mm) (mm) (mm) (mm ) (%) (mm) (mm) Recorded 1816 618 1198 0 0.00 1.00 1.00 0 0.00 1.00 SFB 2025 704 1321 45958 11.50 0.41 0.45 1163 21.71 0.45 The value of 17.13 mm for S, for the Corang River catchment, indicated a very low vegetal cover which is not the case. The low value of S indicated that this catchment has a low value of initial loss. However, the F value of 3.08 mm/day was close to the value for clay loams which is physically realistic for this catchment. The expected value of B for a perennial stream was obtained. Low S and F values meant that only a small amount of moisture is held in the soil moisture storage. Figure 6.18 shows mass curve plot of actual and simulated Q, QS and QB for this catchment. The actual values were not significantly different from predicted values. The values of surface storage capacity (S) for the Corang River Catchment were found to be physically unrealistic, even though the values of predicted runoff fitted well when compared to the recorded runoff. + Q A QB A QS X Qe X QBe o QSe 2500 s : 215004 CO 2000 X O O : X + •a "a150 0 - X + « £ + 2 s „ ; B « 1000 X 2 w o CO A •a * c 500 X X Figure 6.18 Mass Curve of Actual and Simulated Q, QB and QSfor the SFB Model Applied to Corang River Catchment 6.5.7 Shoalhaven River Catchment at Kadoona The last catchment analysed was the Shoalhaven River Catchment at Kadoona. Fifteen years of daily rainfall and runoff data were used. The period of analyses was from 1st January 1972 to 31st December 1986. Chapter Six The SFB Model. 6.38 The values of parameters for modelling the Shoalhaven River Catchment are presented in Table 6.17. Based on guidelines recommended by Boughton (1986), these values were found to be physically realistic. Table 6.17 Parameters of the SFB Model for Shoalhaven River Catchment at Kadoona Parameters S(mm) F (mm/day) B SSQ (mm2) Optimisation Process 47.50 U 1.0 106647 Table 6.18 shows the degree of accuracy for the optimised parameter values for this catchment. After considering all measuring criteria, the simulation results were satisfactory. Table 6.18 Results of SFB Model for Shoalhaven River Catchment at Kadoona Model Q QB QS SSQ AQ E D SAD S MR 2 (mm) (mm) (mm) (mm ) (%) (mm) (mm) Recorded 5212 2037 3175 0 0.00 1.00 1.00 0 0.00 1.00 SFB 4970 2278 2692 106647 -4.66 0.79 0.79 2472 24.64 0.93 The parameters of the SFB model for the Shoalhaven River catchment were physically realistic. The value of S (47.5 mm) was lower than the inferred values for crop land in fallow (50 mm). This catchment is mainly covered by eucalypts and shrubs and a value of 100 mm was expected. The depth of top-soil (about 75 mm) of this catchment is about 50% lower than the Kangaroo Valley catchment (about 150 mm). Lower soil depth reduces the soil moisture capacity of the top-soil layer (ie. lower value of S), and hence, a lower initial loss. The difference between rainfall and runoff in this catchment is lower for low values of rainfall and higher for high values of rainfall, which indicates the existence of different storage capacities. The value of F (4.88 mm/day) was close to the inferred value for sandy loams (5 mm/day). A lower value of F was expected for this catchment but the mixture of Podzolic (with high sand content) and Krasnozems (with high expected value of daily infiltration capacity) made the optimum value of F physically realistic. The value of B (1.0) was expected for a perennial stream like the Shoalhaven River. Figure 6.19 shows a mass curve plotting of actual and simulated Q, QS and QB for this catchment. This shows that the model was able to predict total runoff values which fitted very well with the recorded runoff values. Total predicted streamflow is in agreement Chapter Six The SFB Model. 6.39 with the recorded total streamflow, with correct prediction of each streamflow component. + Q A QB A QS x Qe X QBe o QSe 215008 + CO 5000 O O 4000 2 | • A A ^ 3 E.3000 I" co °* 2000 ********* * 1000 * £ * * «5 3 l * r, , , M—i 1 1 ; — . i 1972 1977 1982 1987 Time (Year) Figure 6.19 Mass Curves of Actual and Simulated Q, QB and QSfor the SFB Model Applied to Shoalhaven River Catchment at Kadoona 6.6 DISCUSSION OF RESULTS 6.6.1 General Results The rainfall-runoff process is very complex and the model structures are usually too simple to adequately explain the processes involved in real catchments. The user should always be aware of uncertainty in the results of rainfall-runoff modelling regardless of the level of sophistication of the model used. There is a large amount of uncertainty in the calculated model parameters and therefore in the estimated runoff. Values of optimised model parameters depend on various factors. These include catchment characteristics, the quality and the length of input data, the optimisation techniques used, the objective function adopted, and the structure of the model. Some of the reasons for achieving a large variation of S, F and B in different calibration periods and different catchments are outlined briefly in the following section. The spatial and temporal distribution of rainfall and catchment properties as well as error in the input data are significant sources of uncertainty in rainfall-runoff modelling. It is obvious that the catchment characteristics that govern the process of transforming rainfall into runoff vary greatly. The distribution of rainfall over the catchment area, and the soil permeability and surface storage capacity, change significantly from point Chapter Six The SFB Model. 6.40 to point within the catchment. Models which lump these spatially varying properties together may give poor results. Streamflow, rainfall and evaporation data are almost certain to contain errors. Areal rainfall estimates based on point rainfall observations are a very weak sampling of the actual catchment rainfall and should be regarded as an index of the true rainfall over a catchment. Errors between 10 to 20% can be regarded as normal. Errors up to 60% can be experienced where mountainous areas or strong wind effects are present (Boughton, 1984). As a result, errors in the input data produce errors in the parameter values. The record length of the input data is rarely long enough to allow for all possible combinations of rainfall and runoff. The input data must also be representative of the local environment. The wet and dry sequences and the degree of variability of the input data cause other problems in the estimation of parameter values. Selection of the best set of parameters is usually based on the minimisation of an objective function. Using different objective functions results in different parameter values. The dominant effect of high flow values over low flow values on the optimised parameter values is significant. Many combinations of the model parameters can satisfy the selected objective function and may give a good fit of total streamflow, but with false prediction of different flow components. Difficulty in calibration arises from many different factors. For example; parameter interaction can cause insensitivity of the model's response to changes in the model parameters. The derived value of S for a modelled catchment depends on the adopted values of B and F. Many combinations of the model parameters can also give very similar values of SSQ. The model structures are usually not a true representation of the processes involved in real catchments. Nonhomogeneity exists in soil storage capacity, soil type, slope, land use, land cover and other catchment characteristics. Spatial and temporal distribution of data and catchment properties (parameters and variables), variations of surface stores, vegetation, geology, soil and infiltration rates within the catchment are important factors which are not explained by most of the rainfall runoff models. The lack of an accurate formula to estimate actual evapotranspiration and simplicity of the models in Chapter Six The SFB Model 641 prediction of baseflow and groundwater recharge is another factor which result in the false values of parameters. 6.6.2 Adequacy of The SFB Model The values of parameters for the eight catchments using the SFB model were found to be quite different. This illustrates that even though all the catchments are located in the same region with similar climatic and physical characteristics, the values of parameters were not dependent on the catchment characteristics. The storage capacity parameter ranges between 0.03 to 113.25 mm. The values of infiltration parameter F ranges between 0.98 to 93.35 mm/day. The value of the baseflow parameter B, which is constrained in the model to he between 0 and 1, ranges between 0.2 to 1.0. These values of S, F and B were not clearly related to differences in catchment physical characteristics. Physically realistic parameter values and good estimates of runoff, as well as its components, can be obtained using the SFB model only in homogeneous catchments and if good data is used. Minimising the SSQ of total flows can be used to measure the goodness of fit but it must be combined with other objective functions such as maximising the coefficient of determination between observed and estimated runoff. Furthermore, the performance of the model in predicting different flow components should also be checked. If the coefficient of determination from a simulation is high, the percentage differences between mean monthly observed and mean monthly estimated runoff is close, and both of the flow components are simulated correctly. In all catchments the model, with its corresponding optimum set of parameters, was able to predict total runoff values which agreed well with the recorded runoff values. Even though the values of predicted runoff were acceptable, values of QB and QS and values of the optimum parameters were found to be physically unrealistic. Reasonable estimates of parameters, Q, QS and QB obtained in only 3 catchments. If the parameters of the model are optimised on the minimisation of SSQB and SSQS, some improvements in the obtained parameters will be made and parameter interactions may be reduced. However, because the model structure is not physically realistic, parameters were unable to give accurate predictions of runoff in these catchments. Accordingly, it seems unlikely that the optimised parameter values of this model can be Chapter Six The SFB Model 6.42 related to the physical characteristics of these types of catchments with any degree of reliability. The results of the SFB model applied to eight catchments shows that that heterogeneity existed in most of the catchments analysed, and the optimum set of parameters of the SFB model do not reflect the catchment actual differences. The poor performance of the SFB model is probably due to a combination of model error, parameter error and input error. Among the factors which are important are the failure of the model to consider different soil storage capacities and parameter interactions. 6.7 SUMMARY In this chapter, comparisons between optimum sets of parameters for eight catchments were made for the SFB model. The results show that the adopted model with its corresponding optimum set of parameters was able to predict runoff values which have similar properties to the recorded runoff. Although the values of predicted runoff were acceptable when compared with the recorded runoff, the values of the optimum parameters for some of the studied catchments were found to be physically unrealistic. The optimum parameter values of the model reproduce a total runoff value in agreement with the recorded total runoff, but with incorrect prediction of QB and QS. In these catchments physically realistic parameter gave a poor prediction of runoff. Parameter interaction caused several sets of parameters S, F, and B to give similar SSQ. Optimisation by noinimising the SSQ of total flows is not sufficient to properly model both surface runoff and baseflow. Physically unrealistic parameters can be obtained when using the optimisation process. Several automatic baseflow separation methods are now available (discussed in Chapter 5), and the accuracy of prediction of both the flow components should be checked. Model parameters can also be estimated directly from the recorded rainfall and streamflow data in several ways: using a water balance of individual storm events and baseflow recharge-recessions; and using partitioned baseflow and surface runoff. These methods show considerable promise. Chapter Six The SFB Model. 6.43 If the parameters of the model are optimised on the minimisation of SSQB and SSQS, some improvements in the obtained parameters will be made and parameter interactions may be reduced. However, because the model's structure is not physically realistic, parameters were unable to give accurate predictions of runoff for these catchments. Accordingly, it seems unlikely that the optimised parameter values of this model can be related to the physical characteristics of these types of catchments with any degree of reliability. In most of the catchments, physically realistic parameters were unable to give accurate predictions of runoff, while the optimised parameter values did not have any physical meaning. The optimum values of parameters described data characteristics rather than physical characteristics. To check the model's ability to produce similar parameter values using different data sets from a single catchment, the split sample test was used. The results indicated that different input data changes the configuration of the response surface. Using different data inputs resulted in different parameter values. Deficiencies in model structure as well as data error are accounted for obtaining different sets of parameter values. The SFB model assumes catchments as having only a uniform storage capacity and this could be a major limitation of the model. It was concluded that the optimised parameter values of this model do not truly represent the movement of water in heterogeneous catchments. The most important deficiency of the SFB model, apparent from this study, is the assumed spatial uniformity in storage capacity over the whole catchment. Because of this it was decided to investigate the AWBM model, which accepts source area and storage capacity variations in the catchment, and has a relatively small number of parameters. CHAPTER SEVEN THE AWBM MODEL J CHAPTER SEVEN THE AWBM MODEL 7.1 INTRODUCTION The previous chapter discussed the results of the SFB model when applied to eight catchments. The results showed that heterogeneity existed in most of the catchments and that the optimum set of parameters of the SFB model did not represent physically realistic properties of the catchments. It was proposed that the main reason for the poor results of this model was the assumed spatial uniformity in storage capacity over the whole catchment. The AWBM model was selected because it models heterogeneity by allowing the catchment to start generating runoff at different times and from different parts of the catchment. The AWBM model also has a small number of parameters, each of which can be identified with particular parts of the hydrologic response. This model is applied to the same catchments used in Chapter 6, with the same sets of data. Following is a description of the model and the results obtained. 7.2 DESCRIPTION OF THE A WBM MODEL The AWBM model is a water balance model developed by Boughton (1993). It is a saturation overland flow model which uses daily (or hourly) rainfall and average monthly estimates of the catchment evapotranspiration as input, to calculate daily values of runoff from gauged or ungauged catchments. It has two major differences when compared with other rainfall runoff models. Firstly, it uses storage excess as opposed to infiltration excess as the dominant process by which surface runoff is generated. Secondly, is the recognition of source area generation of catchment runoff, ie. runoff is generated from different parts of the catchment in different storms and at different times during a single storm. This model has several advantages over many other rainfall-runoff models. Its data requirements are readily available (daily data) and at the same time the model can Chapter Seven The AWBM Model 7.2 accept hourly values. The variable time interval of input data expands the application of this model. Furthermore, the model requires evaluation of only three main parameters and can be used as a one parameter model on ephemeral streams. When concurrent rainfall and streamflow data are available, some of the parameters of the model can be directly evaluated without any need for trial and error optimisation. Models like this, that allow for direct evaluation of the parameters, are highly regarded as they allow the use of prior knowledge of the catchment's properties. This greatly reduces the uncertainty associated with the parameter values. The model calculates surface runoff from different source areas and allows for the various areas to begin generating runoff at different times. This is a significant advantage because in a real catchment surface storage and other catchment properties are not uniform over the catchment area, resulting in generated runoff being spatially and temporarily distributed. The model is relatively simple in structure and simulates all runoff components well. The model is able to calculate the start and patterns of rainfall excess with good accuracy and can be used for flood forecasting (Boughton and Carroll, 1993). When the model was compared to the SCS curve number method (US. SCS Handbook, 1964) by Boughton (1995) it was found to be far more accurate. In addition, a comparison of this model with the SFB model clearly displayed the AWBM model's superiority (Sharifi and Boyd, 1994). 7.3 THEORETICAL BEHAVIOUR OF SURFACE STORAGE IN THE AWBM MODEL The simplest model for generating surface runoff as saturated overland flow is the elementary bucket model. Daily rainfall minus evaporation is added to the bucket. Runoff occurs only when the bucket isfilled. Whenfilled, runof f is made up of the excess over the capacity of the bucket. When the catchment has only one uniform surface storage, and the storage is empty at the start of rainfall, then the simple rainfall-runoff relationship is as shown in Figure 7.1 line (1). The effect of varying antecedent wetness can be seen in Figure 7.1. Depending on the antecedent wetness, the line indicating rainfall-runoff relationship shifts towards the line which slopes at 45° through the origin (line 4). Chapter Seven The AWBM Model, 7 3 Evaporation Rainfall J. Runoff (4) (3) (2) (1) , . (4) ' / fc: ' ' ' / o * ' ' s Capacity 3c ' ' ' / OJ _, / ' '•'' '/ /' •' .y (a) , ' • / il&i^ ' —'—£- ^ Rainfall-Evaporation Capacity Figure 7.1 Rainfall-Runoff Relationship for Catchment with Single Uniform Surface Storage Capacity In real catchments, spatial variability of storage capacity exists. The effect of spatial variability is demonstrated by considering a catchment with two storage capacities. This can be modelled by a two-compartment bucket model as shown in Figure 7.2. Runoff begins when rainfall minus evaporation hasfilled th e smaller storage capacity. If rain continues, the rainfall-runoff relationship will be a line with slope 'Al' which is the fraction of the catchment occupied by the smaller storage capacity. If the amount of rainfall less evaporation is enough to fill the larger capacity, then the rainfall-runoff relationship continues as a line which slopes at 45° to the horizontal axis. The slope of this line indicates that the entire catchment is saturated and is producing surface runoff (Boughton, 1990). Evaporation Rainfall A2 T C2 Rainfall-Evaporation Figure 7.2 Rainfall-Runoff Relationship for Catchment with Two Surface Storage Capacities Figure 7.3 extends the concept of spatial variability in surface storage capacity to show the effect of 3 storages and the resulting rainfall-runoff relationship. This approach could Chapter Seven The AWBM Model. 7.4 be extended to more than 3 soil storages and source areas, but practical considerations such as errors in the data and methods of calibrating parameters, make a substantial argument for using only 3 source areas and storage capacities to represent a catchment (Boughton, 1995). Evaporation Rainfall Runoff •Ali A2 7\ >£-A3 "1 SB CI o £ C3 c 3 _3_ oi V CI ' Rainfall-Evaporation Figure 7.3 Rainfall-Runoff Relationship Variability in Surface Storage Capacity In the AWBM model, the runoff is assumed to be generated by two main sources, namely, surface runoff and baseflow sources. Surface mnoff or overland flow is assumed to be excess above the storage capacity. As a result of the spatial variability in the surface storage capacity of the catchments, runoff generates from different parts of a catchment in different storms and at different times during a single storm. Baseflow is modelled as the discharge from shallow groundwater in the alluvium of the drainage system. Modelling an Ephemeral Semiarid Catchment In ephemeral catchments where runoff is comprised only of surface runoff (BFI=0.0), the model can simulate surface runoff using the three surface storage capacities and the corresponding source area fractions. In the case of ungauged ephemeral catchments, the model can be used as a one parameter model. For ungauged catchments, the user estimates a value of average surface storage capacity (Cav) and the model disaggregates that value into a set of 3 capacities and 3 fractions of the catchment area corresponding to those capacities. The values used for disaggregation of the average capacity are Cl= 0.5*Cav, C2=0.75*Cav, and C3=1.5*Cav. The source area fractions of the catchment represented by each of these capacities are A 1=20%, A2=40% and A3=40%. In this case the model is as shown in Figure 7.3. Chapter Seven The AWBM Model. 7.5 Modelling a Perennial Catchment Baseflow is modelled in the AWBM model as discharge from shallow groundwater in alluvium of the drainage system. There are 2 more parameters used in the model to simulate the baseflow component of the runoff. As shown in Figure 7.4, one parameter determines the recharge to the baseflow storage and the other determines the discharge from storage into the stream. The recharge parameter is the baseflow index (BFI). The discharge parameter is the daily baseflow recession constant K. These parameters can be directly estimated by analysing the streamflow record, as was done in Chapter 5. Modelling with Hourly Data The AWBM requires daily rainfall data for the whole period being modelled, although hourly data can be used for any day to calculate hourly values of rainfall excess. Hourly rainfall data are contained in afile wit h each day of hourly data identified by day, month and year. When a day with hourly data is reached during modelling, AWBM switches from daily to hourly calculation. Daily calculation continues automatically when no hourly data are available. The model does not have any flood hydrograph routines such as unit hydrograph, but the hourly values of excess rainfall can be used as input to any flood hydrograph model. Figure 7.5 is an example of the results from the combination of the model with a runoff routing flood hydrograph model (URBS), taken from Boughton and Carroll (1993). Figure 7.4 AWBM Model Combined with Flood Hydrograph Model Chapter Seven The AWBM Model 7.6 250 200 en O CD E 150 u UJ CD 1C0 tx < o 12:00 12:00 12:00 12:00 12:00 12:00 12:00 w-o 12:00 TIME STARTING Dec 11 12:00:00 1991 Figure 7.5 Actual Runoff Compared with Result from AWBM-URBS System (after Boughton and Carroll, 1993) 7.4 OPERATION OF THE MODEL The structure of the model is illustrated in Figure 7.4. The operation of the model is based on the following assumptions: (1) Daily rainfall being added and daily evapotranspiration being subtracted from the amount of water remaining in each of the storages. It is assumed that the evaporation loss is at potential rate while there is water remaining in the stores. (2) Runoff (rainfall excess) from each of the source areas of the catchment occurs when the surface storage capacity of the corresponding store has been filled by rain. (3) Excess rainfall is partitioned into two components, surface runoff and recharge to the baseflow storage. The following equations are used to calculate these components. QS = (1 - BFI) * excess rainfall 7.1 Recharge to the baseflow = (BFI) * excess rainfall 7.2 Chapter Seven The AWBM Model. 7.7 where QS = surface runoff BFI = the baseflow index, a dimensionless ratio defined as the volume of baseflow divided by the volume of total runoff (4) The baseflow recession is computed by Equation (7.3), which implies that the recession is linear on a semi-log graph. QB* i= QBtK 7.3 where QBt+i = the baseflow rate onetime step after the initial time t QBt = the baseflow rate at time t K = daily recession constant In this model discharge from baseflow storage is also assumed to follow the same pattern and is given by the following relationship QB = (1 - K)*BS 7.4 where QB = the daily discharge from baseflow storage BS = the amount of water currently held in the baseflow storage (5) It is assumed that runoff from the second and third source areas of the catchment occurs when their storage capacities have beenfilled by precipitation. This means that there are events in which runoff occurs only from the area with the smallest capacity. After determining thefirst surface storage, the generated runoff from this storage can be calculated for each event, and it is assumed that additional values of runoff occur from the second smallest storage. This allows the capacity C2 and source area fraction A2 to be calculated. By following the same procedure, all three storage capacities and associated source area fractions can be determined. Chapter Seven The AWBM Model 7.R 7.5 METHODS OF EVALUATING PARAMETERS 7.5.1 General Evaluation of the model parameters generally involves the following common steps: • separation of recorded streamflow into baseflow and surface runoff, and calculation of baseflow index (BFI) • evaluation of the daily baseflow recession constant (K) from semi-log plots of the hydrograph recession or any other methods It is possible to estimate the other parameters of the AWBM using different methods (event analysis, stepwise multiple-regression analysis and automatic multiple regression technique). The following sections review two approaches which can be used to estimate the model parameters. First, parameter estimation using direct storm analyses will be explained using 12 years rainfall and streamflow data from Sandy Creek catchment. This will be followed by a discussion about the model's ability to produce consistent parameters if different data sets from a single catchment is used. Next, Parameter estimation using automatic multiple regression technique will be presented using data from seven other catchments. 7.5.2 Parameter Estimation Using Direct Storm Analysis The model parameters can be directly evaluated from analyses of the rainfall and runoff data. This allows inconsistent events which can produce unrealistic parameter values to be identified and discarded from the data. The following procedures were used to calibrate the model and evaluate parameters. The results are for the Sandy Creek catchment located in the New England Region. (a) To calculate the BFI parameter, it is necessary to separate baseflow from surface runoff in the recorded streamflow data. This was done using method 5 as discussed in Chapter 5, where BFI =0.26. (b) Methods of evaluating the daily recession constant (K) were also discussed in Chapter 5. The segments of baseflow between surface runoff events are combined to Chapter Seven The AWBM Model. 7.9 form a master baseflow recession curve. From this, the baseflow recession constant was directly evaluated. The value was found to be K=0.96. (c) After excluding inconsistent streamflow and rainfall data, using the procedure set out by Boughton (1987b, 1990, 1993), the smallest storage capacity can be fixed by ensuring a reasonable compromise between the number of surface runoff events which are under and overestimated (this gives the storage capacity CI mm). As shown in Table 7.1, when the surface storage capacity is set as small as 5 mm, there are too many estimated events (136 occasions), but none actually occurred, and there are some events (56 occasions) on which runoff actually occurred, but none was estimated. When the surface storage capacity is increased to 15 mm, there are few occasions (61) on which runoff was estimated in the absence of actual runoff, but actual runoff occurred on 115 events when none was estimated. At a surface storage capacity of 10 mm there is a reasonable compromise between the under and overestimated events, therefore a reasonable estimate of CI is 10 mm. By plotting the runoff depth estimated using this value of CI mm (for all small events where runoff is only generated fromfirst sourc e area with area fraction Al) against actual surface runoff, the source area fraction A1, can be determined from the slope of the line (see Tables 7.1 and 7.2, Figures 7.6 and 7.7). The value of Al was found to be 25%. Chapter Seven The AWBM Model. 7.10 70 - •206026 60 - 1 f 50 /°° - °o° / % 40 - ~/° I 30 - / ' I 20 0 /° < 10 • 0 niSffiwirTU^—i 1- 1 1 ' 0 20 40 60 80 Estimated Runoff (mm) from 10mm. Surface Storage Capacity Figure 7.6 Estimated Runoff for Cl-10-mm Compared with Actual Runoff Table 7.1 Errors of Estimating No. of Runoff Events forfirst Surface Storage Capacity Estimated Runoff in mm Number of events for Surface Storage where Capacity-mm 5 8 10 15 Actual runoff event but no runoff 56 67 80 115 estimated Estimated event 26 30 27 28 less than actual Estimated event but no actual 136 114 92 61 runoff (d) By using large events, where runoff is generated on the larger stores as well as the smaller store, the second storage capacity, and its source area fractioincan be determined. The calculated runoff from the smallest store must be subtracted from the recorded runoff. The residual amounts come from the other parts of the catchment. By repeating step (c), the numbers of the estimated runoff events using different capacities can be compared with the residual amounts in such a way that a compromise between the errors of under and overestimation of events is achieved. After fixing the second storage capacity C2, the values of the estimated runoff from the second storage capacity are plotted versus values of the residual amounts of runoff (see Table 7.2 and Figure 7.7). As previously noted, the source area fraction of the second storage is equal to the slope of the line fitted to these two values (Figure 7.7). Chapter Seven The AWBM Model. 7.11 Table 7.2 Errors of Estimating No. of Runoff Events for Second Surface Storage Capacity Number of events Estimated Runoff in mm for where Surface Storage Capacity- mm 10 15 20 30 Actual runoff event but no runoff 80 102 112 122 estimated Estimated runoff 25 20 14 11 less than residual Estimated event but no actual 147 103 76 47 runoff 50 - / '>20602 6 < 40 - 'O O '/ e § 30 - o' o o 20 s s - s a OS « 10 o "3 o .. i .... 3 0 0 20 40 60 80 100 Estimated Runoff (mm) from 15mm. Surface Storage Capacity Figure 7.7 Estimated Runoff from Area ofC2=15 mm Compared with Actual Runoff Minus Runoff from First Source Area (e) The next step involves evaluation of the largest surface storage capacity using those periods when the entire catchment is generating surface runoff. As the third source area fraction is now known by subtraction (A3=100-A1-A2), the evaluation of the corresponding surface storage capacity C3 is possible. The calculated runoff from the two smaller surface storages is subtracted from the recorded values. The residual amounts come from the third source area. We wish to find a surface storage capacity (C3) whose generated runoff (residual amounts after subtraction) if multiplied by the known A3 value closely matches the actual values of all events. As the third source area fractioinis known, the direct evaluation of the corresponding surface storage (C3) can now be ascertained. In order to find out the Chapter Seven The AWBM Model. 7.12 values of estimated runoff corresponding to the residual values, the following equation is used. Qes = ~~rr 7.5 A3 where Qac. = the actual runoff for event i (corresponding residual which should be generated by last i source area) A3 = the third source area fraction Qes = the estimated runoff for event i Once a number of estimated runoff have been calculated for different capacities, the equivalent surface storage capacity which can produce these amounts for different events, can easily be determined by a number of trial and error estimations. (f) After the values of the parameters were determined, initial values of the surface and baseflow stores were chosen by trial and error. In most cases the effect of the initial storage levels did not extend past thefirst year of simulation. Results for Sandy Creek catchment are shown in Figure 7.6, Figure 7.7, and Table 7.3. Table 7.3 Summary of Parameter Values and Statistics of Results for Sandy Creek Catchment Mean Al CI b R Area Annual A2 C2 *—av K BH SSQ a sqkm Flow A3 C3 mm % mm mm 25 10 8 53 20 15 39 0.96 0.26 1045 0.91 0.0 0.92 55 60 Cav= average capacity, Qac=a*Qest + b (b=0 and forced through origin), R= correlation coefficient 7.5.3 Parameter Estimation Using a Split Sample To check the accuracy of the model and estimated model parameters, a split sample test was used. The data was split into two equal periods. Model parameters were evaluated using each half of the data as well as the whole data set. Table 7.4 shows estimated parameter values. Similar parameters are obtained using each of the three periods. The model formulation can be seen to be based on a good physical Chapter Seven The AWBM Model. 7.13 reality as it is able to give similar parameters for the same catchment using different data sets. Table 7.4 Summary of Parameter Values and Statistics of Results for Sandy Creek Catchment Period 1 Period 1 Whole Period Area sq km 8 8 8 Annual Flow mm/yr 86 54 70 Al(-) 25% 28% 25% A2 (-) 15% 18% 20% A3 (-) 60% 54% 55% CI (mm) 10 8 10 C2(mm) 15 15 15 C3(mm) 60 60 60 Cav (mm) 41 37 39 K 0.96 0.96 0.96 BFI 0.18 0.22 0.20 SSQ (mm2) 744 197 1045 a 1.0 0.85 0.99 R 0.94 0.79 0.92 7.5.4 Parameter Estimation Using Automatic Multiple Regression Technique The procedure for evaluation of the baseflow parameters (BFI and K) is well established as discussed in the previous section and Chapter 5. This section illustrates one of the alternative methods for estimating surface storage parameters and their associated source area fractions. The method has been automated for computer calculation by Boughton. A number of surface storage capacities are selected and the amounts of excess are calculated for each capacity in turn using a daily water balance (adding rainfall to and subtracting evaporation from the store, assuming that the entire catchment area has the same storage capacity). For each actual runoff event a value of rainfall excess can be calculated from each of the stores. There will also be events where runoff is calculated but for which actual runoff is zero (Boughton, 1993). To calculate the set of surface storage capacities and their associated source area fractions (CI, C2, C3, and Al, A2, A3) whose combined excess most closely matches the actual runoff values, the multiple linear relationship is used as follows Actj = e1;j Ai + e2,j A2 + e3jj A3 7.6 where Chapter Seven The AWBM Model. 7.14 Actj = the actual runoff in the jth event enj = the calculated excess from capacity Cn for the jth event An = the fraction of the catchment represented by capacity Cn The simplest approach for estimating the soil storage capacities and the associated source area fractions is to use a stepwise multiple regression analysis to solve Equation 7.6. The three storage capacities are variables selected by the regression analysis, while the regression coefficients are the source area fractions. Some modification to this simple approach is required. This is due to regression analysis being minimised into a sum of squares of differences between actual and calculated mnoff. This optimising criterion is well known in catchment modelling for giving too much weight to large runoff events and significantly little weighting to the small runoff events. In addition, the three source area fractions must add up to 1.0 (100%), but there is no constraint on the normal regression coefficients so that they add to unity (Boughton, 1993). To eliminate these problems, the following modifications are required. First, the value of CI is determined by considering small runoff events only, and so avoiding the dominance caused by large events. As the smallest surface capacity generates runoff before or at the same time as the other two larger capacities, the smallest capacity CI, must be selected by trial and error testing of a single capacity for the whole catchment, such that the number of predictions of the calculated runoff best match the actual mnoff events without considering the amounts of calculated runoff. If CI is set too small, there will be too many calculated runoff events when no actual runoff occurres. If CI is set too large, there will be too many actual events when the calculated runoff is zero. Next, C2, A2 and C3, A3 are calculated using the large events. Knowing that Al=100-A2-A3, Equation 7.6 can now be rewritten as: (dj - Actj) = (dj - e2,j )A2 + (eij - e3>j )A3 7.7 Having fixed CI to give the best match of the number of predictions of the calculated and actual runoff events, the actual runoff values and the calculated values of excess for each of the other capacities are subtracted from the calculated excess from CI for each runoff event. This can be seen in Equation 7.7. Capacities C2 and C3 are selected by multiple regression using Equation 7.7. The source area fractions A2 and A3 are the regression coefficients as in Equation 7.7. This ensures that the three source area fractions sum to unity (100%), and also properly considers small runoff events due to the manner of Chapter Seven The AWBM Model. 7.15 choosing CI (Boughton, 1993). The following section presents the results of the model applied to seven other catchments using the automatic multiple regression technique. 7.5.5 Application of The Model to all Catchments Using the Automatic Multiple Regression Technique In section 7.5.2, the parameters of AWBM were evaluated from the 12 years data from the Sandy Creek catchment using direct data analysis. The model was then applied to this catchment over three test periods and the model's reliability in simulating catchment runoff was examined. This section will review the application of this model to the seven other catchments using the automatic multiple regression technique. Also a comparison of results between the simulated and actual surface runoff and baseflow will be carried out. 7.5.5.1 Kangaroo Valley Catchment Parameters of the model for 21 years data from the Kangaroo Valley catchment are presented in Table 7.5. Table 7.5 Parameters of the AWBM Model for Kangaroo Valley Catchment Baseflow Parameters Soil Storage Source Area Parameters (mm) Parameters (%) K BFI CI C2 C3 Al A2 A3 0.914 0.31 4 81 142 27.3 56.9 15.8 Table 7.6 shows the measures of accuracy for the optimised parameter values. Considering all measures, the simulation results can be considered to be satisfactory. Figure 7.8 shows that the predicted values of total streamflow, surface mnoff and baseflow are very close to the actual values. Table 7.6 Results of AWBM Model for Kangaroo Valley Catchment Q QB QS SSQ AQ E R (mm) (mm) (mm) (mm^) (%) Recorded 18925 5948 12977 . - 1.0 1.0 AWBM 18998 5912 13086 318082 0.39 0.89 0.95 Chapter Seven The AWBM Model. 7.16 20000 * « ; 215220 VI 16000 O o • T3 F 12000 ¥ „2 a a «a E xx 9 • x g 2 s 8000 • a® 35 •o JKXXX*3" s 4000 + XAA an 3 < 0 i*fjS i I—i__i , i i i • . • 1970 1975 1980 1985 1990 lime (Year) Figure 7.8 Mass Curves of Actual and Simulated Q, QB and QSfor the AWBM Model Applied to Kangaroo Valley Catchment In the Kangaroo Valley catchment, 21 years of data consisted of 4092 rainy days over 639 events. The model calibration was conducted using pan evaporation data as potential evaporation. Pan coefficient conversion factors discussed in Chapter 2 were also used to convert pan evaporation to potential evapotranspiration. Using the Pan conversion factor, the model simulation was improved, resulting in a reduction of the calculated SSQ by 8.9%. It also decreased the difference of total prediction from 16.8% to 0.39%. Further analysis of the results show that the first source area has a soil moisture capacity of 4 mm and constitutes 27% of the catchment area. This source area generated 25.1% of the total runoff during 4092 rain days of the study period. Out of these days, there were only 1338 rain days (32.7% time) in which there was some concurrent runoff. The second source area has a soil moisture capacity of 8 mm and constitutes 56.9% of the catchment area. This source area generated 34.9% of the total catchment runoff and produced runoff during 16.5% of the rain days. The third source area has a soil storage capacity of 142 mm and covers 15.8% of the catchment area. This source area produced only 8.9% of the total runoff in 14.6% of rain days. The remaining 31% of total runoff comes from baseflow storage. The automatic regression technique gave the parameters and SSQ values presented in Tables 7.5 and 7.6. It is also possible to obtain similar SSQ values for other combinations of A and C values. A wide range of combinations of source area fractions (Al, A2, A3) and associated storage capacities (C1,C2, C3) give a reasonable prediction of runoff. As can be seen in Chapter Seven The AWBM Model. 7.17 Table 7.7, there will always be a problem in choosing the best set of parameters. In general the model overestimated daily baseflow for large events and underestimated it in small events. Table 7.7 Various Combinations of Surface Storage Capacities and Source Area Fractions Giving Similar Q and SSQ in Kangaroo Valley Catchment CI C2 C3 Al A2 A3 SSQ Q (18925 mm) 15 175 220 62.5 16.8 21.2 353666 19316 10 80 160 37.8 1 61.2 300688 18290 8 70 160 37.1 9.6 53.2 303783 18596 8 80 140 27.3 56.9 15.8 347989 18607 8 168 220 54.6 28 17.4 312064 19731 4 164 220 48.8 37.3 13.9 352583 19995 0 11 165 38.9 2.3 58.8 354142 20234 The direct analysis of rainfall and mnoff events were used to identify periods of inconsistent data, ie runoff more than rainfall, by a water balance of selected hydrographs. Of the 693 events that occurred in the study period, 120 events were considered to be inconsistent. These events were replaced by the average of estimated runoffs obtained from the SFB and AWBM models. In this way the erroneous data was eliminated from subsequent calibrations. The model was applied to the relatively error free data. A large improvement in the simulation results became apparent and the SSQ was reduced by 56% while maintaining the previous optimised parameters. 7.5.5.2 Macquarie Rivulet Catchment The values of parameters for the Macquarie Rivulet catchment using forty years of daily rainfall and runoff data are presented in Table 7.8. Table 7.8 Parameters of the AWBM Model for Macquarie Rivulet Catchment Baseflow Parameters Soil Storage Source Area Parameters (mm) Parameters (%) K BR CI C2 C3 Al A2 A3 0.985 0.43 4 543 1171 32.2 46.8 21 Chapter Seven The AWBM Model. 7.18 Table 7.9 shows the measures of accuracies for this catchment. Figure 7.9 shows that the values of predicted total runoff, baseflow and surface runoff are within 10% of the actual values. Table 7.9 Results of AWBM Model for Macquarie Rivulet Catchment Q QB QS SSQ AQ E R (mm) (mm) (mm) (mm^) (%) Recorded 22837 9693 13144 _ _ 1.00 1.00 AWBM 20815 8976 11839 2152271 -8.85 0.33 0.59 + Q A QB A QS X Qe x QBe o QSe 25000 214003 + gj 20000 + + + + + + + ^xx ++ xX 6 + vXx •g s 15000 xxxxx> Further analysis of the results shows that thefirst sourc e area has a soil moisture capacity of 4 mm and constitutes 32.2% of the catchment area. This source area generated 40.9% of the total mnoff during 5533 rain days of the study period. Out of these days, there were only 1960 days (36.7% time) in which there was some concurrent mnoff. The second source area has a soil moisture capacity of 543 mm and constitutes 46.8% of the catchment area. This source area generated 12.4% of the total catchment runoff and produced runoff during 5.7% of the rain days. The third source area has a soil storage capacity of 1171 mm and 21% of the remaining catchment area. This source area produced only 3.5% of the total runoff in 3.2% of rain days (note, the remaining percentage is contributed by baseflow). As already discussed, there were significant differences between the recorded rainfall and mnoff in this catchment. In order to model these differences, large values of storage capacities were obtained in the model calibration, which allowed for large losses of moisture into the soil store, ie. a high continuous loss. The high values of CI and C2 Chapter Seven The AWBM Model. 7.19 (more than 500 mm) in 67.8% of the catchment meant that almost 70% of the rainfall from these stores was lost. This characteristic was also illustrated by low correlations between daily rainfall and mnoff (0.36) and by the results obtained from the SFB model. The low storage capacity of 4 mm in 32.3% of the catchment, the relatively small area (36 km2) and the very steep slope (38.4 m/km) of the catchment are points to be noted. The elimination of periods of erroneous data from calibration did not improve the simulation. Also, using the pan coefficient to convert pan evaporation to potential evaporation increased the SSQ and consequently was not used for this catchment. Figure 7.9 reflects that even though the parameters obtained for the soil storage capacities were not realistic, the values of the total streamflow, surface runoff and baseflow, are close to the actual values. Considering all measures, the simulation results can be considered satisfactory. As in the case of the previous catchment, wide range of combinations of source area fractions (Al, A2, A3) and associated storage capacities (C1,C2, C3) results in the similar prediction of runoff. As can be seen in Table 7.10, the problem of choosing the best set of parameters still remains. In addition, the predicted baseflow exhibits a poor correlation when compared to the total surface mnoff. The model overestimates daily baseflow for large events and underestimates it in small events. Table 7.10 Various Combinations of Surface Storage Capacities and Source Area Fractions Giving Similar Q and SSQ in Macquarie Rivulet Catchment CI C2 C3 Al A2 A3 Q SSQ (22817) 1 543 1171 30.7 61.1 8.2 21653 2163601 6 543 1171 35.8 46.4 20.5 20587 2172721 8 543 1171 33.8 46 20.1 20190 2173771 10 543 1171 34.3 45.8 19.7 201196 2153374 15 543 1171 35.8 45.4 18.8 19761 2158527 7.5.5.3 Bungonia Creek Catchment Six years of daily rainfall and mnoff data were used in this analysis. This catchment was very dry with the average daily mnoff values being consistently low, regardless of the values of the average daily rainfall. Chapter Seven The AWBM Model. 7.20 Parameters for the Bungonia Creek catchment are presented in Table 7.11. The values of the predicted total runoff, baseflow and surface runoff were very close to the actual values. Table 7.11 Parameters of the AWBM Model for Bungonia Creek Catchment Baseflow Parameters Soil Storage Source Area Parameters (mm) Parameters (%) K BFI CI C2 C3 Al A2 A3 0.805 0.23 9 160 220 14 44.4 41.6 Table 7.12 shows the measures of accuracy for the obtained parameter values for the catchment. Table 7.12 Results of AWBM Model for Bungonia Creek Catchment Q QB QS SSQ AQ E R (mm) (mm) (mm) (mm^) (%) Recorded 373 95 278 _ _ 1.00 1.00 AWBM 334 77 257 1727 -10.45 0.72 0.85 Since the catchment has a mixture of forest and grassland as vegetal covers, the value of source area fractions and associated values of soil storages are regarded as physically acceptable. In this catchment, 6 years of available rainfall records consisted of 534 rain days. Calibration of the model wasfirst conducte d using pan evaporation, and next by use of pan coefficient conversion factors. Using the conversion factor improved the model simulation and decreased the calculated SSQ by 33%. Mass curves of simulated and actual Q, QB, and QS against time for the model are shown in Figure 7.10. This shows that the model with its optimum set of parameters was able to predict surface runoff values which were very close (7% difference) to the actual value. However the predicted value of the baseflow was underestimated by 20% which indicates that using BFI to divert a fraction of total runoff to baseflow does not effectively simulate the baseflow in this catchment. Chapter Seven The AWBM Model. 7.21 1981 1982 1983 1984 1985 1986 Time (Year) Figure 7.10 Mass Curves of Actual and Simulated Q, QB and QSfor the AWBM Model Applied to Bungonia Creek Catchment Analysis of the results presented in Table 7.12 shows that thefirst sourc e area has a soil moisture capacity of 9 mm and constitutes 14% of the catchment area. This source area generated 60% of the total runoff during 534 rain days of the study period. Out of these days, there were only 152 days (28.5% time) in which there was some concurrent runoff. The second source area has a soil moisture capacity of 160 mm and constitutes 44.4% of the catchment area. This source area generated 11.7% of the total catchment and produced runoff during 2% of the rain days. The third source area has a soil storage capacity of 220 mm and occupies the remaining 41.6% of the catchment area. This source area produced only 5.3% of the total mnoff in 1.5% of rain days. The remaining 23% of the total runoff is contributed by baseflow. Similar SSQ values were obtained for other combinations of A and C values and presented in Table 7.13. The problem of choosing the best set of parameters still occurs. In general, the model overestimated the daily baseflow for large events and underestimated it in small events. Chapter Seven The AWBM Model. 7.22 Table 7.13 Various Combinations of Surface Storage Capacities and Source Area Fractions Giving Similar Q and SSQ in Bungonia Creek Catchment CI C2 C3 Al A2 A3 Qmm SSQ (373) 0 209 280 12.7 24.5 62.8 391.2 3212 9 169 220 14.1 51.2 34.7 335.5 1734 10 160 220 14.3 44.4 41.3 331 1742 11 161 220 14.6 44.5 40.9 327 1750 15 165 220 15.6 47.2 37.2 312 1808 17 167 220 16.1 48.7 35.2 307 1833 19 169 220 16.7 50.3 33 303 1848 Figure 7.10 shows that even though the parameters for soil storage capacities were not realistic, the values of total streamflow, surface mnoff, and baseflow are close to the actual values. Considering all measures, the simulation results can be taken to be satisfactory. Direct analysis of daily rainfall and mnoff events was used to identify periods of inconsistent data. The elimination of the detected periods of erroneous data from the subsequent calibration did not have a considerable effect on the simulated results. 7.5.5.4 Mongarlowe River Catchment Twenty-three years of daily rainfall and runoff data were used for the calibration of the Mongarlowe River catchment. In this catchment, the recorded mnoff values were small in comparison to the recorded rainfall. The value of daily R between rainfall and runoff (R= 0.20) was the lowest in comparison to the other catchments. However, the values of the monthly and annual correlations between rainfall and mnoff were satisfactory (R=0.83 and 0.96). The average runoff values were always less than the average rainfall. The parameters values for the Mongarlowe River catchment are presented in Table 7.14. Chapter Seven The AWBM Model 7.23 Table 7.14 Parameters of the AWBM Model for Mongarlowe River Catchment Baseflow Parameters Soil Storage Source Area Parameters (mm) Parameters (%) K BFI CI C2 C3 Al A2 A3 0.977 0.58 0.0 230 610 36.3 17.1 46.6 The obtained values of storage capacities were very high and unrealistic. Anomalies within the rainfall data of the catchment were noted. This could justify the high values for soil storage capacities. It is worth noting that even with these errors in rainfall data, and resulting poor parameter values, the AWBM model could still produce total QB and total QS over the period of record with only a 3% difference from the actual values. Table 7.15 shows the measures of accuracy for the optimised parameter values for this catchment. Table 7.15 Results of AWBM Model for Mongarlowe River Catchment Q QB QS SSQ AQ E R (mm) (mm) (mm) (mm^) (%) Recorded 6649 3825 2824 _ _ 1.00 1.00 AWBM 6567 3820 2747 54565 -1.23 0.73 0.85 The predicted values of total mnoff, baseflow and surface runoff were very close to the actual values. Rainfall and mnoff of the Mongarlowe River catchment behaved similarly to the rainfall and runoff of Macquarie Rivulet. The parameters of the Mongarlowe River catchment had similar characteristics to the parameters of the Macquarie Rivulet catchment (ie. very high storage capacities). However, the rainfall and mnoff values for this particular catchment were not as high. This can be seen by slightly smaller values of CI, C2 and C3 obtained. Mass curves of simulated and actual Q, QB, and QS against time for the model are shown in Figure 7.11. Chapter Seven The AWBM Model. 7.24 + Q A. QB A QS x Qe x QBe o QSe 1950 1954 1958 1962 1966 1970 Time (Year) Figure 7.11 Mass Curves of Actual and Simulated Q, QB and QSfor the AWBM Model Applied to Mongarlowe River Catchment In this catchment, 23 years of available recorded rainfall data consisted of 3153 rain days. The model calibration was conducted using both pan evaporation data for potential evapotranspiration as well as conversion factors to estimate the potential evapotranspiration. Using the conversion factor improved model simulations, decreased the calculated SSQ by 31% and improved the total differences between actual and predicted runoff from 20.6% to 1.23%. Analysis of the results revealed that 36.3% of the catchment area (thefirst source area) produced runoff with zero mm of soil moisture capacity. This source area generated 36.8% of the total runoff during 3153 rain days of the study period. Out of these days, only 1192 (37.8% time) had some concurrent runoff. The second source area has a soil moisture capacity of 230 mm and constitutes 17.1% of the catchment area. This source area generated 2.8% of the total catchment runoff during 3.8% of the rain days. The third source area has a soil storage capacity of 610 mm and occupies the remaining 46.6% of the catchment area. This source area produced only 2.4% of the total mnoff in 1.3% of rain days. Different combinations of source area fractions (Al, A2, A3) and associated storage capacities (C1,C2, C3) gave a similar SSQ and reasonable prediction of runoff. Some of the results are presented in Table 7.16. This indicates that the method is not entirely objective and results are often dependent on the user. In general the model overestimated daily baseflow for large events and underestimated it in small events. Chapter Seven The AWBM Model. 7.25 Table 7.16 Various Combinations of Surface Storage Capacities and Source Area Fractions Giving Similar Q and SSQ in Mongarlowe River Catchment CI C2 C3 Al A2 A3 Qmm SSQ (6649) 0 11 500 27.9 3.3 68.8 5566 86331 7 47 320 31.7 2.4 65.9 5648 104640 9 55 310 32.8 2.4 64.8 5629 106877 10 50 310 34.2 1 64.8 5621 107047 12 52 300 35.2 0.7 64.1 5604 111963 5 230 610 41.7 13.6 44.7 6239 56865 1 230 610 37.7 16.1 46.2 6501 54870 As discussed earlier, there were substantial differences between the average rainfall and runoff in this catchment. In order to model these differences, large values of storage capacities were needed in the model calibration, which allowed for significant losses of moisture into the soil store. The high values of CI and C2 (more than 200 mm) in 60% of the catchment meant that almost 60% of the rainfall was lost. This characteristic was also illustrated by low correlations between daily rainfall and runoff and by the very high value of F (93.4 mm/day) obtained from the SFB model. Figure 7.11 shows that even though the parameters obtained for the soil storage capacities were not realistic, the values of total streamflow, surface mnoff, and baseflow are close to the actual values. Considering all measures, the simulation results can be considered satisfactory. Some periods of erroneous data were detected by direct analysis of rainfall and mnoff events. However, the exclusion of this inconsistent data did not improve the results. 7.5.5.5 Endrick River Catchment Ten years of daily rainfall and runoff data were used for the analysis of the Endrick River catchment. The values of monthly recorded mnoff in this catchment followed the trend of the monthly simulated mnoff. Model parameters for the Endrick River catchment are presented in Table 7.17. Values of predicted total and surface mnoff correlated well with the actual values, as the volumes of predicted flows were within 20% of the actual values. Chapter Seven The AWBM Mor,el 7 76 Table 7.17 Parameters of the AWBM Model for Endrick River Catchment Baseflow Parameters Soil Storage Source Area Parameters (mm) Parameters (%) K BFI CI C2 C3 Al A2 A3 0.877 0.22 2 12 15 74.9 7.3 17.8 Table 7.18 shows the measures of accuracy for this catchment. Table 7.18 Results of AWBM Model for Endrick River Catchment Q QB QS SSQ AQ E R (mm) (mm) (mm) (mm^) (%) Recorded 6546 1492 5054 . . 1.00 1.00 AWBM 5278 1166 4112 317778 -19.37 0.74 0.95 The values of storage capacities for the Endrick River catchment were very low and similar to those for the Mongarlowe River catchment (ie. non-porous top soil). A rapid mnoff response was observed for this catchment, which indicated low values of initial and continuous losses. This rapid response may be due to the apparent non-porous top- soil layer. Surface runoff occurs almost immediately after rainfall. As discussed earlier, Podzolic soils produce surface sealing very quickly, which makes the apparent value of infiltration very low. This surface sealing effect may cause low initial and continuous losses, which will cause the rapid occurrence of surface mnoff. Mass curves of simulated and actual Q, QB, and QS against time are presented in Figure 7.12. It shows the model was able to predict total runoff, baseflow and surface mnoff values similar to actual values. Chapter Seven The AWBM Model, ? ™ + Q A QB A QS x Qe X QBe o QSe 7000 <*} + i 215009 + CO 6000 O 6 5000 • + X X • 4000 + 0 ea S X A ° 3000 • X o ° Q B * 6 Sin n d 2000 • Q c X 1000 * * * X 3 i £ i * * u < o i . 1. . A , 1970 1972 1974 1976 1978 1980 Time (Year) Figure 7.12 Mass Curves of Actual and Simulated Q, QB and QSfor the AWBM Model Applied to Endrick River Catchment In the Endrick River catchment, 10 years of available data consisted of 1368 rain days. The model calibration was conducted using pan evaporation data as potential evaporation as well as using the pan coefficient conversion factors discussed earlier. The latter improved model simulation and reduced SSQ of prediction. Further analysis of the results shows that thefirst sourc e area has a soil moisture capacity of 2 mm and which constitutes 74.9% of the catchment area. This source area generated 63.6% of the total runoff during 1368 rain days of the study period. Out of these days, there were only 477 rain days (34.9% time), in which there was some concurrent runoff. The second source area has a soil moisture capacity of 12 mm and constitutes of 7.3% of the catchment area. This source area generated 4.4% of the total catchment mnoff and produced runoff during 17.7% of the rain days. The third source area has a soil storage capacity of 15 mm and occupies the remaining 17.8% of the catchment. This source area produced only 10% of the total runoff in 16% of rain days. The remaining 22% is contributed by baseflow. A range of combinations of source area fractions (Al, A2, A3) and associated storage capacities (CI, C2, C3) gave a reasonable prediction of runoff, as can be seen in Table 7.19. In general, the model overestimated the daily baseflow for large events and underestimated it in small events. Chapter Seven The AWBM Model 7 2R Table 7.19 Various Combinations of Surface Storage Capacities and Source Area Fractions Giving Similar Q and SSQ in Endrick River Catchment CI C2 C3 Al A2 A3 Qmm SSQ (6546) 0 8 20 97.4 0.8 1.8 6300 334989 1 13 15 66.3 17.1 16.6 5313 345296 2 12 15 74.9 7.3 17.8 6340 151882 3 11 12 79.9 14.2 5.8 6273 177646 4 8 11 90.6 6 3.4 6239 177961 5 8 11 90.6 6 3.4 6059 178836 6 8 11 90.6 6 3.4 5898 179630 numbers were obtadne d after exclusion of the neriod s of eironeou s d ata Analysis of rainfall and runoff events were used to identify periods of inconsistent data. Out of 1368 rain days which occurred during the study period, there were 100 days in which the recorded surface runoff was more than rainfall. In 28 days these differences ranged between a maximum of 183 mm to a minimum of 10 mm. The rest had a difference of only 2-10 mm. Runoff in these days were replaced by estimated values obtained from the AWBM model. In this way the erroneous data was eliminated from subsequent calibration. The model was applied again to this relatively error free data. A large improvement in the simulation results was apparent and the SSQ was reduced by 52.2% whilst maintaining the previously optimised parameters. 7.5.5.6 Corang River Catchment Eight years of daily rainfall and mnoff data were used to calibrate the model for the Corang River catchment. The values of parameters for this catchment are presented in Table 7.20. Chapter Seven The AWBM Model. 7.29 Table 7.20 Parameters of the AWBM Model for Corang River Catchment Baseflow Parameters Soil Storage Source Area Parameters (mm) Parameters (%) K BFI CI C2 C3 Al A2 A3 0.955 0.35 10 40 205 47.5 39.8 12.7 Table 7.21 shows the measures of accuracy for this catchment. Table 7.21 Results of AWBM Model for Corang River Catchment Q QB QS SSQ AQ E R (mm) (mm) (mm) (mm^) (%) Recorded 1816 618 1198 _ _ 1.0 1.0 AWBM 1429 500 929 31289 -21.31 0.60 0.79 Figure 7.13 shows a mass curve plot of actual and simulated Q, QS and QB. The actual values were not significantly different from predicted values and were well within 20% of the estimated values. Using the pan coefficient conversion factor in calibrating the model, improved the model simulation, which reduced the calculated SSQ by 44%, and reduced the differences of the total predicted and actual mnoff from 57.8% to 21.31%. Analysis of the results shows that thefirst sourc e area has a soil moisture capacity of 10 mm and constitutes 47.6% of the catchment area. This source area generated 46.3% of the total runoff during 1205 rain days during the study period. Out of these days, there were only 182 rain days (15.1% of period) in which there was some concurrent runoff. The second source area has a soil moisture capacity of 40 mm and constitutes 39.9% of the catchment area. This source area generated 17.9% of the total catchment runoff, and produced runoff during 7.1 % of the rain days. The third source area has a soil storage capacity of 205 mm and occupies the remaining 12.7% of the catchment. This source area produced 0.9% of the total runoff in 0.9% of rain days. The 35% remaining is the contribution of the baseflow storage. Chapter Seven The AWBM Model. 7.30 + Q A QB A QS X Qe X QBe o QSe 2000 1800 r 215004 + CO 1600 O + 1400 X 6 -g 1200 : + X A •a £. 1000 i X A a g, 800 O E A O 600 i ¥ e x 400 * 2 * X "5 200 X s 0 ; $ 1 s 1979 1981 1983 1985 1987 Time (Year) Figure 7.13 Mass Curves of Actual and Simulated Q, QB and QSfor the AWBM Model Applied to Corang River Catchment Different combinations of source area fractions (Al, A2, A3) and associated storage capacities (C1,C2, C3) which resulted in similar SSQ and the reasonable prediction of mnoff are presented in Table 7.22. The problem of choosing the best set of parameters still remains for this catchment. In general, the model underestimated all of the mnoff components by almost 20%. However, once again, it overestimated daily baseflow for large events and underestimated daily baseflow for small events. Table 7.22 Various Combinations of Surface Storage Capacities and Source Area Fractions Giving Similar Q and SSQ in Corang River Catchment CI C2 C3 Al A2 A3 Qmm SSQ (1816) (0.0) 0 5 143 2 66 32 1870 31434 2 8 142 34.3 32.4 33.3 1884 31155 5 135 155 67.9 25.8 6.2 1863 31345 8 128 168 75.2 2.2 22.7 1783 30597 10 130 170 78.4 11.3 10.3 1722 30377 15 65 85 82.5 13.3 4.2 1582 29956 10 40 205 47.6 39.9 12.7 1697 14129 imber in tiie last rov/ were obiaine d after eliminalio n of the periods of erroneou Direct analysis of rainfall and mnoff events were used to identify periods of inconsistent data. Out of 182 days of mnoff in the study period, there were 35 days with data in which the surface runoff was more than the rainfall (1.7-22.4 mm). These events were replaced by estimated runoff obtained from the AWBM model. In this way the erroneous data was eliminated from subsequent calibration. The model was applied again to the Chapter Seven The AWBM Model. 7.31 relatively error free data. A large improvement in the simulation results was apparent and the SSQ was reduced by 55% whilst maintaining the previously optimised parameters. 7.5.5.7 Shoalhaven River Catchment at Kadoona Fifteen years of daily rainfall and mnoff data were used for analysis of the Shoalhaven River catchment at Kadoona. Parameters for this catchment are presented in Table 7.23. Table 7.23 Parameters of the AWBM Model for Shoalhaven River Catchment at Kadoona Baseflow Parameters Soil Storage Source Area Parameters (mm) Parameters (%) K BFI CI C2 C3 Al A2 A3 0.899 0.40 8 88 158 56.3 36.3 7.4 Table 7.24 shows the measures of accuracy for this catchment. Considering all measures, the simulation results were satisfactory. The predicted values are within 3% of the actual values. Monthly surface runoff was predicted very well, while the baseflow prediction was poor. A problem exists in estimating higher values for the baseflow in larger events and underestimating it for smaller events. Table 7.24 Results of AWBM Model for Shoalhaven River Catchment at Kadoona Q QB QS SSQ AQ E R (mm) (mm) (mm) (mm^) (%) Recorded 5212 2037 3175 _ . 1.0 1.0 AWBM 5091 2037 3054 8767 -2.32 0.83 0.91 The difference between recorded rainfall and runoff in this catchment is low for lower values of rainfall and high for higher values of rainfall, which indicates low initial losses and high continuous losses. The values of soil storage capacities for the AWBM model when applied to the Shoalhaven River at Kadoona were similar to the Kangaroo Valley catchment. The value of CI (8 mm) shows the soil storage capacity of the area adjacent to the streamflow network. The value of C2 (88 mm) is close to the inferred value of top soil of this catchment (cropland in fallow) and not too far from the 100 mm expected capacity of eucalyptus and shrubs which mainly covers the catchment. The value of S3 (158 mm) indicates that some part of this catchment has a high moisture capacity. Chapter Seven The AWBM Model. 7.32 Analysis of the results shows that thefirst sourc e area has a soil moisture capacity of 8 mm and constitutes 56.3% of the catchment area. This source area generated 46% of the total runoff during 1988 rain days during the study period. Out of these days, there were only 451 rain days (22.7% of period) in which there was some concurrent runoff. The second source area has a soil moisture capacity of 88 mm and constitutes 36.3% of the catchment area. This source area generated 12.2% of the total catchment runoff, and produced runoff during 6.7% of the rain days. The third source area has a soil storage capacity of 158 mm and is 7.4% of the remaining catchment area. This source area produced only 1.8% of the total runoff in 4.98% of rain days. The remaining 40% is contributed by baseflow. Figure 7.14 shows a mass curve plot of actual and simulated Q, QS and QB for this catchment. This shows that the model was able to predict total and surface runoff values which fitted very well with the actual values. Total predicted baseflow is in agreement with the actual baseflow but the correlation between these values is not as good as the other two components of runoff. + Q AQB AQSxQexQBeoQSe 1972 1977 1982 1987 Time (Year) Figure 7.14 Mass Curves of Actual and Simulated Q, QB and QSfor the AWBM Model Applied to Shoalhaven River Catchment at Kadoona As can be seen from Table 7.25, different combinations of source area fractions (Al, A2, A3) and associated storage capacities (CI, C2, C3) resulted in a reasonable prediction of mnoff for this catchment. However, the problem of choosing the best set of parameters still occurs. In general the model underestimated all mnoff components by almost 20%. However, once again it overestimated daily baseflow for large events and underestimated it in small events. Chapter Seven The AWBM Model. 7.33 Table 7.25 Various Combinations of Surface Storage Capacities and Source Area Fractions Giving Similar Q and SSQ in Shoalhaven River Catchment CI C2 C3 Al A2 A3 Qmm SSQ (5213) (0.0) 1 65 410 35.8 46.5 17.7 4890 90812 5 45 165 39.9 43 17.1 5079 87717 7 47 157 46 35.8 18.2 5054 88004 9 89 159 58.5 33.8 7.7 5061 87850 10 90 160 60.6 31.3 8.1 5036 87998 12 92 162 64.6 27 8.5 4490 88588 15 85 165 69.4 19.7 10.9 4900 89880 7.6 MODEL SENSITIVITY ANALYSIS Sensitivity analysis is a useful tool in modelling rainfall and runoff. The usual method is to examine the model output while all parameters, except one, are kept constant. This gives the user some guidance in the selection of realistic parameters, and helps to prevent parameter interaction. The sensitivity analysis of the model is performed using Sandy Creek catchment data. Table 7.26 shows model sensitivity to parameter variations. The parameters were K, BFI and Cav, where Cav is the area weighted average of all 3 store capacities. The results show that the more effective parameters of the model are the soil storage capacities, BFI and K. The AWBM model was found to be less sensitive to change in parameters compared to the SFB model (Table 7.26). Table 7.26 Model Sensitivity to Parameter Variations for AWBM Model Param Comp change in runoff for % change in param. -20% -10% 10% 20% QS 15.0 7.3 -6.8 -12.8 (Cav) QB 13.0 6.4 -5.9 -11.1 Q 14.5 7.0 -6.7 -12.3 QS 0.0 0.0 0.0 (K) QB 37.0 0.25 - Q 11.0 0.07 0.07 QS 7.0 3.5 -3.5 -7.0 (BFI) QB -17.5 -8.7 8.7 17.5 Q 0.0 0.0 0.0 0.0 Chapter Seven The AWBM Model, 714 The AWBM was also found to be much less sensitive to the periods of data (period 1, period 2, and whole period) used in the calibration, compared to the SFB model. 7.7 SUMMARY This chapter introduced the AWBM model. This model was tested using data from a small experimental catchment located in northern N.S.W. A summary of the model parameters for all catchments is presented in Table 7.27. Table 7.27 Parameters of the AWBM Model for all Catchment Catchments' K BFI CI C2 C3 Al A2 A3 Nat. Index 206026 0.960 0.20 10 15 60 25 20 55 215220 0.914 0.31 4 81 142 27.3 56.9 15.8 214003 0.985 0.43 4 543 1171 32.2 46.8 21 215014 0.805 0.23 9 160 220 14 44.4 41.6 215006 0.977 0.58 0.0 230 610 36.3 17.1 46.6 215009 0.877 0.22 2 12 15 74.9 7.3 17.8 215004 0.955 0.35 10 40 205 47.5 39.8 12.7 215008 0.899 0.40 8 88 158 56.3 36.3 7.4 The results showed that the model was able to predict surface mnoff and baseflow components which were similar to the actual values. In consideration of the results obtained from this part, the model was then applied to seven other catchments. The results are recorded later in this chapter. Thefinal section of this chapter addressed the model's sensitivity. Parameter estimation using water balance of the data allows for inconsistent events, which can produce unrealistic parameter values to be identified and discarded. Considerable effort was needed to determine the appropriate parameter values of the model using this method. The method is tedious and impractical for application to a large number of catchments. Automatic multiple regression was a much faster method for determining parameter values. However, a wide range of combinations of source area fractions (Al, A2, A3) and associated storage capacities (C1,C2, C3) resulted in similar SSQ and the reasonable prediction of runoff, making the selection of the best set of Chapter Seven The AWBM Model. 7.35 parameters difficult. Interaction between parameters caused several sets of parameters to give similar SSQ. However, the AWBM model gave good simulation of actual flow components (surface runoff and baseflow) for all of the catchments studied. The model calibrations werefirst conducte d using pan evaporation data as the potential evaporation. This was followed by using monthly pan coefficient conversion factors (discussed in Chapter 2) which were used to convert pan evaporation to potential evapotranspiration. Using the conversion factor improved the model simulation in most catchments and decreased the calculated SSQ considerably. In seven of the eight catchments the adjustment of pan evaporation data using the pan factor gave best results. In one catchment (Macquarie Rivulet) using pan factor gave poorer results. The reason for this was that in this catchment there were large differences between the recorded rainfall and streamflow data. When pan factors were applied, actual evapotranspiration was grossly underestimated and model calibration required exceptionally large values of soil storage capacity to balance. When pan factors were omitted, the larger value of actual evapotranspiration produced more realistic values of soil storage capacities. Although the AWBM is a great improvement on other models, and predicts the separate components of surface runoff and baseflow quite well, its prediction of baseflow could be improved. In general, the model overestimates daily baseflow for large events and underestimates it in small events. In addition, all of the catchments studied have nonlinear baseflow recessions, while the predicted daily baseflow from the model is proportional to the amount of water in a single baseflow store and is given in a simple linear form. The simplicity of the baseflow simulation in the model is, (based on results), inadequate for simulating the more complex baseflow response of a catchment. The AWBM model was able to predict surface runoff in a more realistic way. Both small and large runoff events are predicted accurately. Since the results were encouraging, the use of this model should be investigated further. Analysis can be carried out with data from different regions with different hydrological characteristics to further verify the performance of the model. Chapter Seven The AWBM Model. 7.36 Furtherfield wor k is needed to check the validity of the source areas predicted by the model. There are a number of ways to specify the source areas of mnoff generation including; comparing observed soil moisture levels and observed flows, investigating soil types and their distribution in the catchment, and by considering the other morphologic features of the basins. Furthermore, the use of the model on ungauged catchments requires experience and field observation. It is believed that the use of the model in ungauged catchments would become more efficient by orienting some study towards improving the structure of the model as well as the method of parameter estimation. This can primarily be done by incorporating a new relationship for governing the discharge and recharge of source areas, as well asfinding a n accurate formula for routing the surface and sub-surface flow through their paths, and finally by defining the default source area fractions more accurately. Overall, the AWBM gives good results, particularly QS and QB and is much better than a model optimised on Q total, such as SFB. CHAPTER EIGHT INVESTIGATION OF MODEL COMPLEXITY AND DATA ERRORS IN RAINFALL-RUNOFF MODELLING CHAPTER EIGHT INVESTIGATION OF MODEL COMPLEXITY AND DATA ERRORS IN RAINFALL-RUNOFF MODELLING 8.1 INTRODUCTION Hydrologic models can be used to obtain an understanding of a system's performance under specific conditions. A large number of models are available, with various levels of complexity. These range from simple mnoff coefficient approaches, to lumped conceptual models such as Stanford's (Crawford and Linsley, 1966), through to complete physical process models such as SHE (Abbott et al, 1986). In an earlier study conducted by Baki (1993), eight models of varying degrees of complexity, including Diskin's model (2 parameters), the SFB model (3 parameters), SDI model of Mount (4 parameters), API model (6 parameters), SDI model of Langford (6 parameters), Boughton model (9 parameters), the Modified Boughton model (10 parameters) and the SDI model of Kuczera (11 parameters) were applied to seven catchments. From the study, the SDI model was found to be the most satisfactory for all catchments analysed. In consideration of this, it was decided to undertake a more detailed investigation of the SDI model in conjunction with SFB and AWBM models. The SFB and the AWBM models were applied to the eight catchments as discussed in chapters 6 and 7. In this chapter, the potential for improving rainfall-runoff modelling by applying a more complex model will be investigated. The more complex SDI model will be applied to the same data sets and the results will be compared. The limitations of modelling when only streamflow is used in the calibration of models will be highlighted. In addition, the effects of data quality on the parameter values will be discussed and a study of error analysis using the AWBM model will be carried out. Chapter Eight Investigation of Model Complexity and Data Errors in Rainfall-Runoff Modelling. 8.2 8.2 THE SDI MODEL The Soil Dryness Index (SDI) model is a water balance model originally developed by Mount (1972). Langford et al. (1978), made several structural changes to this model and used it to simulate the land hydrologic cycle of catchments. This model has undergone further modification by Kuczera (1987). For the purposes of this study the latest version of the model (Kuczera's SDI model) will be used. The description of the 11-parameter SDI model of Kuczera is given in Chapter 3. In a comparative study of 8 models applied to 7 catchments, Baki (1993) showed that the best model for all catchments was Kuczera's SDI model. Accordingly, this model was adopted as a bench mark to investigate the effects of the level of complexity in the performance of rainfall-runoff models. Baki (1993) used the total streamflow data in comparing the models. In the present study, the separated QS and QB (discussed in Chapter 5) were used to compare the calculated values with those recorded. Table 8.1 shows optimum sets of parameters of this model for eight catchments together with the corresponding measures of accuracy over the period of study. Chapter Eight Investigation of Model Complexity and Data Errors in Rainfall-Runoff Modelling R.3 Table 8.1 Parameters and Results of the SDI Model for All Catchments Variables Sandy Kangaroo Macquarie Bungonia Mongarlowe Endrick Corang Shoalhaven Creek Valley Rivulet Creek River River River River KI 0.056 0.5740 0.1560 0.5997 0.0618 1.0 0.4950 0.0932 KG 0.0181 0.0168 0.0076 0.0086 0.0176 0.2063 0.0263 0.0055 ATHRU 0.1 4.2081 3.6163 4.5923 0.0 0.0 0.0 0.2698 BTHRU 0.9 1.1518 1.2062 1.1916 1.0 1.0 0.9103 1.0034 WET 0.08 0.0 0.1750 0.2184 0.0 0.3900 0.1820 0.0 SMAX 37.57 6.3059 9.1598 7.3629 9.1598 13.7690 11.3990 8.9014 CEP 0.105 0.0018 0.0015 0.0011 0.1261 0.2183 0.0483 0.0104 DEEP 0.0 1.0 0.3423 0.5434 0.3730 1.0 0.6340 0.9396 WETS 0.0 0.0055 0.0 0.0008 0.0001 0.0119 0.0143 0.0256 WETH 0.0041 0.0083 0.001 0.0334 0.0011 0.2924 0.0004 0.0078 BEP 0.065 0.0742 0.0268 0.0274 0.0781 0.0672 0.1112 0.0834 initial SDI 280.0 60.0 180.0 175.0 80.0 24.0 85.0 80.0 initial H 0.0 35.0 80.0 0.0 15.0 0.0 0.0 5.0 SSQ mm2 5125 211363 1955699 2462 51348 221241 24605 71385 E 0.73 0.93 0.39 0.60 0.74 0.82 0.68 0.86 D 0.74 0.93 0.40 0.60 0.74 0.85 0.69 0.87 AQ(%) -7.9 -4.78 1.51 6.44 -1.59 -2.98 8.74 7.40 Table 8.1 shows that even though all the catchments are located in the same region the parameter values are not similar. Values of E and D are satisfactory for all studied catchments. All values of AQ were smaller than ±10%. A comparison can be made using dimensionless coefficients E, D and AQ to examine the performance of the model based on the results obtained from differences between the predicted and actual total streamflow. Based on the values of E the catchments, in descending order of accuracy, are: Kangaroo Valley, the Shoalhaven River, the Endrick River, the Mongarlowe River, Sandy Creek, the Corang River, Bungonia Creek and Macquarie Rivulet. Parameters WET, WETS and WETH, represent the soil moisture storage and the saturated soil storage contributions which control the surface runoff in the model. The value of WETS for the Macquarie Rivulet catchment is zero, indicating that the response to runoff for this catchment exhibited a linear function for soil storage and was not as good as the other catchments. This also seems to be apparent for the Bungonia Creek Chapter Eight Investigation of Model Complexity and Data Errors in Rainfall-Runoff Modelling. 8.4 catchment which does not have a good recorded rainfall-runoff response either. The differences between the values of average recorded rainfall and runoff in these catchments were very high, indicating a high loss rate. The values of WETH were high for the Endrick River which had a rapid recorded rainfall-runoff response (very low S value); medium for the Kangaroo Valley, Bungonia Creek and Shoalhaven River catchments, and low for the Corang River, Macquarie Rivulet, the Mongarlowe River and Sandy Creek, all of which exhibited low recorded rainfall-runoff responses. The evapotranspiration loss component (ET) in the model is controlled by three parameters; BEP, CEP and SMAX. Parameters CEP and SMAX relate ET to the soil moisture storage, while BEP relates ET to the recorded pan evaporation (EP). The value of BEP was very low for the Macquarie Rivulet catchment compared to the other catchments, indicating that the value of ET was not reduced by an increase in EP, as for other catchments. These results are consistent when calibrating the AWBM model, indicating higher evaporation rates for this catchment. The value of BEP for the Corang River catchment was much higher than for the other catchments. Even though SMAX was slightly higher, the combination of CEP and SMAX values indicate that, for matching the total streamflow, the model should assign a lower value of evaporation loss. The parameter which controls the interflow components of the mnoff is KI. A high interflow recession constant was expected for this region, but the values of KI were much lower than expected because matching the total streamflow was achieved with flow from other sources in most catchments. The exception was the Endrick River catchment where an immediate recession was achieved because the catchment had a very strong rainfall-runoff response. The value of KG controls the depletion of moisture from the saturated soil store. This value ranged from 0.0055 for the Shoalhaven River to 0.2063 for the Endrick River. A higher value of KG indicates a higher rate of moisture depletion from the saturated soil store. The baseflow is a portion of this depletion, which is controlled by the parameter DEEP (similar to parameter B in the SFB). Catchments which have big differences between rainfall and runoff values have lower values of DEEP eg Sandy Creek, Macquarie Rivulet and the Mongarlowe River. For these respective catchments, about 90%, 70% and 65% of the depletion from the saturated soil store were lost to deep Chapter Eight Investigation of Model Complexity and Data Errors in Rainfall-Runoff Modelling, 8.5 groundwater. For the Macquarie Rivulet catchment, the values of B in the SFB model and DEEP in the SDI model have similar values indicating a similar portion of soil storage depletion was lost to deep groundwater. The highest values of DEEP were obtained for the Kangaroo Valley and Endrick River catchments, where all the baseflow discharges into the stream system. This was followed by the Shoalhaven River catchment where 94% of the infiltration discharged into the stream system. The value of B (SFB model) for the Shoalhaven River catchment was 1.0. Similar values of B for the SFB model (B=l) were found for Kangaroo Valley catchment. For the Corang River and Bungonia Creek catchments the saturated soil storage depletion that was lost via deep seepage to groundwater, was 37% and 46% respectively. Figure 8.1 presents the plot of simulated and actual monthly runoff against time for all catchments. Based on the comparison of total simulated and recorded streamflow the result of the modelling is acceptable. In the following section, a comparison between three rainfall-runoff models followed by a comparison of results between the simulated and actual surface runoff and baseflow will be made to investigate the effects of complexity in the performance of rainfall-runoff models. Chapter Eight Investigation of Model Complexity and Data Errors in Rainfall-Runoff Modelling, 8.6 206026 •••Qa •g* 500 —-Qe LiAAAtLiiGft. 1 14 27 40 53 66 79 92 105 118 131 23 45 67 89 111 133 155 177 199 221 243 Time (month) Time (month) 1 6 11 16 21 26 31 36 41 46 51 56 61 66 71 41 81 121 161 201 241 281 321 361 401 441 Time (month) Time (month) 600 1 25 49 73 97 121 145 169 193 217 241 265 12 23 34 45 56 67 78 89 100 111 Time (month) Time (month) 1 16 31 46 61 76 91 106 121136 151166 1 7 13 19 25 31 37 43 49 55 61 67 73 79 85 91 Time (month) Time (month) Figure 8.1 Plot of Actual and Simulated Monthly Streamflow Using the SDI Model Chapter Eight Investigation of Model Complexity and Data Errors in Rainfall-Runoff Modelling. 8.7 8.3 MODEL COMPARISONS The main issue addressed in this section is to investigate the required complexity in rainfall-runoff models that use only daily rainfall and streamflow data. A review of literature in Chapter 3 showed that the number of model parameters and the complexity of models have increased with the expansion in available computing power. There are many modelers seeking to incorporate processes assumed to be important hydrologically into their modelling structures by including a large number of parameters. This raises the questions of how much of this information is reliable and can be obtained with modelling and does modelling accuracy depend on the number of parameters incorporated in a rainfall-runoff model? To answer these questions, the results obtained from a simple conceptual SFB model and the variable source area AWBM model were compared with the results of the 11 parameter SDI model which was applied to eight catchments. The description of catchments is given in Chapter 4. 8.3.1 Sandy Creek Catchment Mass curves of simulated and actual Q, QB, and QS against time for the SDI model presented in Figure 8.2 show an acceptable simulation of total streamflow for this catchment. Table 8.2 shows the measures of accuracy for the optimised parameter values. Comparison of the obtained results presented in Table 8.2 shows that if only the total mnoff is considered as a basis for checking model performance, all models produce acceptable results. The SDI model can be rankedfirst fo r SSQ, E, R, and AQ. The AWBM model can be ranked second for SSQ, E, R, and third for AQ. Finally the SFB model is ranked last based on SSQ, E, R, and second for AQ. Chapter Eight Investigation of Model Complexity and Data Errors in Rainfall-Runoff Mrvtellinp x s + Q A QB A QS X Qe x QBe O QSe 900 800 + + + 206026 + X o 700 Y X A A A 600 * « s 500 X * ¥ * 2 o O 0 400 A A A A A o 300 ; A o O ° •a O 0 O X X X X 200 I \ X X X X X c X A A 4 100 Figure 8.2 Mass Curves of Actual and Simulated Q, QB and QSfor the SDI Model Applied to Sandy Creek Catchment Although the SDI model performed best in the total simulations (see Figure 8.2), the predicted baseflow and surface runoff are in error. Baseflow is overestimated by 62% while surface runoff is underestimated by 25%. The results presented in Table 8.2 indicate that the best simulation for the different components based on all measures was obtained using the AWBM model followed by the SDI model. The SFB model came third. The same problem can be seen with this model. The simulation of baseflow given by this model is extremely poor. The optimised parameter values of the SFB model for this catchment should not be accepted. Table 8.2 Results of all Models for Sandy Creek Catchment Model Variable Actual Est A% SSQ R E Q 840.2 912 +8.58 9698 0.71 0.50 SFB QB 164.7 586 +255.8 1824 0.55 -2.87 QS 676.1 327 -51.69 8635 0.70 0.43 Q 840.2 721 -14.21 6634 0.81 0.66 AWBM QB 164.7 147 -11.03 446 0.53 0.05 QS 676.1 574 -15.06 5265 0.82 0.66 Q 840.2 775 -7.76 5136 0.86 0.73 SDI QB 164.7 267 +62.09 3342 0.60 -6.10 QS 676.1 508 -24.85 7310 0.81 0.52 8.3.2 Kangaroo Valley Catchment Mass curves of simulated and actual Q, QB, and QS against time for the SDI model presented in Figure 8.3 show a very good simulation of total streamflow for this catchment. Comparison of the results in Table 8.3 shows that if only the total runoff is considered as a basis for checking model performance, the SDI model can be ranked first Chapter Eight Investigation of Model Complexity and Data Errors in Rainfall-Runoff Modelling. 8.9 for SSQ, E, R, and second for the percentage of AQ. The AWBM model can be ranked first for the percentage of AQ and second for other criteria, using the same basis (if only the total runoff is considered). The SFB model is ranked last based on all measures. + Q A QB A QS X Qe x QBe o QSe 1970 1975 1980 1985 1990 Time (Year) Figure 8.3 Mass Curves of Actual and Simulated Q, QB and QSfor the SDI Model Applied to Kangaroo Valley Catchment As can be seen in Figure 8.3, considering the separate prediction of baseflow and surface mnoff provides some interesting insights into rainfall runoff modelling. The results presented in Table 8.3 indicate that by far the best simulation of the different components was obtained using the AWBM model. The SFB model performs much better than the more complex SDI model. The simulation of baseflow given by the SDI model is extremely poor. This is mainly due to the interactions between parameters describing the surface runoff and baseflow components of mnoff. Although the SDI model gives a good prediction of total streamflow, it does not truly represent the movement of water in the catchment. Therefore, the optimised parameter values of the SDI model for this catchment should not be accepted. Chapter Eight Investigation of Model Complexity and Data Errors in Rainfall-Runoff Modelling. 8.10 Table 8.3 Results of Models for Kangaroo Valley Catchment Model Variable Actual Est A% SSQ R E differences Q 18925 17066 -9.82 347383 0.94 0.88 SFB QB 5948 5875 -1.23 76115 0.54 0.28 QS 12977 11191 -13.76 226418 0.95 0.90 Q 18925 18986 +0.32 318082 0.95 0.89 AWBM QB 5948 5899 -0.83 102306 0.74 0.03 QS 12977 13087 +0.84 311806 0.94 0.87 Q 18925 18022 -4.77 211304 0.97 0.93 SDI QB 5948 9034 +51.88 247698 0.54 -1.35 QS 12977 8988 -30.74 403699 0.95 0.83 8.3.3 Macquarie Rivulet Catchment Plot of mass curves time series of Q, QB, and QS for the SDI model is shown in Figure 8.4. + Q A QB A QS X Qe X QBe o QSe 25000 i Figure 8.4 Mass Curves of Actual and Simulated Q, QB and QSfor the SDI Model Applied to Macquarie Rivulet Catchment Table 8.4 presents the results of rainfall-runoff modelling for the Macquarie Rivulet Catchment. This table shows that all models were able to predict monthly mnoff values which were not significantly different from the recorded values. Based on the comparison made between recorded and predicted runoff over the whole period of analysis, the best model was the SDI model. The results presented in Table 8.4 show that the best simulation for the different components was obtained using the AWBM model. The SFB model performs better than the more complex SDI model, however the optimised parameter values of this model were not realistic. The simulation of baseflow and surface mnoff by the SDI model is verv poor This is mainly because of the interactions between optimised parameters Chapter Eight Investigation of Model Complexity and Data Errors in Rainfall-Runoff Modelling. 8.11 representing different flow components of runoff. Although the SDI model presents a good prediction of total streamflow, it does not tmly represent the movement of water through the catchment. The optimised parameter values for this catchment using both SDI and SFB models are found to be physically unrealistic and should not be accepted. Table 8.4 Results of Models for Macquarie Rivulet Catchment Model Variable Actual Est A% SSQ R E Q 22837 24253 +6.20 2254848 0.55 0.30 SFB QB 9693 13497 +39.25 246113 0.40 0.04 QS 13144 10755 -18.18 1709533 0.55 0.28 Q 22837 20815 -8.85 2152271 0.59 0.33 AWBM QB 9693 8976 -7.40 259659 0.41 -0.01 QS 13144 11840 -9.92 1525065 0.60 0.36 Q 22837 23161 +1.42 1965501 0.63 0.39 SDI QB 9693 6106 -37.01 280285 0.33 -0.09 QS 13144 17056 +29.76 1454619 0.63 0.39 8.3.4 Bungonia Creek Catchment Mass curves of simulated and actual Q, QB, and QS against time for the SDI model presented in Figure 8.5 show a good simulation of the total streamflow for this catchment. Table 8.5 shows the measures of accuracy for the optimised parameter values for the catchment. A comparison of the results in Table 8.5 shows that if only the total mnoff is considered as a basis for checking model performance, the AWBM model can be rankedfirst fo r SSQ, E, R, and third for the percentage of AQ. The SFB model can be rankedfirst fo r the percentage of AQ; but last for other criteria, using the same basis. The SDI model is ranked second based on all measures. Chapter Eight Investigation of Model Complexity and Pat. F.rmr. in Rainfall-Runoff Milling * 7? 400 — * 350 :+ Q A QB A QS CO X Qe X QBe o QSe 1 ° o 300 8 + 250 - 215014 A •S J, 200 r A | O-150 •a c 100 CO 50 a {, fit ii"1 ^""X 1981 1982 1983 1984 1985 1986 Time (Year) Figure 8.5 Mass Curves of Actual and Simulated Q, QB and QSfor the SDI Model Applied to Bungonia Creek Catchment The results presented in Table 8.5 indicate that the best simulation of the different components is obtained using the AWBM model. The simulation of baseflow given by the SDI model, for this catchment, is better than Kangaroo Valley and Macquarie Rivulet and is acceptable. The SFB model performs extremely poorly with incorrect prediction of baseflow and surface runoff, and came last. Although the SFB model gives an acceptable prediction of total streamflow, it does not truly represent the movement of water through the catchment. Therefore the optimised parameter values of this model for this catchment should not be accepted. Table 8.5 Results of Models for Bungonia Creek Catchment Actual Est A% SSQ R E Q 373 376 +0.75 4031 0.59 0.34 SFB QB 95 340 +256.15 1765 0.61 -6.20 QS 278 36 -87.00 4333 0.41 -0.02 Q 373 334 -10.45 1727 0.85 0.72 AWBM QB 95 77 -19.19 105 0.77 0.57 QS 278 257 -7.45 1152 0.86 0.73 Q 373 397 +6.38 2455 0.78 0.60 SDI QB 95 18 -81.55 371 0.27 -0.51 QS 278 379 +36.58 2067 0.75 0.51 8.3.5 Mongarlowe River Catchment Based on a comparison made between recorded and predicted total runoff over the whole period of analysis, SDI was the best model . The mass curves of simulated and actual Q, QB, and QS against time for the SDI model presented in Figure 8.6 depicts a very good simulation of the total streamflow for this catchment. Table 8.6 reflects the measures of accuracy for the optimised parameter values for the catchment. In Table 8.6, Chapter Eight—Investigation of Model Complexity and Data Errors in Rainfall-Runoff Modelling. 8.13 a comparison of the results shows that all models performed well if only the total runoff is considered as a basis for checking model performance. The SDI model can be ranked first for SSQ, E, R, and second for the percentage of AQ. The AWBM model can be ranked first for the percentage of AQ; and second for other criteria, using the same basis. The SFB model is ranked last based on all measurements. + Q A QB A QS X Qe X QBe o QSe 7000 X** 6000 215006 CO xx* • xx* O 5000 6 ,x*X |"4000 x •a x** . IAAAA .2 M 3000 'a o ***x**A^AAAAAAAAA E CO 2000 x***^*^^ o0ooooooooo •a iSSAooo" c •3s !00°0 CQ 1950 1954 1958 1962 1966 1970 a Time (Year) Figure 8.6 Mass Curves of Actual and Simulated Q, QB and QSfor the SDI Model Applied to Mongarlowe River Catchment Although the SDI model performed best for the total flow simulations (see Figure 8.6), the simulation of other components is poor. The results presented in Table 8.6 indicate that by far the best simulation for the different components was obtained using the AWBM model. The simulation of baseflow by the SDI model for Mongarlowe River is better than for Kangaroo Valley and Macquarie Rivulet. The SFB model performs extremely poorly with an incorrect prediction of baseflow and surface mnoff. Although the SFB model gives a good prediction of total streamflow, it does not truly represent the movement of water through the catchment. Therefore optimised parameter values for this catchment using this model should not be accepted. Chapter Eight Investigation of Model Complexity and Data Errors in Rainfall-Runoff Modelling. 8.14 Table 8.6 Results of Models for• Mongarhowe River Catchment Actual Est A% SSQ R E Q 6649 6838 +2.84 99859 0.72 0.50 SFB QB 3825 6022 +57.43 30665 0.83 0.24 QS 2824 817 -71.09 88707 0.52 0.13 Q 6649 6567 -1.24 54565 0.85 0.73 AWBM QB 3825 3820 -0.13 15052 0.81 0.63 QS 2824 2747 -2.74 42812 0.77 0.58 Q 6649 6494 -2.34 51349 0.86 0.74 SDI QB 3825 4742 +23.98 29985 0.71 0.26 QS 2824 1752 -37.98 52517 0.77 0.48 8.3.6 Endrick River Catchment The mass curves of simulated and actual Q, QB, and QS against time using the SDI model for ten years of daily rainfall and runoff data is presented in Figure 8.7. This figure shows a very good simulation of the total streamflow for this catchment. Table 8.7 shows the measures of accuracy for the optimised parameter values for the catchment. A comparison of the results obtained shows that if only the total runoff is considered as a basis for checking model performance, all models performed well. The AWBM model can be rankedfirst fo r SSQ, E, R, and second for the percentage of AQ. The SDI model can be ranked first for the percentage of AQ and second for other criteria, using the same basis. The SFB model is ranked last based on all accounts. + Q A QB A QS X Qe X QBe o QSe 7000 =8 6000 CO O 5000 Jj S 4000 •a J. 3 m 3000 £ o> % 2000 a ~ 1000 a 1970 1972 1974 1976 1978 1980 < Time (Year) Figure 8.7 Mass Curves of Actual and Simulated Q, QB and QSfor the SDI Model Applied to Endrick River Catchment Although the SDI model performed second for total flow simulations (Figure 8.7), the simulation of other components was poor. The results presented in Table 8.7 indicate that the best simulation of the different components was obtained using the AWBM model. The simulation of baseflow using the SDI model for the Endrick River Catchment Chapter Eight Investigation of Model Complexity and Data Errors in Rainfall-Runoff Modelling. 8.15 was better than for Kangaroo Valley and Macquarie Rivulet. The SFB model performed poorly because it incorrectly predicted the baseflow and surface runoff. Although the SFB model gives a good prediction of the total streamflow, it did not tmly represent the movement of water through the catchment. Therefore the optimised parameter values of the model for this catchment should not be accepted Table 8.7 Results of the Models for Endrick River Catchment Actual Est A% SSQ R E Q 6546 7855 +19.98 311247 0.90 0.74 SFB QB 1493 33 -97.81 43475 0.41 -0.67 QS 5054 7821 +54.77 266276 0.91 0.73 Q 6546 5278 -19.37 317778 0.95 0.74 AWBM QB 1493 1167 -21.86 12088 0.75 0.53 QS 5054 4112 -18.63 283318 0.95 0.71 Q 6546 6315 -3.54 221287 0.92 0.82 SDI QB 1493 3083 +106.55 124679 0.44 -3.80 QS 5054 3231 -36.06 310992 0.95 0.68 8.3.7 Corang River Catchment Mass curves of simulated and actual Q, QB, and QS against time for the SDI model presented in Figure 8.8 show a very good simulation of the total streamflow for this catchment. Table 8.8 shows the measures of accuracy for the optimised parameter values for the catchment. A comparison of the results shows that if only the total runoff is considered as a basis for checking model performance, all models performed well. The SDI model can be rankedfirst for SSQ, E, R, and AQ. The AWBM model can be ranked second and the SFB model is ranked last based on all measures. Chapter Eitht Investigation of Model Complexity , d Data F.rrnr. in P .nf,n_p„ n a WModellingf . 8.16 + Q A QB A QS x Qe x QBe o QSe 2000 x • * 1800 \ 215004 + g 1600 X X O 1400 X | f 1200 * A 5. S, 100I0 A £ 5, 800 \ X A £ 600 +X • § 400 A + A o 1 200 > 0 0 2 ol i.i ...o, o 1979 1981 1983 1985 1987 lime (Year) Figure 8.8 Mass Curves of Actual and Simulated Q, QB and QSfor the SDI Model Applied to Corang River Catchment The results presented in Table 8.8 indicate that, by far, the best simulation of the different components was obtained using the AWBM model. The SDI model produced the best results for total flow simulations (Figure 8.8), but the simulation for other components was poor. The simulation of baseflow given by this model was extremely poor. This is mainly due to the optimised parameters describing the baseflow components of mnoff. The SFB model performed much better than the SDI model and made it the second best option in this instance. Although the SDI model gives a good prediction of total streamflow, it does not truly represent the movement of water through the catchment. Therefore the optimised parameter values for this catchment using this model should not be accepted. Table 8.8 Results of all Models Model for Corang River Catchment Actual Est A% SSQ R E Q 1816 2025 +11.51 45949 0.67 0.41 SFB QB 618 704 +13.85 2277 0.71 0.42 QS 1198 1321 +10.30 35201 0.66 0.30 Q 1816 1429 -21.32 31289 0.79 0.60 AWBM QB 618 500 -19.17 1910 0.75 0.52 QS 1198 929 -22.43 20619 0.79 0.59 0 1816 1975 +8.76 24612 0.83 0.68 SDI QB 618 1698 +174.70 33372 0.76 -7.47 QS 1198 277 -76.89 43649 0.70 0.14 Chapter Eight—Investigation of Model Complexity and Data Errors in Rainfall-Runoff Modelling,ft 17 8.3.8 Shoalhaven River Catchment at Kadoona The Shoalhaven River Catchment (Kadoona River) was analysed using fifteen years of daily rainfall and runoff data. The period of analysis was from 1st January 1972 to 31st December 1986. For this catchment, mass curves of simulated and actual Q, QB, and QS against time for the SDI model presented in Figure 8.9 show a very good simulation of the total streamflow. A comparison of the results obtained and presented in Table 8.9 shows that if only the total mnoff is considered as a basis for checking model performance, the SDI model can be ranked first for SSQ, E, R, and second for the percentage of AQ. The AWBM model can be ranked first for the percentage of AQ and second for other criteria using the same basis. The SFB model is ranked last on all accounts. + Q A QB A QS X Qe X QBe o QSe 6000 °8 cn 5000 O O 4000 S» B 3 J, 3000 c a 33 °*2000 •a f 1000 a 1987 Time (Year) Figure 8.9 Mass Curves of Actual and Simulated Q, QB and QSfor the SDI Model Applied to Shoalhaven River Catchment at Kadoona The results presented in Table 8.9 shows that all models were able to predict the total mnoff values which were close to the recorded runoff values. The total predicted streamflow was in agreement with the recorded total streamflow with a correct prediction of each streamflow component for all models. The SDI model performed best for the total simulations (Figure 8.9) while the simulation of other components maintains the same level of accuracy. The results presented in Table 8.9 indicate that the best simulation for the different components is obtained using the SDI model based on all measures except AQB. The SDI model was ranked second, based on AQB, after the AWBM model. The simulation of baseflow given by the AWBM model was third for all Chapter Eight Investigation of Model Complexity and Data Errors in Rainfall-Runoff Modelling. 8.18 measures except AQ. The SFB model performed better than the AWBM model and the results obtained by this model are comparable with those of the SDI model. Table 8.9 Results of all Models for Shoalhaven River Catchment at Kadoona Actual Est A% SSQ R E Q 5213 4970 -4.65 106681 0.89 0.79 SFB QB 2037 2279 +11.84 13898 0.71 0.45 QS 3175 2691 -15.24 69011 0.91 0.83 Q 5213 5091 -2.33 87670 0.91 0.83 AWBM QB 2037 2037 -0.00 25855 0.71 -0.02 QS 3175 3054 -3.82 76379 0.93 0.81 Q 5213 5598 +7.40 71400 0.93 0.86 SDI QB 2037 2140 +5.05 12539 0.73 0.51 QS 3175 3458 +8.90 51577 0.94 0.87 8.3.9 Summary In comparing the models, attention was focussed on the prediction of different flow components rather than simply assessing the models on the basis of total predicted and actual runoff. The accuracy of the prediction was assessed based on the results obtained from SSQ of total streamflow. The SDI model gave the best followed by the AWBM and the SFB models. This model gave a good prediction of total streamflow, however it did not predict different flow components correctly and the simulation did not truly represent the movement of water through the catchment. The same problem was observed with the SFB model. The accuracy of the prediction was then assessed based on prediction of different flow components. Despite the SDI being more complex and having more parameters, the AWBM model performed better on 6 catchments. Even in some of the catchments the results of the SFB model were much better than the SDI model. The SDI model gave the best results for only one catchment. It was concluded that additional parameters do not necessarily lead to better results. A physically realistic model structure is more important and can give better results. The models are ranked on the basis of different flow components, using several criteria (Table 8.10). Chapter Eight Investigation of Model Complexity and Data Errors in Rainfall-Runoff Modelling. 8.19 Table 8.10 Ranking of the Models in all Catchments Cat Rank 1 Rank 2 Rank 3 No 0 OB OS 0 OB OS 0 OB OS 1 c b b a c c b a a 2 b b b c a a a c c 3 c b b a c a b a c A% 4 a b b c c c b a a 5 b b b c c c a a a 6 c b b b a c a c a 7 c a a a b b b c c 8 b b b a c c c a a 1 c b b b a c a c a 2 c a a b b b a c c 3 c a c b b b a c a SSO 4 b b b c c c a a a 5 c b b b c c a a a 6 c b a a a b b c c 7 c b b b a a a c c 8 c c c b a a a b b 1 c c b b a c a b a 2 c b a b a c a c b 3 c b c b a b a c a R 4 b b b c a c a c a 5 c a b b b c a c a 6 b b b c c c a a a 7 c c b b b c a a a 8 c c c b a b a b a 1 c b b b a c a c a 2 c a a b b b a c c 3 c b c b a b a c a E 4 b b b c c c a a a 5 c b b b a c a c a 6 c b a b a b a c c 7 c b b b a a a c c 8 c c c b a b a b a SFB la 5a 6a 5a 17a 5a 26a 10a 21a Total AWBM 7b 22b 20b 20b 6b 10b 5b 4b 2b SDI 24c 5c 6c 7c 9c 17c lc 18c 9c *a=SFB,b=AWBM,c=SDI 8.4 ERROR ANALYSIS 8.4.1 Error in Evaporation Data The effects of different assumptions in the values of potential evapotranspiration used in rainfall-runoff models are discussed by comparing the results of the SFB and AWBM models for the Sandy Creek catchment. In order to assess the influence of evaporation during a storm for rainfall-runoff modelling, the evaporation inputs for these models were altered by ±(10-20%) in order to determine the magnitude of the corresponding Chapter Eight Investigation of Model Complexity and Data Errors in Rainfall-Runoff Modelling. 8.20 change in predicted streamflows. The results are presented in Table 8.11. In general, it was found that the effects of evaporation are considerable in both models, however the AWBM model is less sensitive to introduced errors in the evaporation data. Table 8.11 Sensitivity of the SFB and AWBM Models to the Change in the Evaporation Input Variable Model -20% -10% 10% 20% Q SFB 29.13 11.4 -10 -17.8 AWBM 19.6 8.9 -8 -14.5 QB SFB 107 26.3 -34.6 -52 AWBM 19.2 9.4 -6.2 -14.9 QS SFB 13 6 -5.9 -10.7 AWBM 20 8.8 -8.1 -13.9 SSQ SFB 36.3 10.2 -4.8 -5.4 AWBM 10.5 5 0.42 1.6 8.4.2 Error in Rainfall and Streamflow Data Rainfall, streamflow and evaporation data will always contain some errors. The estimation of areal rainfall from point rainfall introduces further errors in the data. Consequently, the data input to rainfall-runoff models is almost certain to contain errors. Where parameter values in the model are optimised in order to calibrate the model against observed runoff data, errors in the rainfall data are compensated for by errors in the parameter values, resulting in spurious values for the parameters. Accordingly, it is very important to know how the results of parameter estimation are going to be affected by data errors. The results presented in section 8.3 showed that the AWBM model performed the best of all three models. An obvious research avenue lies in the examination of the relationships between CI, C2, C3 and Al, A2 and A3 values and physical catchment characteristics. Such procedures will need to account for uncertainties deriving from rainfall and streamflow data errors. In this kind of study it is desirable to have an error free data set against which to make comparisons. After calibrating the AWBM model with 23 years of rainfall, streamflow, and evaporation data for the Kangaroo Valley catchment, the model was run using the calibrated parameters to generate an error free runoff record. This results in zero SSQ. All daily values of the rainfall, streamflow and evaporation data were then increased by ±10 and ±20 and the parameter values in the model were again optimised using different Chapter Eight Investigation of Model Complexity and Data Errors in Rainfall-Runoff Modelling. 8.21 combinations of correct and erroneous data. The values of K and BFI for both error free and error contaminated streamflow data are unchanged (BFI=0.31 and K=0.914), because a percentage change in the streamflow adjusts all streamflow coordinates equally. The different combinations of sets of parameters obtained for each set of the data are shown in Table 8.12. These were obtained by using the automatic multiple regression technique discussed in Chapter 7. Chapter Eight Investigation of Model Complexity and Data Errors in Rainfall-Runoff MnnV.11 inP, R 7.7 Table 8.12 Result of Fitting the AWBM Model with Errors in Rainfall, Streamflow and Evaporation Input Data p Q EP CI C2 C3 Q QB QS SSQ % % % Al A2 A3 mm mm mm mm2 0.0* 0.0 0.0 4 81 142 18925 5948 12977 0.0 27.3 56.9 15.8 -20 0 0 0 43 59 18737 5922 12815 120381 68.3 30.4 1.2 -10 0.0 0.0 4 69 74 18527 5856 12670 35503 52.8 32.3 14.9 +10 0.0 0.0 0 79 164 19820 6264 13556 45102 3 66.9 30 +20 0.0 0.0 15 147 216 21575 6817 14458 217379 0.4 86.4 13.2 0.0 -20 0.0 2 109 180 16143 5103 11031 109381 0.2 95.1 4.7 0.0 -10 0.0 0 51 125 17375 5494 11881 27510 4.1 42.7 53.2 0.0 +10 0.0 0 85 98 20641 6522 14119 24708 34.9 64.8 0.2 0.0 +20 0.0 0 78 85 22343 7058 15285 100025 49.9 34.6 16 0.0 0.0 -20 0 67 155 16043 5074 10969 53472 0.3 73.6 26.2 0.0 0.0 -10 0 58 132 17254 5455 11799 20615 9.9 54 36.1 0.0 0.0 +10 0 78 139 19062 6025 13074 2489 28.3 56.3 15.4 0.0 0.0 +20 0 75 150 19729 6235 13494 5146 33.6 53.7 12.6 -20 -20 0.0 0 59 136 15291 4837 10454 6939 36.3 53.5 10.2 -10 -10 0.0 0 65 116 17147 5421 11725 2387 28.3 49.5 22.2 +10 +10 0.0 0 75 146 21004 6637 14367 6376 11.7 59.6 28.8 +20 +20 0.0 0 65 175 22999 7265 15734 17166 1.4 64.1 34.5 +10 -10 0.0 15 114 1033 18841 5955 12886 147107 4.6 79.9 16.2 -10 +10 0.0 0 62 66 20444 6460 13984 112557 58.1 18 23.8 +20 -20 0.0 0 330 3000 19686 6222 13464 673835 2.2 67.8 30 -20 +20 0.0 0 0.0 0.0 - ~ 100 - - * First row presents the correct values of model parameters and input data After fitting the model to different combinations of error-free and error-contaminated data, the effects of errors on the fitting were studied. The errors in the data seriously affect the parameter values of the model but the output is not greatly affected (Table 8.13, last row). Errors in the model output caused by evaporation errors are less than the Chapter Eight Investigation of Model Complexity and Data Errors in Rainfall-Runoff Modelling. 8.23 evaporation errors themselves, but they can cause a drift in the obtained parameter values of the model (see Table 8.13 rows 9-12). Some of the errors in rainfall data can be cancelled by the processes of evaporation and transpiration as well as being reduced by the different storage capacities of the model, and cannot be detected in the model output (rows 1-4). For errors in the runoff, no opportunity for cancelling exists except by decreasing the smallest storage capacity or increasing the largest storage capacity. In all cases the values of the second storage capacity and its source area fraction (C2 & A2) remain more stable. General results obtained from the analyses highlights the ability of the model to find apparently better values of the objective function SSQ with incorrect parameter values. This ability is shown by best fit parameter estimates similar to each other but different from the correct parameter values. This would imply that the error had affected the parameters of the model so that they cannot be considered to have any physical meaning. Chapter Eight—Investigation of Model Complexity and Data Errors in Rainfall-Runoff Modelling. 8.24 Table 8.13 Percentage Error of Parameters and Predicted Values of the AWBM Model due to the Different Combinations of Error-free and Error-contaminated Data No P% Q% EP% CI C2 C3 Al A2 A3 Q QB QS 1 -20 0 0 -100 -47 -58 150 -47 -92 -1 0 -1 2 -10 0 0 0 -15 -48 93 -43 -6 -2 -2 -2 3 10 0 0 -100 -2 15 -89 18 90 5 5 4 4 20 0 0 275 81 52 -99 52 -16 14 15 11 5 0 -20 0 -50 35 27 -99 67 -70 -15 -14 -15 6 0 -10 0 -100 -37 -12 -85 -25 237 -8 -8 -8 7 0 10 0 -100 5 -31 28 14 -99 9 10 9 8 0 20 0 -100 -4 -40 83 -39 1 18 19 18 9 0 0 -20 -100 -17 9 -99 29 66 -15 -15 -15 10 0 0 -10 -100 -28 -7 -64 -5 128 -9 -8 -9 11 0 0 10 -100 -4 -2 4 -1 -3 1 1 1 12 0 0 20 -100 -7 6 23 -6 -20 4 5 4 13 -20 -20 0 -100 -27 -4 33 -6 -35 -19 -19 -19 14 -10 -10 0 -100 -20 -18 4 -13 41 -9 -9 -10 15 10 10 0 -100 -7 3 -57 5 82 11 12 11 16 20 20 0 -100 -20 23 -95 13 118 22 22 21 17 10 -10 0 275 41 627 -83 40 3 0 0 -1 18 -10 10 0 -100 -23 -54 113 -68 51 8 9 8 19 20 -20 0 -100 307 2013 -92 19 90 4 5 4 Ave 8.4 8.4 3.2 110.5 38.3 160.5 73.3 26.8 65.7 9.2 9.4 9.0 * last row = Absolute Mean 8.5 SUMMARY AND CONCLUSION In the first part of this chapter the three-parameter SFB model, the variable source area AWBM model and a version of a more complex eleven-parameter SDI model were compared in order to investigate the effects of complexity in rainfall-runoff modelling. The models were compared using rainfall, runoff and evaporation data from 8 catchments. Table 8.10 lists the models ranking on the basis of different flow components using several criteria. When models were assessed on the basis of comparing total predicted and actual streamflow, all three models were shown to be capable of reproducing the total monthly flow components. If the models were assessed by comparing the recorded and predicted runoff over the study period, the best would have been the SDI model, which performed the best for seven out of eight catchments. This was followed by the AWBM model and then the SFB model. These kinds of comparisons demonstrate that a model's accuracy apparently increases as the number of parameters increases. In general, when the number of parameters increases, the differences between total predicted and actual runoff can easily Chapter Eight—Investigation of Model Complexity and Data Errors in Rainfall-Runoff Modelling. 8.25 be minimised, but the errors in data propagate to a greater degree into the estimated parameters and increase the uncertainty of optimised parameter values. However, when the models were assessed on the basis of separate surface runoff and baseflow components, the AWBM model gave significantly better results for 6 catchments and therefore ranked first. The SFB model and the SDI model gave the best results for one catchment each. In several catchments the results from the three parameter SFB model were far better than values obtained by the 11-parameter SDI model (see Table 8.3, Kangaroo Valley catchment). The poor performance of the SDI and SFB models relative to the AWBM model is probably due to a combination of model errors, parameter errors and input errors. Among the factors which seem to be more important are the failure of the models to consider the spatial variability in the soil storage capacity of catchments. Additional factors of importance are parameter interactions and errors in the input data. Analyses of the parameters and operation of the SFB and SDI models have shown that interaction exists between the parameters governing the surface mnoff and baseflow. If the simulated surface runoff does not match the surface runoff component of the catchment, this causes a consequent change of the model parameters, which usually leads to overestimation of the baseflow. On the other hand, when an appropriate parameter gives a match of predicted and actual surface runoff, little effect on the final results will be apparent. The physics governing the movement of water through a catchment to the streams involves very complex relationships. The lack of real progress being made in understanding catchment behaviour, as well as a lack of accurate data monitoring, has been compensated by overparameterization of some models. The number of model parameters and the complexity of models have been increased with the expansion in available computing power. It appears to be very unlikely that all of the processes incorporated in complex models can be supported by the limited information obtained from rainfall and streamflow. The degree of complexity does not play a significant role in the correct calibration of the model unless the model structure is based on correct assumptions. Chapter Eight Investigation of Model Complexify and Data Frrnrc in Rainfall-Runoff Modelling. 8.26 The AWBM model performed best in the studied catchments because of its use of source area and storage capacity variations. The model prediction is acceptable for a range of parameter variations. Hence, the AWBM model can be regarded as a promising model for use in ungauged catchments. The simulation of baseflow by the SDI and SFB models is extremely poor for most of the catchments studied. Hence, the optimised parameter values obtained using these models should not be accepted. Interestingly, when the accuracy of the prediction was assessed based on the separate flow components, the AWBM model gave the best results. Also, the simple three-parameter SFB model was found to give good results in catchments having little spatial variability in their soil storage capacity. This suggests that, for accurate simulation and to avoid parameter interactions, the calibration of both baseflow and surface runoff is essential for modelling. The AWBM model derives the K and BFI parameters directly from the streamflow data and has the advantage of prior knowledge of the catchments. In this model, when the total flow is calibrated to match the actual values, the BFI ratio acts to estimate a reasonable amount of baseflow. However, it should be noted that the simplicity of baseflow recharge and discharge in the model is not adequate for simulating the baseflow response and could be improved by the procedure discussed in Chapter 5. In the second part of this chapter the effects of data errors in the optimised parameter values of the AWBM model were discussed. The errors in the data seriously affect the parameter values of the model without introducing big changes in the output. Errors in the model's output caused by evaporation errors are less than those causing evaporation errors, but they can create a drift in the obtained parameter values of the model. Errors in rainfall data can be absorbed by processes of evaporation and transpiration as well as by being reduced by different storage capacities of the model. Errors in mnoff can only be reduced by decreasing the smallest storage capacity or increasing the largest storage capacity. In all cases, the values of the second storage capacity (C2) and its source area fraction (A2) remain more stable. One conclusion that can be drawn from this analysis is that the model is capable of finding smaller, but false, values of the objective function with incorrect parameter values. Chapter Eight Investigation of Model Complexity and Data Errors in Rainfall-Runoff Modelling. 8.27 In the AWBM model, large values of C associated with small values of source area fractions and vice versa indicates that a strong interaction of C and A exists in the model. Each set of parameters gives a quite good reproduction of the total runoff and the SSQ obtained from the comparison of the monthly actual and predicted runoff is not sensitive to the values of paired A and C as long as the combination of them produces the appropriate values of monthly runoff. This presents a relatively weak simulation of baseflow. The reason for these low R and E accuracy measures in the baseflow values are twofold. Firstly the baseflow index (BFI) is a dynamic ratio, lower for high flows and higher for low flows in actual catchments, where it is fixed in the model. Secondly, the simplicity of the baseflow discharge from a single storage was not adequate for simulating the baseflow response in all catchments studied. The AWBM model was found to be very stable for catchment modelling and to present good final results. It can provide acceptable monthly and daily flow estimates for catchments. Furthermore, the model predicts runoff in a realistic manner, as both small and large runoff events are predicted accurately. Since the results were encouraging, the use of this model should be investigated further. Analyses can be carried out using data from different regions and with different hydrological characteristics to verify the performance of this model. CHAPTER NINE SUMMARY, CONCLUSIONS AND RECOMMENDATIONS FOR FUTURE RESEARCH CHAPTER NINE SUMMARY, CONCLUSIONS AND RECOMMENDATIONS FOR FUTURE RESEARCH 9.1 SUMMARY AND CONCLUSIONS 9.1.1 Hydrological Processes in Catchments In Chapter two of this thesis, a review of the hydrological processes in catchments including hydrometeorological factors, process factors, and physical factors was carried out. The roles of precipitation, mnoff and evapotranspiration in the catchment system were discussed. Process factors which include interception, infiltration, soil moisture storage, interflow, subsurface flow, channel processes, and transmission losses were explained. Finally, the physical factors and the features of a basin which directly impact the runoff response characteristics (volume, magnitude and timing) were highlighted. The Hydrologist's interest in rainfall data and its limitations were discussed. Sources of uncertainty in the measured data including systematic and random errors were highlighted and methods of extrapolation of point rainfall to areal rainfall were discussed. Evaporation and transpiration are the most important losses in the hydrologic cycle, significant factors in rainfall-runoff modelling, soil moisture modelling, and crop yield studies. To investigate the importance of catchment evapotranspiration, water balance studies were carried out using data from five catchments in Australia. The results showed that, on average, evapotranspiration accounts for more than 40 percent of total rainfall while surface runoff, subsurface runoff and other losses account for respectively, 32, 16, and 12 percent of total rainfall (Table 2.1). Chapter Nine Summary. Conclusions and Recommendations for Future Research. 9.2 Several approaches to estimating evapotranspiration and the criteria for selection of a method for PET (potential evapotranspiration) estimation were discussed. Pan evaporation measurement and Morton's complementary estimates of evapotranspiration were found to be the most widely accepted methods for the estimation of PET in rainfall- runoff modelling. These methods were compared for five catchments. The comparisons made in this research indicates that the two approaches are essentially similar in monthly and annual scales if the quality of data is good, however, one should be careful in using both methods for smaller time steps. There are a number of problems associated with the use of the complementary approach. This method is developed by neglecting the change in heat stored in the subsurface. Therefore, this method should not be used for short time steps because of subsurface heat storage changes, and because of the lag times associated with the change in storage of heat and water vapour in the atmospheric boundary layer after changes in surface conditions (or the passage of frontal systems). Also, before using the pan evaporation records, the data should be tested for reliability by comparing measured data with adjacent stations. If Morton's Ewet is used alone, additional errors may be introduced into rainfall-runoff models with time intervals of one day and less. If pan evaporation data is used alone, poor data quality and the conversion factor can introduce error in rainfall-runoff models. By taking advantage of the combined merits of both methods, the problems inherent in each method being applied alone can be avoided. The correlation between pan evaporation and Morton's estimates in five catchments resulted in constants in the range of 0.55 to 0.77 with a mean value of 0.662. These constants compared well with conversion factors obtained from the water balance method and showed promise as a conversion factor for relating pan evaporation to potential evapotranspiration. As a result of this investigation, it was decided to adopt pan evaporation with conversion coefficients in the next stage of this study (Section 2.2.2.5). Two major theories explaining surface mnoff generation; the Hortonian theory and the variable source area concept were explained and factors affecting mnoff rates and losses were discussed. As different mnoff processes could be dominant in different areas and none of these theories could explain the processes of runoff generation precisely, it was noted that both rainfall and catchment characteristics are important in generating runoff. Heavy vegetation and well-drained soil in some areas might have an infiltration rate that Chapter Nine Summary, Conclusions and Recommendations for Future Research. 9.3 is far greater than 60 mm/hr, an intensity which an ordinary rainfall would seldom exceed (Hewlett and Hibbert, 1967). Hence, the determination of the runoff producing area is the most important issue both for continuous rainfall-runoff modelling as well as flood studies. Process factors including interception, infiltration, soil moisture storage, interflow, subsurface flow, channel processes, and transmission losses, as well as the physical factors of a catchment which directly impact the runoff response were explained. 9.1.2 Modelling the Rainfall-Runoff Processes A study of hydrological modelling and current rainfall-mnoff models was presented in Chapter three. This included discussing different models, model selection, calibration, parameter estimation, and various optimisation techniques (for finding optimum parameter values of the models). An analysis of rainfall-runoff models was carried out. Most of the reviewed rainfall-runoff models were conceptual models developed in Australia, including: the SFB Model (Boughton, 1984), Boughton's original and modified models (Boughton, 1965, 1966), the SDI Models (Mount, 1972, Langford et al., 1978a, and Kuczera, 1988), AWBM Model (Boughton, 1993). The other models were, the Stanford Watershed Model (Crawford and Linsley, 1966), the Sacramento Model (Burnash et al. 1973), the SHE Model (Abbott et al., 1986), the SCS Model (US SCS, 1964), the analytical API Model (Betson et al., 1969), and the Semi-Arid-Zone Model (Sukvanachaikul, 1983). The problems associated with the selection of an appropriate rainfall-runoff model were also discussed and important keys in model selection were highlighted. Selection of the most appropriate model depends upon the purpose of the research. Selection may be based on a number of criteria, such as, accuracy of prediction and required time interval of prediction, simplicity of the model application and its data requirements, consistency of parameter estimates and sensitivity of results to changes in parameter values. The number of hydrologic processes that are active in generating mnoff and the variability of the hydrologic processes to be modelled are other important factors in the selection of rainfall-runoff models. Chapter Nine Summary. Conclusions and Recommendations for Future Research. 9.4 After selecting a well-formulated model, calibration (for best results) of the model with observed rainfall and streamflow data is essential. Optimisation techniques are used to establish model parameter values. Three main categories of automatic optimisation procedures were discussed. These include; stochastic methods, descent methods (methods for functions of one variable or more) and direct search methods (various simplex methods, steepest descent and conjugate direction methods). Several types of optimisation techniques including, manual, automatic, and semi-automatic were highlighted. The optimised parameter values of most models using optimisation techniques contain substantial errors. Apart from the model's structural deficiency, errors can arise from a number of sources, such as data errors, inconsistency and variation in the input data, the spatial and temporal distribution of catchment properties, including variation of the surface stores, lack of an accurate relationship to convert potential evaporation to actual evapotranspiration, interactions of the parameter values, the method of optimisation used and the objective function adopted. Some of these errors may not be detected when using automatic techniques and may result in the estimation of misleading parameter values. Research has been carried out into replacing automatic optimisation techniques with the manual or semi automatic optimisation of model parameters. The manual and automatic procedures were combined because they often perform better in this form than when used separately. The manual approach, on its own, is time consuming and tedious. Although automatic methods are relatively simple to use, they rely heavily on one pre-specified objective function. If poor data are used or poor initial parameter values are selected, automatic methods may fail to find parameters that are physically meaningful. By taking advantage of the merits of both methods together, the problems associated with their separate use can be avoided. As a result of the survey carried out in this chapter, the SFB and the AWBM were selected as the main models and evaluation of the model parameters was carried out using both the SAPS optimisation technique and taken directly from data. Chapter Nine Summary. Conclusions and Recommendations for Future Research. 9.5 9.1.3 Catchments and Data Description of catchments and preliminary analysis of climatic inputs have been made in the first and second part of Chapter four. Catchment descriptions which included identification of their physical characteristics, geology, soil types and vegetation were made. Based on physical characteristics, and from the results, inferences of parameter values (interception storage, soil moisture storage and infiltration capacity) were made. Prehiriinary analyses of climatic inputs were carried out. The quality of the data was checked. In addition different time series plots of rainfall and streamflow were used to compare the recorded data. Missing data were infilled using data from nearby stations by fitting a regression equation. The average catchment precipitation was calculated based on the Thiessen polygon method. Comparisons between rainfall-runoff and their variabilities were made using average monthly and annual values. Statistical analyses of monthly values were carried out. This involved computing the arithmetic mean, median and mode, computing the parameters which measure the variability of the data (ie., standard deviation, variance and coefficient of variation), computing the correlation between each pair of variables, and a third moment the coefficient of skewness was computed. Finally, the residual mass curves were calculated for distinguishing wet and dry years. For the eight catchments in study area, the coefficient of variation of monthly runoff was between 1.12 to 1.95 and that of monthly rainfall was between 0.74 to 1.03. The median monthly runoff for the study area varies from 1 to 30 mm, while this figure varies between 40 to 140 mm for rainfall data. Summary statistics for all the catchments are shown in Tables 4.5 to 4.12. Table 4.13 shows the results of a linear regression between monthly and annual surface runoff and rainfall. Considerable data variability was evident during the study period. Comparison of the number of years above and below average in all stations shows that there is a fluctuation pattern with sequences of high and low flow years. It was found that there were considerable variations in streamflows in the study. Chapter Nine Summary. Conclusions and Recommendations for Future Research, 9 6 Error in the data can lead to inconsistent results and poor parameter estimates. It is highly desirable that good quality data be selected for modelling and the data be checked for inconsistencies as was done for this study (Chapter three and Appendix B). 9.1.4 Baseflow Recharge and Discharge The total flow in a stream is made up of two main components: baseflow and surface mnoff. These components have quite different properties. In rainfall-runoff modelling, it is possible to predict a total mnoff value in agreement with the recorded total mnoff, but with incorrect prediction of both flow components. When analyzing the hydrology of a catchment it is useful to consider these two components separately by partitioning the streamflow into baseflow and surface runoff. These allow the estimation of several other hydrologic properties of the catchment, including the groundwater store state, low flow prediction, soil store capacity, source area, and the rate of groundwater recharge from infiltration. Five streamflow partitioning methods were investigated in Chapterfive. They include: a) recursive digital filter, method 1 b) automated technique adopted by Boughton, method 2 c) improved frequency-domain filter technique, method 3 d) modified automated technique, method 4 e) proposed method based on travel time of runoff, method 5 Methods 1 and 3 are based on a procedure for modelling time varying data and are commonly used in signal analysis and processing. Methods 2 and 4 are empirical methods which assume baseflow increases during periods of surface runoff by a fraction of the difference between the total runoff and the baseflow on the previous day; or the previous five days. Thefifth metho d separates the baseflow component of the hydrograph based on a relationship between the volume of water in storage, and the baseflow discharge which is similar to the graphical method of baseflow separation and has a physical basis. Streamflow data from two catchments was separated manually, and the performance of method 5 was checked. It was found that the results of the automatic technique (method 5) agreed well with those obtained by the manual approach. Chapter Nine Summary. Conclusions and Recommendations for Future Research. 9.7 The relative performance and consistency of all methods was evaluated using the daily streamflow records for the eight catchments. Analysis of results using the five methods in eight catchments led to the following conclusions: • All of the methods investigated for baseflow separation have their own advantages and disadvantages and, at best, each method will produce an approximation of the actual baseflow. At present there is no practical way of achieving a completely accurate baseflow separation. • Methods 2 to 5 can be used to obtain reasonable estimates of baseflow and surface runoff. The results of this study show that using methods 2 to 5 can be accurate enough for both flood studies and rainfall runoff modelling. These methods can explain up to 95% of the total variations in baseflow and produce a AQB less than ± 5%. • Method 1 underestimates the baseflow relative to the other methods. Methods 1 to 4 underestimate the surface runoff end time for all catchments. Method 5 which is based on the travel time of runoff yields more discriminating and accurate information about the baseflow components of the hydrograph than the other methods. Results obtained from method 5 were found to yield similar outputs to those obtained using graphical techniques and hence this method is recommended. • The contribution of baseflow to total streamflow represents a significant process in the hydrologic cycle which should be investigated further. The validity of the separation methods could not easily be checked. More research is needed to clarify the problems of baseflow and recessions. In section 5.3 of Chapter five the concept of a baseflow index (BFI), a dimensionless ratio which can yield useful information about the proportion of the mnoff that originates from groundwater sources was discussed. The BFI values for eight catchments using five methods were calculated and a discussion of results was presented. The results indicate that method 1 underestimates the BFI values for all catchments relative to the other methods. In overall performance and for larger and more sluggish catchments, there is little difference between the BFI values estimated from method 5 in comparison with those estimated by methods 2 and 4. In general, higher mnoff years experienced higher BFI values, and low runoff years experienced lower BFI values than the average. Hence, Chapter Nine Summary, Conclusions and Recommendations for Future Research 9 8 it is necessary to use a period of record which includes wet and dry sequences in calculating BFI. A relatively low variation for BFI ratios and, as expected, high variations for surface runoff were apparent. The variation of the annual BFI index was found to be lower than single event BFI ratios. The variations of single event BFI (the ratio of event baseflow volume to total streamflow volume for same event) are considerably higher. At a smaller time scale (events), the value of single event BFI is higher for low flow and lower for high flow (Figures 5.6 and 5.7). There does not appear to be a definite relationship between the size of catchment area and the baseflow index for the catchments studied. In the second part of Chapterfive, analyses of hydrograph recessions and groundwater recharge, which are important for rainfall-runoff modelling, were carried out. Both matching strip and analytical methods were applied to the eight catchments. The results indicate that the analytical approach has advantages over the matching strip method for low flow studies. The advantage of this method is that all events can be plotted in one graph and each catchment can be tested to see whether it is linear or no-nlinear. Results from the investigation of the Master Recession and Recharge Curves (Sections 5.5.2 and 5.5.4) indicate that for most of the studied catchments several clusters of recession and recharge curves can be constructed. Each cluster represented a different value of the recession constant or recharge constant. This can be due to factors such as the recession and recharge of different aquifers, expansion and shrinkage of the source areas and seasonal variations in pattern of recession and recharge. Separated streamflows from method 5 were used to check the results of rainfall-runoff models and to estimate model parameters. Estimated values of BFI from all methods are presented in Tables 5.2 to 5.8. The values obtained by method 5 were used as the parameter required by the AWBM model, and also as an index to compare the flow characteristics of different catchments. Calculated values of daily recessions using both methods, for eight catchments, are presented in Tables 5.10. The calculated values were used in rainfall-runoff modelling. Chapter Nine Summary. Conclusions and Recommendations for Future Research, 9.9 9.1.5 The SFB Model The SFB model was selected for this study because of the daily data available for these catchments, its simple model structure, small number of parameters, and the good conceptual basis of the model. Moreover the model parameters can be attributed with the particular properties of the catchment. The Semi-Automatic Pattern Search technique (SAPS), which was reported (Baki, 1993 and Jayasuriya, 1991) to give satisfactory results infinding the optimum set of parameters, was adopted. Daily rainfall data from the Sandy Creek catchment were used in the initial analysis. Daily rainfall data were divided into three periods: the First Period, the Second Period and the Whole Period. This division was made to enable testing of the model's ability to produce similar parameter values using different data sets from the same catchment. This was, therefore, a test of the model itself rather than a test of the correctness of the parameter values. The parameter values for different periods were found to be quite different indicating the dependency of the model on the data used. Model parameters were estimated directly from the recorded rainfall and streamflow data in two ways, by using the water balance of individual storm events and using separated baseflow and surface runoff. These methods showed considerable promise. In the case of the simple 3-parameter SFB model, because the model structure is not physically realistic for a heterogeneous catchment, the abovmentioned methods improved the physical interpretation of the model parameters but parameters were unable to give accurate estimations of runoff. Parameter interactions caused several sets of the parameters S, F, and B to give similar sums of squared-off differences (SSQ). Optimisation by minimising the SSQ of total flows (ie surface runoff combined with baseflow) is not sufficient to properly model both surface runoff and baseflow. Physically unrealistic parameters can be obtained using the optimisation process. Several automatic baseflow separation methods are now available (discussed in Chapter five) which can easily separate recorded streamflow into QS and QB, and the accuracy of prediction of both flow components should be checked. Comparisons between optimum sets of parameters for different catchments were made for the SFB model in Chapter six. The results showed that the SFB model was able to estimate runoff values similar to the recorded mnoff. Although the estimated runoff was Chapter Nine Summary, Conclusions and Recommendations for Future Research. 9 JO acceptable, the values of the optimum parameters for some of the studied catchments were found to be physically unrealistic. In most of the catchments, physically realistic parameters failed to give accurate predictions of runoff, while on the other hand the optimised parameter values gave reasonable runoff predictions but did not have any physical meaning. The optimum values of parameters varied from one data set to another and therefore described data characteristics rather than physical characteristics. It was concluded that the optimised parameter values of this model did not truly represent the movement of water in heterogeneous catchments and could not be related to the physical characteristics of the catchments with any degree of reliability. The poor performance of a model can be attributed to a combination of the following; model error, parameter error and data error. Among the factors which seem to be more important are the failure of the SFB model to consider different soil storage capacities in different parts of these catchments, and parameter interactions. The SFB model is inadequate in catchment modelling because it assumes a spatial uniformity in storage capacity over the whole catchment. It was decided to adopt the AWBM model, which represents the source area and storage capacity variations in the catchments, and also has a small number of parameters for the next phase of this study. 9.1.6 The AWBM Model In the first part of Chapter seven, the AWBM model was described and its results from a small experimental catchment were discussed. Initially parameters were estimated using direct data analysis. Daily rainfall data from the Sandy Creek catchment were used in this analysis. The data were divided into three periods: the First Period, the Second Period and the Whole Period (same as used for the SFB model). The values of parameters for different periods were found to be similar. This indicated that the model was able to provide parameters that were reasonably insensitive to the calibration periods used. The results also showed that the model was able to estimate the surface runoff and baseflow components of the total streamflow which were similar to the actual values. Chapter Nine Summary. Conclusions and Recommendations for Future Research. 9.11 In extending these early results, the model was applied to seven other catchments using the automatic multiple regression technique. Also a study of model sensitivity was carried out. Considerable effort was needed to determine the appropriate parameter values of the model. However, the AWBM model gave a good reproduction of actual flow components (surface and baseflow) for all of the catchments. Interaction between parameters caused several sets of parameters to give similar SSQ. A wide range of combinations between source area fractions (Al, A2, A3) and associated storage capacities (CI, C2, C3) can result in reasonable predictions of SUMQ, SUMQB, SUMQS and it becomes difficult to select the correct set of parameters. The effects of evaporation on model performance were studied. First, the model calibrations were conducted using pan evaporation data as potential evaporation. Next, pan coefficient conversion factors (discussed in Chapter two) were used to convert pan evaporation to potential evapotranspiration. Using the conversion factor improved the model simulation in most of the catchments and decreased the SSQ of prediction considerably. Although total baseflow (summed QB) is estimated well, the baseflow component is not estimated well for individual events. Inspection of daily data showed that there is an overestimation of baseflow for large events and an underestimation for small events. Also, all of the studied catchments have non-linear baseflow recessions while the predicted baseflow from the model is proportional to the amount of water in a single baseflow store and is given in a simple linear form. The simplicity of the baseflow simulation in the model is inadequate for simulating the baseflow response of real catchments. 9.1.7 Effects of Complexity and Data Errors in Rainfall-Runoff Modelling A large number of models with different levels of complexity is available. These range from simple runoff coefficient approaches, to lumped conceptual models such as the Stanford Watershed Model (Crawford and Linsley, 1966), through to complete physical process models such as SHE (Abbott et al., 1986). In an earlier study conducted by Baki (1993), eight models of varying degrees of complexity, were applied to seven Chapter Nine Summary, Conclusions and Recommendations for Future Research. 9.12 catchments. Baki (1993) found the SDI Model of Kuczera to be the most satisfactory model for all catchments analysed. It was decided to investigate the more complex eleven-parameter SDI model in conjunction with SFB and AWBM models in more detail to determine the complexity required in rainfall-runoff modelling. The models were compared using data from eight catchments in Australia. Attention was focused on the estimation of different flow components rather than simply assessing the models on the basis of comparing total predicted and actual mnoff. The summary results showing the rank on the basis of different flow components and using several measuring criteria was presented in Table 8.13, Chapter eight. All three models were shown to be capable of reproducing the total monthly flows. Had the models been assessed by a comparison between recorded and predicted mnoff over the study periods, the best model would have been SDI, as this produced the best results in seven catchments out of eight. This was followed by AWBM and SFB model respectively. This comparison demonstrates that model accuracy increases as the number of parameters (or complexity) increases. However, when the models were assessed on the basis of the baseflow and surface mnoff prediction, the AWBM model gave significantly better results in six catchments and ranked first. The SFB model and the SDI model gave the best results in one catchment each. In some catchments the results from the 3 parameter SFB model was far better than values obtained from the 11 parameter SDI. The poor performance of SDI and SFB models is probably due to a combination of model error, parameter error and input error. Among the factors which seem to be more important are the failure of these models to consider spatial variability in soil storage capacity of catchments. Parameter estimation based only on the total streamflow may give misleading results if errors in predicted baseflow and surface mnoff compensate to give approximately correct total flows. Parameter estimation from separated baseflow and surface mnoff, and parameter estimation directly from individual events shows promise and should be further investigated. Chapter Nine Summary, Conclusions and Recommendations for Future Research. 9.13 It was concluded that when the number of parameters increases, the differences between total predicted and actual runoff can easily be minimised, but the errors in data propagate into the estimated parameters and this increases the uncertainty of optimised parameter values. The next most important factors are parameter interactions, the selected objective function, and errors in the input data. Analyses of the parameters and operation of the SFB and SDI models have shown that interactions exist between the parameters governing the surface runoff and baseflow. If the surface runoff estimated by the model does not match the actual surface runoff, a consequent change of model parameters which usually leads to overestimation of baseflow occurs. On the other hand when an appropriate parameter describes the surface runoff, little effect on the final results will be apparent. The physics governing the movement of water through a catchment involves very complex relationships. The number of model parameters and the apparent complexity of models have been increased with the expansion in available computing power. It is unlikely that all of the processes incorporated in complex models can be supported by the limited information obtained from rainfall and streamflow data. The degree of model complexity does not play a significant role in correct calibration, unless the model structure is constructed based on correct assumptions. As stated before, when the accuracy of the prediction is assessed based on the results obtained from the SSQ of total streamflow, the SDI model gave the best fit followed by the AWBM and SFB models. The SDI model gave a good prediction of total streamflow, however it did not truly represent the movement of water in the catchment. The same problem could be seen with the SFB model. The simulation of the baseflow by these models was extremely poor in some of the studied catchments. Hence, the optimised parameter values obtained in similar cases using these models should not be accepted. Interestingly, when the accuracy of the prediction was assessed based on both of the flow components, the AWBM model gave the best results. The SFB model was also found to give good results in homogeneous catchments; those in which a single soil store capacity occupied most of the catchment area. The AWBM calculated some of the model parameters from the streamflow data and took advantage of prior knowledge of Chapter Nine Summary. Conclusions and Recommendations for Future Research. 9.14 the catchments (BFI from the ratio of baseflow volume to total runoff volume and K from recession of streamflow hydrographs). When the total flow is calibrated to match the actual values, the BFI ratio acts to estimate a reasonable amount of baseflow. However, it should be noted that this method of simulation gives relatively poor estimates of baseflow in some catchments. Because the AWBM model represents the variation of soil store capacity over the catchment surface, the model performed equally well when applied to different data periods and gave good results over a wider range of catchment characteristics. The number of parameters is not the only requirement for model accuracy. The results showed that the AWBM model, with efficient parameterisation and with a good physical basis, performed better than more complex 11-parameter SDI model and it was decided to investigate this model further. Since the accuracy of model parameters can be adversely influenced by erroneous input data, any attempt to relate model parameters to catchment characteristics will need to account for the uncertainties. Such procedures will need to account for the uncertainties deriving from rainfall and streamflow data errors. For this kind of study it was desirable to have an error free data set against which comparisons could be made. After calibrating the AWBM model with 23 years of rainfall, streamflow, and evaporation data for Kangaroo Valley catchment, the model was run using the calibrated parameters to generate an "error free" runoff record. All daily values of the rainfall, streamflow and evaporation data were then varied by ±10 and ±20 and the parameter values of the model were optimised using different combinations of correct and erroneous data. The values of K and BFI for both error free and error contaminated streamflow data were not significantly different. This study gave some insight into the relative physical importance of parameter values and their interactions. The errors in the data seriously affect the parameter values of the model without introducing large changes in the output of the model. The impact of evaporation data errors is not as large as the percentage of entered error, but it can cause a drift in the obtained parameter values of the model. Errors in the rainfall data can be absorbed by the process of evaporation and transpiration as well as by being smoothed Chapter N,ne Summary, Conclusions and Recommendations for Future Research. 9.15 cancelling exists except by decreasing the smallest storage capacity or increasing the largest storage capacity. In all cases the value of the second storage capacity C2 and its source area fraction A2 remains more stable. A general result obtained from the analysis was the ability of the model to find lower values of the objective function SSQ with incorrect parameter values. In the AWBM model, large values of storage capacity (C) were associated with small values of source area fraction (A) and vice versa, indicating that a strong interaction between C and A exists in the model. All sets of parameters give quite a good reproduction of the total runoff and the SSQ obtained from comparison of the monthly actual and predicted streamflow is not sensitive to the values of paired C's and A's as long as the combination of them produces appropriate values of monthly runoff. This method of simulation gives a relatively poor baseflow estimate. The reason for these low R and E accuracy measures for the baseflow estimates are twofold. Firstly, the baseflow index (BFI) is a dynamic ratio, lower for high flows and higher for low flows in actual catchments. Secondly, simulating the simplicity of the baseflow discharge by using a fixed recession parameter is not adequate for simulating the baseflow response of catchments. The AWBM was found to be a very stable catchment model and gave a good prediction of total streamflow. The model predicts runoff in a realistic manner. Furthermore both small and large mnoff events are predicted more accurately than the other models. This model with some refinements may be able to produce new information about the physical controls on catchment response and can eventually be used for ungauged catchments by measurement of parameter values for the catchment. However, at the present stage it seems unlikely that the parameters of the model will lead to values that have a reliable relationship with the physical characteristics of the catchment areas. 9.2 RECOMMENDATIONS FOR FUTURE RESEARCH Future research is needed to clarify evapotranspiration losses in catchments. Establishing the correlation between pan evaporation and Morton's estimates and comparing them with lysimeter measurement would be desirable. Development of a method to produce Chapter Nine Summary, Conclusions and Recommendations for Future Research, 9.16 evaporation estimates for an area which does not have an evaporation station or evaporation maps would be useful for ungauged catchments. For most situations there are usually plenty of rainfall records but the streamflow data is more expensive to establish, is limited and is rarely available. Although precipitation records are more abundant, much of the information is unreliable because of errors in the recorded data, extrapolation from point to areal rainfall and the averaging of several rain gauge records. Apart from the systematic, random and observation errors which have a great impact on the measured values, the elevation difference between the catchment and the raingauge, the wind direction and spatial distributions of rainfall can all create rainfall recording errors. The data should be screened for errors before any modelling commences. Further research into appropriate screening procedures would be desirable. The contribution of baseflow to total streamflow represents a significant component of the hydrologic cycle and consequently should be considered in detail. The validity of different methods of baseflow partitioning should be checked. More research is needed to clarify the problem of baseflow and recessions. Success with thefiltering procedure (methods 1 and 2) and the attractive features of method 5, simplicity, robustness of thefilter's performance and the consistency in the results from year to year, shows promise and these methods should be investigated further. The design of afilter wit h respect to the antecedent condition of the catchment and incorporation of a routing function for the baseflow, and optimisation of the frequency cut off of the slow flow would be beneficial. Catchment hydrology may be more clearly understood if the discharge and recharge of baseflow storage are studied whilst considering runoff from the source areas of the catchments. The process of rainfall-runoff is very complex and the model structures are usually too simple in explaining the processes involved in real catchments. Users should always be aware of the uncertainty of the results of rainfall-runoff modelling regardless of the level of sophistication of the model used. There is a large amount of uncertainty in the calculated model parameters (hence mnoff) caused by different factors (ie model structure deficiency, parameter interactions and data error). Future research in catchment modelling must address the problem of effects of random and systematic data errors on the parameter values and their interactions in conceptual models before any attempt is Chapter Nine Summary. Conclusions and Recommendations for Future Research. 9.17 made to relate them to the physical characteristics of the catchments. The development of a screening model to evaluate the reliability of the input data (rainfall, evaporation and discharge) and to exclude inconsistent data can be thefirst step . The concept of a variable source area of storm mnoff generation is very important in catchment modelling and should be investigated further. Due to the acceptance of the source area and storage capacity variations in the catchment the AWBM model could give significantly better results. Somefield work is also needed to examine the stability and physical significance of the values of source area fractions predicted by the model. There are a number of ways to specify the source areas of mnoff generation which include, comparing observed soil moisture levels and observed flows, investigating soil types and their distribution in the catchment and the other morphologic features of the basins. It seems that the process of transmission loss in streams does not have a big impact on small catchments located in humid regions, but this process should be taken into account if realism is to be achieved in modelling larger catchments. The AWBM model predicts runoff in a realistic manner, both for small and large runoff events as well as baseflow and surface mnoff. Since the results were encouraging, the use of this model should be investigated further. Analysis can be carried out with data from different regions with different hydrological characteristics to further verify the performance of the model. Relating model parameters to the physically measurable catchment characteristics has been one of the most important aims of many researchers over the past three decades, but has met with little success. Results of this study showed that the AWBM model can be regarded as a promising model for use in ungauged catchments. It is proposed that further research be conducted towards improving the documentation and code for the model to make it more useful and realistic. This can primarily be done by incorporating a new relationship governing the recharge and discharge of baseflow storage andfinding a more accurate relationship for routing the surface and subsurface flow through their paths. This refinement can be combined with a methodology to calibrate the model using partitioned baseflow and surface runoff. It is hoped with such improvement that the interactions in the surface storage capacities and corresponding source area fractions will Chapter Nine Summary, Conclusions and Recommendations for Future Research. 9.18 be reduced, and robust and physically meaningful parameters will be obtained from the model. It should be noted that any data errors seriously affect the parameters of the model at this stage and it seems unlikely that parameters of the model will lead to values that have a reliable relationship with the physical characteristics of the catchment areas. REFERENCES AND BIBLIOGRAPHY REFERENCES Abbott, M. B., Bathurst, J. C, Cunge, J. 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APPENDICES APPENDIX A PHOTOGRAPHS OF THE STUDY AREAS Annendix A Photographs of the Study Areas A.2 Picture Al: Vegetation on Sandy Creek Catchment (at Newholme)* * Picture A2: Stream Gauging Station at Hampden Bridge* Appendix A Photographs of the Sludv Areas A.3 Picture A3: Farmland Near Kangaroo Valley Towenship* 3**Ss&*S "^ ISKSll^S^S Picture A4: Vegetation Near Woodhill* Appendix A Photographs of the Study Areas A.4 Picture A5: Vegetation Near the Slope of Barren Ground Escarpment* Picture A6: Farmland in Kangaroo valley' * taken from Baki, 1993 ** taken from Newholme, a progress report of the Newholme Field Laboratory, 1954 APPENDIX B DETAILS OF RECORDING STATIONS AND SUMMARY OF ANALYSED DATA Appendix R Details of recording stations and summary of analysed data B.2 Daily Rainfall Stations (Source: Commonwealth of Australia, Bureau of Meteorology, 1972) CBM. Name Lat. Long. Elev. Start Stop No. (°) (°) (m) 056016 Guyra P.O. 31 13 15140 4430 1975 068000 Albion Park 34 35 150 42 8 1892 068036 Kangaroo Valley Township 34 44 150 32 122 1914 068054 Robertson P.O. 34 35 150 36 740 1890 068085 Nerriga (Tolwong) 34 51 150 08 381 1961 068124 Upper Kangaroo River 34 41 150 36 76 1962 068174 Brogers Creek (No.2) 34 43 150 41 350 1967 068178 Barren Grounds 34 41 150 43 617 1967 _ 068182 Nerriga (Glen Garry) 35 08 150 08 564 1969 068183 Nerriga (Touga) 34 57 150 05 579 1961 — 069000 Araluen P.O. 35 38 149 49 168 1891 1970 069010 Braidwood P.O. 35 26 149 48 655 1887 _ 069041 Charleyong P.O. 35 15 149 55 533 1960 — 069049 Nerriga P.O. 35 07 150 05 625 1898 — 069071 Braidwood 1. 35 28 149 49 710 1877 1901 069081 Sassafras 35 09 150 16 640 1901 1918 069099 Nerriga (The Poplars) 35 06 150 04 549 1969 — 069102 North Araluen P.O. 35 38 149 47 168 1970 — 069105 Araluen (Merricumbene) 35 44 149 55 46 1970 — 070012 Bungonia Inverary Park 34 54 149 59 646 1883 - 070052 Kadoona 35 50 149 39 722 1950 1952 070057 Braidwood (Krawarree) 35 50 149 38 732 1898 - - 070060 Lower Boro 35 12 149 48 689 1903 - 070061 Majors Creek P.O. 35 34 149 44 686 1898 1894 - 070063 Marulan P.O. 34 43 150 00 643 1882 1925 070102 Currandooley 35 18 149 42 620 1942 - 070118 Kyeema 35 07 149 56 605 149 42 610 1961 - 070121 Braidwood (Banoon) 35 17 149 51 579 1961 1970 070122 Windellama (Roseview) 35 01 34 46 149 53 701 1959 - 070141 Windfarthing 35 21 149 39 840 1906 1930 070175 Mulloon Appendix R Details of recording stations and summary of analysed data B.3 Table Bl (continued): Daily Rainfall Stations (Source: Commonwealthof Australia, Bureau ofMeteoroloev. J977) CBM. Name Lat. Long. Elev. Start Stop No. (°) (°) (m) 070183 Budjong Windellima 35 07 149 54 587 1899 1938 070200 Samares 34 49 149 40 640 1890 1912 070219 Braidwood (Khan Yunis) 35 52 149 38 610 1966 070230 Windellama (Buburba) 35 01 149 52 579 1970 070261 Oranmeir (Gilston) 35 41 149 35 610 1971 206026 Sandy Creek 30 25 15139 1040 1976 - WRC 206027 Pipeclay Creek 30 28 15137 1080 1974 - WRC WB Budderoo 34 37 150 42 640 1977 Table B2 Streamgauging Stations (Source: Australian Water Resources Council, 1984) WRC. Stream Location Area Years Complete Auth. No. (Start-Stop) 206026 Sandy Creek Newholme 8 1976 _ WRC 206027 Pipeclay Creek Kirby Farm 8.5 1974 _ WRC 214003 Macquarie Rivulet Albion Park 35 1949 WRC 215001 Shoalhaven River Welcome Reef 2770 1909-1981 72 years WRC 215002 Shoalhaven River Warri 2700 1914-1969 55 years WRC 215003 Mongarlowe River Charleyong 470 1924 _ CBM 215004 Corang River Hockeys 166 1924 — WRC 215005 Mongarlowe River Marlowe 417 1945 — WRC 215006 Mongarlowe River Mongarlowe 130 1949 — WRC 215007 Mongarlowe River Monga 45 1949 — WRC 215008 Shoalhaven River Kadoona 280 1972-1987 15 years WRC 215009 Endrick River Nowra Road 210 1924-1981 57 years WRC 215010 Kangaroo River Kangaroo Valley 241 1954-1975 19 years WRC — 215012 Jerrabutgulla Creek Kain 116 1973 WRC - 215014 Bungonia Creek Bungonia 164 1981 WRC - WB 215220 Kangaroo River Hampden Bridge 330 1970 Note: WRC Australian Water Resources Council WB The Water Board of N.S.W. CBM Commonwealth of Australia, Bureau of Meteorology Appendix R _Details of recording stations and summary of analysed data B.4 Table B3 Pan Evaporation Station (Source: Commonwealth of Australia, Bureau of CBM. Name Lat. Long. Elev. Years Pan Type No. 0 0 (ft) (Start) 056013 Glen Innes (Agric. Res. Stn.) 29 42 15142 3700 1910 A 056018 Inverell (Research Farm) 29 47 15105 2320 1949 A 059099 Dorrigo (Police Stn.) 30 21 152 43 2450 1970 A 068076 Nowra (R.A.N. Albatros) 34 57 150 52 109 1968 A 066037 Sydney (Mascot A M.O.) 33 56 151 11 10 1929 A 067033 Richmond Aero. 33.36 150.42 62 1928 A 070263 Goulburn (St. Josephs) 34.43 149.45 2080 1971 A Table B4 Data Files for Studied Catchments (Disk #1 & #2) Filename Description CAT7687.SAN Rainfall, Runoff And Pan Evaporation Data For The Sandy Creek Catchment (1/6/1975-31/12/1987) CAT7090.KV Rainfall, Runoff And Pan Evaporation Data For The Kangaroo Valley Catchment (1/1/1970-31/12/1990) CAT5089.MR Rainfall, Runoff And Pan Evaporation Data For The Macquarie Rivulet Catchment (1/1/1950-31/12/1989) CAT8186.BUN Rainfall, Runoff And Pan Evaporation Data For The Bungonia Creek Catchment (1/1/1981-31/12/1986) CAT5089.MOR Rainfall, Runoff And Pan Evaporation Data For The Mongarlowe River Catchment (1/1/1950-31/12/1972) CAT7079.ER Rainfall, Runoff And Pan Evaporation Data For The Endrick River Catchment (1/1/1970-31/12/1979) CAT7986.CR Rainfall, Runoff And Pan Evaporation Data For The Corang River Catchment (1/1/1979-31/12/1986) CAT7286.KDN Rainfall, Runoff And Pan Evaporation Data For The Shoalhaven River Catchment (1/1/1972-31/12/1986) Appendix R Details of recording stations and summary of analysed data B.5 > Differenc Between Monthly Estimated Surface runoff and Actual Surface Runof, (QS-EX), Assuming Zero Surface Storage Capacity for Sandy Creek Catchment (206026) QS-EX (mm) Month Year 1 2 3 4 5 6 7 8 9 10 11 12 Total 1975 0 0 0 0 0 43 37 38 26 68 62 97 370 1976 114 62 52 -3 17 63 19 8 4 42 75 25 479 1977 12 126 78 25 58 23 -2 23 28 32 28 15 445 1978 30 2 101 -13 53 20 21 17 19 23 42 71 385 1979 67 -4 1 7 -4 15 5 9 14 45 -7 0 148 1980 0 7 14 0 6 19 20 3 -2 25 -3 26 116 1981 -6 -1 2 2 26 16 32 10 19 17 48 25 189 1982 34 16 39 6 6 0 0 0 3 25 0 0 127 1983 6 20 7 20 58 11 17 3 1 10 28 60 240 1984 53 60 16 16 11 6 3 5 15 3 -1 22 209 1985 3 28 38 33 19 30 49 28 0 5 12 31 277 1986 8 7 16 -2 18 0 64 11 9 5 10 6 151 1987 46 19 14 -1 10 15 20 77 17 24 24 72 337 Total 366 340 379 89 279 261 285 232 152 324 318 449 3473 * Bold numbers indicate months where actual surface runoff exceeds estimated surface runoff Table B6 Differenc Between Monthly Estimated Surface runoff and Actual Surface Runoff, (QS-EX), Assuming Zero Surface Storage Capacity for Kangaroo Valley Catchment (215220) QS-EX (mm) Month Year 1 2 3 4 5 6 7 8 9 10 11 12 Total 1970 78 23 67 13 -14 -30 0 16 65 19 45 124 406 1971 70 22 44 25 43 6 11 51 15 2 60 85 435 1972 85 37 77 39 29 41 0 42 5 95 26 6 483 1973 85 199 23 35 46 91 35 47 28 36 127 15 767 1974 129 115 87 77 235 89 26 182 9 47 26 8 1029 1975 29 129 118 80 12 -2 22 7 36 59 81 8 578 1976 210 112 84 56 24 207 4 19 34 137 18 23 928 1977 24 180 93 21 152 53 5 3 42 0 1 7 581 1978 211 51 30 40 47 145 11 14 62 25 62 56 753 1979 36 24 205 17 86 92 41 18 0 50 58 0 626 1980 92 65 75 8 188 9 14 0 0 5 29 53 537 1981 103 147 6 116 89 43 8 9 3 151 53 193 921 1982 60 27 115 22 0 72 25 16 201 12 0 9 560 1983 33 87 205 37 166 29 8 12 22 159 93 64 915 1984 141 132 96 101 64 68 78 4 22 16 135 90 946 1985 6 67 179 115 70 56 52 7 110 90 81 44 877 1986 111 40 24 97 23 11 7 81 85 35 95 6 617 1987 27 54 78 22 63 20 42 90 0 202 43 75 716 1988 62 77 86 250 -90 29 55 36 57 0 85 98 745 1989 104 62 132 91 26 97 32 18 1 0 96 65 725 1990 48 175 37 73 76 16 34 -35 87 40 1 37 589 1180 1215 1067 14735 Total 1745 1825 1861 1335 1335 1140 509 637 885 numbers indicate months where actual surface runoff exceeds estimated surface runoff Appendix R Details of recording stations and summary of analysed data B.6 Table B7 Differenc Between Monthly Estimated Surface runoff and Actual Surface Runoff, (QS-EX), Assuming Zero Surface Storage Capacity for Macquarie Rivulet Catchment (214003) QS-EX (mm) Month Year 1 2 3 4 5 6 7 8 9 10 11 12 Total 1950 171 146 252 213 166 425 83 -14 44 124 42 12 1664 1951 328 226 24 -13 98 406 71 61 177 6 -6 7 1385 1952 37 58 111 281 39 320 262 307 5 171 36 78 1704 1953 55 64 56 11 410 0 36 25 39 44 44 17 801 1954 122 287 32 5 47 33 74 44 46 102 100 41 933 1955 110 173 62 85 427 61 23 7 26 44 77 153 1248 1956 93 556 316 -134 196 220 69 54 -3 182 -5 17 1562 1957 17 227 31 1 0 108 195 143 -1 5 16 46 788 1958 99 254 215 107 3 141 59 38 34 33 26 46 1056 1959 245 157 65 31 32 105 124 39 44 167 79 35 1122 1960 61 56 87 28 127 103 220 60 80 149 38 213 1222 1961 71 76 260 138 5 130 67 199 92 8 9 170 1225 1962 19 166 58 131 237 0 119 133 149 48 18 150 1230 1963 134 87 357 265 175 139 26 227 92 -6 12 216 1725 1964 49 56 117 370 35 609 13 51 30 60 39 20 1449 1965 7 9 3 82 43 87 104 6 59 224 12 64 700 1966 6 175 189 16 7 185 26 49 73 97 429 56 1308 1967 115 30 124 16 24 270 52 205 127 70 65 15 1114 1968 147 0 81 25 155 2 40 9 0 4 23 83 570 1969 32 164 120 261 64 247 32 156 27 104 261 42 1509 1970 168 30 69 3 0 20 0 1 68 26 43 168 596 1971 -11 392 30 47 25 2 -1 40 10 2 37 133 705 1972 22 45 87 126 8 38 0 54 0 -176 25 16 244 1973 88 183 51 23 35 61 -4 28 0 39 161 5 670 -442 1974 -41 22 88 -69 -70 -98 14 -326 1 45 -6 0 1975 26 114 -49 192 -33 65 81 2 10 36 43 2 489 1976 175 116 219 -16 9 62 45 14 59 144 85 12 923 1977 29 205 207 20 146 33 0 2 25 0 85 2 754 1061 1978 173 48 341 154 29 155 6 6 33 19 40 58 1979 12 3 175 15 92 59 16 2 0 23 38 1 436 1980 58 70 69 6 119 18 4 0 2 2 7 13 368 1374 1981 120 234 10 229 167 81 18 17 7 163 157 169 1982 65 32 183 27 0 100 24 6 168 18 5 28 657 1443 1983 27 123 460 52 254 91 18 25 30 165 126 73 1761 1984 176 390 282 146 38 99 175 10 28 33 286 97 1248 1985 9 67 244 103 159 80 84 15 156 237 57 37 1102 1986 144 46 12 88 34 8 15 396 111 48 189 11 1111 1987 56 36 144 16 92 30 51 194 3 337 73 79 44 71 0 108 162 1643 1988 86 111 55 622 88 52 245 147 54 17 3 0 100 76 1197 1989 108 81 231 348 31 4692 254:S 2346 19215 2795) 2975 2621 41658 Total 3409 5318 547C 405C1 3511 * Bold numbers indicate months where actual surface runoff exceeds estimated surfacerunof f AppendixJR Details of recording stations and summary of analysed data B.7 Differenc Between Monthly Estimated Surface runoff and Actual Surface Runoff, (QS-EX), Assuming Zero Surface Storage Capacity for Bungonia Creek Catchment (215014) QS-EX (mm) Month Year 1 2 3 4 5 6 7 8 9 10 11 12 Total 1981 9 81 12 55 42 23 25 22 7 21 33 59 388 1982 19 2 82 10 0 2 0 0 37 0 0 20 173 1983 51 23 91 28 101 17 4 5 42 88 44 71 565 1984 140 90 52 72 38 49 47 14 21 9 121 49 701 1985 11 12 68 31 40 26 6 30 43 52 35 33 389 1986 73 0 0 48 11 0 25 69 12 33 110 9 388 Total 303 207 305 244 231 117 108 140 162 203 342 241 2602 Bold numbers indicate months where actual surface runoff exceeds estimated surface runoff Table B9 Differenc Between Monthly Estimated Surface runoff and Actual Surface Runoff, (QS-EX), Assuming Zero Surface Storage Capacity for Mongarlowe River Catchment (215006) QS-EX (mm) Month Year 1 2 3 4 5 6 7 8 9 10 11 12 Total 1950 78 113 165 83 135 151 61 6 3 93 38 11 935 1951 81 46 -2 0 12 172 -8 41 110 29 14 2 496 1952 28 25 151 189 21 184 87 88 15 98 89 61 1035 1953 29 22 1 1 227 -1 2 17 11 30 9 31 379 1954 31 89 -2 1 0 12 3 4 9 52 42 8 250 1955 31 92 10 -1 224 3 4 17 4 36 75 27 522 1956 51 163 30 31 116 228 44 11 5 49 -1 4 731 1957 0 42 14 1 0 106 118 47 3 0 8 43 379 1958 57 114 37 23 1 160 9 22 17 25 0 30 495 1959 66 78 81 48 2 251 109 2 14 287 87 9 1033 1960 39 10 49 13 55 21 118 -9 74 35 27 275 708 1961 38 54 195 -5 5 54 164 87 36 89 157 49 920 1962 70 151 15 -1 38 0 38 40 215 20 13 123 721 1963 45 58 80 149 174 18 61 25 46 -3 17 84 755 1964 4 17 35 113 26 26 24 66 21 57 7 30 426 1965 0 0 0 1 0 30 13 3 42 134 1 34 258 1966 9 62 28 0 10 77 23 19 36 44 109 86 504 1967 55 2 20 0 19 28 8 102 64 28 3 0 328 1968 24 0 40 2 91 5 12 4 0 8 31 68 285 1969 13 73 24 61 31 48 5 52 -1 69 54 20 449 1970 110 27 52 23 52 8 0 8 58 24 75 122 559 1971 53 79 17 16 21 0 1 12 5 10 26 34 274 1972 109 22 45 53 23 41 -1 27 0 24 15 0 359 1150 12800 Total 1020 1340 1086 801 1283 1621 894 687 786 1236 896 * Bold numbers indicate months where actual surface runoff exceeds estimated surface runoff ixB Details of recording stations and summary of analysed data B. Table BIO Differenc Between Monthly Estimated Surface runoff and Actual Surface Runoff, (QS-EX), Assuming Zero Surface Storage Capacity for Endrick River QS-EX (mm) Month Year 1 2 3 4 5 6 7 8 9 10 11 12 Total 1970 28 18 23 20 62 9 0 12 41 30 54 71 369 1971 57 34 21 19 19 0 3 23 11 3 58 63 311 1972 0 45 28 29 3 25 0 24 0 35 15 0 204 1973 48 55 41 -3 38 37 23 30 23 19 46 20 376 1974 7 20 1 -23 -73 -48 10 -106 12 46 24 1 -129 1975 29 47 -3 -20 5 -159 14 10 33 31 13 53 51 1976 74 31 -31 11 2 -36 36 6 38 -135 14 10 19 1977 10 5 -15 11 12 5 0 0 36 0 0 10 74 1978 105 2 -161 33 9 -140 23 3 -56 2 -2 26 -155 1979 1 0 83 28 8 -14 19 19 6 -4 38 0 183 Total 359 256 -13 105 84 -322 128 21 143 26 261 255 1304 * Bold numbers indicate months where actual surface runoff exceeds estimated surface runoff Table Bll Differenc Between Monthly Estimated Surface runoff and Actual Surface Runoff, (QS-EX), Assuming Zero Surface Storage Capacity for Corang River Catchment (215004) QS-EX (mm) Month Year 1 2 3 4 5 6 7 8 9 10 11 12 Total 1979 1 0 133 35 14 6 19 20 3 6 37 0 273 1980 43 50 39 17 38 4 -1 6 1 12 12 15 235 1981 8 71 25 33 19 19 29 17 8 11 20 11 272 1982 9 4 75 23 0 8 6 2 29 4 0 6 164 1983 41 17 101 20 53 23 2 13 27 83 26 61 466 1984 142 27 9 46 19 27 19 13 21 17 -11 63 391 1985 10 18 70 21 11 26 1 23 -1 -46 47 19 200 1986 65 -1 0 43 16 4 15 41 -1 19 62 10 271 Total 319 185 452 237 169 117 89 134 87 107 192 185 2273 * Bold numbers indicate months where actual surface runoff exceeds estimated surface runoff Appendix B Details of recording stations and summary of analysed data B.9 Table B12 Differenc Between Monthly Estimated Surface runoff and Actual Surface Runoff, (QS-EX), Assuming Zero Surface Storage Capacity for Shoalhaven , River Catchment (215008) QS-EX (mm) Month Year 1 2 3 4 5 6 7 8 9 10 11 12 Total 1972 134 72 29 29 32 34 0 18 0 67 33 1 449 1973 38 147 15 11 37 15 5 38 11 30 42 44 434 1974 59 71 124 81 96 -16 7 103 2 33 61 5 627 1975 42 114 64 70 8 0 23 3 32 61 7 23 447 1976 89 67 55 13 3 26 4 11 28 -14 19 10 308 1977 38 174 34 58 30 13 5 3 31 3 0 5 393 1978 94 0 184 29 61 67 12 10 82 35 11 9 594 1979 13 2 148 45 27 18 3 12 0 26 35 0 329 1980 41 5 17 2 77 0 0 2 0 16 25 75 260 1981 6 95 11 141 95 13 18 1 6 47 81 60 573 1982 32 21 69 13 0 4 1 0 26 0 0 3 170 1983 48 14 157 39 17 13 2 2 14 67 83 23 479 1984 122 61 51 60 20 31 52 -7 25 11 19 53 498 1985 11 0 97 29 30 17 69 28 55 12 43 6 399 1986 17 7 7 18 8 16 5 68 22 27 53 34 281 Total 785 848 1063 638 543 250 206 291 334 420 512 351 6241 * Bold numbers indicate months where actual surface runoff exceeds estimated surface runoff APPENDIX C LIST OF PROGRAMS USED FOR DATA ANALYSIS, BASEFLOW SEPARATION AND MODELLING AjppendixC lists of programs used for data analysis and modelling C.2 Table CI Lists of Programs and Models Used for Data Analysis, Baseflow Separation and Modelling (Disk #3, #4 & #5) filename APPLICATIONS runoffext extracting daily runoff from Hyd Sys format and writing them to a format suitable for modelling (day/month/year/runoff). rainext program in Quick Basic to extract daily rainfall from Hyd Sys format. This program enables the user to write the data in presentation format or a format suitable for modelling. evapext extracting daily pan data from Bureau of Meteorology format and writing in Hyd Sys format. basefl.bas extracting daily runoff from Hyd Sys format and writing each month in a row for baseflow separation (No. of days of month/il,i2,i3...,day/m/year/rainfall). rrow.bas extracting daily rainfall/runoff from Hyd Sys format and writing each month in a row for use of AWBM model (No. of days of month/ II, 12, i3...,day /m/y/rain). awbmcl convening runoff data from row to column format to be used by the AWBM model mortev program to calculate potential evaporation by Morton's method using sunshine duration, air temperature and wet bulb (or dew point) temperature (Nathan, 1990) checkdat program calculating a coarse check on major inconsistencies between rainfall and runoff (Boughton, 1994). bfip program written in fortran to calculate baseflow index and plot the hydrograph using a recursive digitalfilter (method 1) with a fixed filter parameter of 0.925 and 3 pass (procedure proposed by Lyne and Hollick (1979) and recommended by Nathan and McMahon 1990a). method If this program is written in fortran. It separates baseflow from surface runoff using a recursive digitalfilter (metho d 1) with differentfilter parameter s and calculates BFI. program written in Matlab to separate baseflow from surface runoff, calculate method l.m baseflow index and plot the hydrograph using a recursive digitalfilter (method 1) with differentfilter parameters. program written in Quick Basic to separate baseflow from surface runoff, calculate method lb baseflow index using a recursive digitalfilter (metho d 1) with different filter parameters. It is based on the procedure proposed by Lyne and Hollick (1979) and recommended by Nathan and McMahon 1990a. this program is written in Quick Basic and separates baseflow from surface runoff method2 based on Method 2 presented by Boughton (Boughton, 1988b). The method assumes that baseflow increase is a fraction of the difference between the baseflow on the previous day and the total flow. A^pendixjC, lists of programs used for data analysis and modelling C.3 Table CI (continued) Lists of Programs and Models Used for Data Analysis, Baseflow Separation and Modelling (Disk #3, #4 & #5) this program (method 3) separates and plots baseflow component of runoff based on a method3.m butterworth filter of order 5 using Mathlab pakage. Separation of baseflow based on Method 3 written in Quick Basic. This method separates method3 baseflow from surface and calculates baseflow index using a recursive digitalfilter wit h different filter parameters. ^ is a program to separate baseflow based on Method 4. The method assumes that baseflow method4 increase is a fraction of the difference between the average values of baseflow on the previousfive days and the total flow. is a program to separate baseflow based on Method 5. This method separates baseflow method5 component of the hydrograph based on a relationship between volume of water in storage and baseflow discharge. baseflow is a program in turbo pascal to separate baseflow from surface runoff (method 2). The method assumes that baseflow increase is a fraction of the difference between the baseflow on the previous day and the total flow. It calculates the daily recession constant and baseflow index (Boughton, 1988 & 1995). sfbm This program is written in fortran and converts daily rainfall and potential evaporation data into monthly streamflow values using the SFB rainfall-runoff model (Nathan, 1990). sfbp SFB model program written in turbo pascal. This program converts daily rainfall and average daily values of pan evaporation for each month into monthly streamflow values using the SFB rainfall-runoff model. (Boughton, 1995) sfbf This program converts daily rainfall and pan evaporation data into daily, monthly and yearly streamflow values based on the SFB Rainfall-runoff model. When cocurrent rainfall-runoff is available the model parameters can be optimised using the Pattern Search Optimisation Technique. sdif This program converts daily rainfall and pan evaporation data into daily, monthly and yearly streamflow values based on the SDI Rainfall-runoff model. When cocurrent rainfall-runoff is available the model parameters can be optimised using the Pattern Search Optimisation Technique. . . — base is a program for calibration of the AWBM model parameters to a data set in which baseflow is a component of runoff. This calculates the model parameters using multiple regression technique (Boughton. 1994 & 1995). AWBM model program written in turbo pascal. The essentialfiles to run the program are a awbm daily (hourly) rainfallfile and an evapotranspirationfile. The outputs of the model are monthly totals of calculated runoff, daily (hourly) totals of calculated runoff and partial area runoff from the 3 stores (Boughton, 1994 & 1995). APPENDIX D DETAILS OF RECESSION ANALYSIS IN ALL CATCHMENTS A^pendixJJ Details of recession analysis in all catchments D.2 For calculating MRC twofiles are created for each catchment. File MASTER.CATNO contains the final master recession values. File SEGMENTS.CATNO contains the individual baseflow recession segments used to calculate the master recessions. A list of files is presented in the following table. Table Dl, Master Recession Values and the Individual Baseflow Recession Segments used to Calculate the MRC for all Catchments (Disk #6) FILENAME DISCRTPTION MASTER.SAN final master recession values and the individual baseflow recession segments used to calculate the MRC for Sandy Creek Catchment SEGMENT.SAN MASTER.KV final master recession values and the individual baseflow recession segments SEGMENTS.KV used to calculate the MRC for Kangaroo Valley Catchment MASTER.MR final master recession values and the individual baseflow recession segments SEGMENT.MR used to calculate the MRC for Macquarie Rivulet Catchment final master recession values and the individual baseflow recession segments MASTER.KDN SEGMENT.KDN used to calculate the MRC for Shoalhaven River Catchment final master recession values and the individual baseflow recession segments MASTER.BUN SEGMENT.BUN used to calculate the MRC for Bungonia Creek Catchment final master recession values and the individual baseflow recession segments MASTER.ER SEGMENT.ER used to calculate the MRC for Endrick River Catchment final master recession values and the individual baseflow recession segments MASTER.CR used to calculate the MRC for Corang River Catchment SEGMENT.CR final master recession values and the individual baseflow recession segments MASTER.MOR used to calculate the MRC for Mongarlowe River Catchment SEGMENT.MOR AppendixTJ Details of recession analysis in all catchments D.3 i i- 206026 B o.i 0.91 t§ 0.01 MKWXWMHXNWHM 0.001 0.001 10 20 30 40 0 20 40 60 80 100 Time (days) Time (days) 10 (t 0.001 0.001 50 100 150 200 0 20 40 60 80 100 Time (days) Time (days) 10 10 _ 0.644 0.99 \- 0.80 215009 «*" 0.94 *_ • 215006 0.982 ^**iw. ""' CO 1 0.97 1 t^ i - e §, 0.1 1 ^^ 0.98 s % ^^^ S ^T_i^"_ 0.1 E - o 0.01 0.001 0.01 0 20 40 60 80 100 0 50 100 150 200 250 Time (days) Time (days) 10 V^O.856 215008 - ^%. 0.932 ^W *"' 0.961 E 0.1 0.97 o E 0.01 -- 1. .. . J 1 0.001 0.001 0 20 40 60 80 100 0 20 40 60 80 100 120 140 Time (days) Time (days) Figure Dl Plots of MRC using Matching Strip Method for all Catchments