MEDRC Series of R & D Reports MEDRC Project: 17 - JD - 004
HYDROLOGICAL AND SURFACE WATER MANAGEMENT MODELING OF WADI MUSA WATERSHED
M.Sc. Thesis By
Ahmad Jaser Alzubaidi
Supervisor
Dr. Radwan Al-Weshah, Prof. Civil Engineering Department The University of Jordan
A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree of Master in Civil Engineering
MEDRC Water Research Muscat Sultanate of Oman
June, 2018
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Dedication
To My Beloved Wife,
“ Wasama “
My eternal love …
Ahmad Alzubaidi
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Acknowledgement
First of all, praise to Allah who helped me to complete this thesis.
My sincere thanks and gratitude to my Supervisor Prof. Radwan Al-Weshah for his following up, support and guidance.
Much gratitude to the examining committee, Prof. Rakad Ta'ani, Prof. Nidal
Hadadin, and Dr. Khaldoun Shatanawi for their fruitful discussion and review of the thesis.
My appreciation to my wife Eng. Wasama Abdullah for the help and support.
I offer my thanks and gratitude to the MEDRC fellowship for their generous support; In addition, I would like to offer special thanks to Dr. Khaldoun Shatanawi for his great efforts in introducing and explaining the goals and the contributions of the
MEDRC fellowship to us " the masters' students of Jordan University ".
Finally, I would like to express my deepest gratitude and thanks to my father and mother and to all professors, staff members and colleagues of the Civil Engineering
Department of the University of Jordan for their support.
.
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Table of Contents
Committee Decision ...... Error! Bookmark not defined. Dedication ...... iii Acknowledgement ...... iv Table of Contents ...... v List of Tables ...... viii List of Figures ...... xi List of Appendices ...... xiv List of Abbreviations ...... xv ABSTRACT ...... xvii CHAPTER ONE ...... 1 1.INTRODUCTION...... 1 1.1 Background...... 1 1.2 Problem Statement...... 4 1.3 Description of the Study Area...... 4 1.4 Climate of the Study Area...... 8 1.5 Importance of the Study...... 8 1.6 Objectives of the study...... 9 CHAPTER TWO ...... 10 2.LITERATURE REVIEW...... 10 2.1 Design Storm Characteristics...... 10 2.1.1 Intensity-Duration-Frequency (IDF) Curves ...... 10 2.1.2 Rainfall Distribution Hyetographs ...... 17 2.2 Watershed Hydrological Characteristics ...... 19 2.2.1 Time of Concentration and Lag Time ...... 20 2.2.2 Watershed Delineation ...... 21 2.2.3 Average Rainfall Interpolation ...... 21 2.3 Model Calibration ...... 23 2.4 Peak Discharge Estimation ...... 25 2.4.1 (SCS-CN) Unit Hydrograph ...... 26 2.4.2 SCS Curve Number ...... 28 2.5 Channel Routing ...... 31 2.6 Reservoir Capacity Estimation ...... 36 2.7 Hydrological Modeling Software ...... 40 2.7.1 WMS 10.1 Software ...... 40 2.7.2 EasyFit 5.6 Software ...... 41 vi
2.7.3 ArcGIS (10.2.2) Software ...... 42 2.8 Previous Studies ...... 44 CHAPTER THREE ...... 50 3.METHODOLOGY...... 50 3.1 Design Storm Characteristics...... 50 3.1.1 Intensity-Duration-Frequency (IDF) Curves...... 50 3.1.2 Development of Rainfall Distribution Hyetographs ...... 52 3.2 Watershed Delineation ...... 53 3.3 Model Calibration...... 54 3.4 Peak Discharge Estimation ...... 55 3.5 Channel Routing...... 57 3.6 Reservoir Capacity Estimation ...... 58 CHAPTER FOUR ...... 59 4.DATA ANALYSIS AND MODELLING...... 59 4.1 Rainfall Data (Rainfall stations) ...... 60 4.2 Digital Elevation Model (DEM) and Triangulated Irregular Network (TIN)... 61 4.3 Existing Soil Types ...... 64 4.4 Slope ...... 65 4.5 Land Cover / Land Use ...... 65 4.6 Drainage Area ...... 67 4.7 Watershed Length ...... 67 4.8 Curve Number ...... 69 4.9 Time of Concentration (TC) ...... 70 4.10 Watershed Delineation ...... 71 CHAPTER FIVE ...... 76 5.RESULTS AND DISCUSSION...... 76 5.1 Results...... 76 5.1.1 Design Storm Characteristics...... 76 5.1.2 Model Calibration...... 84 5.1.3 Peak Discharge Estimation...... 90 5.1.4 Channel Routing...... 93 5.1.5 Reservoir Capacity Estimation...... 102 5.1.6 Flood Risk Mitigation using Afforestation...... 107 5.2 Discussion...... 114 5.2.1 Identifying the best IDF construction method. 114 5.2.2 The validity of the model based on the calibrating parameter (CN) ...... 116 5.2.3 Effect of changing CN values on Peak flow values(Qp) ...... 118 vii
5.2.4 Generating the Flow-Duration-Frequency Curves ...... 119 5.2.5 Effect of Routing on Peak Flow ...... 121 5.2.6 Calculating the peak discharge at the watershed outlet using the Sub-integral method ...... 121 5.2.7 Calculating the peak discharge in the Siq using Manning’s equation ...... 126 CHAPTER SIX ...... 128 6.CONCLUSIONS AND RECOMMENDATIONS...... 128 6.1 Conclusions...... 128 REFERENCES ...... 132 APPENDICES ...... 137 Appendix A: Rainfall data ...... 137 (A.1): Rainfall records...... 137 (A.2): Statistical distributions ranking ...... 141 (A.3): (Bell, Chen and Hershfield) Rainfall data...... 143 Appendix B: Extreme quantiles, Intensity values ...... 153 (B.1): Extreme rainfall quantiles ...... 153 (B.2): Rainfall intensities ...... 155 Appendix C: IDF Curves ...... 158 (C.1): IDF Curves ...... 158 (C.2): IDF Curves parameters, Percentage differences ...... 161 Appendix D: Rainfall distributions ...... 167 (D.1): S-Curves ordinates ...... 167 (D.2): Hyetographs ...... 173 Appendix E: Hydrographs ...... 176 Appendix F: Channels Cross Sectional Dimensions Estimation ...... 181 Appendix G: Reservoir capacity estimation ...... 185 (G.1): Runoff calculations ...... 185 (G.2): Reservoir capacity calculations ...... 186 (G.3): Mass curve plots ...... 188 Appendix H: Tables and Figures ...... 191 Abstract in Arabic ...... 196
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List of Tables
Table 2.1 Frequency factors K for log-Pearson Type III distribution….……..…….16 Table 2.2: Characteristics of soils assigned to SCS Soil Groups………….…………27 Table 2.3: Seasonal rainfall limits for AMC ………………………………………...28 Table 2.4: Kinematic wave parameters for different cross sectional shapes…..…….32 Table 2.5: Roughness Coefficient…………………………………………………....33
Table 4.1: Watershed Characteristics sources………………………………………..58 Table 4.2: Locations of the meteorological climate stations in the study area……....60 Table 4.3: CN number for all Sub-watersheds……………………………………….67 Table 4.4: Tc value for all sub-watersheds……………………………………...…....69 Table 4.5: The thirteen sub-watersheds characteristics…………………...……….....71 Table 4.6: The Contribution ratios of the 3 rainfall stations. …………………….….73
Table 5.1 : IDF Curves parameters at 100 years return period……………………..78 Table 5.2 : Sherman Equation for 100 years return period at the three statio.……..79 Table 5.3 : The divided precipitation ordinates at 100 yrs return period…………...80 Table 5.4 : Available rainfall and runoff records at DG0004………………………82 Table 5.5 : The delineated sub-watersheds at the calibration drainage basin…..…..83 Table 5.6 : The observed and simulated values at the calibration drainage basin.…85 Table 5.7 : The CN values for the observed storms at calibration drainage basin.....86 Table 5.8 : Simulated and calculated CN…………………………………………...88 Table 5.9 : Simulated peak discharges volumes at the watershed outlet……….…..90 Table 5.10: Dimensions estimation of reach 65R…………………..…...…………. 95 Table 5.11: Simulated routed peak discharges and volumes at the outlet………...... 98 Table 5.12: Simulated routed peak discharges and volumes at DG0004………...….99 Table 5.13: Critical runoff years at the three rainfall stations………………….…...101 Table 5.14: Runoff calculations at 1965 for Wadi Musa station………………..…..101 Table 5.15: Runoff calculations at 1969 for Wadi Musa station………………..…..102 Table 5.16: Maximum reservoir capacity calculations at A0001…………..……….103.. Table 5.17: Average reservoir capacity calculations at A0001……………………..103 Table 5.18: Maximum and Average Reservoirs Capacity…………………………..104 Table 5.19: Afforestation effects on CN values………………………………..…...106 Table 5.20: Curve number calculations for basin 80 after the afforestation………..107 Table 5.21: Curve number calculations for basin 81 after the afforestation………..107 Table 5.22: Curve number calculations for basin 82 after the afforestation………..108 Table 5.23: Curve number calculations for basin 83 after the afforestation………..108 Table 5.24: Curve number calculations for basin 84 after the afforestation………..108 Table 5.25: Afforestation effect on the peak flow values……………………….….109 Table 5.26: Effect of CN on the variation of Qp…………………………………....116 Table 5.27: Effect of routing on Qp……………….……………………………...... 119 Table 5.28: The peak discharge calculations using the sub-integral method……. …121 Table 5.29: The lagging between each sub-watershed mouth and the main outlet.…121 Table 5.30: The Siq dimensions…………………………..…………………………124
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Appendix A Table A.1.1: Rainfall records for DG0001……...………………………...…….……137 Table A.1.2: Rainfall records for DG0002………..……………………………….…138 Table A.1.3: Rainfall records for DG0003……..………………………………….....139 Table A.2.1: Probability distributions test results for Wadi Musa rainfall station…...140 Table A.2.2: Probability distributions test results for Hai rainfall station………..…..140 Table A.2.3: Probability distributions test results for Petra rainfall station……..…...140 Table A.3.1: Rainfall data for DG0001, Bell method………………………….…….142 Table A.3.2: Rainfall data for DG0001, Hershfield method………………………....144 Table A.3.3: Rainfall data for DG0002, Bell method……………………….……….146 Table A.3.4: Rainfall data for DG0002, Hershfield method…………………….…...147 Table A.3.5: Rainfall data for DG0003, Bell method………………………………..147 Table A.3.6: Rainfall data for DG0003, Hershfield method…………………………149 Table A.3.7: Percentile differences – Bell procedure Vs MWI……………………...127 Table A.3.8: Percentile differences – Chen procedure Vs MWI…………………...... 127 Table A.3.9: Percentile differences - Hershfield procedure Vs MWI……………...... 127
Appendix B Table B.1.1: Extreme rainfall quantiles for DG0001, Bell method……………….....152 Table B.1.2: Extreme rainfall quantiles for DG0001, Hershfield method……….…..152 Table B.1.3: Extreme rainfall quantiles for DG0002, Bell method………………….152 Table B.1.4: Extreme rainfall quantiles for DG0002, Hershfield method……….…..153 Table B.1.5: Extreme rainfall quantiles for DG0003, Bell method………………….153 Table B.1.6: Extreme rainfall quantiles for DG0003, Hershfield method…………...153 Table B.2.1: Rainfall Intensities for DG0001, Bell method…………………………154 Table B.2.2: Rainfall Intensities for DG0001, Chen method……………………..…154 Table B.2.3: Rainfall Intensities for DG0002, Hershfield method…………………..154 Table B.2.4: Rainfall Intensities for DG0002, Bell method………………………....155 Table B.2.5 Rainfall Intensities for DG0002, Chen method…………………..…....155 Table B.2.6: Rainfall Intensities for DG0002, Hershfield method…………………..155 Table B.2.7: Rainfall Intensities for DG0003, Bell method………………...……….156 Table B.2.8: Rainfall Intensities for DG0003, Chen method……………….……….156 Table B.2.9: Rainfall Intensities for DG0003, Hershfield method………………...... 156
Appendix C Table C.2.1: IDF Curves/Intensity Parameters, for 2 yrs. Return period………….160 Table C.2.2: IDF Curves/Intensity Parameters, for 5 yrs. Return period……….…160 Table C.2.3: IDF Curves/Intensity Parameters, for 10 yrs. Return period……...…161 Table C.2.4: IDF Curves/Intensity Parameters, for 25 yrs. Return period………...161 Table C.2.5: IDF Curves/Intensity Parameters, for 50 yrs. Return period………...162 Table C.2.6: IDF Curves/Intensity Parameters, for 100 yrs. Return period……….162 Table C.2.7: IDF Curves/Intensity Parameters, for 200 yrs. Return period……….163 Table C.2.8 Rainfall Intensity Difference, Bell Vs MWI, DG0002……………....163 Table C.2.9 Rainfall Intensity Difference, Chen Vs MWI, DG0002……………..164 Table C.2.10 Rainfall Intensity Difference, Hershfield Vs MWI, DG0002………..164 Table C.2.11: Rainfall Intensity Difference, Bell Vs MWI, DG0003……………....164 Table C.2.12: Rainfall Intensity Difference, Chen Vs MWI, DG0002……………..165 Table C.2.13: Rainfall Intensity Difference, Hershfield Vs MWI, DG0002………..165 x
Appendix D Table D.1.1: The Hyetograph ordinates for 2.75 hrs. from the six return periods…..166 Table D.1.2: The Hyetograph ordinates for 30 min. from the six return periods…....167 Table D.1.3: The Hyetograph ordinates for 1.0 hr. from the six return periods……..168 Table D.1.4: The Hyetograph ordinates for 1.5 hrs. from the six return periods...….169 Table D.1.5: The Hyetograph ordinates for 2.0 hrs. from the six return periods...... 170 Table D.1.6: The Hyetograph ordinates for 2.5 hrs. from the six return periods…....171
Appendix F Table F.1: Channels Dimensions estimation……………………………..……….180
Appendix G Table G.1.1: Runoff calculations at 1972(Avg. year) for Hai station………………184 Table G.1.2: Runoff calculations at 1979(Max year) for Hai station……………….184 Table G.1.3: Runoff calculations at 2008(Avg. year) for Petra station……………..185 Table G.1.4: Runoff calculations at 1991(Max year) for Petra station…………...... 185 Table G.2.1: Maximum reservoir capacity calculations at A0002………………….185 Table G.2.2: Average reservoir capacity calculations at A0002……………………186 Table G.2.3: Maximum reservoir capacity calculations at A0003………………….186 Table G.2.4: Average reservoir capacity calculations at A000…………….……….187
Appendix H Table H.1: Frequency Factors K for log Pearson type III distribution…….……...190 Table H.2: Runoff curve number for urban areas…………………………………192 Table H.3: Runoff curve number for arid and semiarid range lands……...………192 Table H.2: Runoff curve number for cultivated agricultural areas…….…………193 Table H.2: Runoff curve number for other agricultural areas….…………………193 Table H.6: Runoff coefficients for use in the rational method……………………194
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List of Figures
Figure 1.1: Location of the study area…………………………………………...…..6 Figure 1.2: Study area drainage watershed and the surrounding catchments...…...... 7
Figure 2.1: Chen’s Parameters estimation…………….……………………………..13 Figure 2.2: Input S-curve series of a rainfall distribution in WMS……………...... 18 Figure 2.3: Stochastic Run Parameters by WMS………………………………..…..23 Figure 2.4: SCS unit hydrograph………………...…………………………….....….25 Figure 2.5: Hydrograph Routing through a Transfer function………………...…...... 29 Figure 2.6: Attenuation and Translation in a routed hydrograph……………...…...... 30 Figure 2.7: Kinematic wave routing interface, HEC-1……………………….....…....34 Figure 2.8: Ripple diagram……..……………………………………………..……...35 Figure 2.9: HEC-1 Interface, (WMS 10.1,2016)…………………………..…...…….40 Figure 2.10: EasyFit 5.6, 2010 Interface………………………………………...... …..41 Figure 2.11: ArcGIS, 2014 Interface………………………………………….……….42
Figure 3.1: Modeling Wizard (WMS 10.1,2016)……………………………..……...52
Figure 4.1: Distribution of the rainfall stations in the study area…………...……....59 Figure 4.2: The projection and the datum of the data…………………………...…..60 Figure 4.3: Digital Elevation Model of the Study Area (DEM)…………….....…....61 Figure 4.4: Triangulated Irregular Network of the study area (TIN)………….….....61 Figure 4.5: Soil Types Map of the study watershed……………………………..…..62 Figure 4.6: Soil groups map of the study watershed ………….………..…………...63 Figure 4.7: Slope percentage in the study area………………………………….….. 64 Figure 4.8: Land Cover / Land Use in the study area……………...………………..64 Figure 4.9: Drainage system in the study area………………………………. ……..66 Figure 4.10: The watershed with the Main Channel………………..………………...65 Figure 4.11: Tc result window(WMS 10.1, 2016)…………………...……………….68 Figure 4.12: Proposed Outlets in the study area watershed…………………………..70 Figure 4.13: Watersheds in the study area……………………………………….…...72 Figure 4.14: Generated Thiessen Polygons for the three Rainfall Stations…………..73
Figure 5.1: IDF Curves for Wadi Musa station by Bell method………….……….76 Figure 5.2: 2.75-hrs./100-yrs. Rainfall Hyetograph bar chart..…………...……….81 Figure 5.3 : Rainfall Hyetograph at Tc = 2.753 hours…………………………..…82 Figure 5.4: Calibration area………………………………………………………..83 Figure 5.5.a: Simulated hydrograph at 1968 storm for calculated CN………………84 Figure 5.5.b: Simulated hydrograph at 1970 storm for calculated CN…….……..…84 Figure 5.5.c: Simulated hydrograph at 1974 storm for calculated CN…………...…84 Figure 5.6.a: Simulated hydrograph at 1968 storm for calibrated CN……….…..….87 Figure 5.6.b: Simulated hydrograph at 1970 storm for calibrated CN……………....87 Figure 5.6.c: Simulated hydrograph at 1974 storm for calibrated CN……….…..….87 Figure 5.7: SCS Hydrograph at 2.75 hrs. duration with different return periods.....89 Figure 5.8: FDF (Flow-Duration-Frequency) Curves for the study area…….…….91 Figure 5.9 : Determination of channel cross section in WMS………………....…..92 Figure 5.10.a: Cross section #1 in reach R65………………………………….....…..92 xii
Figure 5.10.b: Cross section #2 in reach R65………………………………….....…..93 Figure 5.10.c: Cross section #3 in reach R65………………………………….….….93 Figure 5.10.d: Cross section #4 in reach R65…………………………………...…..93 Figure 5.10.e: Cross section #5 in reach R65…………………………………….....94 Figure 5.10.f: Cross section #6 in reach R65…………………………………..…..94 Figure 5.11: R65 Profile……………………………………………………...…...94 Figure 5.12: Effect of routing on the hydrograph of 2.75 hrs.………………...…..96 Figure 5.13: Routed SCS Hydrograph at 2.75-hrs. storm…………….……….…..97 Figure 5.14: RFDF (Routed-Flow-Duration-Frequency) Curves…………….… ..99 Figure 5.15: Mass Curve of A0001 at 1965………………………………….…...104 Figure 5.16: Afforestation areas in the upstream watersheds of the study area .....105 Figure 5.17: Effect of afforestation scenario for 2 yrs. Return period ……...…....109 Figure 5.18: Effect of afforestation scenario for 5 yrs. Return period ……...…....110 Figure 5.19: Effect of afforestation scenario for 10 yrs. Return period ……...... 110 Figure 5.20: Effect of afforestation scenario for 25 yrs. Return period ……...…..110 Figure 5.21: Effect of afforestation scenario for 50 yrs. Return period ……...... 111 Figure 5.22: Effect of afforestation scenario for 2 yrs. Return period ……...…....111 Figure 5.23: IDF Curve for Wadi Musa rainfall station from MWI…………..….112 Figure 5.24: Bell, Chen, Hershfield, MWI IDF for DG0001…………………...... 113 Figure 5.25: Urbanization at Wadi Musa, 1984, Google Earth………………..….114 Figure 5.26: Urbanization at Wadi Musa, 1994, Google Earth……………..…….115 Figure 5.27: Urbanization at Wadi Musa, 2004, Google Earth……………..…….115 Figure 5.28: Urbanization at Wadi Musa, 2014, Google Earth…………..……….115 Figure 5.29: Urbanization at Wadi Musa, 2018, Google Earth…………..……….116 Figure 5.30: FDF(Flow-Duration-Frequency) Curves for the study area…..……..118 Figure 5.31: Runoff Hydrograph using the Sub-integral method………..………..122
Appendix A Figure A.2.1: Statistical distributions graph for Wadi Musa station………………..141 Figure A.2.2: Statistical distributions graph for Wadi Musa station………………..141 Figure A.2.3: Statistical distributions graph for Wadi Musa station………………..142
Appendix C Figure C.1.1: IDF Curves for DG0001, Bell method……………………………… 157 Figure C.1.2: IDF Curves for DG0001, Chen method…………………...…….…...157 Figure C.1.3: IDF Curves for DG0001, Hershfield method……………….………..157 Figure C.1.4: IDF Curves for DG0002, Bell method…………………………….....158 Figure C.1.5: IDF Curves for DG0002, Chen method……………………………...158 Figure C.1.6: IDF Curves for DG0002, Hershfield method………………….……..158 Figure C.1.7: IDF Curves for DG0003, Bell method…………………………..…...159 Figure C.1.8: IDF Curves for DG0003, Chen method……………………………...159 Figure C.1.9: IDF Curves for DG0003, Hershfield method………………………...159
Appendix D Figure D.2.1: Rainfall Hyetograph at 30 min ………………………………………..172 Figure D.2.2: Rainfall Hyetograph at 1 hour……………………………..…………..172 Figure D.2.3: Rainfall Hyetograph at 1.5 hours……………………………....……...173 Figure D.2.4: Rainfall Hyetograph at 2.0 hours……………………...………..……..173 Figure D.2.5: Rainfall Hyetograph at 2.5 hours………………………………...…....174
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Appendix E Figure E.1: SCS Hydrograph/ 30 min…. ………………………………………....175 Figure E.2: Routed SCS Hydrograph/ 30 min.………..……………………….….175 Figure E.3: SCS Hydrograph/ 1 hr. .……………………………….……………..175 Figure E.4: Routed SCS Hydrograph/ 1hr.. ……………………………...... ……..176 Figure E.5: SCS Hydrograph/ 1.5 hrs. .………………………….….………..…...176 Figure E.6: Routed SCS Hydrograph/ 1.5 hrs.. …………………………...... …..176 Figure E.7: SCS Hydrograph/ 2 hrs. .………………………….….………..……..177 Figure E.8: Routed SCS Hydrograph/ 2hrs.... …………………………...... ……..177 Figure E.9: SCS Hydrograph/ 2.5 hrs. .………………………….….……..….…..177 Figure E.10: Routed SCS Hydrograph/ 2.5 hrs.... ………………………...... ……..178 Figure E.11: SCS hydrograph 2 yrs. ……………………………………………….178 Figure E.12: SCS hydrograph 5 yrs. ……………………………………………….178 Figure E.13: SCS hydrograph 10 yrs. ………………………………………..…….178 Figure E.14: SCS hydrograph 25 yrs. …………………………………………..….178 Figure E.15: SCS hydrograph 50 yrs. …………………………………………..….179 Figure E.16: SCS hydrograph 100 yrs. …………………………………………….179
Appendix G Figure G.3.1: Mass Curve of A0001 at 1969……………………………………..….187 Figure G.3.2: Mass Curve of A0002 at 1979………………………………..……….188 Figure G.3.3: Mass Curve of A0002 at 1972………………………………..……….188 Figure G.3.4: Mass Curve of A0003 at 1991…………………………………..…….189 Figure G.3.5: Mass Curve of A0003 at 2008………………………………..……….189
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List of Appendices
Appendix A: Rainfall Data (1937-2017) ...... 137 Appendix B: Extreme Rainfall (XT) and Rainfall Intensities (I) ...... 152 Appendix C: IDF Curves ...... 157 Appendix D: Rainfall Distribution Hyetographs ...... 166 Appendix E: Hydrographs ...... 175 Appendix F: Channel Dimensions Esimation...... 180 Appendix G: Reservoir Capacity Calculations ...... 184 Appendix H: Tables and Figures ...... 190
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List of Abbreviations AMC Antecedent Moisture Condition CN Curve Number DEM Digital Elevation Model DOS Department of Statistics GIS Geographic Information System WMS Watershed Modelling System HEC Hydrologic Engineering Center HMS Hydrological Modeling System HSG Hydrological Soil Group IDF Intensity Duration Frequency JTM Jordan Transverse Mercator MCE Multi Criteria Evaluation MCDM Multi Criteria Decision Making MCM Million Cubic Meter JMD Jordan Meteorological Department MOA Ministry of Agriculture MOE Ministry of Environment MoPWH Ministry of Public Works and Housing MWI Ministry of Water and Irrigation NRA Natural Resources Authority NRCS Natural Resources Conservation Service RJGC Royal Jordanian Geographic Center SCS Soil Conservation Service USACE United States Army Corps of Engineers USDA U.S. Department of Agriculture USGS United State Geological Survey NEH National Engineering Handbook PEST Parameter Estimation by Sequential Testing WRCB Water Resources Control Board NPTEL National Program on Technology Enhanced Learning GOF Goodness of Fit HEC-RAS River Analysis System xvi
HSPF Hydrological Simulation Program – FORTRAN TIN Triangulated Irregular Network FDF Flow-Duration-Frequency Curves TR-20 Technical Release No. 20 TR-55 Technical Release No. 50 NSS National Streamflow Statistics Program MODRAT Modified Rational HSPF Hydrological Simulation Program-FORTRAN GSSHA Gridded Surface Subsurface Hydrological Analysis USWRC United States Water Resources Council USGS United States Geological Survey USDA United States Department of Agriculture JDOS Jordan Department of Statistics EDF Electricité de France FAO Food and Agriculture Organization WAJ Water Authority of Jordan RFDF Routed Flow-Duration-Frequency Curves
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HYDROLOGICAL AND SURFACE WATER MANAGEMENT MODELING OF WADI MUSA WATERSHED
By Ahmad Jaser Alzubaidi
Supervisor Dr. Radwan Abdullah Al-Weshah, Prof.
ABSTRACT
Wadi Musa watershed needs better understanding of flood characteristics to mitigate the impact of floods on the monuments in Petra, and increase the water availability in the region. The main objectives of this study were to estimate the peak discharge and the reservoir capacity of the watershed. This is to manage and mitigate the floods in critical sites of Wadi Musa watershed. These estimations were obtained through hydrological analysis of the watershed characteristics and physical analysis of the design storm characteristics.
A hydrological model is generated for this watershed using the WMS software. The watershed is delineated and subdivided into 13 sub watersheds. The model is calibrated and then validated for peak discharge and volume forecasting for 6 return periods (2, 5, 10, 25, 50 and 100 years). Intensity-Duration-Frequency curves are constructed for the 3 rainfall stations situated within the watershed (Petra, Hai and Wadi Musa) using the methods of (Chen, Bell and Hershfield). The rainfall data were analyzed to identify the best-fit probability distribution for each period of study on each station using Easy-Fit software. The Log-Pearson type-III distribution is found to be the best-fit probability distribution and is used to obtain the IDF curves. The rainfall distribution hyetographs are constructed using the Alternating Block method.
The curve number (CN) of the watershed is calculated through WMS using the SCS method depending on soil and land use maps and found to be 86. The time of concentration of the basin is calculated through WMS using the SCS method and found to be 2 hrs. and 45 minutes. The kinematic wave routing is applied to the model which resulted in a peak discharge attenuation of 20 % for a storm of the time of concentration with a 100-year frequency. The peak flow and volume at the time of concentration of a 100-year frequency are 299.8 m3/s and 2.3 MCM respectively. Flow-Duration-Frequency (FDF) curves are generated for the watershed. The (FDF) curves are considered to be a footprint of the watershed, where the peak discharge and volume can be found for any storm with a duration ranging between (0.5 hr. to 2.75 hrs.) with any return period. The runoff analysis indicates that only rainfall events exceeding 8.25 mm within the 24 hrs. period would generate runoff. The average and the maximum volume of direct runoff (storage capacity) is calculated using the mass curve procedure and is found to be 3.81 MCM, and 13.63 MCM respectively.
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One management practice was applied on the model in order to mitigate the flood risk. The afforestation of 1643 ha in the upstream watersheds. It resulted in peak flow and flood volume reduction of 39-61% and 28-47% respectively. 1
CHAPTER ONE
1. INTRODUCTION
1.1 Background
The overall drainage system in Jordan consists of two main flow patterns. The first drains water towards the Jordan Rift Valley, through deeply incised wadis and rivers dissecting the Jordan Valley-Dead Sea escarpments, to discharge ultimately into the
Dead Sea. The second drains water through shallow streams, which generally flow eastwards from the western highlands towards the internal desert depressions and mudflats (Nortcliff, et al., 2008).
The three main rivers, Zarqa River, Jordan River and Yarmouk River are major parts of Jordan’s surface water system. The last two rivers are both shared with
Palestine and Syria who leave only a small amount for Jordan (Aquastat, 2008).
Groundwater is the major water resource in Jordan; it is the only water resource in some areas within the country. Twelve groundwater aquifer have been identified in
Jordan (MWI, 2015). One of the major groundwater aquifers is the non-renewable Disi aquifer which is also shared with the northwestern Saudi Arabia. Since Most of the major water resources in Jordan are shared with neighboring countries, this is to be an additional challenge as conditions of water scarcity increase.
The population of Jordan is rising at approximately 3% annually
(Worldometers.com, 2017). More than three-quarters of Jordan’s population lives in urban areas. This number is rising due to factors such as: the population growth, the movement to cities to search for employment, in addition to the influx of refugees from the surrounding countries suffering from political crises. This endless gap between the available water supplies and the demand; apply a huge pressure on Jordan’s precious water resources in all parts of the country. 2
Many communities in Jordan have long experienced extremely low levels of water availability per capita. Especially the southern regions, were economic, social and infrastructural developments are the least among the other regions of the country, including Wadi Musa district. This high population growth rate coupled with the limited water resources in Jordan are applying a huge pressure on the water security of the country, where the water demand far exceeds supply and water allocation is unbalanced.
Wadi Musa (River of Moses) is situated in the southwest area of Jordan. It lies about 200 km to the south of Amman, between the Dead Sea and the Gulf of Aqaba. It is considered to be a major tourist attraction, where "Petra" one of the new Ten wonders of the world is located there. The Nabateans built channels that carried water from this spring to the city of Petra, but Floods and flash floods have historically caused a major threat to the Wadi Musa City.
Hydrologists ranked Wadi Musa as the Wadi with the highest risk/damage in
Jordan. Geomorphologic investigation in the Wadi bed can easily prove that Wadi Musa is considered an ephemeral Wadi with intermittent flash floods of flows that can exceed the 298 m3/s threshold (Alhasanat, 2014).
The floods in Wadi Musa do not flow every year, (historical data for Wadi Musa shows that 4 water year out of 22 was zero flow) (JMD, 2012). Nevertheless, at certain years the extent of flood can be huge, (i.e. (2.42 m3/sec) daily discharge in January
1964) (Alhasanat, 2014).
As documented in most of the available studies (EDF, 1995), the flood that occurred in April, 1963 was an extreme event, probably with a 100-year return period
(Al-Weshah and El-Khoury, 1999). During this event, the intense and sudden rainfall caused flood water to flow from all wadis into Wadi Musa outlet. The flood carried a 3
huge sediment load which blocked most of the hydraulic structures in the Wadi. The dam at the entrance of the Siq was filled with sediment; consequently, flood water overtopped the dam and entered the Siq. Despite the great emergency efforts, twenty
French tourists lost their lives (Al-Weshah and El-Khoury, 1999).
In 1991, another flood, one probably of a 50-year return period, washed away two culverts upstream of the Siq and caused a serious problem for visitors and tourists. The flood water did not enter the Siq. The traces of high water within Wadi Al-Matahah inside the Petra watershed, indicated that the water level reached an elevation of more than 12 m above the Wadi bed.
Other recent major floods in Petra occurred in January, 1995 and November, 1996.
During these events, the Siq entrance area was flooded and tourists had to be rescued. In more recent floods, many deaths have recorded in the same area (Al-Weshah and El-
Khoury, 1999).
Petra's monuments are all situated within the limits of Wadi Musa watershed. The occurring flash floods during rainfall events threaten the tourist activities and apply a great danger on the monuments themselves.
A general comprehensive flood risk modeling and management for Wadi Musa watershed has to be developed using hydrological modeling. The critical water situation in Wadi Musa watershed needs the development of Rainfall- Runoff relationships for better understanding of flood characteristics of the area. This is to mitigate the impact of floods on tourism and on the monuments in Petra. It is also important to increase the water availability in the region.
4
1.2 Problem Statement
Jordan is currently experiencing rapid population growth rate. This high
population consumes more water than is available from renewable sources and
Meeting water demand has become even more critical.
Wadi Musa has an arid climate, where water resources are very scarce, and
drought conditions were evidenced for many years as it is the same case for most of
the areas in Jordan.
The region has a notorious history of extreme floods that had caused severe damage to the installations located in its floodway and considerable distances downstream.
The main water problem in the study area is the increasing water demand and the water scarce conditions. In conjunction with the heavy flash floods that threaten the monuments and the touristic activities. Large amounts of rainwater are wasted annually without benefiting from it in the unbalanced equation of the water demand and supply.
1.3 Description of the Study Area
Wadi Musa is a small town in Ma’an governorate, at the south of Jordan. It has a population density of 2.3 people per dunam (JDOS, 2015).
The city is considered as the main tourist gateway to Petra. It holds a considerable number of restaurants and hotels, in addition to the campus of the College of
Archaeology, Tourism & Hotel Management of Al-Hussein Bin Talal University.
Wadi Musa is located in an arid zone at the southwest of Jordan, between the
Dead Sea and the Gulf of Aqaba. It lies at the Sherah Mountains, overseeing Wadi
Araba at the Jordan Rift Valley. It has coordinates of 30°19′11.96″N and 35°28′42.37″E which is about 100 km north of the Gulf of Aqaba and 250 km south of Amman. The 5
region has an average elevation of 950 m above mean sea level (MAPLOGS.COM,
2018).
There are three rainfall gauging stations in the Wadi Musa watershed. These stations, designated DG0001, DG0002 and DG0003, are operated by the Water
Authority of Jordan (WAJ). Annual total rainfall records are available for these stations since 1937, and daily total rainfall records are available since 1980.
The drainage watershed of Wadi Musa covers an approximate area of 139 km2, as shown in Figure 1.1. The drainage watershed and its surrounding catchments are shown in Figure 1.2. 6
Figure 1.1: Location of the study area 7
Figure 1.2: Study area's drainage watershed and the surrounding catchments 8
1.4 Climate of the Study Area
Wadi Musa region belongs to the Mediterranean climatic zone. The average annual precipitation is around 200 mm. Most rainfall is concentrated between October and April and is mainly of orographic origin. Seasonal mean temperatures vary from 6
°C in January to 22 °C in July (JMD, 1995).
The maximum temperature in summer may reach 39 °C, while the minimum temperature in winter is slightly below 0 °C. Dominant winds in the area are from west and southwest. The average daily evaporation is about 6.8 mm. The reported highest evaporation rate is 9.8 mm/day in June, while the lowest rate is 3.6 mm/ day in
December
(JMD, 1995), (Al-Weshah and El-Khoury, 1999).
1.5 Importance of the Study
The selection of Wadi Musa watershed as a suitable case study was mainly because floods and flash floods have historically caused a major threat to the residential, industrial, social, economic and touristic uses of Wadi Musa city. It threatens the tourist activities in Petra city and forms a great danger on the monuments.
Some of these floods were of flows that exceeded the 298 m3/s threshold; these large amounts of rainwater are wasted annually without benefiting from it in the unbalanced equation of the water demand and supply.
Therefore, flood risk management gains high priority in the study area. As a result, flood risk management schemes conclude to a recommendation on how to mitigate the impacts and risks to reduce the vulnerability of the area to flooding, and increase its share to the water availability.
9
1.6 Objectives of the study
• Identifying the design storm characteristics by constructing the Intensity-
Duration-Frequency (IDF) curves for each of the rainfall stations that exist in the
study area using three procedures (Bell, 1969, Chen, 1983, and Hershfield,
1961). Then determining the most suitable procedure to obtain the IDF
parameters.
• Generating the rainfall distribution hyetographs for 6 storm durations (Time of
concentration (Tc) and 5 other possible shorter storm durations) using the
alternating block method.
• Calibrating the model and validating it for calculating and forecasting the peak
discharge at the watershed outlet, for 6 return periods (2, 5, 10, 25, 50 and 100)
years. Then obtaining the hydrographs using the Soil Conservation Service-
Curve Number (SCS-CN) method.
• Applying The Kinematic Wave Routing on the obtained hydrographs to predict
the changes in each one’s shape as water moves through the channels towards
the outlet.
• Computing the reservoir capacity to identify the required dams in the study area
and the storage capacity of each. This is by computing the runoff volumes from
the main Wadi’s in the study area, using storm by storm analysis and applying
the Mass Curve method.
• Providing recommendations on how to mitigate the impacts and risks to reduce
the vulnerability of the area to flooding and increase its share to the water
availability. Then estimating the efficiency of flood mitigation countermeasures.
10
CHAPTER TWO
2. LITERATURE REVIEW
2.1 Design Storm Characteristics
The design storm serves as the base of the assessment of hydrologic impacts on a specified area. The characteristics of a design storm are identified by defining the precipitation pattern corresponding to the hydrological system to be modeled, this pattern is defined by its (Intensity-Duration-Frequency) curves and its time distribution hyetographs.
A specified design storm duration is used in conjunction with an IDF curve of a given frequency to the maximum average intensity of the design storm hyetograph. Together, the selected intensity and duration are then "shaped" into a design storm hyetograph that conforms to the appearance of historical or synthetic hyetograph patterns, (Adams and
Howard, 1986).
The design storm event is then imported into a rainfall-runoff simulation model to analyze the performance of drainage system.
2.1.1 Intensity-Duration-Frequency (IDF) Curves
The intensity-duration-frequency (IDF) curves are functions that relate the rainfall intensity with the frequency of occurrence (Return periods) over a range of storm durations. It is a probabilistic tool that have proven useful in water resources management.
IDF curves provide accurate estimates of design rainfall in terms of its intensity, duration and frequency which are considered essential inputs in the hydrological designs, and the hydrological impact assessment (Shrethsa, et al. 2013). 11
Intensity-Duration-Frequency relationships are certainly not old fashioned. Even in this computer age they provide a lot of information on the rainfall and they can be used as a base for the determination of design storms (Roeck, 2001).
Most of practical applications for urban highway drainage area gives the depth of rainfall for durations of 30 minutes, 1, 2, 3, 6, 12 and 24 hours for frequencies of recurrence of 2, 5, 10, 25, 50 and 100 years (O'connor, 1979).
The commonly used return periods such as for United States Isohyet maps are (2, 5, 10,
25, 50 and 100) years. It is generally recommended that 2 to 100 years is the sufficient return period for soil and water conservation measures, construction of hydraulic structures, irrigation and drainage works (Faiers, et al., 1997).
The intensity of rainfall (I) is a measure of the amount of rain that falls over time. It is measured in the height of the water layer covering the ground in a period of time and is usually expressed in depth per unit time as:
P I = (2.1) Td where:
(P) = total amount of rainfall depth ]mm[
(Td) = duration of the period ]hrs.[
To derive an equation for calculating the rainfall intensity (I) for the region of interest, there are some required parameters to estimate, for establishing an equation to suit the calculation of rainfall intensity for a certain recurrence interval and specific storm period. These parameters depend mainly on the results obtained from the IDF curves. 12
In this study, Sherman Intensity Equation 2.2 is adopted and used: (Chow, et. al., 1988)
a I = c (2.2) (Td+b) where:
I = Intensity at specific duration (t) and return period (T) [mm/hr.]
a, b, c = Intensity parameters constants [Unit-less]
Td = Storm Duration [hr.]
This equation predicts the rainfall intensity for any return period with a given storm duration and calibrated parameters obtained from the IDF curves, which could be constructed using either of the following methods.
A. Bell Method
Bell (1969), proposed a generalized IDF formula using the rainfall depth for one- hour storm with a10-year recurrence interval (P110) as an index. He provided ratios of the 24-hr rainfall depth to different other durations for the same return period, these rainfall durations are (5-min, 10-min, 20-min, 30-min, 120-min, 180-min, 360-min, and
720-min) and the ratios are (0.13, 0.2, 0.28, 0.34, 0.44, 0.57, 0.63, 0.75 and 0.88) of the
24-hr rainfall depth respectively.
B. Chen method
Chen (1983), developed a generalized IDF formula for any location in the United
States using three base rainfall depths: 1-hour with 10- year return period (P110), 24- hours with 10-years return period (P2410) and 24-hours with 100-years return period
(P1100). 13
Chen's ratios are similar to Bell's (1969). But The rainfall intensity is calculated using Equation 2.3: (Chow, et. al., 1988)
10 (2−x) (x−1) T a1R1 log(10 T ) It = c (2.3) (t+b1) 1
Where:
T It = Intensity at specific duration (t) and return period (T) [mm/hr.]
10 R1 = Precipitation at 1-hr and 10-year period [mm] a1, b1, c1 = Chen's Parameters
T = Return Period [Year]
T = Time Duration [min]
a1, b1, c1 parameters in the Chen’s formula are estimated from Figure 2.1. 14
Figure 2.1: Chen’s Parameters estimation (Chen, 1983)
C. Hershfield Method
Hershfield (1961), studied the relationship between the maximum 1-hr rainfall depth and the other storm short durations. He provided consistent linear ratios for rainfall depths at different short durations to the 1-hr rainfall depth of the same return period. The ratios of the (5-min, 10-min, 20-min, 30-min, and 120-min) rainfall depths are (0.29, 0.45, 0.62, 0.79, and 1.25) of the 1-hr rainfall depth, respectively.
The statistical analysis of the maximum 24-hr rainfall records for the three above mentioned methods will depend on three different frequency methods: Gumbel, Log 15
Pearson III and Normal, whichever is best fitted to the data. To check the goodness of the fit, there are various criteria that could be employed to evaluate the suitability of a probability distribution for describing a set of data. The following goodness-of-fit tests are applied to the rainfall records corresponding to this study for the selection of the best fit probability distribution.
A. The Kolmogorov-Smirnov
This is a nonparametric test of the equality of continuous, one-
dimensional probability distributions that can be used to compare a sample with
a reference probability distribution (one-sample K–S test), or to compare two
samples (Kolmogorov and Smirnov, 1948).
B. The Anderson-Darling
This is a statistical test of whether a given sample of data is drawn from a
given probability distribution. In its basic form, the test assumes that there are no
parameters to be estimated in the distribution being tested, in which case the test
and its set of critical values is distribution-free. (Anderson and Darling, 1952)
C. The Chi-Squared
This is a statistical test where the null hypothesis in the distribution of the
sample is true. It is used to reveal the existence of significant difference
between the expected frequencies and the observed frequencies in one or more
categories.(Cochran and William, 1952).
The tests are applied on the maximum 24-hr rainfall data. The best fit probability distribution was the distribution having the minimum sum of ranking residuals for this particular rainfall data set.
The laws of probability underline any study of the statistical nature of repeated observations or trials. Most hydrologic variables are assumed to be random processes. 16
The common continuous distributions are used to fit historical sequences (Viessman, et. al., 1989). The hydrological processes are governed by the laws of chance, which means that the use of the laws of probability in the hydrological analysis is a must. This is to be able to obtain the best mathematical description of the hydrological processes.
In this study, three probability distributions are tested through the previously mentioned statistical tests to define the best fit distribution, the distributions are as follows:
1. Gumbel Max frequency Distribution
The Gumbel Max frequency Distribution is used to model the distribution of the maximum (or the minimum) of a number of samples of various distributions.
2. Lognormal frequency distribution
The lognormal frequency distribution is a continuous probability distribution of a random variable whose logarithm is normally distributed.
3. Log Pearson type III frequency distribution
The Log-Pearson Type III distribution is a statistical technique for fitting frequency distribution data to predict the design storm rainfall depth at a specified area.
This technique is the standard technique used by Federal Agencies in the United States.
Once the statistical parameters are calculated for the available rainfall records, a frequency distribution can be constructed. Where (XT) corresponding to any return period (T) is calculated through The Log-Pearson Type III distribution using the general equation of the Log Pearson type III frequency distribution, Equation 2.4. (Cowan,
1998)
X = X̅ + (KT × S ) (2.4)
Where:
X = the rainfall depth value of some specified probability
X̅ = the average of the rainfall values 17
S = the standard deviation of the rainfall values sample.
KT= frequency factor
The frequency factor KT is a function of the skewness coefficient and the return period, it is obtained from the frequency factor table shown in Appendix H., (i.e. Table 2.1 illustrates some of these values).
Table 2.1 Frequency Factors K for Log-Pearson Type III Distribution, (Viessman, et. al., 1989) Recurrence Interval In Years
2 5 10 25 50 100 200
SKEW Percent Chance COEFFICIENT
Cs 50 20 10 4 2 1 0.5 3 -0.396 0.42 1.18 2.278 3.152 4.051 4.97 2.9 -0.39 0.44 1.195 2.277 3.134 4.013 4.904 2.8 -0.384 0.46 1.21 2.275 3.114 3.973 4.847 2.7 -0.376 0.479 1.224 2.272 3.093 3.932 4.783 2.6 -0.368 0.499 1.238 2.267 3.071 3.889 4.718 2.5 -0.36 0.518 1.25 2.262 3.048 3.845 4.652 2.4 -0.351 0.537 1.262 2.256 3.023 3.8 4.584 2.3 -0.341 0.555 1.274 2.248 2.997 3.753 4.515 2.2 -0.33 0.574 1.284 2.24 2.97 3.705 4.444 2.1 -0.319 0.592 1.294 2.23 2.942 3.656 4.372 2 -0.307 0.609 1.302 2.219 2.912 3.605 4.298 1.9 -0.294 0.627 1.31 2.207 2.881 3.553 4.223 1.8 -0.282 0.643 1.318 2.193 2.848 3.499 4.147 1.7 -0.268 0.66 1.324 2.179 2.815 3.444 4.069 1.6 -0.254 0.675 1.329 2.163 2.78 3.388 3.99 1.5 -0.24 0.69 1.333 2.146 2.743 3.33 3.91 1.4 -0.225 0.705 1.337 2.128 2.706 3.271 3.828 1.3 -0.21 0.719 1.339 2.108 2.666 3.211 3.745 1.2 -0.195 0.732 1.34 2.087 2.626 3.149 3.661
2.1.2 Rainfall Distribution Hyetographs
The Rainfall - time distribution hyetographs of the design storms are highly needed to run the hydrological model. These distributions are generally categorized as:
I. Uniform distribution 18
Uniform distributions, in a reasonable manner, assumes rainfall is constant over the duration of the event and is generally used with the rational method, it is Unsuitable for use in hydrograph modeling.
II. Nested distribution
Nested intensity distribution hyetographs contain the desired intensity for any given duration within the storm.
In this study, The Alternating Block method -which is a nested intensity rainfall distribution method- was adopted and used to develop graphical charts (hyetographs) of storm rainfall distribution over time depending on the IDF curves data. This method derives a dimensionless rainfall-time distribution from observed storms based on the
SCS procedure of re-arranging the storm pattern so that the greatest depth values occurs at the middle of the total duration. (El-Sayed, 2017).
In this method, dimensionless hyetographs of all storms for the short periods of different return periods should be created. Each storm hyetograph is expressed as cumulative percentages of storm rainfall depth and storm duration and then used as an input S-
Curve series in the HEC-1 model for each corresponding simulation, an example of the
S-curve input series is shown in Figure 2.2. 19
Figure 2.2: 24 –hr. Input S-curve series of a rainfall distribution, (WMS 10.1, 2016)
Advantages and disadvantages of the alternating block method (McKinney, 2007):
• Advantages
A. It provides a special more accurate rainfall distribution derived from local data.
B. It is Applicable to multiple durations.
• Disadvantages
A. The naturally occurring storms do not occur in the proposed re-arranged manner.
B. It yields relatively high peak discharges.
2.2 Watershed Hydrological Characteristics
Watershed hydrological characteristics are factors affecting the hydrological design, the hydrological analysis of a watershed is done through studying the hydrologic reaction of the watershed in relation to rainfall and the flow pattern during floods. 20
2.2.1 Time of Concentration and Lag Time
Time of concertation is generally defined as the time required for a drop of water to travel from the most hydrologically remote point in the watershed to the point of collection (Viessman, et. al., 1989).
The Lag time is the time from the center of mass of rainfall excess to hydrograph peak (NRCS, 2010).
Most basin lag equations include some measure of the basin slope and the length of flow along the main channel within the basin. Some equations also include factors that account for differences in overland or channel flow such as a runoff coefficient,
SCS curve number (CN), or a channel roughness factor. Some equations account for storm characteristics (usually rainfall intensity), but basin lag time is primarily associated with basin characteristics rather than storm characteristics (Sauer, et al.,
1983).
The general form of the lag time and the relation between the lag time and the time of concentration for a given watershed are illustrated in Equation 2.4 and Equation 2.5, respectively (Chow, et. al., 1988).
L×L m T = C × ( ca) (2.4) L t S0.5
TL = 0.6 × Tc (2.5)
Where,
Ct = Watershed slope and storage effect constant. It depends on the studied region
characteristics, such as: the basin slope and the length of flow along the main
channel within the basin.
L = maximum flow length along main channel from point of reference to upstream
watershed boundary. 21
Lca = distance along main channel from point of reference to a point opposite the
centroid.
S = slope of the maximum flow distance path.
m = lag exponent.
There are many different ways to calculate Tc. The Watershed Modeling System
(WMS) provides a number of Tc calculation procedures according to the Peak
Discharge estimation methods. The SCS method was adopted and used through this study. The geometric parameters computed from the Triangulated Irregular Network
(TIN) or the Digital Elevation Model (DEM), are used in empirical equations developed for either time of concentration or lag time.
2.2.2 Watershed Delineation
A watershed is an area of land where surface water converges. It is defined by the area over which any precipitation will eventually contribute water to the watershed outlet.
Watershed delineation is dividing the watershed into discrete land and channel segments to analyze watershed behavior by determining its boundaries using basic topographic maps and applying the definition of a watershed.
2.2.3 Average Rainfall Interpolation
In order to better understand the behavior of a hydrological system, it is important to use accurate predictions of a real average rainfall over the watershed.
The interpolation of rainfall is used to predict the spatial distribution of rainfall. 22
The most frequently used deterministic spatial interpolation methods for measuring rainfall are the Thiessen polygon, Arithmetic Mean, Isohyetal and Inverse Distance
Weighting (Sarann, et.al, 2017).
In this study, the Thiessen polygon interpolation method is applied to assign weight at each gauge station in proportion to the watershed area. This is to achieve accurate estimation of the spatial distribution of rainfall. Any location inside a polygon is closer to the rain gauge at it’s center than to any other rain gauge, and by this assuming that each polygon had the rainfall recorded by the central rain gauge over the entire polygon. The proportion of the drainage basin each polygon takes up is determined Since each polygon has a different size.
The weighted average is calculated by Equation 2.6 (Viessman, et. al., 1989):
n ̅ ∑i=1 PiAi P = n (2.6) ∑i=1 Ai
Where:
P̅ = Average Precipitation [mm]
P = Precipitation [mm]
A = Area [km2]
A TIN or DEM terrain model can be used in WMS to delineate stream networks and drainage basin boundaries. Since the terrain model is an accurate geometric description of the watershed, parameters such as areas, slopes, and flow distances can automatically be computed by WMS (AQUAVEO.COM, 2016).
23
2.3 Model Calibration
Most of the hydrologic models must be "calibrated" to be useful for the solution of practical problems. Calibration is an estimation of some parameter values for the model to enable it to closely match the behavior of the real system it represents (Duan, et.al,
2013). It is a test of a model with known input and output information that is used to adjust or estimate parameters for which data are not available.
There are Several contributions to the hydrological literature that have followed the same classical pattern for hydrologic model calibration.
The emergence of a new and more powerful model calibration paradigm must include recognition of the inherent multi objective nature of the problem and must explicitly recognize the role of model error (Gupta and Sooroshian, 1998).
Hydrological models can be calibrated using a small number of critical events equally well as using all data. Critical events can be identified with the help of their geometrical properties of the set of subsequent precipitation or discharge data (Singh and Bardossy, 2012)
There are two basic methods used for the calibration of hydrologic models:
1. Automated calibration
In this method various computer algorithms are used to achieve the best
simulated reproduction of observed values (Gupta et al., 2000).
2. Guided trial and error calibration
The knowledge of the model and the effect of each parameter on the results, is
used to control changes to parameter values. This is done primarily by
comparing the simulated results with the observed values, especially hydrograph
plots. This procedure is most effective when interactive, graphical software is
available to view the results and make parameter changes. The calibration is 24
finished when the user subjectively determines that the objectives have been
met.
The calibration is conducted through the WMS stochastic model through performing an analysis of observed data and future simulations. Certain parameters can be varied within a range of reasonable values in order to create a number of simulations that provide an efficient guided trial and error procedure. Limits are applied on the range over which parameters values can vary in order to obtain more physically realistic values of the parameters that yield the observed results. This is to compare the parameters and justify the existing difference between the simulated and the observed results, and thus validate the model to be used in the peak discharge forecasting.
The interface window of the stochastic model in WMS is shown in Figure 2.3. 25
Figure 2.3: Stochastic Run Parameters, (WMS 10.1, 2016)
2.4 Peak Discharge Estimation
Peak flow or peak discharge is the main parameter for hydraulic structure design.
It is the maximum rate of discharge during the period of runoff caused by a storm.
Accurate estimates of peak-flow magnitude for various frequencies (recurrence intervals) are necessary for effective structural design, planning purposes and flood management. Underestimates of peak-flow magnitudes may result in disruption of 26
service, costly maintenance, and loss of life, while overestimates may result in excessive construction costs (Sando, 1998).
Peak-flow frequencies at gaged sites are determined by fitting a probability distribution function to the series of annual peak flows (USACE, 1972).
All federal agencies and many State agencies and local consultants follow the procedures described in (USWRC, 1982) for determining the peak-flow magnitude and frequency relations for gaging stations.
The estimation of peak flow that is likely to be experienced in future is very important for the purpose of this study. It can be done using many methods such as synthetic hydrograph and Natural Resources Conservation Service (NRCS) methods.
In this study, The Soil Conservation Service – Curve Number (SCS-CN) synthetic hydrograph method is adopted and used, since it is one of the most popular methods for computing the runoff volume from a rainstorm. It is popular because it is simple, easy to understand and apply, stable, and accounts for most of the runoff producing watershed characteristics. (Mishra and Singh, 2013). It is also most suitable for watersheds larger than (100 km2).
2.4.1 (SCS-CN) Unit Hydrograph
The SCS-CN method was originally developed for its use on agricultural watersheds and has since been extended and applied to rural, forest and urban watersheds. Since the inception of the method, it has been applied to a wide range of environments. In recent years, the method has received much attention in the hydrologic literature. (Mishra and
Singh, 2003). The SCS unit hydrograph represents one unit of the excess rainfall occurring over the entire watershed. It does not represent either the total runoff volume or the design hydrograph. The unit hydrograph is simply used to translate the time 27
distribution of precipitation excess into a runoff hydrograph. The SCS unit hydrograph and its characteristics are shown in Figure 2.4.
Figure 2.4: SCS unit hydrograph (McCuen, 2004)
The unit hydrograph has 37.5% of its volume on the rising side and the remaining
62.5% of the volume on the recession side. The peak flow for one unit of rainfall excess is given by Equation 2.7. The SCS form of the lag time and the Time of concentration and their relations are illustrated in Equation 2.8 through Equation 2.11 (Chow, et. al.,
1988).
484×A ×Q qp = (2.7) Tp
T T = d + T (2.8) p 2 L
Td = 0.133 × TC (2.9)
L0.8×(S+1)0.7 T = (2.10) L 1900×Y0.5 28
TL = 0.6 × TC (2.11)
Where,
3 qp = Peak discharge [ft /s]
A = Area [mi2]
Q = Runoff depth [in]
Tp = Time to peak [hr]
Td = Duration of rain excess [hr]
TL = Lag time [hr]
TC = Time of concentration [hr]
L = Longest path [ft]
S = Maximum retention [in]
1000 S = − 10 [in] CN
Y = Slope [%]
In this study, a Hydrological model Hec-1 is used through WMS software to estimate the runoff peak discharge.
2.4.2 SCS Curve Number
The curve number is an empirical parameter used in hydrology for predicting direct runoff or infiltration from rainfall excess. Each sub-watershed has a Curve number (CN) to characterize the runoff properties for a particular soil and ground cover.
High CN values (such as 98 for pavement) cause most of the rainfall to appear as runoff, with minimal losses. Lower values (such as 58 for certain wooded areas), 29
correspond to an increased ability of the soil to retain rainfall, and will produce much less runoff (NEH, 2004).
The CN value is a primary input parameter for the SCS runoff equation and It is primarily based on the SCS hydrologic soil groups illustrated in Table 2.2. It also depends on land use codes which are an ordinance that combines the zoning and subdivision regulations into a single document.
Table 2.2: Characteristics of Soils Assigned to Soil Groups, (McCuen, 1998) Minimum Infiltration Group Characteristics of Soils Run off Potential rate (mm/hr) Deep sand; deep loess; A 0.76-1.37 Low Runoff Potential aggregated silts Moderately Low runoff B Shallow loess; sandy loam 0.38-0.76 Potential Clay loams; shallow sandy loam; Moderately height C soils low in organic content; soils 0.127-0.38 runoff Potential usually high in clay Soils that swell significantly when D wet; heavy plastic clays; certain 0-0.127 High Runoff Potential saline soils
As different soil types and land covers was found in the same watershed, the weighted CN for each watershed should be computed using Equation 2.12 (Chow, et. al., 1988).
∑(퐶푁 푥 퐴 ) 퐶푁 = 푖 푖 (2.12) ∑ 퐴푖
Where,
퐶푁 = Weighted curve number
퐶푁푖 = Curve Number for Land Cover Type 30
퐴푖 = Area with Curve Number CNi
It is important to note that the SCS developed three conditions of soil moisture that has a large effect on runoff volume and rate that is reflected through the CN values.
These three AMC classifications according to (NRCS, 1972) are:
• AMC I - dry, but above wilting point.
• AMC II - average (normal).
• AMC III - wet (saturated soil).
The appropriate moisture group AMC I, AMC II and AMC III is based on a five-day antecedent rainfall amount and season category (dormant and growing seasons) as shown in Table 2.3 (Silveira et al., 2000), (NEH-4, 1964).
Table 2.3: Seasonal rainfall limits for AMC, (NEH-4, 1964) Total 5-day antecedent rainfall (mm) AMC group Dormant Season Growing Season I Less than 13 Less than 36 II 13 to 28 36 to 53 III More than 28 More than 53
The classification into the antecedent moisture classes AMC I, AMC II and AMC
III, representing dry, average and wet conditions, is an essential matter for the application of the SCS curve number, as illustrated above.
The runoff curve number (CN) values are for condition II, and must be adjusted for the other two conditions using Equation 2.13 and Equation 2.14 (NRCS, 1972).
4.2×CNAMC II CNAMC I = (2.13) 10+0.058×CNAMC II
23×CNAMC II CNAMC III = (2.14) 10+0.13×CNAMC II
31
A list of the CN numbers for average AMC II provided by the Natural Resources
Conservation Service (NRCS, 1972), is shown in Appendix H.
2.5 Channel Routing
Routing is a technique used to predict the changes in the shape of a hydrograph as water moves through a channel or a reservoir to be able to determine the flow hydrograph at a point on a watershed from a known hydrograph upstream through a transfer function as illustrated in Figure 2.5.
Figure 2.5: Hydrograph Routing through a Transfer function (NPTEL, 2012)
As flood wave travels downstream, it undergoes:
• Attenuation - the peak of the outflow hydrograph is smaller than of the inflow
hydrograph. this reduction in peak value is called attenuation.
• Translation (Time lag) - The peak of the outflow occurs after the peak of the
inflow, the time difference between the two peaks is known as lag.
These two important aspects of the flood routing analysis are illustrated in Figure
2.6 32
Figure 2.6: Attenuation and Translation in a routed hydrograph, (NPTEL, 2012)
Routing techniques can be broadly classified as:
A. Lumped / Hydrologic routing:
Flow is calculated as a function of time alone at a particular location.
Hydrologic routing methods employ essentially the equation of continuity and flow/storage relationship
B. Distributed / Hydraulic routing:
Flow is calculated as a function of space and time throughout the system.
Hydraulic methods use continuity and momentum equation along with the equation of motion of unsteady flow.
In general, based on the available field data and goals of the project, one of the routing procedures is selected, where both of these methods apply some form of the continuity equation, the general form of continuity is illustrated in Equation 2.15 (Thompson, 33
dS 1999). = Σ Inflow − Σ Outflow dT
(2.15)
In this study, the kinematic wave hydraulic routing method was adopted and used, since it provides a nonlinear measure without the need of any tedious or costly solution.
The kinematic wave approximation has been proven to be an accurate and efficient method of simulating storm water runoff from small basins for both overland flow and stream channel routing (Overton and Meadows, 1976).
Kinematic waves govern the flow when inertial and pressure forces are not important. Thus, in a kinematic wave, the gravity and frictional terms are balanced, so the flow does not accelerate appreciably. For these kinds of waves, the energy grade line is
Parallel to the channel bottom. The kinematic wave equation for collector and stream flow routing is shown in Equation 2.16 (Viessman, et. al., 1989).
q ̥ = α ̥y̥ m̥ (2.16)
Where,
q ̥ = flow per unit width ]m3/s[
y ̥ = flow depth ] mm[
α ̥ = kinematic wave parameter for a particular cross sectional shape, slope and roughness.
m̥ = kinematic wave parameter for a particular cross sectional shape, slope and roughness.
The values of α and m are different for each differently-shaped cross section and will vary with effective Manning’s "n" and channel slope as well. The differently shaped cross sections along with their corresponding kinematic wave parameters are illustrated 34
in Table 2.4.
Table 2.4: Kinematic wave parameters for different cross sectional shapes
Cross Sectional Α m Shape
Triangular
Rectangular
Circular 1.25
Trapezoidal
The trapezoidal section is usually the most appropriate choice for open channels (HEC,
1993).
The values of the surface roughness coefficients “n” marked as the effective resistance parameter for a channel flow are illustrated in Table 2.5.
Table 2.5: Roughness Coefficient (NEH, 2010)
Surface Description n Smooth surface (concrete, asphalt, gravel, or bare soil 0.011 Fallow (no residue) 0.04 Cultivated soils: Residue cover ≤ 20% 0.06 Residue cover > 20% 0.17 Grass: Short-grass prairie 0.15 Dense grasses 0.24 Bermuda grass 0.41 Range (natural) 0.13 Woods: Light underbrush 0.4 Dense underbrush 0.8
35
HEC-1 contains options for using the kinematic wave theory to compute sub basin outflow hydrographs and to route hydrographs through a stream reach (HEC, 1990).
In this study, the kinematic wave method of channel routing was simulated through the
HEC-1 model in WMS. The trapezoidal section is one of the two basic sections considered by HEC-1 model in WMS. When defining a trapezoidal section, it is important to define the side slopes z and the channel bottom width w accurately.
In HEC-1, the hydrographs are combined and routed downstream at the outlet points. However, routing data must be entered in order to simulate the movement of a flood wave through the channel reaches (AQUAVEO.COM, 2014).
The following channel routing elements are used for routing a hydrograph through a channel reach (USACE, 1993).
A. Channel or stream length (L)
B. Slope (S)
C. Manning’s (n)
D. Area of sub basin (A)
E. Channel shape (Trapezoidal or Circular)
F. Channel dimensions (Width (w), Side slopes (z), or Diameter (D))
G. The upstream hydrograph to be routed through the reach if desired.
The kinematic wave routing interface window in the HEC-1 model by WMS is shown in Figure 2.7. 36
Figure 2.7: Kinematic wave routing interface window (WMS 10.1, 2016)
2.6 Reservoir Capacity Estimation
A Reservoir is a storage space for water. The capacity of the reservoir is very important since the main function of the reservoir is the storage of the water. It is of crucial importance and is the major aspect of the reservoir hydrologic design and flood control.
During high flows, water flowing in a river has to be stored so that a uniform supply of water can be assured, for water resources utilization like irrigation, water supply and power Generation (Bharali, 2015).
The reservoir capacity is calculated by Predicting the runoff volumes from the main wadis in the study area using storm by storm analysis. This is to calculate the maximum and the average reservoir storage capacity at the outlet of the watershed. The flow volume is one of the major hydrological design parameters, taking into consideration the 5-day cumulative runoff depth, the initial abstraction, the maximum 37
retention, the curve number and the antecedent moisture content AMC, as important factors to suggest best water conservation techniques.
In this study, the reservoir capacity is estimated using the Mass Curve Method
(Ripple Curve). It is One of the most widely used methods, which is a plot of cumulative volume of water that can be stored from a stream flow versus time in days, weeks or months. The mass curve of supply (i.e. Accumulated Inflow) is first drawn and then superimposed by the demand line, as illustrated in Figure 2.8.
Figure 2.8: Ripple Diagram, 1883 (Raghunath, 2006) The sum of the two maximum ordinates between supply and demand line is known as the reservoir capacity.
The procedure to construct such diagram is as follows:
➢ Calculating the Runoff depth using The SCS-CN method, which is illustrated in
Equation 2.17 and Equation 2.18 (NRCS, 1972).
• If P > 0.2 S
(P−0.2S)2 Q = (2.17) P+0.8S 38
• If P < 0.2 S
Q = 0 (No runoff occurs) (2.18)
Where:
Q = Runoff [in]
P = Storm Rainfall [in]
CN = Curve Number [Unit-less]
S = Maximum Potential Retention [in]
S is the potential maximum soil moisture retention after runoff begins. It is related to the dimensionless parameter CN in the range of 0 ≤ CN ≤ 100 by Equation 2.19.
1000 S = − 10 (2.19) CN
➢ Calculating the monthly inflow for the critical years (maximum and average
runoff) using Equation 2.20 (Chow, et. al., 1988).
Inflow [MCM] = Catchment Area (km2) × Runoff (m) (2.20)
➢ Calculating the monthly demand which is the amount of water that is required
for a given landscaped area, using Equation 2.21 (Hadadin, 2012).
Demand [MCM] = Monthly Average of Water Supply (2.21) = Sum of water supply divided by 12
➢ Plotting the cumulative demand against time, and then plot the mass curve of
Inflow.
39
➢ Reading the required storage by either of the following:
1. Summing the positive differences between the inflow and the demand or
summing the negative differences.
2. Calculating the difference between the maximum and the minimum difference
between the cumulative inflow and the cumulative demand.
3. Measuring the scaled length of the perpendicular line between the -parallel to the
demand line- tangents to the maximum and the minimum points on the
cumulative inflow curve.
➢ Repeating the procedure for the critical years at the 3 areas (A0001, A0002 and
A0003) assigned to the 3 rainfall stations (DG0001, DG0002 and DG0003), then
determine the maximum and average storage capacity of the whole watershed.
The mass curve method is widely used for the analysis of reservoir capacity demand problems. This method is adopted and used throughout this study considering the evidence of providing convenient results through various studies. However, it is important to mention the defects of this method (Swain, 2016):
• The reservoir release is assumed to be constant (this is not accurate on monthly
basis, because the demand is often seasonal).
• It assumes that the future hydrology is the same as the hydrology of the region in
the past.
• It does not take the evaporation and other losses into consideration through the
analysis. 40
2.7 Hydrological Modeling Software
2.7.1 WMS 10.1 Software
The Watershed Modeling System (WMS) is a water modeling software application. It is used to develop watershed computer simulations. It is a complete all- in-one watershed solution. The software provides tools to provide automated watershed delineation, hydrologic modeling, floodplain mapping, Storm drain modeling and 2D
(Distributed) hydrology. The software supports a number of hydraulic and hydrologic models that can be used to create drainage basin simulations as HEC-1, HEC-RAS,
HEC-HMS, TR-20, TR-55, NSS, Rational, MODRAT, HSPF, GSSHA, and other models (Edsel, 2011).
In this study, a HEC-1 model is adopted and used. The HEC-1 model is a widely used model that is designed by the US Corps of Engineers Hydrologic Engineering
Center. It has been refined and updated over the years. It is designed to simulate the response of the surface runoff of a watershed resulting from the precipitation of a rainfall storm event. This is done by representing the basin as an interconnected system of hydrologic and hydraulic components (stream channels or reservoirs).
The modeling results in hydrographs at points of interest. A variety of methodologies are available to input and model rainfall, losses, runoff transformation and translation and diversion (HEC, 1990).
The HEC-1 Simulation window in (WMS 10.1, 2016) software is shown in Figure 2.9. 41
Figure 2.9: HEC-1 Interface (WMS 10.1, 2016)
2.7.2 EasyFit 5.6 Software
EasyFit allows its user to automatically or manually fit a large number of distributions to the data and select the best model in seconds. It supports all the commonly used continuous distributions Goodness of Fit Tests. The tests show how well the distribution you selected fits to your data. EasyFit supports a huge number of statistical goodness of fit tests including the following GOF tests:
• Kolmogorov-Smirnov
• Anderson-Darling
• Chi-Squared
The Summary report lists the distributions ordered by the GOF statistics, enabling you to select one or more models which best fit to your data (Mathwave.com, 2009). 42
The (EasyFit 5.6, 2010) Software Interface is shown in Figure 2.10.
Figure 2.10: EasyFit 5.6, 2010 Interface, (EasyFit 5.6 help, 2010).
2.7.3 ArcGIS (10.2.2) Software
The Geographic Information System (GIS) is a computer-based tool that allows you to create, manipulate, analyze, store and display information based on its location. ArcGIS for Desktop consists of several integrated applications, including ArcMap, ArcCatalog,
ArcToolbox, ArcScene, and ArcGlobe. ArcCatalog is the data management application, used to browse datasets and files on one's computer, database, or other sources.
ArcCatalog also provides the ability to view and manage metadata for spatial datasets
(Zeiders and Retrieved, 2008).
The (ArcGIS 2014) Software Interface is shown in Figure 2.11. 43
Figure 2.11: ArcGIS, 2014 Interface 44
2.8 Previous Studies
Al-Weshah and El-Khoury, (1999) developed and calibrated a flash flood analysis model for Petra City in Jordan. The flood flow and volume are estimated for storm events of various return periods. They proposed several investigation measures and evaluated their impact on flood peak flow and volume where they conclude that these measures can reduce peak flow and volume by up to 70% in critical sites in Petra like the Siq entrance. The four watershed management scenarios are afforestation of selected parts of the watershed, contour terracing and construction of check dams with afforestation, construction of storage/detention dams and combination of storage/detention dams and afforestation.
Alhasanat, (2014) assessed risks due to potential flash floods hazards at Wadi
Musa and determined the magnitude of flows for flash flood hazards and constructed floodplain zone maps for the selected flood return periods of 25, 50, 75 and 100 years.
The runoff analysis indicated that only rainfall events exceeding 22 mm within the 24- hour period would generate runoff.
.
Shatanawi, (2015) on her master thesis at the University of Jordan, Amman,
Jordan, studied the flash floods mitigation and risk management of wadis draining to the gulf of Aqaba. The study was initiated for characterizing the watersheds facing Aqaba coast. The Hydrologic Modeling System (HEC-HMS) and Watershed Modeling System
(WMS) software predicted the peak discharge fairly close but the difference was due to using different routing methods. The average peak of all methods was 65.8 m³/s. She also estimated the average volume of runoff was 155,000 m³ and 280,000 m³ for the 10- 45
yr and 100-yr return periods resulting from the 24-hour storm respectively and the time to peak was about 2 hrs. indicating the importance of an early warning system.
In Egypt, El- Sayed, (2017) developed synthetic rainfall distribution curves for
Sinai area, she derived a dimensionless rainfall-time distribution from observed storms.
She analyzed one hundred twenty-seven rainfall storms for Sinai area and categorized all of them into four groups according to the total duration, then she obtained an average hyetograph for each group, for the benefit of the developed design storm pattern for
Sinai area. She developed a hydrological model(Hec-1) through (WMS) software to estimate the runoff discharge and volume for each wadi. The research recommends using the developed curves to create storm hyetograph of any design storm for Sinai since the developed synthetic rainfall distribution curves were compared to the other distributions of SCS and the results show significant difference in computing runoff between the proposed distributions and the SCS distributions. SCS distributions gave peak discharge with about 17% lower than the proposed distributions.
Abdelrahim, (2017) studied the suitable areas for rainwater harvesting in southern region of Jordan. She used the daily rainfall records for the period from 1991 to 2015 for Ghor As-Safi, At Tafelah, and Bsera rainfall stations in the study area to develop
Intensity-Duration-Frequency (IDF) curves for the study area, she also estimated the peak of discharge and reservoir capacity. The time to peak was determined using SCS,
Snyder, Clark, and TR-55 methods through developing a Rainfall-Runoff (HEC-1) and
(HEC-HMS) models in (WMS). She marks obtaining a minor difference of (13%) when using the SCS method to calculate a drainage basin’s Time to peak, while obtaining a maximum difference of (57%) when using Snyder method. The research recommends 46
developing IDF curves for other stations in the southern regions of Jordan since (Bell,
Chen, and Hershfield) procedures were found suitable for developing the IDF curves for the stations in the study area.
Al-Hoot, (2006) studied the surface water modeling for Humret Es-sahin area using watershed modelling systems (WMS), the Rainfall-Runoff (HEC-1) model was utilized using Watershed Modeling System (WMS). The results showed that high variations between measured and predicted runoff volumes using the average
Antecedent Moisture Condition (AMC II) before calibration. Additionally, the study concluded that when using the SCS-CN method with rainfall-runoff (HEC-1) model for small watersheds, the model was good in prediction of the surface runoff volumes with storm by storm analysis and it could be used in order to suggest best conservation techniques.
Nazzal and Al-Salihi, (2008) studied the surface water hydrological characterization and modeling of Amman-Zarqa basin, where they found that an average curve number value is reached following the calibration exercise. They also found that both Watershed Modeling System (WMS) and Gridded Surface Subsurface
Hydrologic Analysis (GSSH) models are stable and produce comparable results.
Simulated direct runoff volumes are checked against the available observed daily runoff records for gauging station at new Jerash Bridge.
Alayyash and Nnadi, (2003) studied Wadi Salma catchment in order to collect field rainfall-runoff data. The data with the hydrologic database were used in the model to simulate the runoff and the results were compared to the observed field observation. 47
The results showed that the simulation model simulated the runoff volumes with acceptable estimation compared to the observed runoff. The total volumes of arid lands floods are the important parameter for water resources planning.
Hadadin, (2005) studied the relationship between the rainfall amount, duration, and frequency for Mujib basin in Jordan. Intensity-Duration-Frequency (IDF) equations were developed for eight rainfall-recording stations in the basin. The 8 IDF equations obtained were compared with the curved obtained by Gumbel method and Water
Authority of Jordan (WAJ), the results predicted were closer to the measured values.
Peak discharges were calculated for six locations in the basin; it was found that the peak discharge which gotten from Manning's equation was closed to the peak discharge that gotten from rational method at 25 years return period.
Al Zubi, et. al., (2010) used the United States Soil Conservation Services (SCS) method to estimate runoff depth and runoff volume for Wadi Muheiwir catchment area in the eastern parts of Jordan. The curve number was found equal to 80, taking in consideration the Antecedent Moisture Condition (AMC), the initial abstraction of rainfall, and land use. The calculations of the runoff volume for the study area were determined and statistically analyzed by applying Gumbel distribution. The results were estimated for 10, 25, 50, 100, and 200 years return period, and the long-term average runoff was 0.063 MCM (ranged between zero and 0.544 MCM).
Erturk et. al., (2006) studied the application of Watershed Modeling System
(WMS) for integrated management of a watershed in Turkey. The study was initiated aiming at using the Watershed Modeling System (WMS) version 7.1 for the delineation of boundaries of Koycegiz Lake-Dalyan Lagoon. The study has shown that WMS is 48
applicable to small scale drainages or moderate-sized watersheds, and to sub-watersheds at local or regional scale with steep slopes. Also, it is capable to visualize the results in establishing watershed management strategies.
In Egypt, Abdelkhalik, (2009) carried out hydrological analysis of wadi Waties near Nuweiba city and developed a custom- built rainfall –runoff hydrological model that reflect the hyper arid conditions of the region. Also, he used flash flood manager as early warning system which can provide flash flood warning two days in advance.
Al-Qudah, (2001) studied the floods of southern Jordan where he found that floods are associated with certain synoptic climatic conditions that are mainly influenced by the red sea trough. He also found that geomorphologic factors significantly influence flood generation. He used River Analysis System (HECRAS) to estimate the large flood of February 2006 of found it to be 320 m3/s.
Al-Abed and Al-Sharif, (2008) studied the Zarqa River basin, they calibrated the hydrological component of the Hydrological Simulation Program – FORTRAN (HSPF) model for the Zarqa River Basin. The calibration of the HSPF water quantity parameters was aided by GIS and by the automatic calibration model (PEST). The automatic calibration was done for the years 1988–1991 and the validation was done for the years
1996–1998. The coefficient of determination, R2 for the calibration and verification years of the monthly flows was 0.81 and 0.76, respectively.
Albek, et.al., (2004) developed an HSPF and HEC-1 model for the Seydi Suyu
Watershed in Turkey for the 1991–1994 years. The model was calibrated using the 49
water years 1991–1993, and validated with data from water year 1994. The response of the watershed to various scenarios was determined and compared to base simulations conducted for the 1991–1994 water years. In the scenarios, the effects of an increase in temperature due to climate change, and the effects of maximum and minimum watershed cover modifications were examined. The results indicate that an annual mean temperature increase of 3 Celsius degrees due to climate change will decrease the watershed outflows by 21%. The existence of the deep-rooted vegetation covering the whole of the watershed is observed to cause a decrease in the total stream outflow by
37% compared to base model results. In the case of no deep-rooted vegetation the total stream outflow increases by 40% compared to the current vegetation distribution.
Awawdeh, et. al., (2012) study aimed to evaluate the potential for potable and non-potable water savings by using rainwater at Yarmouk University and to provide recommendations for increasing water efficiency use to minimize water waste and reduce the water bill. Results showed that a maximum of 99,000 m3/year of rainwater can be collected, 37,000 m3/year of it from roofs of buildings and 62,000 m3/year from open impervious areas, provided that all surfaces are used and all runoff from the surfaces are collected. The estimated potential for potable water savings is 125 to 145% of the total domestic water supply. Chemical and biological analysis of harvested water indicated the requirement of water treatment for nitrate and pathogenic organisms.
50
CHAPTER THREE
3. METHODOLOGY
The general methodology considered in this study was to:
• Delineate the basin then divide it to twenty-nine watersheds.
• Construct the IDF curves using the three methods (Bell, Chen, and Hershfield).
• Generate the time distribution hyetographs using the alternating block method.
• Calibrate the model and validate it for peak discharge forecasting.
• Calculate the routed Peak Discharge for the entire watershed using the (SCS-
CN) Method and the Kinematic wave routing method.
• Estimate the maximum and average reservoirs capacity using the Mass Curve
method.
• Mitigate the flood risks at the study area.
3.1 Design Storm Characteristics
3.1.1 Intensity-Duration-Frequency (IDF) Curves
The three methods for constructing the IDF Curves are: Bell method, Chen method and Hershfield method. These methods are explained below:
A. Bell method
The main steps of the Bell method are:
• Obtaining the daily precipitation records for the three rainfall stations in the
study area.
• Determining the maximum 24-hrs. rainfall for each year for the three rainfall
stations.
• Determining the rainfall for the storm durations (5, 10, 20, 30, 60, 120, 180, 360,
720, 1440) min using Bell ratios. 51
• Applying statistical analysis on the 24-hr rainfall data for the three rainfall
stations using different probability distributions, such as; Normal distribution,
Gumbel Max and Log Pearson III.
• Recognizing the best fit distribution using the three tests (Kolmogorov-Smirnov,
Anderson-Darling and Chi-Square).
• Calculating the rainfall quantiles (mm) for (2, 5, 10, 25, 50, 100) year return
periods for each storm duration for the three rainfall stations.
• Calculating the rainfall intensities (mm/hr.).
• Plotting the IDF Curves on log-log papers.
B. Chen method
Chen procedure has the same steps as Bell method. The only difference is how to
estimate the rainfall intensities. Rainfall intensities (mm/hr.) are estimated by
applying the previously explained Chen’s formula in Equation 2.3.
C. Hershfield method
Hershfield method has the same steps as Bell method. The only difference is
that Hershfield ratios for storm durations are different from those of Bell’s as
illustrated in Section 2.1.1 of this document.
The parameters a, b, and c of Sherman Intensity Equation 2.1 for the IDF curves are estimated by using nonlinear regression analysis - The Solver function in (Microsoft
Excel, 2016) - which employs an iterative least squares fitting routine to produce the optimal goodness of fit between data and function. The IDF curves relationships are then constructed for the three stations in study area.
The best method of IDF curves estimation is the method that yields the least percentage difference from the IDF curves generated by the MWI for the study area, and it is recommended for future analysis. 52
3.1.2 Development of Rainfall Distribution Hyetographs
The Alternating Block method is used to build a graphical chart of rainfall distribution over time. The rainfall distribution hyetographs are generated using the following steps:
• Obtaining the Sherman intensity equation parameters for the three rainfall
stations and for each return period using the three methods (Bell, Chen and
Hershfield).
• Identifying the best IDF estimation method to use the corresponding (a, b and c)
parameters. Then generate the intensity equation as illustrated in (Section 3.1.3)
of this document.
• Dividing each of the total time of concentration and the short periods into 30
ordinates intervals (Δt, 2Δt, 3Δt…etc.)
• Calculating the rainfall intensity for each of the divided ordinates using the
generated intensity equation for all of the return periods and for the three rainfall
stations.
• Calculating the rainfall amount P (mm) = Intensity * Duration: P = i t for
each of the divided ordinates of the short periods for all of the return periods
and the three rainfall stations.
• Calculating the incremental precipitation ΔP for each of the divided ordinates of
the short periods for all of the return periods and the three rainfall stations,
using the relation: P = I 2 2 t − P1
• Rearranging each storm so that the greatest incremental depth value is set at the
middle of the storm duration (The center of the hyetograph). The lower values
are set on both right and left sides alternatively. 53
• Obtaining the average rainfall distribution hyetograph of each short duration
from the six return periods (2, 5, 10, 25, 50 and 100) years, and for the three
rainfall stations (Wadi, Hai and Petra).
• Calculating the cumulative S-curve ordinates by dividing each incremental
precipitation by the summation of the precipitation. This is done for all of the
divided duration ordinates to obtain 18 S-curves in addition to their average for
each of the seven short durations.
• Transferring the hyetograph entries of the six storm durations into WMS as an
S-Curve series input data.
3.2 Watershed Delineation
The watershed is delineated into sub-watersheds using (WMS 10.1,2016) software. Where, after identifying the watershed outlets, the “Delineate
Watershed” feature is used to delineate the watershed as shown in Figure 3.1
(AQUAVEO.COM, 2014). 54
Figure 3.1: Modeling Wizard (WMS 10.1,2016)
• Hydrological data were collected from the three rainfall stations. The
coordinates of each station are imposed on the study area map using (WMS 10.1
2016) Software.
• Thiessen polygons were constructed after which the effective rainfall stations
were selected, and the weighted ratio for each was calculated from the
constructed Thiessen polygons.
3.3 Model Calibration
A runoff recording station “DG0004”, is located at the Wadi Musa area. The model
was calibrated for its CN values at the watersheds upstream of the Wadi Musa
drainage basin (Jabal Al-Zubaira and Al-Hai watersheds). This is done through the
following steps:
• Obtaining the observed peak discharge and volume for different available storms
at the runoff station DG0004. 55
• Identifying the area corresponding to DG0004 station, which will only be
representing the observed data and thus calibrated.
• Generating the model for the identified area to be calibrated with the same
DEM, TIN, Soil and Land-use layers as those of the total study area.
• Automatically computing the CN (AMC II) values for the calibrated area from
the GIS attributes calculator in WMS. Modify the CN if needed for the
corresponding AMC.
• Defining the precipitation of the rainfall stations contributing to the model as the
precipitation from the available observed storms at DG0004.
• Running the HEC-1 simulation in WMS for the model and obtaining the routed
hydrograph for the calibrated drainage area.
• Reading the simulated routed peak discharge and the volume at the calibrated
drainage area outlet from the obtained hydrograph. Compare it with the observed
peak discharge and volume at DG0004.
• Applying a trial and error CN value calibration for the model. This is done by
applying a stochastic run simulation by which you define realistic limits on the
range of the CN value for the model.
• Obtaining the exact CN values that yield the observed peak discharge and
volume for the available recorded storms.
• Comparing the calculated CN values with the simulated CN values for the
observed results of the available recorded storms. Then validating the model
based on the results.
3.4 Peak Discharge Estimation
The peak discharge is estimated using the following procedure: 56
• Automatically delineate the watershed into sub-watersheds using the digital
terrain data by WMS.
• Applying the Thiessen Polygon feature by WMS to assign the contribution
weight of each rainfall station for the total watershed area, or assign weighted
ratios from each rainfall station for each sub-basin.
• Calculating the time of concentration by the SCS method simulated on WMS for
the sub-watersheds and for the total watershed area.
• Determining the rainfall depth and the rainfall intensity for (2, 5, 10, 25, 50, and
100) yrs. design period. This is done for each station at six different storm
durations. as illustrated in the IDF curves construction section of this document.
• Generating the rainfall distribution hyetographs for each different short duration
using the alternating block method as illustrated in the rainfall distribution
hyetographs section of this document.
• Running the model 36 times, each with its unique rainfall depth for the specified
return period and storm duration assigned to the three rainfall stations. Also with
the rainfall distribution S-Curve for the given storm duration. Then obtaining the
hydrograph for each sub-watershed and for the total watershed at its outlet.
• Reading the total peak discharge and the total volume values at the watershed
outlet from the hydrographs of the 36 simulations. This is to obtain a simulated
pattern of the peak discharge at the study area for different return periods and
different short durations.
• Plotting the Peak discharge values against the short durations for each return
period to obtain the Runoff-Duration-Frequency (RDF) curves for the study
area.
57
3.5 Channel Routing
After applying the very first six steps illustrated in the peak discharge estimation,
Section 3.5 of this document, and before running the model, the following procedure should be followed. This is to obtain the routed peak discharge through the channels in the model:
• Identifying the reaches to be routed.
• Estimating each reach channel dimensions. This is done by activating only the
drainage coverage and taking several cross sections by the Arc feature in the
channels map module. Then edit the DEM elevations to obtain the average
channel width (w), and the average channel side slopes (z).
• Determining the channel’s (n) based on the average channel’s conditions using
Table 2.5 in Section 2.5 of this document.
• Using the Kinematic wave routing feature in WMS from the routing data of each
outlet in the drainage coverage Tree.
• Identifying the channel length (L) automatically by the channel routing feature
in WMS or it could be scaled from a drainage map of the basin.
• Identifying the channel slope (S) automatically by the channel routing feature in
WMS or it could be estimated using the topographic maps.
• Identifying the channel shape (Trapezoidal, Circular or Deep). The chosen shape
should provide the best fit to the channel shape for the given flow.
• Running the HEC-1 simulation on WMS 36 times. Then obtaining the routed
hydrograph for the total watershed at its outlet.
• Reading the total routed peak discharge at the watershed outlet from the
hydrographs of the 36 simulations. This is to obtain a simulated pattern of the 58
routed peak discharge at the study area for different return periods and different
storm durations.
• Plotting the routed peak discharge values against the short durations for each
return period to obtain the Routed-Flow-Duration-Frequency (RFDF) curves for
the study area.
3.6 Reservoir Capacity Estimation
The maximum and the average reservoir capacity were computed using the Mass
Curve method. This is illustrated through the following steps:
• Calculating the weighted curve number for the watershed using SCS-CN method
through the WMS Software.
• Recognizing the appropriate moisture group AMC, I, AMC II and AMC III. It is
based on the calculated five-day accumulative rainfall amount and season
category (dormant and growing seasons).
• Adjusting the CN values according to the identified AMC.
• Calculating the maximum retention (S).
• Identifying the effective rainfall (P) from the daily rainfall records of the three
rainfall stations, at which the amount of rainfall exceeds the value of (0.2 S).
• Calculating the effective runoff (Pe) for all the storms and for the three rainfall
stations. This is done using the effective rainfall and the modified maximum
retention according to the adjusted CN to the appropriate AMC.
• Obtaining the monthly and the yearly cumulative runoff to identify the critical
years for each of the three rainfall stations (Maximum and Average yearly
cumulative runoff).
• Calculating the inflow volume for each month of the critical years. This is done
by multiplying the monthly cumulative runoff for each of the stations and for the 59
specified critical years with the area assigned to each station by the Thiessen
polygons.
• Calculating the cumulative inflow volume for the specified critical year.
• Calculating the monthly demand for the 12 months and then calculating the
cumulative demand.
• Calculating the difference between the inflow (Supply) and the demand for each
month.
• Plotting the cumulative demand line and the cumulative inflow curve against
time.
• Calculating the reservoir capacity for the maximum and the average storm for
each of the three rainfall stations. Then obtaining the total maximum and
average reservoir capacity for the whole watershed.
CHAPTER FOUR
4. DATA ANALYSIS AND MODELLING
Watershed Characteristics are the factors affecting the hydrological design. Thus, it is necessary to obtain these factors for the specified study area, because they differ from region to region. Some of the important factors are the size, slope, and land use of the watershed, etc…
In this study, some of the watershed characteristics were obtained from official governmental bodies in Jordan. The other characteristics were analyzed and obtained during the hydrological modeling. 60
The sources from which each of the watershed characteristics was obtained are illustrated in Table 4.1.
Table 4.1: Watershed Characteristics sources Watershed Characteristics Sources
Stations and rainfall data Ministry of water and Irrigation (MWI) Digital elevation model Ministry of water and Irrigation (MWI) Existing soil types Ministry of Agriculture (MoA) Slope Derived from the DEM Land use/Land cover Royal Jordanian Geographic Center (RJGC) Roads Ministry of Public Works and Housing (MOPWH) Obtained from the analysis of the DEM and the hydro Drainage area lines in the hydrological model of the watershed Obtained from the analysis of the hydro lines in the Watershed length hydrological model of the watershed Curve number Calculated through WMS Time of concentration Calculated through WMS
4.1 Rainfall Data (Rainfall stations)
There are three rainfall gauging stations at the Wadi Musa watershed. These stations designated DG0001, DG0002, and DG0003 are operated by the Ministry of
Water and Irrigation (MWI) . Annual total rainfall records are available for these stations since 1937 (Al-Weshah and El-Khoury, 1999).
A runoff station DG0004 located in the Wadi Musa area was installed in 1984. It is operated by the Meteorological Department of Jordan (JMD).
The rainfall data of the three rainfall stations in the study area were obtained from
(MWI). These three rainfall stations are Wadi Musa, Hai, and Petra. 61
The 24-hour maximum rainfall values in (mm) of the years (1937 – 2017) for Wadi
Musa, Hai, and Petra stations are represented in Appendix A.1.
The locations of the rainfall stations in the Study area are shown in Figure 4.1.
Figure 4.1: Distributions of the rainfall stations in the Study Area (MWI, 2015)
The rainfall stations and their locations in the Study Area are presented in Table 4.2.
Table 4.2: Locations of the Meteorological Climate Stations in the Study Area Station Station Station Coordinates Average type name ID North East Elevation rainfall (mm) Wadi 165.13 DG0001 1000000 488510 1100 Musa Rainfall Hai DG0002 198550 247025 1500 157.8 Petra DG0003 154930 188855 1000 128.42
4.2 Digital Elevation Model (DEM) and Triangulated Irregular Network (TIN)
The Digital Elevation Model (DEM) is a digital representation used widely for topographic maps. It is based on a grid file of elevation and it can be derived into 62
topographic contours to compute elevation slope and aspect. For Wadi Musa watershed, the Digital Elevation Model (DEM) is obtained from the Ministry of Water and
Irrigation of Jordan (MWI). The datum of the data is Jordan JTM (Jordan Transverse
Mercator), and The DEM has 30 m resolution.
The projection and the datum of the data are displayed in Figure 4.2.
Figure 4.2: The projection and the datum of the data The Digital Elevation Model (DEM) of the study area is shown in Figure 4.3. 63
Figure 4.3: Digital Elevation Model of the study area (DEM) (MWI, 2015)
The Triangulated Irregular Network (TIN) of the study area is shown in Figure 4.4.
Figure 4.4: Triangulated Irregular Network of the study area (TIN) (MWI, 2015) 64
4.3 Existing Soil Types
About 4000 soil types were identified and classified based on the potential of runoff into four hydrologic soil group A, B, C, and D by the U.S. Soil Conservation
Service (SCS) (McCuen, 1998). Soil groups characteristics are discussed in Table 2.2 in
Section 2.4.2 of this document.
The soil map of the study area is obtained from the Ministry of Agriculture of
Jordan (MoA). The existing soil types in the study area are illustrated in Figure 4.5. The hydrological soil groups dominating in the study area are illustrated in Figure 4.6.
Figure 4.5: Soil types map of the study watershed (MoA, 2015)
65
Figure 4.6: Soil groups map of the study watershed
4.4 Slope
The slope is the rate of elevation change with respect to distance along main course path. It is an important factor of the momentum of runoff which has a reflection on flood magnitude. The slope is derived from the DEM. The slope percentage variation in the study area is illustrated in Figure 4.7.
4.5 Land Cover / Land Use
Land Cover is the material covering the land surface such as; asphalt, grass, clay, etc.., each has different infiltration rate or surface storage. Consequently, land cover affects the runoff volume, the runoff timing, and the maximum flood flow rates.
The Land Cover / Land Use map of the study area was obtained from the Royal
Jordanian Geographic Center (RJGC). It describes the type of vegetation that covers the study area and uses of lands as shown in Figure 4.8.
66
Figure 4.7: Slope percentage in the study area
Figure 4.8: Land cover / Land use in the study area (RJGC, 2015).
67
4.6 Drainage Area
The drainage area is the land area where precipitation falls off into creeks, streams, rivers, lakes, and reservoirs. It is a land feature that can be identified by tracing a line along the highest elevation between two areas on a map, it can be estimated manually or automatically. The Drainage area is important to determine the curve number of the watershed and to be able to compute the volume generated from the rainfall. Therefore, the drainage area is an input for the hydrological model to estimate the peak flow and the volume of the runoff. In this study. The Drainage area is 139.11 km2.
The drainage system in the study area is shown in Figure 4.9
4.7 Watershed Length
Watershed length is the distance between the watershed outlets along the main channel to the divide point, it increases as area increase. It is important to estimate the time of concentration. The longest path at The study area watershed is 22.1 km and is shown in Figure 4.10.
Figure 4.10: The watershed with the main channel. 68
Figure 4.9: Drainage system at the watershed
69
4.8 Curve Number
The SCS runoff curve number (CN) is an index for the combination of a hydrologic soil group, and land use. It is calculated in WMS after delineating the watershed. This is done by importing the soil classes into ArcGIS and then convert it to a shapefile format. The shapefile and its CN attributes are imported into WMS, after which a new Land-use coverage is created to convert the CN shapefile to the land-use coverage. The CN attributes are mapped as the land use ID. The Curve number for each land use ID and soil type that matches the ID are defined. Then the SCS CN values are calculated through the hydrologic modeling module GIS attributes calculators.
The obtained CN number for each of the delineated sub-watershed is illustrated in
Table 4.3.
Table 4.3: CN number for all sub-watersheds
Watershed ID Area (km2) CN CN*Area
1 24.74 84.14 2081.62 2 9.65 84.82 818.51 3 16.78 83.07 1393.91 4 24.02 83.17 1997.74 5 9.98 88.70 885.23 6 7.10 89.12 632.75 7 17.65 89.00 1570.85 8 1.64 88.09 144.47 9 1.80 87.57 157.63 10 6.47 87.76 567.81 11 6.44 89.00 573.16 12 7.47 89.33 667.30 13 5.37 89.00 477.93 Weighted CN = 86
The curve number (CN) value ranges from 83.07 to 89.03 for the sub-watersheds in the study area. The weighted curve number of the whole watershed is (86) according to Equation 2.12. 70
4.9 Time of Concentration (TC)
In this study, the travel times for the sub-watersheds are calculated using the (Compute Travel Time) command in the Calculators menu in WMS.
The computed travel time was then assigned to the relevant input parameter for the selected hydrologic model. The Tc computation result window in WMS is shown in
Figure 4.11.
Figure 4.11: Tc result window (WMS 10.1, 2016)
The Tc values for each of the delineated sub-watersheds are illustrated in Table 4.4.
71
Table 4.4: Tc value for all sub-watersheds
Watershed ID Tc (SCS) (hrs.)
1 1.57
2 1.34
3 1.68
4 1.77
5 0.98
6 0.60
7 1.09
8 0.30
9 0.49
10 0.86
11 0.93
12 0.88
13 0.47
The Time of concentration (Tc) value ranges from 0.30 hr. to 1.77 hr. for the sub- watersheds in the study area. The total Tc for the whole watershed is (2.75 hrs.) which is the Tc for the longest path in the watershed.
4.10 Watershed Delineation
Thirteen sites are automatically identified using the WMS as an output points of watersheds based on the flow accumulation direction. The possible points for outlets of these Watersheds are shown in Figure 4.12. 72
Figure 4.12: Proposed outlets in the study area watershed 73
Thirteen sub-watersheds in the study area were delineated. Streams in these watersheds Illustrate how water flows from the beginning of the watershed to the outlet in all of the studied sub-watersheds.
The delineated sub-watersheds characteristics are illustrated in Table 4.5. The delineated sub-watersheds in the study area are shown in Figure 4.13.
Table 4.5: The thirteen sub-watersheds characteristics
Inlet Outlet Elevation Initial Tc Tc Area Longest Slope ID Elevation Elevation Difference CN Abstraction (Kirpich) (SCS) (km2) Path (m) % (m) (m) (m) (mm) (hr) (hr)
1 24.74 1695.64 978 717.64 9393.2 7.64 84.14 9.58 1.00 1.57
2 9.65 1658.85 1053.42 605.43 7250.6 8.35 84.82 9.09 0.79 1.34
3 16.78 1626.25 1041 585.25 8722 6.71 83.07 10.35 1.00 1.68
4 24.02 1625.81 1040.2 585.61 9415 6.22 83.17 10.28 1.09 1.77
5 9.98 1278.77 854.86 423.91 6782.6 6.25 88.70 6.47 0.84 0.98
6 7.10 1157.12 864.02 293.10 4645 6.31 89.12 6.20 0.63 0.60
7 17.65 1145.33 456.92 688.41 9601.3 7.17 89.00 6.28 1.05 1.09
8 1.64 1032.01 455 577.01 2345.6 24.60 88.09 6.87 0.22 0.30
9 1.80 1127.14 438.65 688.49 3418.5 20.14 87.57 7.21 0.32 0.49
10 6.47 1180.98 454.74 726.24 5880.5 12.35 87.76 7.09 0.58 0.86
11 6.44 1248.92 554.09 694.83 5809.6 11.96 89.00 6.28 0.58 0.93
12 7.47 1509.90 854.86 655.04 6274.4 10.44 89.33 6.07 0.65 0.88
13 5.37 1119.07 554.09 564.98 4216.3 13.40 89.00 6.28 0.44 0.47
74
Figure 4.13: Watersheds in the study area 75
The weighted ratio for each effective rainfall station is calculated through applying
the Thiessen polygons using (WMS 10.1, 2016) Software. The Thiessen polygons for the
three rainfall stations in the study area watershed are shown in Figure 4.14.
Figure 4.14: Generated Thiessen polygons for the three rainfall stations.
The contributing ratio of each rainfall station to the total 139.11 km2 area, and the contribution area of each rainfall station, are shown in Table 4.6.
Table 4.6: The Contribution ratios of the three rainfall stations Contribution Contribution Station name Station ID Area ID ratio area (Km2) Wadi Musa DG0001 0.3 42.55 A0001
Hai DG0002 0.25 34.58 A0002
Petra DG0003 0.45 61.98 A0003
Σ=139.11
76
CHAPTER FIVE
5. RESULTS AND DISCUSSION 5.1 Results
Based on the hydrological literature review, after following the previously discussed hydrological analysis aspects, measures, data analysis and modelling and applying the proposed methodology, the following results are obtained.
5.1.1 Design Storm Characteristics
5.1.1.1 Intensity-Duration-Frequency (IDF) Curves
The rainfall records for each rainfall station were tested using (Easy-Fit 5.6, 2012) software for Normal, Gumbel, and Log Pearson Type III distributions. This is to check and select the best distribution function. The distributions are ranked according to the closeness of maintaining the confidence interval of 0.05 which is associated with the null hypothesis. Where the distribution ranked with number 1; succeeded in accomplishing the 0.05 confidence interval. This is according to the statistical parameters of the three tests (The Kolmogorov-Smirnov, The Anderson-Darling, and
The Chi-Squared). As a result, the Log Pearson Type III probability distribution was found to be the most appropriate probability distribution function. It describes the given data series for all the considered stations. The statistical rankings for the stations
(DG0001, DG0002 and DG0003) are presented in Appendix A.2
The statistical distributions graphs for the rainfall records at stations (DG0001, DG0002 and DG0003) are presented in Appendix A.2.
A. Bell Method 77
The storm duration rainfall records for Wadi Musa station using Bell method are calculated, See Appendix A.3 for the rainfall records for the three stations using Bell method
The extreme rainfall quantiles (XT) for the selected durations and return periods are computed using the Log Pearson Type III distribution. The calculated extreme rainfall values for the three rainfall sations, using Bell method are illustrated in Appendix B.1.
The values of rainfall intensities (I) for the selected durations and return periods are determined using the Log Pearson Type III distribution. This is done by dividing extreme rainfall values obtained previously on their respective durations. For instance, the values of rainfall intensities for the three rainfall stations, using Bell method are illustrated in Appendix B.2.
IDF curves plotting
The IDF curves are plotted on a Log-Log scale. The IDF curves are plotted using the predicted values of rainfall intensity for Wadi Musa, Hai and Petra stations for different durations and return periods.
The IDF curves for Wadi Musa station by Bell method are shown in Figure 5.1. 78
Figure 5.1: IDF curves for Wadi Musa station by Bell method
See Appendix C.1 for the rest of the IDF curves for all stations using Bell method.
B. Chen Method
The storm duration rainfall records for Wadi Musa station using Chen method are calculated and are the same as those for Bell method.
The calculated extreme rainfall values for the three rainfall stations, using Chen method are the same as those for Bell method which are shown in Appendix B.1.
The values of rainfall intensities (I) for the selected durations and return periods were determined using the Log Pearson Type III distribution by the Chen formula illustrated in Equation 2.2.
The values of rainfall intensities for the three rainfall stations, using Chen method are illustrated in Appendix B.2.
See Appendix C.1 for the IDF curves for all stations using Chen method.
C. Hershfield Method 79
The storm duration rainfall records for the three rainfall stations using Bell method are calculated and illustrated in Appendix A.3.
The calculated extreme rainfall values for the three rainfall stations, using
Hershfield method are shown in Appendix B.1.
The values of rainfall intensities for the three rainfall stations, using Hershfield method are illustrated in Appendix B.2.
See Appendix C.1 for the IDF curves for all stations using Hershfield method.
The rainfall intensity values are used as input data for estimating the IDF parameters. The estimation of IDF parameters (a, b and c) of Sherman equation were done using the Solver tool in (Microsoft Excel, 2016). This is done for the durations of
5 mins, 10 mins, 20 mins, 30 mins, 1, 2, 3, 6, 12 and 24 hours and return periods of 2, 5
,10, 25, 50 and 100 years. The results of the 100 years return period are summarized in
Table 5.1. 80
Table 5.1: IDF curves at 100 years return period for the three stations using the three methods
100-YR IDF Equation Parameters Station Name Method a b c
Bell 698.53 8.60 0.77
Chen 679.30 8.42 0.77 Wadi Musa Hershfield 519.72 6.39 0.71
Average 632.52 7.80 0.75
Bell 849.14 8.01 0.77
Chen 856.00 8.34 0.77 Hai Hershfield 637.42 5.85 0.70
Average 780.85 7.40 0.75
Bell 656.12 5.55 0.72
Chen 859.60 8.39 0.77 Petra Hershfield 658.83 6.39 0.71
Average 724.85 6.78 0.73
See Appendix C.1 for the rest of the IDF parameters values for all return periods.
According to Equation 2.1, the rainfall intensity values at 100 years return period for the three stations by the three methods (Bell, Chen, and Hershfield) are tabulated in
Table 5.2. 81
Table 5.2: Sherman rainfall intensity equation for 100 years return period at the three stations using the three methods
Station Name Method Intensity Equation
698.53 Bell I = 0.77 (Td + 8.60)
679.30 Wadi Musa Chen I = 0.77 (Td + 8.42)
519.72 Hershfield I = 0.71 (Td + 6.39)
849.14 Bell I = 0.77 (Td + 8.01)
856 Hai Chen I = .77 (Td + 8.34)
637.42 Hershfield I = 0.7 (Td + 5.85)
656.12 Bell I = 0.72 (Td + 5.55)
859.60 Petra Chen I = 0.77 (Td + 8.39)
658.83 Hershfield I = 0.71 (Td + 6.39)
Intensity equations based on Bell method were used to generate the S-Curve hyetographs; since the Bell method IDF curves have the least deviation from the MWI
IDF curves. This is shown in the discussion Section 5.2 of this document.
5.1.1.2 Development of Rainfall Distribution Hyetographs
The divided precipitation ordinates for the 6 storm durations, where the alternating block method is applied, for 100 yrs. return periods are illustrated in Table 5.3. 82
Table 5.3: The divided precipitation ordinates at 100 yrs. return period 100 yrs.
30 min 60 min 90 min 120 min 150 min 165 min (1/2 hr.) (1 hr.) (1.5 hrs.) (2 hrs.) (2.5 hrs.) (2.75 hrs.= Tc)
t P t P t P t P t P t P (min) (mm) (min) (mm) (min) (mm) (min) (mm) (min) (mm) (min) (mm) 2 0.34 4 0.38 6 0.41 8 0.44 10 0.46 8 0.48 3 0.36 6 0.40 9 0.44 12 0.46 15 0.49 14 0.51 4 0.38 8 0.43 12 0.46 16 0.49 20 0.52 20 0.54 5 0.41 10 0.46 15 0.50 20 0.53 25 0.55 25 0.57 6 0.44 12 0.50 18 0.53 24 0.56 30 0.59 31 0.62 7 0.48 14 0.54 21 0.58 28 0.61 35 0.64 36 0.67 8 0.53 16 0.59 24 0.63 32 0.67 40 0.70 42 0.73 9 0.58 18 0.65 27 0.70 36 0.73 45 0.77 48 0.80 10 0.65 20 0.73 30 0.78 40 0.82 50 0.86 53 0.90 11 0.74 22 0.84 33 0.90 44 0.94 55 0.98 59 1.03 12 0.86 24 0.99 36 1.05 48 1.10 60 1.14 64 1.22 13 1.02 26 1.20 39 1.28 52 1.34 65 1.39 70 1.50 14 1.26 28 1.54 42 1.67 56 1.75 70 1.81 76 2.00 15 1.64 30 2.15 45 2.40 60 2.55 75 2.65 81 3.08 16 5.14 32 8.69 48 11.38 64 13.53 80 15.33 87 13.90 17 1.91 34 2.69 51 3.09 68 3.33 85 3.50 92 4.30 18 1.43 36 1.79 54 1.96 72 2.07 90 2.14 98 2.41 19 1.13 38 1.35 57 1.45 76 1.52 95 1.57 104 1.71 20 0.93 40 1.08 60 1.16 80 1.21 100 1.25 109 1.34 21 0.80 42 0.91 63 0.97 84 1.01 105 1.05 115 1.12 22 0.69 44 0.78 66 0.83 88 0.88 110 0.91 120 0.96 23 0.62 46 0.69 69 0.74 92 0.78 115 0.81 126 0.85 24 0.55 48 0.62 72 0.66 96 0.70 120 0.73 132 0.76 25 0.50 50 0.56 75 0.60 100 0.64 125 0.67 137 0.70 26 0.46 52 0.52 78 0.55 104 0.59 130 0.61 143 0.64 27 0.43 54 0.48 81 0.51 108 0.54 135 0.57 148 0.59 28 0.40 56 0.45 84 0.48 112 0.51 140 0.53 154 0.56 29 0.37 58 0.42 87 0.45 116 0.48 145 0.50 160 0.52 30 0.35 60 0.39 90 0.42 120 0.45 150 0.47 165 0.49 *The middle of the storm duration is shaded in grey, at which the highest precipitation is set and the lower values are set on both upper and lower sides alternatively.
The generated hyetograph bar chart for a storm with the study area’s own time of concentration (2.75 hrs.), and with 100 yrs. return period is illustrated in Figure 5.2.
83
Rainfall hyetograph bar chart
16.00
14.00
12.00
10.00
8.00
6.00 Precipitation (mm)
4.00
2.00
0.00 8 14 20 25 31 36 42 48 53 59 64 70 76 81 87 92 98 104 109 115 120 126 132 137 143 148 154 160 165 Duration (min)
Figure 5.2: 2.75hrs./100-yrs. Rainfall hyetograph bar chart
The S-curve cumulative ordinates for the 6 storm durations (30 min, 1 hr., 1.5 hrs., 2 hrs., 2.5 hrs. And 2.75 hrs.) with each return period are illustrated in Appendix D.1.
These ordinated are transferred into the WMS model as the rainfall distribution for the simulations that simulate the study area at each specified storm duration.
The generated S-curve for a storm with the study area’s own time of concentration (2.75 hrs.), and with 100 yrs. return period is illustrated in Figure 5.3. 84
Rainfall distribution for the study area at Tc 1 2 yrs/Wadi 0.9 5 yrs/Wadi 0.8 10 yrs/Wadi 25 yrs/Wadi 0.7 50 yrs/Wadi 0.6 100 yrs/Wadi 0.5 2 yrs/Hai 5 yrs/Hai 0.4 10 yrs/Hai 0.3 25 yrs/Hai
Cumulative Cumulative precipiation ordinate 0.2 50 yrs/Hai
0.1 100 yrs/Hai 2 yrs/Petra 0 0 50 100 150 200 5 yrs/Petra t(min) 10 yrs/Petra
Figure 5.3: Rainfall hyetograph at Tc = 2.75 hours
The rest of the rainfall hyetograph distributions are illustrated in Appendix D.2.
5.1.2 Model Calibration
The observed peak discharge and volume for the three available storms at the runoff station are illustrated in Table 5.4 (Al-Weshah and El-Khoury, 1999), (WAJ,
1995).
Table 5.4: Available rainfall and runoff records at DG0004 (WAJ, 1995) Date Daily rainfall (mm) Peak flow (m3/s) 25/11/1968 21.00 1.79 10/3/1970 46.10 2.18 17/1/1974 22.60 0.41
The area corresponding to the runoff station DG0004 is equal to 26.5 km2. It is illustrated in Figure 5.4. 85
Figure 5.4: Calibration area
The calibrated area is delineated into two sub-watersheds. Table 5.5 represents the characteristics of these two sub-watersheds.
Table 5.5: The delineated sub-watersheds in the drainage area to be calibrated. Watershed Watershed Area CN CN CN ID Name (Km2) (AMC II) (AMC 1) (AMC III) 82B Hay 9.62 84.50 69.60 92.61 Jabal Al- 80B 16.73 83.00 67.22 91.82 Zubaira
The obtained hydrograph at the model of calibration drainage area, simulated with the calculated CN numbers for the three storms are illustrated in Figure 5.5.a through
Figure 5.5.c. 86
Figure 5.5.a: Simulated hydrograph at 1968 storm for calculated CN.
Figure 5.5.b: Simulated hydrograph at 1970 storm for calculated CN
Figure 5.5.c: Simulated hydrograph at 1974 storm for calculated CN
87
A comparison made between the simulated peak discharge at the area of interest and the observed three storms at the same area is illustrated in Table 5.6.
Table 5.6: The observed and the simulated results at the calibration drainage watershed
Rainfall Jabal Al-Zubaira Hay (CN II) (mm) (CN II) Storm 1968 Wadi Hay 83 84.5 Musa
Observed Qp 3 3 Simulated Qp (m /s) (m /s) 18.8 22.5
1.79 3.09
Rainfall Jabal Al-Zubaira Hay (mm) (CN I) (CN I) Storm 1970 Wadi Hay 67.22 69.6 Musa
Observed Qp 3 3 Simulated Qp (m /s) (m /s) 43 55.5
2.18 6.24
Rainfall Jabal Al-Zubaira Hay (mm) (CN II) (CN II) Storm 1974 Wadi Hay 83 84.5 Musa
Observed Qp 3 3 Simulated Qp (m /s) (m /s) 15.7
0.14 0.69
88
The CN values for Jabal Al-Zubaira and Hay are calibrated in order to yield the
observed peak discharges for the three storms. This is determined using the trial and
error stochastic run simulation and illustrated in Table 5.7.
Table 5.7: The CN values for the observed storms at the calibration drainage watershed Rainfall Jabal Al-Zubaira Hay (mm) Storm 1968 Wadi Hay 81.57 81.57 Musa
Observed Qp 3 3 Simulated Qp (m /s) (m /s) 18.8 22.5
1.79 1.79
Rainfall Jabal Al-Zubaira Hay (mm) Storm 1970 Wadi Hay 62.65 62.65 Musa
Observed Qp 3 3 Simulated Qp (m /s) (m /s) 43 55.5
2.18 2.18
Rainfall Jabal Al-Zubaira Hay (mm) Storm 1974 Wadi Hay 82.9 82.9 Musa
Observed Qp 3 3 Simulated Qp (m /s) (m /s) 15.7
0.41 0.41
The obtained hydrographs from the model of the calibration drainage watershed, with
the calibrated CN numbers and for the 3 storms are illustrated in Figure 5.6.a through
Figure 5.6.c. 89
Figure 5.6.a: Simulated hydrograph at 1968 storm for calibrated CN
Figure 5.6.b: Simulated hydrograph at 1970 storm for calibrated CN
Figure 5.6.c: Simulated hydrograph at 1974 storm for calibrated CN 90
A comparison made between the simulated CN values at the time of the recorded storms and the calculated CN values at this study, is illustrated in Table 5.8.
Table 5.8: Simulated and calculated CN
Simulated CN at each storm Calculated CN date Storm Date
Jabal Al-Zubaira Hay Jabal Al-Zubaira Hay
11/25/1968 81.57 81.57 83 84.5
3/10/1970 62.65 62.65 67.22 69.6
1/14/1974 82.9 82.9 83 84.5
5.1.3 Peak Discharge Estimation
Hydrographs
The SCS hydrograph was obtained at the outlet of the watershed. It was obtained by HEC-1 model for a storm with the time of concentration duration with different return periods. The hydrograph is shown in Figure 5.7.
91
Figure 5.7: SCS hydrograph at 2.75 hrs. duration with different return periods
The rest of the SCS hydrographs for the 36 simulations along with the routed hydrographs are illustrated in Appendix E.
The peak discharge and the volume values at the outlet of the watershed was simulated 36 times. Each simulation with different return period and different storm duration. The results are illustrated in Table 5.9.
92
Table 5.9: Simulated peak discharge values and volumes at the watershed outlet for different return periods with different storm durations (36 simulation)
Return Duration Flow Volume Return Duration Flow Volume period (hours) (m3/s) (MCM) period (hours) (m3/s) (MCM)
0.50 6.56 0.02 0.50 35.70 0.14
1.00 17.76 0.09 1.00 64.22 0.33
1.50 23.60 0.15 1.50 78.75 0.49 2 years 5 years 2.00 31.44 0.23 2.00 99.91 0.67
2.50 34.12 0.27 2.50 106.14 0.71
Tc=2.75 40.32 0.28 Tc=2.75 108.94 0.76
0.50 62.93 0.25 0.50 104.15 0.42
1.00 102.90 0.53 1.00 158.61 0.81
10 1.50 125.18 0.75 25 1.50 188.45 1.11 years years 2.00 154.23 1.00 2.00 228.58 1.43
2.50 162.75 1.05 2.50 244.12 1.56
Tc=2.75 165.08 1.12 Tc=2.75 251.97 1.59
0.50 137.50 0.56 0.50 172.39 0.70
1.00 201.92 1.03 1.00 246.08 1.25
50 1.50 237.11 1.38 100 1.50 286.19 1653.33 years 2.00 286.31 1.76 years 2.00 344.26 2.09
2.50 304.21 1.86 2.50 364.33 2.30
Tc=2.75 306.55 1.95 Tc=2.75 375.20 2.40
93
The resulting FDF (Flow-Duration-Frequency) curves for the study area are illustrated in Figure 5.8.
FDF (Flow-Duration-Frequency) curves for Wadi Musa watershed 400.00
350.00
300.00
250.00 2 yrs. /s)
3 5 yrs. 200.00 10 yrs. Qp (m 150.00 25 yrs. 50 yrs. 100.00 100 yrs. 50.00
0.00 0.00 0.50 1.00 1.50 2.00 2.50 3.00 Duration (hr.)
Figure 5.8: FDF (Flow-Duration-Frequency) curves for the study area
Flow-Duration-Frequency curves are footprint for the watershed; in which the determination of any flow value is applicable by applying the storm duration and the required frequency into the FDF curves.
5.1.4 Channel Routing
After analyzing the study area, the identified channel contains 25 reaches to be routed. The dimensions of each channel reach are estimated by taking several cross sections ranging from two sections in the profile of the shortest reach, to thirteen sections in the profile of the longest reach, to obtain the average channel width (w), and the average channel side slopes (z).
An example of the 129 cross-sections taken in the channels profiles using the arc feature in WMS, is illustrated in Figure 5.9. 94
Figure 5.9: Determination of channel cross-section in WMS
The 6 cross sections taken in the profile of channel R65 are shown in Figure 5.10.a through Figure 5.10.f , and Figure 5.11 shows the profile of the channel 65R.
Figure 5.10.a: Cross section #1 in reach 65R
95
Figure 5.10.b: Cross section #2 in reach 65R
Figure 5.10.c: Cross section #3 in reach 65R
Figure 5.10.d: Cross section #4 in reach 65R 96
Figure 5.10.e: Cross section #5 in reach 65R
Figure 5.10.f: Cross section #6 in reach 65R
Figure 5.11: 65R Profile 97
The obtained bed elevation, max first side elevation, max second side elevation, horizontal distance to the first side, the horizontal distance to the second side and the calculations of the average channel width (w), and the average channel side slopes (z) for the above shown six sections of reach R65, are all shown in Table 5.10.
Table 5.10: Dimensions estimation of 65R Max 1st Max Horizonta Bed Horizonta Channe Sectio Width side 2nd side l distance Av elevatio l distance Z1 Z2 l ID n (m) elevatio elevatio to 2nd g Z n to 1st side n n side 6.6 7.1 6.9 1.00 43.00 1134.00 1156.00 1155.00 147.00 340.00 8 4 1 6.4 9.1 7.7 2.00 42.95 1123.00 1153.14 1155.72 193.00 536.25 0 8 9 7.2 9.8 8.5 3.00 49.00 1105.92 1126.00 1126.94 145.00 400.19 2 1 2 R65 6.8 8.5 7.7 4.00 50.00 1091.00 1109.94 1110.16 130.00 343.91 6 5 1 5.2 4.5 4.9 5.00 65.00 1071.08 1092.00 1119.00 109.35 393.70 3 8 0 4.5 4.4 4.4 6.00 45.00 1054.00 1081.00 1090.00 122.45 327.30 4 4 9 6.7 Avg W = 49.16 Avg Z = 2
The remaining 24 reaches cross sections estimation are illustrated in Appendix F.
The channel’s “n” is chosen to be 0.04 based on the average channel’s conditions and using Table 2.5 in Section 2.5 of this document, corresponding to the manning’s” n” for the areas that are (Fallow (no residue)). The routing feature is applied on the 36 simulations on WMS.
Routed Hydrographs
The SCS routed hydrograph was obtained at the outlet of the watershed. It was obtained by HEC-1 model for a storm with the time of concentration duration and 100- yrs return period. The routed and the un-routed hydrograph is shown in Figure 5.12, It illustrates the attenuation and the time lag resulting from the movement of water through the channels towards the outlet.
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Figure 5.12: Effect of routing on the hydrograph of 2.75 hrs. storm with 100-yrs. return period
The rest of the routed SCS hydrographs for the remaining 35 simulations are illustrated in Appendix E.
The obtained SCS routed hydrographs by HEC-1 model for the time of concentration storm at the six different return periods, are illustrated in Figure 5.13.
99
Figure 5.13: Routed SCS Hydrograph at 2.75 hrs. storm 100
The routed peak discharge at the outlet of the watershed was simulated 36 times.
Each simulation with different return period and different storm duration. The results are illustrated in Table 5.11.
Table 5.11: Simulated routed peak discharge values at the watershed outlet for different return periods with different storm durations Routing Using Routing Using Return Duration KMW Return Duration KMW Period (hours) Period (hours) Flow (m3/s) Flow (m3/s) 0.50 1.55 0.50 15.65 1.00 7.37 1.00 39.79 1.50 13.60 1.50 53.91 2 years 5 years 2.00 19.22 2.00 68.06 2.50 22.22 2.50 73.24 Tc =2.75 24.32 Tc =2.75 75.15 0.50 34.62 0.50 65.78 1.00 71.68 1.00 117.97
10 1.50 89.48 25 1.50 141.69 years 2.00 111.43 years 2.00 171.34 2.50 117.30 2.50 184.35 Tc =2.75 119.28 Tc =2.75 193.93 4.00 90.86 0.50 119.27 6.00 154.35 1.00 191.63
50 9.00 182.89 100 1.50 226.92 years 12.00 221.71 years 2.00 271.00 18.00 235.07 2.50 285.61 Tc =2.75 243.26 Tc =2.75 299.82
The resulting Routed-Flow-Duration-Frequency (RFDF) curves for the study area, are illustrated in Figure 5.14. 101
RFDF (Routed-Flow-Duration-Frequency) curves for Wadi Musa watershed 350.00
300.00
250.00
2 yrs.
200.00 /s)
3 5 yrs. 10 yrs.
Qp (m 150.00 25 yrs.3 50 yrs. 100.00 100 yrs.
50.00
0.00 0.00 0.50 1.00 1.50 2.00 2.50 3.00 Duration (hr.)
Figure 5.14: RFDF (Routed-Flow-Duration-Frequency) Curves for the study area
The peak discharge and the volume values at DG0004 of the watershed for (2, 5,
10, 25, 50 and 100) yrs. Return periods are illustrated in Table 5.12.
Table 5.12: Simulated routed peak discharge values and volumes at DG0004 for different return periods at 24 hrs.
DG0004: Simulated Qp and Vol.
Qp Vol. P(mm) (m3/s) (m3) Return period (yrs.) W. Hay CN (Calculated ) Musa 2 30.66 34.77 15.46 187.2198 5 44.14 45.75 34.03 379.1529 10 52.59 50.7 45.95 499.0473 25 62.72 55.43 60.52 640.9611 50 69.89 58.19 70.68 740.0745 100 76.77 60.48 80.32 834.1677
See the obtained hydrographs at each return period for DG0004 at Appendix E.
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5.1.5 Reservoir Capacity Estimation
Effective Runoff
The effective rainfall storms for the three stations are identified at (P > = 8.26 mm), and the effective runoff values for the three stations are calculated. The identified critical years (maximum and average cumulative yearly runoff) are shown in Table 5.13 along with the cumulative yearly runoff values for the specified critical years.
Curve Number Estimation
The curve number for each sub-watershed is calculated considering the effect of the soil type and land cover using the WMS GIS Attributes calculator. The weighted curve number is calculated through Equation 2.12. The CN results for Wadi Musa station at the critical (maximum and average) year are shown in Table 5.14 and Table
5.15, respectively.
Antecedent Moisture Conditions Determination
The AMC for each storm at the three rainfall stations for the given records are identified and then the weighted AMC II curve number is adjusted to the other AMC using Equation 2.13 and Equation 2.14, The AMC results for Wadi Musa station at the critical (max and average) year are shown in Table 5.14 and Table 5.15, respectively.
Maximum Retention
After adjusting the curve number, the values of the maximum potential retention
(S) are calculated through Equation 2.20, which indicates the initial abstraction (Ia) of rainfall via vegetation and soil. The S results for Wadi Musa at the critical (max and average) year station are shown in Table 5.14 and Table 5.15, respectively.
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Table 5.13: Critical runoff years at the three rainfall stations Max. AVG. Cumulative Cumulative Station Name Station ID Year Year yearly runoff yearly runoff (mm) (mm) Wadi Musa DG0001 167.287 1965 38.92 1969 Hai DG0002 108.25 1979 36.65 1972 Petra DG0003 98.33 1991 26.34 2008
The effective daily rainfall, the effective runoff depth, the AMC, the adjusted CN, S and the cumulative monthly and yearly runoff of Wadi Musa station, are all shown in
Table 5.14 and Table 5.15.
Table 5.14: Runoff calculations at 1965 (max runoff year) for Wadi Musa station
Effective 5 Day daily Runoff Monthly Yearly Cumulative AMC Weighted S Year month rainfall Depth Runoff Runoff Rainfall Condition CN (mm) (mm) P≥ (mm) (mm) (mm) (mm) 8.26
13 17 AMC II 86.02 41.3 0.49
35 29 AMC III 93.4 18 19.99
22 57 AMC III 93.4 18 9.32
39 96 AMC III 93.4 18 23.5 Jan 159.34 53 149 AMC III 93.4 18 36.24 1965 167.3 24 173 AMC III 93.4 18 10.86
68 72 AMC III 93.4 18 50.37
21 89 AMC III 93.4 18 8.57
17 17 AMC II 86.02 41.3 1.53 Mar 7.94 18 35 AMC III 93.4 18 6.42
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Table 5.15: Runoff calculations at 1969(avg runoff year) for Wadi Musa station
Effective 5 Day daily Runoff Monthly Yearly Cumulative AMC Weighted S Year month rainfall Depth Runoff Runoff Rainfall Condition CN (mm) (mm) P≥ (mm) (mm) (mm) (mm) 8.26
11 17 AMC II 86.02 41.3 0.17 15 20 AMC II 86.02 41.3 0.95 10 20.5 AMC II 86.02 41.3 0.07 9.8 6 AMC I 72.09 98.3 1.1 AMC 18.5 28.3 93.4 18 6.77 Jan III 1.19 1969 AMC 38.92 12 47.8 93.4 18 2.68 III AMC 20.5 68.3 93.4 18 8.2 III AMC 11.5 70 93.4 18 2.42 III AMC Apr 31 37.7 93.4 18 16.56 16.56 III
See Appendix G.1 for the rest of the storm by storm runoff calculations at the three rainfall Stations.
Reservoir Capacity Estimation
The total max and the average reservoir capacity is the sum of the max and the average reservoir capacity for the three areas assigned the three rainfall stations (A0001,
A0002, A0003), which are determined by applying the mass curve method on each.
The max and the average reservoir capacity estimation for A0001, are illustrated in Table 5.16 and Table 5.17, respectively.
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Table 5.16: Maximum reservoir capacity calculations at A0001 A0001 = 42.55 Km2
Cum. Runoff Inflow Cum. Cum. Inflow - Inflow - Year Month depth volume Demand inflow demand Cum. Demand (mm) (MCM) demand
OCT 0.00 0.00 0.00 0.59 0.59 -0.59 -0.59 NOV 0.00 0.00 0.00 0.59 1.19 -1.19 -0.59 DEC 0.00 0.00 0.00 0.59 1.78 -1.78 -0.59 JAN 159.34 6.78 6.78 0.59 2.37 4.41 6.19 Max. FEB 0.00 0.00 6.78 0.59 2.97 3.81 -0.59 MAR 7.94 0.34 7.12 0.59 3.56 3.56 -0.26 1965 APR 0.00 0.00 7.12 0.59 4.15 2.97 -0.59 MAY 0.00 0.00 7.12 0.59 4.75 2.37 -0.59 JUN 0.00 0.00 7.12 0.59 5.34 1.78 -0.59 JUL 0.00 0.00 7.12 0.59 5.93 1.19 -0.59 AUG 0.00 0.00 7.12 0.59 6.52 0.59 -0.59 SEP 0.00 0.00 7.12 0.59 7.12 0.00 -0.59 Reservoir capacity = 6.19
Table 5.17: Average reservoir capacity calculations at A0001 A0001 = 42.55 Km2
Cum. Runoff Inflow Cum. Cum. Inflow - Inflow - Year Month depth Volume Demand Inflow Demand Cum. Demand (mm) (MCM) Demand
JAN 1.19 0.05 0.05 0.14 0.14 -0.09 -0.09 FEB 0.00 0.00 0.05 0.14 0.28 -0.23 -0.14 MAR 21.17 0.90 0.95 0.14 0.41 0.54 0.76 APR 16.56 0.70 1.66 0.14 0.55 1.10 0.57 Avg. MAY 0.00 0.00 1.66 0.14 0.69 0.97 -0.14 JUN 0.00 0.00 1.66 0.14 0.83 0.83 -0.14 1969 JUL 0.00 0.00 1.66 0.14 0.97 0.69 -0.14 AUG 0.00 0.00 1.66 0.14 1.10 0.55 -0.14 SEP 0.00 0.00 1.66 0.14 1.24 0.41 -0.14 OCT 0.00 0.00 1.66 0.14 1.38 0.28 -0.14 NOV 0.00 0.00 1.66 0.14 1.52 0.14 -0.14 DEC 0.00 0.00 1.66 0.14 1.66 0.00 -0.14 Reservoir Capacity = 1.33
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The mass curve plot for A0001 at 1965, is illustrated in Figure 5.15.
8
7
6
5
4 Cumulative Demand
3 Cumulative Inflow Volume Volume (MCM) 2
1
0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 -1 Time(Months)
Figure 5.15: Mass curve of A0001 at 1965
The maximum and the average reservoirs capacity at the study area, are illustrated in
Table 5.18.
Table 5.18: Maximum and average reservoirs capacity
Max reservoir AVG reservoir Reservoir Label Area(Km2) Capacity (MCM) Capacity (MCM)
A0001 42.55 6.19 1.33 A0002 34.58 2.44 1.12 A0003 61.98 5 1.36 Total Watershed 139.11 13.63 3.81
For the reservoirs capacity calculations and the mass curves plots of A0002 and
A0003, see Appendix G.2 and Appendix G.3.
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5.1.6 Flood Risk Mitigation using Afforestation
In this study, the afforestation scenario was applied in the upstream watersheds of the Siq entrance. The afforestation scenario is important to mitigate the flood risks at the
Siq entrance; that leads to the monuments area in Petra. In addition to the flood severity reduction, it provides reduction of the atmospheric pollution by increasing the vegetation cover.
Afforestation can be applied on selected bare ground areas in the upstream watersheds of Hay, Jabal Zubaira, Qurnat Bani Saad, Kafr Suheym represented by the following watershed ID's respectively;(B 80, B 81, B 82, B 83, B 84), as shown in
Figure 5.16.
Figure 5.16: Afforestation areas in the upstream watersheds of the study area 108
The afforestation scheme to cover a total area of (1643 ha), distributed among the upstream watersheds as shown in the previous figure.
The areas to be afforested in each watershed were calculated based on the investigation of good afforestation practices. According to the definition of SCS (1986), the good afforestation conditions are applicable when the total vegetated area is greater than 75% of the arable land of the watershed (Al-Weshah and El-Khoury, 1999). Based on this ratio, each area of afforestation in each watershed has been calculated as a 75% of the arable bare land, which is the minimum requirement in order to achieve the good afforestation practices.
A proposed curve number was generated to account for the afforestation in each of these areas. These proposed curve numbers are shown in Table 5.19.
Table 5.19: Afforestation effect on CN values Upstream Watersheds to Siq Entrance Calculated Proposed Watershed ID Area ( km2 ) CN CN 80.00 16.77 83.09 76.63 81.00 3.12 88.83 81.53 82.00 9.66 84.69 78.97 83.00 2.07 85.67 77.78 84.00 18.83 81.86 77.27 Total area of the upstream watersheds 50.45
It is obvious that CN decreases due to afforestation, since it affects the infiltration- runoff process in the watersheds; It is suggested that the afforestation consists mainly of combination of oak, aspen and olive trees. The calculations of the proposed CN's for the five watersheds are illustrated in Table 5.20 through Table 5.24.
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Table 5.20: Curve number calculations for watershed 80 after the afforestation Watershed ID 80.00 Afforested area = 615 Ha Assigned Area CN X Soil Group Land Use CN (Km2) Area D Bare Ground 89.00 0.44 39.07 D Residential 86.00 0.36 31.22 D Mixed Forest Land 79.00 0.25 19.59 D Shrub and Grass Rangeland 90.00 0.02 1.71 C Mixed Forest Land 73.00 0.21 15.33 C Bare Ground 87.00 2.05 178.48 C Mixed Barren Lands 91.00 1.53 138.96 C Oak and Cupressus 67.00 0.59 39.66 C Cropland and Pasture 79.00 4.89 385.92 C Sandy Areas and Other Beaches 85.00 0.08 6.46 Orchards Groves Vineyards C Nurseries 80.00 0.21 16.80 C Oak and Cupressus 67.00 6.15 412.35 Weighted CN 76.63
Table 5.21: Curve number calculations for watershed 81 after the afforestation Watershed ID 81.00 Afforested area = 152 Ha Assigned Area CN X Soil Group Land Use CN (Km2) Area D Bare Ground 89.00 0.51 45.03 D Sandy Areas and Other Beaches 88.00 0.24 21.21 D Mixed Barren Lands 94.00 0.33 31.40 D Cropland and Pasture 84.00 0.07 6.22 D Mixed Forest Land 79.00 0.09 7.35 D Shrub and Grass Rangeland 90.00 0.02 1.71 C Bare Ground 87.00 0.33 29.06 D Oak and Cupressus 74.00 1.52 112.33 Weighted CN 81.53
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Table 5.22: Curve number calculations for watershed 82 after the afforestation Watershed ID 82.00 Afforested area = 334 Ha Assigned Area CN X Soil Group Land Use CN (Km2) Area D Bare Ground 89.00 0.52 46.08 D Sandy Areas and Other Beaches 88.00 0.41 36.43 D Cropland and Pasture 84.00 0.02 1.60 D Mixed Barren Lands 94.00 0.13 12.41 D Residential 86.00 0.02 1.63 C Sandy Areas and Other Beaches 85.00 0.43 36.81 C Bare Ground 87.00 0.60 52.29 D Mixed Forest Land 79.00 0.09 7.43 C Mixed Barren Lands 91.00 1.02 92.55 C Cropland and Pasture 79.00 2.92 230.52 C Oak and Cupressus 67.00 1.94 130.05 D Oak and Cupressus 74.00 1.55 114.94 Weighted CN 78.97 Table 5.23: Curve number calculations for watershed 83 after the afforestation Watershed ID 83.00 Afforested area = 90 Ha Assigned Area CN X Soil Group Land Use CN (Km2) Area D Bare Ground 89.00 0.12 10.28 C Bare Ground 87.00 0.19 16.23 C Mixed Barren Lands 91.00 0.06 5.37 C Cropland and Pasture 79.00 0.49 38.87 D Cropland and Pasture 84.00 0.22 18.23 D Mixed Barren Lands 94.00 0.10 9.21 C Oak and Cupressus 67.00 0.56 37.69 D Oak and Cupressus 74.00 0.34 24.98 Weighted CN 77.78 Table 5.24: Curve number calculations for watershed 84 after the afforestation Watershed ID 84.00 Afforested area = 452 Ha Assigned CN X Soil Group Land Use CN Area (Km2) Area C Bare Ground 87.00 1.51 131.54 C Cropland and Pasture 79.00 11.50 908.58 D Bare Ground 89.00 0.16 13.80 C Mixed Barren Lands 91.00 0.72 65.34 D Mixed Barren Lands 94.00 0.04 3.67 C Sandy Areas and Other Beaches 85.00 0.14 11.56 C Oak and Cupressus 67.00 0.16 10.39 C Orchards Groves Vineyards Nurseries 80.00 0.06 4.64 C Mixed Forest Land 73.00 0.04 2.85 C Oak and Cupressus 67.00 4.52 303.01 Weighted CN 77.27 111
The flood peak flow and volumes are evaluated at the Siq entrance based on the proposed curve numbers, taking into account the routing effect also. The results of this analysis are shown in Table 5.25.
Table 5.25: Afforestation effect on the peak flow values Existing conditions Scenario (1): Afforestation At Siq entrance At Siq entrance Return Volume Return Volume period Qp (m3/s) (103 m3) period Qp (m3/s) (103 m3) 2.00 32.72 381.14 2.00 12.69 201.54 5.00 69.47 730.18 5.00 34.98 460.37 10.00 91.00 930.00 10.00 49.33 616.35 25.00 115.27 1152.07 25.00 66.54 796.14 50.00 131.23 1299.69 50.00 78.21 918.89 100.00 146.40 1434.34 100.00 89.13 1031.50
The afforestation of oak, cypresses and olive reduced the flood Peak flow by
39-61 %, which is a very high percentage, and reduced the flood volumes by 28-47%, this indicates that this scenario is very effective in flood management for the long-term period. It is very clear that the afforestation impact is very pronounced for frequent return periods. The resulting hydrographs of Afforestation scenario are shown in Figure
5.17 through Figure 5.22.
Figure 5.17.: Effect of afforestation scenario for 2 yrs. return period 112
Figure 5.18: Effect of afforestation scenario for 5 yrs. return period
Figure 5.19.: Effect of afforestation scenario for 10 yrs. return period
Figure 5.20: Effect of afforestation scenario for 25 yrs. return period 113
Figure 5.21.: Effect of afforestation scenario for 50 yrs. return period
Figure 5.22: Effect of afforestation scenario for 100 yrs. return period
Other scenarios can be applied and evaluated, such as the terracing, the construction of check dams. Where a detailed field survey to check the channels dimensions, channels slopes and outlets are required. In addition to feasibility and costs study to be conducted. Also, topographic maps are needed to derive the elevations.
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5.2 Discussion
5.2.1 Identifying the best IDF construction method.
The obtained IDF curves from the MWI for Wadi Musa rainfall station are illustrated in Figure 5.23.
Rainfall Intensity (mm/hr) for Wadi Mousa Station
1000.0
100.0
10.0 Intensity(mm/hr)
100Y 2Y 50Y 10Y 5Y 1.0 1 10 100 1000 10000 Duration(minute)
Figure 5.23: IDF curves for Wadi Musa rainfall station from MWI
The percentage differences between the intensity obtained from the IDF curves by
Bell, Chen and Hershfield method for Wadi Musa station and the intensity obtained from the MWI for Wadi Musa station are illustrated in Appendix C.2.
A comparison made between the average of the average of the percentile differences between Bell, Chen and Hershfield methods with the IDF curves obtained from MWI. 115
Through this comparison, it has been observed that for Wadi Musa station, Bell method has the least deviation from the MWI IDF Curve with a deviation of 36.61 %.
A graphical comparison between Bell, Chen, Hershfield, and MWI IDF curves for
Wadi Musa station at 100-year return period is illustrated in Figure 5.24.
100 yrs. Intensity from: Bell,Chen,Hershfield and MWI 1000
100
10
1 1.00 10.00 100.00 1000.00 10000.00
Bell Chen Hershfield MWI
Figure 5.24: Bell, Chen, Hershfield, and MWI IDF curves for Wadi Musa station at 100 years return period
A comparison made between the average of the average of the percentile differences between Bell, Chen and Hershfield methods with the IDF curves obtained from MWI.
Through this comparison, it has been observed that for Hai and Petra stations Bell
Method has also the less deviation from the MWI IDF curves with a deviation of 44.92
% and, 36.65 %, respectively.
116
5.2.2 The validity of the model based on the calibrating parameter (CN)
It is shown that the soil curve number is the major and only parameter used in order to validate the model. This is very clear when comparing the observed Qp of the three major storms of 1968, 1970 and 1974 at DG4 runoff station with the simulated Qp values depending on the calculated CN and the calibrated ones. Where as shown in
Table 5.29 that CN1968 < CN1970 < CN1974 < Calculated, since we notice the increment increase in the calibrated CN values in each storm, but for the storm that occured in
1974, the antecedent moisture conditions were wet when referring to the records, so the assigned CN is of type (I), these calibrated CNs when simulated in the model, they give exactly the observed Qp values of the three storms, so based on this, we can validate the model to cover the whole watershed area.
This by default confirms that CN naturally increases with time due to many reasons, the most prominent factor is the urban sprawl and the expansion of the infrastructure in the area. This is shown in Figure 5.25 through Figure 5. 29, also the climate change plays a major role; where the accumulative increase of temperature with time leaves the soil dry and thus increases the CN.
Figure 5.25: Urbanization at Wadi Musa as highlighted by red circles (1984), Google Earth 117
Figure 5.26: Urbanization at Wadi Musa as highlighted by red circles (1994), Google Earth
Figure 5.27: Urbanization at Wadi Musa as highlighted by red circles (2004), Google Earth
Figure 5.28: Urbanization at Wadi Musa as highlighted by red circles (2014), Google Earth 118
Figure 5.29: Urbanization at Wadi Musa as highlighted by red circles (2018), Google Earth
5.2.3 Effect of changing CN values on Peak flow values(Qp)
The main uncertain parameter that affects the results of the simulation process is the CN value (Ponce and Hawkins, 1996). A stochastic HEC-1 model via WMS was conducted in order to assess the effect of CN variation on Qp at the outlet of Wadi Musa watershed. The results are shown in Table 5.26. It is clear that the flood peak is most sensitive to the less severe more frequent floods.
Table 5.26: Effect of CN on the variation of Qp Q2 year Q5 year Q10 year Q25 year Q50 year Q100 year CN (m3/s) (m3/s) (m3/s) (m3/s) (m3/s) (m3/s) 80 33.05 99.80 156.60 237.80 303.28 369.80 81 38.80 112.50 172.50 258.10 327.60 394.90 82 45.40 125.40 190.44 281.40 352.13 421.90 83 53.00 140.20 210.00 305.30 377.40 451.00 84 61.10 156.00 230.00 330.00 402.70 480.60 85 70.20 172.40 250.20 355.00 431.20 510.60 86 80.80 191.40 273.30 380.00 460.70 540.10 87 91.80 211.50 297.50 406.10 489.80 570.70 88 105.10 231.90 322.30 434.60 520.30 601.20 89 118.60 253.50 347.50 464.50 550.50 632.50 Range 85.55 153.70 190.90 226.70 247.22 262.70 S.D 27.38 49.29 61.29 72.29 79.12 84.45
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5.2.4 Generating the Flow-Duration-Frequency Curves
The FDF curves are a footprint of the watershed, where it is considered to be a special property of the watershed. It shows the probable peak discharge at the outlet of the watershed at different storm durations of different return periods. This provides a major tool that can be used for research and design purposes; such as designing hydraulic structures in the area where you can pick up the Qp needed from the FDF curve shown in Figure 5.30.
120
Figure 5.30: RFDF (Routed-Flow-Duration-Frequency) curves for the study area 121
5.2.5 Effect of Routing on Peak Flow
As illustrated in Table 5.27, routing is more effective and sensitive for more frequent events. Reduction percentages are the highest for short return periods, while they are the least for the longer frequencies. At the same time, it is noticed that routing is more effective for the short duration storms, and the reduction percentages decrease with the increase of storm durations.
Table 5.27: Effect of routing on Qp Percentage peak flow reduction after kinematic wave routing
Storm duration (hrs.) 2 yrs. 5 yrs. 10 yrs. 25 yrs. 50 yrs. 100 yrs.
0.05 76.37 56.16 44.99 36.84 33.92 30.81
1.00 58.50 38.04 30.34 25.62 23.56 22.13
1.50 42.37 31.54 28.52 24.81 22.87 20.71
2.00 38.87 31.88 27.75 25.04 22.56 21.28
2.50 34.88 31.00 27.93 24.48 22.73 21.61
2.75 39.68 31.02 27.74 23.03 20.65 20.09
5.2.6 Calculating the peak discharge at the watershed outlet using the Sub-integral method
For the purpose of calculating the peak discharge using the Sub-integral method, the time of concentration was calculated for each sub-watershed using Kirpich formula
(1940) (Chow, et. al., 1988):
1 L1.155 T = × (5.1) c 52 H0.385
122
Where,
Tc= Time of concentration (hrs.)
L= Watershed length (m)
H= Elevation difference between the inlet and the outlet (m)
The peak discharge resulting from each watershed was calculated using the
Rational method Equation 5.1 (Chow, et. al., 1964):
Qp = 0.278 CIA × Cf (5.2)
Where,
C= Runoff coefficient (Unit-less)
I = Intensity (mm/hr)
A = Area (km2)
The runoff coefficient for each sub-watershed at the study area was determined depending on the surface characteristics, the slope of the watershed and the return period. The table of the runoff coefficients for use in rational method is illustrated in
Appendix H. (Chow, et.al, 1964).
The frequency correction factor was included within the runoff coefficient factor table as it takes the return period into consideration.
The intensities were obtained from the IDF curves of Wadi Musa watershed for each time of concentration, all the intensities are tabulated as shown in Table 5.28. The peak discharge calculations are also illustrated in Table 5.28.
123
Table 5.28: The peak discharge calculations using the sub-integral method Tc Watershed Slope Runoff Area Intensity Volume (kirpich) Qp (m3/s) ID (%) Coefficient (km2) (mm/hr) (MCM) (min) 1 7.64 0.54 24.74 33.78 60.00 125.46 0.45 2 8.35 0.54 9.65 41.37 47.64 59.93 0.17 3 6.71 0.51 16.78 33.93 59.76 80.72 0.29 4 6.22 0.51 24.02 32.76 65.16 111.57 0.44 5 6.25 0.51 9.98 39.56 50.58 55.98 0.17 6 6.31 0.51 7.10 47.48 37.68 47.80 0.11 7 7.17 0.54 17.65 33.24 62.70 88.07 0.33 8 24.60 0.54 1.64 83.28 13.20 20.50 0.02 9 20.14 0.54 1.80 67.20 19.02 18.16 0.02 10 12.35 0.54 6.47 49.22 34.86 47.81 0.10 11 11.96 0.54 6.44 49.14 34.98 47.51 0.10 12 10.44 0.54 7.47 46.60 39.12 52.26 0.12 13 13.40 0.54 5.37 56.92 26.16 45.89 0.07 Total volume = 2.39
The lag time between each sub-watershed mouth and the main outlet was calculated using the kirpich formula (Chow, et. al., 1988). The calculations are illustrated in Table
5.29.
Table 5.29: The lagging between each sub-watershed mouth and the main outlet. Outlet Channel Tc (Kirpich) Lagging Watershed ID Inlet Elev. (m) Elev. (m) Length (m) (min) (min) 9 438.60 438.60 0.00 0.00 0.00 10 438.60 454.00 532.70 9.46 9.46 8 454.00 456.90 407.40 13.20 22.66 7 454.00 456.90 407.40 13.20 22.66 11 456.90 554.00 1552.80 16.02 38.68 13 456.90 554.00 1552.80 16.02 38.68 1 554.00 978.00 3031.70 19.67 58.35 6 554.00 864.00 3317.90 24.63 63.31 12 864.00 864.90 1359.80 83.33 146.63 5 864.00 864.90 1359.80 83.33 146.63 2 864.90 1053.40 5572.75 54.29 200.92 3 864.90 1053.40 5572.75 54.29 200.92 4 864.90 1053.40 5572.75 54.29 200.92
The hydrograph of Wadi Musa watershed using the sub-integral method resulted in a peak discharge of approximately 268 m3/s for a 2.75 hrs. (Tc) storm duration, with 100- years frequency. The resulted hydrograph is illustrated in Figure 5.31. 124
125
Figure 5.31: Runoff Hydrograph for Wadi Musa watershed using the Sub-integral method 126
5.2.7 Calculating the peak discharge in the Siq using Manning’s equation
Hydrology is generally related to the study of rainfall and water in connection to geography and geology. It transforms rainfall amount into quantity of runoff.
Hydraulics is more oriented towards physics, the study of the motion of liquids in relation to disciplines such as fluid mechanics and fluids dynamics. It takes the calculated quantity of runoff from hydrology to determine a flood level.
The peak discharge was calculated at the watershed outlet using Manning’s
Equation 5.3 ( Sturm, T.W., 2010). This is to show the correlation between the science of hydraulics and the science of hydrology.
1 2 Q = VA = ( ) AR3√S (5.3) n
Where,
Q = Flow rate, (m3/s)
V = Velocity, (m/s)
A = Flow area, (m2) n = Manning’s roughness coefficient
R = Hydraulic radius, (m)
S = Channel Slope, (m/m)
The channel’s “n” is chosen to be 0.04 based on the average channel’s conditions and using Table 2.5 in Section 2.5 of this document, corresponding to the manning’s” n” for the areas that are (Fallow (no residue)).
The height of the Siq was taken as the recorded height of water in 1963; a 100-year frequency flood. Where flood water overtopped the dam and entered the Siq instead of being diverted through the tunnel of Wadi Al-Mudhlim. Eyewitnesses stated that the 127
flood water depth was about 10-12 m in some areas of the Siq passage. (Al-Weshah and
El-Khoury, 1999).
The rest of the Siq cross sectional dimensions were obtained from the Digital Elevation
Model (DEM), using WMS, and are illustrated in Table 5.30.
Table 5.30: The Siq dimensions
Channel Mannig's width Height Area Perimeter Hydraulic Volume Qp (m3/s) slope n (m) (m) (m2) (m) radius (m) (MCM) (m/m)
297.98 0.04 5.2 10 52 25.20 2.06 0.02 2.95
The Manning’s peak discharge was found to be 297.98 m3/s. This value of peak discharge is convergent to the peak discharge value of 299.8 m3/s, that was forecasted using the SCS method through WMS. It is also very close to the peak discharge value of
268 m3/s, that was obtained manually using the sub-integral method.
Also, as indicated in the literature, during flood events, water velocity may reach a velocity of 5 m/s. Based on this, a rough estimation of the flow can be derived using
Manning’s equation. This is by multiplying the cross sectional area by a flood velocity of 5 m/s. The estimated peak flow is equal to 260 m3/s. This is also compatible with the attained results.
In addition, The volume of the flow obtained from Manning’s equation
(Hydraulically), was found to be equal to 2.95 MCM which is close to the estimated flow volume of 3.8 MCM, using the Ripple diagram method (Hydrologically).
This confirms the ability to apply hydraulic and hydrological methods to engineering applications in an integrated way. It supports the fact that the sciences of
Hydraulics and Hydrology are an extremely correlated engineering disciplines.
128
CHAPTER SIX
6. CONCLUSIONS AND RECOMMENDATIONS
Based on the discussed results in the previous chapter of this document, the general conclusions of this study are totalized in the following points:
6.1 Conclusions
1. The curve number for Wadi Musa watershed is found to be 86 using the SCS
method depending on the land use & soil map of Wadi Musa area, this update of
CN can be used in other studies for the purposes of modeling and flood
estimation.
2. The calculated time of concentration for Wadi Musa watershed using the SCS
method is 2.75 hrs.
3. The model is calibrated by altering the CN of the two sub watersheds (Jabal
Zubaira and Al-Hay) , since they contribute to the flow at DG0004 , the results
of calibration were successful and logical, thus, the model is validated to
forecast flow discharge and volume at the watershed outlet and DG4 for
different time periods and for different return periods.
4. The peak flow and volume determined by this study for the average time of
concentration storm duration of a 100-year return period equal 299.8 m3/s and
2.27 MCM respectively. And the peak flow and volume determined by this
study for a 24-hours storm duration of a 100-year return period equal 553.33
m3/s and 5.43 MCM respectively.
5. Flow-Duration-Frequency curves for Wadi Musa watershed are developed
through this study, these FDF curves are a footprint for Wadi Musa watershed,
which enable us to estimate the peak discharge at any storm duration for any
return period. 129
6. The kinematic wave routing application on the model yielded a reduction of 20
% on the peak flow of 2.75 hrs storm duration of a 100-year return period, and a
23% reduction of the peak flow of 24 hrs storm duration of a 100-year return
period.
7. Rainfall events exceeding 8.25 mm will eventually generate runoff in Wadi
Musa Watershed.
8. The amount of runoff that can be stored in a reservoir is found to be 3.81 MCM
on average basis, and 13.63 MCM on maximum basis.
9. The availability of rainfall and flood records are major constraints on model
calibration and reliability. Only three flood records were used in the calibration.
The maximum of these flood records has a return period of 2 years most
probably. Though calibration yielded accepted CN values, but the certainty of
the flood volumes data has to be questioned when considering the reliability of
the simulation.
10. Afforestation of 1643 ha with oak, cupressus and olives in the upstream
watersheds (B80, B81, B82, B83, B84) provides about 39% to 61% reduction in
the peak flows which is a very high percentage, and reduced the flood volumes
by 28-47%, this indicates that this scenario is very effective in flood risk
management for the long-term period. It is very clear that the afforestation
impact is very pronounced for frequent return periods.
130
6.2 Recommendations
1. Peak flow and volume are the major parameters for the purposes of research,
flood forecasting and designing hydraulic structures in the region such as
(detention dams, culverts, or check dams); I recommend using the peak flow and
volume values which are generated for 6 return periods (2 ,5, 10, 25,50,100), for
storm durations ranging from 2.75 hrs up to 24 hrs. In this study, FDF curves are
developed for Wadi Musa watershed; I recommend using these curves for the
previously mentioned purposes.
2. The CN value of Wadi Musa watershed is 86, I recommend researchers to use
this value in their studies, since CN is a very critical parameter to estimate the
peak discharge and watershed storage capacity. The time of concentration is
found to be 2.75 hrs based on SCS method, but when analyzing the jump
occurring in the flow-duration-frequency curves, it appears that the watershed's
Tc is 6 hrs, I recommend conducting more studies about the Flow-Duration-
Frequency curves in order to interpret the physical meaning of the jumps and the
possibility of deriving the time of concentration from these curves.
3. The storage capacity of Wadi Musa watershed based on the average and
maximum runoff were 3.81 MCM & 13.63 MCM respectively, I recommend
establishing a dam at the outlet of the watershed with a storage capacity of 3.81
MCM, which will help the area to cover its needs of water use.
4. Lack of rain gauges, runoff gauging stations and employees is one of the major
problems that southern regions in Jordan suffer from, including Wadi Musa area,
where there is only one runoff gauging station and not working anymore; I 131
recommend hiring more employees and installing more rain and flow gauges at
this area.
5. Afforestation is a very effective mitigation measure which shall be implemented
to face the flood risk problems, I recommend this measure to be used rather than
the structural measures which interfere in the integrity and aesthetics of the
historical sitting in Petra.
6. Floods pose a very dangerous risk on people, properties and monuments
especially in the Siq area, so there is a necessary need to mitigate the flood risks
in this area. The afforestation is encouraged to be used since it is more attractive
and better suited. It also does not interfere in the integrity and aesthetics of the
historical setting of Petra.
132
REFERENCES
Abdelkhalek, A. Y. 2009, Development of an Early Warning System for Flash Floods in Wadi Watier – Sinai Desert. PhD Dissertation, Vrije Universiteit Brussel, Belgum.
Abdelrahim, T. 2017, Hydrological Analysis for Water Harvesting Potential at Southern Region of Jordan. Master-Thesis, University of Jordan, Amman-Jordan.
Adams and Howard, 1986. Design Storm Pathology, Canadian Water Resources Journal.
Al Zubi, Y. and Abu Baker, S., 2010. Hydrology and water harvesting techniques of Wadi Muheiwir catchment area – The case study of Jordan. Journal of Applied Sciences, 10 (4), pp: 298-304.
Al-Abed,N. and Al-Sharif,M , 2008. Hydrological Modeling of Zarqa River Basin – Jordan Using the Hydrological Simulation Program – FORTRAN (HSPF) Model, Water Resource Management (2008) 22:1203–1220.
Al-Ayyash, S. and Nnadi, F.N., 2003. “Surface Water Management tool for Arid Lands Using GIS - Jordan Desert" XI World Water Congress of IWRA, Madrid, Spain, 5-9 October.
Albek, M., Ogutveren, U.B., Albek,E, 2004. Hydrological modeling of Seydi Suyu watershed (Turkey) with HSPF, Journal of Hydrology 285 (2004) 260–271.
Alhasanat, H., 2014, Flash Flood Assessment for Wadi Mousa City-Jordan, Procedia Economics and Finance 18 (2014) 675 – 683.
Al-Hoot, M. 2006, Surface water modelling for Humret Es-sahin area using watershed modelling systems, Master – Thesis. The University of Jordan, Amman, Jordan.
Al-Qudah K.A 2001, Floods as Water Resource and as a Hazard in Arid Regions: a Case Study in Southern Jordan. Jordan Journal of Civil Engineering, 5(1).
Al-Weshah, R. A. and El-Khoury, F., 1999. Flood analysis and mitigation for Petra area in Jordan. Journal of water resources planning and management, 125(3), 170- 177.
Apayden, H., Sonomez, F.K., Yildirim, Y.E., 2004. Spatial interpolation techniques for climate data in the GAP region in Turkey, Department of Farm Structures 133
and Irrigation, Faculty of Agriculture, University of Ankara, 06110 Diskapi, Ankara, Turkey.
Awawdeh, M., Al Shraideh, S., Al Qudah, K., and Jaradat, R., (2012). Rainwater harvesting assessment for a small size urban area in Jordan. International Journal of Water Resources and Environmental Engineering Vol. 4(12), pp. 415-422.
Baharli, B., 2015. Estimation of Reservoir Storage Capacity by using Residual Mass Curve, Assam downtown University, Guwahati, Assam, India.
Bell, F.C., 1969. Generalized Rainfall-Duration-Frequency Relationships. Journal of Hydraulic Division, ASCE, 95, 311-327.
Chen, C.L., 1983. Rainfall Intensity-Duration-Frequency Formulas. Journal of Hydraulic Engineering. Vol. 109, No. 12, pp: 1603-1621.
Chow, V.T., Maidment, D.R., and Mays, L.W., 1988. Applied Hydrology. McGraw- Hill, New York, pp: 572. Cowan, G., 1998. Statistical Data Analysis, oxford science publications, Clarendon Press. Department of Agriculture, Natural Resources Conservation Service, 1972. Hydrographs. National Engineering Handbook, Part 630, Section 4. Washington, DC.
Edsel, B.D., 2011. Watershed Modeling and its Applications: A State-of-the-Art Review. The Open Hydrology Journal. 5, pp: 26–50.
Electricité de France, 1995. French electric utility company, largely owned by the French state, Paris.
El-Sayed, E., 2017. Development of synthetic rainfall distribution curves for Sinai area. Ain Shams Engineering Journal, pp. 217-233.
Erturk, A. L. I., Gurel, M., Baloch, M. A., Dikerler, T., Varol, E., Akbulut, N., & Tanik, A., 2006. Application of watershed modeling system (WMS) for integrated management of a watershed in Turkey. Journal of Environmental Science and Health Part A, 41(9), 2045-2056.
Faiers, G.E., Keim, B.D., and Muller, R.A., 1997. Rainfall Frequency/Magnitude Atlas for the South Central United States. Geosciences Publications. Louisiana State University. Baton Rouge, LA.
Goodman, L. A., 1954. Kolmogorov-Smirnov tests for psychological research. Psychological Bulletin, 51(2), 160-168.
Gupta, H.V, Sorooshian, S., Yapo, P.O, 1998. Toward improved calibration of hydrologic models: Multiple and no commensurable, Water Resources Research – pp: 751-763.
134
Hadadin, N, 2005. Rainfall Intensity-Duration-Frequency Relationship in the Mujib Basin in Jordan, Journal of Applied Siences-pp:1777-1784.
Hadadin, N., 2012. Evaluation of several techniques for estimating storm water runoff in arid watersheds. Environmental Earth Sciences, pp: 1773–1782.
Hershfield, D.M., 1961. Estimating the probable maximum precipitation. Proceedings American Society of Civil Engineers, Journal Hydraulics Division 87 (HY5), pp: 99–106.
Hydrologic Engineering Center (HEC), 1990. HEC-1 Technical Reference Manual, U.S. Army Corps of Engineers Hydrologic Engineering Center, Davis, CA.
Hydrologic Engineering Center (HEC), 2000. HEC-HMS Technical Reference Manual, U.S. Army Corps of Engineers Hydrologic Engineering Center, Davis, CA.
Jordan Department of Statistics (JDOS), 2015.
Jordan Ministry of Agricultural (MOA), 2015.
Jordan Ministry of Public Works and Housing (MoPWH), 2015.
Jordan Ministry of Water and Irrigation (MWI), 2009. Water for Life: Jordan’s Water Strategy, (2008 – 2022).
Jordan Ministry of Water and Irrigation (MWI), 2015. Jordan Water Sector Facts and Figures. Report.
McCuen, R.H., 1998. Hydrologic Analysis and Design, 2nd ed., Prentice Hall, Upper Saddle River, NJ.
Meteorological Department of Jordan (JMD), 2005. Open File.
Mishra S.K., Singh V.P, 2013, Soil conservation service curve number (SCS-CN) methodology, Springer Science & Business Media.
National Program on Technology Enhanced Learning (NPTEL), 2012. Lecture1, Introduction to flood routing, Bombai.
National Resources Conservation Service (NRCS), 1972. Chapter 22, Design hydrographs.
National Resources Conservation Service (NRCS), 2010. Chapter 3, Premelinary investigations.
Natural Resources Authority of Jordan (NRA), 2015.
135
Nazzal, M. S. and Al-Salihi, A., 2008. Surface Water Hydrological Characterization and Modeling of Amman Zarqa Basin. Master-Thesis. The University of Jordan, Amman, Jordan.
Nortcliff, S., Carr, G., Potter, R.B., and Darmame, K., 2008 “Jordan’s Water Resources: Challenges for the Future.” Geographical Paper No. 185, the University of Reading, United Kingdom.
O'connor, D.S., 1979. Design of Urban Highway Drainage Federal Highway Administration. Saint Louis, Missouri.
Overton, D.E., Meadows, M.E., 1976. Stormwater Design, Part IV: Stochastic Stormwater Modeling, Tennesse.
Raghunath, H.M., 2006. Hydrology Principles Analysis Design. New Age International, New Delhi, pp: 280.
Roeck. G, 2001. Stochastic system identification for operational modal analysis: a review - Journal of Dynamic Systems, Measurement. Royal Jordanian Geographic Center (RJGC), 2015.
Sando, S.K., 1998. Techniques for Estimating Peak-Flow Magnitude and Frequency Relations for South Dakota Streams- Water-Resources Investigations Report.
Shatanawi, S., 2015. Flash floods mitigation and risk management of Wadis draining to the Gulf of Aqaba. Master-Thesis, The University of Jordan, Amman, Jordan.
Shrestha S., Shrestha M., Shrestha P.K., 2017. “Evaluation of the SWAT model performance for simulating river discharge in the Himalayan and tropical basins of Asia”
Silveira, L., Charbonneir, F., Genta, J.A. 2000. The antecedent soil moisture condition of the curve number procedure- Hydrological Sciences Journal.
Singh, S.K. and Bardossy A., 2012. Improving the calibration strategy of the physically-based model WaSiM-ETH using critical events. Hydrological Sicences Journal-pp:1487-1505.
Smirnov, N. 1948. "Table for estimating the goodness of fit of empirical distributions". Annals of Mathematical Statistics. pp: 279–281.
Swain, P., 2016. Water Resources Engineering, Lecture Notes- Department of Civil Engineering, VSSUT, Burla.
Thiessen, A.H., 1911. Precipitation averages for large areas, Monthly weather Review, Vol. 39. pp: 1082-1084.
Thiessen, A.H., 1911. Precipitation averages for large areas, Monthly weather Review, Vol. 39. pp: 1082-1084. 136
Thompson, S.A., 1999. Hydrology for water management- A.A. Balkema, Netherlands.
U.S Army Corps of Engineers, 1993. Kinematic wave routing using HEC-1 simulation. Washington, DC.
U.S. Department of Agriculture, Natural Resources Conservation Service, 2010. Time of Concentration. National Engineering Handbook, Part 630, Chapter 15. Washington, DC.
U.S. Department of Agriculture, Natural Resources Conservation Service, 2007. Hydrographs. National Engineering Handbook, Part 630, Chapter 16. Washington, DC.
U.S. Department of Agriculture, Natural Resources Conservation Service, 2004. Estimation of direct runoff from storm rainfall. National Engineering Handbook, Part 630, Chapter 10. Washington, DC.
U.S. Department of Agriculture, Natural Resources Conservation Service, 1983. National Engineering Handbook, Hydrology, Section 4. Washington, DC.
U.S. Department of Agriculture, Natural Resources Conservation Service, 1964, reprinted in 1985. National Engineering Handbook, Section 4. Hydrology, Washington, DC.
USWRC, 1982. Guidelines for determining flood flow frequency, Bull, U.S Gov. print. office, Washinton, DC.
Viessman W., Jr., Gary L., John W., 1989. Introduction to hydrology, New York Harper.
Water Authority of Jordan (WAJ) and Meteorological Department of Jordan (JMD), Open File, 2005.
Water Authority of Jordan (WAJ), 2005. Open File.
Water Authority of Jordan WAJ, 2015.
Zeiders, M. (2002, Retrieved 2008). Introduction to ArcMap. Penn State.
Websites:
137
United States Geological Survey (USGS) Website (http://gdex.cr.usgs.gov/gdex/)
World Wide Elevation Map Finder (MAPLOGS) (http://elevation.maplogs.com/)
Global water information system developed by FOA (http://www.fao.org/nr/water/aquastat/main/index.stm)
Online platform for live world statistics (http://www.worldometers.info/ar/)
Mathwave Data Analysis and Simulation (http://www.mathwave.com/products/easyfit.html)
National Program on Technology Enhanced Learning (http://nptel.ac.in/)
ArcGIS Desktop 10.1 help, ESRI, (2012), (http://webhelp.esri.com/arcgisdesktop/10.1/index.cfm?TopicName=Using%20kriging) Accessed, September 2015.
XMS Wiki (WMS: WMS User Manual 10.1) (http://www.xmswiki.com/wiki/Main_Page) Accessed, March 2017.
ArcGIS Desktop 10.1 help, ESRI, (2012), (http://webhelp.esri.com/arcgisdesktop/10.1/index.cfm?TopicName=Using%20kriging) Accessed, December 2017.
XMS Wiki (WMS: WMS User Manual 10.1) (http://www.xmswiki.com/wiki/Main_Page) Accessed, October 2017.
APPENDICES
Appendix A: Rainfall data (A.1): Rainfall records.
Table A1.1: Rainfall records for DG0001, MWI 138
Max P Max P in Max P in in 24 No Year 24 hrs. No Year 24 hrs. No Year hrs. (mm) (mm) (mm) 1 11/19/1937 21 29 1/18/1965 68 57 1/12/1993 25 2 11/7/1938 49 30 11/9/1966 24 58 3/12/1994 53 3 11/9/1939 49 31 3/24/1967 24 59 2/6/1995 25 4 12/29/1940 26 32 2/1/1968 20 60 1/18/1996 35.6 5 3/2/1941 45 33 4/15/1969 31 61 2/21/1997 26 6 2/19/1942 27.5 34 3/10/1970 43 62 1/11/1998 55.2 7 3/21/1943 56 35 12/28/1971 43 63 2/8/1999 45 8 12/31/1944 24.5 36 3/21/1972 30 64 1/30/2000 24 9 3/26/1945 35 37 12/16/1973 10.6 65 5/2/2001 34 10 1/9/1946 19.5 38 1/31/1974 41 66 2/13/2002 38.2 11 3/18/1947 33 39 2/20/1975 69 67 1/15/2003 13.4 12 2/15/1948 35 40 3/12/1976 30 68 1/13/2004 66.4 13 2/8/1949 30 41 4/12/1977 22 69 1/5/2005 37.3 14 2/21/1950 25.5 42 12/11/1978 18 70 2/14/2006 35 15 2/18/1951 42 43 1/9/1979 35 71 2/3/2007 27 16 2/2/1952 42 44 12/11/1980 51 72 1/31/2008 37 17 4/1/1953 17 45 3/27/1981 15 73 12/7/2009 28.6 18 4/2/1954 58 46 11/8/1982 22 74 2/26/2010 42.2 19 12/7/1955 20 47 1/24/1983 35 75 2/1/2011 7 20 3/28/1956 14 48 12/13/1984 22 76 1/30/2012 14.1 21 4/6/1957 21 49 2/15/1985 32 77 2/2/2013 41 22 12/8/1958 11 50 4/3/1986 20.5 78 5/7/2014 32.8 23 3/1/1959 30 51 3/18/1987 37.5 79 10/25/2015 22.4 24 1/1/1960 26 52 3/4/1988 67 80 3/26/2016 48.5 25 2/1/1961 25.5 53 3/14/1989 19.6 81 2/17/2017 24.6 26 1/25/1962 21.5 54 4/2/1990 53 27 12/3/1963 54 55 3/22/1991 65 28 12/11/1964 23 56 2/10/1992 30.6
Table A.1.2: Rainfall records for DG0002, MWI
No Year P in 24 hrs. (mm)
1 12/3/1963 47.2 2 2/29/1964 28 139
3 3/22/1965 21 4 2/2/1966 29 5 5/15/1967 40 6 11/25/1968 22.5 7 3/22/1969 22 8 3/10/1970 55.5 9 12/27/1971 40 10 3/21/1972 30 11 11/22/1973 10.5 12 2/11/1974 23 13 2/20/1975 67 14 3/12/1976 33.8 15 12/14/1977 27.7 16 12/12/1978 22 17 11/29/1979 40 18 12/10/1980 66.3 19 3/25/1981 19.5 20 11/8/1982 19.5 21 1/19/1983 44 22 3/26/1984 21.5 23 3/22/1985 27.7
Table A.1.3: Rainfall records for DG0003, MWI P in 24 hrs. P in 24 hrs. No Year (mm) No Year (mm) 1 12/4/1963 41 29 3/22/1991 64 2 2/2/1964 29 30 12/14/1992 25.5 140
3 3/23/1965 14.5 31 1/12/1993 24 4 2/1/1966 14.1 32 3/12/1994 50.2 5 2/4/1967 12.2 33 2/6/1995 23 6 11/25/1968 19.5 34 1/18/1996 36.3 7 3/22/1969 19 35 2/21/1997 24 8 3/10/1970 43 36 11/1/1998 43 9 12/18/1971 27 37 12/27/1999 37.2 10 4/29/1972 24 38 1/30/2000 26 11 1/15/1973 14 39 5/2/2001 32 12 2/11/1974 33 40 2/13/2002 37.1 13 2/20/1975 84.7 41 1/15/2003 14.1 14 3/12/1976 33.8 42 1/13/2004 56.6 15 1/6/1977 32 43 12/26/2005 4.5 16 12/12/1978 18 44 12/27/2006 30 17 1/9/1979 30 45 1/3/2007 28 18 2/24/1980 24.5 46 1/31/2008 35 19 4/30/1981 17 47 12/7/2009 30 20 1/31/1982 11 48 2/26/2010 42.4 21 3/25/1983 6.4 49 4/4/2011 16 22 1/14/1984 29.2 50 1/30/2012 14.1 23 2/15/1985 32 51 1/31/2013 61.2 24 4/2/1986 23.2 52 5/7/2014 33 25 3/24/1987 5.1 53 10/25/2015 22.6 26 3/4/1988 20 54 3/26/2016 48 27 3/14/1989 19 55 2/17/2017 24 28 4/2/1990 50
141
(A.2): Statistical distributions ranking
Table A.2.1: Probability distributions test results for Wadi Musa rainfall station
Table A.2.2: Probability distributions test results for Hai rainfall station
Table A.2.3: Probability distributions test results for Petra rainfall station
142
Figure A.2.1: Statistical distributions graphs for Wadi Musa station
Figure A.2.2: Statistical distributions graphs for Hai station 143
Figure A.2.3: Statistical distributions graphs for Petra station (A.3): (Bell, Chen and Hershfield) Rainfall data.
Table A.3.1: Rainfall data for DG0001, Bell method 24 5- 10- 20- 30- 60- 120- 180- 360- 720- Year hrs. min min min min min min min min Min 11/19/1937 21 2.73 4.20 5.88 7.14 9.24 11.97 13.23 15.75 18.48 11/7/1938 49 6.37 9.80 13.72 16.66 21.56 27.93 30.87 36.75 43.12 11/9/1939 49 6.37 9.80 13.72 16.66 21.56 27.93 30.87 36.75 43.12 12/29/1940 26 3.38 5.20 7.28 8.84 11.44 14.82 16.38 19.50 22.88 3/2/1941 45 5.85 9.00 12.60 15.30 19.80 25.65 28.35 33.75 39.60 2/19/1942 27.5 3.58 5.50 7.70 9.35 12.10 15.68 17.33 20.63 24.20 3/21/1943 56 7.28 11.20 15.68 19.04 24.64 31.92 35.28 42.00 49.28 12/31/1944 24.5 3.19 4.90 6.86 8.33 10.78 13.97 15.44 18.38 21.56 3/26/1945 35 4.55 7.00 9.80 11.90 15.40 19.95 22.05 26.25 30.80 1/9/1946 19.5 2.54 3.90 5.46 6.63 8.58 11.12 12.29 14.63 17.16 3/18/1947 33 4.29 6.60 9.24 11.22 14.52 18.81 20.79 24.75 29.04 2/15/1948 35 4.55 7.00 9.80 11.90 15.40 19.95 22.05 26.25 30.80 2/8/1949 30 3.90 6.00 8.40 10.20 13.20 17.10 18.90 22.50 26.40 2/21/1950 25.5 3.32 5.10 7.14 8.67 11.22 14.54 16.07 19.13 22.44 2/18/1951 42 5.46 8.40 11.76 14.28 18.48 23.94 26.46 31.50 36.96 2/2/1952 42 5.46 8.40 11.76 14.28 18.48 23.94 26.46 31.50 36.96 4/1/1953 17 2.21 3.40 4.76 5.78 7.48 9.69 10.71 12.75 14.96 4/2/1954 58 7.54 11.60 16.24 19.72 25.52 33.06 36.54 43.50 51.04 12/7/1955 20 2.60 4.00 5.60 6.80 8.80 11.40 12.60 15.00 17.60 3/28/1956 14 1.82 2.80 3.92 4.76 6.16 7.98 8.82 10.50 12.32 4/6/1957 21 2.73 4.20 5.88 7.14 9.24 11.97 13.23 15.75 18.48 12/8/1958 11 1.43 2.20 3.08 3.74 4.84 6.27 6.93 8.25 9.68 144
Table A.3.1: Rainfall data for DG0001, Bell method-Contd. 24 5- 10- 20- 30- 60- 120- 180- 360- 720- Year hrs. min min min min min min min min Min 3/1/1959 30 3.90 6.00 8.40 10.20 13.20 17.10 18.90 22.50 26.40 1/1/1960 26 3.38 5.20 7.28 8.84 11.44 14.82 16.38 19.50 22.88 2/1/1961 25.5 3.32 5.10 7.14 8.67 11.22 14.54 16.07 19.13 22.44 1/25/1962 21.5 2.80 4.30 6.02 7.31 9.46 12.26 13.55 16.13 18.92 12/3/1963 54 7.02 10.80 15.12 18.36 23.76 30.78 34.02 40.50 47.52 12/11/1964 23 2.99 4.60 6.44 7.82 10.12 13.11 14.49 17.25 20.24 1/18/1965 68 8.84 13.60 19.04 23.12 29.92 38.76 42.84 51.00 59.84 11/9/1966 24 3.12 4.80 6.72 8.16 10.56 13.68 15.12 18.00 21.12 3/24/1967 24 3.12 4.80 6.72 8.16 10.56 13.68 15.12 18.00 21.12 2/1/1968 20 2.60 4.00 5.60 6.80 8.80 11.40 12.60 15.00 17.60 4/15/1969 31 4.03 6.20 8.68 10.54 13.64 17.67 19.53 23.25 27.28 3/10/1970 43 5.59 8.60 12.04 14.62 18.92 24.51 27.09 32.25 37.84 12/28/1971 43 5.59 8.60 12.04 14.62 18.92 24.51 27.09 32.25 37.84 3/21/1972 30 3.90 6.00 8.40 10.20 13.20 17.10 18.90 22.50 26.40 12/16/1973 10.6 1.38 2.12 2.97 3.60 4.66 6.04 6.68 7.95 9.33 1/31/1974 41 5.33 8.20 11.48 13.94 18.04 23.37 25.83 30.75 36.08 2/20/1975 69 8.97 13.80 19.32 23.46 30.36 39.33 43.47 51.75 60.72 3/12/1976 30 3.90 6.00 8.40 10.20 13.20 17.10 18.90 22.50 26.40 4/12/1977 22 2.86 4.40 6.16 7.48 9.68 12.54 13.86 16.50 19.36 12/11/1978 18 2.34 3.60 5.04 6.12 7.92 10.26 11.34 13.50 15.84 1/9/1979 35 4.55 7.00 9.80 11.90 15.40 19.95 22.05 26.25 30.80 12/11/1980 51 6.63 10.20 14.28 17.34 22.44 29.07 32.13 38.25 44.88 3/27/1981 15 1.95 3.00 4.20 5.10 6.60 8.55 9.45 11.25 13.20 11/8/1982 22 2.86 4.40 6.16 7.48 9.68 12.54 13.86 16.50 19.36 1/24/1983 35 4.55 7.00 9.80 11.90 15.40 19.95 22.05 26.25 30.80 12/13/1984 22 2.86 4.40 6.16 7.48 9.68 12.54 13.86 16.50 19.36 2/15/1985 32 4.16 6.40 8.96 10.88 14.08 18.24 20.16 24.00 28.16 4/3/1986 20.5 2.67 4.10 5.74 6.97 9.02 11.69 12.92 15.38 18.04 3/18/1987 37.5 4.88 7.50 10.50 12.75 16.50 21.38 23.63 28.13 33.00 3/4/1988 67 8.71 13.40 18.76 22.78 29.48 38.19 42.21 50.25 58.96 3/14/1989 19.6 2.55 3.92 5.49 6.66 8.62 11.17 12.35 14.70 17.25 4/2/1990 53 6.89 10.60 14.84 18.02 23.32 30.21 33.39 39.75 46.64 3/22/1991 65 8.45 13.00 18.20 22.10 28.60 37.05 40.95 48.75 57.20 2/10/1992 30.6 3.98 6.12 8.57 10.40 13.46 17.44 19.28 22.95 26.93 1/12/1993 25 3.25 5.00 7.00 8.50 11.00 14.25 15.75 18.75 22.00 3/12/1994 53 6.89 10.60 14.84 18.02 23.32 30.21 33.39 39.75 46.64 2/6/1995 25 3.25 5.00 7.00 8.50 11.00 14.25 15.75 18.75 22.00 1/18/1996 35.6 4.63 7.12 9.97 12.10 15.66 20.29 22.43 26.70 31.33 2/21/1997 26 3.38 5.20 7.28 8.84 11.44 14.82 16.38 19.50 22.88 1/11/1998 55.2 7.18 11.04 15.46 18.77 24.29 31.46 34.78 41.40 48.58 2/8/1999 45 5.85 9.00 12.60 15.30 19.80 25.65 28.35 33.75 39.60 1/30/2000 24 3.12 4.80 6.72 8.16 10.56 13.68 15.12 18.00 21.12 145
Table A.3.1: Rainfall data for DG0001, Bell method-Contd. 24 5- 10- 20- 30- 60- 120- 180- 360- 720- Year hrs. min min min min min min min min min 5/2/2001 34 4.42 6.80 9.52 11.56 14.96 19.38 21.42 25.50 29.92 2/13/2002 38.2 4.97 7.64 10.70 12.99 16.81 21.77 24.07 28.65 33.62 1/15/2003 13.4 1.74 2.68 3.75 4.56 5.90 7.64 8.44 10.05 11.79 1/13/2004 66.4 8.63 13.28 18.59 22.58 29.22 37.85 41.83 49.80 58.43 1/5/2005 37.3 4.85 7.46 10.44 12.68 16.41 21.26 23.50 27.98 32.82 2/14/2006 35 4.55 7.00 9.80 11.90 15.40 19.95 22.05 26.25 30.80 2/3/2007 27 3.51 5.40 7.56 9.18 11.88 15.39 17.01 20.25 23.76 1/31/2008 37 4.81 7.40 10.36 12.58 16.28 21.09 23.31 27.75 32.56 12/7/2009 28.6 3.72 5.72 8.01 9.72 12.58 16.30 18.02 21.45 25.17 2/26/2010 42.2 5.49 8.44 11.82 14.35 18.57 24.05 26.59 31.65 37.14 2/1/2011 7 0.91 1.40 1.96 2.38 3.08 3.99 4.41 5.25 6.16 1/30/2012 14.1 1.83 2.82 3.95 4.79 6.20 8.04 8.88 10.58 12.41 2/2/2013 41 5.33 8.20 11.48 13.94 18.04 23.37 25.83 30.75 36.08 5/7/2014 32.8 4.26 6.56 9.18 11.15 14.43 18.70 20.66 24.60 28.86 10/25/2015 22.4 2.91 4.48 6.27 7.62 9.86 12.77 14.11 16.80 19.71 3/26/2016 48.5 6.31 9.70 13.58 16.49 21.34 27.65 30.56 36.38 42.68 2/17/2017 24.6 3.20 4.92 6.89 8.36 10.82 14.02 15.50 18.45 21.65
Table A.3.2: Rainfall data for DG0001, Hershfield method 24 5- 10- 20- 30- 60- Year 120-min hrs. min min min min min 11/19/1937 21 2.68 4.16 5.73 7.30 9.24 11.55 11/7/1938 49 6.25 9.70 13.37 17.03 21.56 26.95 11/9/1939 49 6.25 9.70 13.37 17.03 21.56 26.95 12/29/1940 26 3.32 5.15 7.09 9.04 11.44 14.30 3/2/1941 45 5.74 8.91 12.28 15.64 19.80 24.75 2/19/1942 27.5 3.51 5.45 7.50 9.56 12.10 15.13 3/21/1943 56 7.15 11.09 15.28 19.47 24.64 30.80 12/31/1944 24.5 3.13 4.85 6.68 8.52 10.78 13.48 3/26/1945 35 4.47 6.93 9.55 12.17 15.40 19.25 1/9/1946 19.5 2.49 3.86 5.32 6.78 8.58 10.73 3/18/1947 33 4.21 6.53 9.00 11.47 14.52 18.15 2/15/1948 35 4.47 6.93 9.55 12.17 15.40 19.25 2/8/1949 30 3.83 5.94 8.18 10.43 13.20 16.50 2/21/1950 25.5 3.25 5.05 6.96 8.86 11.22 14.03 2/18/1951 42 5.36 8.32 11.46 14.60 18.48 23.10 2/2/1952 42 5.36 8.32 11.46 14.60 18.48 23.10 4/1/1953 17 2.17 3.37 4.64 5.91 7.48 9.35 4/2/1954 58 7.40 11.48 15.82 20.16 25.52 31.90 12/7/1955 20 2.55 3.96 5.46 6.95 8.80 11.00 3/28/1956 14 1.79 2.77 3.82 4.87 6.16 7.70 4/6/1957 21 2.68 4.16 5.73 7.30 9.24 11.55 12/8/1958 11 1.40 2.18 3.00 3.82 4.84 6.05 146
Table A.3.2: Rainfall data for DG0001, Hershfield method-Contd. 24 5- 10- 20- 30- 60- Year 120-min hrs. min min min min min 3/1/1959 30 3.83 5.94 8.18 10.43 13.20 16.50 1/1/1960 26 3.32 5.15 7.09 9.04 11.44 14.30 2/1/1961 25.5 3.25 5.05 6.96 8.86 11.22 14.03 1/25/1962 21.5 2.74 4.26 5.87 7.47 9.46 11.83 12/3/1963 54 6.89 10.69 14.73 18.77 23.76 29.70 12/11/1964 23 2.93 4.55 6.27 7.99 10.12 12.65 1/18/1965 68 8.68 13.46 18.55 23.64 29.92 37.40 11/9/1966 24 3.06 4.75 6.55 8.34 10.56 13.20 3/24/1967 24 3.06 4.75 6.55 8.34 10.56 13.20 2/1/1968 20 2.55 3.96 5.46 6.95 8.80 11.00 4/15/1969 31 3.96 6.14 8.46 10.78 13.64 17.05 3/10/1970 43 5.49 8.51 11.73 14.95 18.92 23.65 12/28/1971 43 5.49 8.51 11.73 14.95 18.92 23.65 3/21/1972 30 3.83 5.94 8.18 10.43 13.20 16.50 12/16/1973 10.6 1.35 2.10 2.89 3.68 4.66 5.83 1/31/1974 41 5.23 8.12 11.18 14.25 18.04 22.55 2/20/1975 69 8.80 13.66 18.82 23.98 30.36 37.95 3/12/1976 30 3.83 5.94 8.18 10.43 13.20 16.50 4/12/1977 22 2.81 4.36 6.00 7.65 9.68 12.10 12/11/1978 18 2.30 3.56 4.91 6.26 7.92 9.90 1/9/1979 35 4.47 6.93 9.55 12.17 15.40 19.25 12/11/1980 51 6.51 10.10 13.91 17.73 22.44 28.05 3/27/1981 15 1.91 2.97 4.09 5.21 6.60 8.25 11/8/1982 22 2.81 4.36 6.00 7.65 9.68 12.10 1/24/1983 35 4.47 6.93 9.55 12.17 15.40 19.25 12/13/1984 22 2.81 4.36 6.00 7.65 9.68 12.10 2/15/1985 32 4.08 6.34 8.73 11.12 14.08 17.60 4/3/1986 20.5 2.62 4.06 5.59 7.13 9.02 11.28 3/18/1987 37.5 4.79 7.43 10.23 13.04 16.50 20.63 3/4/1988 67 8.55 13.27 18.28 23.29 29.48 36.85 3/14/1989 19.6 2.50 3.88 5.35 6.81 8.62 10.78 4/2/1990 53 6.76 10.49 14.46 18.42 23.32 29.15 3/22/1991 65 8.29 12.87 17.73 22.59 28.60 35.75 2/10/1992 30.6 3.90 6.06 8.35 10.64 13.46 16.83 1/12/1993 25 3.19 4.95 6.82 8.69 11.00 13.75 3/12/1994 53 6.76 10.49 14.46 18.42 23.32 29.15 2/6/1995 25 3.19 4.95 6.82 8.69 11.00 13.75 1/18/1996 35.6 4.54 7.05 9.71 12.37 15.66 19.58 2/21/1997 26 3.32 5.15 7.09 9.04 11.44 14.30 1/11/1998 55.2 7.04 10.93 15.06 19.19 24.29 30.36 2/8/1999 45 5.74 8.91 12.28 15.64 19.80 24.75 1/30/2000 24 3.06 4.75 6.55 8.34 10.56 13.20 147
Table A.3.2: Rainfall data for DG0001, Hershfield method-Contd. 24 5- 10- 20- 30- 60- Year 120-min hrs. min min min min min 5/2/2001 34 4.34 6.73 9.28 11.82 14.96 18.70 2/13/2002 38.2 4.87 7.56 10.42 13.28 16.81 21.01 1/15/2003 13.4 1.71 2.65 3.66 4.66 5.90 7.37 1/13/2004 66.4 8.47 13.15 18.11 23.08 29.22 36.52 1/5/2005 37.3 4.76 7.39 10.18 12.97 16.41 20.52 2/14/2006 35 4.47 6.93 9.55 12.17 15.40 19.25 2/3/2007 27 3.45 5.35 7.37 9.39 11.88 14.85 1/31/2008 37 4.72 7.33 10.09 12.86 16.28 20.35 12/7/2009 28.6 3.65 5.66 7.80 9.94 12.58 15.73 2/26/2010 42.2 5.38 8.36 11.51 14.67 18.57 23.21 2/1/2011 7 0.89 1.39 1.91 2.43 3.08 3.85 1/30/2012 14.1 1.80 2.79 3.85 4.90 6.20 7.76 2/2/2013 41 5.23 8.12 11.18 14.25 18.04 22.55 5/7/2014 32.8 4.19 6.49 8.95 11.40 14.43 18.04 10/25/2015 22.4 2.86 4.44 6.11 7.79 9.86 12.32 3/26/2016 48.5 6.19 9.60 13.23 16.86 21.34 26.68 2/17/2017 24.6 3.14 4.87 6.71 8.55 10.82 13.53
Table A.3.3: Rainfall data for DG0002, Bell method 24 - 5- 10- 20- 30- 60- 120- 180- 360- 720- Year hrs. min min min min min min min min min 12/3/1963 47.2 6.14 9.44 13.22 16.05 20.77 26.90 29.74 35.40 41.54 2/29/1964 28.00 3.64 5.60 7.84 9.52 12.32 15.96 17.64 21.00 24.64 3/22/1965 21.00 2.73 4.20 5.88 7.14 9.24 11.97 13.23 15.75 18.48 2/2/1966 29.00 3.77 5.80 8.12 9.86 12.76 16.53 18.27 21.75 25.52 5/15/1967 40.00 5.20 8.00 11.20 13.60 17.60 22.80 25.20 30.00 35.20 11/25/1968 22.50 2.93 4.50 6.30 7.65 9.90 12.83 14.18 16.88 19.80 3/22/1969 22.00 2.86 4.40 6.16 7.48 9.68 12.54 13.86 16.50 19.36 3/10/1970 55.50 7.22 11.10 15.54 18.87 24.42 31.64 34.97 41.63 48.84 12/27/1971 40.00 5.20 8.00 11.20 13.60 17.60 22.80 25.20 30.00 35.20 3/21/1972 30.00 3.90 6.00 8.40 10.20 13.20 17.10 18.90 22.50 26.40 11/22/1973 10.50 1.37 2.10 2.94 3.57 4.62 5.99 6.62 7.88 9.24 2/11/1974 23.00 2.99 4.60 6.44 7.82 10.12 13.11 14.49 17.25 20.24 2/20/1975 67.00 8.71 13.40 18.76 22.78 29.48 38.19 42.21 50.25 58.96 3/12/1976 33.80 4.39 6.76 9.46 11.49 14.87 19.27 21.29 25.35 29.74 12/14/1977 27.70 3.60 5.54 7.76 9.42 12.19 15.79 17.45 20.78 24.38 12/12/1978 22.00 2.86 4.40 6.16 7.48 9.68 12.54 13.86 16.50 19.36 11/29/1979 40.00 5.20 8.00 11.20 13.60 17.60 22.80 25.20 30.00 35.20 12/10/1980 66.30 8.62 13.26 18.56 22.54 29.17 37.79 41.77 49.73 58.34 3/25/1981 19.50 2.54 3.90 5.46 6.63 8.58 11.12 12.29 14.63 17.16 11/8/1982 19.50 2.54 3.90 5.46 6.63 8.58 11.12 12.29 14.63 17.16 148
Table A.3.3: Rainfall data for DG0002, Bell method-Contd. 24 - 5- 10- 20- 30- 60- 120- 180- 360- 720- Year hrs. min min min min min min min min min 1/19/1983 44.00 5.72 8.80 12.32 14.96 19.36 25.08 27.72 33.00 38.72 3/26/1984 21.50 2.80 4.30 6.02 7.31 9.46 12.26 13.55 16.13 18.92 3/22/1985 27.70 3.60 5.54 7.76 9.42 12.19 15.79 17.45 20.78 24.38
Table A.3.4: Rainfall data for DG0002, Hershfield method Year 24 hrs. 5-min 10-min 20-min 30-min 60-min 120-min 12/3/1963 47.20 6.14 9.44 13.22 16.05 20.77 26.90 2/29/1964 28.00 3.64 5.60 7.84 9.52 12.32 15.96 3/22/1965 21.00 2.73 4.20 5.88 7.14 9.24 11.97 2/2/1966 29.00 3.77 5.80 8.12 9.86 12.76 16.53 5/15/1967 40.00 5.20 8.00 11.20 13.60 17.60 22.80 11/25/1968 22.50 2.93 4.50 6.30 7.65 9.90 12.83 3/22/1969 22.00 2.86 4.40 6.16 7.48 9.68 12.54 3/10/1970 55.50 7.22 11.10 15.54 18.87 24.42 31.64 12/27/1971 40.00 5.20 8.00 11.20 13.60 17.60 22.80 3/21/1972 30.00 3.90 6.00 8.40 10.20 13.20 17.10 11/22/1973 10.50 1.37 2.10 2.94 3.57 4.62 5.99 2/11/1974 23.00 2.99 4.60 6.44 7.82 10.12 13.11 2/20/1975 67.00 8.71 13.40 18.76 22.78 29.48 38.19 3/12/1976 33.80 4.39 6.76 9.46 11.49 14.87 19.27 12/14/1977 27.70 3.60 5.54 7.76 9.42 12.19 15.79 12/12/1978 22.00 2.86 4.40 6.16 7.48 9.68 12.54 11/29/1979 40.00 5.20 8.00 11.20 13.60 17.60 22.80 12/10/1980 66.30 8.62 13.26 18.56 22.54 29.17 37.79 3/25/1981 19.50 2.54 3.90 5.46 6.63 8.58 11.12 11/8/1982 19.50 2.54 3.90 5.46 6.63 8.58 11.12 1/19/1983 44.00 5.72 8.80 12.32 14.96 19.36 25.08 3/26/1984 21.50 2.80 4.30 6.02 7.31 9.46 12.26 3/22/1985 27.70 3.60 5.54 7.76 9.42 12.19 15.79
Table A.3.5: Rainfall data for DG0003, Bell method 24 – 5- 10- 20- 30- 60- 120- 180- 360- 720- Year hrs. min min min min min min min min min 12/4/1963 41.00 5.33 8.20 11.48 13.94 18.04 23.37 25.83 30.75 36.08 2/2/1964 29.00 3.77 5.80 8.12 9.86 12.76 16.53 18.27 21.75 25.52 3/23/1965 14.50 1.89 2.90 4.06 4.93 6.38 8.27 9.14 10.88 12.76 2/1/1966 14.10 1.83 2.82 3.95 4.79 6.20 8.04 8.88 10.58 12.41 2/4/1967 12.20 1.59 2.44 3.42 4.15 5.37 6.95 7.69 9.15 10.74 11/25/1968 19.50 2.54 3.90 5.46 6.63 8.58 11.12 12.29 14.63 17.16 3/22/1969 19.00 2.47 3.80 5.32 6.46 8.36 10.83 11.97 14.25 16.72 3/10/1970 43.00 5.59 8.60 12.04 14.62 18.92 24.51 27.09 32.25 37.84 149
Table A.3.5: Rainfall data for DG0003, Bell method-Contd. 24 – 5- 10- 20- 30- 60- 120- 180- 360- 720- Year hrs. min min min min min min min min min 12/18/1971 27.00 3.51 5.40 7.56 9.18 11.88 15.39 17.01 20.25 23.76 4/29/1972 24.00 3.12 4.80 6.72 8.16 10.56 13.68 15.12 18.00 21.12 1/15/1973 14.00 1.82 2.80 3.92 4.76 6.16 7.98 8.82 10.50 12.32 2/11/1974 33.00 4.29 6.60 9.24 11.22 14.52 18.81 20.79 24.75 29.04 2/20/1975 84.70 11.01 16.94 23.72 28.80 37.27 48.28 53.36 63.53 74.54 3/12/1976 33.80 4.39 6.76 9.46 11.49 14.87 19.27 21.29 25.35 29.74 1/6/1977 32.00 4.16 6.40 8.96 10.88 14.08 18.24 20.16 24.00 28.16 12/12/1978 18.00 2.34 3.60 5.04 6.12 7.92 10.26 11.34 13.50 15.84 1/9/1979 30.00 3.90 6.00 8.40 10.20 13.20 17.10 18.90 22.50 26.40 2/24/1980 24.50 3.19 4.90 6.86 8.33 10.78 13.97 15.44 18.38 21.56 4/30/1981 17.00 2.21 3.40 4.76 5.78 7.48 9.69 10.71 12.75 14.96 1/31/1982 11.00 1.43 2.20 3.08 3.74 4.84 6.27 6.93 8.25 9.68 3/25/1983 6.40 0.83 1.28 1.79 2.18 2.82 3.65 4.03 4.80 5.63 1/14/1984 29.20 3.80 5.84 8.18 9.93 12.85 16.64 18.40 21.90 25.70 2/15/1985 32.00 4.16 6.40 8.96 10.88 14.08 18.24 20.16 24.00 28.16 4/2/1986 23.20 3.02 4.64 6.50 7.89 10.21 13.22 14.62 17.40 20.42 3/24/1987 5.10 0.66 1.02 1.43 1.73 2.24 2.91 3.21 3.83 4.49 3/4/1988 20.00 2.60 4.00 5.60 6.80 8.80 11.40 12.60 15.00 17.60 3/14/1989 19.00 2.47 3.80 5.32 6.46 8.36 10.83 11.97 14.25 16.72 4/2/1990 50.00 6.50 10.00 14.00 17.00 22.00 28.50 31.50 37.50 44.00 3/22/1991 64.00 8.32 12.80 17.92 21.76 28.16 36.48 40.32 48.00 56.32 12/14/1992 25.50 3.32 5.10 7.14 8.67 11.22 14.54 16.07 19.13 22.44 1/12/1993 24.00 3.12 4.80 6.72 8.16 10.56 13.68 15.12 18.00 21.12 3/12/1994 50.20 6.53 10.04 14.06 17.07 22.09 28.61 31.63 37.65 44.18 2/6/1995 23.00 2.99 4.60 6.44 7.82 10.12 13.11 14.49 17.25 20.24 1/18/1996 36.30 4.72 7.26 10.16 12.34 15.97 20.69 22.87 27.23 31.94 2/21/1997 24.00 3.12 4.80 6.72 8.16 10.56 13.68 15.12 18.00 21.12 11/1/1998 43.00 5.59 8.60 12.04 14.62 18.92 24.51 27.09 32.25 37.84 12/27/1999 37.20 4.84 7.44 10.42 12.65 16.37 21.20 23.44 27.90 32.74 1/30/2000 26.00 3.38 5.20 7.28 8.84 11.44 14.82 16.38 19.50 22.88 5/2/2001 32.00 4.16 6.40 8.96 10.88 14.08 18.24 20.16 24.00 28.16 2/13/2002 37.10 4.82 7.42 10.39 12.61 16.32 21.15 23.37 27.83 32.65 1/15/2003 14.10 1.83 2.82 3.95 4.79 6.20 8.04 8.88 10.58 12.41 1/13/2004 56.60 7.36 11.32 15.85 19.24 24.90 32.26 35.66 42.45 49.81 12/26/2005 4.50 0.59 0.90 1.26 1.53 1.98 2.57 2.84 3.38 3.96 12/27/2006 30.00 3.90 6.00 8.40 10.20 13.20 17.10 18.90 22.50 26.40 1/3/2007 28.00 3.64 5.60 7.84 9.52 12.32 15.96 17.64 21.00 24.64 1/31/2008 35.00 4.55 7.00 9.80 11.90 15.40 19.95 22.05 26.25 30.80 12/7/2009 30.00 3.90 6.00 8.40 10.20 13.20 17.10 18.90 22.50 26.40 150
Table A.3.5: Rainfall data for DG0003, Bell method-Contd. 24 – 5- 10- 20- 30- 60- 120- 180- 360- 720- Year hrs. min min min min min min min min min 2/26/2010 42.40 5.51 8.48 11.87 14.42 18.66 24.17 26.71 31.80 37.31 4/4/2011 16.00 2.08 3.20 4.48 5.44 7.04 9.12 10.08 12.00 14.08 1/30/2012 14.10 1.83 2.82 3.95 4.79 6.20 8.04 8.88 10.58 12.41 1/31/2013 61.20 7.96 12.24 17.14 20.81 26.93 34.88 38.56 45.90 53.86 5/7/2014 33.00 4.29 6.60 9.24 11.22 14.52 18.81 20.79 24.75 29.04 10/25/2015 22.60 2.94 4.52 6.33 7.68 9.94 12.88 14.24 16.95 19.89 3/26/2016 48.00 6.24 9.60 13.44 16.32 21.12 27.36 30.24 36.00 42.24 2/17/2017 24.00 3.12 4.80 6.72 8.16 10.56 13.68 15.12 18.00 21.12
Table A.3.6: Rainfall data for DG0003, Hershfield method Year 24 hrs. 5-min 10-min 20-min 30-min 60-min 120-min 12/4/1963 41.00 5.23 8.12 11.18 14.25 18.04 22.55 2/2/1964 29.00 3.70 5.74 7.91 10.08 12.76 15.95 3/23/1965 14.50 1.85 2.87 3.96 5.04 6.38 7.98 2/1/1966 14.10 1.80 2.79 3.85 4.90 6.20 7.76 2/4/1967 12.20 1.56 2.42 3.33 4.24 5.37 6.71 11/25/1968 19.50 2.49 3.86 5.32 6.78 8.58 10.73 3/22/1969 19.00 2.42 3.76 5.18 6.60 8.36 10.45 3/10/1970 43.00 5.49 8.51 11.73 14.95 18.92 23.65 12/18/1971 27.00 3.45 5.35 7.37 9.39 11.88 14.85 4/29/1972 24.00 3.06 4.75 6.55 8.34 10.56 13.20 1/15/1973 14.00 1.79 2.77 3.82 4.87 6.16 7.70 2/11/1974 33.00 4.21 6.53 9.00 11.47 14.52 18.15 2/20/1975 84.70 10.81 16.77 23.11 29.44 37.27 46.59 3/12/1976 33.80 4.31 6.69 9.22 11.75 14.87 18.59 1/6/1977 32.00 4.08 6.34 8.73 11.12 14.08 17.60 12/12/1978 18.00 2.30 3.56 4.91 6.26 7.92 9.90 1/9/1979 30.00 3.83 5.94 8.18 10.43 13.20 16.50 2/24/1980 24.50 3.13 4.85 6.68 8.52 10.78 13.48 4/30/1981 17.00 2.17 3.37 4.64 5.91 7.48 9.35 1/31/1982 11.00 1.40 2.18 3.00 3.82 4.84 6.05 3/25/1983 6.40 0.82 1.27 1.75 2.22 2.82 3.52 1/14/1984 29.20 3.73 5.78 7.97 10.15 12.85 16.06 2/15/1985 32.00 4.08 6.34 8.73 11.12 14.08 17.60 4/2/1986 23.20 2.96 4.59 6.33 8.06 10.21 12.76 3/24/1987 5.10 0.65 1.01 1.39 1.77 2.24 2.81 3/4/1988 20.00 2.55 3.96 5.46 6.95 8.80 11.00 3/14/1989 19.00 2.42 3.76 5.18 6.60 8.36 10.45 4/2/1990 50.00 6.38 9.90 13.64 17.38 22.00 27.50 3/22/1991 64.00 8.17 12.67 17.46 22.25 28.16 35.20 12/14/1992 25.50 3.25 5.05 6.96 8.86 11.22 14.03 151
Table A.3.6: Rainfall data for DG0003, Hershfield method-Contd. Year 24 hrs. 5-min 10-min 20-min 30-min 60-min 120-min 1/12/1993 24.00 3.06 4.75 6.55 8.34 10.56 13.20 3/12/1994 50.20 6.41 9.94 13.69 17.45 22.09 27.61 2/6/1995 23.00 2.93 4.55 6.27 7.99 10.12 12.65 1/18/1996 36.30 4.63 7.19 9.90 12.62 15.97 19.97 2/21/1997 24.00 3.06 4.75 6.55 8.34 10.56 13.20 11/1/1998 43.00 5.49 8.51 11.73 14.95 18.92 23.65 12/27/1999 37.20 4.75 7.37 10.15 12.93 16.37 20.46 1/30/2000 26.00 3.32 5.15 7.09 9.04 11.44 14.30 5/2/2001 32.00 4.08 6.34 8.73 11.12 14.08 17.60 2/13/2002 37.10 4.73 7.35 10.12 12.90 16.32 20.41 1/15/2003 14.10 1.80 2.79 3.85 4.90 6.20 7.76 1/13/2004 56.60 7.22 11.21 15.44 19.67 24.90 31.13 12/26/2005 4.50 0.57 0.89 1.23 1.56 1.98 2.48 12/27/2006 30.00 3.83 5.94 8.18 10.43 13.20 16.50 1/3/2007 28.00 3.57 5.54 7.64 9.73 12.32 15.40 1/31/2008 35.00 4.47 6.93 9.55 12.17 15.40 19.25 12/7/2009 30.00 3.83 5.94 8.18 10.43 13.20 16.50 2/26/2010 42.40 5.41 8.40 11.57 14.74 18.66 23.32 4/4/2011 16.00 2.04 3.17 4.36 5.56 7.04 8.80 1/30/2012 14.10 1.80 2.79 3.85 4.90 6.20 7.76 1/31/2013 61.20 7.81 12.12 16.70 21.27 26.93 33.66 5/7/2014 33.00 4.21 6.53 9.00 11.47 14.52 18.15 10/25/2015 22.60 2.88 4.47 6.17 7.86 9.94 12.43 3/26/2016 48.00 6.12 9.50 13.09 16.68 21.12 26.40 2/17/2017 24.00 3.06 4.75 6.55 8.34 10.56 13.20
152
Table A.3.7: Percentile differences – Bell method Vs MWI – Wadi Musa station
Difference percentage Duration 2 yrs. 5 yrs. 10 yrs. 25 yrs. 50 yrs. 100 yrs. Average 5.00 64.39% 13.29% 2.72% 2.85% 5.45% 5.50% 15.70% 10.00 89.68% 37.60% 21.93% 20.93% 12.62% 3.23% 31.00% 20.00 94.74% 53.72% 42.92% 33.03% 22.19% 5.84% 42.08% 30.00 81.20% 67.26% 48.61% 36.01% 19.18% 9.68% 43.66% 60.00 93.50% 69.17% 50.74% 30.90% 21.34% 10.20% 45.97% 120.00 73.11% 83.65% 68.03% 22.65% 38.20% 8.50% 49.02% 180.00 97.37% 71.57% 54.31% 28.50% 24.69% 9.60% 47.67% 360.00 88.99% 42.97% 24.27% 6.59% 4.30% 4.20% 28.55% 1440.00 70.30% 46.64% 24.27% 4.98% 6.75% 1.90% 25.81% Average of the Average = 36.61%
Table A.2.8: Percentile differences – Chen method Vs MWI – Wadi Musa station
Difference percentage Duration 2 yrs. 5 yrs. 10 yrs. 25 yrs. 50 yrs. 100 yrs. Average 5 90.60% 15.30% 0.15% 6.70% 8.84% 29.24% 25.14% 10 83.60% 42.62% 21.07% 18.27% 10.57% 3.92% 30.01% 20 91.08% 62.95% 45.13% 33.05% 22.70% 7.48% 43.73% 30 82.10% 73.62% 47.78% 27.74% 17.19% 9.06% 42.91% 60 89.70% 73.99% 48.52% 28.63% 18.22% 10.17% 44.87% 120 88.00% 79.56% 57.38% 37.48% 27.99% 20.48% 51.81% 180 72.10% 69.45% 46.01% 26.13% 16.66% 8.27% 39.77% 360 91.04% 41.56% 17.87% 0.42% 10.24% 16.62% 29.62% 1440 93.33% 51.78% 23.22% 2.53% 8.57% 13.83% 32.21% Average of the Average = 37.79%
Table A.2.9: Percentile differences - Hershfield method Vs MWI – Wadi Musa station
Difference percentage Duration 2 yrs. 5 yrs. 10 yrs. 25 yrs. 50 yrs. 100 yrs. Average 5 61.35% 90.11% 23.23% 10.22% 0.11% 3.53% 31.43% 10 87.78% 80.20% 50.97% 31.96% 25.69% 15.89% 48.75% 20 89.73% 81.54% 65.98% 52.22% 36.06% 22.79% 58.05% 30 73.41% 71.69% 75.60% 66.09% 21.69% 15.41% 53.98% 60 82.09% 66.30% 70.21% 64.79% 19.05% 9.60% 52.01% 120 91.20% 63.50% 73.54% 50.62% 30.10% 22.50% 55.24% 1440 69.02% 59.09% 62.51% 35.85% 10.20% 3.07% 39.96% Average of the Average = 48.49% 153
Appendix B: Extreme quantiles, Intensity values (B.1): Extreme rainfall quantiles
Table B.1.1: Extreme rainfall quantiles for DG0001, Bell method Extreme rainfall quantiles Duration(min) 2 yr 5 yr 10 yr 25 yr 50 yr 100 yr 200 yr 5 4.52 5.95 6.59 7.21 7.56 7.86 8.12 10 6.95 9.15 10.14 11.09 11.64 12.10 12.49 20 9.74 12.81 14.20 15.52 16.29 16.94 17.48 30 11.82 15.56 17.24 18.85 19.78 20.56 21.23 60 15.30 20.13 22.31 24.39 25.60 26.61 27.47 120 19.82 26.08 28.90 31.59 33.17 34.48 35.59 180 21.91 28.82 31.94 34.92 36.66 38.10 39.33 360 26.08 34.31 38.03 41.57 43.64 45.36 46.82 720 30.60 40.26 44.62 48.78 51.21 53.22 54.94 1440 34.77 45.75 50.70 55.43 58.19 60.48 62.43
Table B.1.2: Extreme rainfall quantiles for DG0001, Hershfield method Extreme rainfall quantiles (mm) Duration 2 yr. 5 yr. 10 yr. 25 yr. 50 yr. 100 yr. 200 yr. (mm) 5 4.44 5.84 6.47 7.07 7.42 7.72 7.97 10 6.89 9.06 10.04 10.97 11.52 11.98 12.36 20 9.49 12.48 13.83 15.12 15.87 16.50 17.03 30 12.09 15.90 17.62 19.27 20.23 21.02 21.70 60 15.30 20.13 22.31 24.39 25.60 26.61 27.47 120 19.13 25.16 27.89 30.49 32.00 33.27 34.34 1440 34.77 45.75 50.70 55.43 58.19 60.48 62.43
Table B.1.3: Extreme rainfall quantiles for DG0002, Bell method Extreme rainfall quantiles (mm)
Duration 2 yr. 5 yr. 10 yr. 25 yr. 50 yr. 100 yr. 200 yr. (mm) 5 3.99 5.74 6.84 8.15 9.09 9.98 10.85 10 6.13 8.83 10.52 12.54 13.98 15.35 16.69 20 8.58 12.36 14.72 17.56 19.57 21.50 23.36 30 10.42 15.01 17.88 21.32 23.76 26.10 28.37 60 13.49 19.42 23.14 27.59 30.75 33.78 36.71 120 17.48 25.16 29.97 35.75 39.84 43.76 47.56 180 19.32 27.81 33.13 39.51 44.03 48.36 52.57 360 22.99 33.11 39.44 47.04 52.42 57.58 62.58 720 26.98 38.85 46.28 55.19 61.50 67.56 73.43 1440 30.66 44.14 52.59 62.72 69.89 76.77 83.44 154
Table B.1.4: Extreme rainfall quantiles for DG0002, Hershfield method Extreme rainfall quantiles (mm) Duration 2 yr 5 yr 10 yr 25 yr 50 yr 100 yr 200 yr (min) 5 3.99 5.74 6.84 8.15 9.09 9.98 10.85 10 6.13 8.83 10.52 12.54 13.98 15.35 16.69 20 8.58 12.36 14.72 17.56 19.57 21.50 23.36 30 10.42 15.01 17.88 21.32 23.76 26.10 28.37 60 13.49 19.42 23.14 27.59 30.75 33.78 36.71 120 17.48 25.16 29.97 35.75 39.84 43.76 47.56 1440 30.66 44.14 52.59 62.72 69.89 76.77 83.44
Table B.1.5: Extreme rainfall quantiles for DG0003, Bell method Extreme rainfall quantiles (mm) Duration 2 yr 5 yr 10 yr 25 yr 50 yr 100 yr 200 yr (mm) 5 3.44 5.27 6.46 7.92 8.96 9.97 10.95 10 5.28 8.11 9.95 12.18 13.78 15.33 16.85 20 7.40 11.36 13.92 17.05 19.30 21.47 23.58 30 8.98 13.79 16.91 20.71 23.43 26.07 28.64 60 11.63 17.85 21.88 26.80 30.32 33.74 37.06 120 15.06 23.13 28.35 34.71 39.28 43.70 48.01 180 16.65 25.56 31.33 38.37 43.42 48.30 53.06 360 19.82 30.43 37.30 45.67 51.69 57.50 63.17 720 23.25 35.70 43.76 53.59 60.64 67.47 74.12 1440 26.42 40.57 49.73 60.90 68.91 76.67 84.23
Table B.1.6: Extreme rainfall quantiles for DG0003, Hershfield method Extreme rainfall quantiles (mm) Duartion 2 yr 5 yr 10 yr 25 yr 50 yr 100 yr 200 yr (min) 5 3.37 5.18 6.35 7.77 8.79 9.78 10.75 10 5.23 8.03 9.85 12.06 13.64 15.18 16.68 20 7.21 11.07 13.57 16.61 18.80 20.92 22.98 30 9.19 14.10 17.29 21.17 23.95 26.65 29.28 60 11.63 17.85 21.88 26.80 30.32 33.74 37.06 120 14.53 22.31 27.35 33.49 37.90 42.17 46.33 1440 26.42 40.57 49.73 60.90 68.91 76.67 84.23
155
(B.2): Rainfall intensities
Table B.2.1: Rainfall Intensities for DG0001, Bell method Rainfall Intensities (mm/hr.)
Duration (hr.) 2 yr 5 yr 10 yr 25 yr 50 yr 100 yr 200 yr
0.08 54.25 71.37 79.10 86.47 90.77 94.35 97.39 0.17 41.73 54.90 60.84 66.51 69.83 72.58 74.92 0.33 29.21 38.43 42.59 46.56 48.88 50.81 52.44 0.50 23.65 31.11 34.48 37.69 39.57 41.13 42.45 1.00 15.30 20.13 22.31 24.39 25.60 26.61 27.47 2.00 9.91 13.04 14.45 15.80 16.58 17.24 17.79 3.00 7.30 9.61 10.65 11.64 12.22 12.70 13.11 6.00 4.35 5.72 6.34 6.93 7.27 7.56 7.80 12.00 2.55 3.36 3.72 4.06 4.27 4.44 4.58 24.00 1.45 1.91 2.11 2.31 2.42 2.52 2.60
Table B.2.2: Rainfall Intensities for DG0001, Chen method Rainfall Intensities (mm/hr.) Duration 2yr. 5yr. 10yr. 25yr. 50yr. 100yr. 200 yr. (hr.) 0.08 66.72 72.64 77.12 83.04 87.52 91.99 96.47 0.17 52.27 56.90 60.41 65.05 68.56 72.06 75.57 0.33 37.42 40.74 43.25 46.57 49.08 51.59 54.10 0.50 29.66 32.29 34.28 36.92 38.91 40.90 42.89 1.00 19.02 20.70 21.98 23.67 24.94 26.22 27.50 2.00 11.71 12.75 13.53 14.57 15.36 16.14 16.93 3.00 8.72 9.49 10.07 10.85 11.43 12.02 12.60 6.00 5.20 5.66 6.01 6.47 6.82 7.17 7.52 12.00 3.08 3.35 3.56 3.83 4.04 4.24 4.45 24.00 1.81 1.97 2.09 2.26 2.38 2.50 2.62
Table B.2.3: Rainfall Intensities for DG0002, Hershfield method Rainfall Intensities (mm/hr) Duration (hr.) 2 yr 5 yr 10 yr 25 yr 50 yr 100 yr 200 yr
0.08 53.25 70.05 77.64 84.87 89.10 92.61 95.59 0.17 41.31 54.35 60.24 65.85 69.13 71.85 74.17 0.33 28.46 37.44 41.50 45.36 47.62 49.50 51.09 0.50 24.18 31.81 35.25 38.53 40.45 42.05 43.40 1.00 15.30 20.13 22.31 24.39 25.60 26.61 27.47 2.00 9.56 12.58 13.94 15.24 16.00 16.63 17.17 24.00 1.45 1.91 2.11 2.31 2.42 2.52 2.60
156
Table B.2.4: Rainfall Intensities for DG0002, Bell method Rainfall Intensities (mm/hr.) Duration 2 yr 5 yr 10 yr 25 yr 50 yr 100 yr 200 yr (hr.) 0.08 47.83 68.86 82.04 97.84 109.03 119.76 130.17 0.17 36.79 52.97 63.11 75.26 83.87 92.12 100.13 0.33 25.75 37.08 44.17 52.68 58.71 64.49 70.09 0.5 20.85 30.02 35.76 42.65 47.52 52.20 56.74 1 13.49 19.42 23.14 27.59 30.75 33.78 36.71 2 8.74 12.58 14.99 17.87 19.92 21.88 23.78 3 6.44 9.27 11.04 13.17 14.68 16.12 17.52 6 3.83 5.52 6.57 7.84 8.74 9.60 10.43 12 2.25 3.24 3.86 4.60 5.13 5.63 6.12 24 1.28 1.84 2.19 2.61 2.91 3.20 3.48
Table B.2.5 Rainfall Intensities for DG0002, Chen method. Rainfall Intensities (mm/hr.) Duration 2 yr 5 yr 10 yr 25 yr 50 yr 100 yr 200 yr (hr.) 0.08 47.83 68.86 82.04 97.84 109.03 119.76 130.17 0.17 36.79 52.97 63.11 75.26 83.87 92.12 100.13 0.33 25.75 37.08 44.17 52.68 58.71 64.49 70.09 0.5 20.85 30.02 35.76 42.65 47.52 52.20 56.74 1 13.49 19.42 23.14 27.59 30.75 33.78 36.71 2 8.74 12.58 14.99 17.87 19.92 21.88 23.78 3 6.44 9.27 11.04 13.17 14.68 16.12 17.52 6 3.83 5.52 6.57 7.84 8.74 9.60 10.43 12 2.25 3.24 3.86 4.60 5.13 5.63 6.12 24 1.28 1.84 2.19 2.61 2.91 3.20 3.48
Table B.2.6: Rainfall Intensities for DG0002, Hershfield method Rainfall Intensities (mm/hr.) Duration 2 yr 5 yr 10 yr 25 yr 50 yr 100 yr 200 yr (hr.) 0.08 47.83 68.86 82.04 97.84 109.03 119.76 130.17 0.17 36.79 52.97 63.11 75.26 83.87 92.12 100.13 0.33 25.75 37.08 44.17 52.68 58.71 64.49 70.09 0.5 20.85 30.02 35.76 42.65 47.52 52.20 56.74 1 13.49 19.42 23.14 27.59 30.75 33.78 36.71 2 8.74 12.58 14.99 17.87 19.92 21.88 23.78 24 1.28 1.84 2.19 2.61 2.91 3.20 3.48
157
Table B.2.7: Rainfall Intensities for DG0003, Bell method Rainfall Intensities (mm/hr.) Duration 2 yr. 5 yr. 10 yr. 25 yr. 50 yr. 100 yr. 200 yr. (hr.) 0.08 41.22 63.29 77.58 95.00 107.51 119.61 131.40 0.17 31.71 48.68 59.68 73.08 82.70 92.01 101.07 0.33 22.20 34.08 41.77 51.16 57.89 64.40 70.75 0.5 17.97 27.59 33.82 41.41 46.86 52.14 57.28 1 11.63 17.85 21.88 26.80 30.32 33.74 37.06 2 7.53 11.56 14.17 17.36 19.64 21.85 24.01 3 5.55 8.52 10.44 12.79 14.47 16.10 17.69 6 3.30 5.07 6.22 7.61 8.61 9.58 10.53 12 1.94 2.98 3.65 4.47 5.05 5.62 6.18 24 1.10 1.69 2.07 2.54 2.87 3.19 3.51
Table B.2.8: Rainfall Intensities for DG0003, Chen method Rainfall Intensities (mm/hr.) Duration 2yr. 5yr. 10yr. 25yr. 50yr. 100yr. 200 yr. (hr.) 0.08 50.01 64.55 75.54 90.08 101.08 112.08 123.07 0.17 39.18 50.56 59.18 70.57 79.18 87.80 96.41 0.33 28.05 36.20 42.37 50.52 56.69 62.85 69.02 0.5 22.23 28.70 33.58 40.05 44.94 49.83 54.71 1 14.25 18.40 21.53 25.68 28.81 31.94 35.08 2 8.78 11.33 13.26 15.81 17.74 19.67 21.60 3 6.53 8.43 9.87 11.77 13.20 14.64 16.08 6 3.90 5.03 5.89 7.02 7.88 8.74 9.59 12 2.31 2.98 3.48 4.15 4.66 5.17 5.68 24 1.36 1.75 2.05 2.45 2.75 3.04 3.34
Table B.2.9: Rainfall Intensities for DG0003, Hershfield method Rainfall Intensifies (mm/hr.) Duration 2 yr. 5 yr. 10 yr. 25 yr. 50 yr. 100 yr. 200 yr. (hr.) 0.08 40.46 62.12 76.15 93.25 105.52 117.40 128.97 0.17 31.39 48.20 59.08 72.35 81.87 91.09 100.06 0.33 21.63 33.20 40.70 49.84 56.40 62.75 68.93 0.5 18.37 28.21 34.57 42.34 47.91 53.30 58.56 1 11.63 17.85 21.88 26.80 30.32 33.74 37.06 2 7.27 11.16 13.68 16.75 18.95 21.09 23.16 24 1.10 1.69 2.07 2.54 2.87 3.20 3.51
158
Appendix C: IDF Curves
(C.1): IDF Curves
Figure C.1.1: IDF Curves for DG0001, Bell method
Figure C.1.2: IDF Curves for DG0001, Chen method
Figure C.1.3: IDF Curves for DG0001, Hershfield method 159
Figure C.1.4: IDF Curves for DG0002, Bell method
Figure C.1.5: IDF Curves for DG0002, Chen method
Figure C.1.6: IDF Curves for DG0002, Hershfield method
160
Figure C.1.7: IDF Curves for DG0003, Bell method
Figure C.1.8: IDF Curves for DG0003, Chen method
Figure C.1.9: IDF Curves for DG0003, Hershfield method 161
(C.2): IDF Curves parameters, Percentage differences
Table C.2.1: IDF curves/Intensity equation parameters, for 2 yrs. return period 2-YR IDF equation parameters Station name Method a b c Bell 372.15 7.92 0.76 Chen 523.93 9.00 0.78 Wadi Musa Hershfield 298.80 6.39 0.71 Average 398.29 7.77 0.75 Bell 327.86 7.86 0.76 Chen 402.33 8.48 0.77 Hai Hershfield 256.41 5.88 0.70 Average 328.87 7.41 0.74 Bell 256.71 7.31 0.73 Chen 346.98 8.41 0.77 Petra Hershfield 227.03 6.38 0.71 Average 276.91 7.37 0.74
Table C.2.2: IDF curves/Intensity equation parameters, for 5 yrs. return period 5-YR IDF equation parameters Station name Method a b c Bell 601.17 9.50 0.81 Chen 538.59 8.48 0.77 Wadi Musa Hershfield 393.10 6.38 0.71 Average 510.95 8.12 0.76 Bell 499.66 8.42 0.77 Chen 621.56 9.94 0.81 Hai Hershfield 366.53 5.85 0.70 Average 495.91 8.07 0.76 Bell 459.05 8.45 0.77 Chen 465.79 8.36 0.77 Petra Hershfield 348.58 6.38 0.71 Average 424.48 7.73 0.75 162
Table C.2.3: IDF curves/Intensity equation parameters, for 10 yrs. return period 10-YR IDF equation parameters Station name Method a b c Bell 625.67 9.65 0.78 Chen 540.41 8.5 0.77 Wadi Musa Hershfield 435.65 6.38 0.71
Average 533.91 8.18 0.75 Bell 567.59 8.11 0.76 Chen 594.05 8.47 0.77 Hai Hershfield 436.64 5.85 0.7
Average 532.76 7.48 0.74 Bell 637.1 8.77 0.8 Chen 557.96 8.4 0.77 Petra Hershfield 427.29 6.38 0.71
Average 540.78 7.85 0.76
Table C.2.4: IDF curves/Intensity equation parameters, for 25 yrs. return period 25-YR IDF equation parameters Station name Method a b c Bell 776.79 9.99 0.82 Chen 613.07 8.42 0.77 Wadi Musa Hershfield 476.29 6.39 0.71 Average 622.05 8.27 0.77 Bell 719.65 8.33 0.77 Chen 698.24 8.41 0.77 Hai Hershfield 520.73 5.85 0.70 Average 646.20 7.53 0.75 Bell 528.65 5.95 0.72 Chen 678.10 8.40 0.77 Petra Hershfield 523.29 6.39 0.71 Average 576.68 6.91 0.73
163
Table C.2.5: IDF curves/Intensity equation parameters, for 50 yrs. return period 50-YR IDF Equation Parameters Station Name Method a b c Bell 741.04 8.76 0.80 Chen 642.10 8.33 0.77 Wadi Musa Hershfield 499.95 6.38 0.71 Average 627.70 7.82 0.76 Bell 716.59 7.05 0.76 Chen 779.58 8.40 0.77 Hai Hershfield 580.30 5.85 0.70 Average 692.16 7.10 0.74 Bell 687.97 7.27 0.74 Chen 771.68 8.42 0.77 Petra Hershfield 200.00 7.00 0.71 Average 553.22 7.56 0.74
Table C.2.6: IDF Curves/Intensity equation Parameters, for 100 yrs. Return period 100-YR IDF Equation Parameters Station Name Method a b c
Bell 698.53 8.60 0.77
Chen 679.30 8.42 0.77 Wadi Musa Hershfield 519.72 6.39 0.71
Average 632.52 7.80 0.75
Bell 849.14 8.01 0.77
Chen 856.00 8.34 0.77 Hai Hershfield 637.42 5.85 0.70
Average 780.85 7.40 0.75
Bell 656.12 5.55 0.72
Chen 859.60 8.39 0.77 Petra Hershfield 658.83 6.39 0.71
Average 724.85 6.78 0.73
164
Table C.2.7: IDF Curves/Intensity equation Parameters, for 200 yrs. Return period
Station 200-YR IDF Equation Parameters Method Name a b c
Bell 638.61 7.03 0.76
Chen 711.81 8.40 0.77 Wadi Musa Hershfield 536.41 6.38 0.71
Average 628.94 7.27 0.75
Bell 867.85 7.51 0.75
Chen 948.48 8.47 0.77 Hai Hershfield 692.84 5.85 0.70
Average 836.39 7.28 0.74
Bell 926.85 7.98 0.77
Chen 950.61 8.39 0.77 Petra Hershfield 723.71 6.38 0.71
Average 867.06 7.59 0.75
Table C.2.8: Rainfall intensity difference percentage, Bell Vs MWI, DG0002 Difference percentage Duration 2 yr 5 yr 10 yr 25 yr 50 yr 100 yr Average 5.00 44.94% 9.31% 6.54% 9.93% 13.57% 7.88% 15.36% 10.00 67.24% 32.76% 26.46% 36.83% 35.27% 22.83% 36.90% 20.00 71.70% 48.32% 48.23% 50.52% 46.77% 34.35% 49.98% 30.00 78.19% 61.38% 54.14% 47.57% 43.15% 39.21% 53.94% 60.00 77.50% 63.22% 56.34% 49.97% 45.74% 41.93% 55.78% 120.00 82.04% 77.20% 74.27% 68.62% 65.99% 63.28% 71.90% 180.00 74.02% 65.54% 60.05% 53.14% 49.76% 45.24% 57.96% 360.00 66.63% 37.95% 28.89% 20.61% 14.95% 11.58% 30.10% 1440.00 82.50% 41.49% 28.89% 18.78% 12.00% 10.30% 32.33% Average of the Average = 44.92%
165
Table C.2.9: Rainfall intensity difference percentage, Chen Vs MWI, DG0002 Difference percentage Duration 2 yr 5 yr 10 yr 25 yr 50 yr 100 yr Average 5.00 64.48% 9.39% 3.88% 6.32% 10.10% 10.18% 17.39% 10.00 93.27% 35.30% 25.56% 34.77% 33.54% 21.96% 40.73% 20.00 102.93% 54.59% 50.52% 51.61% 48.18% 36.42% 57.38% 30.00 106.24% 64.71% 53.27% 45.55% 41.53% 38.42% 58.29% 60.00 103.55% 65.06% 54.04% 46.57% 42.77% 39.83% 58.64% 120.00 98.45% 70.34% 63.22% 56.66% 54.57% 52.93% 66.03% 180.00 91.63% 60.76% 51.43% 43.73% 40.89% 37.42% 54.31% 360.00 83.95% 34.29% 22.25% 13.47% 8.40% 10.11% 28.74% 1440.00 110.62% 43.99% 27.80% 16.83% 10.42% 9.37% 36.51% Average of the Average = 46.45%
Table C.2.10 Rainfall intensity difference percentage, Hershfield Vs MWI, DG0002 Difference percentage Duration 2 yr 5 yr 10 yr 25 yr 50 yr 100 yr Average 5 44.94% 90.01% 30.22% 27.06% 22.50% 24.75% 39.91% 10 67.24% 81.52% 58.16% 50.82% 52.49% 48.59% 59.80% 20 71.70% 62.85% 76.69% 76.78% 67.73% 61.21% 69.49% 30 78.19% 71.90% 55.98% 83.82% 64.44% 57.24% 68.60% 60 77.50% 78.50% 60.20% 86.45% 67.13% 60.09% 71.64% 120 82.04% 76.52% 61.89% 85.09% 87.91% 82.33% 3.04% 1440 82.50% 81.69% 68.55% 53.71% 32.37% 23.03% 35.00% Average of the Average = 49.64%
Table C.2.11:Rainfall intensity difference percentage, Bell Vs MWI, DG0003 Difference percentage Duration 2 yr 5 yr 10 yr 25 yr 50 yr 100 yr Average 5.00 24.91% 0.46% 0.75% 6.75% 11.99% 7.99% 8.81% 10.00 44.13% 22.02% 19.59% 32.87% 33.38% 22.68% 29.11% 20.00 47.98% 36.32% 40.18% 46.16% 44.72% 34.18% 41.59% 30.00 53.58% 48.32% 45.76% 43.29% 41.15% 39.03% 45.19% 60.00 52.98% 50.01% 47.84% 45.63% 43.71% 41.75% 46.99% 120.00 56.89% 62.85% 64.80% 63.74% 63.67% 63.07% 62.51% 180.00 49.98% 52.14% 51.35% 48.71% 47.67% 45.06% 49.15% 360.00 43.61% 26.78% 21.89% 17.12% 13.35% 11.44% 22.36% 1440.00 57.29% 30.03% 21.89% 15.34% 10.44% 10.16% 24.19% Average of the Average = 36.65%
166
Table C.2.12: Rainfall intensity difference percentage, Chen Vs MWI, DG0002 Difference percentage Duration 2 yr 5 yr 10 yr 25 yr 50 yr 100 yr Average 5.00 42.40% 0.48% 1.77% 3.31% 8.63% 10.30% 11.15% 10.00 67.33% 24.28% 18.74% 30.96% 31.76% 21.80% 32.48% 20.00 75.70% 42.00% 42.34% 47.32% 46.20% 36.25% 48.30% 30.00 78.56% 51.30% 44.94% 41.44% 39.64% 38.25% 49.02% 60.00 76.24% 51.61% 45.66% 42.42% 40.86% 39.66% 49.41% 120.00 71.82% 56.47% 54.35% 52.23% 52.51% 52.73% 56.68% 180.00 65.92% 47.67% 43.20% 39.66% 39.01% 37.24% 45.45% 360.00 59.26% 23.35% 15.60% 10.26% 6.95% 11.00% 21.07% 1440.00 82.36% 32.27% 20.86% 13.52% 8.95% 9.23% 27.86% Average of the Average = 37.94%
Table C.2.13 Rainfall intensity difference percentage, Hershfield Vs MWI, DG0002 Difference percentage Duration 2 yr 5 yr 10 yr 25 yr 50 yr 100 yr Average 5 22.61% 88.25% 20.87% 21.10% 18.56% 22.29% 32.28% 10 42.69% 90.32% 48.07% 44.99% 48.85% 46.91% 53.64% 20 44.17% 83.64% 62.79% 67.25% 61.14% 56.87% 20.32% 30 57.01% 79.99% 85.87% 82.49% 65.77% 60.55% 33.50% 60 52.98% 71.52% 83.87% 81.05% 64.79% 59.89% 69.02% 120 51.39% 60.60% 92.61% 94.74% 78.79% 75.71% 75.64% 1440 57.29% 93.50% 59.39% 49.26% 30.52% 22.87% 52.14% Average of the Average = 48.08%
167
Appendix D: Rainfall distributions
(D.1): S-Curves ordinates
Table D.1.1: The hyetograph S-curve ordinates for Tc = 2.75 storm Tc=2.75 hrs. 2 yrs. 5 yrs. 10 yrs. 25 yrs. 50 yrs. 100 yrs. Avg. t(min) Petra Musa Hay Petra Musa Hay Petra Musa Hay Petra Musa Hay Petra Musa Hay Petra Musa Hay 0 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 8.38 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 13.98 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 19.58 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 25.18 0.05 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.05 0.04 0.04 0.04 0.04 0.04 0.05 0.04 0.04 0.04 30.78 0.06 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.06 0.06 0.05 0.06 0.05 0.05 0.06 0.05 0.05 0.05 36.38 0.07 0.07 0.07 0.07 0.06 0.07 0.06 0.07 0.07 0.07 0.07 0.07 0.07 0.06 0.07 0.07 0.07 0.07 0.07 41.98 0.09 0.08 0.08 0.08 0.07 0.08 0.07 0.08 0.08 0.09 0.08 0.08 0.09 0.07 0.08 0.09 0.08 0.08 0.08 47.58 0.11 0.10 0.10 0.10 0.09 0.10 0.09 0.10 0.10 0.11 0.10 0.10 0.10 0.09 0.10 0.11 0.10 0.10 0.10 53.18 0.13 0.12 0.12 0.12 0.11 0.12 0.11 0.12 0.12 0.13 0.12 0.12 0.12 0.11 0.12 0.13 0.12 0.12 0.12 58.78 0.15 0.14 0.14 0.14 0.13 0.14 0.13 0.14 0.14 0.15 0.14 0.14 0.15 0.13 0.14 0.15 0.14 0.14 0.14 64.38 0.18 0.17 0.17 0.17 0.15 0.17 0.15 0.17 0.17 0.18 0.17 0.16 0.17 0.15 0.17 0.18 0.16 0.17 0.17 69.98 0.21 0.20 0.20 0.20 0.19 0.20 0.19 0.20 0.20 0.21 0.20 0.20 0.21 0.19 0.20 0.21 0.20 0.20 0.20 75.58 0.26 0.25 0.25 0.25 0.23 0.24 0.23 0.25 0.25 0.26 0.25 0.24 0.25 0.23 0.24 0.25 0.24 0.24 0.25 81.18 0.33 0.32 0.32 0.32 0.31 0.32 0.31 0.32 0.32 0.32 0.32 0.32 0.32 0.31 0.31 0.32 0.32 0.32 0.32 86.78 0.62 0.62 0.62 0.62 0.63 0.62 0.63 0.62 0.62 0.62 0.62 0.63 0.62 0.63 0.63 0.63 0.62 0.63 0.63 92.38 0.72 0.73 0.73 0.73 0.74 0.73 0.74 0.72 0.72 0.72 0.72 0.73 0.72 0.74 0.73 0.72 0.73 0.73 0.73 97.98 0.77 0.78 0.78 0.78 0.80 0.78 0.80 0.78 0.78 0.77 0.78 0.79 0.78 0.80 0.79 0.77 0.79 0.79 0.78 103.58 0.81 0.82 0.82 0.82 0.83 0.82 0.84 0.82 0.82 0.81 0.82 0.82 0.82 0.84 0.82 0.81 0.82 0.82 0.82 109.18 0.84 0.85 0.85 0.85 0.86 0.85 0.86 0.85 0.85 0.84 0.85 0.85 0.85 0.86 0.85 0.84 0.85 0.85 0.85 114.78 0.87 0.87 0.87 0.88 0.89 0.88 0.89 0.88 0.87 0.87 0.87 0.88 0.87 0.89 0.88 0.87 0.88 0.88 0.88 120.38 0.89 0.90 0.89 0.90 0.91 0.90 0.91 0.90 0.89 0.89 0.89 0.90 0.89 0.91 0.90 0.89 0.90 0.90 0.90 125.98 0.91 0.91 0.91 0.91 0.92 0.92 0.92 0.92 0.91 0.91 0.91 0.92 0.91 0.92 0.91 0.91 0.92 0.92 0.91 131.58 0.92 0.93 0.93 0.93 0.94 0.93 0.94 0.93 0.93 0.92 0.93 0.93 0.93 0.94 0.93 0.92 0.93 0.93 0.93 137.18 0.94 0.94 0.94 0.94 0.95 0.95 0.95 0.95 0.94 0.94 0.94 0.95 0.94 0.95 0.94 0.94 0.95 0.94 0.94 142.78 0.95 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.95 0.96 0.96 0.95 0.96 0.96 0.95 0.96 0.96 0.96 148.38 0.97 0.97 0.97 0.97 0.97 0.97 0.97 0.97 0.97 0.97 0.97 0.97 0.97 0.97 0.97 0.97 0.97 0.97 0.97 153.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 0.98 159.58 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 0.99 165.18 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
168
Table D.1.2: The hyetograph S-curve ordinates for 30 min. storm 30 min t (min) 100 yr 50 yr 25 yr 10 yr 5 yr 2 yr Avg 2 0.01 0.01 0.01 0.01 0.01 0.01 0.01 3 0.03 0.03 0.03 0.03 0.03 0.03 0.03 4 0.04 0.04 0.04 0.04 0.04 0.04 0.04 5 0.06 0.06 0.06 0.06 0.06 0.06 0.06 6 0.08 0.08 0.08 0.08 0.08 0.08 0.08 7 0.10 0.10 0.10 0.10 0.10 0.10 0.10 8 0.12 0.12 0.12 0.12 0.12 0.12 0.12 9 0.14 0.14 0.14 0.14 0.14 0.14 0.14 10 0.16 0.17 0.17 0.17 0.17 0.17 0.17 11 0.19 0.20 0.20 0.20 0.20 0.20 0.20 12 0.23 0.23 0.24 0.24 0.24 0.24 0.23 13 0.27 0.28 0.28 0.28 0.28 0.28 0.28 14 0.32 0.33 0.33 0.33 0.33 0.33 0.33 15 0.38 0.39 0.39 0.39 0.39 0.39 0.39 16 0.58 0.58 0.58 0.58 0.58 0.58 0.58 17 0.66 0.65 0.65 0.65 0.65 0.65 0.65 18 0.72 0.71 0.70 0.70 0.70 0.71 0.71 19 0.76 0.75 0.75 0.75 0.75 0.75 0.75 20 0.80 0.79 0.79 0.79 0.79 0.79 0.79 21 0.83 0.82 0.82 0.82 0.82 0.82 0.82 22 0.86 0.85 0.85 0.85 0.85 0.85 0.85 23 0.88 0.88 0.88 0.87 0.88 0.88 0.88 24 0.90 0.90 0.90 0.90 0.90 0.90 0.90 25 0.92 0.92 0.92 0.92 0.92 0.92 0.92 26 0.94 0.94 0.94 0.94 0.94 0.94 0.94 27 0.96 0.95 0.96 0.95 0.96 0.96 0.96 28 0.97 0.97 0.97 0.97 0.97 0.97 0.97 29 0.99 0.99 0.99 0.99 0.99 0.99 0.99 30 1.00 1.00 1.00 1.00 1.00 1.00 1.00
169
Table D.1.3: The hyetograph S-curve ordinates for 1 hr. storm 60 min t (min) 100 yr 50 yr 25 yr 10 yr 5 yr 2 yr Avg 4 0.01 0.01 0.01 0.01 0.01 0.01 0.01 6 0.02 0.02 0.02 0.02 0.02 0.02 0.02 8 0.04 0.04 0.04 0.04 0.04 0.04 0.04 10 0.05 0.05 0.05 0.05 0.05 0.05 0.05 12 0.07 0.07 0.06 0.06 0.06 0.06 0.06 14 0.08 0.08 0.08 0.08 0.08 0.08 0.08 16 0.10 0.10 0.10 0.10 0.10 0.10 0.10 18 0.12 0.12 0.12 0.12 0.12 0.12 0.12 20 0.14 0.14 0.14 0.14 0.14 0.14 0.14 22 0.17 0.17 0.17 0.17 0.17 0.17 0.17 24 0.20 0.20 0.20 0.20 0.20 0.20 0.20 26 0.24 0.24 0.24 0.24 0.24 0.24 0.24 28 0.28 0.29 0.29 0.29 0.29 0.29 0.29 30 0.35 0.35 0.35 0.36 0.36 0.36 0.35 32 0.61 0.61 0.60 0.60 0.60 0.60 0.61 34 0.69 0.69 0.69 0.69 0.69 0.69 0.69 36 0.75 0.74 0.75 0.74 0.74 0.74 0.75 38 0.79 0.79 0.79 0.79 0.79 0.79 0.79 40 0.82 0.82 0.82 0.82 0.82 0.82 0.82 42 0.85 0.85 0.85 0.85 0.85 0.85 0.85 44 0.87 0.87 0.88 0.87 0.88 0.88 0.88 46 0.90 0.90 0.90 0.90 0.90 0.90 0.90 48 0.91 0.91 0.92 0.92 0.92 0.92 0.92 50 0.93 0.93 0.93 0.93 0.93 0.93 0.93 52 0.95 0.95 0.95 0.95 0.95 0.95 0.95 54 0.96 0.96 0.96 0.96 0.96 0.96 0.96 56 0.98 0.98 0.98 0.98 0.98 0.98 0.98 58 0.99 0.99 0.99 0.99 0.99 0.99 0.99 60 1.00 1.00 1.00 1.00 1.00 1.00 1.00
170
Table D.1.4: The hyetograph S-curve ordinates for 1.5 hrs. storm 90 min t (min) 100 yr 50 yr 25 yr 10 yr 5 yr 2 yr Avg 6 0.01 0.01 0.01 0.01 0.01 0.01 0.01 9 0.02 0.02 0.02 0.02 0.02 0.02 0.02 12 0.03 0.03 0.03 0.03 0.03 0.03 0.03 15 0.05 0.05 0.04 0.05 0.04 0.05 0.05 18 0.06 0.06 0.06 0.06 0.06 0.06 0.06 21 0.08 0.08 0.07 0.07 0.07 0.07 0.07 24 0.09 0.09 0.09 0.09 0.09 0.09 0.09 27 0.11 0.11 0.11 0.11 0.11 0.11 0.11 30 0.13 0.13 0.13 0.13 0.13 0.13 0.13 33 0.16 0.16 0.15 0.15 0.15 0.15 0.15 36 0.19 0.18 0.18 0.18 0.18 0.18 0.18 39 0.22 0.22 0.22 0.22 0.22 0.22 0.22 42 0.26 0.27 0.26 0.27 0.26 0.26 0.26 45 0.33 0.33 0.33 0.33 0.33 0.33 0.33 48 0.63 0.62 0.63 0.62 0.62 0.62 0.63 51 0.71 0.71 0.71 0.71 0.71 0.71 0.71 54 0.77 0.76 0.77 0.76 0.77 0.77 0.77 57 0.80 0.80 0.81 0.81 0.81 0.81 0.81 60 0.83 0.84 0.84 0.84 0.84 0.84 0.84 63 0.86 0.86 0.87 0.86 0.87 0.86 0.86 66 0.88 0.88 0.89 0.89 0.89 0.89 0.89 69 0.90 0.90 0.91 0.91 0.91 0.91 0.91 72 0.92 0.92 0.93 0.92 0.92 0.92 0.92 75 0.94 0.94 0.94 0.94 0.94 0.94 0.94 78 0.95 0.95 0.95 0.95 0.95 0.95 0.95 81 0.96 0.97 0.97 0.97 0.97 0.97 0.97 84 0.98 0.98 0.98 0.98 0.98 0.98 0.98 87 0.99 0.99 0.99 0.99 0.99 0.99 0.99 90 1.00 1.00 1.00 1.00 1.00 1.00 1.00
171
Table D.1.5: The hyetograph S-curve ordinates for 2 hrs. storm 120 min t (min) 100 yr 50 yr 25 yr 10 yr 5 yr 2 yr Avg 8 0.01 0.01 0.01 0.01 0.01 0.01 0.01 12 0.02 0.02 0.02 0.02 0.02 0.02 0.02 16 0.03 0.03 0.03 0.03 0.03 0.03 0.03 20 0.05 0.04 0.04 0.04 0.04 0.04 0.04 24 0.06 0.06 0.05 0.06 0.05 0.06 0.06 28 0.08 0.07 0.07 0.07 0.07 0.07 0.07 32 0.09 0.09 0.08 0.09 0.08 0.09 0.09 36 0.11 0.11 0.10 0.10 0.10 0.10 0.10 40 0.13 0.13 0.12 0.12 0.12 0.12 0.12 44 0.15 0.15 0.14 0.15 0.14 0.15 0.15 48 0.18 0.18 0.17 0.17 0.17 0.17 0.17 52 0.21 0.21 0.20 0.21 0.20 0.21 0.21 56 0.25 0.25 0.25 0.25 0.25 0.25 0.25 60 0.32 0.32 0.31 0.32 0.31 0.32 0.32 64 0.64 0.64 0.64 0.64 0.64 0.64 0.64 68 0.72 0.72 0.73 0.72 0.73 0.73 0.73 72 0.77 0.78 0.78 0.78 0.78 0.78 0.78 76 0.81 0.81 0.82 0.82 0.82 0.82 0.82 80 0.84 0.84 0.85 0.85 0.85 0.85 0.85 84 0.87 0.87 0.87 0.87 0.87 0.87 0.87 88 0.89 0.89 0.90 0.89 0.89 0.89 0.89 92 0.91 0.91 0.91 0.91 0.91 0.91 0.91 96 0.92 0.93 0.93 0.93 0.93 0.93 0.93 100 0.94 0.94 0.94 0.94 0.94 0.94 0.94 104 0.95 0.95 0.96 0.96 0.96 0.96 0.95 108 0.97 0.97 0.97 0.97 0.97 0.97 0.97 112 0.98 0.98 0.98 0.98 0.98 0.98 0.98 116 0.99 0.99 0.99 0.99 0.99 0.99 0.99 120 1.00 1.00 1.00 1.00 1.00 1.00 1.00
172
Table D.1.6: The hyetograph S-curve ordinates for 2.5 hrs. storm 150 min t (min) 100 yr 50 yr 25 yr 10 yr 5 yr 2 yr Avg 10 0.01 0.01 0.01 0.01 0.01 0.01 0.01 15 0.02 0.02 0.02 0.02 0.02 0.02 0.02 20 0.03 0.03 0.03 0.03 0.03 0.03 0.03 25 0.05 0.04 0.04 0.04 0.04 0.04 0.04 30 0.06 0.06 0.05 0.05 0.05 0.05 0.05 35 0.07 0.07 0.07 0.07 0.07 0.07 0.07 40 0.09 0.09 0.08 0.08 0.08 0.08 0.08 45 0.11 0.10 0.10 0.10 0.10 0.10 0.10 50 0.13 0.12 0.11 0.12 0.12 0.12 0.12 55 0.15 0.14 0.14 0.14 0.14 0.14 0.14 60 0.17 0.17 0.16 0.17 0.16 0.17 0.17 65 0.21 0.20 0.19 0.20 0.19 0.20 0.20 70 0.25 0.24 0.24 0.24 0.24 0.24 0.24 75 0.31 0.31 0.30 0.31 0.30 0.30 0.30 80 0.65 0.65 0.65 0.65 0.65 0.65 0.65 85 0.73 0.73 0.74 0.74 0.74 0.74 0.74 90 0.78 0.78 0.79 0.79 0.79 0.79 0.79 95 0.82 0.82 0.83 0.82 0.83 0.82 0.82 100 0.84 0.85 0.86 0.85 0.86 0.85 0.85 105 0.87 0.87 0.88 0.88 0.88 0.88 0.88 110 0.89 0.89 0.90 0.90 0.90 0.90 0.90 115 0.91 0.91 0.92 0.91 0.92 0.91 0.91 120 0.92 0.93 0.93 0.93 0.93 0.93 0.93 125 0.94 0.94 0.95 0.94 0.95 0.94 0.94 130 0.95 0.96 0.96 0.96 0.96 0.96 0.96 135 0.97 0.97 0.97 0.97 0.97 0.97 0.97 140 0.98 0.98 0.98 0.98 0.98 0.98 0.98 145 0.99 0.99 0.99 0.99 0.99 0.99 0.99 150 1.00 1.00 1.00 1.00 1.00 1.00 1.00
173
(D.2): Hyetographs
Rainfall distribution for the study area at 30 min. storm 1.20
1.00
0.80 30 min 100 yr 30 min 50 yr 0.60 30 min 25 yr 30 min 10 yr 30 min 5 yr 0.40 30 min 2 yr
30 min Avg Cumulative precipitation ordinate precipitation Cumulative 0.20
0.00 0 5 10 15 20 25 30 35 t (min)
Figure D.2.1: Rainfall hyetograph at 30 min. storm
Rainfall distribution for the study area at 1 hr. storm 1.20
1.00
0.80 60 min 100 yr 60 min 50 yr 0.60 60 min 25 yr 60 min 10 yr 60 min 5 yr 0.40 60 min 2 yr
Cumulative Cumulative precipitation ordinate 60 min Avg
0.20
0.00 0 10 20 30 40 50 60 70 t (min)
Figure D.2.2: Rainfall hyetograph at 1 hr. storm 174
Rainfall distribution for the study area at 1.5 hrs. storm 1.20
1.00
0.80 90 min 100 yr 90 min 50 yr 0.60 90 min 25 yr 90 min 10 yr 90 min 5 yr 0.40 90 min 2 yr
Cumulative Cumulative precipitation ordinate 90 min Avg
0.20
0.00 0 20 40 60 80 100 t (min)
Figure D.2.3: Rainfall hyetograph at 1.5 hrs. storm
Rainfall distribution for the study area at 2 hrs. storm 1.20
1.00
0.80 120 min 100 yr 120 min 50 yr 0.60 120 min 25 yr 120 min 10 yr 120 min 5 yr 0.40 120 min 2 yr
Cumulative Cumulative precipitation ordinate 120 min Avg
0.20
0.00 0 20 40 60 80 100 120 140 t (min)
Figure D.2.4: Rainfall hyetograph at 2 hrs. storm 175
Rainfall distribution for the study area at 2 hrs. storm 1.20
1.00
0.80 150 min 100 yr 150 min 50 yr 0.60 150 min 25 yr 150 min 10 yr 150 min 5 yr 0.40 150 min 2 yr
Cumulative Cumulative precipitation ordinate 150 min Avg 0.20
0.00 0 20 40 60 80 100 120 140 160 t (min)
Figure D.2.5: Rainfall Hyetograph at 2.5 hrs. storm
176
Appendix E: Hydrographs
Figure E.1: SCS hydrograph for 30 min storm with different return periods
Figure E.2: SCS routed hydrograph for 30 min storm with different return periods
Figure E.3: SCS hydrograph for 1 hr. storm with different return periods
177
Figure E.4: SCS routed hydrograph for 1 hr. storm with different return periods
Figure E.5: SCS hydrograph for 1.5 hrs. storm with different return periods
Figure E.6: SCS routed hydrograph for 1.5 hrs. storm with different return periods 178
Figure E.7: SCS hydrograph for 2 hrs. storm with different return periods
Figure E.8: SCS routed hydrograph for 2 hrs. storm with different return periods
Figure E.9: SCS hydrograph for 2.5 hrs. storm with different return periods 179
Figure E.10: SCS routed hydrograph for 2.5 hrs. storm with different return periods
The routed SCS Hydrographs for different return periods at DG0004 are illustrated in Figure E.11 through Figure E.16.
Figure E.11: SCS hydrograph - 2 yrs. Figure E.12: SCS hydrograph - 5 yrs.
180
Figure E.13: SCS hydrograph - 10 yrs. Figure E.14: SCS hydrograph - 25 yrs.
Figure E.15: SCS hydrograph - 50 yrs. Figure E.16: SCS hydrograph - 100 yrs.
181
Appendix F: Channels Cross Sectional Dimensions Estimation
Table F.1: Channels cross sectional dimensions estimation Max 1st Max 2nd Horizontal Horizontal Width Bed Avg. ID Section side side distance to distance to Z1 Z2 (m) elevation Z elevation elevation 1st side 2nd side
1.00 43.00 1045.10 1067.00 1063.00 235.27 406.30 10.74 7.15 8.95 R63 2.00 61.18 1044.00 1064.00 1058.00 203.93 367.00 10.20 7.28 8.74 Avg W = 52.09 Avg Z = 8.84 1.00 41.00 1038.00 1053.00 1108.00 88.45 413.10 5.90 4.05 4.97 2.00 81.43 1025.00 1034.00 1109.00 223.85 590.00 24.87 3.39 14.13 3.00 83.87 1016.00 1042.00 1113.00 209.68 503.23 8.06 2.16 5.11 4.00 63.41 994.00 1042.00 1043.00 158.52 348.75 3.30 2.59 2.95 5.00 43.68 979.00 1000.00 991.00 152.88 109.20 7.28 9.10 8.19 6.00 54.59 990.00 1048.00 1025.00 191.10 300.31 3.29 8.58 5.94 7.00 26.00 976.00 1026.00 992.00 132.24 79.33 2.64 4.96 3.80 R62 8.00 56.00 988.00 1006.00 1028.00 308.00 168.00 17.11 4.20 10.66 9.00 66.80 915.00 948.00 1012.00 267.21 178.15 8.10 1.84 4.97 10.00 45.55 914.00 935.00 1043.00 113.89 205.00 5.42 1.59 3.51 11.00 69.20 900.00 926.00 1064.00 298.34 275.38 11.47 1.68 6.58 12.00 69.37 893.00 912.00 1045.00 277.45 277.46 14.60 1.83 8.21 13.00 46.24 884.00 930.00 925.00 185.28 231.60 4.03 5.65 4.84 14.00 65.66 887.00 925.00 908.00 153.23 197.00 4.03 9.38 6.71 Avg W = 58.06 Avg Z = 6.47 1.00 66.80 915.00 948.00 1012.00 267.21 178.15 8.10 1.84 4.97 2.00 45.55 914.00 935.00 1043.00 113.89 205.00 5.42 1.59 3.51 3.00 69.20 900.00 926.00 1064.00 298.34 275.38 11.47 1.68 6.58 R61 4.00 69.37 893.00 912.00 1045.00 277.45 277.46 14.60 1.83 8.21 5.00 46.24 884.00 930.00 925.00 185.28 231.60 4.03 5.65 4.84 6.00 65.66 887.00 925.00 908.00 153.23 197.00 4.03 9.38 6.71 Avg W = 60.47 Avg Z = 5.80 1.00 29.10 869.00 911.00 908.00 261.57 174.37 6.23 4.47 5.35 2.00 42.90 863.00 892.00 896.00 128.70 128.70 4.44 3.90 4.17 3.00 62.14 864.00 888.00 903.00 124.24 186.30 5.18 4.78 4.98 R59 4.00 41.62 863.00 888.00 894.00 166.48 145.67 6.66 4.70 5.68 5.00 31.65 862.00 886.00 895.00 316.15 189.65 13.17 5.75 9.46 6.00 20.70 861.00 873.00 890.00 103.50 144.90 8.63 5.00 6.81 Avg W = 38.02 Avg Z = 6.07 1.00 41.84 853.00 884.00 883.00 230.40 104.70 7.43 3.49 5.46 R56 2.00 53.00 853.00 887.00 878.00 159.00 106.05 4.68 4.24 4.46 Avg W = 47.42 Avg Z = 4.96 1.00 60.80 852.00 888.00 902.00 121.50 151.90 3.38 3.04 3.21 2.00 32.60 854.00 882.00 928.00 130.30 130.30 4.65 1.76 3.21 R55 3.00 62.33 857.00 881.00 1002.00 156.00 249.57 6.50 1.72 4.11 Avg W = 51.91 Avg Z = 3.51 182
Table F.1: Channels cross sectional dimensions estimation-Contd. Max 1st Max 2nd Horizontal Horizontal Width Bed Avg. ID Section side side distance to distance to Z1 Z2 (m) elevation Z elevation elevation 1st side 2nd side 1.00 92.15 851.00 1013.00 1037.00 311.46 280.30 1.92 1.51 1.71 2.00 90.30 845.00 981.00 942.00 210.80 180.70 1.55 1.86 1.71 3.00 30.60 814.00 992.00 922.00 122.60 91.90 0.69 0.85 0.77 4.00 31.50 850.00 937.00 960.00 157.50 220.50 1.81 2.00 1.91 5.00 45.00 834.00 991.00 971.00 202.20 247.00 1.29 1.80 1.55 6.00 42.00 864.00 995.00 970.00 210.30 147.10 1.61 1.39 1.50 7.00 62.30 846.00 997.00 892.00 228.70 124.00 1.51 2.70 2.11 R54 8.00 82.80 825.00 948.00 985.00 145.00 269.30 1.18 1.68 1.43 9.00 104.8 857.00 1020.00 989.00 251.50 230.60 1.54 1.75 1.64 10.00 124.4 779.00 976.00 922.00 311.20 228.20 1.58 1.60 1.59 11.00 61.30 720.00 833.00 824.00 163.70 184.20 1.45 1.77 1.61 12.00 82.20 671.00 807.00 850.00 191.90 219.40 1.41 1.23 1.32 13.00 53.70 670.00 735.00 724.00 161.30 107.50 2.48 1.99 2.24 Avg W = 69.47 Avg Z = 1.62 1.00 61.80 674.00 759.00 882.00 247.30 350.40 2.91 1.68 2.30 2.00 82.50 680.00 805.00 896.00 330.30 371.50 2.64 1.72 2.18 3.00 41.00 666.00 778.00 757.00 266.40 184.50 2.38 2.03 2.20 4.00 63.20 646.00 839.00 978.80 157.80 221.00 0.82 0.66 0.74 R50 5.00 86.10 646.00 739.00 773.00 150.60 129.10 1.62 1.02 1.32 6.00 86.50 640.00 712.00 796.00 129.80 129.90 1.80 0.83 1.32 7.00 56.60 652.00 772.00 801.00 169.80 169.70 1.42 1.14 1.28 8.00 80.60 640.00 717.00 860.00 134.30 188.00 1.74 0.85 1.30 Avg Avg W = 69.79 Z = 1.58 1.00 83.80 629.00 848.00 710.00 167.70 167.70 0.77 2.07 1.42 2.00 54.70 625.00 720.00 819.00 191.30 164.00 2.01 0.85 1.43 3.00 84.10 610.00 757.00 763.00 196.30 140.10 1.34 0.92 1.13 R49 4.00 83.30 594.00 697.00 881.00 166.80 250.10 1.62 0.87 1.25 5.00 82.80 564.00 612.00 898.00 110.30 275.80 2.30 0.83 1.56 Avg Avg W = 77.74 Z = 1.36 1.00 82.50 1036.00 1048.00 1052.00 227.00 165.10 18.92 10.32 14.62 2.00 84.00 1035.00 1061.00 1058.00 126.00 462.00 4.85 20.09 12.47 R58 3.00 82.70 1031.00 1083.00 1051.00 185.90 371.80 3.58 18.59 11.08 Avg Avg W = 83.07 Z = 12.72 1.00 62.10 1031.00 1097.00 1058.00 228.00 559.50 3.45 20.72 12.09 2.00 61.10 1032.00 1073.00 1093.00 183.10 213.60 4.47 3.50 3.98 3.00 65.80 1033.00 1091.00 1082.00 175.70 153.80 3.03 3.14 3.08 R57 4.00 62.80 1034.00 1081.00 1088.00 156.80 188.10 3.34 3.48 3.41 5.00 114.7 1034.00 1080.00 1091.00 172.00 172.00 3.74 3.02 3.38 6.00 103.4 1015.00 1083.00 1091.00 227.40 206.70 3.34 2.72 3.03 Avg W = 78.32 Avg Z = 4.83 R53 1.00 168.6 1011.00 1038.00 1076.00 126.50 168.60 4.69 2.59 3.64 183
Table F.1: Channels cross sectional dimensions estimation-Contd. Max 1st Max 2nd Horizontal Horizontal Width Bed Avg. Section side side distance to distance to Z1 Z2 (m) elevation Z elevation elevation 1st side 2nd side 2.00 144.4 1010.00 1036.00 1051.00 185.75 165.10 7.14 4.03 5.59 3.00 62.00 1010.00 1044.00 1056.00 268.70 248.00 7.90 5.39 6.65 4.00 63.10 1006.00 1073.00 1075.00 189.44 189.40 2.83 2.74 2.79 5.00 62.70 997.00 1025.00 1010.00 125.30 83.50 4.48 6.42 5.45 6.00 77.80 977.00 1001.00 1002.00 73.80 155.50 3.08 6.22 4.65 Avg W = 96.43 Avg Z = 4.79 1.00 88.00 977.00 1075.00 996.00 374.20 110.10 3.82 5.79 4.81 2.00 82.80 979.00 1064.00 1030.00 331.30 276.00 3.90 5.41 4.65 3.00 67.70 978.00 1013.00 1025.00 203.00 225.70 5.80 4.80 5.30 R52 4.00 128.8 974.00 1009.00 1006.00 231.80 334.90 6.62 10.47 8.54 5.00 105.3 967.00 997.00 1008.00 131.70 342.50 4.39 8.35 6.37 6.00 95.60 967.00 988.00 1001.00 19.60 191.30 0.93 5.63 3.28 Avg W = 94.70 Avg Z = 5.49 1.00 86.40 958.00 1002.00 988.00 172.80 172.80 3.93 5.76 4.84 2.00 85.00 957.00 994.00 1006.00 141.80 198.40 3.83 4.05 3.94 3.00 125.7 952.00 1016.00 1037.00 188.50 251.40 2.95 2.96 2.95 4.00 94.30 947.00 986.00 1042.00 188.50 345.60 4.83 3.64 4.24 5.00 115.8 946.00 975.00 1027.00 202.70 260.50 6.99 3.22 5.10 R51 6.00 89.20 943.00 973.00 1015.00 267.60 208.20 8.92 2.89 5.91 7.00 61.90 923.00 982.00 959.00 309.80 123.90 5.25 3.44 4.35 8.00 60.10 656.00 938.00 810.00 330.70 330.80 1.17 2.15 1.66 9.00 105.9 592.00 723.00 909.00 254.00 317.50 1.94 1.00 1.47 Avg W = 91.59 Avg Z = 3.83 1.00 59.50 550.00 628.00 723.00 178.20 267.30 2.28 1.55 1.91 2.00 60.50 538.00 670.00 684.00 151.40 211.80 1.15 1.45 1.30 3.00 60.30 537.00 714.00 6682.00 210.90 271.20 1.19 0.04 0.62 4.00 64.80 525.00 708.00 618.00 194.30 194.30 1.06 2.09 1.58 5.00 123 517.00 608.00 728.00 276.80 215.20 3.04 1.02 2.03 6.00 64.40 499.00 610.00 574.00 214.58 150.20 1.93 2.00 1.97 R47 7.00 59.89 486.00 548.00 668.00 269.30 329.20 4.34 1.81 3.08 8.00 88.50 477.00 550.00 613.00 206.44 353.90 2.83 2.60 2.72 9.00 82.80 474.00 511.00 629.00 103.50 331.20 2.80 2.14 2.47 10.00 60.90 466.00 503.00 575.00 121.80 182.60 3.29 1.68 2.48 11.00 110.3 460.00 507.00 482.00 198.60 198.60 4.23 9.03 6.63 Avg W = 75.90 Avg Z = 2.43 1.00 84.10 454.00 468.00 458.00 84.05 56.10 6.00 14.03 10.01 2.00 61.50 452.00 504.00 474.00 184.40 143.50 3.55 6.52 5.03 R46 3.00 60.00 452.00 481.00 467.00 119.90 120.00 4.13 8.00 6.07 Avg W = 68.53 Avg Z = 7.04 1.00 63.00 450.00 492.00 476.00 189.00 126.10 4.50 4.85 4.68 R45 2.00 44.70 449.00 492.00 476.00 156.30 134.10 3.63 4.97 4.30 3.00 64.70 450.00 508.00 498.00 226.30 226.00 3.90 4.71 4.31 184
Table F.1: Channels cross sectional dimensions estimation-Contd. Max 1st Max 2nd Horizontal Horizontal Width Bed Avg. Section side side distance to distance to Z1 Z2 (m) elevation Z elevation elevation 1st side 2nd side 4.00 42.40 450.00 474.00 491.00 127.10 211.80 5.30 5.17 5.23 5.00 61.70 448.00 477.00 467.00 123.30 92.50 4.25 4.87 4.56 6.00 44.00 446.00 482.00 491.00 132.11 220.20 3.67 4.89 4.28 7.00 56.90 445.00 471.00 474.00 89.80 149.80 3.45 5.17 4.31 8.00 62.20 445.00 462.00 481.00 82.90 165.80 4.88 4.61 4.74 9.00 84.50 445.00 510.00 512.00 169.10 225.50 2.60 3.37 2.98 10.00 94.90 443.00 477.00 476.00 126.50 158.20 3.72 4.79 4.26 11.00 53.20 441.00 460.00 462.00 106.35 106.30 5.60 5.06 5.33 12.00 41.40 440.00 451.00 452.00 62.10 82.90 5.65 6.91 6.28 Avg W = 59.47 Avg Z = 4.60 1.00 42.40 450.00 474.00 491.00 127.10 211.80 5.30 5.17 5.23 2.00 61.70 448.00 477.00 467.00 123.30 92.50 4.25 4.87 4.56 3.00 44.00 446.00 482.00 491.00 132.11 220.20 3.67 4.89 4.28 4.00 56.90 445.00 471.00 474.00 89.80 149.80 3.45 5.17 4.31 5.00 62.20 445.00 462.00 481.00 82.90 165.80 4.88 4.61 4.74 R44 6.00 84.50 445.00 510.00 512.00 169.10 225.50 2.60 3.37 2.98 7.00 94.90 443.00 477.00 476.00 126.50 158.20 3.72 4.79 4.26 8.00 53.20 441.00 460.00 462.00 106.35 106.30 5.60 5.06 5.33 9.00 41.40 440.00 451.00 452.00 62.10 82.90 5.65 6.91 6.28 Avg W = 60.13 Avg Z = 4.66
185
Appendix G: Reservoir capacity estimation
(G.1): Runoff calculations
Table G.1.1: Runoff calculations at 1972 (avg. year) for Hai station
Effective 5 Day daily Cumulative AMC Weighted S Runoff Monthly Yearly Year month rainfall Rainfall Condition CN (mm) Depth Runoff Runoff (mm) (mm) P≥8.26
28.00 28.00 AMC II 86.02 41.29 6.39 Mar 27.00 61.50 AMC III 93.40 17.95 13.25 35.36 1972 30.00 34.00 AMC III 93.40 17.95 15.72 36.65 Apr 12.50 13.00 AMC II 86.02 41.29 0.40 0.40 Nov 10.70 10.70 AMC I 72.09 98.34 0.90 0.90
Table G.1.2: Runoff calculations at 1979 (max. year) for Hai station
Effective 6 Day daily Cumulative AMC Weighted S Runoff Monthly Yearly Year month rainfall Rainfall Condition CN (mm) Depth Runoff Runoff (mm) (mm) P≥8.27
55.00 55.00 AMC III 93.40 17.95 38.11 Jan 39.48 8.70 8.70 AMC I 72.09 98.34 1.38 17.00 17.00 AMC II 86.02 41.29 1.53 Feb 9.36 20.00 37.00 AMC III 93.40 17.95 7.84 13.00 18.00 AMC II 86.02 41.29 0.49 Mar 1.71 16.00 16.00 AMC II 86.02 41.29 1.22 1979 Nov 40.00 40.00 AMC III 93.40 17.95 24.39 24.39 108.25 9.50 55.50 AMC III 93.40 17.95 1.46 10.30 17.50 AMC II 86.02 41.29 0.10 38.00 55.50 AMC III 93.40 17.95 22.61 Dec 33.31 21.30 21.30 AMC II 86.02 41.29 3.13 13.60 34.90 AMC III 93.40 17.95 3.58 11.50 46.40 AMC III 93.40 17.95 2.42
186
Table G.1.3: Runoff calculations at 2008 (avg. year) for Petra station
Effective 5 Day daily Cumulative AMC Weighted S Runoff Monthly Yearly Year month rainfall Rainfall Condition CN (mm) Depth Runoff runoff (mm) (mm) P≥8.26
17 21.5 AMC II 86.02 41.29 1.53 Jan 21.52 2008 35 39 AMC III 93.40 17.95 19.99 26.35 Feb 25 25 AMC II 86.02 41.29 4.83 4.83
Table G.1.4: Runoff calculations at 1991 (max. year) for Petra station
Effective 5 Day daily Cumulative AMC Weighted S Runoff Monthly Yearly Year month rainfall Rainfall Condition CN (mm) Depth Runoff runoff (mm) (mm) P≥8.26
12.00 15.00 AMC II 86.02 41.29 0.31 16.00 16.00 AMC II 86.02 41.29 1.22 Jan 11.09 17.00 33.00 AMC III 93.40 17.95 5.73 14.00 47.00 AMC III 93.40 17.95 3.82 1991 Feb 9.00 9.00 AMC I 72.09 98.34 1.30 1.30 98.33 9.80 9.80 AMC I 72.09 98.34 1.10 36.00 45.80 AMC III 93.40 17.95 20.86 Mar 85.94 64.00 64.00 AMC III 93.40 17.95 46.57 32.00 96.00 AMC III 93.40 17.95 17.41 (G.2): Reservoir capacity calculations
Table G.2.1: Maximum reservoir capacity calculations at A0002 A0002 = 34.58 Km2 Cum. Runoff Inflow Cumulative Cumulative inflow- Inflow- Year Month depth Volume Demand Inflow Demand Cum. Demand (mm) (MCM) demand OCT 0 1.37 1.37 0.31 0.31 1.05 1.05 NOV 24.39 0.32 1.69 0.31 0.62 1.07 0.01 DEC 33.31 0.06 1.75 0.31 0.94 0.81 -0.25 JAN 39.48 0 1.75 0.31 1.25 0.5 -0.31 MAX FEB 9.36 0 1.75 0.31 1.56 0.19 -0.31 1979 MAR 1.71 0 1.75 0.31 1.87 -0.12 -0.31 APR 0 0 1.75 0.31 2.18 -0.44 -0.31 MAY 0 0 1.75 0.31 2.5 -0.75 -0.31 JUN 0 0 1.75 0.31 2.81 -1.06 -0.31 JUL 0 0 1.75 0.31 3.12 -1.37 -0.31 AUG 0 0.84 2.59 0.31 3.43 -0.84 0.53 187
SEP 0 1.15 3.74 0.31 3.74 0 0.84 Reservoir Capacity = 2.44 Table G.2.2: Average reservoir capacity calculations at A0002 Cum. Runoff Inflow Cumulative Cumulative inflow- Inflow- Year Month depth Volume Demand Inflow Demand Cum. Demand (mm) (MCM) demand OCT 0 0 0 0.11 0.11 -0.11 -0.11 NOV 0.9 0 0 0.11 0.21 -0.21 -0.11 DEC 0 1.22 1.22 0.11 0.32 0.91 1.12 JAN 0 0.01 1.24 0.11 0.42 0.81 -0.09 AVG FEB 0 0 1.24 0.11 0.53 0.71 -0.11 MAR 35.36 0 1.24 0.11 0.63 0.6 -0.11 1972 APR 0.4 0 1.24 0.11 0.74 0.5 -0.11 MAY 0 0 1.24 0.11 0.85 0.39 -0.11 JUN 0 0 1.24 0.11 0.95 0.29 -0.11 JUL 0 0 1.24 0.11 1.06 0.18 -0.11 AUG 0 0.03 1.27 0.11 1.16 0.11 -0.07 SEP 0 0 1.27 0.11 1.27 0 -0.11 Reservoir Capacity = 1.12
Table G.2.3: Maximum reservoir capacity calculations at A0003 A0003 = 61.98 Km2 Cum. Runoff Inflow Cumulative Cumulative inflow- Inflow- Year Month depth Volume Demand Inflow Demand Cum. Demand (mm) (MCM) demand OCT 0 0.69 0.69 0.51 0.51 0.18 0.18 NOV 0 0.08 0.77 0.51 1.02 -0.25 -0.43 DEC 0 5.33 6.09 0.51 1.52 4.57 4.82 JAN 11.09 0 6.09 0.51 2.03 4.06 -0.51 MAX FEB 1.3 0 6.09 0.51 2.54 3.56 -0.51 MAR 85.94 0 6.09 0.51 3.05 3.05 -0.51 1991 APR 0 0 6.09 0.51 3.56 2.54 -0.51 MAY 0 0 6.09 0.51 4.06 2.03 -0.51 JUN 0 0 6.09 0.51 4.57 1.52 -0.51 JUL 0 0 6.09 0.51 5.08 1.02 -0.51 AUG 0 0 6.09 0.51 5.59 0.51 -0.51 SEP 0 0 6.09 0.51 6.09 0 -0.51 Reservoir Capacity = 5
188
Table G.2.4: Average reservoir capacity calculations at A0003 Cum. Runoff Inflow Cumulative Cumulative inflow- Inflow- Year Month depth Volume Demand Inflow Demand Cum. Demand (mm) (MCM) demand OCT 0 1.33 1.33 0.14 0.14 1.2 1.2 NOV 0 0.3 1.63 0.14 0.27 1.36 0.16 DEC 0 0 1.63 0.14 0.41 1.22 -0.14 JAN 21.5 0 1.63 0.14 0.54 1.09 -0.14 AVG FEB 4.83 0 1.63 0.14 0.68 0.95 -0.14 MAR 0 0 1.63 0.14 0.82 0.82 -0.14 2008 APR 0 0 1.63 0.14 0.95 0.68 -0.14 MAY 0 0 1.63 0.14 1.09 0.54 -0.14 JUN 0 0 1.63 0.14 1.22 0.41 -0.14 JUL 0 0 1.63 0.14 1.36 0.27 -0.14 AUG 0 0 1.63 0.14 1.5 0.14 -0.14 SEP 0 0 1.63 0.14 1.63 0 -0.14 Reservoir Capacity = 1.36
(G.3): Mass curve plots
1.8
1.6
1.4
1.2
1
0.8 Cumulative Demand Cumulative Inflow
0.6 Volume Volume (MCM)
0.4
0.2
0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 -0.2 Time(Months)
Figure G.3.1: Mass curve of A0001 at 1969
189
4
3.5
3
2.5
2 Cumulative Demand
1.5 Cumulative Inflow Volume Volume (MCM)
1
0.5
0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
-0.5 Time(Months)
Figure G.3.2: Mass curve of A0002 at 1979
1.6
1.4
1.2
1
0.8 Cumulative Demand
0.6 Cumulative Inflow Volume Volume (MCM) 0.4
0.2
0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
-0.2 Time(Months)
Figure G.3.3: Mass curve of A0002 at 1972
190
7
6
5
4
3 Cumulative Demand Cumulative Inflow
Volume Volume (MCM) 2
1
0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
-1 Time(Months)
Figure G.3.4: Mass curve of A0003 at 1991
1.8
1.6
1.4
1.2
1
0.8 Cumulative Demand Cumulative Inflow
0.6 Volume Volume (MCM)
0.4
0.2
0 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 -0.2 Time(Months)
Figure G.3.5: Mass curve of A0003 at 2008
191
Appendix H: Tables and Figures
Table H.1: Frequency factors K for Log-Pearson Type III distribution, (Viessman, et. al., 1989).
Recurrence interval in years
2 5 10 25 50 100 200 Percent chance SKEW COEFFICIENT Cs 50 20 10 4 2 1 0.5
3 -0.396 0.42 1.18 2.278 3.152 4.051 4.97 2.9 -0.39 0.44 1.195 2.277 3.134 4.013 4.904 2.8 -0.384 0.46 1.21 2.275 3.114 3.973 4.847 2.7 -0.376 0.479 1.224 2.272 3.093 3.932 4.783 2.6 -0.368 0.499 1.238 2.267 3.071 3.889 4.718 2.5 -0.36 0.518 1.25 2.262 3.048 3.845 4.652 2.4 -0.351 0.537 1.262 2.256 3.023 3.8 4.584 2.3 -0.341 0.555 1.274 2.248 2.997 3.753 4.515 2.2 -0.33 0.574 1.284 2.24 2.97 3.705 4.444 2.1 -0.319 0.592 1.294 2.23 2.942 3.656 4.372 2 -0.307 0.609 1.302 2.219 2.912 3.605 4.298 1.9 -0.294 0.627 1.31 2.207 2.881 3.553 4.223 1.8 -0.282 0.643 1.318 2.193 2.848 3.499 4.147 1.7 -0.268 0.66 1.324 2.179 2.815 3.444 4.069 1.6 -0.254 0.675 1.329 2.163 2.78 3.388 3.99 1.5 -0.24 0.69 1.333 2.146 2.743 3.33 3.91 1.4 -0.225 0.705 1.337 2.128 2.706 3.271 3.828 1.3 -0.21 0.719 1.339 2.108 2.666 3.211 3.745 1.2 -0.195 0.732 1.34 2.087 2.626 3.149 3.661 1.1 -0.18 0.745 1.341 2.066 2.585 3.087 3.575 1 -0.164 0.758 1.34 2.043 2.542 3.022 3.489 0.9 -0.148 0.769 1.339 2.018 2.498 2.957 3.401 0.8 -0.132 0.78 1.336 1.993 2.453 2.891 3.312 0.7 -0.116 0.79 1.333 1.967 2.407 2.824 3.223 0.6 -0.099 0.8 1.328 1.939 2.359 2.755 3.132 0.5 -0.083 0.808 1.323 1.91 2.311 2.686 3.041 0.4 -0.066 0.816 1.317 1.88 2.261 2.615 2.949 0.3 -0.05 0.824 1.309 1.849 2.211 2.544 2.856 0.2 -0.033 0.83 1.301 1.818 2.159 2.472 2.763 0.1 -0.017 0.836 1.292 1.785 2.107 2.4 2.67 0 0 0.842 1.282 1.751 2.054 2.326 2.576 -0.1 0.017 0.846 1.27 1.716 2 2.252 2.482 -0.2 0.033 0.85 1.258 1.68 1.945 2.178 2.388 -0.3 0.05 0.853 1.245 1.643 1.89 2.104 2.294 -0.4 0.066 0.855 1.231 1.606 1.834 2.029 2.201 -0.5 0.083 0.856 1.216 1.567 1.777 1.955 2.108 -0.6 0.099 0.857 1.2 1.528 1.72 1.88 2.016 -0.7 0.116 0.857 1.183 1.488 1.663 1.806 1.926 -0.8 0.132 0.856 1.166 1.448 1.606 1.733 1.837 -0.9 0.148 0.854 1.147 1.407 1.549 1.66 1.749 -1 0.164 0.852 1.128 1.366 1.492 1.588 1.664 -1.1 0.18 0.848 1.107 1.324 1.435 1.518 1.581 192
Recurrence interval in years
2 5 10 25 50 100 200 SKEW COEFFICIENT Percent chance Cs 50 20 10 4 2 1 0.5 -1.4 0.225 0.832 1.041 1.198 1.27 1.318 1.351 -1.5 0.24 0.825 1.018 1.157 1.217 1.256 1.282 -1.6 0.254 0.817 0.994 1.116 1.166 1.197 1.216 -1.7 0.268 0.808 0.97 1.075 1.116 1.14 1.155 -1.8 0.282 0.799 0.945 1.035 1.069 1.087 1.097 -1.9 0.294 0.788 0.92 0.996 1.023 1.037 1.044 -2 0.307 0.777 0.895 0.959 0.98 0.99 0.995 -2.1 0.319 0.765 0.869 0.923 0.939 0.946 0.949 -2.2 0.33 0.752 0.844 0.888 0.9 0.905 0.907 -2.3 0.341 0.739 0.819 0.855 0.864 0.867 0.869 -2.4 0.351 0.725 0.795 0.823 0.83 0.832 0.833 -2.5 0.36 0.711 0.711 0.793 0.798 0.799 0.8 -2.6 0.368 0.696 0.747 0.764 0.768 0.769 0.769 -2.7 0.376 0.681 0.724 0.738 0.74 0.74 0.741 -2.8 0.384 0.666 0.702 0.712 0.714 0.714 0.714 -2.9 0.39 0.651 0.681 0.683 0.689 0.69 0.69 -3 0.396 0.636 0.66 0.666 0.666 0.667 0.667
193
Table H.2: Runoff curve number for urban areas, (NRCS, 1999)
Table H.3: Runoff curve number for arid and semi-arid rangelands, (NRCS, 1999)
194
Table H.4: Runoff curve number for cultivate agricultural lands, (NRCS, 1999)
Table H.5: Runoff curve number for other agricultural lands, (NRCS, 1999)
195
Table H.6: Runoff coefficients for use in the rational method (Chow, et.al, 1964) Return Period Character of surface 2 5 10 25 50 100 500
Developed Asphaltic 0.73 0.77 0.81 0.86 0.90 0.95 1.00 Concrete/roof 0.75 0.80 0.83 0.88 0.92 0.97 1.00 Grass areas (lawns, parks, etc.)
Poor Condition (grass cover less than 50% of the area)
Flat, 0-2% 0.32 0.34 0.37 0.40 0.44 0.47 0.58 Average, 2-7% 0.37 0.40 0.43 0.46 0.49 0.53 0.61 Steep, over 7% 0.40 0.43 0.45 0.49 0.52 0.55 0.62
Fair Condition (grass over on 50% to 75% of the area)
Flat, 0-2% 0.25 0.28 0.30 0.34 0.37 0.41 0.53 Average, 2-7% 0.33 0.36 0.38 0.42 0.45 0.49 0.58 Steep, over 7% 0.37 0.40 0.42 0.46 0.49 0.53 0.60
Good condition (grass cover larger than 75% of the area)
Flat, 0-2% 0.21 0.23 0.25 0.29 0.32 0.36 0.49 Average, 2-7% 0.29 0.32 0.35 0.39 0.42 0.46 0.56 Steep, over 7% 0.34 0.37 0.40 0.44 0.47 0.51 0.58
Undeveloped Cultivated Land
Flat, 0-2% 0.31 0.34 0.36 0.40 0.43 0.47 0.57 Average, 2-7% 0.35 0.38 0.41 0.44 0.48 0.51 0.60 Steep, over 7% 0.39 0.42 0.44 0.48 0.51 0.54 0.61
Pasture / Range
Flat, 0-2% 0.25 0.28 0.30 0.34 0.37 0.41 0.53 Average, 2-7% 0.33 0.36 0.38 0.42 0.45 0.49 0.58 Steep, over 7% 0.37 0.40 0.42 0.46 0.49 0.53 0.60
Forest / Woodlands
Flat, 0-2% 0.22 0.25 0.28 0.31 0.35 0.39 0.48 Average, 2-7% 0.31 0.34 0.36 0.40 0.43 0.47 0.56 Steep, over 7% 0.35 0.39 0.41 0.45 0.48 0.52 0.58
196
النمذجة الهيدرولوجية لمياه حوض وادي موسى السطحية وإدارتها
إعداد أحمد جاسر الزبيدي
المشرف االستاذ الدكتور رضوان عبد هللا الوشاح
الملخص
تحتاج منطقة حوض وادي موسى إلى فهم أفضل لخصائص الفيضانات التي تتعرض لها. وذلك للتخفيف من تأثير هذه الفيضانات على اآلثار في مدينة البتراء، باإلضافة إلى إمكانية االستفادة من مياه الفيضانات كمصدر مائي. إن األهداف الرئيسية لهذه الدراسة هي تقدير القيمة العظمى لتدفق المياه وحجمها باإلضافة إلى سعة التخزين العظمى لحوض مياه وادي موسى. وذلك إلدارة وتخفيف أثر الفيضانات على المواقع ذات األهمية السياحية في منطقة حوض وادي موسى. لقد تم التوصل إلى هذه األهداف من خالل التحليل الهيدرولوجي لخصائص الحوض المائي، والتحليل الفيزيائي لخصائص عاصفة التصميم.
تم إنشاء نموذج هيدرولوجي لحوض مياه وادي موسى باستخدام برنامج النمذجة الهيدرولوجية (WMS).و تم التعرف على حدود الحوض ومن ثم تقسيمه إلى 29 حوض. تمت معايرة النموذج ومن ثم التحقق من صحته للتنبؤ بالتدفق وحجم المياه ل 6 فترات الحتمالية التكرار )2 و5 و10 و25 و50 و100 سنة(. تم إنشاء منحنيات شدة المطر وديمومته وتكراره (IDF) لمحطات هطول األمطار الثالثة الواقعة داخل الحوض )وادي موسى والحي والبتراء( باستخدام طرق ) BellوChen وHershfield(. تم تحليل بيانات هطول األمطار للحصول على التوزيع االحتمالي األكثر مالئمة لكل فترة دراسة على كل محطة باستخدام برنامج التحليل االحصائي Easy-Fit. وتم اختيار التوزيع االحتمالي (Log-Pearson III) ليكون التوزيع االحتمالي األنسب وتم استخدامه للحصول على منحنيات ال IDF باستخدام الطرق النظرية. كما تم إنشاء منحنيات توزيع هطول األمطار(Hyetographs) باستخدام طريقة (Alternating Block).
تم حساب قيمة رقم المنحنى الكلي )CN( باستخدام طريقة(SCS) عبر برنامج النمذجة واعتما ًدا على خرائط التربة وانماط االستخدام، وتبين أن قيمته هي 86. تم حساب الوقت الالزم لتجمع المياه عند مخرج الحوض باستخدام طريقة (SCS) عبر برنامج النمذجة، وتبين أن قيمته هي 2 ساعة و45 دقيقة. وتم تتبع التدفق باستخدام طريقة الموجة الحركية على النموذج مما أدى إلى تقليل القيمة العظمى للتدفق- لعاصفة مدتها 24 ساعة واحتمالية تكرارها 100 عام - بنسبة 23٪. تم ايجاد القيمة العظمى لتدفق المياه وحجمها لعاصفة مدتها 24 ساعة واحتمالية تكرارها 100 عام، وكانت قيمة التدفق تساوي 553.3 م3/ث. أما حجم المياه فقد فتبين انه يساوي 5.43 مليون متر مكعب. بعد ذلك، تم إنشاء منحنيات تدفق المياه وديمومتها وتكرارها (FDF) لمياه الحوض. تعتبر هذه المنحنيات بمثابة بصمة خاصة للحوض، حيث يمكن الحصول على القيمة العظمى لتدفق وحجم المياه ألي عاصفة تتراوح مدتها بين )2.75 إلى 24 ساعة( مع أي احتمالية تكرار.
يشير تحليل الجريان إلى أن عواصف هطول األمطار التي تتجاوز 8.25 مم فقط خالل فترة 24 ساعة ستولد جريانًا. بنا ًء على ذلك، تم حساب القيمة العظمى والقيمة المتوسطة للجريان 197
السطحي المباشر للتنبؤ بحجم المياه )سعة التخزين( باستخدام طريقة (Mass Curve). تبين أن سعة التخزين المتوسطة تبلغ 3.81 مليون متر مكعب، بينما تبلغ سعة التخزين العظمى قيمة 13.63 مليون متر مكعب.
تم تطبيق اثنتان من خطط إدارة المصادر المائية للتخفيف من آثار الفيضانات على النموذج الذي يمثل .المنطقة األولى هي تشجير 1643 هكتارا في األحواض الفرعية التي تقع في أعلى الحوض. والثانية هي بناء الحواجز والسدود في نقطة تجميع الحوض. نتج عن تطبيق التشجير تخفيض في القيمة العظمى للتدفق وحجم مياه الفيضانات بنسبة 39-61٪ و28-47٪ على التوالي. ونتج عن إقامة الحواجز تخفيض في القيمة العظمى للتدفق وحجم مياه الفيضانات بنسبة ٪53-36 و21-36٪ على التوالي.