NUMERICAL SIMULATION FOR SEDIMENT FLUSHING IN RESERVOIRS
Year: 2014
MUHAMMAD ASIF CHAUDHRY 2006-Ph.D.-Civil-03
DEPARTMENT OF CIVIL ENGINEERING UNIVERSITY OF ENGINEERING AND TECHNOLOGY LAHORE, PAKISTAN
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NUMERICAL SIMULATION FOR SEDIMENT FLUSHING IN RESERVOIRS
Year: 2014 MUHAMMAD ASIF CHAUDHRY 2006-Ph.D-Civil-03
INTERNAL EXAMINER EXTERNAL EXAMINER (PROF. DR. HABIB-UR-REHMAN) (DR. KHAWJA BILAL AHMED)
CHAIRMAN DEAN Civil Engineering Department Faculty of Civil Engineering
Thesis submitted in partial fulfillment of the requirements for the Degree of Doctor of philosophy in Civil Engineering
DEPARTMENT OF CIVIL ENGINEERING UNIVERSITY OF ENGINEERING AND TECHNOLOGY LAHORE, PAKISTAN
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Dedicated to
My father Chaudhry Muhammad Sadiq
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ACKNOWLEDGEMENTS
All the praises and thanks to the Almighty Allah, the most gracious and merciful, who enabled me with the power and means to contribute a drop to the existing ocean of knowledge.
I would like to express my heartiest gratitude to Professor Dr. Habib-ur-Rehman for his kind supervision and continuous encouragement throughout during my Doctoral program at University of Engineering and Technology, Lahore. His knowledge and experience in this research area made this research a success. Owing to his valuable suggestions and kind supervision this study owes its existence. The support and encouragement he provided made the years of research with him enjoyable and memorable.
My heartiest appreciations to Professor Dr. Hamza Gabriel, NUST Institute, Islamabad for his cooperation in the collection of relevant literature.
Profound thanks are due to Prof. Dr. Muhammad Ashraf (Late), Prof. Dr. A.S. Shakir, Dr. Muhammad Ilyas, Prof. Dr. Chaudhry Zulfiqar Ali, Prof. Dr. Ashiq Kharl, Prof. Dr. Khalid Farooq, Prof. Dr. Aziz Akbar, Prof. Dr. Noor Muhammad, Dr. Syed Iftikhar Ahmed, Dr. Ammad Hassan Khan, Dr. Burhan Sharif, Engr. Naeem Akhtar, Engr. Hassan Mujtaba Shahzad and Engr. Muhammad Yusuf for their constructive guidance, suggestions and cooperation.
I am thankful to Mr. Ghulam Rasool Senior Clerk and Mr. Muhammad Munir Administrative Officer of Chairman Office Civil Engineering Department, Muhammad Shahbaz Senior Clerck HEC focal person office, Mr. Rashid Bhatti Administrative Officer Audit Branch, Muhammad Afzal Junoir Clerck Dues Section, Muhammad Riaz Senior Clerk Cheque Section for their help regarding administrative and accounts matters.
My thanks are also due to my fellow Ph.D. students Dr. Zia-ur-Rehman, Dr. Abdul Ghaffar, Dr. Syed Hassan Farooq, Dr. Muhammad Rizwan, Dr. Usman Naeem, Engr. Majid Sarwar Wattoo, Engr. Abid Latif, Dr. Mazhar Hussain and Dr. Hafiz Ahmad Bakhsh for their help, support and encouragement at the moments of worries.
I could not have achieved this work without the prayers of my brothers, sisters and my family for me. The support, love and encouragement and prays of my mother are unforgettable.
I would like to thank Punjab Irrigation Department for their continuous administrative support through, granting study leave and help in gathering sufficient data during study.
Finally I would like to pay gratitude to Higher Education Commission (HEC), Islamabad for the administrative and financial help for my studies without which study program was not possible to be concluded.
Muhammad Asif Ch. February, 2014
ABSTRACT
Globally there are about 50,000 large dams and among them 25,500 are the storage reservoirs with storage volume of about 6,464 Bm3. World’s annual reservoir storage loss in different regions due to sedimentation varies between 0.08-2.3%, with an average of about 0.6%. It is estimated that in 2030 the demand of water would be 8500 Bm3, but the existing storage would be around 7000 Bm3. To meet 1500 Bm3 shortfall, about 8100 reservoirs are needed and construction of so many reservoirs in future seems to be difficult. The only solution is to conserve the existing reservoirs by enhancing their lives by adopting appropriate measures.
Various methods employed globally to conserve storage capacities of reservoirs are watershed management, conventional dredging, dry excavation, hydrosuction, sediment routing/sluicing, sediment bypassing, density current venting, and sediment flushing through the reservoir, used independently or in combination.
Present study focuses on the flushing operation to enhance the lives of reservoirs and to answer several questions related to flushing operation, like, is reservoir flushable?, if yes, then what would be the flushing efficiency?, how many times in a year it should be flushed?, when it should be flushed?, how much would be the flushing discharge required?, how much should be the duration of flushing?, how much water would be sacrificed for the flushing operation?, and what would be the recovery in capacity of the reservoir considering the flushing operation?, etc.
Flushing is a method by which the flow velocities in a Reservoir are increased to such a level that deposited sediments are mobilized and transported through low level outlets in the dam. Flushing sediments through reservoirs has been practiced successfully and found to be inexpensive in many cases, however, a significant amount of water is required during flushing operation. Hence, there is need to numerically model the flushing scenarios to check the performances of reservoirs in restoring the reservoir capacities.
Flushing probably has been implemented on many hundreds reservoirs of the world, but in literature only about 50 reservoirs are documented as flushed, and flushing data is available for only 25 reservoirs. Among them in literature about 6 reservoirs had been
reported as successfully flushed i.e. Baira-India, Gebidem-Switzerland, Gmund-Austria, Hengshan-China, Palagnedra-Switzerland, and Santo-Domingo-Venezuela reservoirs.
Various flushing indicators used to assess the feasibility of sediment flushing from reservoirs are Sediment Balance Ratio-SBR, Long Term Capacity Ratio-LTCR,
Drawdown Ratio-DDR, Sediment Balance Ratio during full drawdown-SBRd, Flushing Width Ratio-FWR and Top Width Ratio-TWR. The usually adopted critical values of
these indicators are: SBR >1, LTCR approaching to unity, DDR >0.7, SBRd >1, FWR > 1 and TWR = 1-2.
In the present study, the values of these six flushing indicators were computed for the selected 14 flushed reservoirs of various regions of the world and were compared with their critical values. Out of 14 selected reservoirs, 6 were successfully flushed and 08 were partially flushed. From the analysis it was found that for successfully flushed reservoirs critical values of all six flushing indicators were satisfied, but for the partially flushed reservoirs critical values were also satisfied except for the Flushing Indicator LTCR. It shows the significance of LTCR over the other Flushing Indicators. So it was learnt that LTCR is the most important flushing indicator among the six indicators to assess the feasibility of sediment flushing through the reservoirs.
Analysis results of the 14 reservoirs also show that among the successfully flushed reservoirs maximum value of LTCR is 1 for Santo-Domingo and Palagnedra Reservoirs, whereas, Hengshan Reservoir has the least value of LTCR, i.e., 0.77, which is a successfully flushed reservoir, hence it was concluded that the critical value of LTCR may be taken as 0.77 instead of 1 for the successfully flushed reservoirs.
Equations were developed for SBR and LTCR by Multiple Non-linear Regression Analysis, using the data of six successfully flushed reservoirs. These equations were tested on foreign and Pakistani reservoirs and the comparison revealed that developed equations results match well with the results of Atkinson equations.
To get confidence in numerically simulating the flushing scenarios, flushing operations were modeled for three successfully flushed reservoirs for which data of entire flushing
activities were available to calibrate and validate the Flushing Models. To numerically simulate flushing operations, initially these three reservoirs were modeled for the sediment deposition processes. These reservoirs are Baira of India, Gebidem of Switzerland and Gmund of Austria. Flushing processes have been modeled using three Models, i.e. SHARC, HEC-RAS 4.1.0, and Tsinghua University Equation. Results of the study show that SHARC Model well simulates the sediment deposition processes, but it underestimates the flushing durations. Results of the HEC-RAS 4.1.0 Model show that it can well simulate sediment depositions and sediment flushing operations. Then Tsinghua University Equation was used for simulating the sediment flushing operations through these three reservoirs. Results of the Tsinghua University Equation reveal that Model well simulates sediment flushing operations through these reservoirs.
All small reservoirs of Punjab Small Dams Organization (SDO) of Pakistan were investigated, and 20 reservoirs were selected based on detailed data availability to assess their feasibility for sediment flushing. The results reveal that based on the computed Flushing Indicators, 5 reservoirs can be ranked as likely to be successfully flushed, these are Jammargal, Phalina, Dharabi, Talikna, and Jabbi reservoirs.
Among the five likely to be successfully flushed reservoirs, Jabbi reservoir having 3.8 Mm3 storage capacity was selected for modeling the sediment deposition and flushing processes. Jabbi Reservoir was created after the construction of Dam across Jabbi Nullah by the end of year 1990. Hydrographic survey of the reservoir was conducted after 10 years of operation in 2000, which was used for the validation of the sediment deposition process in the reservoir. The survey results show that sediment deposition in 10 years was about 0.418 Mm3.
As results of the flushing modeling on the three foreign reservoirs proved that HEC-RAS 4.1.0 and Tsinghua University Equation well simulate the flushing processes, hence flushing operations of Jabbi Reservoir were modeled using two Models i.e. HEC-RAS 4.1.0 and Tsinghua University Equation, under two scenarios, i.e. flushing after one year and ten years of sediment deposition. Results of the both the Models and both the options for sediment deposition show good agreement with the observed deposited sediments. A complete flushing operation includes the emptying of reservoir, flushing the sediment
through the reservoir and refilling of the reservoir. Considering the results of complete flushing operation it was estimated that refilling time required for the reservoir is about 64% of the year as inflows to the reservoir are intermittent, hence annual flushing of the reservoir looks infeasible, however, large quantity of water for the flushing operation of the reservoir may be sacrificed after 10 years.
HEC-RAS 4.1.0 and Tsinghua University Model Results were used to formulate the complete strategy for flushing the reservoir. Model results revealed that for flushing the Jabbi Reservoir after 10 years deposition, appropriate flushing months are July and August; suitable flushing discharge is 3 cumecs; time required to empty the reservoir is 0.34 day; time required to refill the reservoir is 235 days; flushing duration required to flush 10 years deposited sediments is about 4 days; average flushable sediment diameter is 10 mm; and water required for flushing the reservoir would be about 4.4 Mm3.
Following the knowledge earned from this research work, similar procedures can be applied to other reservoirs of the world to check the degree of success in flushing operation, moreover, flushing plans / strategies can be formulated and relevant recovery in the reservoir capacities can be assessed.
TABLE OF CONTENTS Description Page #
Dedication ...... i Acknowledgements ...... ii Abstract ...... iv Table of Contents ...... viii List of Figures ...... xiv List of Tables ...... xix List of Abbreviations & Symbols...... xx
Chapter 1 INTRODUCTION
1.1 GENERAL ...... 1 1.2 PROBLEM STATEMENT ...... 4 1.3 OBJECTIVES ...... 4 1.4 SCOPE OF RESEARCH WORK ...... 5 1.5 UTILIZATION OF RESEARCH ...... 7 1.6 THESIS OVERVIEW ...... 8
Chapter 2 LITERATURE REVIEW
2.1 INTRODUCTION ...... 10 2.2 RESERVOIR SEDIMENTATION ...... 10
2.2.1 Reservoir sedimentation mechanism ...... 11 2.2.2 Consequences of reservoir sedimentation ...... 13 2.2.3 Methods to enhance the life of reservoir ...... 14 2.2.3.1 Watershed management ...... 14 2.2.3.2 Conventional dredging ...... 15 2.2.3.3 Dry excavation ...... 15 2.2.3.4 Hydrosuction ...... 15 2.2.3.5 Sediment routing/sluicing ...... 16 2.2.3.6 Sediment bypassing ...... 17
2.2.3.7 Density current venting ...... 18 2.2.3.8 Sediment flushing through reservoir ...... 19
2.3 EMPIRICAL MODELING OF RESERVOIR SEDIMENTATION ...... 19 2.3.1 Suspended sediment inflow into the reservoir ...... 19 2.3.2 Bed load into the reservoir ...... 20 2.3.2.1 Meyer-Peter and Muller formula ...... 20 2.3.2.2 Parker Formula...... 21 2.3.2.3 Brown-Einstein Equation ...... 22 2.3.2.4 DuBoys Formula ...... 23 2.3.2.5 Shields Formula ...... 23 2.3.2.6 Modified Einstein procedures for unmeasured sediment load ...... 24 2.3.3 Total sediment load into the reservoir ...... 24 2.3.4 Trap efficiency of reservoir ...... 24 2.3.4.1 Brune’s Curve ...... 25 2.3.4.2 Churchill’s Method ...... 26 2.3.5 Trapped sediment load in the reservoir ...... 26 2.3.6 Delta modeling in the reservoir ...... 26
2.4 SEDIMENT REMOVAL FROM RESERVOIRS BY FLUSHING ...... 29 2.4.1 General ...... 29 2.4.2 Worldwide experiences of sediment flushing from reservoirs ...... 30 2.4.3 Sediment Management Experiences on Pakistani Large Reservoirs ...... 36 2.4.4 Classification of Techniques ...... 40 2.4.4.1 Empty Flushing ...... 40 2.4.4.2 Flushing with Partial Drawdown ...... 43 2.4.5 Downstream Environmental Effects of Flushing ...... 44 2.4.6 Flushing phases ...... 46 2.4.7 Erosion Processes during flushing ...... 48 2.4.7.1 Slumping at the Dam ...... 49 2.4.7.2 Slope Failure ...... 50 2.4.7.3 Retrogressive Erosion ...... 50
2.4.7.4 Progressive Erosion ...... 52
2.4.8 Flushing Efficiency ...... 53 2.4.8.1 Flushing Efficiency with Partial Drawdown ...... 54 2.4.8.2 Flushing Efficiency with Emptying ...... 54 2.4.9 Factors affecting the flushing efficiency ...... 56 2.4.10 Indicators to assess flushing feasibility of reservoir ...... 60 2.4.10.1 Sediment Balance Ratio ...... 60 2.4.10.2 Long Term Capacity Ratio ...... 61 2.4.10.3 Drawdown Ratio ...... 62 2.4.10.4 Sediment Balance Ratio with Full Drawdown...... 62 2.4.10.5 Flushing Width Ratio ...... 62 2.4.10.6 Top Width Ratio ...... 63
2.5 PROCESS BASED MODELING OF RESERVOIR SEDIMENTATION ...... 63
2.5.1 One Dimensional Numerical Models ...... 64 2.5.1.1 HEC-6 ...... 65 2.5.1.2 HEC-RAS 4.1.0 ...... 67 2.5.1.3 SHARC ...... 73 2.5.1.4 RESSASS ...... 75 2.5.1.5 FLUVIAL-12 ...... 75 2.5.1.6 Tsinghua University Model ...... 77 2.5.2 Two Dimensional Numerical Models ...... 81 2.5.2.1 GSTARS 4.0 ...... 82 2.5.2.2 TABS ...... 84 2.5.2.3 DIVAST ...... 85 2.5.3 Three Dimensional Numerical Models ...... 87 2.5.3.1 SSIIM ...... 88 2.5.3.2 FLUENT ...... 89 2.6 SUMMARY ...... 90
Chapter 3 METHODOLOGY
3.1 INTRODUCTION ...... 93 3.2 DATA COLLECTION ...... 93 3.3 EXPLORING MOST IMPORTANT FLUSHING INDICATOR AND ITS CRITICAL VALUE...... 96
3.4 DEVELOPMENT OF EQUATIONS FOR SBR AND LTCR OF RESERVOIRS ...... 96
3.5 MODELING SEDIMENT DEPOSITION AND FLUSHING OPERATION THROUGH RESERVOIRS USING SHARC ...... 99
3.5.1 Data input to Model ...... 99 3.5.2 Modeling sediment deposition and sediment flushing in reservoirs ...... 100 3.5.2.1 Baira Reservoir of India ...... 100 3.5.2.2 Gebidem Reservoir of Switzerland ...... 103 3.5.2.3 Gmund Reservoir of Austria ...... 106 3.6 MODELING SEDIMENT DEPOSITION AND FLUSHING OPERATION THROUGH RESERVOIRS USING HEC-RAS 4.1.0 ...... 109
3.6.1 Baira Reservoir of India ...... 109 3.6.2 Gebidem Reservoir of Switzerland ...... 114 3.6.3 Gmund Reservoir of Austria ...... 118 3.7 MODELING SEDIMENT FLUSHING OPERATION THROUGH RESERVOIR USING TSINGHUA UNIVERSITY EQUATION ...... 123
3.7.1 Baira Reservoir of India ...... 124 3.7.2 Gebidem Reservoir of Switzerland ...... 125 3.7.3 Gmund Reservoir of Austria ...... 126 3.8 ASSESSMENT OF FLUSHING EFFICIENCIES OF SMALL RESERVOIRS ...... 127 3.9 MODELING JABBI RESERVOIR FOR SEDIMENT FLUSHING OPERATION ...... 129
3.9.1 Modeling Jabbi Reservoir for and flushing operation using HEC-RAS 4.1.0 ...... 130
3.9.2 Modeling Jabbi Reservoir for flushing operation using Tsinghua University Equation ...... 136
3.10 PROPOSED FLUSHING STRATEGIES FOR JABBI RESERVOIR ...... 136 3.11 SUMMARY ...... 137
Chapter 4 RESULTS AND DISCUSSIONS
4.1 INTRODUCTION ...... 139 4.2 EXPLORING MOST IMPORTANT FLUSHING INDICATOR AND ITS CRITICAL VALUE ...... 139
4.3 DEVELOPMENT OF EQUATIONS FOR SBR AND LTCR OF RESERVOIRS ...... 143
4.4 MODELING SEDIMENT DEPOSITION AND SEDIMENT FLUSHING THROUGH RESERVOIRS USING SHARC ...... 146
4.4.1 Baira Reservoir of India ...... 146 4.4.2 Gebidem Reservoir of Switzerland ...... 149 4.4.3 Gmund Reservoir of Austria ...... 152
4.5 MODELING SEDIMENT DEPOSITION AND SEDIMENT FLUSHING THROUGH RESERVOIRS USING HEC-RAS 4.1.0 ...... 157
4.5.1 Baira Reservoir of India ...... 157 4.5.2 Gebidem Reservoir of Switzerland ...... 159 4.5.3 Gmund Reservoir of Austria ...... 162
4.6 MODELING SEDIMENT FLUSHING THROUGH RESERVOIRS USING TSINGHUA UNIVERSITY EQUATION ...... 165
4.6.1 Modeling sediment flushing in Baira Reservoir ...... 165 4.6.2 Modeling flushing in Gebidem Reservoir ...... 167 4.6.3 Modeling flushing in Gmund Reservoir ...... 169
4.7 ASSESSMENT OF FLUSHING EFFICIENCIES FOR SMALL RESERVOIRS ...... 174 4.8 MODELING SEDIMENT DEPOSITION AND FLUSHING OPERATION IN JABBI RESERVOIR USING HEC-RAS 4.1.0 ...... 175
4.9 MODELING SEDIMENT FLUSHING OPERATION IN JABBI RESERVOIR USING TSINGHUA UNIVERSITY EQUATION ...... 179
4.10 PROPOSING FLUSHING STRATEGIES FOR JABBI RESERVOIR ...... 182
4.10.1 Appropriate time to flush sediments from the reservoir ...... 183 4.10.2 Suitable flushing discharge required during flushing process ...... 184 4.10.3 Time required to empty the reservoir ...... 185 4.10.4 Time required to refill the reservoir ...... 186 4.10.5 Flushable sediment size ...... 186 4.10.6 Required flushing duration ...... 187 4.10.7 Volume of water required for flushing operation ...... 188 4.11 SUMMARY ...... 188
Chapter 5 CONCLUSIONS AND RECOMMENDATIONS
5.1 GENERAL ...... 191 5.2 CONCLUSIONS ...... 191 5.3 RECOMMENDATIONS ...... 193
REFERENCES ...... 194
LIST OF FIGURES
Description Page # Figure- 2.1 Regional distribution of reservoir sedimentation 11 Figure-2.2 Generalized depositional zones in a reservoir 12 Figure-2.3 Dredging process in a reservoir (ARAS T., 2009) 16 Figure-2.4 Photograph of sediment removal at Cogswell Reservoir (courtesy Los 16 Angeles County) Figure-2.5 Siphon dredging system at Tianjiawan Reservoir (Zhang & Xie, 1993) 17 Figure-2.6 Turbid water being discharged from the low-level outlet at Steeg 18 Reservoir, Oued Fodda, Algeria (Morris and Fan, 2010) Figure-2.7 Trap efficiencies curves from Brune (1953) and Churchill (1948) 25 Figure-2.8 Worldwide distribution of storage reservoirs 30 Figure-2.9 Worldwide distributions of water storages 31 Figure 2.10 Worldwide distribution of flushed reservoirs 31 Figure 2.11 Mode of flushing used in the reservoirs, worldwide 32 Figure 2.12 Dashikau irrigation reservoir in China, emptied before flood season 42 (Morris and Fan, 2010) Figure 2.13 Sanmanxi Reservoir, China, during sediment flushing. (Morris and 42 Fan, 2010) Figure 2.14 Welbedacht dam, South Africa, during sediment flushing (Olesen and 43 Basson, 2004) Figure 2.15 Hydraulic and sediment characteristics for channel formation and 47 channel maintenance during flushing event. Figure 2.16 Slumping of fine-grained deposits near the dam in the small Santa 49 Maria Reservoir on Río Samala, Guatemala (Morris and Fan, 2010) Figure 2.17 Characteristics of retrogressive erosion from flume test. (Morris and 51 Fan, 2010) Figure 2.18 Cross section immediately u/s of the dam for simplified reservoir 62 geometry (Atkinson, 1996b) Figure-2.19 Flow chart showing major steps of computation for FLUVIAL Model 77 Figure-2.20 Sketch showing the coordinate system used and the definition of some 87 of the variables, here u= u1 , v = u2 , w = u3 Figure-3.1 Flow diagram representing Methodology adopted to achieve the 95 objectives Figure 3.2 Input data given to the Deposition Model of SHARC 101 Figure 3.3 Fall velocities of different sizes suspended sediments load 101 Figure 3.4 Bed material sizes entering into Baira Reservoir 102 Figure 3.5 Suspended sediment sizes entering into Baira Reservoir 102
Figure 3.6 Input data given to the Sluicing Model for Baira Reservoir 103 Figure 3.7 Input data given to the Deposition Model for Gebidem Reservoir 104 Figure 3.8 Fall velocities of different sizes suspended sediments load for Gebidem 104 Reservoir Figure 3.9 Bed material sizes entering into Gebidem Reservoir 105 Figure 3.10 Suspended material sizes entering into Gebidem Reservoir 105 Figure 3.11 Input data given to the Sluicing Model for Gebidem Reservoir 106 Figure 3.12 Input data given to the Deposition Model for Gmund Reservoir 107 Figure 3.13 Fall velocities of different sizes suspended sediments load for Gmund 107 Reservoir Figure 3.14 Bed material sizes entering into Gmund Reservoir 108 Figure 3.15 Suspended material sizes entering into Gmund Reservoir 108 Figure 3.16 Input data given to the Sluicing Model for Gmund Reservoir 109 Figure 3.17 Schematic diagrams showing the cross section locations used during 110 delta modeling for Baira Reservoir Figure 3.18 Flow Hydrographs at Baira dam site used as upstream boundary 112 condition Figure 3.19 Schematic diagram showing the cross section locations used during 116 delta modeling for Gebidem Reservoir Figure 3.20 Flow Hydrographs at Gebidem dam site used as upstream boundary 116 condition Figure 3.21 Schematic diagram showing the cross section locations used for the 120 delta modeling for Gmund Reservoir Figure 3.22 Flow Hydrographs at Gmund dam site used as upstream boundary 120 condition Figure 3.23 Schematic diagram showing the cross section locations used for the 132 delta modeling for Jabbi Reservoir Figure 3.24 Flow Hydrographs at Jabbi dam site used as upstream boundary 132 condition for annual deposition Figure 3.25 Bed material gradation curve of Jabbi Reservoir for annual sediment 134 deposition Figure 4.1 SBR values of flushed reservoirs of world 140 Figure 4.2 DDR values of flushed reservoirs of world 140
Figure 4.3 SBRd values of flushed reservoirs of world 141 Figure 4.4 FWR values of flushed reservoirs of world 141 Figure 4.5 TWR values of flushed reservoirs of world 142 Figure 4.6 LTCR values of flushed reservoirs of world 143 Figure 4.7 Comparison between the given and calculated SBR values of foreign 144 reservoirs
Figure 4.8 Comparison between the given and calculated LTCR values of foreign 144 resorvoirs Figure 4.9 Comparison of results for SBR computed by Atkinson (1996) method 145 and developed equations for Pakistani reservoirs Figure 4.10 Comparison of results for LTCR computed by Atkinson (1996) method 145 and developed equations for Pakistani reservoirs Figure 4.11 Longitudinal delta profile after 1.5 years deposition in Baira Reservoir 146 Figure 4.12 In-transport gradation curves at start and end of deposition process in 147 Baira Reservoir Figure 4.13 Bed material gradation curves at u/s & d/s of Baira Reservoir 147 Figure 4.14 Bed levels during sediment flushing in Baira Reservoir 148 Figure 4.15 Concentration leaving the Baira Reservoir during flushing operation 148 Figure 4.16 Longitudinal sediment delta profile after 1 year deposition in Gebidem 149 Reservoir Figure 4.17 In-transport gradation curves at start and end of deposition process for 150 Gebidem Reservoir Figure 4.18 Bed material gradation curves at u/s & d/s of Gebidem Reservoir 151 Figure 4.19 Bed levels during sediment flushing in Gebidem Reservoir 151 Figure 4.20 Concentration leaving the Gebidem Reservoir during flushing 152 operation Figure 4.21 Longitudinal sediment delta profile after 1 year deposition in Gmund 153 Reservoir Figure 4.22 In-transport gradation curves at start and end of deposition process in 153 Gmund Reservoir Figure 4.23 Bed material gradation curves at u/s & d/s of Gmund Reservoir 154 Figure 4.24 Bed levels during sediment flushing in Gmund Reservoir 155 Figure 4.25 Concentration leaving the Gmund Reservoir during flushing operation 156 Figure 4.26 Water surface profile before delta modeling for Baira Reservoir 157 Figure 4.27 Simulated Longitudinal Sediment Delta Profile for Baira Reservoir due 158 to 1.5 year sediment deposition Figure 4.28 Bed profile of Baira Reservoir before flushing based on 1 year 158 Sediment deposition Figure 4.29 Longitudinal profile of Baira Reservoir after flushing the 1.5 years 159 deposited sediments Figure 4.30 Water surface profile before delta modeling for Gebidem Reservoir 160 Figure 4.31 Simulated Longitudinal Delta Profile for Gebidem Reservoir after 1 160 year sediment deposition Figure 4.32 Bed profile of Gebidem Reservoir before flushing sediment deposition 161 Figure 4.33 Bed Profile of Gebidem Reservoir after flushing sediment deposition 161 Figure 4.34 Water surface profile before delta modeling for Gmund Reservoir 162 Figure 4.35 Simulated Longitudinal Delta Profile for Gmund Reservoir after 163
sediment deposition Figure 4.36 Bed profile of Gmund Reservoir before flushing Sediment deposition 163 Figure 4.37 Bed profile of Gmund Reservoir after flushing Sediment deposition 164 Figure 4.38 Determination Erodibility Coefficient () for Baira Reservoir 165 Figure 4.39 Comparison between observed flushing duration and simulated 166 flushing duration for Baira Reservoir Figure 4.40 Simulated flushing durations against flushing discharges for Baira 166 Reservoir Figure 4.41 Determination of Erodibility Coefficient () for Gebidem Reservoir 167 Figure 4.42 Comparison between observed flushing duration and simulated 168 flushing duration for Gebidem Reservoir Figure 4.43 Comparison between observed flushed sediments and simulated 168 flushed sediments for Gebidem Reservoir Figure 4.44 Simulated flushing durations against various flushing discharges 169 for Gebidem Reservoir Figure 4.45 Determination of Erodibility Coefficient () for Gmund Reservoir 170 Figure 4.46 Comparison between observed flushing duration and simulated 170 flushing duration for Gmund Reservoir Figure 4.47 Simulated flushing durations against various flushing discharges 171 for Gmund Reservoir Figure 4.48 LTCR values of 20 selected small reservoirs 175 Figure 4.49 Water surface profile before delta modeling for Jabbi reservoir 176 Figure 4.50 Simulated Longitudinal Delta Profile for Jabbi Reservoir after 1 year 176 sediment deposition Figure 4.51 Bed profile of Jabbi Reservoir before flushing 1 year sediment 177 deposition Figure 4.52 Bed profile of Jabbi Reservoir after flushing the 1 year deposited 177 sediment Figure 4.53 Bed profile of Jabbi Reservoir after 10 years sediment deposition 178 Figure 4.54 Bed profile of Jabbi Reservoir before flushing 10 years sediment 178 deposition Figure 4.55 Bed profile of Jabbi Reservoir after flushing 10 years sediment 179 deposition Figure 4.56 Flushing durations against flushing discharges of Jabbi resorvoir for 1 180 year flushing Figure 4.57 Flushing durations against flushing discharges of Jabbi resorvoir for 10 181 years flushing Figure 4.58 Average daily flows and minimum flushing discharge required for 183 Jabbi Reservoir (year 1991-2000) Figure 4.59 Flow mass curve for proposed flushing durations 183 Figure 4.60 Flushing durations required to flush 1 year/10 years deposited 184 sediments Figure 4.61 Calculated reservoir emptying time 185
Figure 4.62 Re-filling time for Jabbi Reservoir 185 Figure 4.63 Mean velocities at various river stations during annual flushing 186 operation Figure 4.64 Mean velocities at various river stations during flushing 10 years 187 deposited sediments Figure 4.65 Critical water velocities as function of mean grain size (ASCE Task 187 Committee, 1967)
LIST OF TABLES
DESCRIPTION PAGE#
Table 2.1 Bed load correction 24 Table 2.2 Successfully flushed reservoirs 33 Table 2.3 Partially flushed reservoirs 34 Table 2.4 Different definitions of flushing efficiency 53 Table 2.5 Overflow drawdown flushing 55 Table 2.6 Flushing efficiency for reservoir emptying 56 Table 2.7 values recommended by various sources Table 3.1 Data input to develop equation for flushing indicators 97 Table 3.2 Thirty five cross sections used for Baira Reservoir during delta 111 modeling Table 3.3 Twenty five cross sections used for Gebidem Reservoir during 115 delta modeling Table 3.4 Twenty nine cross sections used for Gmund Reservoir during 119 delta modeling Table 3.5 Flushing data of foreign reservoirs 123 Table 3.6 Input data of 20 reservoirs of small dams organization, Islamabad 128 Table 3.7 Twenty eight cross sections used for the delta modeling for Jabbi 131 Reservoir Table 3.8 Flushing data of Jabbi Reservoir 136 Table 4.1 Comparison between observed and simulated flushing durations 156 using SHARC Table 4.2 Comparison between simulated and observed flushing durations 165 using HEC-RAS 4.1.0 Table 4.3 Comparison between simulated and observed flushing durations 172 using Tsinghua University Equation Table 4.4 Summary of results by 3 Models 173
Table 4.5 Modeling Results for Jabbi Reservoir 182
Table 4.6 Flushing summary for Jabbi Reservoir 188
xix
ABBRIVIATIONS & SYMBOLS
A list of all the special symbols used in this thesis along with their brief description is given below:
SYMBOL DESCRIPTION BCM Billion Cubic Meter DACSE Design Analysis for Canal Sediment Extractors DDR Drawdown Ratio DORC Design of Regime Canals DOSSBAS Design of Sluiced Settling Basins FWR Flushing Width Ratio GPS Global Positioning System GSTARS General Stream Tube Model for Alluvial River Simulation GUI graphical user interface HEC-RAS Hydraulic Engineering Center- River Analysis System ICOLD International commission on large dams IWR Institute for Water Resources LiDAR Light Distancing and Ranging LTCR Long Term Capacity Ratio MCM Million Cubic Meter PIDA Punjab Irrigation and Drainage Authority RESSASS Reservoir Survey Analysis and Sediment Simulation SBR Sediment Balance Ratio SBRd Sediment Balance Ratio with full Drawdown SHARC Sediment and Hydraulic Analysis for Rehabilitation of Canals SSIIM Sediment Simulation In Intake with Multiple option SSL Suspended Sediments Load TWR Top Width Ratio
Cd characteristics sediment coefficient
f Specific weight of liquid (water)
da Arithmetic mean sediment size g Gravitational acceleration
qB Bed load d Sediment size
qB Sediment bed load per unit time and per unit width of the channel. Fall velocity
* Shield stress
xx
Kinematics viscosity
Cd Characteristics sediment coefficient S Foreset slope of delta deposit
c c Critical shear stress d 90 Size of sediment at which material is finer by 90% A Critical sediment mobility parameter A Cross-sectional area of the flow
BAVE average width of the channel C coefficient
Ci Total sediment concentration of inflow
CL Sediment concentration in lower zone
Cm Sediment discharge concentration
Co Total sediment concentration of outflow
Cv Sediment capacity concentration (by volume) D Effective water depth D Diameter of bed material on topset slope d Maximum channel depth at dominant discharge
d50 Sediment particle size of which 50% is finer
D90 Diameter of bed material for 90 percent finer in millimeters
dgr dimensionless grain diameter d50 dm Median size diameter of the sediments.
ds Mean particle diameter E Flushing efficiency f’ Engelund and Hansen’s transport function
Fgr Ackers and White’s mobility number
Fr Froude number G Unit wt of water
Ggr Ackers and White’s sediment transport function
gs Unit sediment transport
gs Total sediment transport
xxi
gsb Bed load sediment transport
gssL Suspended sediment transport in lower zone gssM Suspended sediment transport in middle zone
gssU Suspended sediment transport in upper zone
hf friction loss Allowance for the watershed area between the upstrseam gauging station K and the dam site K Coefficient equal to 0.19 (English units) or 0.058 (SI units)
Kr Roughness coefficient
’ Kr Roughness coefficient based on grains
Ld Annual quantity of sediment deposited
Li Annual quantity of sediment inflow
Lo Annual quantity of sediment flushed out
Mf Mass flushed n Manning’s roughness n' roughness due to grain n’ grain Manning’s roughness nv Temperature exponent Q Water discharge
Q/QB Ratio of the total flow
Qf Flushing discharge
Qs Sediment discharge
qt total bed-material load per unit channel width R Hydraulic mean radius r sediment particle radius s Specific gravity of sediments S Topset slope of delta S S Energy slope s s Specific gravity of the sediments
So Original bed slope of the river Te Estimated trapping efficiency of the reservoir Tf Flushing duration (days)
Tf Fraction of year used for flushing TL Total sediment inflow
xxii
Tr Fraction of year that the river’s sediment load will take to refill.
U* shear velocity V Average channel velocity
V1 Storage capacity of reservoir before flushing
V2 Storage capacity of the reservoir after flushing
Vd Volume of deposit flushed out
Vi Inflowing water volume
Vo Outflowing water volume
Vori Original live storage capacity
Vsi Inflowing sediment volume during flushing
Vso Outflowing sediment volume during flushing
Wf Width of eroded channel Ws Settling velocity of the sediment particles. X Sediment concentration ρ Fluid density Erodibility coefficient Unit wt of water
s Unit wt of solid particles
b Bed shear stress
o’ Bed shear stress due to grain resistance
xxiii
CHAPTER 1
INTRODUCTION
1.1 GENERAL
Globally there are about 50,000 large dams and among them 25,500 are the storage reservoirs with storage volume of about 6,464 Bm3 (Caston et al., 2009; White et al., 2000). All the reservoirs are subjected to some degree of sedimentation resulting in the reduction of the storage capacities of the reservoirs and other harmful consequences. When a dam is constructed across a river, the area of flow increases for the same discharge, which reduces velocity of flow such that sediments settle in the impoundment resulting in the reservoir sedimentation. Most of the world reservoirs are losing their storage capacities due to reservoir sedimentation. Regionwise annual reservoir storage losses vary from 0.08 to 2.3 percent, with the average annual world storage loss of about 0.6 percent (White, 2010). The maximum average annual storage loss is in China, i.e., 2.3%, whereas the minimum is in North Africa, i.e., 0.08%. Average annual storage losses, in percentage, in other regions are: Middle East 1.5, Central Asia 1, South Asia 0.52, South East Asia 0.30, Pacific Rim 0.27, Sub-Saharan Africa 0.23, North Europe 0.2, North America 0.2, South Europe 0.17, and South America 0.1.
In the present study, flushing method is investigated in detail to answer the several questions related to flushing operation. Flushing is a method by which the flow velocities in a reservoir are increased to such intensities that deposited sediments are mobilized and transported through low level outlets in the dam (White, 2010; Emamgholizadeh, 2008). Flushing sediments through a reservoir has been practiced successfully and found to be inexpensive in many cases, however, a great amount of water consumed in the flushing operation might affect it (Fi-John et al., 2003). Reservoir sediment flushing may be categorized as; complete drawdown flushing or emptying and flushing, and partial drawdown flushing, also called as pressure flushing (Emamgholizadeh, 2006).
The oldest known practice of flushing was referred to by D’Rohan (1911), who described the method adopted in Spain in the 16th century, where bottom-outlet gates known as the
1 CHAPTER 1 INTRODUCTION
Spanish gates or undersluices were used. Another early example of flushing sediments with large-capacity sluices was reported by Jordana (1925) in the Peña Reservoir, Spain.
Flushing is being practiced for hundreds of the reservoirs of the world. In literature there are about fifty reservoirs which are reported to be flushed. Among them flushing data is available for only twenty five flushed reservoirs (White et al., 2000). Out of these reservoirs, Atkinson (1996b) used the data of fourteen reservoirs to assess the feasibility of sediment flushing. He concluded that among the selected reservoirs, six reservoirs proved to be successful for flushing, while the remaining eight reservoirs were partially flushed. The selected fourteen reservoirs were: Baira and Ichari of India, Gebidem and Palagnedra of Switzerland, Gmund of Austria, Hengshan, Gaunting, Heisonglin, Sanmenxia and Shuicaozi of China, Santo-Domingo of Venezuela, Guernsey of USA, Ouchi-Kurgan of Former USSR, and Sefid-Rud of Iran. Successfully flushed reservoirs are: Baira of India, Gebidem and Palagnedra of Switzerland, Gmund of Austria, Hengshan of China, and Santo-Domingo of Venezuela.
Various flushing indicators used to assess feasibility of sediment flushing from reservoirs are: Sediment Balance Ratio, SBR, Long Term Capacity Ratio, LTCR, Drawdown Ratio,
DDR, Sediment Balance Ratio during full drawdown, SBRd, Flushing Width Ratio, FWR, and Top Width Ratio, TWR. The critical values of these indicators are: SBR > 1,
LTCR approaching to unity, DDR > 0.7, SBRd > 1, FWR > 1 and TWR 1-2 (Atkinson, 1996b; White et.al, 2000). The values of these six flushing indicators were computed for above mentioned fourteen flushed reservoirs. Flushing indicators qualifying for successfully flushed reservoirs and which do not qualify for partially flushed reservoirs were categorized.
In the present study, from analysis, it was found that critical values of most of the flushing indicators were satisfied for the flushed reservoirs, except the critical value of LTCR, which was satisfied for only successfully flushed reservoirs. So LTCR was found to be the most important flushing indicator among the six flushing indicators to assess the feasibility of sediment flushing from reservoir.
2 CHAPTER 1 INTRODUCTION
To numerically simulate flushing operations, initially reservoirs were modeled for deposition processes. Flushing operations were modeled for three successfully flushed reservoirs, for which data of entire flushing operations were available to calibrate and validate the Models. These reservoirs are Baira of India, Gebidem of Switzerland and Gmund of Austria. Flushing processes had been modeled using three Models, i.e. SHARC, HEC-RAS 4.1.0 and Tsinghua University Equation. SHARC Model well simulates the sediment deposition process, but it underestimates flushing duration. HEC- RAS 4.1.0 Model was calibrated for the three reservoirs. Results of the Model show that it can well simulate sediment deposition and flushing operations. Tsinghua University Equation was also used for simulation of sediment flushing through reservoirs. Then it was calibrated for the three reservoirs. The Results of Tsinghua Equation revealed that Model well simulates sediment flushing operations through reservoirs.
Equations were developed for SBR and LTCR by Multiple Non-linear Regression Analysis, using the data of six successfully flushed foreign reservoirs. These equations were tested for foreign and five Pakistani reservoirs: Talikna, Jabbi, Jammargal, Dharabi, and Phalina. The values obtained were much closer to values computed by Atkinson (1996b) procedure.
Among the sixty small reservoirs under the control of Small Dams Organization of Punjab Irrigation Department, using the data of twenty reservoirs, the values of LTCR were computed to assess the suitability for sediment flushing through these reservoirs. Based upon the computed values of LTCR, it was observed that five reservoirs Jabbi, Talikna, Dharabi, Phalina, and Jammargal were suited for sediment flushing operation.
Finally, Jabbi Reservoir in District Attock of Punjab, was selected for modeling sediment deposition and proposed flushing operation. This reservoir was modeled using HEC-RAS 4.1.0 and Tsinghua University Equation to simulate flushing operations. Flushing strategies were also proposed for this reservoir.
3 CHAPTER 1 INTRODUCTION
1.2 PROBLEM STATEMENT
Pakistan has two major storage reservoirs, Mangla and Tarbela, having initial storage capacities of about 7.259 Bm3 and 14.344 Bm3 respectively. These two reservoirs are depleting their capacities due to sedimentation at an alarming rate. According to the hydrographic surveys conducted in 2013, Mangla and Tarbela Reservoirs have lost 22.16% and 34.87% of their original storage capacities (Wapda, 2013).
Apart from that, Pakistan has a number of small reservoirs which are losing their capacities due to sedimentation. In Punjab, sixty small reservoirs are losing their capacities at alarming rates. As per hydrographic surveys conducted, Tainpura-I, Tainpura-II, Dungi, Jammargal, Pira Fatehal, Rawal and Jabbi reservoirs are losing their capacities at average annual losses in, percentage, as: 1.13, 0.97, 1.84, 4.15, 3.36, 0.53 and 1.1 respectively (PID, 2013).
It is the need of time that methods should be adopted to enhance the lives of these reservoirs. Approaches used to desilt the reservoirs are dredging, dry excavation, hydrosuction, sediment routing/sluicing, sediment bypassing, density current venting, and flushing sediments through reservoir. Among these methods, sediment flushing from reservoir is one of the economical methods used to desilt the reservoirs, with the condition that sufficient water is available. Hence there is need to explore the strategies to flush sediments through the reservoirs, so that the lives of these reservoirs may be enhanced.
Moreover in Pakistan, there are a large number of reservoirs at feasibility and design stage, and there is a dire need to model flushing scenarios using Numerical Simulation Models. Confidence in modeling is only possible by simulating the flushing operations for those reservoirs which have sufficient observed data related to flushing operations.
1.3 OBJECTIVES
Following were the major objectives of the Ph.D. research work:
4 CHAPTER 1 INTRODUCTION
i. Evaluation of six flushing indicators: SBR, LTCR, DDR, SBRd, FWR, and TWR for assessing the feasibility of sediment flushing through reservoirs. Among the six flushing indicators, to explore the most important flushing indicator, and to investigate its critical value considering data of fourteen flushed reservoirs of the world.
ii. To develop equations for the two important flushing indicators, i.e., SBR, and LTCR, for assessing the feasibility of sediment flushing through the reservoirs, using the data of six successfully flushed reservoirs, i.e., Baira, Gebidem, Gmund, Hengshan, Palagnedra, and Santo-Domingo by using Multiple Non-Linear Regression Analysis.
iii. Evaluation of two 1-D Sediment Transport Numerical Simulation Models, i.e. SHARC and HEC-RAS 4.1.0 for modeling the sediment deposition and sediment flushing through the reservoirs.
iv. Evaluation of Tsinghua University Equation for modeling the sediment flushing through the reservoirs.
v. Assessment of flushing potential in 20 small reservoirs in the Punjab province of Pakistan by computing flushing indicators and then ranking of these reservoirs with respect to their flushing potentials.
vi. To formulate flushing strategy/plan for one of the small reservoir of the Punjab, Pakistan, i.e. Jabbi Reservoir.
1.4 SCOPE OF RESEARCH WORK
Considering the objectives of the research work, the following scope of the research work was set: Literature survey relevant to the research area was conducted throughout the research period using technical literature existing in libraries and the internet explorer. Complete research about reservoir sedimentation worldwide was made and different reservoirs of the world were studied regarding reservoir sedimentation. Also the mechanism of
5 CHAPTER 1 INTRODUCTION
reservoir sedimentation was studied. The consequences of reservoir sedimentation were also studied.
As the reservoirs are losing their capacities due to sedimentation, the methods to minimize reservoir sedimentation and the methods to enhance the lives of reservoirs being implemented worldwide were studied in the thesis
Among the various methods to enhance lives of reservoirs, sediment flushing through reservoirs is an important way to desilt reservoirs. Various reservoirs of the world, where sediment flushing is being implemented, were studied. Also the factors affecting sediment flushing efficiency were discussed. The indicators to assess flushing feasibility of reservoirs were also explored and their applicability was studied and discussed.
As none of Pakistani reservoir is being flushed successfully, for modeling of sediment deposition and sediment flushing through reservoirs, three foreign reservoirs of the world, Baira Reservoir of India, Gebidem Reservoir of Switzerland and Gmund Reservoir of Austria were selected and numerical simulations were carried out using three Numerical Models, SHARC, HEC-RAS 4.1.0 and Tsinghua University Model. The performances of these three Models were evaluated regarding simulating sediment deposition and sediment flushing through reservoirs.
Among the sixty small reservoirs of Punjab, under the control of Punjab Small Dams Organization of Punjab Irrigation Department, twenty reservoirs were selected and evaluated for sediment flushing feasibility through these reservoirs.
Finally among these twenty reservoirs, Jabbi Reservoir in District Attock was selected for modeling sediment deposition and sediment flushing through this reservoir, using two sediment Models HEC-RAS 4.1.0, and Tsinghua University Model. Complete flushing plan for this Reservoir was proposed and the recommendations were made regarding flushing the deposited sediments from the Jabbi Reservoir.
6 CHAPTER 1 INTRODUCTION
1.5 UTILIZATION OF RESEARCH
There are two main storage reservoirs, Mangla and Tarbela which are losing their capacities, gradually, due to sedimentation. Moreover there are sixty small reservoirs in Punjab under the control of Small Dams Organization, Islamabad and many other reservoirs in other provinces of Pakistan which are subjected to sediment deposition, resulting storage loss of these reservoirs. Using Numerical Models, their sediment flushing operations can be modeled and strategies may be proposed to desilt these reservoirs and their lost capacities may be restored by any of the methods described above to enhance the storage lives of these reservoirs. Study may be made to assess the feasibility of sediment flushing through these reservoirs, and after that flushing provision may be made in the reservoirs, and also flushing strategies may be proposed to sustain the storage capacities of the reservoirs for longer life spans.
Nowadays, Pakistan has energy crisis, load shedding is the persistent feature which is being faced by the whole Pakistani Nation. We are of fortune enough to have huge potential for hydropower generation and many suitable sites are available for the construction of hydropower plants. Many projects are identified and they are either in feasibility study phase or in other phases. The projects identified by WAPDA are numerous, some of the projects are: Diamer Basha Dam, Kalabagh Dam, Gomal Zam Dam, Mirani Dam, Sabakzai Dam, Satpara Dam, Kurram Tangi Dam, Akhori Dam, Nai Gaj Dam, Skardu/Katzara Dam, Sukleji Dam, Winder Dam, Naulong Dam, Hingol Dam, Munda Dam, Allai Khwar Project, Khan Khwar Project, Duber Khwar Project, Jinnah Hydropower Project, Neelum-Jhelum Hydropower Project, Golen Gol Hydropower Project, Dasu Hydropower Project, Bunji Hydropower Project, Keyal Khwar Hydropower Project, Lawi Hydropower Project, Spat Gah & Chor Nullah Project, Kohala Hydropower Project, Phandar Hydropower Project, and Basho Hydropower Project. Feasibility studies of the above projects and present study approaches and results will help in analysing the sediment flushing feasibility through these reservoirs. So it may be said that construction of new reservoirs is the need of the day for Pakistan, but reservoir conservation is essential for the sustainability of these reservoirs, so, this study is very much concerned and can contribute a lot to enhance their lives.
7 CHAPTER 1 INTRODUCTION
This study will definitely give confidence to consultants who are preparing feasibility reports, and several Models have been evaluated to simulate the flushing operations considering the data of observed flushing operations.
1.6 THESIS OVERVIEW
Research work related to sediment flushing is described in five chapters. Introduction is presented in Chapter 1, which describes the worldwide reservoir sedimentation problems and the methods to sustain the storage capacity of reservoir, special focus on the method of sediment flushing from reservoir. Problem statement, objectives of the study, scope of research work have also been described and finally utilization of the research in Pakistan has been described in detail.
Literature Review is described in Chapter 2, it describes in detail reservoir sedimentation occurring in the world. Then methods to enhance the lives of the reservoirs have been described, focusing to the method of sediment flushing from reservoirs. Strategies for sediment flushing through the reservoir have been described.
Methodology of study has been described in Chapter 3. Data of fourteen reservoirs; 6 successfully flushed and 8 partially flushed reservoirs, was used to determine most import flushing indicator. Data of six successfully flushed foreign reservoirs was used to develop equations to calculate the values of two important flushing indicators, Sediment Balance Ratio, SBR, and Long Term Capacity Ratio, LTCR, using Multiple Non-Linear Regression Analysis. Data of three foreign reservoirs was used to numerically simulate sediment deposition and sediment flushing through reservoirs, using three Numerical Models SHARC, HEC-RAS 4.1.0 and Tsinghua University Equation, and also evaluation of these three Models was made for their performance for numerical simulation of reservoirs. Ranking of twenty small reservoirs in Pakistan for their feasibility towards sediment flushing was also done. Modeling of sediment deposition and flushing through reservoir was done for one small reservoir, Jabbi, using two Numerical Models HEC- RAS 4.1.0 and Tsinghua University Equation. Finally flushing strategies for Jabbi Reservoir had been proposed.
8 CHAPTER 1 INTRODUCTION
Results and Discussions had been described in Chapter 4. Based upon the flushing data of fourteen flushed foreign reservoirs LTCR was declared as the most important flushing indicator. Developed equations to calculate SBR and LTCR were tested on six foreign reservoirs and 5 Pakistani Small reservoirs. Evaluation for the performance of three Numerical Models to numerically simulate sediment deposition and flushing through reservoirs was made. Ranking of twenty small Pakistani reservoirs was made and five reservoirs, Talikna, Dharabi, Jammargal, Phalina and Jabbi were declared that they may be successfully flushed. Then Jabbi Reservoir was modeled for sediment deposition and sediment flushing through reservoirs using two Numerical Models HEC-RAS 4.1.0 and University Equation. Finally suitable strategies were proposed for flushing sediments through small Jabbi Reservoir.
Conclusions and recommendations of the study are reported in Chapter 5.
9
CHAPTER 2
LITERATURE REVIEW
2.1 INTRODUCTION
This chapter describes the state of the art knowledge on sediment deposition in reservoirs, worldwide experience on sediment flushing through the reservoirs and their related theory. The topics discussed in the chapter are reservoir sedimentation, empirical modeling of reservoir sedimentation, various approaches to enhance the lives of reservoirs, various ways to evacuate sediments from the reservoirs, removal of sediments from the reservoir by flushing, various indicators to assess the feasibility of sediment flushing through reservoirs, process based modeling of reservoir sedimentation and flushing of sediments through the reservoirs. At the end of this chapter whole literature findings are summarized.
2.2 RESERVOIR SEDIMENTATION
Mostly natural rivers are approximately balanced with respect to the sediment inflow and outflow. When a dam is constructed across the river, this balance is entirely changed and the area of flow increases for the same discharge which reduces velocity of flows such that sediments start settling in the impoundment resulting in the reservoir sedimentation. One of the major consequences of the reservoir sedimentation is the reservoir storage loss. Most of the world reservoirs are losing their storage capacities due to reservoir sedimentation. Annual reservoir storage loss due to sedimentation in different countries varies from 0.08 to 2.3 percent, with average annual world storage loss of about 0.6 percent. The maximum annual storage loss is in China, i.e., 2.3%, whereas the minimum storage loss is in North Africa., i.e., 0.08%. Average annual storage losses, in percentage, in other regions are: Middle East 1.5, Central Asia 1, South Asia 0.52, South East Asia 0.30, Pacific Rim 0.27, Sub-Saharan Africa 0.23, North Europe 0.2, North America 0.2, South Europe 0.17, and South America 0.1 (white, 2010), as depicted in Figure 2.1
10
CHAPTER 2 LITERATURE REVIEW
3
2.5 2.3 2 1.5 1.5 1 1
0.5 0.52 0.3 0.27 0.23
(%) Loss Storage Annual 0.2 0.2 0.17 0.1 0.08 0
China M.East S. Asia N. Euro. N. Euro. S. S.E.Asia N. Africa N. Amer. S. Amer. Pac. Rim
S. S.Africa Centr. Asia Centr.
Reigon
Figure 2.1 Regional distribution of reservoir sedimentation
2.2.1 Reservoir Sedimentation Mechanism When a sediment laden tributary enters into the reservoir, then due to the wider cross sectional area of the reservoir, flow velocity reduces, and the sediment transport capacity is decreased. This causes deposition of sediments in the reservoir. Sedimentation process may be described by another way that in a flowing river the water is in high turbulence and when water enters into the reservoir, turbulence is reduced and the sediment particles cannot remain in suspension further, and begin to settle in the reservoir (Boreland, 1971).
The bed load and coarse fraction of the suspended load are deposited just at the upstream of the reservoir, to form delta deposit. Delta deposit mostly consists of gravel and sand. The particles of median sizes are the next to be deposited, while fine sediments with lower settling velocities and some portion of coarser particles i.e. sand are transported further downstream of the reservoir to form the bottom set deposits (Morris and Fan, 2010). Delta deposition may be further distinguished as topset deposit, foreset deposit and bottomset deposit. Topset deposit contains the early settling coarser particles and mainly consists of the bed material of the reservoir. Topset deposit bed slope is about half of the bed slope of the reservoir. Foreset deposit is the face of the delta advancing into the reservoir and is distinguished from topset deposit by an increase in slope and decrease in
11
CHAPTER 2 LITERATURE REVIEW grain size. Foreset depositional portion is unstable and subject to slumping, its slope is 6.5 times the topset slope.
Another important transport mode for fine sediments, i.e., silt and clay, is the turbidity current. Turbid density current is the gravity-induced movement of one fluid, under or over another fluid, caused by density difference between two fluids. Turbidity currents occur when sediment laden water enters an impoundment, plunges beneath the clear water, and travels downstream along the submerged Thalweg. Turbidity currents are driven by an excess gravity force (negative buoyancy) due to the presence of sediment- laden water in a clear surrounding fluid. These low velocity currents are capable of transporting large quantities of sediment over long distances. Their role of sediment deposition is less than deltaic deposit processes and usually they create mud deposits near the dam as bottomset deposits (Sloff, 1997). A sketch of deposition process is shown in Figure 2.2
Figure 2.2 Generalized depositional zones in a reservoir
12
CHAPTER 2 LITERATURE REVIEW
2.2.2 Consequences of Reservoir Sedimentation The main consequences of reservoir sedimentation are: (i) Storage Loss: Sediment deposition in reservoir will reduce and ultimately eliminates useable storage capacity, making the reservoir useless for water supply or power generation. If the spillway capacity is based on flood storage within the reservoir, sedimentation can cause the dam unsafe when the flood storage is lost. (ii) Delta deposition: The coarser portion of the inflowing sediment load is deposited on the upstream of the reservoir, forming delta deposits which not only reduce reservoir storage, but can also cause channel aggradation extending many kilometers upstream of the reservoir. Channel aggradation can increase flooding of infrastructure, communities and agricultural lands on the flood plains, and groundwater level rise, creating water logging and salinity. (iii)Navigation: Both commercial and recreational navigation can severely impaired by sediment accumulation, especially in the delta area and in the vicinity of locks. (iv) Air pollution: In seasonally empty irrigation reservoirs, desiccated deposits of fine sediment can be eroded and transported by wind, creating a nuisance and health hazard to nearby communities (Danielevsky, 1993; Tolouie, 1993) (v) Earthquake hazard: Sediment deposits have greater mass than water, and some research indicates that the presence of sediment against the dam can significantly increases the force of earthquake shaking against the structure (Chen and Hung, 1993). Sediments accumulating near the dam may be liquefied by earthquake shaking so that they flow forward and bury bottom outlets, entering and clogging any conduits that are open. At the large Tarbela dam on the Indus River in Pakistan, it was estimated that 6 to 12 months would be required to restore irrigation and hydropower service after occurrence of an event of this nature (Lowe and Fox, 1995). (vi) Abrasion: Sediments coarser than 0.1 mm will greatly accelerate the erosion of turbine runners and pelton wheel nozzles. This reduces the power generation efficiency and requires the removal of generating units from service for repair. (vii) Energy loss: when a series of hydropower satiations are constructed along a river, delta deposition can elevate the streambed and tail race water level, reducing the
13
CHAPTER 2 LITERATURE REVIEW
available power head and possibly flooding the power station if there is no essential remedial measures. (viii) Intakes and outlets clogging: sediment can block or clog intakes and low level outlets at the dam and damages them. During extreme floods, deposition of many meters of material can occur in a few hours. Sediments and debris 17m deep were deposited in front of Valdesia dam in the Dominican Republic during the passage of hurricane David in 1979, clogging the power intakes for approximately 6 months (Morris and Fan, 2010). (ix) Downstream degradation: On the downstream of the dam the water is sediment hungry and it causes degradation downstream of the reservoir.
2.2.3. Methods to Enhance the Life of Reservoir There are several methods by which the life of the reservoir can be enhanced; otherwise reservoir may be silted up within a few years due to sedimentation. These method employed, are: watershed management, conventional dredging, dry excavation, hydrosuction, sediment routing/sluicing, sediment bypassing, density current venting, and sediment flushing through reservoir, used independently or in combination (Palmeri et al., 2003). These methods are briefly described below:
2.2.3.1. Watershed Management In watershed management, the erosion of sediments which eventually enter into the reservoir is minimized although it can not be reduced to zero. Literally there are hundreds of specific structural and non structural measures which can be employed to reduce the sediment yield. The techniques used to reduce the erosion are: Structural or Mechanical Measures: These measures control the movement of water over the earth, reducing the flow velocity and safely dispose off the surface runoff with much less erosion of the soil (Morgan, 1995). The measures are: (i) Structural terraces (ii) Diversion channels, grassed waterways, and other flow conveyance structures. (iii) Channel protection and stabilization measures like riprap, gabions, and check dams (iv) Sediment traps, debris basins, detention basin etc.
14
CHAPTER 2 LITERATURE REVIEW
Vegetative or Agronomic Measures: These measures are the growth of crop and crop residue to protect the soil from erosion. Vegetation is inexpensive and self renewing. However a significant effort is required for the initial development of vegetation, particularly in the dredged places and semi-arid areas. Operational Measures: These are management and scheduling measures adopted to minimize the erosion potential. It includes the scheduling the construction so as minimize the area of exposed soil.
2.2.3.2. Conventional Dredging The process of excavating deposited sediments from underwater is termed as conventional Dredging (Figure 2.3). Conventional hydraulic dredging is often much more expensive than the cost of storage replacement and it is generally not economically feasible to remove all sediments from reservoirs by means of dredging alone. Disposal of dredged material may also generates environmental problems and suitable mitigation measures may be quite expensive. If the material is not deposited downstream of the dam then large expenses of landfill may be required.
2.2.3.3 Dry Excavation In Dry excavation (also known as trucking) the sediment is excavated and transported for disposal using traditional earth moving equipments. Excavation and disposal costs are high, and as such this technique is generally used for relatively small reservoirs in the developed countries. Dry excavation has been carried out at Cogswell Reservoir in California (Figure 2.4).
The sediment from this reservoir has been excavated with conventional earth moving equipments and has been used as engineered landfill in the hills adjacent to the reservoir (Morris and Fan, 2010).
2.2.3.4 Hydrosuction Hydrosuction method differs from that of traditional dredging. Hydraulic head available at the dam is used as the energy for dredging instead of pumps powered by electricity or diesel (Figure 2.5). As such, where there is sufficient head available, the operating costs of Hydrosuction method are substantially lower than those of traditional dredging. This practice has been performed at Taijiawan Reservoir in China (Liu, et. al., 2002).
15
CHAPTER 2 LITERATURE REVIEW
Figure 2.3 Dredging process in a Reservoir (ARAS, T, 2009)
2.2.3.5. Sediment Routing/Sluicing To pass the sediment through or around the impoundment while minimizing objectionable deposition is called Sediment Routing. Sediment Routing focuses on either minimizing deposition or balancing deposition and scouring during flood periods, whereas flushing removes accumulated sediment after they have been deposited.
Figure 2.4 Photograph of sediment removal at Cogswell Reservoir (Morris and Fan, 2010)
16
CHAPTER 2 LITERATURE REVIEW
Figure 2.5 Siphon dredging system at Tianjiawan Reservoir (Zhang & Xie, 1993)
A major disadvantage of sediment routing is that a large amount of water must be released during floods to transport sediments. Sediment routing is most feasible at hydrologically small reservoirs. Sediment routing may not be able to remove previously deposited sediment or pass the coarsest part of the inflowing load beyond the dam. Thus, routing needs to begin as early as possible after dam construction to preserve capacity, and supplemental measures (e.g., flushing, dredging) may also be required (Morris and Fan, 2010).
2.2.3.6 Sediment Bypassing Rivers, especially sediment-laden rivers, carry most of the annual sediment load during the flood season. Bypassing heavily sediment-laden flows through a channel or tunnel may avoid serious reservoir sedimentation. The bypassed flows may be used for warping, where possible. Such a combination may bring about high efficiency in sediment management. When heavily sediment-laden flows are bypassed through a tunnel or channel, reservoir sedimentation may be alleviated to some extent. In most cases, however, the construction cost of such a facility is high. Where a unique topography is available, the cost of construction may be reduced and bypassing facilities may be practical.
17
CHAPTER 2 LITERATURE REVIEW
2.2.3.7 Density Current Venting A density current is the gravity-induced movement of one fluid under, through, or over another fluid, caused by density difference between two fluids (Wanyonyi, 2002). Turbidity currents occur when sediment laden water enters an impoundment, plunges beneath the clear water, and travels downstream along the submerged Thalweg (Cesare, 2001). If the current reaches the dam, it will form a submerged muddy lake and the turbid water reaching the dam can be vented if low level outlets are opened. Turbidity current can be sustained only as long as inflow continues; if the duration of the turbid inflow is less than the travel time required to reach the dam, the current will dissipate. In some reservoirs of Algeria and China over the half of the inflowing sediment load from individual flood events has been passed through the impoundment as turbidity current and vented from the dam through low level sluices. The greatest amount of turbidity can be released when the discharge capacity of the outlet approximately matches the flow rate of turbidity current reaching the dam. This method is practiced at Steeg Dam in Algeria (Figure 2.6). Density current venting is an attractive way of releasing sediment laden flows because unlike flushing operation, it does not require the lowering of the reservoir level (Morris and Fan, 2010).
Figure 2.6 Turbid water being discharged from the low-level outlet at Steeg Reservoir, Oued Fodda, Algeria (Morris and Fan, 2010)
18
CHAPTER 2 LITERATURE REVIEW
2.2.3.8. Sediment Flushing through Reservoir Flushing is a method by which the flow velocities in a Reservoir are increased to such a level that deposited sediments are mobilized and transported through low level outlets in the dam (Emamgholizadeh, 2008). Flushing sediments through a reservoir has been practiced successfully and found to be inexpensive in many cases. However, a great amount of water consumed in the flushing operation might affect it (Fi-John et al. 2003). Reservoir sediment flushing may be categorized as; complete drawdown flushing which also called empty flushing and partial drawdown flushing, also called pressure flushing (Emamgholizadeh, 2006).
In complete drawdown flushing the reservoir is emptied before the flood season, resulting riverine flow conditions in the reservoir. Low level outlets for flushing operation are provided close to the original riverbed level with sufficient hydraulic capacity to achieve full drawdown (White et al. 2000). Flushing is most effective in preserving reservoir storage, when outlets are placed near the original streambed level and reservoir is completely emptied (Morris and Fan, 2010).
Every reservoir of the world cannot be flushed successfully due to the non-availability of sufficient water for flushing and geometric parameters like flatter bed slope and wider section etc. Flushing also causes sediments to be released from the reservoir at a much higher concentration than occurs in the natural fluvial system which may creates unacceptable environmental impacts downstream, however, these impacts are less severe as compared to no flushing at all (Chaudhry and Rehman, 2007).
2.3 EMPIRICAL MODELING OF RESERVOIR SEDIMENTATION
2.3.1 Suspended Sediment Inflow into the Reservoir Suspended sediment load computations for the reservoir may be carried-out by considering sediment data of gauging station, normally at upstream gauging station of the dam site and transformed value at the dam site by giving proper allowance for the watershed area in between the upstream gauging station as given in Equation below:
19
CHAPTER 2 LITERATURE REVIEW
SSLdam 1 K SSLu / s gauging station (2.1)
Where, SSL is the suspended sediments load; K is the allowance for the watershed area between the upstrseam gauging station and the dam site
Suspended sediment load at the dam site may also be computed by using the data of other gauging stations on downstream of the dam. After determining the annual suspended loads using the data of each gauging station, an average value is taken as the suspended sediment inflow to the dam site. Taking average density of the deposited sediments (in tons/m3), the average suspended sediment load in terms of volume to the dam site comes out in Mm3.
Though annual suspended sediment loads are available for a specific period of data records, the daily suspended sediment loads may be generated from the instantaneous data records by plotting the suspended sediment rating curves for each stream gauging stations.
2.3.2 Bed Load into the Reservoir Bed load is the rate of movement of sediment particles along the stream bed in the processes of rolling, sliding and/or hopping (saltation). Generally, the amount of bed load transported by a large, deep river is about 5 to 25 % of the suspended load (Simon, 1992). Bed loads may be computed on the daily basis for the entire temporal range for which the instantaneous suspended sediment discharge data is available. Bed load computations may be done by using Meyer Peter & Muller formula, Parker formula, Einstein-brown formula, Duboys formula and Shields formula.
2.3.2.1 Meyer-Peter and Muller formula Mayer-Peter and Muller (1948) equation was one of the earliest equations developed and is still one of the most widely used. It is exactingly a bedload equation developed from flume experiments of sand and plane bed conditions. The equation was introduced based on data collected as: sediment sizes: 0.4- 29 mm, flow depths: 0.01-1.2 m, specific gravity of sediments: 1.25-4, energy gradient: 4 x 10-4- 2 x 10-2, average channel velocity: 0.37- 2.86 m/s, Channel width: 0.15-2m
20
CHAPTER 2 LITERATURE REVIEW
Following empirical equation was developed
f Rb S n 3 / 2 f 1/ 3 qB 2 / 3 1 ( ) 0.047 0.25( ) ( ) 1/ 3 (2.2) n ( s f )d a g s ( s f ) d a
Where n is the Manning’s roughness, Rb is the hydraulic mean radius, S is the energy
slope, s is the specific weight of solids, f is the specific weight of liquid (water), d a is the arithmetic mean sediment size, g is gravitational acceleration, qB is the bed load rate
1 / 6 d90 in lb/ft/sec, n' , d90 is the size of sediment at which material is finer by 90%. 26 According to many researchers Meyer Peter and Muller equation overestimates the bed load transport rates of about same order as the suspended load with (n’/n) value keeping at the lower limit of 0.5. Where n’ is the grain Manning’s roughness and n is the Manning’s roughness. The range of (n’/n) varies from 0.5 to 1, it is 0.5 for strong bed forms and 1 in absence of bed forms (Chang, 1988).
2.3.2.2 Parker Formula The bed load equation developed by Parker (1982) is for stream of mostly gravel and coarser bed material. Such streams usually possess a surface layer markedly coarser than the substrate. This layer, referred to as the pavement, is different from the immobile armour. In paved gravel bed streams, bed motion is considered as a normal event. In that the bed is active for infrequent periods of flood. The coarser pavement grains are often mobile, whereas the armored bed is immobile. Based on data collected for gravel bed streams, Parker developed the following relationship for bed load transport:
* ** 3/ 2 qB 0.0218 G (2.3) Where ** (2.4) 0.0386
* qB qB Rgd d (2.5)
21
CHAPTER 2 LITERATURE REVIEW
** (2.6) R g d
4.5 0.853 G() 54741 for > 1.59 (2.7)
G() exp14.2 1 9.28 12 for 1 1.59 (2.8)
G() 14.2 for 1 (2.9)
For a high sediment transport rate ** 1.5 1.59 qB ( ) (2.10)
Where d is the sediment size and qB is the sediment bed load per unit time and per unit width of the channel. Parker also showed that with this relationship, data with d= 28.6 mm fall below Einstein’s curve and data with d = 0.5 mm fall above Einstein’s curve.
2.3.2.3 Brown-Einstein Equation This formula is a modification of the 1942 Einstein formula by Rouse, Boyer and Laursen. The formula applies the parameters and and their relationship is represented by the following equations: 3 1 3 (2.11) 40 40 * where 5.5 * 0.182
0.465 e 0.391 where 5.5 (2.12) in which
qB 3 1/ 2 (2.13) S F s 1 g d
1 S (2.14) O *
1/ 2 1/ 2 2 36 2 36 2
F 3 3 (2.15) 3 gd (s 1) gd (s 1)
22
CHAPTER 2 LITERATURE REVIEW
Where * is the shield stress, is the kinematics viscosity, s is the specific gravity of the sediments and d d median size diameter of the sediments, is unit weight of 50 water, s is unit weight of solids, o is bed shear stress, and qB is the sediment bed load per unit time and per unit width of the channel.
2.3.2.4 DuBoys Formula The bed load formula by DuBoys (1879) assumes that uniform sediment grains move as a series of superimposed layers with each other thickness d of the same magnitude as the grain diameter. According to Duboys, the bed load transport equation is written as:
qb Cd o o c (2.16)
Here qb is bedload discharge per unit channel width, C d is the characteristics sediment coefficient, and c is the critical shear stress, o is bed shear stress. Relations for C d and
c were found by Straub (1935) based upon experiments in small laboratory flumes with a sand bed. The relations are given by following equations.
0.17 C (m3/kg/s) (2.17) d d 3 / 4
2 c 0.061 0.093 d , (Kg/m ), where d is in mm (2.18)
2.3.2.5 Shields (1936) Formula A dimensionless formula based on the excess shear stress was proposed by Shields (1936) as:
o c q S qb 10 S (2.19) 1 ( s ) d
Where qb is bedload discharge per unit channel width, o is bed shear stress, c is the
critical shear stress, is unit weight of water, s is unit weight of sediments, S is energy
23
CHAPTER 2 LITERATURE REVIEW slope, d is mean sediment diameter, and q is discharge per unit width of channel. The equation is dimensionally homogenous and can be used in any system of units.
2.3.2.6 Modified Einstein Procedures for Unmeasured Sediment Load A useful guide for evaluating the unmeasured sediment load is the bed load correction shown in Table 2.1 (Bureau, 1987). Five conditions are given for defining bed load depending upon suspended sediment concentration and size analysis of stream bed and suspended materials.
2.3.3 Total Sediment Load into the Reservoir Finally, the daily total sediment loads at dam site can be computed by adding the bed load in the suspended sediment load.
QStotal Qsuspended Qbedload (2.20)
Table 2.1 Bed load correction
Suspended Texture of Percentage bed sediment Stream bed Condition suspended load in terms of concentration material material suspended load (mg/L) 11 <1000 Sand 20 to 50% sand 25 to 150 12 1000 to 7500 Sand 20 to 50% sand 10 to 35 3 >7500 Sand 20 to 50% sand 5 24 Any concentration Compacted clay, Up to 25% 5 to 15 gravel, cobbles, or sand boulders 5 Any concentration Clay and silt No sand <2
1Special sampling program for Modified Einstein computations required under these conditions. 2A bed load sampler such as the Helley-Smith bedload sampler may be used, or computations made by use of two or more of the bedload equations when bed material is gravel or cobble size.
2.3.4 Trap Efficiency of Reservoir The amount of sediment deposited within a reservoir depends on the trap efficiency. The trap efficiency of a reservoir is defined as the ratio of the quantity of deposited sediment to the total sediment inflow. It depends mainly upon the fall velocity of the various sediment particles, flow rate and velocity through the reservoir (Strand and Pemberton, 1982) and certain characteristics of reservoirs like; the size, depth, shape, and operation rules of the reservoir. The particle fall velocity is dependent on particle size, shape, and density, water viscosity, and the chemical composition of the water and sediment. The
24
CHAPTER 2 LITERATURE REVIEW rate of flow through the reservoir is determined by the volume of inflow with respect to available storage and by the rate of outflow.
Methods mostly used for estimating reservoir trap efficiency are Brune’s Curve and Churchill’s Curve. These methods are empirically based upon measured sediment deposits in a large number of reservoirs and are stated below:
2.3.4.1 Brune’s Curve Brune (1953) developed an empirical relationship to estimate the long-term reservoir trap efficiency for large storage or normal pond reservoir based on the correlation between the relative reservoir size and the trap efficiency observed in Tennessee Valley Authority reservoirs in the Southeastern United States. Using this relationship, reservoirs with the capacity to store more than 10 percent of the average annual inflow would be expected to trap between 75 and 100 percent of the inflowing sediment. Reservoirs with the capacity to store 1 percent of the average annual inflow would be expected to trap between 30 and 55 percent of the inflowing sediment. When the reservoir storage capacity is less than 0.1 percent of the average annual inflow, then the sediment trap efficiency would be near zero.
Figure 2.7 provides a good comparison of the Brune and Churchill methods for computing trap efficiencies (Murthy, 1980).
Figure 2.7 Trap efficiencies curves from Brune (1953) and Churchill (1948)
25
CHAPTER 2 LITERATURE REVIEW
2.3.4.2 Churchill’s Method Churchill (1948) developed a trap efficiency curve for settling basins, small reservoirs, flood retarding structures, semi-dry reservoirs, and reservoirs that are frequently sluiced. Using data from Tennessee Valley Authority reservoirs, Churchill (1948) developed a relationship between the percent of incoming sediment passing through a reservoir and the sedimentation index of the reservoir (Figure 2.7). The sedimentation index is defined as the ratio of the period of retention to the mean velocity through the reservoir. The Churchill curve has been converted to a dimensionless expression by multiplying the sedimentation index by g, acceleration due to gravity. Churchill’s curve can be used to estimate trap efficiency for settling basin, small reservoir, or reservoirs which are continuously sluiced.
A general guideline is to use the Brune method for large storage or normal ponded reservoirs and the Churchill curve for settling basins, small reservoirs, flood retarding structures, semi-dry reservoirs, or reservoirs that are continuously sluiced.
2.3.5 Trapped Sediment Load in the Reservoir The entire sediment load entering into the reservoir is not accumulated into the reservoir. Some portion of the coarser load is settled upstream of the reservoir: a portion may spill out through spillway, some portion may enter into the power tunnel and some may go downstream through the sluice gate.
When the trap efficiency of the reservoir is estimated using Brune Curve or Churchill Curve depending upon the size of the reservoir, then trapped load or may called deposited load is calculated by the following relation: (2.21) Qs trapped Qs total Te
Where; Qs trapped is the trapped load in reservoir, Qs total is the total sediment inflow, Te is the estimated trapping efficiency of the reservoir
2.3.6 Delta Modeling in the Reservoir Topset slope of delta may be computed by the following methods:
26
CHAPTER 2 LITERATURE REVIEW
(a) Statistical analysis of existing delta slopes of the reservoirs of the world reveal that topset slope of the delta is approximately equals to the half of the original river bed slope.
S 0.5S o (2.22)
Where, S is the topset slope of delta, So is the original bed slope of the river
(b) Topset slope from comparable existing reservoir
(c) Zero bed load transport slope from a bed load equations such as:
Schoklitsch equation
Meyer-Peter & Muller equation
3 / 2 Q n s K 1 / 6 D Q B D S 90 (2.23) d
Where;
S is topset slope, K is coefficient equal to 0.19 (English units) or 0.058 (SI units), Q/QB is ratio of the total sediment inflow to sediment inflow over the bed, D is diameter of bed material on topset slope (mm); D90 = diameter of bed material for 90 percent finer than, in millimeters, d is maximum channel depth at dominant discharge (feet or meter), and ns is Manning’s roughness coefficient for the bed of channel normally computed as D 1/ 6 90 26 The average of foreset slopes observed in Bureau of Reclamation reservoir resurveys is 6.5 times the topset slope. However, some reservoirs exhibit a foreset slope considerably greater than this; for example, Lake Mead’s foreset slope is 100 times the topset. By adopting a foreset slope of 6.5 times the topset, the first trial delta fit can be computed.
S 6.5 S (2.24)
27
CHAPTER 2 LITERATURE REVIEW
Where, S is the foreset slope of delta deposit, S is the topset slope of the delta deposit
Bottomset slope is equal to the bed slope of the reservoir formed during sediment movement near the dam. The bottomset slope of delta deposits mainly consists of fine sand and silt particles, because these delta deposits of sediments can be transported easily. Bottomset slope of the delta is almost equal to the original bed slope of the channel. As shown in the above Figure 2.2. i.e.,
S So (2.25)
Where, S is the bottomset slope of delta, So is the original bed slope of the reservoir
Location of Pivot Point of Delta Deposits Pivot point is located between topset slope and foreset slope depends primarily on operation of the reservoir and on the existing channel slope in the delta area. If the reservoir is operated near the top of the conservation pool a large portion of the time, the elevation of the top of the conservation pool will be the pivot point elevation. Conversely, if the reservoir water surface has frequent fluctuations and a deeply entrenched inflow channel, a mean operating pool elevation should be used to establish the pivot point. In the extreme situation when a reservoir is emptied every year during the flood peak flows for sluicing sediment, there will be no pivot point. The location of pivot point is shown in the Figure. 2.2 The location of the pivot point can be determined empirically by the following formulae as given by Nazia (2007). S ' S (2.26) Where: S' is the foreset slope of delta and S is the topset slope of the delta deposit V W (2.27) BAvg 2 W R (2.28) LPP L R (2.29)
28
CHAPTER 2 LITERATURE REVIEW
Where; W is Wedge Area, V is Cumulative volume, LPP = Location of Pivot Point,
L = Reservoir Length and BAVE is the average width of the channel D R (2.30)
(2.31) D S o R Where:
R is Arc Radius, D is Delta Depth, D is Total Flow Depth
(RL)BedatPP (RL)BedatUS (So LPP) (2.32)
(RL) PivotPo int (RL) BedatPivotPo int D (2.33)
(RL) Bed (RL)U / S FinesDepth (2.34) Where RL = Reduced Level
2.4. SEDIMENTS REMOVAL FROM RESERVOIRS BY FLUSHING
2.4.1 General White et al. (2000) had reported about 50 reservoirs on which flushing have been attempted. Among them flushing data is available for only 25 flushed reservoirs. Among these 25 flushed reservoirs Atkinson (1996b) further selected the fourteen reservoirs and using the data he analyzed these reservoirs for feasibility of sediment flushing. He concluded that among the selected fourteen reservoirs, six reservoirs proved to be successful for flushing, while the remaining eight reservoirs proved to flush partially. These selected fourteen reservoirs are: Baira and Ichari of India, Gebidem and Palagnedra of Switzerland, Gmund of Austria, Hengshan, Gaunting, Heisonglin, Sanmenxia and Shuicaozi of China, Santo-Domingo of Venezuela, Guernsey of USA, Ouchi-Kurgan of Former USSR, and Sefid-Rud of Iran. Successfully flushed reservoirs
29
CHAPTER 2 LITERATURE REVIEW are: Baira of India, Gebidem and Palagnedra of Switzerland, Gmund of Austria, Hengshan of China, and Santo-Domingo of Venezuela.
2.4.2 Worldwide Experiences of Sediment Flushing from Reservoirs
Globally there are about 25,500 storage reservoirs with the total storage volume of about 6,464 Bm3 (ICOLD, 1998; White et al., 2000; White, 2001). The maximum number of reservoirs is in North America, i.e., 7,205, with the storage volume of about 1,845 Bm3, whereas the minimum number of reservoirs is in Central Asia, i.e., 78, with the storage volume of 148 Bm3. The numbers of storage reservoirs with storage volumes (in Bm3) in other regions are: South Asia 4131(319), South Europe 3220(145), Pacific Rim 2278(277), North Europe 2277(938), China 1895(649), South America 1498(1039), Sub Saharan Africa 966(575), Middle East 895(224), North Africa 280(188), and South East Asia 277(117) (Figure 2.8, Figure 2.9).
2400
2000 1845 1600 1200
1039 938 800 649 575 319
400 277 224 Storage Volume (Bcm) 188
148 145 117 0
China
South Asia South Pacific Rim Middle East North Africa North Central Asia Central North Europe South Europe South
America North South America South South East Asia Sub Saharan Africa Saharan Sub Reigon
Figure 2.8 Worldwide distributions of storage reservoirs (White et al., 2000)
30
CHAPTER 2 LITERATURE REVIEW
9000 8000 7205 7000
6000 5000 4131 4000
3220
3000 2778 2277 No. of resrvoirs of No.
2000 1895 1498 966 1000 895
280 277 0 78
China M.East S. Asia S.
SE.Asia S. Euro. Euro. N. N. Africa N. N. Amer. N. S. Amer. Pac. Rim Pac. Centr. Centr. Asia S. S. Africa Region
Figure 2.9 Worldwide distributions of water storages (White et al., 2000)
There are about 50 reservoirs which are documented to be flushed, out of which flushing data is available for about 25 reservoirs (White et al., 2000). The maximum numbers of reservoirs are flushed in China, 21. The number of flushed reservoirs in different countries as: Switzerland 5, Former USSR 4, India 3, USA 3, Puerto Rico 2, Algeria 1, Austria 1, Costa Rica 1, Guatemala 1 Iran, Japan 1, New Zealand 1, Pakistan 1, Sudan 1, Taiwan 1, Tunisia 1, and Venezuela 1 (Figure 2.10).
24 21 21 18
15
12 9
6 5 4 No. of Flushed Reservoirs 3 3
3 2
1 1 1 1 1 1 1 1 1 1 1 1 0 Iran
USA India China Japan USSR Sudan Austria Algeria Tunisia Taiwan Pakistan Costa Rica Venezuela Guatemala Puerto Rico Puerto Switzerland Newzealand Countries
Figure 2.10 Worldwide distribution of flushed reservoirs
31
CHAPTER 2 LITERATURE REVIEW
Flushing has been successfully implemented at Baira-India, Gebidem-Switzerland, Gmund-Austria, Hengshan-China, Palagnedra-Switzerland, Santo-Domingo-Venezuela Reservoirs, while the partially flushed reservoirs are: Chinese reservoirs, Gaunting, Heisonglin, Sanmenxia, Shuicaozi, Naodehai, Nanqin, Guernsey-USA, Ichari-India, Ouchi-Kurgan and Zemo-Afchar of former USSR, Sefid-Rud-Iran, Warsak-Pakistan, Jensanpei-Taiwan, KHASHM EL GIBRA-Sudan, Mangahao-Newzealand, and Cachi of Costa Rica (White, 2001; Emamgholizadeh et al., 2006). The reservoirs Guernsey, Ichari, Shuicaozi and Warsak seem to be partially flushed due to absence of any flushing outlet and flushing is being done through the spillway at higher elevation. Different modes of sediment removal from the reservoirs are: Flushing alone, Flushing alongwith Routing, Flushing alongwith Density Current Venting, Flushing aided both by Routing and Density Current Venting, and Density Current Venting alongwith Flushing. Among the 50 flushed reservoirs 42 reservoirs are desilted by Flushing mode, whereas 3 reservoirs by Flushing alongwith Routing, 2 reservoirs by Flushing alongwith Density Current Venting, 2 reservoirs by Flushing alongwith Routing and Density Current Venting and 1 reservoir by Density Current Venting alongwith Flushing (Figure 2.11).
50
40
30
20
No. of Reservoirs of No. 10
0 F FR FD FRD DF Mode of sediment removal
Figure 2.11 Mode of flushing used in the reservoirs, worldwide
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CHAPTER 2 LITERATURE REVIEW
TABLE 2.2 Successfully Flushed Reservoirs
Capacity S.No. Reservoir Sedimentation experience Flushing Experience (Mm3) Assumed annual rate as Used diversion tunnel, clearing 0.38 Baira 1 9.6 0.092, but 0.45 Mm3 Mm3 in 40 hours, interruption to India accumulated in 18 months. generation, annual flushing thereafter. Reservoir emptied for 2-4 days per Virtually no sediment year and about 3 Mm3 water was used, Gebidem accumulation, because of 2 9.0 virtually no sediment accumulation, Switzerland gorge type geometry and because of gorge-type and annual annual flushing. flushing. 0.2 Mt/yr initially, Flushing undertaken intermittently Gmund reducing to 0.07 Mt/yr between 1946-1960 and annual 3 0.93 Austria after u/s reservoir built in flushing thereafter. 1967. 3.19 Mm3 deposition between 1966- 3.19 Mm3 deposited 1966- 73.Emptied and flushed for 37 days in Hengshan 4 13.3 73, reaching depth of 27m 1974, removing 0.8 Mm3 of deposits; China at dam. 52 days flushing in 1979 removed 1.03 Mm3 deposits. 1978 flood caused 1.8 Mm3 deposition, 1978 flood caused 1.8 flushing between November 1978- Palagnedra Mm3 deposition (33% of March 1979 removed 2.4 Mm3 5 5.5 Switzerland original storage) and deposits, virtually full capacity of submerged bottom outlet. reservoir can be maintained in the long term. Only one flushing operation in May Santo- 0.58 Mm3 deposited in 2 1978, after 4 years of operation and 6 Domingo 3 years, 1976-78; 0.77 Mm3 flushed 50-60% of deposition in 3 Venezuela in 4 years, 1974-78. days. Concluded that flushing should be annual.
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CHAPTER 2 LITERATURE REVIEW
TABLE 2.3 Partially Flushed Reservoirs
Capacity S.No. Reservoir Sedimentation experience Flushing Experience (Mm3) 350 Mm3 deposited in 1953- 60; subsequently many u/s Only one flushing operation in 1954 Guanting 1 2270 reservoirs constructed, removing 10% of annual flow partly China substantially reducing venting by density current. sediment inflows. 39.3% of storage lost Attempted in four years 1959-62, but between 1927-57, when Guernsey not considered effective, as recovered 2 91 sediment contributing U.S.A less than 0.2% of the original capacity catchment reduced from of reservoir. 14000 to 1800 Km2. From 1962, density current venting 1.62 Mm3 deposition in first and flood season sluicing reduced trap three years of operation (6% efficiency to about 15% ; lateral Heisonglin 3 8.6 storage loss per year); erosion technique successfully China capacity reduced to 5.87 implemented from 1980, recovering Mm3 by 1973 some lost storage; long term capacity expected to be 30-35% of original. Sedimentation reached spillway crest after 1 year; No bottom outlet built for flushing Ichari 85% trapping much greater 4 11.6 and reservoir flushed annually by India than indicated by Brune fully opening spillway gates. curve; anticipated long term capacity about 35% Bed levels rose upto 23m by Ouchi- 1969; sediment volume Sluiced for 3-4 months annually since 5 Kurgan 56.4 appears to have stabilized at 1963. U.S.S.R 30 Mm3 since 1968 Rehabilitation from 1966 included construction of larger low level Sanmenxia Severe, with 1800Mt 6 9640 outlets; flushed for 4 months annually; China deposited in first 18 months six development stages are described in literature. Flushing (about 4 months/year) commenced in 1980; after 7 years Severe, causing loss of 21% 26% of lost storage had been of the storage capacity per Sefid-Rud recovered; from 1992 flood plain 7 1760 year upto 1980 (T.E.=73%) Iran erosion enhanced using diversion most of sediment releases channels ; expected that long term occurred in density currents. capacity could be upto 90% of original reservoir capacity. 8.18 Mm3 (85% of storage) Implemented experimentally from Shuicaozi lost between 1958-81; bed 1965; but limited by high elevation 8 9.6 China levels at dam only 7m of spillway and short duration below impounding level. annually to about one third of inflow. Capacity reduced to about Bottom outlets ungated prior to Naodehai 9 168 60% by 1950, but recovered 1970, so flushing appears to have China to about 80% by early 1970s been natural. Storage loss 53% by 1983 Density current venting commenced Nanqin 10 10.2 life span then expected to be in 1977 discharging about 2.43 Mt of China upto 2000 if flushing not suspended sediment load between
34
CHAPTER 2 LITERATURE REVIEW
instigated. 1977-84. Experimental flushing from 1984 with good results concluded that flushing should be undertaken for 4 days every 3-4 years. Implemented from 1939, with full Zemo- Not found 76% of capacity lost in 10 drawdown and appeared to keep 11 Afchar in years. situation stable upto 1955, removing U.S.S.R literature about 1 Mm3 per year 30 Mm3 deposition between No bottom outlet provided. Five 1960-70, by 1980 reservoir flushing operation over spillway crest Warsak was totally sedimented, 12 170 performed between 1976-79, with Pakistan except 60m wide 6m deep total duration 20 days and scoured 4.2 channel on right bank Mm3 of deposited sediments. leading to power intakes. Flushing commenced since 1955 for 2.5 months annually, virtually Storage loss 4.26 Mm3 Jensanpei arresting subsequent sedimentation, 13 7 between 1938-55 Taiwan but not restoring capacity, minor representing 3.4% per year. raising of impounding level in 1942 and 1958. KHASHM Flushing operations in 1971 and 1973 14 EL GIBRA 950 Capacity seriously depleted each removed 85 MTons. Sudan Flushed in 1969 through low level Mangahao diversion tunnel and 73% of Not found 59% storage loss by 1958; accumulated sediment removed in one 15 in problem become serious by New month; subsequently annual emptying literature mid 1960. Zealand and flushing performed during 3 week closure of power house. Estimated that 18% flows Commenced since 1973 and 14 Cachi without deposition , 54% flushing operations performed in 18 16 54 Costa Rica passes by density current years and reduced trapping efficiency venting and 28% deposited from 82% to 27 %. 0.57 Mm3 deposited per Water level lowered in flood season, year. From 1960-63 in resulting in substantial reduction in Honglingjin 17 8.6 impounding mode, storage loss 0.45 Mm3 per year 1964- China representing 3.5% storage 73; technique is essentially loss per year routing/sluicing Lost 53% of capacity Mechanical method employed Loiza between 1953-94; three unsuccessfully in 1994; dredging 18 27 Puerto Rico 1.1m low level outlets considered in 19995; technique blocked. employed is routing/sluicing 4.3 Mm3 deposited per year Water level lowered in flood season, between 1959-61, in Zhenziliang reduction in storage loss 0.77 Mm3 19 36.6 impounding mode, China per year between 1962-73; technique representing 12% storage is essentially routing/sluicing loss per year.
Whereas in Figure 2.11, F: flushing alone applied for desilting the reservoir; FR: flushing alongwith sediment routing; FD: flushing alongwith density current venting; FRD:
35
CHAPTER 2 LITERATURE REVIEW flushing alongwith sediment routing and density current venting; DF: density current venting alongwith flushing. deposition and sediment flushing through the reservoirs.
2.4.3 Sediment Management Experiences on Pakistani Large Reservoirs
Pakistan has two major reservoirs, Tarbela Reservoir and Mangla Reservoir. Sediment management in these Reservoirs is described in subsequent paragraphs.
Tarbla Reservoir, Pakistan Tarbela Dam is one of the largest earth and rock filled dams in the world, and its reservoir is the largest storage project in Pakistan. The dam was built across the Indus River and completed in 1974. The dam is operated by the Water and Power Development Authority (WAPDA). The original capacity of the reservoir was 14.344 BCM and the length 96 km. The dam has a height of 145 m above the bed level. The dam has five main tunnels; three tunnels (no. 1, 2 and 3) are equipped with power houses with generation capacity of 3470 MW. The other two tunnels (no. 4 and 5) are reserved for irrigation flows and low level flushing, if opted. Operation of the reservoir over the last 39 years has resulted in a capacity loss of 34.87% (Wapda, 2013). Sedimentation in the reservoir developed a huge underwater delta; whose pivot point is just 10 km from the dam toe. Liquefaction of the delta in the case of an earthquake poses a serious threat to the serviceability of the dam, as it may overwhelm the tunnels intakes. (Noor and Tingsanchali, 2009).
The catchment area of Indus at Tarbela is 169,600 km2, which is unique in the sense that it contains seven of the world’s ten highest peaks and seven of the world largest glaciers. The mean annual flow at Tarbela is 79 Bm3 (Haq and Abbas, 2006).
Various problems, which arise as a result of heavy sedimentation of the reservoir, are as follows:-
36
CHAPTER 2 LITERATURE REVIEW a) A loss of live storage, which is causing gradual reduction in the regulated yield of reservoir. This in turn would result in reduction in water availability for the agriculture for Rabi and early Kharif seasons. b) Reduction in the firm energy available from the Project. c) The physical effect of sediment, which includes the risk of clogging of low level tunnel outlet particularly in a seismic activity, the erosive action of sediment- laden water on outlet concrete structures and Power turbines will result in exorbitant maintenance costs.
For maximizing the benefits of Tarbela reservoir the following four options can be considered (TAMS, 1998): (i) Manage the distribution of sediments within the reservoir. (ii) Minimize the flow of sediments into the reservoir. (iii) Maximize evacuation of sediments from the reservoir. (iv) Increase the live storage volume of reservoir. Each of the above options has been analyzed below in the light of its practicality, safety and sustainability:
(i) The sedimentation pattern within the reservoir can be managed by means of reservoir operational policy and by protecting low level tunnel intakes from sediment clogging. Raising the minimum reservoir level every year by 1.2 m would result in deposition of sediments in the upper reaches of reservoir only and thus would delay the advancement of sediment delta. Though this option entails no capital cost but would progressively result in increased loss of live storage. Minimum reservoir level of 417 m fixed in 1998 is being maintained in order to use optimally the available storage.
(ii) Protection of tunnel intakes against sediment clogging by construction of an underwater dyke in front of the intakes as proposed by the Consultants had been studied. This option not only involves tremendous stability and construction problems but also its benefits in the absence of sediment flushing from the reservoir seem minimal.
37
CHAPTER 2 LITERATURE REVIEW
Reduction of sediment influx either by watershed management or by construction of check dams in the upper catchment is impractical as about 90% of total runoff is dominated by snow / glacier melt. Nothing can be done at this altitude on the steep mountains. Most of the catchment area is out of the monsoon zone. Watershed Management is being implemented by the NWFP Forest Department upto Besham and it has very little effect. Diamer Basha Dam shall have some positive impact as it would enhance its life.
(iii) Evacuation of 200 million tons of yearly sediments by flushing through four low level high capacity outlets from the left bank has been proposed by the consultants.
(a) This option would comprise four 12 m diameter tunnels driven through the left abutment, possibly underneath the auxiliary spillway and discharging into its plunge pool. The abutment is weak. There have been a lot of problems and it has stabilized after a lot of remedial works. This proposal carries a large number of grey areas which need to be carefully addressed before taking it to a feasibility stage.
WAPDA considers the underwater dyke and the four tunnels an unprecedented option, the example of which does not exist elsewhere in the World. Moreover, this option would in no time adversely affect the downstream hydropower Project of Ghazi Barotha and Chashma and kill them much earlier.
(b) Measures in terms of dredging of sediments from this mega reservoir are almost impossible. The dredging of sediment is generally carried out at seashores where mobilization from open seas is possible. The dredging option in case of Tarbela reservoir is not only prohibitive in cost but also is without any precedence and impractical. Any dredging proposal to be effective must provide for removal and disposal of 550,000 tons of sediments every day. Realistically, the target is unattainable even if hundred of dredgers and ancillary equipment are deployed over the reservoir stretch of 50 Km2 to work round the clock.
(iv) Measure to increase the live storage capacity of reservoir would entail raising of crest of all embankment dams. Considering the existing foundation conditions at the site and
38
CHAPTER 2 LITERATURE REVIEW other geotechnical problems of the embankment dams, this option poses serious stability threats to the Project. Therefore, this option is also discounted as being unfeasible and impractical.
As the delta comes closer, the trap efficiency reduces and the sediments starts passing through the existing outlets. Studies are underway to flush the sediments through the existing outlets. If another reservoir is available to store the water downstream, we can operate Tarbela reservoir at low level and flush a part of the yearly sediments.
For flushing the delta should be close to the dam. The reservoir has to be depleted to its lowest level. Powerhouse has to be closed. Discharges of the order of 5600 cumecs passed over the exposed delta, so that they can create shear velocity and entrain the deposited sediments. Large low level outlet capacity is required to pass the discharge. The outlets need to be steel lined to withstand the abrasion otherwise after flushing they would erode and it may not be possible to close the gates to refill the reservoir as happened in Volta dam (Haq and Abbas, 2006). It may not be possible to refill the reservoir in a drought year. The reservoir is operated on irrigation demand and cannot be operated in flushing mode without the surety of its refilling. Mangla Reservoir, Pakistan Mangla reservoir was impounded in 1967 after the construction of the dam. The reservoir had a gross storage capacity of 7.259 Bm3. Average annual water inflow into the reservoir is 28.8 Bm3 and average annual sediment inflow is 41.2 Mm3. By the year 2006, as per hydrographic survey conducted in 2005, about 20.54% of the gross storage capacity had been depleted due to reservoir sedimentation. The delta was moving towards the dam face and pivot point of the delta had reached at a distance of about 7.9 km upstream of main dam (Haq and Abbas, 2006).
To compensate the storage loss due to sedimentation, raising of the crest of the dam by 12 m was kept in the original design. In fact, 18 million US Dollars were spent at the time of original construction to keep provision in foundations of dams and other structures for 12m future raising.
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Study of Mangla dam raising was assigned to Mangla Joint Venture which comprises National Engineering Services Pakistan (Pvt.) Limited (NESPAK) as lead firm, Barqaab Consulting Services (Pvt.) Limited, Binnie and Partners from U.K. and Harza Engineering Company U.S.A. Studies carried out by the joint venture had examined various options for the height of dam raising. Raising of the dam by 3m or 6m had not been favoured as the reservoir capacity gained by 3m raising will not even compensate for the capacity lost so far to silt deposition and raising of the dam by 6m would require second time raising and displacement of population in future, which was not a practical option both from technical and socio-environmental considerations.
The feasibility study had further shown that raising the dam by 9m and 12m was technically feasible and economically viable. However the incremental benefits of raising the dam from 9m to 12m were relatively small against substantial costs and displacements of population. In view of these considerations, a final choice of 9m raising the dam had been made. Raising of the dam by 9m from El. 376.2m to El. 385.2m would allow raising of the reservoir conservation level by 12.2m from El. 366.5m to El. 378.7m.
So the raising of Mangla dam was started in 2004 and the project was completed in December, 2009. After raising of Mangla dam the gross storage capacity of the reservoir has been enhanced to 9.132 Bm3 (net increase of 3.55 Bm3) and power generation had been increased to 1180 MW (an increase in installed capacity of 180 MW). The crest raising of Mangla dam shall extend the life of reservoir about 80 years and compensate for the progressive depletion of the storage capacity (Haq and Abbas, 2006).
2.4.4 Classification of Techniques Reservoir sediment flushing may be categorized as; complete drawdown flushing or empty flushing and partial drawdown flushing or pressure flushing (Emamgholizadeh et al., 2006).
2.4.4.1 Emptying and Flushing In complete drawdown flushing the Reservoir is emptied before the flood season, resulting riverine flow in the reservoir. Low level outlets for flushing operation are provided close to the original riverbed level with sufficient hydraulic capacity to achieve
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CHAPTER 2 LITERATURE REVIEW full drawdown (White et al., 2000). Flushing is most effective in preserving reservoir storage when outlets are placed near the original streambed level and reservoir is completely emptied (Chaudhry, et al., 2013). Empty flushing may also be categorized according to the conditions whether it occurs during the flood season or the nonflood season. While both strategies have been employed successfully, flood season flushing is usually more effective because it offers larger discharges with more erosive energy, and floodborne sediments may be routed through the impoundment.
Emptying and Flushing during Flood Season Some irrigation reservoirs in China are emptied for flushing during the first part of the flood season, passing early season floods through the impoundment without significant detention. The reservoir is refilled during the latter part of flood season. This is being practiced at Jensenpei reservoir in Taiwan, Dashikou irrigation reservoir in China. After the operation of Dashikou reservoir it was felt that the reservoir began to fill with the sediments rapidly, so the reservoir operation strategy was modified and an outlet dimensioning 1.5x 3 m was installed close to the bed of the river. During the initial part of flood season the reservoir remains empty to pass the early season flood which eroded the accumulated sediments and passes it through low level outlets and then at the end of flood season the reservoir gate is closed to fill it for winter irrigation. By adopting this strategy the sediment accumulation in the reservoir is much reduced. The photograph showing the emptying of the reservoir before flood season to flush the previously accumulated sediments is shown in Figure 2.12.
Empty flushing has also been implemented on Sanmanxia reservoir of china and Welbedacht dam, South Africa as shown in Figures 2.13 and 2.14.
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Figure 2.12 Dashikau irrigation reservoir in China, emptied before flood season (Morris and Fan, 2010)
Figure 2.13 Sanmanxia Reservoir, China, during sediment flushing (Morris and Fan 2010)
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Figure 2.14 Welbedacht dam, South Africa, during sediment flushing (Olesen and Basson, 2004)
Emptying and Flushing during Non-Flood Season Flushing may also be successful during the nonflood season, but will classically requires a longer flushing period than flood flushing, because of the lower discharge. Limited discharge and incapability to route inflowing sediments along flood water can enhance the tendency for coarse sediments to accumulate, and, because flood season inflow is not routed through the flushing channel, the rate of sediment deposition on floodplain areas can also be expected to be higher as is the case of non flood season flushing through Sefid-Rud Reservoir in Iran (Tolouie, 1993).
2.4.4.2 Flushing with Partial Drawdown Empty flushing or drawdown flushing is most effective in maintaining the storage capacity of the reservoir, because the outlet gates are located near the original streambed of the reservoir which may be completely emptied. Sometimes due to the limitation in drawdown of the reservoir or the higher invert level of flushing outlet, the reservoir level may be partially drawdown, resulting in the partial drawdown flushing, also called pressure flushing. Under pressure flushing the reservoir is lowered down to the minimum operating level and then the bottom outlets are opened, allowing the formation of a
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CHAPTER 2 LITERATURE REVIEW conical scour hole in front of the outlet, while maintaining the minimum operating level. Sediment from the upper portion of the reservoir is transported towards the dam during drawdown, but only material in the scour hole in front of the outlets can be evacuated. At Gebidem Reservoir, model tests indicated that the scour hole could be evacuated in only 2 to 3 hours, but it would take 20 to 30 hours to refill the hole with sediment. To discharge the anticipated 400,000 to 500,000 m3/yr of sediment inflow at this site, 10 to 15 drawdowns would be required annually (Ullmann, 1970). This is generally not an effective flushing method. However pressure flushing is being practiced at many reservoirs of the world like Gaunting, Liujixia, Shuicaozi of China, Guernsey-USA, Ichari-India, Ouchi-Kurgan-former USSR and Warsak Reservoir of Pakistan.
2.4.5 Downstream Environmental Effects of Flushing Sediment flushing from the reservoirs has some negative environmental effects downstream of the reservoirs. Due to flushing, sediments released downstream of the reservoir are of much higher concentration than occurs in the natural fluvial system. The released sediment concentration typically ranges from 100 g/L to even upto 1000 g/L (Morris and Fan, 2010). These extreme concentrations can create unacceptable impacts downstream. Extreme sediment concentrations can choke irrigation canals and heat exchangers for industrial cooling systems. Environmental harm can be great; high sediment concentration which suffocates benthic organisms and clogs fish gills and can kill virtually all the organisms in a stream (Ghoreishi, 2007).
Some of the earliest observations on downstream effects of flushing were made by Kanthak (1924) at the Alicante Dam, Spain, who noticed considerable damage downstream of the dam caused by a sudden release of water and mud during flushing. Schoklitsch (1935) was another early observer who pointed out the negative environmental impacts in the downstream reach due to the sudden release of sediment- laden flows. During the flushing process, extreme quantities of suspended matter are stirred up and carried in suspension down the river over long stretches. For the most part, these are again deposited in the next reservoir and nearly always leads to complaints from owners of land below the dam and from lease holders of fisheries.
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Due to the deposition of sediment in reservoirs, the downstream river reach often responds with degradation. If flushing is done, the introduction of sediment into the downstream river reach will reduce the rate of bed degradation, however, it will not have any effect if the sediment is transported as wash load (Breusers et al., 1982). Parhami (1986) noted that downstream from the Sefid-Rud Reservoir, Iran, where scouring had occurred after dam closure, flushing had a positive effect on the river bed. Another example is the Ladzhanuri Reservoir, Georgia, where the sediments flushed out ultimately arrived on the Black Sea coast and played a favourable role in stability of the beach (Kereselidze et al., 1986).
The sudden release of large volumes of sediment may create serious problems downstream, such as, channel aggradation and flooding, interference with water supply and cooling water intakes, as well as adverse impacts on fisheries and the environment (Morris, 1995). Furthermore, exceptional sediment concentrations are a threat to benthos fauna and flora as well as fish populations and their spawn, cause a reduction of water oxygen content, cause deterioration of riparian biotopes and cultivated lands due to sediment depositions, and cause reactivation of contaminated deposits (Scheuerlein, 1995).
Several studies on dissolved oxygen have been made. In the Niobrara River, USA, Hesse and Newcomb (1982) noted unacceptable low levels of dissolved oxygen during flushing (3.5 mg /L). However, Gray and Ward (1982) observed that the level of dissolved oxygen remained high in the North Platte River, USA, during flushing of the Guernsey Reservoir. Roux (1984) noted depletion of dissolved oxygen during flushing of the Verbois and Génnissiat reservoirs in Switzerland and France, respectively. A sudden drop to anoxic conditions could be attributed to an increased amount of organic matter in the flow (Roux, 1984). Buermann et al. (1995) observed, for the Olifants River, South Africa, a decrease of dissolved oxygen resulting in extreme hypoxic conditions. Both Buermann et al. (1995) and Scheuerlein et al. (1996), found that downstream from the hydropower plant Bad Tölz, Germany, the amount dissolved oxygen increased with downstream distance from the dam.
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Studies on macroinvertebrates during flushing have, for example, been made by Hesse and Newcomb (1982) in the Niobrara River and by Gray and Ward (1982) during a sediment release from the Guernsey Reservoir on the North Platte River, USA. Generally, the numbers of macroinvertebrates decreased, but Gray and Ward (1982) noted that some species actually increased in numbers during flushing. Amman and Kast (1996) pointed out that the invertebrates are important for the water’s ability of self purification. They stated that sediment in suspension is dangerous for all fish’s gills and probably also for macroinvertebrates. Deposited sediment on the river bed will fill the pores in underlying material and prevent the macroinvertebrates to migrate or live there.
Hesse and Newcomb (1982) suggested that to minimize the impact of flushing, it should be avoided during spawning, it should follow an annual flushing schedule to maximize insect recolonization efforts, and the reservoir should be refilled over a period of time such that dewatering downstream does not reduce flows below 60% of the historical mean monthly flows. This will avoid stranding of fish eggs and larvae and reduce the loss of macroinvertebrate populations (Hesse and Newcomb, 1982). Buermann et al. (1995) stated that the management strategy of flushing to improve storage capacity is ecologically unacceptable. Scheuerlein (1995) suggested that sediment concentration due to flushing actions should not exceed the upper limit measured already at historical natural-flood events, and as soon as the concentration exceeds this limit the flushing discharge should be reduced.
As a conclusion it can be said that several opportunities to decrease the negative downstream effects of flushing exist, and still more ideas will be presented in the future. Important, however, is that appropriate measures are included in the management or design of the reservoirs and dams as soon as possible, to reduce the risks of species extinction or costly measures to restore the rivers to pre-reservoir conditions.
2.4.6 Flushing Phases Each flushing event has three distinct stages: drawdown, erosion, and refill. The characteristic behaviour of hydraulic and sediment parameters during flushing are summarized in Figure 2.15. Drawdown stage may usually be divided into two parts.
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Preliminary drawdown stage, which brings the reservoir to the minimum operational level by delivering waters for irrigation purpose or to hydropower turbines and typically occurs over a period of days or weeks. Final drawdown entails rapid emptying of the reservoir below the minimum operational level opening undersluices and usually occurs over a short period of time, of about a few hours in smaller reservoirs. Complex patterns of sediment movement can occur during drawdown. During drawdown, sediments from the upper end of the reservoir can be mobilized and transported downstream where they will be redeposited in the lowered pool.
Figure 2.15 Hydraulic and sediment characteristics for channel formation and channel maintenance during flushing event (Morris and Fan, 2010).
The erosion stage occurs when riverine flow is established along the full length of the impoundment, producing high flow velocities that scour fine sediment from the channel and transport the eroded sediments through the dam. Erosion may continue for a few days or for weeks, depending on the site, with longer flushing periods required for higher sediment loads or lower flushing discharges.
The refill stage begins on closure of the bottom outlet, and rising backwater causes sediment to deposit within the impoundment. Water having a lower sediment concentration may be released during this period to help scour deposited sediment out of the river channel below the dam.
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Brown (1943) declared that flushing is most efficient during the first hours, but Gvelesiani and Shmal'tzel (1968) noticed that during flushing process, the most vigorous scour occurs in a period of eight to ten hours after the practical erosion begins. In a later article they reported from the flushing of former USSR reservoirs, that sediment concentration reached upto the values of 400-500 g/L, especially at the initial period of flushing (Gvelesiani and Shmal'tzel, 1971). After a certain period of time the value of sediment concentration becomes stabilized. They recommended that this being the time when flushing should be stopped, because the flushing channel has been developed and only useful water is being carried out.
Ramírez and Rodríguez (1992) divided the flushing of the Cachí Reservoir, Costa Rica, into three phases. The first phase, initial drawdown stage, consists of 25 days of slow water release, lowering the reservoir water level one meter per day down to a few meters above minimum level for power generation. The second phase, final drawdown stage consists of rapid release of the remaining water, approximately within five hours. The third phase, erosion stage, consists of free flow of water through the reservoir for two or three days. In case of Cachí Reservoir, Gebidem Reservoir, Switzerland, flushing process can be divided into three phases (Rechsteiner, 1996), and another example, the Margaritze Reservoir, Austria, where the phases of flushing process are described and can be found in Wagner et al. (1996). The amount of material removed varies for different reservoirs and also the different phases. Most material is released in the second phase at Cachí Reservoir, but in the third phase at Gebidem Reservoir. However, the transition from drawdown to riverine flow during a flushing event is always distinguished by a dramatic increase in the sediment concentration discharged from the dam (Morris and Fan, 2010).
2.4.7 Erosion Processes during Flushing Sediment discharges released during flushing are distinguished by both excessive and highly variable sediment concentrations. The main processes occurred during erosion process are (i) slumping at the dam (ii) slope failure (iii) retrogressive erosion (iv) progressive erosion.
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2.4.7.1 Slumping at the Dam At the start of flushing when bottom outlet is opened and if poorly consolidated fine sediments had been accumulated above the outlet, slope failure of sediments start which result into slumping and plastic flow of the deposits. The slumping process at the small Santa María Dam in Guatemala is shown in Figure 2.16. Similarly at Hengshan Dam in China, slush on the floodplain of the reservoir slid slowly into the flushing channel and then released through the undersluices within a period of couple of days (Fan, 1985). A similar pattern has been observed at small reservoirs in Puerto Rico.
Figure 2.16 Slumping of fine-grained deposits near the dam in Santa Maria Reservoir, Guatemala (Morris and Fan, 2010)
At Hengshan Dam in China, slush on the floodplain surface within 350 m of the dam slid gradually into the channel and was released through the bottom outlet over a period of several days (Fan, 1985). At the 20-MW hydropower Mangahao Reservoir, in New Zealand, 59 percent of the reservoir capacity had been depleted by sedimentation just after 45 years of operation and the bottom outlet was buried under 13 m of silt after 25 years without sluice operation. When the flushing was attempted, there was no sediment flow during the first day the gate was opened. But on the second day silt began to extrude from the undersluice, emptying the reservoir and leaving a crater-like depression above the sluice entrance. About 75 percent of the accumulated sediment was flushed during the
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CHAPTER 2 LITERATURE REVIEW subsequent month. Thereafter flushing was undertaken annually (Jowett, 1984; Brandt, 1999).
2.4.7.2 Slope Failure Due to the erosive action of flushing flow banks of the flushing channel become unstable and slide into the channel. Bank failure is the principal mechanism involved in the widening of flushing channels. The main flushing channel may erode until it attains pre- impoundment river bed level, after which further erosion may occur only by widening of channel by bank failure. The type of slope failure and the stable angle of repose depend on the sediment characteristics. Cases of several forms of bank slides have been observed at Sefid-Rud Reservoir in Iran, during flushing (Morris and Fan, 2010).
2.4.7.3 Retrogressive Erosion A channel erosion process characterized by a zone of high slope and rapid erosion, moving upstream along a channel having a lower slope and erosion rate, is termed retrogressive erosion (Morris and Fan, 2010; Ghoreishi, 2007). The highest rate of erosion occurs along the steep drop at the downstream end of the deposit, causing this area of maximum erosion to move upstream through a headcutting process similar to gully erosion. The point of slope change is also called the pivot point or the nickpoint, and the term nickpoint erosion is also used to express retrogressive erosion. Multiple headcuts can be formed along the length of an eroding channel. Retrogressive erosion is the major process for the formation of flushing channels through reservoir deposits. The opening of deep outlets which establishes flow across deposits having a relatively mild slope, with an abrupt drop or even a waterfall at the downstream end initiates retrogressive erosion, creating a nickpoint that can move upstream rapidly depending on the nature of the deposits and the erosive forces. (Morris and Fan, 2010; Ghoreishi, 2007).
A retrogressive erosion results from the change in hydraulic energy caused by the discontinuous longitudinal profile, and it is not dependent on any specific grain size in the deposit, although erosional patterns are influenced by the deposit characteristics. Retrogressive erosion can occur in coarse sediments on a river delta and in fine grained and cohesive sediments (Randle and Lyons, 1995). In non-cohesive or unconsolidated
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CHAPTER 2 LITERATURE REVIEW cohesive sediments retrogressive erosion tends to proceed upstream (Figure 2.17a). In consolidated deposits the eroding face tends to be more nearly vertical (Figure 2.17c). As retrogressive erosion proceeds, there is a gradual transition of the foreset and topset slopes to a unified slope (Figure 2.17b). The most intense erosion occurs in the area of highest slope and the nickpoint continuously moves upstream, causing the foreset slope to decrease. At the same time channel erosion causes the topset slope to increase, until a unified slope is achieved. At this point retrogressive erosion has ended and the erosion process may now be termed progressive erosion. Jiang (1992) reports that sediment transport computations based on unit stream power have been used to predict rates of retrogressive erosion.
Figure 2.17 Characteristics of retrogressive erosion from flume test (Morris and Fan, 2010)
At Hengshan Reservoir in China during flushing, a channel was quickly formed which deepened continuously and extended upstream in the floodplain deposits. This process is known as retrogressive erosion and is often initiated at the scoured funnel close to the
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CHAPTER 2 LITERATURE REVIEW dam (Fan, 1985). Depending on the characteristics of the deposits, the resulting channel form may differ. In the Shuicaozi Reservoir, China, Du and Zhang (1989) observed a steeper slope in the region where cohesive sediment is predominant. Yoon (1992) pointed out that the cutting down due to retrogressive erosion develops along the longitudinal profile only while the lateral widening is weak.
Zhang (1995) noted that scour depressions in the bed profile are distinct features during the process of headward erosion in cohesive material. Continuous headward erosion, i.e. with a smooth bed profile, will take place if dry density is less than 1,200 Kg/m3 to 1,250 Kg/m3. If density is larger, it will appear as local drop headward erosion (bluff erosion), i.e. with a stepped bed profile. He also pointed out that headward erosion in coarse beds only can develop as continuous.
2.4.7.4 Progressive Erosion The term progressive erosion refers to a channel erosion process which occurs uniformly from the upstream end of the reach and progress downstream, scouring relatively thin layers of sediments from the surface of the deposits. In general, when the suspended- sediment concentration of inflowing water is less than the sediment carrying capacity, the flow will carry sediment from the channel bed. When clear water enters a zone of erodible deposits having uniform slope and grain size, it will gradually carry sediment by eroding the deposit. The rate of bed erosion at the start will be rapid because of the large available sediment-carrying capacity of clear water. As the flow progresses downstream and carries sediment, its capacity to scour and transport additional sediment will decrease, eventually reaching to zero (Morris and Fan, 2010). In this manner progressive erosion can cause a high rate of bed erosion at the upstream end of a deposit and less erosion at the downstream end.
At Gurnsey Reservoir on the North Platte River, USA, the effects of retrogression were lowering of the thalweg in the middle part of the reservoir, 3.5 to 12.5 km above the dam, but also raising of the thalweg at the closest 3.5 km to the dam, due to re-deposition (Lara, 1973). Vorob’ev et al. (1990) noted that an increase of the cross-sectional area of the reservoir, due to flushing, leads to some lowering of water level of the flushing flow
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CHAPTER 2 LITERATURE REVIEW in the main channel. The slope in the upstream stretch of the reservoir will then increase, increasing the flow velocity and effectiveness of erosion of the sediments.
2.4.8 Flushing Efficiency
Flushing efficiency (Fe) is defined as the ratio of volume of eroded sediment deposits to the water volume used during flushing over any specified time interval (Morris and Fan, 2010). Different authors define flushing efficiency in different ways; some are described in Table 2.4. Table 2.4 Different Definitions of Flushing Efficiency Sr. Efficiency Author Remarks No. Expression
V Vo outflowing water volume 1 E o Qian (1982) 3 Vd Vd volume of deposit flushed out (m )
L Ackers and Lo annual sediment flushed out 2 E o Li Thompson (1987) Li annual sediment inflow (Kg)
V V V2 reservoir storage capacity after flushing 3 E 2 1 Mahmood (1987) 3 Vo V1 storage capacity before flushing (m ) V V 2 1 3 4 E Mahmood (1987) Vori original live storage capacity (m ) Vori
Tr fraction of year in which sediment load refill Tr 5 E Mahmood (1987) reservoir restored capacity (V2 - V1) 1T f Tf fraction of year consumed during flushing
L Lo annual sediment flushed out 6 E o Atkinson (1996b) Ld Ld sediment deposited annually (Kg)
V V Lai and Shen Vso outflowing sediments during flushing 7 E so si 3 Vo (1996) Vsi inflowing sediments during flushing (m ) 3 Vi inflowing water volume (m )
V C V C Morris and Fan Co outflowing total sediment concentration 8 E o o i i Vo (2010) Ci inflowing total sediment concentrations (kg/m)
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2.4.8.1 Flushing Efficiency with Partial Drawdown Due to some constraints like operational requirements or the higher elevation of outlets reservoir cannot be completely drawndown, resulting partial drawdown flushing. When flushing flow is released through outlets located at much high above the level of the deposits, creating a pool of impounded water before the dam, the flushing efficiency is usually very low. Flushing efficiencies for some reservoirs where sediment was released through high-level outlets are summarized in Table 2.5. Flushing under conditions of partial drawdown may erode upstream sediments and redeposit them near the dam, and, if a low-level outlet is opened, some of the eroded sediment may be vented as a turbidity current, but this is an inefficient means of removing sediment from a reservoir.
Flushing with partial drawdown may be efficient under specific circumstances. For example, drawdown and sediment release through a high-level outlet was undertaken at the Guernsey Reservoir in Wyoming River, to deliver fine sediment to a downstream unlined irrigation canal. The sediment partially sealed the canal bottom and reduced canal seepage losses. Although sediments were scoured from the upper portion of the reservoir during the 1961, 1962, and 1963 drawdowns, the suspended solids concentration in water released from the reservoir never exceeded 0.8 g/l (Jarecki and Murphy, 1963). The principal effect of these and subsequent drawdowns has not been to release sediment, but to redistribute sediment within the reservoir by removing it from the upper pool and re- depositing it closer to the dam (Lara, 1973).
2.4.8.2 Flushing Efficiency with Emptying The flushing efficiencies attained at several reservoirs during empty flushing is summarized in Table 2.6. These are mean values for the entire event, including the initial period with extremely high sediment removal as well as the latter period of lower concentration discharge and low flushing efficiency. Observed values for flushing efficiency vary widely and are much influenced by flushing duration, and will also heavily influenced by the amount of sediment inflow during the previous impounding period.
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Table 2.5 Overflow drawdown flushing Discharg Water: Outflow Years of Durat Flushing Reservoir e sediment situation operation ion efficiency m3/s ratio
Guernsey Overflow 56.6 - 10-18 1960-1962 0.00017 5880 USA spillway 198 days Total Warsak Overflow 1976-1979, 1410 490.5 0.00169 592 Pakistan spillway 5 flushings h Overflow outlets Liujiaxia 1981,1984, 1660 - 103 - 0.0023 - water level 435-141 1985,1988 2090 177 h 0.0071 china lowered 4.4-7.8 m 1965,1966, Shuicaoz Overflow 3-4 0.012 - 1974, 1978, 21.4-230 83-23 spillway 0.043 China 1980, 1981 days (Fan, 1995)
Lai and Shen (1996) observed during laboratory tests of reservoir flushing that about half the total volume of sediment removed was eroded during the first one third of the flushing period, initially high flushing efficiency (about 0.10) when retrogressive erosion was started, it declined asymptotically to a lower level of about 0.025. A high flushing efficiency is not necessarily synonymous with desirable or effective sediment management. For example, the flushing efficiency for the removal of coarse material will inevitably be lower than of fine materials, and if a reservoir is operated to maximize flushing efficiency, it may continuously accumulate coarse sediment. High flushing efficiency may also create sediment concentrations downstream which are excessive from the viewpoint of other users or the environment.
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Table 2.6 Flushing efficiency for reservoir emptying Water: Years of Discharge Flushing Flushing Reservoir sediment operation m3/s duration efficiency ratio Gebidem, 1969-1994 39 35h/yr 0.048-0.060 21-17 Switzerland Barenburg, 1985 * 20 h 0.060 17 Switzerland Ferrera, 1985 * n.d. 0.026 38 Switzerland Gen-shan-pei, 1958-1983 * 53 days/yr 0.0897 11 China Santo Domingo, 1978 8-10 n.d. 0.09-0.13 11-8 Venezuela Donfanghong, 1984 51 n.d. 0.056-0.083 18-12 China Sefid-Rud, 1980-1987 * 61-157 days 0.022-0.067 45-15 Iran Zemo-Afchar, 1939-1966 72-688 13-76 h 0.015-0.096 67-10 U.S.S.R Chirurt, 1968 400-500 5 days 0.04 25 U.S.S.R. (Fan, 1995), * not described in literature
2.4.9 Factors Affecting the Flushing Efficiency There are several factors that affect sediment-flushing efficiency. Wilson (1903) (ref. Brown, 1943) declared that sluice bottom outlets have less sediment flushing efficiency if the area of the opening is less. Ortho (1934) pointed out a number of factors affecting the flushing efficiency, these are described below: Lesser the depth of impoundment during flushing better will be flushing results. Greater the discharge of the flushing, more will be the flushing efficiency. Flushing discharge of atleast twice the mean annual flow or the flushing volume atleast 10% of mean annual runoff is recommended (Attewill et al., 1998; Atkinson, 1996b).
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The size of flushing outlet has much effect on the flushing performance. Flushing is most effective when the reservoir is fully drawn down to a level to the pre- impounding state, hence creating riverine flow condition within the reservoir. This can be only possible when the flushing outlet has sufficient hydraulic capacity to maintain minimum reservoir level during flushing process. Flushing performance is much affected by the elevation of flushing outlet. Lower the elevation of outlet, more will be flushing efficiency, higher the elevation of outlet, less will be the flushing efficiency (White, 2001). If the sill level of outlet is at lower elevation, the area of flow is decreased for the same discharge, resulting in high erosive velocity and hence more deposited sediment will be eroded out through the reservoir. Longer the flushing duration, more may be the flushing efficiency, lesser the duration, less will be the flushing efficiency. Flushing is performed by forming the flushing channel within the reservoir. The narrower reservoirs are suited for efficient flushing. If the reservoir is wide the channel will be formed within the smaller area of the reservoir and less accumulated sediment will be eroded through the reservoir producing less amount of flushing, but if the flushing channel width is close to the bed width of the reservoir, most of the deposited sediments will be evacuated, giving higher efficiency (Atkinson, 1996b). Flushing performance is also influenced by the original stream gradient through the reservoir. Steeper is the gradient of the reservoir, more will be flushing efficiency, because the velocity of flow increases and it erodes more sediments and if the gradient of the stream is less, flow velocity will be less, hence eroding lesser sediments through the reservoir. For shorter reservoir, flushing efficiency will be more, and for longer reservoir the flushing efficiency will be lower. Flushing performance is also influenced by the shape of the reservoir. If the reservoir is straighter, flushing performance will be better, but if the reservoir has loops of bends, then the velocity of the flow is reduced due to this shape and also the eroded sediments are not carried upto the undersluice due to its shape.
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Flushing performance is also influenced by the position of deposited sediments in the reservoir. If the sediments are close to the dam site it can be easily flushed out through the undersluice, but if they are at the upstream of the reservoir then by flushing they may be advanced towards the dam but difficult to flush out of the reservoir. Sediment type also affects the flushing performance. Finer particles can be easily eroded as require less flushing velocity, but the coarser particles are more difficult to erode, because they require more erosive velocity to erode out of the reservoir. Sediment shape also affects the flushing performance. Irregular shaped particles are difficult to erode, whereas rounder particles can be eroded easily. The age of deposited sediments also affect the flushing efficiency. If the sediments are freshly deposited they can be easily flushed, whereas the sediments deposited a long time ago get consolidated and difficult to scour.
The effect of water level on flushing efficiency has been studied by Jarecki and Murphy (1965) at the Guernsey Dam, USA. The study showed that during flushing, sediment releases were greater during low water levels and that the rate of drawdown had no apparent effect. Based on a long data set from the period 1939-1966 at the Zemo-Afchar hydropower station, USSR, Gvelesiani and Shmal'tzel (1968) investigated the influence of water discharge on flushing efficiency. They observed that larger discharges proved to remove more sediment, but produced lower mean sediment concentrations. From the same reservoir, they also noted that there exists an optimal flushing discharge; when discharge is greater than the optimum value its efficiency decreases due to backwater effects. If flushing discharge is less than optimum, erosion is decreased because stream power of the flushing flow is below its critical value. Partl’s (1976) study on reservoirs in Austria showed that the higher the flood discharge, the more sediments will be eroded by flushing. Flushing is hardly effective if the river flow is less than three times the annual mean flow.
The importance of less consolidated sediments was shown by Guo and Li (1984) at the Hengshan Reservoir, China, where the highest sediment-to-water ratio was obtained
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CHAPTER 2 LITERATURE REVIEW when the flushing process eroded in a previously eroded flushing channel filled with sediment. White and Bettess (1984) stated that flushing to be effective, there must be general movement of water and sediment in the reservoir, caused by both flow from the low level outlets and inflow to the reservoir. If outlets are too small, material eroded from the delta deposits will redeposit closer to the dam.
The effectiveness of erosion can be increased by rainfall and wind, as in the Sefid-Rud Reservoir, Iran (Parhami, 1986). To keep a high flushing efficiency for a long period of time, Ackers and Thompson (1987) suggested that flexibility in the design of a reservoir should be included by constructing many low level outlets in the dam, and because the conditions may vary, rigid operation rules should not be laid down. The importance of the outlets’ dimensions on the efficiency of flushing was investigated by Paul and Dhillon (1988). They noted that flushing will be more effective, wider the sluice. The difference of sediment removal between reservoirs can be illustrated by the Cherry Creek Dam, USA, whereas in contrast to the above cases, it does not appear that the different magnitudes or durations of the discharge have much effect on the removal of sediments (Buchholz and Knofczynski, 1988). Scheuerlein (1989) stated that effective sluicing and flushing must be pointed towards minimum of drawdown and sluicing time. He also presented straightforward approaches, by means of graphs, to estimate roughly the drawdown level corresponding to a desired flushing of a certain grain size.
High flushing efficiency is not necessarily synonymous with effective sediment management. In reservoirs having a significant load of fine and coarse sediments, short flushing periods may be effective in removing fines, but longer flushing periods and larger flushing flows will be required to remove the inflowing load of coarse material. Therefore, if a site is operated to maximize flushing efficiency, it may continuously accumulate coarse sediments. Morris and Fan (2010) also noted that maximum sediment release will occur when emptying coincides with high flows and that the amount of sediment released in each stage of flushing varies from one event to another. Furthermore, effective sediment removal through a high-level outlet can be achieved only after the bed of the deposits has risen to the level of the outlet.
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2.4.10 Indicators to Assess Flushing Feasibility of Reservoir Before planning to flush, there must be some indicators to assess flushing feasibility. Atkinson (1996b) describes six indicators to evaluate feasibility of sediment flushing from reservoir. These indicators are: Sediment Balance Ratio (SBR), Long Term
Capacity Ratio (LTCR), Drawdown Ratio (DDR), SBR with Full Drawdown (SBRd), Flushing Width Ratio (FWR) and Top Width Ratio (TWR).
Among the flushing indicators, SBR and LTCR are the governing criterions to decide flushing feasibility. For the successful flushing the limits of these indicators are: - SBR
>1, LTCR ≈ 1, DDR > 0.7, SBRd >1, FWR > 1 and TWR 1-2 (Atkinson 1996b). Following are the formulae given to calculate the values of these indicators by the given data.
2.4.10.1. Sediment Balance Ratio Sediment Balance Ratio (SBR) is defined as the ratio between sediments mass flushed annually to the sediments mass deposited annually (Atkinson, 1996b). If; SBR > 1.0 ; reservoir is feasible for sediment flushing; SBR is too low, flushing may be feasible at higher discharges, by increasing flushing period or larger flushing outlets. Following is the procedure to calculate the value of estimated SBR;
M f SBR (2.35) M dep
Where Mf is sediments mass flushed annually and Mdep is the sediments mass deposited annually.
Wres Wbot 2SSres El f Elmin (2.36) 0.5 W f 12.8 Q f (2.37)
Minimum of Wres and Wf Will be used for calculation purpose El El S max f L (2.38) Q 1.6 S 1.2 Q f S W 0.6 (2.39)
(Qs)modified is calculated by dividing Qs by a factor of 3, as the reservoir is different from Chinese reservoirs (Atkinson, 1996b).
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M 86400 T Q f f S (2.40) M TE M in dep 100 (2.41)
2.4.10.2. Long Term Capacity Ratio LTCR is defined as the ratio between sustainable capacity to the original capacity of the reservoir; whereas sustainable capacity is the total volume of the reservoir which can be maintained due to the flushing of the reservoir (Atkinson, 1996b) If; LTCR upto 1: reservoir can be flushed successfully; LTCR > 0.5: reservoir can be flushed partially; LTCR = 0.5: Minimum value of criteria, reservoir may be considered for flushing
A f LTCR (2.42) Ar W W 2SS El El tf S max f (2.43)
Wt Wbot SS res Elmax Elmin (2.44)
If Wtf < Wt Then
Wtf W Af Elmax Elmin (2.45) 2
If Wtf > Wt
Then 2 Af W h f h f hl hm SS s hl SS res (2.46)
Where, hm , hl and hf are defined in Figure 2.18 and calculated below as;
W W h res m (2.47) 2SS S SS res
h El El h l max f m (2.48)
h El El f max f (2.49)
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Wt W Ar bot El El 2 max min (2.50)
Figure 2.18 Cross section immediately u/s of the dam for simplified reservoir geometry (Atkinson, 1996b)
2.4.10.3. Drawdown Ratio
Drawdown Ratio is defined as:
El f Elmin DDR 1 (2.51) Elmax Elmin
2.4.10.4. Sediment Balance Ratio with Full Drawdown The calculation of sediment balance ratio with full drawdown is in the same manner as
SBR, the only difference is that in the calculation, Elf is taken equal to Elmin
2.4.10.5. Flushing Width Ratio Flushing width ratio is defined as the ratio width of flow at the bed of flushing channel to the bottom width of the reservoir
W (2.52) FWR f Wbot
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2.4.10.6. Top Width Ratio The top width ratio for the flushed reservoir, TWR is defined as the ratio between the top width of scoured channel after drawdown, Wtd to the reservoir width at the top water level Wt, i.e.
Wtd TWR (2.53) Wt
Wtd is computed by the following formula
Wtd Wbot 2SSs Elmax Elmin (2.54) 2 Where Af is the cross sectional area of valley scoured out by flushing (m ), Ar is the cross 2 sectional area of reservoir in reach immediately upstream from dam (m ), Elf is the water surface elevation at the dam during flushing (m), Elmax is the elevation of top water level
(m), Elmin is the minimum river bed elevation immediately upstream from the dam (m), L is the reservoir length (m), Mdep is the mass of sediments which deposits annually in the reservoir (Tons), Mf is the mass of sediments flushed annually from the reservoir (Tons),
Min is the mean annual sediments inflow (Tons), Qf is the discharge passing through 3 reservoir during flushing (m /s), Qs is the sediment load during flushing (Tons/s), S is the longitudinal slope during flushing, SSres is the representative side slope for the reservoir, SSs is side slope for the deposits exposed by flushing, TE is the trapping efficiency of reservoir
(%), Tf is the duration of flushing (days), W is the width of flow for flushing conditions (m),
Wbot is the bottom width for the reservoir (m), Wf is the width of flow at the bed of the flushing channel (m), Wres is the reservoir width in the reach upstream from the dam at flushing water surface elevation (m), Wtf is the top width of the scoured valley at the top water level (m), ψ is the multiplier in the Tsinghua University method for sediment load prediction during flushing.
2.5 PROCESS BASED MODELING OF RESERVOIR SEDIMENTATION
Numerical modeling has become very popular in the last few decades, mainly due to the increasing availability of more powerful and compatible computers. Particularly in the
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CHAPTER 2 LITERATURE REVIEW fields of water flow and its turbulence, water quality, sediment transport, much advancement has been made. Many computer models are now available for users to purchase. Some of the models are in public domain and can be obtained free of charge. Graphical user interfaces, automatic grid generators, geographic information systems, and improved data collection techniques, such as LiDAR (Light Distancing and Ranging) expedite the use of numerical models as a popular tool for solving river engineering problems.
All numerical models are developed by recognition of physical relationships with modeled prototype. The equations and coefficients for nearly all flow process in hydraulic engineering are of empirical nature and solution of schemes is very complex in numerical modeling. The mostly used methods for solving these equations in numerical modeling are (i) Finite Difference Method (ii) Finite Element Method (iii) Finite Volume Method
Finite difference is used intensively because in this solution schemes algorithms can be also be solved on computers like other methods. The finite difference method can be applied for the solution of water profiles. Using simple differential schemes to more complex three-dimensional problems. Finite Element Method allows more accurate representation of model boundaries for two and three-dimensional problems, but requires intensive computational effort and face convergence problems. In Finite Volume Method partial differential equations are transformed into total differential equations through an integration procedure. The water body is divided into single volume, which allows an easy representation of boundaries. Euler, Power law, Maccormac etc. are the known algorithms of this method. This was basically designed for the aeronautical engineering but now extensively using in hydraulic engineering as well.
In the numerical modeling the models used are of one dimensional, two dimensional, three dimensional, which are described in the subsequent sections.
2.5.1 One-Dimensional Numerical Models Most of the Sediment Transport Models used in river engineering are one dimensional, especially those used for long-term simulation of a long river reach. The numerical
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CHAPTER 2 LITERATURE REVIEW solutions are more stable and require the least amount of computer time and capacity. One-dimensional models generally require the least amount of field data for calibration and testing. One-dimensional models are not suitable, however, for simulating truly two- or three-dimensional local phenomena. One-dimensional models are usually based on the same conservation principles as the multidimensional models, i.e., the conservation of mass and momentum. Conservation of mass (continuity equation) can be expressed as
A Q q (2.55) t x l Where A = cross-sectional area of the flow, Q = water discharge, and
ql = lateral inflow per unit length. Whereas the conservation of momentum is expressed as:
Q Q 2 gA gAS f SO 0 t x A x (2.56)
Where Sf is friction slope, So is bed slope, and β is momentum correction coefficient (β 1) Equations (2.55) and (2.56) are known as the de Saint Venant equations. The advantage of one-dimensional modeling lies in the simple simulation of long river reaches and flow simulation for long time series. However the main disadvantages are that, three dimensional effects of the secondary flow and those local phenomena e.g. flow around islands cannot be simulated. The mostly used 1-D Models are HEC-6, HEC-RAS 4.1.0, SHARC, RESSASS, and FLUVIAL which are described below:
2.5.1.1 HEC-6 HEC-6 was initially developed by William Thomas at the U.S. Army's Hydrologic Engineering Center in 1973 and was handed over for use within the Corps. HEC-6 Model (U.S. Army, 1991) is probably the most widely used model in the United States for the simulation of sediment transport in rivers and reservoirs. The model has been modified and enhanced through new releases, and the current version handles both deposition and scour of sediment sizes from clay to boulders. 65
CHAPTER 2 LITERATURE REVIEW
HEC-6 is a one-dimensional movable-boundary open-channel flow model that computes sediment scour and deposition by simulating the interaction between the hydraulics of the flow and the rate of sediment transport, with the assumption that equilibrium conditions are achieved between the flow and the bed material transport within each time step. But this assumption observed to be violated during rapidly rising and falling hydrographs, which can limit the model’s ability to simulate single event. (Gist et al., 1996).
HEC-6 can simulate a main river, its tributaries and local inflows. The hydraulic profile is simulated by the standard step method and Manning’s equation to solve the one dimensional energy equation, with the user specifying n values for both channel and overbank areas at each cross section. Sediment transport capacity is calculated at each time interval. Transport potential is calculated for each grain size class in the bed. Dredging can be simulated and sediment deposition in the reservoir can also be analyzed with this model. The main capabilities of the model are:
. It is designed to predict sediment movement in the reservoir thereby sediment deposition and progressive reduction in the storage capacity incorporating interaction between flow hydraulics, sediment transport, channel roughness and related changes in boundary.
. It simulates a river system consisting of main river, tributaries and local inflow/outflow points. Sediment transport is calculated in primary rivers and tributaries.
. It can simulate the effect on sediment deposition due to various operating rule curves.
. It has capability to simulate options of flushing of deposited sediments in the reservoir.
Main advantage of this model includes good documentation, continuing support and development by the Hydrological Engineering Center (Gee, 1992). A particular feature of the HEC-6 Model for reservoir analysis is its ability to simulate both deposition and scour for a wide range of grain sizes, including silts and clays. Whereas many other Reservoir
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Sedimentation Models do not incorporate the facility to simulate fines (Morris and Fan, 2010). Flushing of sediments option can also be applied through the model.
2.5.1.2 HEC-RAS 4.1.0 The HEC-RAS 4.1.0 software was developed at the Hydrologic Engineering Centre (HEC), which is a division of the Institute for Water Resources (IWR), U.S. Army Corps of Engineers in 1995. The software was designed by Mr. Gary W. Brunner, leader of the HEC-RAS 4.1.0 development team. HEC-RAS 4.1.0 is an integrated system of software, designed for interactive use in a multi-tasking, multi-user network environment. The system is comprised of a graphical user interface (GUI), separate hydraulic analysis and sediment transport analysis components, data storage and management capabilities, graphics and reporting facilities.
HEC-RAS 4.1.0 is designed to perform one dimensional sediment transport calculation for a full network of natural and constructed channels. Sediment component was recently incorporated and version 4.1.0 was released in 2010. The following is description of the major capabilities of HEC-RAS 4.1.0 (U.S., 2005).
Cross Section Locations The inline weir and gated spillway routines in HEC-RAS 4.1.0 require the same cross sections as the bridge and culvert routines. For modelling, minimum four cross sections in the vicinity of the structure, two upstream and two downstream are required. In general, there should always be additional cross sections downstream from any structure. The locations of these minimum four cross sections are; One cross section sufficiently downstream such that the flow is fully expanded, one at the downstream end of the structure (representing the tail water location), one at the upstream end of the structure (representing the headwater location), one cross section located far enough upstream at the point in which the flow begins to contract.
Quasi – unsteady flow simulation Current sediment capabilities in HEC-RAS 4.1.0 are based on quasi-unsteady hydraulics. The quasi-unsteady approach approximates a flow hydrograph by a series of steady flow profiles associated with corresponding flow durations. Because these types of analysis
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CHAPTER 2 LITERATURE REVIEW require different information than steady or unsteady flow, so it is necessary to provide different input alongwith boundary condition.
Boundary conditions Different boundary conditions are available in HEC-RAS 4.1.0. Each upstream boundary (the most upstream cross section of an open ended upstream reach) must have a Flow Series boundary condition specified. Optional internal boundaries include Lateral Flow Series and Uniform Lateral Flow Series. Each downstream boundary (the downstream most cross section of an open ended downstream reach) can be either: Stage Time Series, Rating Curve, or Normal Depth.
Flow series Since Quasi-unsteady flow can have irregular (varying) time steps, each specified flow must also be accompanied by a time duration (over which the flow is constant). Additionally, a computational time step must be entered for each record. Flow Duration: to approximate a flow hydrograph as a series of steady flows, each steady flow profile must have flow duration. The duration is then broken up into a series of computational increments over which the sediment routing occurs. Due to the non-linear nature of alluvial sediment movement, transport is usually concentrated during large, peak flow events. These events are usually of relatively short duration and are characterized by rapidly changing flow. Because of this non-linearity, an irregular time step is desirable. Low flows, corresponding to small or moderate transport (or bed change), are often approximated with large time steps.
Temperature Because of several aspects of sediment transport mechanics, particularly fall velocity, incipient motion and sediment transport are sensitive to water temperature, hence, HEC- RAS 4.1.0 requires temperature information. Only one temperature per time step can be specified for the entire model.
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Bed gradation curve Bed gradation curve is also given as input to the Model for simulating sediment deposition or sediment flushing.
HEC-RAS 4.1.0 can be used:
. To evaluate sediment deposition in reservoirs
. Predict the removal of sediments from the reservoir by hydraulic flushing
. Estimate maximum possible scour during large flood events
. Evaluate sedimentation in fixed channels
Sediment transport functions used in the Model are Ackers-White (1973) function, Engelund-Hansen (1967) function, Laursen-Copelnd (1968) function, Meyer-Peter Muller (1948) function, Toffaleti (1968) function, and Yang (1973) function described below.
Ackers - White (1973) function Ackers-White transport function is a total load function and developed in terms of particle size, mobility, and transport. Dimensionless size parameter is used to distinguish between fine, transitionary, and coarse sediment sizes. The general transport equation for Acker-White functions for a single grain size as;
Ggr s d s X n (2.57) u D * V and
F (2.58) G C gr 1 gr A
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Where X is sediment concentration, in parts per part, Ggr is sediment transport parameter, s is specific gravity of sediments, ds is mean particle diameter, D is effective depth, u* is shear velocity, V is average channel velocity, n is transition exponent, depending on sediment size, C is coefficient, Fgr is sediment mobility parameter, A is critical sediment mobility parameter. Engelund-Hansen (1967) function Engelund-Hansen function is a total load predictor which gives adequate results for sandy rivers with substantial suspended load. It is based on flume data with sediment sizes between 0.19 and 0.93 mm. It has been extensively tested, and found to be fairly consistent with the field data. The general transport equation for Engelund-Hansen function is represented as,
3 / 2 2 d 50 o g s 0.05 s V d s s 50 g 1 (2.59)
Where gs is unit sediment transport, is unit weight of water, s is unit weight of sediment particles, V is average channel velocity, o is bed level shear stress, d50 is particle size of which 50% is smaller.
Laursen-Copelnd (1958) function The Laursen method is a total sediment load predictor, derived from a combination of qualitative analysis, original experiments, and supplementary data. Transport of sediments is primarily defined based on hydraulic characteristics of mean channel velocity, depth of flow, energy gradient, and on the sediment characteristics of gradation and fall velocity. The range of applicability is 0.011 mm to 29 mm, median particle diameter.
The general sediment transport function Laursen (Copeland) function for a single grain size is presented as,
7 / 6 d ' u (2.60) s o * Cm 0.01 1 f D c
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Where Cm is sediment discharge concentration, in weight/volume, is unit weight of water, ds is mean particle diameter, D is effective depth of flow, o is bed shear stress, c
u is critical bed shear stress, f * is function of shear velocity to fall velocity. Toffaleti (1968) function The toffaleti is modified-Einstein total load sediment transport function that breaks suspended load distribution into vertical zones, replacing two dimensional sediment movement. Four zones are used to define sediment distribution. They are the upper zone, the middle zone, the lower zone and the bed zone. Sediment transport is calculated independently for each zone and summed to arrive on total sediment transport. The method was developed using extensive collection of field and flume data. Flume experiment used sediment sizes ranging from 0.3 mm to 0.93 mm, however successful application of the method suggests that mean particle diameter should be as low as 0.095 mm.
The general transport equations for toffaleti function for a single grain size is presented as:
1n 0.756 z R 1n 0.756 z 2dm 11.24 g ssL M (lower zone) (2.61) 1 n 0.756 z
0.244 z 1n z 1n z R R R (middle zone) (2.62) 11.24 2.5 11.24 g ssM M 1 n z
0.244 z 0.5Z 1n 1.5z R R 1n 1.5 z R R (upper zone) (2.63) 11.24 2.5 2.5 g ssU M 1 n 1.5z 1n 0.756z (bed zone) (2.64) gsb M 2dm
0.756zn (2.65) M 43.2 CL 1 n VR
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g s g ssL g ssM g ssU g sb (2.66)
Where gssL is suspended sediment transport in lower zone (tons/day/ft), gssM is suspended sediment transport in middle zone (tons/day/ft), gssU is suspended sediment transport in upper zone (tons/day/ft), gsb is bed load sediment transport (tons/day/ft), gs is total sediment transport (tons/day/ft), M is sediment concentration parameter, CL is sediment concentration in lower zone, dm is median particle diameter, z is exponent describing the relationship between the sediment and hydraulic characteristics, and n is temperature coefficient.
Yang (1973) function Yang’s (1973) method is developed under the hypothesis that unit stream power is the dominating factor for the determination of total sediment concentration. The research is supported by the data obtained by flume experiments and field data under wide range conditions found in alluvial channels. Principally sediment sizes range is from 0.062 to 7 mm with total sediment concentration ranging from 10 to 585,000 PPM, channel widths rage range from 0.44 to 1746 ft, depths from 0.037 to 49.4 ft, water temperature from 0o to 34.3o Celsius, average channel velocity from 0.75 to 6.45 fps, and slopes from 0.000043 to 0.029.
The general sediment transport equations for sand and gravel using Yang function for single grain size is represented as:
d u logC 5.435 0.286log m 0.457log * t d u V S Vcr S (2.67) 1.799 0.409log m 0.314log * *log for sand dm < 2 mm d u logC 6.681 0.633log m 4.816log * t d u V S Vcr S (2.68) 2.784 0.305 log m 0.282log * *log for gravel dm 2 mm
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2.5.1.3 SHARC
1-D numerical Model SHARC was developed by HR Wallingford, DFID (SHARC Manual, 2001; Westrich, and Juraschek, 1985). SHARC is a suite of incorporated programs designed to assist in the identification and solution of sediment problems at intakes in rivers and canal systems. There are six modules that usually are used in this Model: Problem Diagnosis and Initial Options, Preliminary Economic Screening, Design Tools, Hydraulic Simulation, Environmental Impact, and Economic Analysis. Among the above six modules Design Tools module has the four programs i.e.: Intake Model, DORC (Design of Regime Canals), DACSE (Design Analysis for Canal Sediment Extractors) and DOSSBAS (Design of Sluiced Settling Basins). It includes two numerical Modules; Deposition Modules and Sluicing Modules that simulate the performance of basins operating in the deposition and sluicing modes. The Simulation Model assists the design of settling basins by allowing a designer to predict the impact of a basin. The design can then be refined or optimized using trial and error procedure. Geometric data input to DOSSBAS Model is linear, i.e. bottom widths of reservoir at upstream and downstream sides, average side slopes, bed elevations at upstream and downstream of reservoir. Besides the simulation of sediment deposition and sediment flushing in canals, it can also be used for the simulation of reservoir sediment deposition and reservoir sediment flushing.
Sediment Deposition Model is based on Westrich and Juraschek transport function (Westrich and Juraschek, 1985) given in equation (2.69).
0.0018 bV Cv (2.69) s 1 gDWs
Where, Cv is the sediment capacity concentration (by volume), b is the bed shear stress, s is the specific gravity of silt, ρ is the fluid density, g is the acceleration due to gravity,
D is the water depth, and Ws is the settling velocity of the sediment particles.
Westrich and Juraschek developed the sediment transport equation for silt-sized material. The equation is derived in the laboratory with particles having a settled velocity ranging from 0.06 mm/s to 9 mm/s. The predicted transport capacities obtained from this formula
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CHAPTER 2 LITERATURE REVIEW do not depend on bed material composition, but only on the material in suspension (Yang, 2006).
Sediment deposition is modeled by splitting a settling basin into a number of short reaches. The simulation period is also split into short time steps, with steady state flow conditions assumed within each time step. Calculations for an individual time step begin with a backwater computation, to obtain the water levels along the basin, from the known water level at the downstream end. The discharge and bed levels at the start of the time step are inputs for this computation. The roughness of the bed formed by deposited sediment is predicted using an alluvial friction predictor, and turbulence intensities in the basin are calculated. Sand sized sediments entering the basin are split into ten size fractions, the concentration of each size fraction being traced along the basin. The concentration change between one section and the next downstream (i.e. within a sub- reach) is computed using sediment transport functions and a bed boundary condition. The transport and deposition of fine sediments, silts and clays in the cohesive size range are treated separately, using a transport function based on the settling velocities of the fine sediment mixtures entering each sub reach. Computed deposition rates for sand fractions and silt fractions are combined to obtain total bed level rise for each section of the basin. Up-dated bed levels, and the bed material size grading for each sub reach, are then used as input to the computations in the next time step.
Three types of data are required as input to Deposition Model: geometric data, flow and concentration data and sediment properties. (i) geometric data: reservoir length, initial bed width, bed width at the upstream end of the reservoir, upstream and downstream bed elevations of the reservoir, side slope of reservoir, sill height of the outlet from the river bed level at dam site, and the normal operating level, (ii) flow and concentration data: annual water inflow, annual sediment inflow, average daily discharge and sediment concentration, period of the sediment deposition, flushing discharge and flushing duration. (iii) Sediment properties: specific gravity of sand and fine sediments, settled density for sand and fine sediments.
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The Sluicing Model is based on the Van Rijn transport functions (Van Rijn, 1984a; Van Rijn, 1984b) given in equations (2.70) and (2.71). 0.5 0.5 1.5 0.3 2.1 qb 0.053s 1 g d50 D* T (2.70)
qsus FVhCa (2.71)
2 Where qb is the bed load transport rate (m /s), qsus is the suspended load transport rate 2 (m /s), s is the relative density, g is the acceleration due to gravity, d50 is the mean particle size, D* is the particle parameter, T is the bed shear parameter, F is the shape factor, h is the water depth, V is the mean velocity, and Ca is the reference concentration.
The Sluicing Model uses initial bed levels, bed sediment sizes and densities from the Deposition Model as the starting point for sluicing simulations. The model assumes that during sluicing erosion occurs from the downstream face of the sediment deposits in a settling basin. Sluicing is thus modeled with the assumption of erosion of a series of wedges of bed material.
2.5.1.4 RESSASS (Reservoir Survey Analysis and Sediment Simulation) RESSASS is a Mathematical Model, developed by HR Walling ford, one dimensional sediment simulation model enables to quantify reservoir storage reduction due to sedimentation. Its main capabilities are:
. It predicts delta movement within the reservoir
. It enables to quantify reduction in reservoir storage volume due sedimentation
. It predicts the impact of future sedimentation in the storage and the effect of reservoir operation polices in reducing sediment deposition rates.
2.5.1.5 FLUVIAL-12 The FLUVIAL-12 is a private one-dimensional model developed by Howard Chang in 1988. The fundamental features of the FLUVIAL Model are described by Chang (1988). It has been implemented on North Fork Feather River, U.S.A. to simulate reservoir sediment deposition. Model has the following five major components of hydraulics and geometry.
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. Hydraulics routing Water routing provides temporal and spatial variations of the stage, discharge, energy gradient and other hydraulic parameters in the channel. The water routing component has the following three major features: (i) Numerical solution of the continuity and momentum equations for longitudinal flow, (ii) evaluation of flow resistance due to longitudinal and transverse flows, and (iii) upstream and downstream boundary conditions. . Sediment routing The sediment routing component of the model has the following major features. (i) Computation of sediment transport capacity using a suitable formula for the physical conditions, (ii) determination of actual sediment discharge by making corrections for sorting and diffusion, (iii) upstream conditions for sediment inflow, and (iv) numerical solution of the continuity equation for sediment. These features are evaluated at each time step; the results so obtained are used in determining the changes in channel configuration. . Simulation of changes in channel width: For a certain time, the amount of width change depends on the sediment rate, bank configuration and bank erodibility. The slope of an erodible bank is limited by the angle of repose of the material. The rate of width change depends on the rate at which sediment material is removed or deposited along the banks. . Simulation of changes in channel bed profile: Distributions of erosion and deposition at a cross section are typically not uniform. Generally speaking, deposition tends to start from the low point and is more evenly distributed because it tends to build up the channel bed in nearly horizontal layers. This process of deposition is often accompanied by channel widening. On the other hand, channel-bed erosion tends to be more confined with greater erosion in the thalweg. This process is usually associated with a reduction in width as the banks slip back into the channel. In the model, the allocation of scour and fill across a section during each time step is assumed to be a power function of the effective tractive force (o- c). . Simulation of changes in transverse bed geometry due to curvature: Sediment transport, in the presence of transverse flow, has a component in that direction. Sediment movement in the transverse direction contributes to the adjustment of transverse bed profile. In an unsteady flow, the transverse bed profile varies with time, and it is
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CHAPTER 2 LITERATURE REVIEW constantly adjusted toward equilibrium through scour and deposition. The transverse bed load per unit channel length q b is related to the streamwise transport qb (Ikeda, 1982).
A distinct feature of the model is its ability to simulate the development of the transverse bed slope in a curved reach with the condition to have sufficient field data for calibration. The flow diagram of the model process is shown in Figure 2.19.
Figure 2.19 Flow chart showing major steps of computation for FLUVIAL Model
2.5.1.5 Tsinghua University Model The Tsinghua equation was developed for drawdown flushing using flushing data from reservoirs in China. The equation was independently verified by laboratory experiments (Lai and Shen, 1996) and is utilized by many researchers, such as Chang et al. (2003) and Kawashima et al. (2003), to estimate the sediment quantity evacuated from reservoirs. The equation is as follows:
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1.6 1.2 Q f S Qs 0.6 (2.72) W f
Where Qs is sediment load during flushing (tons/s), is erodibility coefficient, Qf is 3 flushing discharge (m /s), Wf is bottom width of flushing channel (m), S is longitudinal energy slope during flushing
The parameters required to calculate the sediment discharge, Qs (tons/s), are flushing 3 discharge Qf (m /s), longitudinal energy slope S during flushing (dimensionless), width of the flushing channel Wf (m) and erodibility coefficient (). The longitudinal energy slope
S and the width Wf are estimated as proposed by Kawashima et al. (2003) and Atkinson (1996b) as follows: Normal Operating Level of Re servoir Re servoir Water Level during Flushing S (2.73) Length of Re servoir and
0.5 W f 12.8 Q f (2.74)
The erodibility coefficient () depends on characteristics of suspended sediment and bed load. IRTCES (1985) proposed representative values of (Table 2.7) for various sediment characteristics in the case of drawdown flushing. These values are derived using 3 flushing data from reservoirs in China ranging from Qf 0.1-5730 (m /s), S 0.06-16‰, Wf 10-1000 (m) and Qs 0.0006-777 (tons/s). Drawdown flushing occurs when the reservoir level is low enough to create riverine conditions. Atkinson (1996b) checked the values of coefficient against the flushing data for four reservoirs in USA, USSR, and India. Atkinson (1996b) concluded that the ( values proposed by IRTCES (1985) overestimates the flushed sediment volumes by a factor of three, if the conditions are different from those in China. Atkinson’s recommended values of are also shown in Table (2.7). Atkinson (1996b) further recommended that if the water depth during flushing is not less than 30% of the maximum water depth in the reservoir, the flushing should be further constrained by adjusting the ( values. As such, the user of the
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Tsinghua University Model should provide suitable values of ( based on the observed data or based on recommended values in literature.
Table 2.7 ( values recommended by various sources Case Description IRTCES Atkinson (1985) (1996b) I Loess 1600 530
I I Sediment with d50 < 0.1 mm 650 225
I I I Sediment with d50 > 0.1 mm 300 100 IV Flushing with low discharge 180 60
Most sediment transport equations were developed for rivers and channels, and make assumptions that restrict their application outside the range for which they were developed. They may not be valid, for example, for flows in reservoirs. Tsinghua University Equation (IRTCES, 1985) is an empirical equation especially derived for calculating the transport capacity of flushing flows in reservoirs. Furthermore University Equation is capable to compute sediment transport capacity for all size fractions, irrespective of particle size.
Tsinghua University Equation has been implemented in GSTARS-4. It has been tested and used specifically for reservoir sedimentation problems. Other equations that have been developed using river data, but that have been applied to reservoir engineering with various degrees of success are the Ackers and White (1973) and the Yang’s (1973) equation.
Apart from the Models described above, engineers used other developed models. For example, the earliest mathematical models reported in the literature were, naturally, one- dimensional. Fan and Jiang (1980) developed a model for retrogressive erosion and the methods of its computation under the conditions of a sudden drawdown of water level (Fan, 1985; Fan and Morris, 1992).
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Cavor and Slavic (1982), developed a one-dimensional mathematical model for the Sefid- Rud Reservoir, Iran (Sloff, 1991). This model, extended with a procedure to find the most efficient pattern of operating the bottom outlets, was reported by Bruk et al. (1983). Other early one-dimensional-models to simulate flushing were made by Shuyou et al. (1988), where sedimentation as well as retrogressive erosion were included (Sloff, 1991). Hotchkiss (1989), and Du and Zhang (1989) studied for retrogressive erosion of cohesive sediment in reservoirs ( Sloff, 1991).
Wang and Locher (1989) used the one-dimensional HEC-6 Model to develop operational procedures to minimize the accumulation of sediment in the Cowlitz Falls Reservoir, USA. Pemberton and Orvis (1991) used the STARS Model, to simulate scouring rates for flushing of settling basins in Mexico and Nepal. Morris and Hu (1992) used the HEC-6 Model to analyze the impact of changing gate operations when routing sediment through the Loíza Reservoir during floods. Zarn (1992) used the one-dimensional MORMO Model to simulate flushing of the reservoir of Reichenau Hydropower Station, Switzerland. He concluded that the model simulates bed geometry, grain-size distributions, and suspended-sediment concentrations satisfactorily, provided the sediment data is reliable. A one-dimensional diffusion model, where sediment transport is represented by a unit stream power equation, was successfully used by Ju (1992) to calculate bed profiles during headward erosion.
Lai and Shen (1995) developed an unsteady mobile-bed model to simulate degradation flushing processes. Sen and Srivastava (1995) used Fan and Jiang’s (1980) model for calculating the desiltation of the Baira Reservoir, India. The mathematical model obtained from the Baira desiltation was then applied on the Kurichu Reservoir, Bhutan. Atkinson (1996a) developed a numerical model for simulating sediment movement and scoured channel formation. Di Silvio (1996) described a one-dimensional model to describe bottom evolution during flushing, whereas Kern et al. (1996) used a one- dimensional model to simulate erosion and deposition in the Lauffen Reservoir, Germany. Krok et al. (1997) employed a one-dimensional model to simulate the bed profile evolution and the amount of sediment removed during flushing, whereas Petitjean
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CHAPTER 2 LITERATURE REVIEW et al. (1997) used one-dimensional MOBILI Model on the Escale Reservoir, France and the model showed to have a total error of 30% when validated.
2.5.2 Two Dimensional Numerical Models Two-dimensional models for flow and sediment transport are widely used due to the introduction of fast personal computers and the availability of a significant number of commercial models.
Two-dimensional models can be categorized into two-dimensional vertically averaged and two dimensional horizontally averaged models. The former scheme is used where depth-averaged velocity or other hydraulic parameters can sufficiently describe the variation of hydraulic conditions across a channel. The latter scheme is used where width-averaged hydraulic parameters can sufficiently describe the variation of hydraulic conditions in the vertical direction. Most two-dimensional sediment transport models are depth-averaged models; hence, described in this section. Two-dimensional, depth- averaged models result from vertically averaging the governing equations, known as Navier-Stoke equations after a few simplifying assumptions. Two-dimensional models require a geometry which is divided in a two dimensional grid. Most commonly used grids are rectangular or non orthogonal.
Conservation of momentum equation
u u u F 1 p u i i j i i u' u' i j (2.75) t x j x j x j x j Where i, j = cartisian directions (for x = 1, y = 2, z =3)
j = cartisian directions perpendicular to i
ui = Cartesian component of the velocity
= fluid density
Fi = component of the body forces per unit volume in the i-directoin
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ρu'i u' j = turbulence stresses
Conservation of mass equation
u i 0 (2.76) xi
2.5.2.1 GSTARS 4.0 The first version of GSTARS (General Stream Tube Model for Alluvial River Simulation) was developed by the U.S. Bureau of Reclamation (Molinas and Yang, 1986) to simulate the flow conditions in a semi-two-dimensional manner and the change of channel geometry in a semi three-dimensional manner. Significant efforts were made to improve the first version, and GSTARS 2.1 and GSTARS-3 were released by Yang and Simoes (2000, 2002). Current version released is GSTARS 4.0 developed by Yang and Jungkyu (2011).
GSTARS is a steady nonuniform flow, one-dimensional model which simulates certain aspects of two-dimensional flow by using the stream tube concept for hydraulic computations. GSTARS-4 consists of four major parts:-
The first part is the use of both the energy and the momentum equations for the backwater computations. This feature allows the program to compute the water surface profiles through combinations of subcritical and supercritical flows. In these computations, GSTARS-4 can handle irregular cross sections regardless of whether single channel or multiple channels separated by small islands or sand bars. The major update was made for hydraulic calculation. Previous GSTARS Models have the capability of steady or quasi-steady hydraulic computation, whereas, GSTARS 4.0 can simulate both steady and truly unsteady flow.
The second part is the use of the stream tube concept, which is used in the sediment routing computations. Hydraulic parameters and sediment routing are computed for each stream tube, thereby providing a transverse variation in the cross section in a semi-two dimensional manner. Although no flow can be transported across the boundary of a stream tube, transverse bed slope and secondary flows are phenomena accounted for in
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GSTARS-4 that contribute to the exchange of sediments between stream tubes. The position and width of each stream tube may change after each step of computation. The scour or deposition computed in each stream tube gives the variation of channel geometry in the vertical (or lateral) direction. The water surface profiles are computed first. The channel is then divided into a selected number of stream tubes with the following characteristics: (1) the total discharge carried by the channel is distributed equally among the stream tubes; (2) stream tubes are bounded by channel boundaries and by imaginary vertical walls; (3) the discharge along a stream tube is constant (i.e., there is no exchange of water through stream tube boundaries). Bed sorting and armoring in each stream tube follows the method proposed by Bennett and Nordin (1977), and the rate of sediment transport can be computed using any of the methods: DuBoys (1879) , Meyer-Peter and Muller's (1948) , Laursen (1958) , Modified Laursen method by Madden (1993), Toffaleti’s (1968) , Engelund and Hansen (1972), Ackers-White (1973), Revised Ackers and White (1990) , Ashida and Michiue’s (1972), Tsinghua University method (IRTCES, 1985), Krone's (1962) and Ariathurai and Krone's 1976 methods for cohesive sediment transport. GSTARS4 uses the same numerical scheme as that in GSTARS3 for sediment routing part with some minor revisions. The third part is the use of the theory of minimum energy dissipation rate (Yang, 1971, 1976; Yang and Song, 1979, 1986) in its simplified version of minimum total stream power to compute channel width and depth adjustments. The use of this theory allows the channel width to be treated as an unknown variable. Treating the channel width as an unknown variable is one of the most important capabilities of GSTARS-4. Whether a channel width or depth is adjusted at a given cross section and at a given time step depends on which condition results in less total stream power. For the use of theory of minimum energy dissipation rate, GSTARS-4 is the same as the previous GSTARS-3 Model. The fourth part is the inclusion of a channel bank side stability criteria based on the angle of repose of bank materials and sediment continuity. GSTARS-4 uses identical procedure of GSTARS 3 for the calculation of bank side stability.
GSTARS-4 is based on GSTARS-3 with the following modifications and improvements:
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• Unsteady flow simulation was added. • More options for non-equilibrium sediment transport were added. • Input option of percentage of wash load was expanded in case of high sediment concentration laden flows. • Spatial variation of bed material density can be applicable. • More options for gradation of incoming sediment from the upstream boundary. • Water and sediment exchanges between the main channel and tributaries were added. • Another output file for water and sediment discharges at the downstream boundary is added for other uses, such as downstream impact routing. • Expanded user’s manual
GSTARS-4 has the following limitations:- GSTARS-4 is a semi 2D and semi 3D Model for flow simulation and simulation of channel geometry change respectively. It should not be applied to situations where a truly 2-D or truly 3-D model is required. However, GSTARS-4 is adequate for solving many river engineering problems. GSTARS-4 is based on the stream tube concept. Secondary currents are empirically taken up. The phenomena of diffusion and super elevation are ignored. Many of the methods and concepts used in GSTARS-4 are simplified by approximations of real phenomena.
2.5.2.2 TABS TABS-2 is a collection of generalized computer programs integrated into a numerical modeling system for analyzing two-dimensional hydraulics, transport, and sedimentation problems in rivers and reservoirs (Thomas and McAnally, 1985). In the model there are three basic modules incorporated: RMA-2, STUDH and RMA-4, which are described below: . RMA-2 computes two-dimensional hydraulic flows; it is a finite element solution of the Reynolds form of the Navier-Stokes equations for turbulent flows. Friction is computed by Manning's equation and eddy viscosity coefficients are used to
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define turbulence characteristics. The model automatically identifies dry elements and corrects the mesh accordingly. . STUDH computes sediment transport; it solves the convection-diffusion equation with bed source terms, and is developed for either sand or cohesive sediments. Clay erosion is based on Partheniades equation (1962) and the deposition of clay uses Krone's equation (1962). Deposited material forms layers, and the STUDH allows upkeep upto 10 layers at each node for maintaining separate material types, deposit thickness, and deposit age. . RMA-4 computes water-quality parameters. Transport calculations with RMA-4 are made by the use of convection-diffusion equation. Upto seven conservative or decaying substances can be routed.
qe 1 (2.77) cr
Where qe is mass of sediment eroded per unit area of bed surface per unit of time 2 2 (Kg/m /s), τ is shear stress (N/m ), τcr is critical stress at which erosion commences (N/m2), and α is coefficient of erodibility.
qd Cv1 (2.78) d
Where Cv is volumetric sediment concentration, ω is fall velocity, and τd is critical stress for sediment deposition. A microcomputer version of TABS-2, with pre and post processing software for mesh generation and flow visualization, is available from vendors such as Boss International, 6612 Mineral Point Road, Madison, WI 53703 (Internet http://www.bossintl.com).
2.5.2.3 DIVAST (Depth- Integrated Velocities and Solute Transport) It is a two dimensional model developed Model by Roger Faulkner of Cardiff University for solute and sediment transport. Its main capabilities are:
. It computes water surface elevation and velocities in two dimensions.
. It calculates sediment deposition and re-erosion in the reservoir.
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. It predict change in elevation/storage curve as a result of sedimentation
Numerous 2-D numerical models are used in the world for sediment deposition and flushing, some are described here: Ruland and Rouvé (1992) used a two-dimensional model and a probabilistic approach to model the risk of erosion in reservoirs during drawdown. Based on a case study, they concluded that the model generally is feasible for describing such processes.Westrich and Al-Zoubi (1992) used both a one-dimensional and a two-dimensional model to determine the dimensions of a flushing channel in the Isar River, Germany. After dredging the channel, the flushing flows have eroded in the dredged areas. However, due to rapid water level lowering, during flushing, slope failures have occurred, reshaping the dredged channel. Shen et al. (1993) described a two- dimensional mobile-bed model to predict bed evolution in a reservoir and they concluded that the model shows the capability of simulating lateral variation of bed elevation. Jin (1995) used a two-dimensional model for reservoir erosion to improve navigation possibilities and Spork et al. (1995) described two-dimensional modeling of erosion, transport, and deposition of sediment in the Haus Ley reservoir, Germany, using the RISMO model.
Tu et al. (1995) used the quasi two-dimensional FLUVIAL-12 Model for a series of tests at Rock and Cresta Dams, USA. The tests were conducted for various flow and drawdown conditions to simulate hydraulic and sediment transport processes. Chang et al. (1996) did an evaluation of the feasibility and effectiveness of sediment-pass-through of these reservoirs and Chang and Fan (1996) presented tests and calibration of the FLUVIAL-12 Model for the reservoirs.
Al-Zoubi and Westrich (1996) used a two-dimensional model for simulating flushing in a reservoir on the Danube River, Germany, where use of a flushing channel decreased amounts deposited material significantly. Petitjean et al. (1996) described the SUBIEF Model, a two-dimensional model for reservoir sedimentation and flushing, and Jacobsen (1997) used the 2d/3d numerical Model SSIM applied to the Lake Roxburgh, New Zealand, to study erosion and deposition during flood drawdown.
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2.5.3 Three-Dimensional Numerical Models The flow phenomena in natural rivers are three dimensional, especially those at or near a meander bend, local expansion and contraction, or a hydraulic structure. Turbulence is an essentially three-dimensional phenomenon, and three-dimensional models are particularly useful for the simulation of turbulent heat and mass transport. These models are usually based on the Reynolds-averaged form of the Navier-Stokes equations, using additional equations of varied degree of complexity for the turbulence closure.
The Navier-Stokes equations represent the statement of Newton's second law for fluids (i.e., the conservation of momentum), and in the Cartesian coordinate system and for incompressible fluids, they can be written as:
u u u F 1 p u i i j i i u' u' i j (2.79) t x j C x j x j x j Where i, j is cartisian directions ( for x = 1, y = 2, z =3); j is cartisian directions perpendicular to i; ui is Cartesian component of the velocity ; is fluid density;
Fi is component of the body forces per unit volume in the i-directoin ; -ρu'i u' j is turbulence stresses. Whereas conservation of mass can be expressed by the continuity equation for incompressible fluids as:
u i 0 (2.80) xi The above terms are described in Figure 2.20
Figure 2.20 Sketch showing the coordinate system used and the definition of some of the variables, here u= u1 , v = u2 , w = u3
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The main disadvantage with the three dimensional approach is that, it necessitates a more complex computer code and data that can increase the cost.
2.5.3.1 SSIIM SSIIM is an abbreviation for Sediment Simulation In Intake with Multiple option. The program is made for use in river, environmental, hydraulic and sedimentation engineering. Originally the main aim of the creation of the program was to simulate sediment movements in rivers and canals. The SSIIM Model developed by Nils Olsen uses a finite volume method to solve the Navier Stokes equations with model in three dimensions on a general non-orthogonal grid. A control volume method is used for the discretization, together with power law scheme or the second order upwind scheme. The SIMPLE (Semi-Implicit Method for Pressure-Linked Equations) method is used for the pressure coupling. An implicit solver is used, producing velocity field in the geometry. The velocities are used when solving convection-diffusion equations for different sediment sizes. This gives trap efficiency and sediment deposition pattern.
The primary motivation for development of this model was the difficulty of simulating fine sediments in physical models. Particle animation is provided to aid flow visualization. Application of this model to the analysis of sediment accumulation at two hydropower reservoirs in Costa Rica has been reported by Olsen et al. (1994).
The main strength of SSIIM as compared to other CFD programs is its ability for modeling sediment transport with moveable bed in a complex geometry. This includes multiple sediment sizes, sorting, bed load and suspended load, bed forms and effects of sloping beds. The latest modules for wetting and drying in the unstructured grid enable geo-morphological modeling. The model runs under the OS/2 operating system, and is available at no cost from the developer, Nils Olsen at the Norwegian Institute of Technology. It may be located by conducting a search for SSIIM 13.ZIP or SSIIM using an Internet search tool (Morris and Fan, 2010).
Some of the limitations of SSIIM program are:
. The program neglect non-orthogonal diffusive terms.
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. The program neglects stress terms for elements that are not at the boundary.
. The grid lines in the vertical direction have to be exactly vertical.
. Internal walls cannot be used within two cells from a multi-block connection.
. The flow must be fully turbulent.
As other three-dimensional models, the SSIIM Model requires massive data for simulation and that much data is rarely available in local network of data collecting agencies. For hydropower projects situated in northern areas of Pakistan data collection is very difficult to meet the demand of the model.
2.5.3.2 FLUENT FLUENT is a three dimensional software package which is used for numerical simulation of fluids. It uses finite volume approach to solve 3D incompressible continuity and Reynolds-averaged Navier-Stokes equations. Different types of discretization schemes (QUICK, MUSCL, First Order upwind scheme, Second Order upwind scheme, Power Law etc.) are available in it. A number of turbulence models such as k - , RNG k - , k- , Reynolds stress model, Spalart-Allmaras model, shear stress transport k - model, large eddy simulation, detached eddy simulation models etc. are offered by this numerical code. This code gives a number of options for simulation of two phase flow including Lagrangian particle tracking technique, Discrete phase modeling, Eulerian-Eulerian two phase modeling technique etc. This code is widely used for research and design purposes. In civil engineering, it is used in open channel and pipe flows and for modeling the flow structures and sediment transport and deposition in meandering rivers. It has also been tested in past for simulation of different geomorphologic cases. It is used for all types of external and internal flow situations. Its validity is being enhanced with the passage of time.
The pressure velocity coupling can be done using SIMPLE, SIMPLEC or PISO algorithms. FLUENT provides the facility of importing the grid generated in TGrid, Gambit, PreBFC, ICEMCFD, GeoMesh, and FIDAP etc. In this simulation work the grid generator Gambit has been used. FLUENT provides a broad range of built-in boundary conditions such as flow values at inlet and outlet, pressure value at inlet, axis and
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CHAPTER 2 LITERATURE REVIEW symmetry boundary conditions, wall boundary condition, mass flow and velocity inlet boundary conditions, pressure outlet and pressure far-field boundary conditions, periodic boundary condition, fan boundary condition etc.
Following are some aspects of FLUENT: 1. It has an excellent built-in post processor. 2. It provides a good grid checking capability. 3. Different types of surfaces can be generated within an existing grid if required. 4. A range of physical properties of different materials are available in it. These include density, viscosity, radiation properties, standard state enthalpies, mass diffusion coefficients, thermal conductivity, kinetic theory parameters, molecular heat transfer coefficient etc. 5. It can handle multiphase flows. It offers more than one ways to tackle such situations. 6. Options are available for implicit, explicit, steady, unsteady, segregated, collocated grids etc. A number of researchers used FLUENT for open channel flow studies (Dargahi, 2004). These researchers utilized FLUENT for modeling the flow structures and sediment transport and deposition in meandering rivers. A number of researchers also attempted open channel flows/ meandering channels using their own codes such as Zhang and Shen, (2008) and Nguyen, et al. (2007).
2.6 SUMMARY
This chapter describes about sediment deposition in reservoir and sediment flushing from the reservoirs and their related theory. The topics discussed in the chapter are Reservoir sedimentation, Empirical Modeling of reservoir sedimentation, Sediments removal from reservoir by flushing, Process based Modeling of reservoir sedimentation and flushing sediments through reservoirs.
In reservoir sedimentation, mechanism of sedimentation process has been discussed in detail. Ultimate consequences of reservoir sedimentation process have been elaborated. Lost reservoir storage can be restored by various methods globally. These methods are 90
CHAPTER 2 LITERATURE REVIEW watershed management, conventional dredging, dry excavation, hydrosuction, sediment routing/sluicing, sediment bypassing, density current venting and sediment flushing through reservoir. All these methods have been discussed elaborately.
Empirical Modeling of reservoir sedimentation has been described in this chapter. In the empirical modeling first of all suspended sediment load entering in a certain reservoir is calculated. Then bed load into the reservoir is estimated by various bed load functions like Meyer Peter and Muller (1948) equation, Parker (1982) formula, Brown-Einstein equation, DuBoys (1879) formula, Sheilds (1936) formula and Modified Einstein Procedure for unmeasured sediment load. After estimating bed load into the reservoir, total sediment load into the reservoir may be computed by summing up the estimated suspended load and bed load into the reservoir.
When sediment load enters into the reservoir most of the load is settled in the reservoir and some portion passes through the reservoir downstream alongwith water. This settled load is called trapped sediment load into the reservoir. The trapped sediment load can be calculated by multiplying the sediment into the reservoir and trap efficiency of the reservoir. Trap efficiency can be calculated by Brune’s curve or Churchill’s curve. Brune’s curve is used to calculate the trap efficiency of large sized reservoir, whereas, Churchill’s curve is used to compute the trap efficiency of small sized reservoir. Empirically sediment delta can be modeled. In delta modeling topset slope, foreset slope and bottomset slope is determined for sediment delta. Pivot point of delta is then located.
In reservoir sediment flushing, various reservoirs of the world, where sediment flushing has been implemented are described in detail. There are about 50 reservoirs which are reported to be flushed. Out of these 50 flushed reservoirs flushing data of 25 reservoirs is available, which is being discussed in the chapter. Out of these flushed reservoirs six are the successfully flushed reservoir, while the remaining are partially flushed reservoirs. Flushing may be implemented as complete drawdown flushing or partial drawdown flushing, but the complete drawdown flushing is more effective. There are two main drawbacks in flushing. One is that the reservoir has to be emptied and second is the extreme sediment concentration downstream of the reservoir may create unacceptable
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CHAPTER 2 LITERATURE REVIEW environmental conditions. Flushing process has three main phases, drawdown phase, erosion/ flushing phase, and refilling phase. Erosion process has many sub-processes like slumping at dam site, slope failure, retrogressive erosion and progressive erosion.
Flushing efficiency of a reservoir is defined as the volume of eroded sediment deposits to the water volume used during flushing over any specified time interval. Flushing efficiency has been described by various authors given in this chapter. Flushing efficiency with emptying is more than the flushing efficiency during partial drawdown flushing. Factors affecting flushing efficiency have been also discussed. The main factors are depth of water in reservoir during flushing, flushing discharge, size and configuration of flushing outlet, length, and width of reservoir. Flushing indicators to assess sediment flushing feasibility from the reservoir are, Sediment Balance Ratio (SBR), Long Term
Capacity Ratio (LTCR), Drawdown Ratio (DDR), SBR During full drawdown (SBRd), Flushing Width Ratio (FWR) and Top Width Ratio (TWR). These indicators are described in detail in the relevant section.
Numerical modeling may be performed to simulate sediment deposition processes and sediments flushing operations. Numerical Models are of three types: 1-D Models, 2-D Models, and 3-D Models. Among 1-D Models, mostly used are HEC-6, HEC-RAS 4.1.0, SHARC, RESSASS, FLUVIAL, and Tsinghua University Model. While in 2-D Models, the mostly used Models are GSTARS, TABS, and DIVAST. In 3-D Models, the commonly used Models are SSIIM and FLUENT.
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CHAPTER 3
METHODOLOGY
3.1 INTRODUCTION
This chapter briefly describes the methodology adopted to achieve the research objectives. It discusses the data collection for modeling of successfully flushed and partially flushed foreign reservoirs. Selected successfully flushed reservoirs are Baira of India, Gebidem of Switzerland and Gmund of Austria. Among the six flushing indictors, the most important flushing indicator, LTCR had been selected. Development of equations for two main flushing indicators, SBR, and LTCR had been described. Modeling of three foreign flushed reservoirs, Baira, Gebidem, and Gmund had been described, using three 1-D Numerical Models SHARC, HEC-RAS 4.1.0, and Tsinghua University Equation. Among the sixty small reservoirs of Punjab Small Dams Organization, twenty were selected to assess their feasibility for sediment flushing by computing their LTCR values. Jabbi Reservoir in District Attcok was selected among the twenty analyzed small reservoirs for modeling sediment deposition and proposed sediment flushing of deposited sediments using two 1-D numerical Models HEC-RAS 4.1.0, and Tsinghua University Equation. Finally, proposed flushing strategies were described for Jabbi Reservoir. Overall research methodology is explained in the Figure 3.1.
3.2 DATA COLLECTION
Data for Baira Reservoir was retrieved from White et al. (2000), Atkinson (1996b) and Jaggi & Kashyap (1984); for Gebidem Reservoir, from White et al. (2000), Morris and Fan (2010), Atkinson (1996b), Dawans et al. (1982) and IRTCES (1985), whereas, for Gmund Reservoir from White et al. (2000), Atkinson (1996b), and Rienossl and Schnelle (1982). For all the three discussed reservoirs, geometric data (reservoir length, bottom width, side slope, sill height of outlet from river bed, normal operating level, upstream and downstream bed elevations), hydraulic data (annual water inflow, average daily discharge, flushing discharge, flushing duration), sediment data (annual sediment inflow,
93
CHAPTER 3 and the respective sediment concentration, sediment type) were taken from Atkinson(1996b). Some other parameters were also explored from White et al. (2000). The data for Jabbi Reservoir had been taken from Small Dams Organization of Punjab Irrigation Department.
94 CHPTER 3 METHODOLOGY
Methodology
Modeling Modeling Flushing Modeling Jabbi Data Exploring Equations by SHARC Modeling Assessing Jabbi Reservoir Flushing Collection imp developme & HEC- by LTCR Of Reservoir by strategies flushing nt RAS Tsinghua Small by HEC- Tsinghua for Jabbi indicators Equation Reservoir RAS Equation Reservoir
Data input Input Data Reservoir Input Data Reservoir Geometric Modeling Modeling Data
Flow Data Regression Geometric Modeling Geometric Modeling Analysis Data sediment Data sediment deposition deposition
Sediment Eqns Flow Data Modeling Flow Data Modeling Data developme sediment sediment nt flushing flushing
Flushing Equations Sediment Determine Sediment Determine Data testing Data flushing Data flushing duration duration
Flushing Flushing Data Data
Figure 3.1 Flow diagram representing Methodology adopted to achieve the objectives
95 CHPTER 3 METHODOLOGY
3.3 EXPLORING MOST IMPORTANT FLUSHING INDICATOR AND ITS CRITICAL VALUE
Before planning to flush sediments from a reservoir, there must be some indicators to assess flushing feasibility. Atkinson (1996b) describes the six indicators to evaluate feasibility of sediment flushing from reservoir as discussed earlier.
Fourteen flushed reservoirs of the world were selected to find the most important flushing indicator. The selected fourteen reservoirs are: Baira and Ichari of India, Gebidem and Palagnedra of Switzerland, Gmund of Austria, Hengshan, Gaunting, Heisonglin, Sanmenxia and Shuicaozi of China, Santo-Domingo of Venezuela, Guernsey of USA, Ouchi-Kurgan of Former USSR, and Sefid-Rud of Iran. The values of the six flushing indicators, SBR, DDR, SBRd, FWR and TWR, and LTCR were computed, using the data of these reservoirs. The computed values of flushing indicators were compared with their critical values. The analysis results show that all the flushed reservoirs and some partially flushed reservoirs satisfy the critical values of flushing indicators, but none of the partially flushed reservoirs satisfy the critical value of LTCR, hence it was concluded that LTCR might be the most important flushing indicator to decide sediment flushing feasibility. Moreover based upon values of LTCR of successful reservoirs critical value of LTCR might be taken as 0.77, instead of 1.
3.4 DEVELOPMENT OF EQUATIONS FOR SBR AND LTCR OF RESERVOIRS
Successfully flushed reservoirs satisfy the critical value of LTCR indicator, whereas partially flushed reservoirs do not fulfill this criterion at all. Considering the important parameters, equations for LTCR and SBR were developed.
The main parameters affecting the sediment flushing from reservoir are flushing
discharge, Qf, flushing duration, Tf, Reservoir length, L, sediment size, d50, longitudinal slope of reservoir during flushing, S, bed width, W, shape of reservoir, surface area of the reservoir, A, dimensions of flushing outlet, capacity inflow ratio of the reservoir, Co/Vin, and sill height of flushing outlet.
96 CHPTER 3 METHODOLOGY
The main influencing parameters selected among the various parameter involved are: Qf,
Tf , L, S, Co/Vin, and area of the reservoir, A.
To compute the values of flushing indicators, SBR, and LTCR, a number equations are involved (Atkinson, 1996b), which is a laborious work, hence, simple empirical equations were developed for the flushing indicators, SBR, and LTCR, using the six selected flushing parameters of six successfully flushed Reservoirs: Baira of India, Gebidem of Switzerland, Gmund of Austria, Hengshan of China, Palagnedra of Switzerland, Santo-Domingo of Venezuela. Data input to the Regression Model is shown in Table 3.1
Table 3.1 Data Input to Develop Equation for Flushing Indicators L V C Q T S A Sr No. Reservoir in o f f m Mm3 Mm3 m3/s hrs m/m m2 1 Baira 4100 1900 9.6 150 31 0.0124 2341 2 Gibidem 1400 420 9 15 96 0.0807 6428 3 Gmund 930 200 0.93 25 168 0.0323 1000 4 Hengshan 1000 15.8 13.3 2 888 0.0650 13300 5 Palagandra 2600 304 5.5 1.25 2160 0.0212 2115 6 Santo Domingo 1000 450 3 5 72 0.0470 3000
The equations of dependent variables SBR and LTCR as a function of independent variables (parameters) may be expressed as:
SBR K La C /V b Q c T d S e A f (3.1) o in f f
LTCR K La C /V b Q c T d S e A f (3.2) o in f f
Where K are the coefficients of equations for SBR and LTCR respectively, whereas a, b, c, d, e and f are exponents for equation (3.1), and (3.2).
Multiple Non- Linear regression analysis for the development of equations
By taking natural log on both sides of the above equations
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ln(SBR) ln K a ln(L) b ln (Co /Vin ) c ln(Q f ) d ln(Tf ) e ln(S) f ln(A) (3.3) ln(LTCR) ln K a ln(L) b ln(C /V ) c ln(Q ) d ln(T ) e ln(S) f ln(A) O in f f (3.4)
The above two equations can be written in the form:
X 1 K aX 2 bX 3 cX 4 dX 5 eX 6 f X 7 (3.5)
The above equation is the multiple regression equation and for its solution the following seven normal equations are used:
X 1 NK aX 2 bX 3 cX 4 dX 5 eX 6 f X 7 (3.6)
2 X 1 X 2 KX 2 aX 2 bX 2 X 3 cX 2X 4 dX 2 X 5 eX 2 X 6 f X 2 X 7 (3.7)
2 X 1 X 3 KX 3 aX 2 X 3 bX 3 cX 3X 4 dX 3 X 5 eX 3 X 6 f X 3 X 7 (3.8)
2 X 1 X 4 KX 4 aX 2 X 4 bX 3 X 4 cX 4 dX 4 X 5 eX 4 X 6 f X 4 X 7 (3.9)
2 X 1 X 5 KX 5 aX 2 X 5 bX 3 X 5 cX 4 X 5 dX 5 eX 5 X 6 f X 5 X 7 (3.10)
2 X 1 X 6 KX 6 a X 2 X 6 bX 3 X 6 cX 4 X 6 dX 5 X 6 eX 6 f X 6 X 7 (3.11)
2 X 1 X 7 KX 7 aX 2 X 7 bX 3 X 7 cX 4 X 7 dX 5 X 7 eX 6 X 7 f X 7 (3.12)
Where:
X 1 ln(SBR) ln(LTCR)
X 2 ln(L) X 3 ln(CO /Vin )
X 4 ln(Q f ) X 5 ln(T f )
X 6 E ln(S) X 7 ln(A)
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After carrying out the Multiple Non-linear Regression Analysis, equations were developed for SBR and LTCR, and the values of both indicators, for the six reservoirs, were computed using the developed equations. These equations were then tested by comparing the obtained value with the values obtained for the same by Atkinson (1996b) method, and then equations were validated by applying them to 5 Pakistani small reservoirs. The values of SBR and LTCR for these reservoirs were closer to the values obtained for the same by Atkinson (1996b) procedure and these are discussed in Chapter- 4 Results and Discussions.
3.5 MODELING SEDIMENT DEPOSITION AND FLUSHING OPERATIONS THROUGH RESERVOIRS USING SHARC
1-D numerical Model SHARC was used to model the observed sediment deposition and flushing performed to evacuate the sediments from the reservoir. The modeling was done for the three foreign reservoirs, Baira-India, Gebidem-Switzerland, and Gmund-Austria. First of all data input to the Model was given and observed longitudinal profiles of sediment deposition were modeled and the simulated deposited sediments volumes were compared with the observed ones.
To model the sediment flushing through the reservoirs, for the simulated sediment deposition, data input was given to the Sluicing Model, and then Model was run for the three flushed reservoirs and the simulated flushing durations were determined for each reservoirs and compared with the observed flushing durations for each reservoir.
3.5.1 Data input to Model Three types of data were given as input to Deposition Model: geometric data, flow and concentration data and sediment properties. (i) Geometric data: It includes reservoir length, initial bed width, bed width at the upstream end of the reservoir, upstream and downstream bed elevations of the reservoir, side slope of reservoir, sill height of the outlet from the river bed level at dam site, and the normal operating level.
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(ii) Flow and concentration data: It covers annual water inflow, annual sediment inflow, average daily discharge and sediment concentration, period of the sediment deposition, flushing discharge, and flushing duration. (iii) Sediment properties: It is the data for specific gravities of sand and fine sediments, settled densities for sand and fine sediments, gradation curves for bed material and suspended sediments.
3.5.2 Modeling Sediment Deposition and Sediment Flushing in Reservoirs
3.5.2.1 Baira Reservoir of India
Modeling Sediment Deposition in Baira Reservoir
Data inputs to the model were: (i) Geometric data: Reservoir length 4100 m, initial bed width 25 m, upstream bed elevation 1122 m, downstream bed elevation 1072 m, side slope of the reservoir 2.0, entry ramp slope 0 m/m and width of the channel at upstream 60 m.
(ii) Hydraulic flow and concentration data: Average daily discharge 100 cumecs, downstream water level 1123 m, water temperature 200 C, Manning roughness coefficient 0.04, total sediment concentration entering the dam 150 PPM (sand concentration 18 PPM, fine sediments concentration 132 PPM), time duration for model run 13,140 hours.
(iii) Sediment properties: specific gravities for both sand and fine sediments 2.65, settled densities for sand and fine sediments were taken as 1.55 and 1.12 Tons/m3, respectively. Data input given to the Model is presented in Figure 3.2.
Fall velocities were computed for diameters corresponding to 0, 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 % finer material in the suspended load. The fall velocities are the function of sediment diameters. The maximum fall velocity accepted by the DOSSBAS
100 CHPTER 3 METHODOLOGY was 10 mm/s. Fall velocities given as input to the Deposition Model are shown in Figure 3.3.
Figure 3.2 Input data given to the Deposition Model of SHARC
Figure 3.3 Fall velocities of different seizes of suspended sediments load
Bed load sediment sizes obtained from bed material gradation curve are given to Model as shown in Figure 3.4.
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Figure 3.4 Bed material sizes entering into Baira Reservoir
The minimum size of sediment is 0.04 mm, whereas the maximum size of the sediment is 32 mm. Suspended sediment load sizes, obtained from suspended sediment gradation curve was given as input to the Model, shown in Figure 3.5.
The Deposition Model can model both suspended sediment and bed load within the range of 0.04 mm to 250 mm and settling velocities having the range of 0.0001 mm/s to 10 mm/s.
Figure 3.5 Suspended sediment sizes entering into Baira Reservoir
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Modeling Sediment Flushing in Baira Reservoir For the observed accumulated sediments of 0.45 Mm3, 0.383 Mm3 were flushed through the reservoir. To simulate the observed flushed amount of 0.383 Mm3, input parameters to the Sluice Model were taken as: sluicing discharge 150 cumecs, downstream water level during sluicing 1072.11 m, and water temperature 200 C. Sluice Model was run at a downstream water level 1072.11 m, with time step length of 2 hours. Inputs to Sluice Model are shown in Figure 3.6.
Figure 3.6 Input data given to the Sluicing Model for Baira Reservoir
3.5.2.2 Gebidem Reservoir of Switzerland Modeling Sediment Deposition in Gebidem Reservoir
Data inputs to the Model were: (i) Geometric data: Reservoir length 1400 m, initial bed width 6 m, upstream bed elevation 1435 m, downstream bed elevation 1323 m, side slope of the reservoir 1.3, entry ramp slope 0 m/m and width of the channel at upstream 10 m. (ii) Hydraulic flow and concentration data: Average daily discharge 13.5 cumecs, downstream water level 1436 m, water temperature 200 C, Manning roughness coefficient 0.024, total sediment concentration entering the dam 900 PPM
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(sand concentration 108 PPM, fine sediments concentration 792 PPM), time duration for Model run 8,760 hours. (iii) Sediment properties: specific gravities for both sand and fine sediments 2.65, settled densities for sand and fine sediments were taken as 1.55 and 1.12 Tons/m3, respectively. Input data to Model is shown in Figure 3.7.
Fall velocities were computed for diameters corresponding to 0, 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 % finer material in the suspended load. Input data given to the Deposition Model is shown in Figure 3.8.
Figure 3.7 Input data given to the Deposition Model for Gebidem Reservoir
Figure 3.8 Fall velocities of different sizes suspended sediment load for Gebidem Reservoir
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Figure 3.9 Bed material sizes entering into Gebidem Reservoir
Figure 3.10 Suspended material sizes entering into Gebidem Reservoir
Bed load and suspended sediment load sizes were also given to the Model obtained from the bed material gradation curve and suspended sediment gradation curve as shown in Figures 3.9 and 3.10 respectively.
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Modeling Sediment Flushing in Gebidem Reservoir
For the observed accumulated sediments of 0.27 Mm3, the same were flushed through the reservoir. To simulate the observed flushed amount of 0.27 Mm3, input parameters given to the Sluice Model were: sluicing discharge, 15 cumecs, downstream water level during sluicing, 1323.2 m, and water temperature, 200 C. Sluice Model was run at a downstream water level of 1323.2 m, during sluicing, and time step length of 1 hour, as shown in Figure 3.11.
Figure 3.11 Input data given to the Sluicing Model for Gebidem Reservoir
3.5.2.3 Gmund Reservoir of Austria Modeling Sediment Deposition in Gmund Reservoir
Data inputs to the Model were: (iv) Geometric data: Reservoir length 930 m, initial bed width 6 m, upstream bed elevation 1189 m, downstream bed elevation 1160 m, side slope of the reservoir 3 m/m, entry ramp slope 0 m/m, and width of the channel at upstream 10 m. (iii) Hydraulic flow and concentration data: Average daily discharge, 6.34 cumecs, downstream water level, 1190 m, water temperature, 200 C, Manning roughness coefficient, 0.024, total sediment concentration entering the dam, 550 PPM (sand concentration 66 PPM, fine sediments concentration 484 PPM), time duration for Model run, 8,760 hours.
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(iv) Sediment properties: specific gravities for both sand and fine sediments, 2.65, settled densities for sand and fine sediments were taken as 1.55, and 1.12 Tons/m3 respectively. Input data given to Model is shown in Figure 3.12.
Figure 3.12 Input data given to the Deposition Model for Gmund Reservoir
Fall velocities were computed for diameters corresponding to 0, 10, 20, 30, 40, 50, 60, 70, 80, 90 and 100 % finer material in the suspended load. Fall velocities input given to the Deposition Model is shown in Figure 3.13.
Figure 3.13 Fall velocities of different sizes suspended sediments load for Gmund Reservoir
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Bed load and suspended sediment load sizes were also given to the Model, obtained from the bed material gradation curve and suspended sediment gradation curve, shown in Figures 3.14 and 3.15 respectively.
For bed load sediment sizes range from 0.04 mm to 32 mm, whereas, suspended sediments sizes range from 0.04 mm to 2 mm.
Figure 3.14 Bed material sizes entering into Gmund Reservoir
Figure 3.15 Suspended material sizes entering into Gmund Reservoir
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Modeling Sediment Flushing in Gmund Reservoir For the observed accumulated sediments of 0.076 Mm3, 0.065 were flushed through the reservoir. To simulate the observed flushed amount of 0.065 Mm3, input parameters given to the Sluice Model are: sluicing discharge, 25 cumecs, downstream water level during sluicing, 1160.3 m, and water temperature, 200 C. By running the Sluice Model at downstream water level, 1160.3m, during sluicing, with time step length, 7 hours. Input parameters given to Sluice Model are shown in Figure 3.16.
Figure 3.16 Input data given to the Sluicing Model for Gmund Reservoir
3.6 MODELING SEDIMENT DEPOSITION AND FLUSHING OPERATION THROUGH RESERVOIRS USING HEC-RAS 4.1.0
3.6.1. Baira Reservoir of India The data required to perform various computations with HEC-RAS 4.1.0 are divided into the following categories: Geometric data, Quasi-unsteady flow data, Sediment data, Bed gradation curve, and inline structure data.
The setting up of the Model was carried out by considering 8.8 km river length with, 35 cross-sections as shown in Figure 3.17. The dam site (inline structure) is situated at section number 0.9, whereas upstream most cross section is 24, and downstream most cross section is 0. In river system schematic, river Baira was drawn in geometric data
109 CHPTER 3 METHODOLOGY editor option of HEC-RAS 4.1.0. In the geometric data editor the name for river reach was specified. Geometric Data The basic geometric data consist of establishing the connectivity of the river system (River system schematic); cross section data, reach lengths, and stream junction information. Thirty five cross sections were given as input to the Model and inline structure was also created in the geometric data in between section numbers 0.95 and 0.8. Boundary geometry for the analysis of flow in river was specified in terms of ground surface profiles (cross sections) and the measured distance between these (reach lengths at each cross-section). The cross sectional data of river Baira was entered in HEC-RAS 4.1.0 by the cross sectional data editor. Cross sections from both the ends of inline structure (dam structure), upstream and downstream, were plotted. The data entered into the cross section data editor comprises of River station information, Pairs of station and elevation, Demarcation of main channel bank station, Downstream reach lengths (i.e., the distance up to next downstream cross section.) for main channel, left over bank and right over bank, and Manning’s roughness coefficient (both vertical and horizontal variation of n- values were considered). The detailed information about the locations of cross sections is given in Table 3.2.
Figure 3.17 Schematic diagram showing the cross section locations used during delta modeling for Baira Reservoir
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Manning value of n was taken as 0.08 for main channel and 0.07 for left over bank and right over bank. Contraction and expansion coefficients used are 0.1 and 0.3 respectively. Dam coordinates were (2, 1123) and (137, 1123), weir coefficient, 1.4, and weir was taken as broad crested shape.
Table 3.2 Thirty Five Cross Sections used for Baira Reservoir during Delta Modeling
Sr. No. River Station Distance to d/s (m) Remarks 1 24 200 u/s of Reservoir Area 2 23 200 u/s of Reservoir Area 3 22 200 u/s of Reservoir Area 4 21 200 u/s of Reservoir Area 5 20 200 Reservoir Area 6 19 200 do 7 18 200 do 8 17 200 do 9 16 200 do 10 15 200 do 11 14 200 do 12 13 200 do 13 12 200 do 14 11 200 do 15 10 200 do 16 9 200 do 17 8 200 do 18 7 200 do 19 6 200 do 20 5 200 do 21 4 200 do 22 3 200 do 23 2 200 do 24 1 300 do 25 0.95 5 do 26 0.9 Inline structure 27 0.8 195 D/S of dam site 28 0.7 500 do 29 0.6 500 do 30 0.5 500 do 31 0.4 500 do 32 0.3 500 do 33 0.2 500 do 34 0.1 500 do 35 0 0 do
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Quasi-Unsteady Flow Data The flow data, which was synthesized from the previous historical data, was entered in the Quasi-unsteady flow data editor which comprised of following two boundary conditions. a) Upstream boundary condition b) Downstream boundary condition a) Upstream Boundary Condition
Mean monthly inflow hydrograph for 8 years (1982-1989) was assigned to the Model as u/s boundary condition as shown in Figure 3.18
Figure 3.18 Flow Hydrographs at Baira dam site used as upstream boundary condition b) Downstream Boundary Condition
Normal depth was taken as downstream boundary condition with friction slope equal to the average river bed slope in the reservoir area at the downstream end, i.e., 0.0124
Variation of temperature with change in month was given as input to Model. As several aspects of sediment transport mechanics, particularly fall velocity, incipient motion and sediment transport are sensitive to water temperature, and hence, HEC-RAS 4.1.0
112 CHPTER 3 METHODOLOGY requires temperature information. Only one temperature per time step can be specified for the entire Model.
Sediment Data Once the geometric data was entered, the sediment data was entered to develop a delta profile of sediment transport. The sediment data was entered in sediment data editor which comprised of following conditions. (a) Initial Conditions and Transport Parameters
(b) Boundary Condition
(a) initial conditions and transport parameters: The initial condition and transport parameters specified for HEC-RAS 4.1.0 for a reservoir were as following at each cross section. i Transport Function: A transport function can be selected from the drop down box near the top of the form. Transport function selected was Tofalleti, as the function gives relatively suitable results. ii Sorting Method The sorting method was used to compute active layer thickness and vertical bed layer tracking assumption. The Exner 5 method was used. It is a three layer active bed model that includes the capability of forming a coarse surface layer that will limit erosion of deeper material thereby simulating bed armoring. iii Fall Velocity Approach Several methods are available for computing fall velocity, but Toffaleti was used for delta modeling of the Baira reservoir. It was also selected after carrying out the sensitivity analysis of various fall velocity formulae available in the software. iv Maximum Depth In the HEC-RAS 4.1.0 sediment framework, a sediment control volume is associated with each cross section. The control volume starts midway from the next cross section upstream and ends midway to the next cross section downstream. The maximum erodible depth used for Model was 10 m. (b) Sediment boundary condition The equilibrium load was used as u/s sediment boundary condition for delta modeling.
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To investigate the flushing performance of the reservoir, flushing scenario was modeled using HEC-RAS 4.1.0 Model. For modeling, the same 35 basic locations of cross sections were used as the geometric data, except those which were modified as obtained after one year delta modeling.
For flushing modeling, similarly a quasi unsteady file was prepared. As boundary condition, daily flow of 150 m3/s was taken as the constant flushing discharge for the entire flushing duration. Sediment transport function, sorting method, and fall velocity method used were Engelund-Hansen, Exner 5 and Ruby respectively. Flushing durations required to flush the deposited sediments in 1.5 years was determined, which came out as 34 days. The temperature of the water was assigned for each day as it affects the sediment transport processes. The normal depth was given by assigning a value of friction slope as 0.0124. Sediment transport function, sorting method, and fall velocity method are Engelund, Exner 5, and Ruby respectively.
For the sediment boundary condition, sediment rating curve derived for the dam site based on long past data record was used.
3.6.2 Gebidem Reservoir of Switzerland The setting up of the Model was carried out by considering 3.8 km river length with 25 cross-sections as shown in Figure 3.19. The dam site is situated at cross section number 0.9, whereas upstream most cross section is number 18 and downstream most cross section is 0, In river system schematic, river Gebidem was drawn in geometric data editor option of HEC-RAS 4.1.0. In the geometric data editor the name for river reach was specified.
Data inputs to the Model were Geometric Data, Quasi-Unsteady Flow Data and Sediment Data.
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Table 3.3 Twenty Five Cross Sections Used For Gebidem Reservoir during Delta Modeling
Sr. No. River Station Distance to d/s (m) Remarks 1 18 100 u/s of Reservoir Area 2 17 100 u/s of Reservoir Area 3 16 100 u/s of Reservoir Area 4 15 100 u/s of Reservoir Area 5 14 100 Reservoir Area 6 13 100 do 7 12 100 do 8 11 100 do 9 10 100 do 10 9 100 do 11 8 100 do 12 7 100 do 13 6 100 do 14 5 100 do 15 4 100 do 16 3 100 do 17 2 100 do 18 1 95 do 19 0.95 5 do 20 0.9 Inline structure 21 0.8 500 D/S of dam site 22 0.6 500 do 23 0.4 500 do 24 0.2 500 do 25 0 0 do
Geometric Data: 25 cross sections were assigned as input data to the Model. Description of the cross sections is given in Table 3.3. The schematic diagram showing the locations of cross sections used for delta modeling is shown in the following Figure 3.19. Manning value of n was taken as 0.08 for main channel and 0.07 for left over bank and right over bank. Contraction and expansion coefficients used were 0.1 and 0.3 respectively. Dam coordinates are (2, 1436) and (240, 1436) Weir coefficient was taken as 1.4, and weir supposed to be broad crested shape.
Quasi-Unsteady Flow Data
Mean monthly inflow hydrograph for 8 years (1990-1997) was assigned to the Model as u/s boundary condition as shown in Figure 3.20
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Figure 3.19 Schematic diagram showing the cross section locations used during delta modeling for Gebidem Reservoir
Figure 3.20 Flow Hydrographs at Gebidem dam site used as upstream boundary condition
Normal depth of 0.0807 was taken as downstream boundary condition with friction slope equal to the average river bed slope in the reservoir area at the downstream end. Variation of temperatures with change in month was given as input to Model. As several aspects of sediment transport mechanics, particularly fall velocity, incipient motion and sediment transport are sensitive to water temperature, and hence, HEC-RAS 4.1.0 requires temperature information. Only one temperature per time step can be specified for the entire Model.
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Sediment Data Once the geometric data was entered, the sediment data was entered to develop a delta profile of sediment transport. The sediment data was entered in sediment data editor which comprised of following conditions. (a) Initial Conditions and Transport Parameters.
(b) Boundary Condition
(a) Initial Conditions and Transport Function: The initial condition and transport parameters specified for HEC-RAS 4.1.0 for a reservoir are as following at each cross section. i Transport Function: . The transport function selected was Tofalleti, which gave reasonable results. ii Sorting Method Sorting method is used by Model to compute active layer thickness and vertical bed layer tracking assumption. The Exner 5 method was used Sorting Method for this reservoir. iii Fall Velocity Approach Several methods are available for computing fall velocity, but Toffaleti was used for delta modeling of the reservoir. iv Maximum Depth The maximum erodible depth used for Model was 10 m for this reservoir.
(b) Sediment Boundary Condition The equilibrium load was used as u/s sediment boundary condition for delta modeling. By giving as input data to Model, the accumulated sediment deposition of 0.27 Mm3 was simulated. Deposited volume worked out by the Model came out to be 0.27 Mm3, same as the observed one; hence the sediment deposition was simulated.
To investigate the flushing performance of the reservoir, flushing scenario was modeled using HEC-RAS 4.1.0 Model. For modeling, the same 25 basic locations of cross sections were used as the geometric data, with modified coordinates of the cross sections in the deposited area of the reservoir during delta modeling.
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For flushing modeling, similarly a quasi unsteady file was prepared. As boundary condition, daily flow of 15 m3/s was taken as the constant flushing discharge for the entire flushing operation. Flushing durations required to flush the deposited sediments in one year were determined, which came out as 102 days. The temperature of the water was assigned for each day as it affects the sediment transport processes. The normal depth was given by assigning a value of friction slope as 0.0807. Sediment transport function, sorting method, and fall velocity method were Laursen (Copeland), Exner 5 and Ruby respectively.
For the sediment boundary condition, sediment rating curve derived for the dam site based on long past data record was used. 3.6.3 Gmund Reservoir of Austria The setting up of the Model was carried out by considering 2.23 km river length with 29 cross-sections as shown in Figure 3.21. The dam site is situated at cross section number 0.9, whereas upstream most cross section is number 18 and downstream most cross section is 0, In river system schematic, river Gmund was drawn in geometric data editor option of HEC-RAS 4.1.0. In the geometric data editor the name for river reach was specified. Data inputs to the Model were Geometric Data, Quasi-Unsteady Flow Data and Sediment Data.
Geometric Data 29 cross sections were given as input to the Model. Description of the cross sections is given in Table 3.4.
Manning value of n was taken as 0.08 for main channel and 0.07 for left over bank and right over bank. Contraction and expansion coefficients used were 0.1 and 0.3 respectively. Dam coordinates were (2, 1190) and (146.1, 1190). Weir coefficient was taken as 1.4 and weir was considered to be broad crested shape.
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Table 3.4 Twenty Nine Cross Sections used for Gmund Reservoir during Delta Modeling
Sr. No. River Station Distance to d/s (m) Remarks 1 18 100 u/s of Reservoir Area 2 17 100 u/s of Reservoir Area 3 16 100 u/s of Reservoir Area 4 15 100 u/s of Reservoir Area 5 14 100 u/s of Reservoir Area 6 13 80 Reservoir Area 7 12 60 do 8 11 90 do 9 10 50 do 10 9 100 do 11 8 90 do 12 7 100 do 13 6 90 do 14 5 90 do 15 4 50 do 16 3 50 do 17 2 50 do 18 1 30 do 19 0.95 5 do 20 0.9 Inline structure 21 0.8 95 D/S dam site 22 0.7 100 do 23 0.6 100 do 24 0.5 100 do 25 0.4 100 do 26 0.3 100 do 27 0.2 100 do 28 0.1 100 do 29 0 0 do
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Figure 3.21 Schematic diagram for cross section locations during delta modeling for Gmund Reservoir
Quasi-Unsteady Flow Data Mean monthly inflow hydrograph for 8 years (1960-1967) was assigned to the Model as u/s boundary condition as shown in Figure 3.22
Figure 3.22 Flow Hydrographs at Gmund dam site used as upstream boundary condition
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Normal depth was taken as downstream boundary condition with friction slope equal to the average river bed slope in the reservoir area at the downstream end, i.e., 0.0323
Variation of temperatures with change in month is given as input to Model. As several aspects of sediment transport mechanics, particularly fall velocity, incipient motion and sediment transport are sensitive to water temperature, and hence, HEC-RAS 4.1.0 requires temperature information. Only one temperature per time step can be specified for the entire Model.
Sediment Data Once the geometric data was entered, the sediment data was entered to develop a delta profile of sediment transport. The sediment data was entered in sediment data editor which comprised of following conditions. (a) Initial Conditions and Transport Parameters.
(b) Boundary Condition
(a) Initial Conditions and Transport Parameters: The initial condition and transport parameters specified for HEC-RAS 4.1.0 for a reservoir are as following at each cross section. i Transport Function: A sediment transport function can be selected from the drop down box near the top of the form. The transport function selected was Tofalleti. This function gave relatively suitable results. ii Sorting Method Sorting method was used by Model to compute active layer thickness and vertical bed layer tracking assumption. The Exner 5 method was used in the Model. iii Fall Velocity Approach Several methods are available for computing fall velocity, but Toffaleti was used for delta modeling of the reservoir. iv Maximum Depth In the HEC-RAS 4.1.0 sediment framework, a sediment control volume is associated with each cross section. The control volume starts midway from the next cross section upstream and ends midway to the next cross section downstream. The maximum erodible depth used
121 CHPTER 3 METHODOLOGY for model was 10 m.
(b) Sediment Boundary Condition The equilibrium load is used as u/s sediment boundary condition for delta modeling. By giving as input data to Model the annual sediment deposition of 0.076 Mm3 was simulated. Deposited volume worked out by the Model as 0.076 Mm3, same as the observed one; hence the sediment deposition for Gmund Reservoir was simulated.
To investigate the flushing performance of the reservoir, flushing scenario is modeled using HEC-RAS 4.1.0 Model. For modeling, the same 29 basic locations of cross sections were used as the geometric data, except that those were modified as obtained after one year delta modeling.
To investigate the flushing performance of the reservoir, flushing scenario was modeled using HEC-RAS 4.1.0. For modeling, the same 29 basic locations of cross sections were used as the geometric data, with modified coordinates of the cross sections in the deposited area of the reservoir during delta modeling.
For flushing modeling, similarly a quasi unsteady file was prepared. As boundary condition, daily flow of 25 m3/s was taken as the constant flushing discharge for the entire flushing operation. Flushing durations required to flush the deposited sediments in one year were determined, which came out as 180 days.
The temperature of the water was assigned for each day as it affects the sediment transport processes. The normal depth was given by assigning a value of friction slope as 0.0323. Sediment transport function, sorting method, and fall velocity method were Toffaleti, Exner 5, and Toffaleti respectively.
For the sediment boundary condition, sediment rating curve derived for the dam site based on long past data record was used.
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3.7 MODELING SEDIMENT FLUSHING OPERATION THROUGH RESERVOIRS USING TSINGHUA UNIVERSITY EQUATION
Tsinghua University Equation, developed in Tsinghua University is used to model sediment flushing through reservoir. Tsinghua University Equation is described below in equation (3.13)
1.6 1.2 Q f S Qs 0.6 (3.13) W f
Where Qs is sediment load during flushing (tons/s), is erodibility coefficient, Qf is 3 flushing discharge (m /s), Wf is bottom width of flushing channel (m), S is longitudinal energy slope during flushing.
Flushing data of three reservoirs: Baira Reservoir of India, Gbidem Reservoir of Switzerland and Gmund Reservoir of Austria, was used to model flushing operation for these reservoirs. Flushing data used during modeling for the reservoirs are given below in Table 3.5
Table 3.5 Flushing Data of Foreign Reservoirs Sr. Reservoir Parameter Unit No. Gebidem Gmund Flushed Sediment 1 Mm3 0.383 0.27 0.065 Volume 31 (0900 hrs 14Aug 1983- 168 2 Flushing Period hour 96 (1991) 1600 hrs 15 Aug 1983 (1968)
Following are the steps for modeling sediment flushing and determination of flushing duration: Erodibility coefficient () was determined by measuring the slope of the line
obtained by plotting the graph between the two flushing parameters, Qos and
Q 1.6 S 1.2 f 0.6 W f
Bottom width of flushing channel, Wf , was calculated by the equation (3.14)
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0.5 (3.14) W f 12.8 Q f
Longitudinal energy slope S during flushing may be computed by equation (3.15)
Normal Operating Level of Re servoir Re servoir Water Level during Flushing (3.15) S Length of Re servoir
Flushing duration, Tf,, required to flush different masses of deposited sediments may be computed by the equation (3.16)
M W 0.6 (3.16) T f f f 1.6 1.2 86400 Q f S
Here Mf is the sediment mass flushed (tons)
Flushed mass from the reservoir may be determined by the equation (3.17)
Q 1.6 S 1.2 M 86400 T f f f 0.6 (3.17) W f
3.7.1 Baira Reservoir of India Input parameters given to the Tsinghua University Model were: observed flushing 3 discharge, Qf, 150 cumecs, mass flushed, Mf, 0.383 Mm , flushing duration, Tf, 31 hours (1.29 days), and longitudinal energy slope, S, 0.00854. The value of erodibility coefficient () for this particular flushing event was determined as 8.1328. As there was only one flushing event available in literature for Baira Reservoir, so by using HEC-RAS 4.1.0, the values of flushing durations were determined for various flushing discharges, varying from 150 to 500 cumecs. The results of HEC-RAS 4.1.0 Model are reliable, as its results had been calibrated previously for the three flushed foreign reservoirs. So the values of flushing durations determined by HEC-RAS 4.1.0 Model may be treated as
124 CHPTER 3 METHODOLOGY observed flushing durations. The values of flushing durations were then also determined for the flushing discharges varying from 150 to 500 cumecs, using Tsinghua University Model. A comparison was made by plotting the graph between the values determined by Tsinghua University Model and observed values. The graph shows that simulated flushing durations well match with the observed flushing durations, within error of 10% The values of flushing durations were also determined by Tsinghua University Equation, for the flushed masses, 0.2 Mm3, 0.383 Mm3, and 0.6 Mm3, with the flushing discharges varying from 100 to 500 cumecs. From the analysis, it was observed that, for larger amounts of sediment masses to be flushed, more flushing durations were required for a constant flushing discharge, and vice versa. Moreover it was also depicted that for certain sediment mass to flush, more was the flushing discharges, less was the flushing duration required, and vice versa as discussed in Chapter 4-Results and Discussions.
3.7.2 Gebidem Reservoir of Switzerland In literature flushing data for Gebidem Reservoir is available for the period 1969 to 1977 (IRTCES, 1985) and 1982 to 1991 (White et al., 2000; Morris and Fan, 2010). For the year 1996, flushing duration was 96 hours with the flushing discharge of 15 cumecs and the total flushed sediment volume was 0.27 Mm3, which was used for simulation. For the years 1969 to 1991, available flushing parameters are, flushing durations, Tf, sediment masses flushed, Mf, flushing discharges, Qf. With the help of these parameters a graph
1 .6 1 .2 Q f S was linearly plotted between Qos and . The slope of the line 0 .6 W f determined by the plot of these two flushing parameters gives the value of Erodibility Coefficient () as 2.7774. The values of simulated flushing durations were computed for given flushing discharges (Equation 3.16), and the computed flushing durations were compared with the observed flushing durations by plotting the graph between observed and simulated values. The observed flushing durations well match with the simulated flushing durations. Similarly values of masses flushed were computed by the Model for the observed flushing discharges (Equation 3.17). It was observed that simulated masses flushed were close to the field values.
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The values of flushing durations were computed for various flushing discharges, varying from 5 cumecs to 35 cumecs, for different flushing masses, 0.27 Mm3, 0.5 Mm3, and 1.0 Mm3. From the analysis it was observed that for larger amounts of sediment masses to be flushed, more flushing durations were required for a constant flushing discharge, and vice versa. Moreover it was also depicted that for certain sediment mass to flush, more was the flushing discharge, less was the flushing duration required, and vice versa as discussed in Chapter 4-Results and Discussions.
3.7.3 Gmund Reservoir of Austria Input parameters given to the Tsinghua University Model were: observed flushing 3 discharge, Qf, 25 cumecs, mass flushed, Mf, 0.0654 Mm , flushing duration, Tf, 168 hours (7 days), and longitudinal energy slope, S, 0.03011. The value of erodibility coefficient () for this particular flushing event was determined as 0.4837. As there was only one flushing event available in literature for Gmund Reservoir, so by using HEC-RAS 4.1.0 Model, the values of flushing durations were determined for different flushing discharges, 25 cumecs to 60 cumecs, with the increment of 5 cumecs. The results of HEC-RAS 4.1.0 are reliable, as its results had been calibrated in the previous sections for three flushed foreign reservoirs. So the values of flushing durations determined by HEC- RAS 4.1.0 was treated as observed flushing durations. Then for the same values of flushing discharges, as used in HEC-RAS 4.1.0 Model, the values of flushing durations were determined by Tsinghua University Model (Equation 3.16). A comparison was made by plotting the graph between the values of flushing durations determined by Tsinghua University Model and the observed one. The values of flushing durations were also determined for the flushed masses 0.2 Mm3, 0.383 Mm3, and 0.6 Mm3, with the flushing discharges varying from 25 to 60 cumecs and a graph was also plotted between the values of flushing durations and the respective flushing discharges, for the different masses flushed, 0.2 Mm3, 0.383 Mm3, and 0.6 Mm3. From the analysis, it was observed that for larger amounts of sediment masses to be flushed, more flushing durations were required for certain flushing discharge, and vice versa. Moreover it was also depicted that for certain sediment mass to be flushed, more was the flushing discharges, less were the flushing durations required, and vice versa as discussed in Chapter 4-Results and Discussions.
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3.8 ASSESSMENT OF FLUSHING EFFICIENCIES OF SMALL RESERVOIRS
There are sixty small dams under Small Dam Organization of Punjab Irrigation Department, whereas many are under consideration. Among the sixty existing reservoirs, twenty reservoirs were selected on the basis of data availability to calculate the flushing indicator to assess the flushing efficiency of the reservoir. Among these flushing indicators which have been described above, Long Term Capacity Ratio, LTCR, directly gives the value of flushing efficiency. Hence the values of LTCR were calculated for the selected reservoirs. The input parameters for calculation of LTCR are given in Table 3.6.
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Table 3.6 Input data of 20 reservoirs of Small Dams Organization (SDO), Islamabad
Co L Elmax Elmin Elf Wbot SSres SSs Vin Min TE Qf Tf SNo. Reservoir District Ψ Co/Vin Mm3 m m m m m m/m mm Mm3 Tons % cumecs days 1 Rawal Islamabad 39.1 3750 534.1 498.0 503.1 800.0 1.5 2.0 103.7 98752 300 0.4 82 6.6 10 2 Dungi Rawalpindi 2.17 1006 458.1 439.4 444.4 83 1.5 2 2 9986 180 1.1 90 0.1 10 3 Jabbi Attock 3.8 2715 385.7 367.3 370 24.0 1.8 1.5 4.1 12010.7 650 0.9 88 0.32 10 4 Pira Fatehal Chakwal 9.13 2500 571.1 546.7 551.7 95 1.5 2 5.8 13813 300 1.57 90.0 0.37 10 5 Jammargal Jhelum 3.0 1880 270.1 257 262 80 1.5 2 2.1 6750 180 1.43 84 0.13 10 6 Tain Pura I Jhelum 9 2750 304.9 280.3 285.3 72 1.5 2 6.4 558261 300 1.41 88 0.4 10 7 Mial Chakwal 3.89 3050 437.4 420.1 425.1 80 1.5 2 3.7 99190 180 1.05 88 0.2 10 8 Lehri Jhelum 7.04 3150 304.9 275.3 280.3 84 1.5 2 7.8 13332 300 0.903 87 0.5 10 9 Khai Chakwal 7.31 2500 623.5 587.8 592.8 90 1.5 2 2.7 71966 300 2.71 93 0.2 10 10 Ghazial Chakwal 2.47 2170 482.6 463.7 468.7 74 1.5 2 2 12862 300 1.074 89 0.15 10 11 Domeli Jhelum 1.73 3900 358.2 327.4 332.4 43 1.5 2 1 162200 300 11.92 97 0.6 10 12 Salial Jhelum 0.65 900 349.7 329 334 65 1.5 2 1 1950 300 1.066 89 0.04 10 13 Sawal Attock 2.96 1005 421 395.4 400.4 52 1.5 2 2.5 15953 300 1.84 90 0.2 10 14 Talikna Attock 2.53 1240 423.6 408.1 413.1 83 1.5 2 1.9 1234 300 1.33 90 0.12 10 15 Jabba Attock 1.06 1210 367.5 344.5 349.5 27 1.5 2 1.9 8572 300 0.558 90 0.1 10 16 Jalwal Attock 6.17 1585 294.1 278.4 283.4 73 1.5 2 6.2 39081 300 0.995 89 0.4 10 17 Dharabi Chakwal 45.67 4610 488.1 463.3 469.3 170 1.5 2 24.2 601723 300 1.89 92 4.5 10 18 Minwal Chakwal 2.47 2210 476.2 454.7 459.7 33 1.5 2 1.4 2555 180 1.74 91 0.1 10 19 Shah Habib Jhelum 2.04 1450 280 259.1 264.1 23 1.5 2 0.1 8334 180 34 98 0.004 10 20 Phalina Rawalpindi 4.81 2500 503.6 484.1 489.1 125 1.5 2 0.7 23602 300 6.87 95 0.45 10
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Where Co is the gross capacity of reservoir, L is length of reservoir, Elmax is normal operating level, Elmin is bed level at dam site, Elf is water surface elevation at dam, Wbot is bottom width of reservoir, SSres is side slope of reservoir, SSs is the side slope of reservoir deposits after flushing, Vin is average annual water inflow, Min is average annual sediment inflow, is erodibility coefficient, Co/Vin is capacity inflow ratio, TE is trap efficiency of reservoir, Qf is flushing discharge, and Tf is the required flushing duration.
3.9 MODELING JABBI RESERVOIR FOR SEDIMENT FLUSHING OPERATION
After impoundment of a reservoir, hydrographic surveys are conducted to assess the sediment deposition in the reservoir. Hydrographic survey to assess the sediment deposition in the reservoir had been conducted in the year 1985 for Rawal Lake; in 2000 for Jabbi Reservoir in Attock; in 2002 for Tainpura Reservoirs in District Jhelum; in the year 2003 for 3 reservoirs, Jammargal Reservoir in District Jhelum, Pira Fatehal Reservoir in Chakwal and Dungi Reservoir in Rawalpindi. Hence hydrographic survey data of only these six reservoirs is available.
Hydrographic surveys of the reservoirs require resources and the couple of time. Today is the era of the computer modeling and a number of Numerical Models are available to model the sediment deposition in the reservoir. Hydrographic survey of Jabbi Reservoir in District Attock was conducted by Iirrigation Research Institute, Punjab Irrigation Department, in April 2000, after about 10 years of its operation.
In this particular study Jabbi Reservoir in District Attock was selected to model sediment deposition and sediment flushing in the reservoir using 2 different Models HEC-RAS 4.1.0 and Tsinghua University Equation.
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3.9.1 Modeling Jabbi Reservoir for Sediment Deposition and Flushing Operation Using HEC-RAS 4.1.0
The data required to perform various computations with HEC-RAS 4.1.0 are divided into the following categories: Geometric data, Quasi-unsteady flow data, Sediment data, Bed gradation curve, and inline structure data.
The setting up of the Model was carried out by considering 3.75 km river length with, 28 cross-sections as shown in Figure 3.17. The dam site (inline structure) is situated at section number 0.9, whereas upstream most cross section is 21, and downstream most cross section is 0, In river system schematic, Jabbi Stream was drawn in geometric data editor option of HEC-RAS 4.1.0. In the geometric data editor the name for Stream reach was specified.
(i) Geometric data: 28 cross sections were given as input to the Model. Description of the cross sections is given in Table 3.7.
The schematic diagram showing the locations of cross sections used for delta modeling is shown in the following Figure 3.23
Manning value of n is taken as 0.08 for main channel and 0.07 for left over bank and right over bank. Contraction and expansion coefficients used are 0.1 and 0.3 respectively. Dam coordinates are (174.6, 385.7) and (749.3, 385.7).weir coefficient was taken as 1.4 and weir shape considered as broad crested.
(ii) Quasi-Unsteady flow data i. Mean monthly flow data for 10 years-1991 to 2000 was given to the Model as u/s boundary condition as shown in Figure 3.24
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Table 3.7 Twenty Eight Cross Sections used during Delta Modeling for Jabbi Reservoir
Sr. No. River Station Distance to d/s (m) Remarks 1 21 101 u/s of Reservoir Area 2 20 134.5 u/s of Reservoir Area 3 19 150 Reservoir Area 4 18 122 do 5 17 150 do 6 16 185.4 do 7 15 119.4 do 8 14 75.3 do 9 13 201.6 do 10 12 226 do 11 11 285 do 12 10 122 do 13 9 132 do 14 8 139 do 15 7 132 do 16 6 86 do 17 5 117 do 18 4 152 do 19 3 106.7 do 20 2 106.7 do 21 1 106.7 do 22 0.95 5 do 23 0.9 Inline structure (dam site) 24 0.8 195 D/S of dam site 25 0.6 200 do 26 0.4 200 do 27 0.2 200 do 28 0 0 do
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Figure 3.23 Schematic diagram showing the cross section locations used for the delta modeling for Jabbi Reservoir
Figure 3.24 Flow Hydrographs at Jabbi dam site used as upstream boundary condition for annual deposition
ii. Normal depth (bed slope) as d/s boundary condition Normal depth is taken as downstream boundary condition with friction slope equal to the average river bed slope in the reservoir area at the downstream end, i.e., 0.00677 iii. Temperature of water
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Mean monthly temperature was input to the Model. As several aspects of sediment transport mechanics, particularly fall velocity, incipient motion and sediment transport are sensitive to water temperature, hence, HEC-RAS 4.1.0 requires temperature information. Only one temperature per time step was specified for the entire Model. (iii) Sediment Data Once the geometric data was entered, the sediment data was given to Model to develop a delta profile of sediment transport. The sediment data was entered in sediment data editor which comprised of following conditions. (a) Initial Conditions and Transport Parameters (b) Boundary Condition (a) Initial Conditions and Transport Parameters: The initial condition and transport parameters specified for HEC-RAS 4.1.0 for a reservoir are as following at each cross section. i Transport Function: The transport functions used in this Model is Engelund-Hansen. This function gives relatively suitable results closer as observed for the reservoir. ii Sorting Method The sorting method to compute active layer thickness and vertical bed layer tracking assumption. The Exner 5 method was used in the Model. iii Fall Velocity Approach Several methods are available for computing fall velocity. But Report 12 was used for delta modeling of the reservoir. iv Maximum Depth The maximum erodible depth used for Model is 10 m for this reservoir v Bed Gradation
HEC-RAS 4.1.0 first requires the creation of bed material gradation curve. Soil samples were taken from the bed of Jabbi Reservoir with the help of concerned Subdivisional Officer, Chaudhry Azeem, and other staff of Small Dams Organization on 13th February, 2012. The soil samples were analyzed in Geotechnical Laboratory of Civil Engineering Department, University of Engineering and Technology, Lahore. By sieve analysis test it was worked out that bed of the Reservoir has Gravel 4%, Sand 69%, and fine sediments
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27%. Fine sediments were further analyzed by Hydrometer test, and finally Bed Gradation Curve was developed, shown in Figure 3.25. Bed Gradation Sizes were given to the Model as input.
100
90 80 70 60
50
% finer 40 30 20
10 0 0.001 0.01 0.1 1 10 100 dia (mm)
Figure 3.25 Bed material gradation curve of Jabbi Reservoir for annual sediment deposition
(b) Sediment Boundary Condition The equilibrium load is used as upstream sediment boundary condition for delta modeling. By giving as input data to Model the accumulated sediment deposition of 0.0418 Mm3 was simulated. Annual deposited volume worked out by the Model was 0.0417 Mm3, about same as the observed one, 0.0418 Mm3, hence the sediment deposition was well simulated.
To investigate the flushing performance of the reservoir, flushing scenario was modeled using HEC-RAS 4.1.0 Model. For modeling, the same 28 basic locations of cross sections were used as the geometric data, except those which were modified as obtained after one year delta modeling. For flushing modeling, similarly a quasi unsteady file was prepared. As boundary condition, daily flow of 0.32 m3/s was taken as the constant flushing discharge for the entire flushing process. Flushing duration required to flush the deposited sediments in one year was determined, which came out as 1.42 days (34 hours). The temperature of the water was assigned for each day as it affects the sediment
134 CHPTER 3 METHODOLOGY transport processes. The normal depth was given by assigning a value of friction slope as 0.00677.
For the sediment boundary condition, sediment rating curve derived for the dam site based on long past data record was used. Transport function, Sorting Method, Fall Velocity Method used, were Engelund, Exner 5, and Ruby respectively.
Sediment deposition in 10 year was 0.418 Mm3, as determined by the conducted hydrographic survey. Sediment deposition and flushing 10 years deposited sediments was simulated same as simulated annual sediment deposition and flushing.
To simulate sediment deposition in 10 years, the geometric data and quasi-unsteady flow data used was the same as in simulating annual sediment deposition. In the sediment data for initial conditions and transport parameters, transport function fall velocity, sorting method, were Toffaleti, Exner 5, and Report 12 respectively. Bed gradation used was the same as used in annual simulation. .as and maximum depth of scour was assumed as 10m. Similarly equilibrium load was used as upstream sediment boundary condition for delta modeling. By giving as input data to Model the accumulated sediment deposition of 0.418 Mm3 was simulated. Sediment deposition volume worked out by the Model for 10 years was 0.417 Mm3, about same as the observed one, 0.418 Mm3, hence the sediment deposition was well simulated.
For modeling sediment flushing, the same 28 basic locations of cross sections were used as the geometric data, except those which were modified as obtained after modeling 10 years sediment deposition determined by the Model. For flushing modeling, similarly a quasi unsteady file was prepared. As boundary condition, daily flow of 1.5 m3/s was taken as the constant flushing discharge for the entire flushing process. Flushing duration required to flush the deposited sediments in 10 years was determined, which came out as 4 days (96 hours). The temperature of the water was assigned for each day as it affects the sediment transport processes. The normal depth was given by assigning a value of friction slope as 0.00677.
135 CHPTER 3 METHODOLOGY
For the sediment boundary condition, sediment rating curve derived for the dam site based on long past data record was used. Transport function, Sorting Method, Fall Velocity Method used, were Laursen (Copeland), Exner 5, and Report 12 respectively.
3.9.2 Modeling Jabbi Reservoir for Flushing Operation Using Tsinghua University Equation In the study, sediment flushing processes were modeled for annual sedimentation of 0.0418 Mm3 and also for deposition of 10 years, 0.418 Mm3 using Tsinghua University Equation. The procedure adopted to model flushing is the same as described in detail in the preceding section 3.7 using the equations (3.14) through (3.17).
The sediment volumes which have to be flushed are the annual sediment deposition and sediment deposition in time period of 10 years. Flushing durations required to flush annual sediment deposition and sediment accumulated in 10 years given in Table 3.8.
Table 3.8 Flushing data of Jabbi Reservoir Flushing Jabbi Reservoir Sr. No Parameter Unit Flushing after 10 Annual flushing Years Proposed Flushed 1 Mm3 0.0326 0.326 Sediment Volume Proposed Flushing 2 hour 34 96 Duration
3.10 PROPOSED FLUSHING STRATEGIES FOR JABBI RESERVOIR To formulate flushing plan, one has to give answers of the following questions: Appropriate time to flush sediments from the reservoir? Suitable flushing discharge required during flushing process? Time required for emptying the reservoir? Flushing duration required to flush annual sediment deposition and deposition in in time period of 10 years?
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Time required for refilling the reservoir? Sediment sizes which are flushable? Volume of water required during flushing operation? To answer these questions, it is necessary to study the flushing feasibility for the jabbi reservoir and their answers are given and discussed in chapter 4-Results and Discussions.
3.11 SUMMARY
In this chapter data of three successfully flushed reservoirs, Baira of India, Gebidem of Switzerland, and Gmund Reservoir of Austria was collected from various sources, reference papers of Reservoirs, research papers through internet explorer and Google Earth. Data collected for these reservoirs was, of three types: Geometric data, Sediment data, and Flow data. In Geometric data, reservoir length, bottom width of reservoir, side slope, reservoir cross sections at various river stations, reach lengths between two adjacent cross sections, Manning value of n, coefficient of contraction and expansion, weir shape and weir coefficient, coordinates of dam structure, shape of weir, sill height of outlet from river bed, normal operating level, upstream and downstream bed elevations; for flow data, annual water inflow, average daily discharge, flushing discharge, flushing duration, normal depth (bed slope) of reservoir, and temperature of water; for sediment data, annual sediment inflow, and the respective sediment concentration, sediment type, bed gradation curve, suspended sediment rating curve, amount of deposited sediments, amount of sediments flushed, was gathered for modeling these reservoirs for sediment deposition and sediment flushing using three numerical Models, SHARC, HEC-RAS 4.1.0, and Tsinghua University Model.
Flushing indicators to assess sediment flushing through reservoirs are Sediment Balance Ratio, SBR, Long Term Capacity Ratio, LTCR, Drawdown Ratio, DDR, Sediment
Balance Ratio during Full Drawdown, SBRd, Flushing Width Ratio, FWR, and Top Width Ratio, TWR. By analysis it was attempted to find the most important flushing indicator, which is sign for successful flushing of these reservoirs.
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Using the data of six successfully flushed reservoirs, attempt had been made to develop equations to calculate SBR and LTCR of any reservoir. The data used for these reservoirs 3 are, length of reservoir, L (m), average annual water inflow, Vin (Mm ), gross capacity of 3 reservoir, Co (Mm), flushing discharge, Qf (cumecs), flushing duration, Tf (hours), longitudinal slope of the reservoir, S (m/m), and average flow area of the reservoir, A (m2).
There are sixty small reservoirs under the control of Punjab Small Dams Organization of Punjab Irrigation Department. Among these sixty reservoirs, twenty reservoirs were selected to compute LTCR values to assess flushing feasibility. Data used in computation are, gross capacity of reservoir, Co, reservoir length, L, Normal operating level, Elmax, river bed level at dam site, Elmin, bottom width of reservoir, Wbot, side slope of reservoir,
SSres, side slope after flushing, SSs, average annual water inflow, Vin, sediment type, (), trap efficiency of reservoir, TE, flushing discharge, Qf, and flushing duration, Tf
Among the twenty selected reservoirs for analysis, Jabbi Reservoir having gross storage capacity, 3.8 Mm3, was selected for modeling sediment deposition and proposed flushing. The reservoir was constructed in 1991 and after about 10 years in April 2000 hydrographic survey was conducted. Survey revealed that 0.418 Mm3 sediments had been deposited in the reservoir suggesting average annual sedimentation of about 0.0418 Mm3 and annual storage loss of about 1.1 %
Jabbi Reservoir had been modeled for annual sedimentation and also for 10 years deposited sediments using two numerical Models, HEC-RAS 4.1.0 and Tsinghua University Model. Flushing sluices had been proposed for the reservoir and proposed flushing had also been modeled for the Reservoir. Finally complete flushing plan had been devised for the Jabbi Reservoir.
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RESULTS AND DISCUSSIONS
4.1 INTRODUCTION
This chapter describes the results and relevant discussions for the investigated critical value of most important flushing indicator, developed equations for SBR and LTCR, modeling sediment deposition and flushing for three foreign reservoirs using SHARC Model, and HEC-RAS 4.1.0, modeling sediment flushing through the three reservoirs using Tsinghua University Model, assessment of flushing efficiencies of small Pakistani reservoirs, modeling sediment deposition and flushing operations for Jabbi Reservoir using HEC-RAS 4.1.0, and Tsinghua University Model, and proposing flushing strategies for Jabbi Reservoir. At the end all results are summarized.
4.2 EXPLORING MOST IMPORTANT FLUSHING INDICATOR AND ITS CRITICAL VALUE
Fourteen flushed reservoirs of the world were selected to find out the most important flushing indicator. The selected fourteen reservoirs were, Baira and Ichari of India, Gebidem and Palagnedra of Switzerland, Gmund of Austria, Hengshan, Gaunting, Heisonglin, Sanmenxia and Shuicaozi of China, Santo-Domingo of Venezuela, Guernsey of USA, Ouchi-Kurgan of Former USSR, and Sefid-Rud of Iran. The values of six flushing indicators were computed for the flushed reservoirs. Then the most important flushing indicator was selected. Computed values of SBR for fourteen reservoirs are shown in Figure 4.1.
Figure 4.1 shows that all the successfully flushed reservoirs satisfy the critical value of Sediment Balance Ratio, SBR. It was observed that most of the partially flushed reservoirs satisfy the critical value of SBR, so it may be said that SBR may not be the most important flushing indicator to assess feasibility of sediment flushing from reservoirs.
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40 successful
35 33 reservoir calculated SBR 30 critical SBR 25 21 20 SBR 15
11 10 7 7 7 7 4.6 4
3.4 5 3 1 0.7 0.2 0
Baira Ichari Grnsy Gntng Gbidm Gmund Hngshn Hsnglin Plgndra Sfd Rud Sfd Shcaozi Snmnxia S. Dmngo Ochi Krgn Ochi Reservoirs
Figure 4.1 SBR values of flushed reservoirs of world
1.2 successful reservoir
1 1 calculated DDR
1 0.96 0.93 critical DDR 0.89 0.81 0.77 0.77 0.8 0.75 0.68 0.6 DDR
0.44
0.4 0.37 0.31 0.2 0.14
0
Baira Grnsy Ichari Gntng Gbidm Gmund Hngshn Hsnglin Plgndra Rud Sfd Shcaozi Snmnxia S. Dmngo Reservoirs Krgn Ochi
Figure 4.2 DDR values of flushed reservoirs of world
Figure 4.2 shows the computed values of DDR for all the flushed reservoirs. Figure shows that almost all the successful reservoirs satisfy the critical value of Drawdown Ratio (DDR) and four partially flushed reservoirs, Sefid-Rud, Guanting, Heisonglin, and Sanmenxia also meet the critical value of DDR.
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130 120 succe ssful re se rvoir
110 110 calculated SBRd 100 critical SBRd 90 80 d 70 58 SBR 60 50 40 33 33
30 24 20
20 15
11 4.8 4.3
10 4 3.2 1
0.3 0
Baira Ichari Grnsy Gntng Gbidm
Gmund Hngshn Hsnglin Plgndra Rud Sfd Shcaozi Snmnxia S. Dmngo S. Krgn Ochi Reservoirs
Figure 4.3 SBRd values of flushed reservoirs of world
Figure 4.3 shows the computed values of SBR during full Drawdown (SBRd) for the flushed reservoirs. Figure shows that all the reservoirs, successfully flushed and partially
flushed reservoirs satisfy the critical value of SBRd except one partially flushed reservoir
Guanting. So SBRd is not the flushing indicator which may distinguish between successfully flushed and partially flushed reservoirs.
12 succe ssful re se rvoir 10 9.9 calculated FWR
critical FWR 8 6.7 6 FWR 5.2
4 3.4
2 2 1.4 1.4 1.4 1
0.3 0.26 0.1 0 0.06 0.04
Baira Ichari Grnsy Gntng
Gbidm Gmund Hngshn Hsnglin Plgndra Rud Sfd Shcaozi Snmnxia S. Dmngo S. Krgn Ochi Reservoirs
Figure 4.4 FWR values of flushed reservoirs of world
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CHAPTER 4 RESULTS AND DISCUSSIONS
Figure 4.4 shows the computed values of Flushing Width Ratio (FWR) of the analyzed flushed reservoirs. Figure shows that all the flushed reservoirs, successfully flushed and partially flushed, meet the critical value of FWR except five reservoirs, Sefid-Rud,
Sanmenxia, Hengshan, Heisonglin, and Guanting. So SBRd may not be the flushing indicator which may be selected to distinguish between successfully flushed reservoirs and partially flushed reservoirs.
8
7.1 successful reservoir calculated TWR
6 critical TWR
4
TWR
2.1 1.8
2 1.6 1.5 1.4 1.3 1 0.9 0.8 0.5 0.3 0.26
0.1 0
Baira Ichari Grnsy
Gbidm Gmund Hngshn Plgndra Sfd Rud Sfd Shcaozi Snmnxia Guanting S. Dmngo S. Ochi Krgn Reservoirs Heisonglin
Figure 4.5 TWR values of flushed reservoirs of world
Figure 4.5 shows the computed values of Top Width Ratio (TWR) of the analyzed fourteen flushed reservoirs. Figure shows that all the six successfully flushed reservoirs and two partially flushed reservoirs Shuicaozi and Ichari satisfy the critical value of TWR. So TWR may not be considered as a flushing indicator which can distinguish between successful reservoirs and partially successful reservoirs because two of the eight partially flushed reservoirs satisfy the critical value of TWR.
Figure 4.6 shows computed values of Long Term Capacity Ratio (LTCR) for the fourteen flushed reservoirs. The Figure shows that out of six successfully flushed reservoirs four reservoirs Santo-Domingo, Palagnedra, Gebidem and Gmund satisfy the critical value of LTCR. Whereas two reservoirs Baira and Hengshan have LTCR values of 0.85 and 0.77 respectively. Although these two do not fully satisfy the criteria but their values are close to the critical value of LTCR. None of the partially flushed reservoirs satisfy the critical
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value of 1.2 1 1 0.99 1 0.98
0.85 successful reservoir 0.8 0.77 calculated LTCR 0.6 critical LTCR LTCR 0.39 0.39
0.4 0.36 0.3 0.26 0.2 0.2 0.13 0.1
0 Baira Ichari Grnsy Gntng Gbidm Gmund Hngshn Hsnglin Plgndra Sfd Rud Sfd Shcaozi Snmnxia Ochi Krgn Ochi
S. Domingo Reservoirs LTCR.
Figure 4.6 LTCR values of flushed reservoirs of world
From Figure 4.1 through Figure 4.6 it was observed that among the six flushing indicators, LTCR was the only indicator which did not satisfy any of the partially flushed reservoirs. So LTCR is the criteria which may be used to distinguish between successfully flushed and partially flushed reservoirs. So it is the flushing indicator which is the most important, and it may be used to predict the feasibility of sediment flushing from the reservoirs. From Figure 4.6 it was observed that successfully flushed reservoir
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CHAPTER 4 RESULTS AND DISCUSSIONS
which had the minimum value of LTCR, is Hengshan Reservoir. The said reservoir has the value of LTCR 0.77. So it may be deduced that the critical value of LCR may be taken as 0.77, instead of 1.
4.3 DEVELOPMENT OF EQUATIONS FOR SBR AND LTCR OF RESERVOIRS
After carrying out the Multiple Non-linear Regression Analysis, following equations (4.1) and (4.2) were developed for SBR and LTCR, respectively.
Q 0.036 T 0.0566 S 0.015 A0.097 f f (4.1) SBR 0.152 0.587 L Co /Vin
L 0.072 T 0.028 S 0.152 LTCR f 0.077 0.019 0.019 (4.2) Co /Vin Qf A Developed equations for SBR and LTCR were tested for both, foreign and Pakistani reservoirs. For foreign reservoirs, using the data of these reservoirs values of SBR and LTCR were computed by the developed empirical equations and compared with the values given by Atkinson (1996b). Comparison results indicated that computed values were much closer to the given values, with an error of about -0.52% for SBR and -1.08% for LTCR as depicted in Figures 4.7 and 4.8. It is because that the data utilized to develop the equations of SBR and LTCR is taken from these six successfully flushed reservoirs.
35
30 Atkinson Value 25 Calculated Value 20
SBR 15 10
5 0
Baira
Gmund Santo- Gebidem Domingo Hengshan Palganedra
Reservoir
Figure 4.7 Comparison between the given and calculated SBR values for foreign 144
CHAPTER 4 RESULTS AND DISCUSSIONS
reservoirs
1.2 Atkinson Value Calculated Value 0.8
LTCR 0.4
0.0
Baira
Gmund Gebidem Hengshan Palganedra Reservoir Santo-Domingo Figure 4.8 Comparison between the given and calculated LTCR values for foreign reservoirs Developed equations were also applied to 5 Pakistani small reservoirs. Comparison of results for the computed SBR and LTCR values, obtained by developed equations and Atkinson (1996b) method, are shown in Figures 4.9 and 4.10. Comparison of the results depicted that the maximum difference, compared with the results by Atkinson method (1996b), for SBR and LTCR were 9% and 11% respectively.
0.60 Atkinson Value 0.50 Calculated Value
0.40
0.30 SBR 0.20
1.2 0.10 Atkinson Value 1.0 Calculated Value 0.00 Talikna Jabbi Jammargal Dharabi Phalina 0.8 Reservoir 0.6 LTCR Figure 0.4 4.9 0.2
0.0 145 Jammargal TaliknaDharabi Phalina Jabbi Reservoir CHAPTER 4 RESULTS AND DISCUSSIONS
Comparison of results for SBR computed by Atkinson (1996b) method and developed equations for Pakistani reservoirs.
Figure 4.10 Comparison of results for LTCR computed by Atkinson (1996b) method and developed equations for Pakistani reservoirs.
From the Figures 4.7 to 4.10 it was observed that the values of SBR and LTCR calculated by the developed equations were close to the values obtained by Atkinson (1996b) method. So the equations may be applied confidently for reservoirs to check the sediment flushing feasibility.
4.4 MODELING SEDIMENT DEPOSITION AND SEDIMENT FLUSHING THROUGH RESERVOIRS USING SHARC
4.4.1 Baira Reservoir of India
146
CHAPTER 4 RESULTS AND DISCUSSIONS
For Baira Reservoir, SHARC Model (Deposition Model) was run for 1.5 years simulation time, with normal operating level of 1123 m. Figures 4.11-4.13 are the output results of the Deposition Model, while Figures 4.14 and 4.15 are the output results of Sluicing Model.
Figure 4.11 Longitudinal delta profile after 1.5 years deposition in Baira Reservoir
Figure 4.11 shows the longitudinal profile of sediment delta deposition in the reservoir, after 1.5 years. Figure also shows that the pivot point of the delta had moved a distance of 0.8 km towards the dam face, whereas, the level of pivot point had attained an elevation of 1120 m. Sand and silt trap efficiencies as given by the Model were 100% and 71.3%, respectively. Volumes of sand and silt deposited were 0.0549 Mm3, 0.397 Mm3 respectively, hence total deposited sediments in the reservoir were 0.452 Mm3, which was close to the observed sediment deposition of 0.45 Mm3. During sediment deposition in the reservoir, the average sand and silt concentrations close to the dam site were 0 and 38 PPM, respectively.
Figure 4.12 shows
147
CHAPTER 4 RESULTS AND DISCUSSIONS
suspended sediments gradation curves, for suspended material before and after simulation during delta modeling of the reservoir. The Figure also shows that sediment sizes were ranging from 0.04 mm to 2 mm, and they reduced to the range of 0.04-0.045 mm, after 1.5 years deposition. Figure 4.13 shows the variation in the bed material gradation curves due to delta formation in the reservoir. At the upstream end of the reservoir, sediments were coarser ranging in sizes from 0.04 mm to 32 mm, whereas, at downstream end of the reservoir sediment sizes were reduced, ranging from 0.04 to 12.6 mm. It is due to the fact that sand and gravel were deposited on the upstream of the reservoir.
Figure 4.12 In-transport gradation curves at start and end of deposition process in Baira Reservoir
Figure 4.13 Bed material gradation curves at u/s and d/s of Baira Reservoir
148
CHAPTER 4 RESULTS AND DISCUSSIONS
Figure 4.14 Bed levels during sediment flushing in Baira Reservoir
Figure 4.14 shows bed profiles at different time intervals during sediment flushing in the reservoir. The Figure also shows that bed levels were gradually reduced with the passage of time until the reservoir restored its original bed profile. Amount of sediment flushed by the Model was 0.385 Mm3, close to the observed sediment deposition of 0.383 Mm3.
Figure 4.15 Concentration leaving the Baira Reservoir during flushing operation
149
CHAPTER 4 RESULTS AND DISCUSSIONS
Figure 4.15 shows the sediment concentration during flushing operation, downstream of the Baira Reservoir, with flushing discharge of 150 cumecs. The total simulated duration of sediment flushing was 9.1 hours, whereas, observed flushing duration was 31 hours. This shows that simulated flushing duration is 3.4 times lesser than observed one. Figure also depicts that, at start of flushing operation, sediment concentration was 780,251 PPM, later it reduced to 138,670 PPM at 3.84 hours, and then reduced to the value of 80,346 PPM at the end of flushing operation i.e., 9.10 hours. Hence the flushing scenario in the reservoir can be explained by this bi-linear curve. Its initial negative slope shows that at the start of flushing operation, sediment concentration was maximum and it reduced gradually, and minimum at the end of flushing operation, because most of the sediments had been flushed at that time.
4.4.2 Gebidem Reservoir of Switzerland For Gebidem Reservoir, Model was run for 1.0 year simulation time, with the normal operating level of 1436 m. Figures 4.16 to 4.18 show the output results of Deposition Model, while Figures 4.19 and 4.20 are the output results of Sluicing Model for Gebidem Reservoir.
Figure 4.16 Longitudinal delta profile after 1.0 year deposition for Gebidem Reservoir
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CHAPTER 4 RESULTS AND DISCUSSIONS
Figure 4.16 is the longitudinal profile of delta deposition in Gebidem Reservoir, after 1 year. The Figure shows that the pivot point of the delta had moved a distance of 0.29 Km towards the dam face, whereas, the level of pivot point had attained an elevation of 1436 m. Sand and silt trap efficiencies as estimated by the Model were 100% and 80.1%, respectively. Total volumes of sand and silt deposited were 0.0297 Mm3 and 0.241 Mm3, respectively, hence total simulated deposited sediments in the reservoir amounted to be 0.271 Mm3, which were close to the observed deposited sediments of 0.27 Mm3. During
deposition of sediments in the reservoir, the average sand and silt concentrations were 0 and 158 PPM respectively, close to the dam.
Figure 4.17 In-transport gradation curves at start and end of deposition process for Gebidem Reservoir
Figure 4.17 shows in-transport gradation curves, before and after simulation, during delta modeling. The Figure also shows that suspended sediment material in transport was ranging from 0.04 mm to 2 mm, whereas, it reduced to the range of 0.04 to 0.14 mm, after one year deposition.
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CHAPTER 4 RESULTS AND DISCUSSIONS
Figure 4.18 Bed material gradation curves at u/s & d/s of Gebidem Reservoir
Figure 4.18 shows the variations in the bed material gradation curves due to delta formation in the reservoir. The Figure also shows that at the upstream of the reservoir sediment sizes were coarser, ranging from 0.04 mm to 32 mm, whereas, at downstream of the reservoir, sediment sizes were much reduced, to the range of 0.04 mm to 2 mm depicting that coarser particles had been settled in the upstream of the reservoir.
Figure 4.19 Bed levels during sediment flushing in Gebidem Reservoir
Figure 4.19 shows longitudinal bed profiles at different time intervals during flushing. The Figure also shows that bed levels were being reduced with the passage of time until the reservoir restored its original bed profile. The flushed sediments were 0.271 Mm3,
152
CHAPTER 4 RESULTS AND DISCUSSIONS
which were almost equal to the observed flushed sediments of 0.27 Mm3.
Figure 4.20 Concentration leaving the Gebidem Reservoir during flushing operation
Figure 4.20 shows the sediment concentration during the flushing operation, downstream of the Gebidem Reservoir, with flushing discharge of 15 cumecs. The total simulated duration of sediment flushing through reservoir was 30.59 hours, whereas, observed flushing duration was 96 hours. It shows that simulated duration of flushing was lesser than observed one, roughly by one third. Figure also depicts that at start of flushing operation sediment concentration was 9,99,892 PPM and was reduced at later stage to 9,58,384 PPM at 14.8 hours, and then at the end of flushing operation i.e., 30.59 hours it abruptly again increased to 9,99,892 PPM The Figure also depicts that at the start sediment concentration was highest, because deposited sediments were close to the dam to be flushed, and it then reduced steadily, and was highest at the last hour , as most of the sediments were available just upstream of the dam for flushing.
4.4.3 Gmund Reservoir of Austria For Gmund Reservoir, Model was run for 1 year simulation time with the normal operating level of 1190 m. Figures 4.21 to 4.23 are the output results of the Deposition Model, whereas Figures 4.24 and 4.25 are the output results of the Sluicing Model.
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CHAPTER 4 RESULTS AND DISCUSSIONS
Figure 4.21 Longitudinal sediment delta profile after 1 year deposition in Gmund Reservoir
Figure 4.21 is the longitudinal profile of delta deposition in the Gmund Reservoir after 1 year. The Figure shows that the pivot point of the delta had moved a distance of 0.29 km towards the dam face, whereas, the level of pivot point reached at an elevation of 1190 m. Sand and silt trap efficiencies during delta formation were 100% and 78.7%, respectively. Total volumes of sand and silt deposited were 0.008514 Mm3 and 0.067987 Mm3 respectively, hence total deposited sediments in the reservoir amounted to be 0.0765 Mm3, almost equal to the observed deposited sediments of 0.076 Mm3. During sediment deposition, the average sand and silt concentrations, close to the dam, were 0 and 103 PPM respectively.
Figure 4.22 In-transport gradation curves at start and end of deposition
154
CHAPTER 4 RESULTS AND DISCUSSIONS
process in Gmund Reservoir
Figure 4.22 shows suspended sediments gradation curves at start and end of simulation, during delta modeling for Gmund Reservoir. The Figure shows that suspended sediment material in transport was ranging in size from 0.04 mm to 2 mm, whereas, it reduced to the range of 0.04 to 0.14 mm, after 1 year deposition period.
Figure 4.23 Bed material gradation curves at u/s & d/s of Gmund Reservoir
Figure 4.23 shows the variation in the bed material gradation curves due to delta formation in the reservoir. The Figure also shows that at the upstream of the reservoir, the sediments were coarser, ranging from 0.04 mm to 32 mm, whereas, on downstream, the sizes were much reduced to the range of 0.04 to 18 mm. It is due to the fact that sand and gravel were deposited in the upper reach of the reservoir.
Figure 4.24 shows longitudinal bed profiles at different time intervals during sediment flushing in the reservoir. The Figure also shows that bed levels were being reduced with the passage of time until the reservoir restored its original bed profile within flushing duration of 34.56 hours. Flushed sediments were 0.0655 Mm3, close to the observed flushed sediments of 0.065 Mm3.
155
CHAPTER 4 RESULTS AND DISCUSSIONS
Figure 4.24 Bed levels during sediment flushing in Gmund Reservoir
Figure 4.25 shows the change of sediment concentration, during the flushing operation, downstream of the reservoir. Figure also shows that the total duration estimated during sediment flushing through reservoir was 34.56 hours, whereas, observed flushing duration was 168 hours. This shows that simulated duration of flushing was lesser than the observed, roughly by 4.8 times. The Figure depicts that at start of flushing operation sediment concentration was 9,08,650 PPM, and it abruptly reduced to the value of 6,58,640 PPM, and then reduced gradually to 3,50,675 PPM within a period of 5.77 hours, and then further reduced to 1,21,170 PPM at 34.48 hours, and then abruptly increased to 7,06,837 PPM at the end of flushing operation i.e. at 34.56 hours. Figure depicts that sediment discharge was higher at the beginning and end of flushing operation. At the start of flushing operation fine sediments were available in the vicinity of outlet, whereas, at the end, most of the delta material reached close to the dam face which had increased sediment concentration in the flow at a rapid rate. The summary of all results is presented in Table 4.1
156
CHAPTER 4 RESULTS AND DISCUSSIONS
Figure 4.25 Concentration leaving the Gmund Reservoir during flushing operation
Table 4.1 Comparison between Observed and Simulated Flushing Durations Using SHARC Model
Parameter Unit Baira Gebidem Gmund
Observed 0.450 0.270 0.0760 Deposited sediments Mm3 Simulated 0.452 0.271 0.0765
Observed 0.383 0.270 0.0650 3 Flushed sediments Mm Simulated 0.385 0.271 0.0655
Observed 31 96 168 Flushing duration hours Simulated 9.1 30.59 34.56
Flushing duration hrs/hrs Observed/Simulated 3.4 3.2 4.8
Average 3.8
Sluicing Model SHARC does not have any calibrating parameters which could be tuned to obtain the results closer to observed values. Perhaps this underestimation of flushing duration is due to high erosive capacity of Van Rijn transport equations (Van Rijn, 1984a; 1984b) which had been used in the SHARC Model. Van Rijn transport function used in Sluicing Model is limited to the particle sizes ranging from 0.064 mm to 2 mm
157
CHAPTER 4 RESULTS AND DISCUSSIONS
(Embaye, 2009; Wubneh, 2007), but the delta deposits mainly contain sand and particles coarser than sand. Model takes only silt and sand from the deposited material due to its inherent limitation and hence flushed the sediments too earlier, than observed flushing duration. This is the main reason for shorter simulated flushing duration, and also major limitation in the accurate performance of the Model in simulating flushing durations.
4.5 MODELING SEDIMENT DEPOSITION AND SEDIMENT FLUSHING THROUGH RESERVOIRS USING HEC-RAS 4.1.0
4.5.1 Baira Reservoir of India To model the Baira Reservoir, input data given to the Model were geometric data: 35 river cross section, Manning value of n 0.07 for Leftover Bank (LOB) and Rightover Bank (ROB) and 0.08 for main channel, contraction and expansion coefficients 0.01 and 0.03 respectively, Dam coordinates (2, 1123), (137, 1123), weir coefficient 1.4 , shape of weir Broad Crested; Quasi-Unsteady flow data- Mean monthly flow data for 8 years- 1982 to 1989, Normal depth (bed slope) 0.0124, Temperatures of water; Sediment data- Transport Function Tofalleti, Sorting Method Exner 5, Fall Velocity Approach Tofalleti, maximum erodible depth 10m, Bed Gradation Curve, equilibrium load was used as u/s sediment boundary condition.
Figure 4.26 Water surface profile before delta modeling for Baira Reservoir
158
CHAPTER 4 RESULTS AND DISCUSSIONS
Figure 4.27 Simulated Longitudinal Delta Profile for Baira Reservoir after 1.5 year sediment deposition
The Figure 4.26 shows the water surface profile with normal operating level of 1123m before sedimentation. The Model was run for a simulation period of 1.5 years and output deposition result is presented in figure 4.27. Simulated sediment deposition was 0.45 Mm3 equals the observed sediment deposition of 0.45 Mm3.
Figure 4.28 Bed profile of Baira Reservoir before flushing based on 1 year sediment deposition
Longitudinal profile of the delta which was used as input for the flushing scenario is
159
CHAPTER 4 RESULTS AND DISCUSSIONS
shown in Figure 4.28.
Figure 4.29 Longitudinal profile of Baira Reservoir after flushing the deposited sediments
Figure 4.29 shows the reservoir bed profile, after flushing the deposited sediments in the reservoir, which were accumulated in 1.5 years. The simulated flushing duration required to flush the deposited sediments by the Model was 32 hours, whereas observed flushing duration was 31 hours. This shows that Model well simulates sediment flushing duration through reservoir.
4.5.2 Gebidem Reservoir of Switzerland
To model the Gebidem Reservoir, input data given to the Model were, geometric data: 25 river cross sections, Manning value of n 0.07 for Leftover Bank (LOB) and Rightover Bank (ROB), and 0.08 for main channel, contraction and expansion coefficients, 0.01, and 0.03 respectively, Dam coordinates (2, 1436), (240, 1436), weir coefficient 1.4 , shape of weir Broad Crested; Quasi-Unsteady flow data: Mean monthly flow data for 8 years-1990 to 1997, Normal depth (bed slope) 0.0807, Temperatures of water, Sediment data: Transport Function Tofalleti, Sorting Method Exner 5, Fall Velocity Approach Tofalleti, maximum erodible depth 10m, Bed Gradation Curve, Equilibrium load was used as u/s sediment boundary condition.
160
CHAPTER 4 RESULTS AND DISCUSSIONS
Figure 4.30 Water surface profile before delta modeling for Gebidem Reservoir
The Figure 4.30 shows the water surface profile with normal operating level of 1436m before sedimentation.
Figure 4.31 Simulated Longitudinal Delta Profile for Gebidem Reservoir after 1 year sediment deposition
The Model was run for a simulation period of 1 year and output resulted deposition is presented in figure 4.31.
161
CHAPTER 4 RESULTS AND DISCUSSIONS
Figure 4.32 Bed profile of Gebidem Reservoir before flushing sediment deposition
Longitudinal profile of the delta which was used as input for the flushing scenario is shown in Figure 4.32.
Figure 4.33 Bed profile of Gebidem Reservoir after flushing sediment deposition
Figure 4.33 shows the reservoir bed profile after flushing the deposited sediments in the reservoir which were accumulated in 1 year. The flushing duration required to flush the deposited sediments was 102 hours with flushing discharge 15 m3/s. All the deposited sediments had been flushed during this flushing period.
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CHAPTER 4 RESULTS AND DISCUSSIONS
4.5.3 Gmund Reservoir of Austria
To model the Gmund Reservoir for sediment deposition and flushing input data given to the Model were geometric data: 29 river cross sections, Manning value of n 0.07 for Leftover Bank (LOB) and Rightover Bank (ROB), 0.08 for main channel, contraction and expansion coefficients 0.01 and 0.03 respectively, Dam coordinates (2, 1190), (146.1, 1190), weir coefficient 1.4 , shape of weir Broad Crested; Quasi-Unsteady flow data: Mean monthly flow data for 8 years-1967 to 1974, Normal depth (bed slope) 0.0323, Temperatures of water, Sediment data: Transport Function, Tofalleti, Sorting Method Exner 5, Fall Velocity Approach Tofalleti, maximum erodible depth 10m, Bed Gradation Curve, equilibrium load was used as u/s sediment boundary condition. .
Figure 4.34 Water surface profile before delta modeling for Gmund Reservoir
Figure 4.34 shows the water surface profile with normal operating level of 1190 m before sedimentation The Model was run for a simulation period of 1 year and output resulted deposition is presented in Figure 4.35.
163
CHAPTER 4 RESULTS AND DISCUSSIONS
Figure 4.35 Simulated Longitudinal Delta Profile for Gmund Reservoir after sediment deposition
Figure 4.36 Bed profile of Gmund Reservoir before flushing Sediment deposition
Longitudinal profile of the delta which was used as input for the flushing scenario in Gmund Reservoir is shown in Figure 4.36.
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CHAPTER 4 RESULTS AND DISCUSSIONS
Figure 4.37 Bed profile of Gmund Reservoir after flushing Sediment deposition
Figure 4.37 shows the reservoir bed profile after flushing the deposited sediments in the reservoir which were accumulated in 1 year.
The observed flushing duration required to flush the deposited sediments of 0.065 Mm3 was 168 hours (7 days), whereas simulated flushing duration by the Model was 180 hours (7.5 days), with flushing discharge of 25 cumecs. Due to flushing operation some aggradations had been obtained on the upstream of the dam site. It was due to the fact that the sill level of the flushing sluices is sufficiently higher than the bed level and hence initially it had to be filled with sediments. However, there was an increase in the degradation of bed profile on the downstream of the dam site. Comparison between the simulated and observed results are presented in Table 4.2
165
CHAPTER 4 RESULTS AND DISCUSSIONS
Table 4.2 Comparison between Simulated and Observed Flushing Durations using HEC-RAS 4.1.0
Parameter Unit Baira Gebidem Gmund
Observed 0.450 0.270 0.076 Deposited sediments Mm3 Simulated 0.450 0.270 0.076
Observed 0.383 0.270 0.065 Flushed sediments Mm3 Simulated 0.385 0.270 0.068
Observed 31 96 168 Flushing duration hours Simulated 32 102 180
Flushing duration hrs/hrs Simulated/ Observed 1.03 1.06 1.07
% Error 3 6 7 average 5
4.6 MODELING SEDIMENT FLUSHING THROUGH RESERVOIRS USING TSINGHUA UNIVERSITY EQUATION 4.6.1 Modeling Sediment Flushing in Baira Reservoir
Q 1.6 S 1.2 By plotting graph between Qs and ( f ) the value of Erodibility Coefficient () 0.6 W f was determined as shown in Figure 4.38. Slope of the curve gives the value of () equal to 8.13 having coefficient of determination (R2) value, 0.9883. The value of is low showing that water level during flushing was higher eroding less sediments during flushing operation for Baira Reservoir.
100
(T/s) s y = 8.1328x R2 = 0.9883 10
Sediment Discharge, Q Discharge, Sediment 1 0.101.6 1.2 1.00 10.00 Q f S W 0.6 f
166
CHAPTER 4 RESULTS AND DISCUSSIONS
Figure 4.38 Determination of Erodibility Coefficient () for Baira Reservoir
35
30
25 +10% 20 -10% 15
10
Simulated Flushing Duration (hrs) Duration Flushing Simulated 5
5 101520253035
Observed Flushing duration (hrs)
Figure 4.39 Comparison between observed flushing duration and simulated flushing duration for Baira Reservoir
Figure 4.39 shows the comparison between the observed flushing durations and the flushing durations determined by the Model for various flushing discharges. From the Figure it is clear that the observed flushing durations well match with the flushing durations determined by the Model.
90.0 Mf = 0.2 MCM 80.0 Mf = 0.383 MCM 70.0 Mf = 0.6 MCM 60.0 50.0 40.0
30.0
20.0 Flushing (hrs) duration 10.0 0.0 100 150 200 250 300 350 400 450 500 Flushing discharge (cumecs)
Figure 4.40 simulated flushing durations against flushing discharges for Baira Reservoir
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CHAPTER 4 RESULTS AND DISCUSSIONS
Figure 4.40 depicts that to flush a certain amount of deposited sediments, flushing durations reduce with increase in flushing discharges and vice versa. Moreover it is also clear from Figure that for a certain flushing discharge, more are the sediments to be flushed, more is the flushing duration required. For example Figure shows that for constant flushing discharge of 150 cumecs, flushing durations required for different masses flushed, 0.2 Mm3, 0.383 Mm3, 0.6 Mm3, are 15.4 hours, 30 hours, and 46 hours, respectively for Baira Reservoir.
4.6.2 Modeling Flushing in Gebidem Reservoir
Q 1.6 S 1.2 By plotting graph between Qs and ( f ) the value of Erodibility Coefficient () 0.6 W f was determined as shown in Figure 4.41. Slope of the curve gives the value of () equal to 2.78 having coefficient of determination (R2) value 0.9491. The value of (is low, showing that water level during flushing was higher, eroding less sediments during flushing operation for Gebidem Reservoir.
10.0
y = 2.7774x R2 = 0.9491 1.0
Sediment Discharge, Qos (T/s) Qos Sediment Discharge, 0.1 1.6 1.2 0.1Qf S 1.0 0.6 Wf
Figure 4.41 Determination of Erodibility Coefficient () for Gebidem Reservoir
168
CHAPTER 4 RESULTS AND DISCUSSIONS
100
90
80 +10 % 70
(hrs) 60 -10 % 50 40 Simulated Flushing Duration Duration Flushing Simulated 30 30 40 50 60 70 80 90 100 Observed Flushing duration (hrs)
Figure 4.42 Comparison between observed flushing duration and simulated flushing duration for Gebidem Reservoir
Figure 4.42 shows the comparison between the observed flushing durations and simulated flushing durations. The Figure shows that values of flushing durations determined by the Tsinghua University Model are very close to the observed flushing durations at different flushing discharges within error of ± 10% shown by the green band.
0.28
0.26 0.24 0.22 0.20 0.18 +10% 0.16 0.14 0.12 -10% 0.10 0.08 0.06
(MCM) Flushed Mass Simulated 0.04 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28 Observed Mass Flushed (MCM)
169
CHAPTER 4 RESULTS AND DISCUSSIONS
Figure 4.43 Comparison between observed flushed sediments and simulated flushed sediments for Gebidem Reservoir
Figure 4.43 shows the comparison between the observed flushed sediments and simulated flushed sediments. The Figure shows that values of various flushed sediment masses determined by the Tsinghua University Model are very close to the observed flushed sediment masses within error of ± 10% shown by the green band.
1600 Mf = 0.27 MCM 1400 Mf = 0.5 MCM
1200 Mf = 1.0 MCM 1000
800
600
400 Flushing duration (hrs) duration Flushing 200
0 0 5 10 15 20 25 30 35 40 Flushing discharge (cumecs)
Figure 4.44 Simulated flushing durations against various flushing discharges for Gebidem Reservoir
Figure 4.44 depicts that flushing durations reduce with increase in flushing discharges for a certain amount of flushed mass and vice versa. Moreover it is also clear from Figure that for a specific flushing discharge, flushing durations increase with the increase in the flushed masses and vice versa. For example Figure shows that flushing durations required for constant flushing discharge of 15 cumecs, for different masses flushed, 0.27 Mm3, 0.5 Mm3, 1.0 Mm3, are 90 hours, 166 hours, and 332 hours respectively for Gebidem Reservoir.
4.6.3 Modeling Flushing in Gmund Reservoir Figure 4.45 shows that Erodibility coefficient () determined is 0.49, having coefficient 2 of determination (R ) 0.9638, which was determined by plotting correlation between Qs
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CHAPTER 4 RESULTS AND DISCUSSIONS
Q 1.6 S 1.2 and ( f ). The value of (is low showing that water level during flushing was 0.6 W f higher eroding less sediments during flushing operation for Gmund Reservoir.
1.0
(T/s)
os y = 0.49x R2 = 0.9638
Sediment Discharge, Q 0.1 0.1 1.0
Figure 4.45 Determination of Erodibility Coefficient () for Gmund Reservoir
Figure 4.46 shows the comparison between the observed flushing durations and simulated flushing durations. The Figure shows that values of flushing durations determined by the Tsinghua University Model are very close to the observed flushing durations at different flushing discharges within ± 14% standard error of estimation. . 600 Mf = 0.0654 MCM 500 Mf = 0.12 MCM Mf = 0.18 MCM 400
300
200
Duration Flushing (hrs) 100
0 25 30 35 40 45 50 55 60 Flushing Discharge (cumecs)
Figure 4.46 Comparison between observed flushing duration and simulated flushing durations for Gmund Reservoir
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CHAPTER 4 RESULTS AND DISCUSSIONS
200 180
160
140 +10% 120
(hrs) 100 -10% 80
60
Simulated Flushing Duration 40 40 60 80 100 120 140 160 180 200 Observed Flushing Duration (hrs)
Figure 4.47 Simulated flushing durations against various flushing discharges for Gmund Reservoir
Figure 4.47 depicts that flushing duration is inversely proportional to the flushing discharge, means that with increase in flushing discharge, flushing duration is decreased and vice versa. Moreover it is also clear from the Figure that for a given flushing discharge, flushing durations increases with increase in the sediment masses to be flushed, and vice versa. For example Figure shows that flushing durations required for constant flushing discharge of 25 cumecs, for different masses flushed, 0.0654 Mm3, 0.12 Mm3, and 1.0 Mm3, are 192 hours, 336 hours, and 503 hours, respectively for Gmund Reservoir.
The results of Tsinghua University Equation are summarized in Table 4.3, showing that Model well simulates sediment flushing through reservoirs and flushing duration required to flush the deposited sediments.
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CHAPTER 4 RESULTS AND DISCUSSIONS
Table 4.3 Comparison Between Simulated And Observed Flushing Durations using Tsinghua University Equation
Parameter Unit Baira Gebidem Gmund
observed 0.383 0.270 0.065 flushed sediments Mm3 simulated 0.401 0.280 0.0601
observed 31 96 168 flushing duration hours simulated 30 90 192
% Error 3 6 14
average 7
Table 4.4 shows the summary of the results of three Models SHARC, HEC-RAS 4.1.0 and Tsinghua University Equation. All the three Models have very good results of modeling and discussed below:
Model SHARC is ideal while simulating sediment mass deposited and sediment mass flushed. The values of calculated mass deposition and mass flushed are very close to the observed values, but it does not well simulate sediment flushing durations and underestimates it. So while simulating the sediment flushing duration, the Model should be used with care. Overall, for the three reservoirs, on average, flushing duration is 4 times lesser than the observed values. So the values obtained by Model may be enhanced by 4 times to make the values realistic.
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CHAPTER 4 RESULTS AND DISCUSSIONS
Table 4.4 Summary Of Results By three Models
TSINGHUA RESERVOIR SCENARIO PARAMETER UNIT SHARC HEC-RAS EQUATION Deposited Mm3 0.45 0.45 - Sediments Flushed Observed Mm3 0.383 0.383 0.383 Sediments Flushing Hours 31 31 31 Duration BAIRA Deposited Mm3 0.452 0.45 - Sediments
Flushed Simulated Mm3 0.385 0.385 0.401 Sediments
Flushing Hours 9.1 34 30 Duration Observed/Simulated Flushing Duration 3.4 0.9 1 Deposited Mm3 0.27 0.27 - Sediments Flushed Observed Mm3 0.27 0.27 0.27 Sediments Flushing Hours 96 96 96 Duration GEBIDEM Deposited Mm3 0.271 0.266 - Sediments
Flushed Simulated Mm3 0.271 0.266 0.28 Sediments
Flushing Hours 30.59 102 90 Duration Observed/Simulated Flushing Duration 3.2 0.9 1.1 Deposited Mm3 0.076 0.076 - Sediments Flushed Observed Mm3 0.065 0.065 0.065 Sediments Flushing GMUND Hours 168 168 168 Duration Deposited Mm3 0.0765 0.076 - Sediments Simulated Flushed Mm3 0.0655 0.068 0.0601 Sediments
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CHAPTER 4 RESULTS AND DISCUSSIONS
Flushing Hours 34.56 180 192 Duration Observed/Simulated Flushing Duration 4.8 0.93 1
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CHAPTER 4 RESULTS AND DISCUSSIONS
As regard HEC-RAS 4.1.0 Model is concerned, the results are reasonably closer to the observed one. Sediment deposition computed by the Model for three different reservoirs equals the observed sediment deposition in the reservoirs. Model also well simulates sediment flushing amounts through the reservoirs. The results of the flushing durations obtained by HEC-RAS 4.1.0 are very close to the observed sediment flushing durations within an error of ±10%.
Tsinghua University Equation used in this study well simulates sediment flushing and flushing durations required during flushing operations. So far the results of Tsinghua University Equation are concerned, it well simulates sediment flushing scenarios in the reservoirs, i.e., sediments mass flushed and the flushing durations. Tsinghua University Equation results for flushing durations for Baira, Gebidem, and Gmund reservoirs are within errors of 3%, 6%, and 14% respectively.
4.7 ASSESSMENT OF FLUSHING EFFICIENCIES FOR SMALL RESERVOIRS
Among the sixty small dams of Punjab, in Pakistan, twenty were selected to calculate the flushing criterions that assess the flushing efficiency of the reservoirs. Among the various flushing indicators, Long Term Capacity Ratio, LTCR gives the value of flushing efficiency. Hence LTCR was calculated for these reservoirs. Input parameters to compute
LTCR are: original capacity of reservoir, Co, normal operating level of reservoir, Elmax, minimum bed level of river, Elmin, water surface elevation at dam during flushing, Elf, representative bottom width of reservoir, Wbot, side slope of reservoir, SSres, side slope of
the exposed sediment after flushing, SSs, mean annual inflow, Vin, mean annual sediment
inflow, Min, Tsinghua University multiplying factor for sediment load prediction, (Ψ),
flushing discharge, Qf , and flushing duration, Tf.
Then after calculating the value of LTCR for the reservoirs, they were ranked in descending order and shown in Figure 4.48. The Figure shows the LTCR values for these reservoirs. Ten reservoirs which have LTCR value less than 0.5, may not be feasible for flushing. These reservoirs are Pira fatehal, Salial, Tain pura I, Lehri, Domeli, Khai, Sawal, Jabba, Minwal, and Shah Habib.
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CHAPTER 4 RESULTS AND DISCUSSIONS
Five reservoirs may be flushed partially as their values are more than 0.5 but not close to unity. These reservoirs are Jalwal, Mial, Dungi, Rawal, and Ghazial. While 5 reservoirs may be successfully flushed. These reservoirs have LTCR values greater than 0.77, the criteria explored by the author for successful flushing of reservoirs. These reservoirs are Jammargal, Talikna, Dharabi, Phalina, and Jabbi. LTCR values for these reservoirs are 0.9, 0.84, 0.81, 0.79, and 0.78 respectively.
1.25 LTCR Critical LTCR
1.00 0.90
0.84 0.81 0.79 0.78 0.75 0.67 0.66 0.63 0.56 LTCR 0.53
0.50 0.47
0.41 0.35 0.33
0.30 0.28 0.27 0.26 0.25 0.25 0.22
0.00
Mial Khai Salial Lehri Jabbi Dungi Sawal Jabba Rawal Jalwal Domeli Minwal Talikna Phalina Ghazial Dharabi Tain Pura I Pura Tain Shah Habib
Jammargal Pira FatehalPira Reservoir
Figure 4.48 LTCR values of 20 selected small reservoirs
4.8 MODELING SEDIMENT DEPOSITION AND FLUSHING OPERATION IN JABBI RESERVOIR USING HEC-RAS 4.1.0
To model the Jabbi Reservoir input data given to the Model was geometric data: 28 river cross sections, Manning value of n for Leftover Bank (LOB) 0.7, Rightover Bank (ROB), 0.7 and Main Channel 0.8, contraction and expansion coefficients 0.1 and 0.3 respectively, Dam coordinates (175.6, 385.7) and (749.3, 385.7), weir coefficient 1.4, shape of weir, Broad crested weir; Quasi-Unsteady flow data: Mean monthly flow data for 10 years-1991 to 2000, Normal depth (bed slope) 0.00677, Temperature of water; Sediment data: Transport Function, Engelund-Hansen, Sorting Method, Exner 5, Fall
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CHAPTER 4 RESULTS AND DISCUSSIONS
Velocity Approach, Report 12, maximum erodible depth, 10m, Bed Gradation Curve, equilibrium load was used as u/s sediment boundary condition.
Figure 4.49 Water surface profile before delta modeling for Jabbi Reservoir
Figure 4.49 shows the water surface profile with normal operating level of 385.7 m, before sedimentation. The Model was run for a simulation period of 1 year, and output resulted deposition is presented in Figure 4.50. Average annual sediment deposition in 1 year was 0.0418 Mm3, whereas simulated annual sedimentation came out to be 0.0418 Mm3, equal to the observed average annual sediment deposition.
Figure 4.50 Simulated Longitudinal Delta Profile for Jabbi Reservoir after 1 year sediment deposition
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CHAPTER 4 RESULTS AND DISCUSSIONS
Longitudinal sediment delta profile which was used as input for the flushing scenario in Jabbi Reservoir is shown in Figure 4.51.
Figure 4.51 Bed profile of Jabbi Reservoir before flushing 1 year deposited sediments.
Figure 4.52 Bed profile of Jabbi Reservoir after flushing the 1 year deposited sediments
Figure 4.52 shows the reservoir bed profile after flushing the deposited sediments in the reservoir which were accumulated in 1 year.
The flushing duration required to flush annual sediment deposition was 1.33 days (32 hours) with flushing discharge of 0.32 m3/s. Due to flushing operation, slight
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CHAPTER 4 RESULTS AND DISCUSSIONS
aggradations had been occurred on the upstream of the dam. It is due to the fact that sill level of the flushing sluices was sufficiently higher than the bed level and hence it had to be filled with sediments.
Figure 4.53 Bed profile of Jabbi Reservoir after 10 years sediment deposition
Simulation of sediment deposition for 10 years was also done by the Model. The Model was run for a simulation period of 10 years and output resulted deposition is presented in Figure 4.53. Simulated sediment deposition is 0.4177 Mm3, close to the observed deposition of 0.418 Mm3.
Figure 4.54 Bed profile of Jabbi Reservoir before flushing 10 years sediment deposition
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CHAPTER 4 RESULTS AND DISCUSSIONS
Longitudinal profile of the delta used as input for the flushing scenario for 10 years sediment deposition in Jabbi Reservoir is shown in Figure 4.54.
Figure 4.55 Bed profile of Jabbi Reservoir after flushing 10 years sediment deposition
Bed profile of the reservoir after flushing 10 years deposited sediments is shown in Figure 4.55. Figure shows that deposited sediments were almost flushed, and there were also some aggradations just upstream of the dam, which was due to the fact that the sill level of the flushing sluices was sufficiently higher than the bed level and hence it had to be filled with sediments.
4.9 MODELING SEDIMENT FLUSHING IN JABBI RESERVOIR USING TSINGHUA UNIVERSITY EQUATION
Tsinghua University Model was used to model sediment flushing through Jabbi Reservoir. Modeling was done to flush annual sediment deposition and deposition after 10 years. The value of Erodibility Coefficient () was determined for flushing annual deposition and also flushing 10 years deposited sediments. Erodibility coefficient () for
1.6 1.2 Q f S s flushing annual sediment deposition determined by the plot between Q and ( 0.6 ) W f was 2788.2. Higher value of (shows that sediments were easily eroded through the
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CHAPTER 4 RESULTS AND DISCUSSIONS
reservoir. The higher value of (is also due to the reason that water level during flushing was much lowered, resulting high erosive velocity during sediment flushing operation.
45 Mf = 0.02 MCM 40 Mf = 0.0326 MCM 35 Mf = 0.04 MCM 30 25 20 15 10
Flushing(hrs) Duration 5
0 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Flushing Discharge (cumecs)
Figure 4.56 Flushing durations against flushing discharges for Jabbi Reservoir for 1 year flushing
Figure 4.56 shows the effect of flushing discharge on flushing duration. The Figure depicts that flushing duration is inversely proportional to the flushing duration, means that with increasing flushing discharge, flushing duration is decreased and vice versa. Moreover it is also clear from Figure that more are the sediments to be flushed, more is the flushing duration required. For example Figure shows that flushing durations required for constant flushing discharge of 0.32 cumecs, for different masses flushed, 0.02 Mm3, 0.0326 Mm3, and 0.04 Mm3 are 21 hours, 34 hours, and 42 hours respectively for annual flushing operation in Jabbi Reservoir.
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CHAPTER 4 RESULTS AND DISCUSSIONS
300 270 Mf = 0.2 MCM 240 Mf = 0.326 MCM Mf = 0.4 MCM 210 180 150 120 90 60 Flushing duration (hrs) duration Flushing 30 0 1.5 2.25 3 3.75 4.5 5.25 6 6.75 7.5 Flushing discharge (cumecs)
Figure 4.57 Flushing durations against flushing discharges for Jabbi Reservoir for 10 years flushing
Results of Tsinghua University Model for flushing 10 years sediment deposition are shown in Figure 4.57. The value of Erodibility coefficient () for flushing annual
1.6 1.2 Q f S s sediment deposition was determined by the plot between Q and ( 0.6 ). The value W f obtained is 538.2. Figure depicts that flushing durations required to flush a certain amount of deposited sediments, reduce with increase in flushing discharges and vice versa. Moreover, it is also evident from the Figure that, for a certain flushing discharge, more are the sediments to be flushed, more is the flushing duration required. For example, Figure shows that flushing durations required for constant flushing discharge of 3 cumecs, for different masses flushed, 0.02 Mm3, 0.0326 Mm3, and 0.04 Mm3 are 59 hours, 96 hours, and 118 hours, respectively, for annual flushing operation in Jabbi Reservoir. Results of HEC-RAS 4.1.0 and Tsinghua University Models are presented in Table 4.5 below.
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CHAPTER 4 RESULTS AND DISCUSSIONS
Table 4.5 Modeling Results for Jabbi Reservoir
Parameter Unit HEC-RAS Tsinghua
observed 0.0418 deposited Mm3 sediments simulated 0.0418 - Flushing observed - - annual flushed sediments Mm3 sediments simulated 0.0326 0.0326 deposition observed - flushing duration hours simulated 32 34
observed 0.418 deposited Mm3 sediments simulated 0.4177 - Flushing observed - - 10 years flushed sediments Mm3 sediments simulated 0.326 0.326 deposition observed - flushing duration hours simulated 96 96
4.10 PROPOSING FLUSHING STRATEGIES FOR JABBI RESERVOIR To study the annual flushing of a reservoir, one has to give answers of the following questions: What would be the appropriate time to flush sediments from the reservoir? How much suitable flushing discharge and flushing duration are required during flushing process? How much time is required to empty the reservoir? How much time is required to refill the reservoir? Which sediment sizes flushable? How much volume of water is required for flushing?
These questions are answered in this section to study the strategies for sediment flushing through the reservoir.
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CHAPTER 4 RESULTS AND DISCUSSIONS
4.10.1 Appropriate time to flush sediments from the reservoir The average daily hydrograph for the flow of the Jabbi stream is shown in Figure 4.58. Figure shows that the flow discharges in the stream, at Jabbi dam site are intermittent. In the months of January, February, June, and December, the flows are minimum. The flows in the months of July, and August are higher having the peak flows 3.07 m3/s, and 7.62 m3/s respectively, meeting well the flushing discharge of 0.32 m3/s. So it may be said that two months July, and August are appropriate for annual flushing operation through the reservoir.
8 hydrograph 7 flushing discharge
6
5
4
flow (m3/s) 3
2
1
0 0 30 60 90 120 150 180 210 240 270 300 330 360 time (days)
Figure 4.58 Average daily flows and minimum flushing discharge required for Jabbi Reservoir (year 1991-2000)
70
60
/s) 3 50
40
30
20 Cumnulative flows (m flows Cumnulative 10
0 0 30 60 90 120 150 180 210 240 270 300 330 360 time (days) Figure 4.59 Flow mass curve for proposed flushing durations
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CHAPTER 4 RESULTS AND DISCUSSIONS
Figure 4.59 is flow mass curve to ensure continuous flow availability for the proposed flushing durations. Figure shows that continuous flows are available for the required flushing duration.
4.10.2 Suitable flushing discharge required during flushing process Mean daily inflow into the reservoir is 0.16 cumecs. Different authors referred that to flush successfully the sediments through the reservoir flushing discharge should be at least twice the mean annual flow; hence the adopted flushing discharge was taken as 0.32 cumecs.
6 flushing after 1 year 5 flushing after 10 years
4
3
2
1 Duration(days) Flushing 0 0.1 1 10 100 Flushing Discharge (m3/s)
Figure 4.60 Flushing durations required to flush one year/10 years deposited sediments
Flushing annual deposition through the reservoir was modeled using HEC-RAS 4.1.0. The Model was run for various flushing discharges, and flushing durations were determined. Flushing was modeled for the range of flushing discharges varying from 0.16 m3/s to 0.96 m3/s. Flushing the sediment deposition of 10 years was also modeled by the Model, using flushing discharges varying from 1.5 to 7.5 m3/s. Variation of flushing durations with varying flushing duration is shown in Figure 4.60. For flushing annual sediment deposition, suitable flushing discharges are 0.32 m3/s to 0.48 m3/s for flushing durations of 34 to 20 hours respectively. For flushing sediment deposition of 10 years, suitable flushing discharges are 3 m3/s to 4.5 m3/s, for flushing durations of 96 hours to 64 hours respectively.
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CHAPTER 4 RESULTS AND DISCUSSIONS
4.10.3 Time required to empty the reservoir? Assumptions made to compute the emptying time of reservoir are that 3 no. sluice gates of the dimensions 1mx8m were provided with sill level 370m, about 3m above the river bed at dam site. The total time required to empty the whole reservoir upto the level of 370 was about 8 hours (0.33 day), as depicted in Figure 4.61.
8
7 6
5
4
3
Emptying time (hrs) time Emptying 2
1
0 370 372 374 376 378 380 382 384 386
Reservoir Level (m)
Figure 4.61 calculated reservoir emptying time
250 Refilling Time = 235 days 200
150
NOL = 385.6 m 100
Filiing Time (days) Time Filiing 50
0 370 372 374 376 378 380 382 384 386 Reservoir Level (m) Figure 4.62 Re-filling time for Jabbi Reservoir
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CHAPTER 4 RESULTS AND DISCUSSIONS
4.10.4 Time required to refill the reservoir After flushing the reservoir, sluice gates would be closed and the reservoir was to be refilled to the normal operating level of 385.7m. Figure 4.62 shows the reservoir refilling times for different reservoir levels. To refill the reservoir upto normal operating level of 385.7m, about 235 days are required. This is the main limitation in flushing Jabbi reservoir that due to low and intermittent daily flow, about more than 7.8 months are required to refill the reservoir, which makes it impractical to carry out flushing operation at Jabbi Reservoir every year.
4.10.5 Flushable sediment size For discharge of 0.32 cumecs, the velocities of flows at various sections are given in Figure 4.63. The maximum velocity is attained at river station No. 14, i.e., 0.79 m/s. for this critical velocity, maximum sediment size that can be flushed is of 8 mm. diameter as determined by the Figure 4.63 (findings of ASCE Task Committee, 1967).
0.8
0.7
0.6
0.5 0.4 0.3 0.2 (m/s) Velocity Mean 0.1
0 0 2 4 6 8 10 12 14 16 18 20 River station
Figure 4.63 Mean velocities at various river stations during annual flushing operation.
Critical velocities at various river stations during flushing 10 years deposited sediments are presented Figure 4.64. The maximum critical velocity is 0.9 m/s at river station 16. From the Figure 4.65 it is found that 10 mm diameter sediment particles can be flushed with this critical velocity.
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CHAPTER 4 RESULTS AND DISCUSSIONS
1 0.9 0.8
0.7 0.6 0.5 0.4
0.3
(m/s) Velocity Mean 0.2 0.1 0 0 2 4 6 8 10 12 14 16 18 20 River Station
Figure 4.64 Mean velocities at various river stations during flushing 10 years deposited sediments
Figure 4.65 Critical water velocities as function of mean grain size (ASCE Task Committee, 1967)
4.10.6 Required flushing duration For whole flushing process, from emptying to refilling total duration required is 237 days for annual flushing with flushing discharge 0.32 cumecs and 240 days for flushing 10 years deposited sediments with flushing discharge of 3 cumecs.
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CHAPTER 4 RESULTS AND DISCUSSIONS
4.10.7 Volume of water required for flushing operation For annual flushing operation with flushing discharge 0.32 cumecs, 3.3 Mm3 volume of water is required and if flushing is performed with flushing discharge of 3 cumecs, to flush 10 years deposited sediments then 4.4 Mm3 water is required for whole flushing operation. Flushing strategies are summarized in Table 4.6
Table 4.6 Flushing summary for Jabbi Reservoir Values S. Description Unit 1 Year 10 Years No. Deposition Deposition July, July, 1 Appropriate time to flush the reservoir month August August 2 Suitable flushing discharge Cumecs 0.32 3 3 Emptying time for the reservoir days 0.34 0.34 4 Flushing duration days 1.33 4 5 Refilling time days 235 235 6 Total time of flushing operation days 237 240 7 Flushable sediment diameter mm 8 10 Volume of water is required per 8 Mm3 3.3 4.4 flushing
Considering the total time required for flushing operation, one flushing is recommended after 10 years. Every year it is very difficult to sacrifice the irrigation releases for a long duration of 237 days. 4.11 SUMMARY
Critical value of LTCR for successful flushing operation has been investigated as 0.77, instead of 1. Using the data of six foreign successfully flushed reservoirs, empirical equations had developed to compute the values of SBR and LTCR by Non-linear Multiple Regression Analysis. Then these equations were tested by applying on the same six foreign successfully flushed reservoirs, and the results were close to the values computed by Atkinson (1996b) method. Then to validate equations, these were applied on 5 small reservoirs of Pakistan: Jammargal, Talikna, Dharabi, Phalina, and Jabbi. The values obtained were close the values determined by Atkinson (1996b) approach, within an error of 3 % to 13 % for SBR and 4 % to 11 % for LTCR, and hence the developed equations can be applied to assess SBR and LTCR.
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CHAPTER 4 RESULTS AND DISCUSSIONS
Three foreign reservoirs, Baira of India, Gebidem of Switzerland and Gmund of Austria were modeled for reservoir sediment deposition and sediment flushing, using 1-D numerical Model SHARC. By modeling these reservoirs it was observed that Model well simulates sediment deposition and sediment flushing through the reservoir, however, it underestimates the flushing durations.
Modeling of the same three foreign reservoirs; Baira, Gebidem and Gmund for sediment deposition and flushing, were carried out by using another 1-D Model HEC-RAS 4.1.0. Model results show that Model well simulates sediment deposition, sediment flushing, and flushing duration.
Modeling of the said three foreign reservoirs; Baira, Gebidem and Gmund was also carried out using Tsinghua University Equation. Model results show that Model well simulates sediment flushing, and flushing duration.
LTCR values of 20 reservoirs of Small Dams Organization were calculated and assessed the feasibility of these reservoirs for sediment flushing, and it was worked out that 5 reservoirs may be flushed successfully, as the values of LTCR were close to unity. These reservoirs are: Jammargal, Talikna, Dharabi, Phalina, and Jabbi, having the respective LTCR values of 0.9, 0.84, 0.81, 0.79, and 0.78 respectively.
Modeling sediment diposition and proposed flushing was carried out using numerical Model HEC-RAS 4.1.0. Flushing duration to flush annually deposited sediments with flushing discharge of 0.32 cumecs, came out to be 1.33 days (32 hours). Modeling of sediment flushing was performed using Tsinghua University Equation. Flushing duration with flushing discharge of 0.32 cumecs, estimated by the Model was 1.42 days (34 hours), and to flush 10 years deposited sediments the estimated flushing duration by the Model was 4 days.
Finally flushing strategies to flush the annual sedimentation and 10 years deposited sediments through the reservoir are planned. In the strategies appropriate time to flush the reservoir, suitable flushing discharge, emptying time for the reservoir, , refilling time for the reservoir, total flushing duration required, sediment size flushable, volume required during flushing and finally flushing efficiency of the reservoir are worked out. For annual
191
CHAPTER 4 RESULTS AND DISCUSSIONS
flushing, suitable flushing discharge is 0.32 cumecs, 0.29 days are required to empty the reservoir, time utilized during flushing operation is 1.42 days, 235 days are required to refill the reservoir, sediment diameter flushable with this discharge is 8 mm and the total water consumed during whole flushing operation is 3.3 Mm3. So far as flushing 10 years deposited sediments is concerned, suitable flushing discharge is 3 cumecs, time of emptying reservoir is 0.29 days, flushing duration required is 4 days, refilling time required is 235 days, 10 mm diameter sediments may be flushed with this flushing discharge and total volume of water required during whole flushing operation is 4.4 Mm3.
As long duration, about 235 days is required to refill the reservoir, so instead of annual flushing, flushing after 10 years looks feasible. Moreover the months when flushing is feasible are July and August based upon the daily flow hydrograph for the reservoir. One flushing after 10 years is recommended to desilt the reservoir.
192
CHAPTER 5
CONCLUSIONS AND RECOMMENDATIONS
5.1 GENERAL
The research for this study was made by analyzing the results of partially flushed reservoirs and successfully flushed reservoirs of the world. By analyzing flushing data of these reservoirs it was ascertained that among the six flushing indicators, LTCR is the most important flushing indicator to assess feasibility of sediment flushing from reservoirs, moreover in literature it is stated that for successful flushing the critical value of LTCR should be close to unity, but by analyzing flushing data of successfully flushed it was established that for successful flushing the critical value of LTCR is 0.77. Then using the flushing data of three foreign successfully flushed reservoirs equations were developed to compute the values of LTCR and SBR with the help of Multiple Non-Linear Regression Analysis. Then using the observed data, sediment depositions processes and sediments flushing operations for three foreign reservoirs were modeled using two 1-D Numerical Models SHARC, and HEC-RAS 4.1.0. Flushing operations for these reservoirs were also modeled using Tsinghua University Equation. Among the sixty small reservoirs of Punjab LTCR values of twenty reservoirs were computed to assess the flushing efficiencies of these reservoirs and it was realized that among these reservoirs five reservoirs might be flushed successfully. Then based upon the geometry of reservoir and the availability of data, small reservoir, Jabbi, was selected to model sediment deposition processes and flushing operations. Sediment deposition processes and flushing operations for this reservoir were modeled using 1-D numerical Model HEC-RAS 4.1.0. Then Tsinghua University Equation was employed to model sediment flushing processes for this reservoir. Based upon the analysis of results for Jabbi Reservoir complete flushing plan for this reservoir was devised.
5.2 CONCLUSIONS
From the analysis of fourteen flushed reservoirs of the world it was concluded
that among the six Flushing Indicators, i.e. SBR, LTCR, DDR, SBRd, FWR and
191
CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS
TWR, the LTCR can be ranked as the most important Flushing Indicator. This Indicator must be evaluated to predict the state of the sediment flushing through a reservoir. In literature it is described that for successful flushing of a reservoir, the critical value of LTCR should be close to unity, but based on analysis of six successfully flushed and eight partially flushed reservoirs of the world, it was investigated that the critical value of LTCR may be taken as 0.77 instead of 1. In the light of modeling results for three successfully flushed reservoirs, Baira of India, Gebidem of Switzerland and Gmund of Austria, it was revealed that SHARC Model well simulates sediment deposition and sediment flushing processes in reservoirs, however, it underestimates the flushing durations. Values of sediments deposited, sediments flushed and flushing durations estimated by the HEC-RAS 4.1.0 Model match well with the observed values, so it was concluded that HEC-RAS 4.1.0 Model might be used to simulate the sediment depositions, sediment flushing and flushing durations. Tsinghua University Equation well simulates sediment flushing operation through the reservoirs by estimating the amount of sediment mass flushed during flushing operation and flushing durations. Based upon the availability of data and the geometries of sixty small reservoirs, twenty Pakistani small reservoirs were analyzed for feasibility of sediment flushing through reservoirs. It was assessed that only five reservoirs Jabbi, Talikna, Dharabi, Phalina and Jammargal seem to be flushed successfully. For Jabbi reservoir, about 64% excedance time of a year is required for the whole flushing operation including three phases i.e. reservoir emptying, flushing and refilling, this much time utilized for complete flushing operation is certainly unaffordable every year, hence annual flushing looks infeasible. Flushing operation to flush 10 years deposited sediments requires about 66% excedance time of a year; hence sediment flushing for Jabbi Reservoir may be performed after 10 years.
192
CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS
5.3 RECOMMENDATIONS
To analyze the reservoirs for sediment flushing, the critical value of LTCR may be taken from 0.77-1. SHARC may be used with care while simulating sediment flushing durations. Annual flushing of Jabbi Reservoir is not recommended, however, flushing may be carried out after each 10 years. Flushing facilities may be provided for 5 small reservoirs of Punjab to enhance their lives, which seem to be feasible for flushing i.e., Jabbi, Talikna, Dharabi, Phalina and Jammargal.
193
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