NUMERICAL SIMULATION FOR SEDIMENT FLUSHING IN …prr.hec.gov.pk/jspui/bitstream/123456789/2470/1/2851S.pdf · As results of the flushing modeling on the three foreign reservoirs proved

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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

ii

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

iii

Dedicated to

My father Chaudhry Muhammad Sadiq

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




3.8 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




4.5 MODELING SEDIMENT DEPOSITION AND SEDIMENT FLUSHING

THROUGH RESERVOIRS USING HEC-RAS 4.1.0 ...............................................157




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 Angeles County)

16

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

Reservoir, Oued Fodda, Algeria (Morris and Fan, 2010) 18

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 (Morris and Fan, 2010)

42

Figure 2.13 Sanmanxi Reservoir, China, during sediment flushing. (Morris and Fan, 2010)

42

Figure 2.14 Welbedacht dam, South Africa, during sediment flushing (Olesen and

Basson, 2004)

43

Figure 2.15 Hydraulic and sediment characteristics for channel formation and channel maintenance during flushing event.

47

Figure 2.16 Slumping of fine-grained deposits near the dam in the small Santa Maria Reservoir on Río Samala, Guatemala (Morris and Fan, 2010)

49

Figure 2.17 Characteristics of retrogressive erosion from flume test. (Morris and Fan, 2010)

51

Figure 2.18 Cross section immediately u/s of the dam for simplified reservoir geometry (Atkinson, 1996b)

62

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 of the variables, here u= u1 , v = u2 , w = u3

87

Figure-3.1 Flow diagram representing Methodology adopted to achieve the objectives

95

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 Reservoir

104

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 Reservoir

107

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 delta modeling for Baira Reservoir

110

Figure 3.18 Flow Hydrographs at Baira dam site used as upstream boundary condition

112

Figure 3.19

Schematic diagram showing the cross section locations used during delta modeling for Gebidem Reservoir

116

Figure 3.20 Flow Hydrographs at Gebidem dam site used as upstream boundary condition

116

Figure 3.21 Schematic diagram showing the cross section locations used for the delta modeling for Gmund Reservoir

120

Figure 3.22 Flow Hydrographs at Gmund dam site used as upstream boundary condition

120

Figure 3.23 Schematic diagram showing the cross section locations used for the delta modeling for Jabbi Reservoir

132

Figure 3.24 Flow Hydrographs at Jabbi dam site used as upstream boundary condition for annual deposition

132

Figure 3.25

Bed material gradation curve of Jabbi Reservoir for annual sediment deposition

134

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 reservoirs

144

Figure 4.8 Comparison between the given and calculated LTCR values of foreign resorvoirs

144

Figure 4.9 Comparison of results for SBR computed by Atkinson (1996) method and developed equations for Pakistani reservoirs

145

Figure 4.10 Comparison of results for LTCR computed by Atkinson (1996) method and developed equations for Pakistani reservoirs

145

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 Baira Reservoir

147

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 Reservoir

149

Figure 4.17 In-transport gradation curves at start and end of deposition process for Gebidem Reservoir

150

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 operation

152

Figure 4.21 Longitudinal sediment delta profile after 1 year deposition in Gmund Reservoir

153

Figure 4.22 In-transport gradation curves at start and end of deposition process in Gmund Reservoir

153

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

to 1.5 year sediment deposition 158

Figure 4.28 Bed profile of Baira Reservoir before flushing based on 1 year Sediment deposition

158

Figure 4.29 Longitudinal profile of Baira Reservoir after flushing the 1.5 years deposited sediments

159

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 year sediment deposition

160

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 flushing duration for Baira Reservoir

166

Figure 4.40 Simulated flushing durations against flushing discharges for Baira Reservoir

166

Figure 4.41 Determination of Erodibility Coefficient () for Gebidem Reservoir 167

Figure 4.42 Comparison between observed flushing duration and simulated flushing duration for Gebidem Reservoir

168

Figure 4.43 Comparison between observed flushed sediments and simulated flushed sediments for Gebidem Reservoir

168

Figure 4.44 Simulated flushing durations against various flushing discharges for Gebidem Reservoir

169

Figure 4.45 Determination of Erodibility Coefficient () for Gmund Reservoir 170

Figure 4.46 Comparison between observed flushing duration and simulated flushing duration for Gmund Reservoir

170

Figure 4.47 Simulated flushing durations against various flushing discharges for Gmund Reservoir

171

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 sediment deposition

176

Figure 4.51 Bed profile of Jabbi Reservoir before flushing 1 year sediment deposition

177

Figure 4.52 Bed profile of Jabbi Reservoir after flushing the 1 year deposited sediment

177

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 deposition

178

Figure 4.55 Bed profile of Jabbi Reservoir after flushing 10 years sediment deposition

179

Figure 4.56 Flushing durations against flushing discharges of Jabbi resorvoir for 1 year flushing

180

Figure 4.57 Flushing durations against flushing discharges of Jabbi resorvoir for 10 years flushing

181

Figure 4.58 Average daily flows and minimum flushing discharge required for Jabbi Reservoir (year 1991-2000)

183

Figure 4.59 Flow mass curve for proposed flushing durations 183

Figure 4.60 Flushing durations required to flush 1 year/10 years deposited sediments

184

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 operation

186

Figure 4.64

Mean velocities at various river stations during flushing 10 years deposited sediments

187

Figure 4.65 Critical water velocities as function of mean grain size (ASCE Task Committee, 1967)

187

xix

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

modeling 111

Table 3.3 Twenty five cross sections used for Gebidem Reservoir during delta modeling

115

Table 3.4 Twenty nine cross sections used for Gmund Reservoir during delta modeling

119

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

Reservoir 131

Table 3.8 Flushing data of Jabbi Reservoir 136

Table 4.1 Comparison between observed and simulated flushing durations using SHARC

156

Table 4.2 Comparison between simulated and observed flushing durations using HEC-RAS 4.1.0

165

Table 4.3 Comparison between simulated and observed flushing durations using Tsinghua University Equation

172

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

xx

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 LoadTWR Top Width Ratio

dC characteristics sediment coefficient

f Specific weight of liquid (water)

ad Arithmetic mean sediment size g Gravitational acceleration

Bq Bed load

d Sediment size

Bq Sediment bed load per unit time and per unit width of the channel.

Fall velocity

* Shield stress

xxi

Kinematics viscosity

dC Characteristics sediment coefficient S Foreset slope of delta deposit

c c Critical shear stress

90d 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

50d 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

xxii

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

K Allowance for the watershed area between the upstrseam gauging station 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 radiusr 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 reservoirTf Flushing duration (days)

Tf Fraction of year used for flushing

TL Total sediment inflow

xxiii

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

CHAPTER 1

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

CHAPTER 1 INTRODUCTION

2

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.


3

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.


4

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:


5

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


6

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.


7

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.


8

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.


9

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.

CHAPTER 2

10

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

CHAPTER 2 LITERATURE REVIEW

11

2.3

1.5

1

0.52

0.3

0.27

0.23

0.2

0.2

0.17

0.1

0.08

0

0.5

1

1.5

2

2.5

3

Ch

ina

M.E

ast

Cen

tr.

Asi

a

S.

Asi

a

S.E

.Asi

a

Pac

. R

im

S.

S.A

fric

a

N.

Eu

ro.

N.

Am

er.

S.

Eu

ro.

S.

Am

er.

N.

Afr

ica

Reigon

An

nu

al S

tora

ge

Lo

ss (

%)

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


12

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


13

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


14

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.


15

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).


16

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)


17

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.


18

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)


19

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:


20

stationgaugingsudam SSLKSSL /1 (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


21

Following empirical equation was developed

afss

Bf

afs

bf

d

q

gd

SR

n

n3/1

3/23/12/3

)(

1)()(25.0047.0

)()(

(2.2)

Where n is the Manning’s roughness, bR 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), ad is

the arithmetic mean sediment size, g is gravitational acceleration, Bq is the bed load rate

in lb/ft/sec, 6/1

90

26'

dn , 90d is the size of sediment at which material is finer by 90%.

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:

GqB

2/3*** 0218.0 (2.3)

Where

0386.0

** (2.4)

dRgdqq BB

* (2.5)


22

dgR

** (2.6)

5.4

853.015474)(

G for > 1.59 (2.7)

2128.912.14exp)( G for 59.11 (2.8)

2.14)( G for 1 (2.9)

For a high sediment transport rate

59.1 5.1** )(Bq (2.10)

Where d is the sediment size and Bq 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*

3

401

40

where 5.5 182.0* (2.11)

391.0465.0 e where 5.5 (2.12)

in which

2/131 dgsF

q

S

B

(2.13)

*

1

O

S (2.14)

2/1

3

22/1

3

2

)1(

36

)1(

36

3

2 F

sgdsgd

(2.15)


23

Where * is the shield stress, is the kinematics viscosity, s is the specific gravity of

the sediments and 50dd median size diameter of the sediments, is unit weight of

water, s is unit weight of solids, o is bed shear stress, and Bq 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:

coodb Cq (2.16)

Here qb is bedload discharge per unit channel width, dC is the characteristics sediment

coefficient, and c is the critical shear stress, o is bed shear stress. Relations for dC 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.

4/3

17.0

dCd (m3/kg/s) (2.17)

dc 093.0061.0 , (Kg/m2), 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:

d

Sqq

sS

co

b

)(1

10

(2.19)

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


24

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.

bedloadsuspendedtotal QQQS (2.20)

Table 2.1 Bed load correction

Condition

Suspended sediment concentration (mg/L)

Stream bed material

Texture of suspended material

Percentage bed load in terms of suspended load

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,

gravel, cobbles, or boulders

Up to 25% sand

5 to 15

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


25

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)


26

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:

etotalstrappeds TQQ (2.21)

Where; trappedsQ is the trapped load in reservoir, totalsQ 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:


27

(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.

oSS 5.0 (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

d

DD

n

Q

QK

S

s

B

2/3

6/190

(2.23)

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

26

6/190D

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.

SS 5.6 (2.24)


28

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.,

oSS (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).

SS ' (2.26)

Where:

'S is the foreset slope of delta and S is the topset slope of the delta deposit

AvgB

VW

(2.27)

W

R

2

(2.28)

RLLPP (2.29)


29

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

RD (2.30)

RSD o (2.31)

Where:

R is Arc Radius, D is Delta Depth, D is Total Flow Depth

)()()( LPPSRLRL oBedatUSBedatPP (2.32)

DRLRL PoBedatPivotPivotPo intint )()( (2.33)

FinesDepthRLRL SUBed /)()( (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



Ouchi-Kurgan of Former USSR, and Sefid-Rud of Iran. Successfully flushed reservoirs


30

1845

1039

938

649

575

319

277

224

188

148

145

117

0

400

800

1200

1600

2000

2400

No

rth

Am

eric

a

So

uth

Am

eric

a

No

rth

Eu

rop

e

Ch

ina

Su

b S

ahar

an A

fric

a

So

uth

Asi

a

Pac

ific

Rim

Mid

dle

Eas

t

No

rth

Afr

ica

Cen

tral

Asi

a

So

uth

Eu

rop

e

So

uth

Eas

t A

sia

Reigon

Sto

rag

e V

olu

me

(Bcm

)

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).

Figure 2.8 Worldwide distributions of storage reservoirs (White et al., 2000)


31

7205

4131

3220

2778

2277

1895

1498

966

895

280

277

78

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

N.

Am

er.

S.

Asi

a

S.

Eu

ro.

Pac

. R

im

N.

Eu

ro.

Ch

ina

S.

Am

er.

S.

S.

Afr

ica

M.E

ast

N.

Afr

ica

SE

.Asi

a

Cen

tr.

Asi

a

Region

No

. o

f re

srvo

irs

21

5

4

3 3

2

1 1 1 1 1 1 1 1 1 1 1 1

0

3

6

9

12

15

18

21

24

Ch

ina

Sw

itze

rlan

d

US

SR

Ind

ia

US

A

Pu

erto

Ric

o

Alg

eria

Au

stri

a

Co

sta

Ric

a

Gu

atem

ala

Iran

Jap

an

New

zeal

and

Pak

ista

n

Su

dan

Tai

wan

Tu

nis

ia

Ven

ezu

ela

Countries

No

. o

f F

lush

ed R

eser

voir

s

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).

Figure 2.10 Worldwide distribution of flushed reservoirs


32

0

10

20

30

40

50

F FR FD FRD DFMode of sediment removal

No

. o

f R

eser

voir

s

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).

Figure 2.11 Mode of flushing used in the reservoirs, worldwide


33

TABLE 2.2 Successfully Flushed Reservoirs

S.No. Reservoir Capacity

(Mm3) Sedimentation experience Flushing Experience

1 Baira India

9.6 Assumed annual rate as 0.092, but 0.45 Mm3

accumulated in 18 months.

Used diversion tunnel, clearing 0.38 Mm3 in 40 hours, interruption to generation, annual flushing thereafter.

2 Gebidem

Switzerland 9.0

Virtually no sediment accumulation, because of gorge type geometry and annual flushing.

Reservoir emptied for 2-4 days per year and about 3 Mm3 water was used, virtually no sediment accumulation, because of gorge-type and annual flushing.

3 Gmund Austria

0.93

0.2 Mt/yr initially, reducing to 0.07 Mt/yr after u/s reservoir built in 1967.

Flushing undertaken intermittently between 1946-1960 and annual flushing thereafter.

4 Hengshan

China 13.3

3.19 Mm3 deposited 1966-73, reaching depth of 27m at dam.

3.19 Mm3 deposition between 1966-73.Emptied and flushed for 37 days in 1974, removing 0.8 Mm3 of deposits; 52 days flushing in 1979 removed 1.03 Mm3 deposits.

5 Palagnedra Switzerland

5.5

1978 flood caused 1.8 Mm3 deposition (33% of original storage) and submerged bottom outlet.

1978 flood caused 1.8 Mm3 deposition, flushing between November 1978-March 1979 removed 2.4 Mm3 deposits, virtually full capacity of reservoir can be maintained in the long term.

6 Santo-

Domingo Venezuela

3 0.58 Mm3 deposited in 2 years, 1976-78; 0.77 Mm3 in 4 years, 1974-78.

Only one flushing operation in May 1978, after 4 years of operation and flushed 50-60% of deposition in 3 days. Concluded that flushing should be annual.


34

TABLE 2.3 Partially Flushed Reservoirs

S.No. Reservoir Capacity

(Mm3) Sedimentation experience Flushing Experience

1 Guanting

China 2270

350 Mm3 deposited in 1953-60; subsequently many u/s reservoirs constructed, substantially reducing sediment inflows.

Only one flushing operation in 1954 removing 10% of annual flow partly venting by density current.

2 Guernsey

U.S.A 91

39.3% of storage lost between 1927-57, when sediment contributing catchment reduced from 14000 to 1800 Km2.

Attempted in four years 1959-62, but not considered effective, as recovered less than 0.2% of the original capacity of reservoir.

3 Heisonglin

China 8.6

1.62 Mm3 deposition in first three years of operation (6% storage loss per year); capacity reduced to 5.87 Mm3 by 1973

From 1962, density current venting and flood season sluicing reduced trap efficiency to about 15% ; lateral erosion technique successfully implemented from 1980, recovering some lost storage; long term capacity expected to be 30-35% of original.

4 Ichari India

11.6

Sedimentation reached spillway crest after 1 year; 85% trapping much greater than indicated by Brune curve; anticipated long term capacity about 35%

No bottom outlet built for flushing and reservoir flushed annually by fully opening spillway gates.

5 Ouchi-Kurgan U.S.S.R

56.4

Bed levels rose upto 23m by 1969; sediment volume appears to have stabilized at 30 Mm3 since 1968

Sluiced for 3-4 months annually since 1963.

6 Sanmenxia

China 9640

Severe, with 1800Mt deposited in first 18 months

Rehabilitation from 1966 included construction of larger low level outlets; flushed for 4 months annually; six development stages are described in literature.

7 Sefid-Rud

Iran 1760

Severe, causing loss of 21% of the storage capacity per year upto 1980 (T.E.=73%) most of sediment releases occurred in density currents.

Flushing (about 4 months/year) commenced in 1980; after 7 years 26% of lost storage had been recovered; from 1992 flood plain erosion enhanced using diversion channels ; expected that long term capacity could be upto 90% of original reservoir capacity.

8 Shuicaozi

China 9.6

8.18 Mm3 (85% of storage) lost between 1958-81; bed levels at dam only 7m below impounding level.

Implemented experimentally from 1965; but limited by high elevation of spillway and short duration annually to about one third of inflow.

9 Naodehai

China 168

Capacity reduced to about 60% by 1950, but recovered to about 80% by early 1970s

Bottom outlets ungated prior to 1970, so flushing appears to have been natural.

10 Nanqin China

10.2 Storage loss 53% by 1983 life span then expected to be upto 2000 if flushing not

Density current venting commenced in 1977 discharging about 2.43 Mt of suspended sediment load between


35

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:

instigated. 1977-84. Experimental flushing from 1984 with good results concluded that flushing should be undertaken for 4 days every 3-4 years.

11 Zemo-Afchar U.S.S.R

Not found in

literature

76% of capacity lost in 10 years.

Implemented from 1939, with full drawdown and appeared to keep situation stable upto 1955, removing about 1 Mm3 per year

12 Warsak Pakistan

170

30 Mm3 deposition between 1960-70, by 1980 reservoir was totally sedimented, except 60m wide 6m deep channel on right bank leading to power intakes.

No bottom outlet provided. Five flushing operation over spillway crest performed between 1976-79, with total duration 20 days and scoured 4.2 Mm3 of deposited sediments.

13 Jensanpei Taiwan

7 Storage loss 4.26 Mm3 between 1938-55 representing 3.4% per year.

Flushing commenced since 1955 for 2.5 months annually, virtually arresting subsequent sedimentation, but not restoring capacity, minor raising of impounding level in 1942 and 1958.

14 KHASHM EL GIBRA

Sudan 950

Capacity seriously depleted

Flushing operations in 1971 and 1973 each removed 85 MTons.

15

Mangahao

New Zealand

Not found in

literature

59% storage loss by 1958; problem become serious by mid 1960.

Flushed in 1969 through low level diversion tunnel and 73% of accumulated sediment removed in one month; subsequently annual emptying and flushing performed during 3 week closure of power house.

16 Cachi

Costa Rica 54

Estimated that 18% flows without deposition , 54% passes by density current venting and 28% deposited

Commenced since 1973 and 14 flushing operations performed in 18 years and reduced trapping efficiency from 82% to 27 %.

17 Honglingjin

China 8.6

0.57 Mm3 deposited per year. From 1960-63 in impounding mode, representing 3.5% storage loss per year

Water level lowered in flood season, resulting in substantial reduction in storage loss 0.45 Mm3 per year 1964-73; technique is essentially routing/sluicing

18 Loiza

Puerto Rico 27

Lost 53% of capacity between 1953-94; three 1.1m low level outlets blocked.

Mechanical method employed unsuccessfully in 1994; dredging considered in 19995; technique employed is routing/sluicing

19 Zhenziliang

China 36.6

4.3 Mm3 deposited per year between 1959-61, in impounding mode, representing 12% storage loss per year.

Water level lowered in flood season, reduction in storage loss 0.77 Mm3 per year between 1962-73; technique is essentially routing/sluicing


36

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:-


37

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.


38

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


39

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.


40

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


41

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.


42

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)


43

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


44

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.


45

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.


46

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.


47

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.


48

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.


49

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


50

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


51

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


52

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


53

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.

No.

Efficiency

Expression Author Remarks

1 d

o

V

VE Qian (1982)

Vo outflowing water volume

Vd volume of deposit flushed out (m3)

2 i

o

L

LE

Ackers and

Thompson (1987)

Lo annual sediment flushed out

Li annual sediment inflow (Kg)

3

oV

VVE 12 Mahmood (1987)

V2 reservoir storage capacity after flushing

V1 storage capacity before flushing (m3)

4

oriV

VVE 12 Mahmood (1987) Vori original live storage capacity (m3)

5 f

r

T

TE

1 Mahmood (1987)

Tr fraction of year in which sediment load refill

reservoir restored capacity (V2 - V1)

Tf fraction of year consumed during flushing

6 d

o

L

LE Atkinson (1996b)

Lo annual sediment flushed out

Ld sediment deposited annually (Kg)

7

o

siso

V

VVE

Lai and Shen

(1996)

Vso outflowing sediments during flushing

Vsi inflowing sediments during flushing (m3)

8

o

iioo

V

CVCVE

Morris and Fan

(2010)

Vi inflowing water volume (m3)

Co outflowing total sediment concentration

Ci inflowing total sediment concentrations

(kg/m)


54

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.


55

Table 2.5 Overflow drawdown flushing

Reservoir Outflow

situation

Years of

operation

Discharg

e

m3/s

Durat

ion

Flushing

efficiency

Water:

sediment

ratio

Guernsey

USA

Overflow spillway

1960-1962 56.6 -

198

10-18 days

0.00017 5880

Warsak

Pakistan

Overflow spillway

1976-1979, 5 flushings

1410

Total 490.5

h

0.00169 592

Liujiaxia

china

Overflow outlets

water level lowered

4.4-7.8 m

1981,1984, 1985,1988

1660 - 2090

103 - 177 h

0.0023 - 0.0071

435-141

Shuicaoz

China

Overflow spillway

1965,1966, 1974, 1978, 1980, 1981

21.4-230 3-4

days

0.012 - 0.043

83-23

(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.


56

Table 2.6 Flushing efficiency for reservoir emptying

Reservoir Years of

operation

Discharge

m3/s

Flushing

duration

Flushing

efficiency

Water:

sediment

ratio

Gebidem, Switzerland

1969-1994 39 35h/yr 0.048-0.060 21-17

Barenburg, Switzerland

1985 * 20 h 0.060 17

Ferrera, Switzerland

1985 * n.d. 0.026 38

Gen-shan-pei, China

1958-1983 * 53 days/yr 0.0897 11

Santo Domingo, Venezuela

1978 8-10 n.d. 0.09-0.13 11-8

Donfanghong, China

1984 51 n.d. 0.056-0.083 18-12

Sefid-Rud, Iran

1980-1987 * 61-157 days 0.022-0.067 45-15

Zemo-Afchar, U.S.S.R

1939-1966 72-688 13-76 h 0.015-0.096 67-10

Chirurt, U.S.S.R.

1968 400-500 5 days 0.04 25

(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).


57

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.


58

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


59

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.


60

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;

dep

f

M

MSBR (2.35)

Where Mf is sediments mass flushed annually and Mdep is the sediments mass deposited annually.

min2 ElElSSWW fresbotres (2.36)

5.08.12 ff QW

(2.37) Minimum of Wres and Wf Will be used for calculation purpose

L

ElElS f max

(2.38)

6.0

2.16.1

W

SQQ f

S (2.39)

(Qs)modified is calculated by dividing Qs by a factor of 3, as the reservoir is different from

Chinese reservoirs (Atkinson, 1996b).


61

Sff QTM 86400 (2.40)

100

TEMM in

dep (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

r

f

A

ALTCR (2.42)

fStf ElElSSWW max2 (2.43)

minmax ElElSSWW resbott (2.44)

If Wtf < Wt Then

minmax2ElEl

WWA tf

f

(2.45)

If Wtf > Wt

Then

reslsmlfff SShSShhhhWA 2 (2.46)

Where, hm , hl and hf are defined in Figure 2.18 and calculated below as;

resS

resm SSSS

WWh

2

(2.47)

mfl hElElh max (2.48)

ff ElElh max (2.49)


62

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:

minmax

min1ElEl

ElElDDR f

(2.51)

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

bot

f

W

WFWR

(2.52)

minmax2ElEl

WWtAr bot

(2.50)


63

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 is computed by the following formula minmax2 ElElSSWW sbottd (2.54)

Where Af is the cross sectional area of valley scoured out by flushing (m2), Ar is the cross

sectional area of reservoir in reach immediately upstream from dam (m2), 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

reservoir during flushing (m3/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

t

td

W

WTWR (2.53)


64

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


65

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

lqx

Q

t

A

(2.55)

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:

02

Of SSgAx

gAA

Q

xt

Q

(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.


66

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


67

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


68

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.


69

1

A

FCG gr

gr

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;

n

sgr

V

uD

dsGX

*

(2.57)

and

(2.58)


70

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,

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,

(2.60)

(2.59)

2/3

50

502

1

05.0

d

g

dVg

s

o

s

ss

*

6/7

1'

01.0u

fD

dC

c

osm


71

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

is critical bed shear stress,

*uf 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:

(middle zone) (2.62)

(upper zone) (2.63)

znmsb dMg 756.012 (bed zone) (2.64)

nzL VRnCM 756.012.43 (2.65)

(lower zone) (2.61)

zn

RRR

Mg

znznz

ssM

1

24.115.224.11

11244.0

zn

RR

RR

Mg

znzn

Zz

ssU 5.11

5.25.224.11

5.115.11

5.0244.0

zn

dR

Mg

znm

zn

ssL 756.01

224.11

756.01756.01


72

sbssUssMssLs ggggg (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:

for sand dm < 2 mm

(2.68)

for gravel dm 2 mm

(2.67)

SVcrSVud

udC

m

mt

log*log314.0log409.0799.1

log457.0log286.0435.5log

*

*

SVcrSVud

udC

m

mt

log*log282.0log305.0784.2

log816.4log633.0681.6log

*

*


73

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).

s

bv gDWs

VC

1

0018.0

(2.69)

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


74

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.


75

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).

1.23.0*

5.150

5.05.01053.0 TDdgsqb (2.70)

asus FVhCq (2.71)

Where qb is the bed load transport rate (m2/s), qsus is the suspended load transport rate

(m2/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.


76

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


77

constantly adjusted toward equilibrium through scour and deposition. The transverse bed

load per unit channel length bq 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:


78

(2.72)

Where Qs is sediment load during flushing (tons/s), is erodibility coefficient, Qf is

flushing discharge (m3/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

discharge Qf (m3/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:

servoirofLength

FlushingduringLevelWaterservoirservoirofLevelOperatingNormalS

Re

ReRe (2.73)

and

5.08.12 ff QW (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

flushing data from reservoirs in China ranging from Qf 0.1-5730 (m3/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

6.0

2.16.1

f

fs

W

SQQ


79

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

(1985)

Atkinson

(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).


80

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


81

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

jij

i

jj

i

j

jii uux

u

xx

pF

x

uu

t

u''

1

(2.75)

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


82

ρ ji uu '' = turbulence stresses

Conservation of mass equation

0

i

i

x

u (2.76)

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


83

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:


84

• 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


85

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.

1

creq

(2.77)

Where qe is mass of sediment eroded per unit area of bed surface per unit of time

(Kg/m2/s), τ is shear stress (N/m2), τcr is critical stress at which erosion commences

(N/m2), and α is coefficient of erodibility.

dvd Cq

1 (2.78)

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.


86

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.


87

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:

jij

i

jj

i

j

jii uux

u

xx

p

C

F

x

uu

t

u''

1

(2.79)

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 ; -ρ ji uu '' is

turbulence stresses. Whereas conservation of mass can be expressed by the continuity

equation for incompressible fluids as:

0

i

i

x

u (2.80)

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


88

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.


89

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


90

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


91

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


92

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.

CHAPTER 3

93

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,

CHAPTER 3

94

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.

CHPTER 3 METHODOLOGY

95

Methodology

Data Collection

Modeling Jabbi

Reservoir by

Tsinghua Equation

Flushing strategies for Jabbi Reservoir

Modeling

Jabbi Reservoir by HEC-

RAS

Assessing LTCR Of

Small Reservoir

FlushingModeling

by Tsinghua Equation

Modeling

by SHARC & HEC-

RAS

Equations developme

nt

Exploring imp

flushing indicators

Geometric Data

Flow Data

Sediment Data

Flushing Data

Data input

Regression Analysis

Eqns developme

nt

Equations testing

Input Data

Reservoir Modeling

Geometric Data

Flow Data

Sediment Data

Flushing Data

Modeling sediment

deposition

Modeling sediment flushing

Determine flushing duration

Input Data

Reservoir Modeling

Geometric Data

Flow Data

Sediment Data

Flushing Data

Modeling sediment

deposition

Modeling sediment flushing

Determine flushing duration

Figure 3.1 Flow diagram representing Methodology adopted to achieve the objectives


96

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



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.


97

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

Sr No. Reservoir L Vin Co Qf Tf S A 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:

fedf

cf

bino

a ASTQVCLKSBR / (3.1)

fedf

cf

bino

a ASTQVCLKLTCR / (3.2)

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


98

)ln()ln()ln()ln()/(ln)ln(ln)ln( AfSeTdQcVCbLaKSBR ffino (3.3)

)ln()ln()ln()ln()/ln()ln(ln)ln( AfSeTdQcVCbLaKLTCR ffinO

(3.4) The above two equations can be written in the form:

7654321 XfXeXdXcXbXaKX (3.5)

The above equation is the multiple regression equation and for its solution the following

seven normal equations are used:

7654321 XfXeXdXcXbXaNKX (3.6)

72625242322

2221 XXfXXeXXdXXcXXbXaXKXX (3.7)

736353432

332331 XXfXXeXXdXXcXbXXaXKXX (3.8)

7464542

44342441 XXfXXeXXdXcXXbXXaXKXX (3.9)

75652

5545352551 XXfXXeXdXXcXXbXXaXKXX (3.10)

762

665646362661 XXfXeXXdXXcXXbXXaXKXX (3.11)

2

77675747372771 XfXXeXXdXXcXXbXXaXKXX (3.12)

Where:

)ln(1 SBRX )ln(LTCR

)ln(2 LX )/ln(3 inO VCX

)ln(4 fQX )ln(5 fTX

)ln(6 SEX )ln(7 AX


99

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.


100

(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


101

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.


102

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


103

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


104

(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.


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


105

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.


106

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.


107

(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


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


108

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


109

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


110

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


111

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 Area2 23 200 u/s of Reservoir Area3 22 200 u/s of Reservoir Area4 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 site28 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


112

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


113

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.


114

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.


115

Table 3.3 Twenty Five Cross Sections Used For Gebidem Reservoir during Delta Modeling


1 18 100 u/s of Reservoir Area2 17 100 u/s of Reservoir Area3 16 100 u/s of Reservoir Area4 15 100 u/s of Reservoir Area5 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 site22 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.


Mean monthly inflow hydrograph for 8 years (1990-1997) was assigned to the Model as



116

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




the entire Model.


117

Sediment Data Once the geometric data was entered, the sediment data was entered to develop a delta



(a) Initial Conditions and Transport Parameters.


(a) Initial Conditions and Transport Function:


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.


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.



sections were used as the geometric data, with modified coordinates of the cross sections

in the deposited area of the reservoir during delta modeling.


118



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.



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.


119

Table 3.4 Twenty Nine Cross Sections used for Gmund Reservoir during Delta Modeling


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


120

Figure 3.21 Schematic diagram for cross section locations during delta modeling for Gmund Reservoir


Mean monthly inflow hydrograph for 8 years (1960-1967) was assigned to the Model as


Figure 3.22 Flow Hydrographs at Gmund dam site used as upstream boundary condition


121

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




the entire Model.

Sediment Data

Once the geometric data was entered, the sediment data was entered to develop a delta



(a) Initial Conditions and Transport Parameters.


(a) Initial Conditions and Transport Parameters:



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.


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


122

for model was 10 m.


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


sections were used as the geometric data, except that those were modified as obtained

after one year delta modeling.


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.



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.




123

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)

6.0

2.16.1

f

fs

W

SQQ (3.13)

Where Qs is sediment load during flushing (tons/s), is erodibility coefficient, Qf is

flushing discharge (m3/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.

No. Parameter Unit

Reservoir

Gebidem Gmund

1 Flushed Sediment

Volume Mm3 0.383 0.27 0.065

2 Flushing Period hour 31 (0900 hrs 14Aug 1983-

1600 hrs 15 Aug 1983 96 (1991)

168

(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

6.0

2.16.1

f

f

W

SQ

Bottom width of flushing channel, Wf , was calculated by the equation (3.14)


124

5.08.12 ff QW (3.14)

Longitudinal energy slope S during flushing may be computed by equation (3.15)

servoirofLength

FlushingduringLevelWaterservoirservoirofLevelOperatingNormalS

Re

ReRe

(3.15)

Flushing duration, Tf,, required to flush different masses of deposited sediments

may be computed by the equation (3.16)

2.16.1

6.0

86400 SQ

WMT

f

fff

(3.16)

Here Mf is the sediment mass flushed (tons)

Flushed mass from the reservoir may be determined by the equation (3.17)

6.0

2.16.1

86400f

fff

W

SQTM (3.17)

3.7.1 Baira Reservoir of India

Input parameters given to the Tsinghua University Model were: observed flushing

discharge, Qf, 150 cumecs, mass flushed, Mf, 0.383 Mm3, 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


125

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.


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

was linearly plotted between Qos and

6.0

2.16.1

f

f

W

SQ. The slope of the line

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.


126

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.


Input parameters given to the Tsinghua University Model were: observed flushing

discharge, Qf, 25 cumecs, mass flushed, Mf, 0.0654 Mm3, 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.


127

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.


128

Table 3.6 Input data of 20 reservoirs of Small Dams Organization (SDO), Islamabad

SNo. Reservoir District Co L Elmax Elmin Elf Wbot SSres SSs Vin Min

Ψ Co/Vin TE Qf Tf

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


129

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.


130

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


131

Table 3.7 Twenty Eight Cross Sections used during Delta Modeling for Jabbi Reservoir


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


132

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


133

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


(a) Initial Conditions and Transport Parameters


(a) Initial Conditions and Transport Parameters:



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


134

0

10

20

30

40

50

60

70

80

90

100

0.001 0.01 0.1 1 10 100

dia (mm)

% f

iner

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.

Figure 3.25 Bed material gradation curve of Jabbi Reservoir for annual sediment deposition


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.



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


135

transport processes. The normal depth was given by assigning a value of friction slope as

0.00677.


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.


136


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

Sr. No Parameter Unit

Flushing Jabbi Reservoir

Annual flushing Flushing after 10

Years

1 Proposed Flushed

Sediment Volume Mm3 0.0326 0.326

2 Proposed Flushing

Duration hour 34 96

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?


137

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.


138

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

are, length of reservoir, L (m), average annual water inflow, Vin (Mm3), gross capacity of

reservoir, Co (Mm3), 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.

CHAPTER 4

139

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.

CHAPTER 4 RESULTS AND DISCUSSIONS

140

33

21

11

7 7 7 7

4.6

4 3.4

3

1 0.7

0.2

0

5

10

15

20

25

30

35

40

Plg

nd

ra

Gm

un

d

S.

Dm

ng

o

Och

i K

rgn

Ich

ari

Gb

idm

Bai

ra

Sh

cao

zi

Sfd

Ru

d

Sn

mn

xia

Hn

gsh

n

Grn

sy

Hsn

gli

n

Gn

tng

Reservoirs

SB

R

successfulreservoircalculated SBR

critical SBR

1 1

0.96

0.93

0.89

0.81

0.77

0.77

0.75

0.68

0.44

0.37

0.31

0.14

0

0.2

0.4

0.6

0.8

1

1.2

S.

Dm

ng

o

Plg

nd

ra

Sfd

Ru

d

Gb

idm

Gm

un

d

Gn

tng

Hn

gsh

n

Hsn

gli

n

Sn

mn

xia

Bai

ra

Grn

sy

Sh

cao

zi

Ich

ari

Och

i K

rgn

Reservoirs

DD

R

successful reservoircalculated DDRcritical DDR

Figure 4.1 SBR values of flushed reservoirs of world

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.


141

110

58

33 33

24 20

15 11

4.8

4.3

4 3.2

1 0.3

0102030405060708090

100110120130

Och

i K

rgn

Gm

un

d

Plg

nd

ra

Ich

ari

Bai

ra

Gb

idm

Sh

cao

zi

S.

Dm

ng

o

Sn

mn

xia

Sfd

Ru

d

Hn

gsh

n

Grn

sy

Hsn

gli

n

Gn

tng

Reservoirs

SB

Rd

successful reservoir

calculated SBRd

critical SBRd

9.9

6.7

5.2

3.4

2

1.4

1.4

1.4

1

0.3

0.26

0.1

0.06

0.04

0

2

4

6

8

10

12

Ich

ari

Gb

idm

Gm

un

d

Bai

ra

Och

i K

rgn

S.

Dm

ng

o

Plg

nd

ra

Grn

sy

Sh

cao

zi

Sfd

Ru

d

Sn

mn

xia

Hn

gsh

n

Hsn

gli

n

Gn

tng

Reservoirs

FW

R


calculated FWR

critical FWR

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.

Figure 4.4 FWR values of flushed reservoirs of world


142

7.1

2.1

1.8

1.6

1.5

1.4

1.3

1 0.9

0.8

0.5

0.3

0.26

0.1

0

2

4

6

8

Hn

gsh

n

Sh

cao

zi

S.

Dm

ng

o

Bai

ra

Gb

idm

Ich

ari

Gm

un

d

Plg

nd

ra

Sn

mn

xia

Hei

son

gli

n

Gu

anti

ng

Och

i K

rgn

Grn

sy

Sfd

Ru

d

Reservoirs

TW

R


calculated TWR

critical TWR

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.

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


143

1 1 0.99

0.98

0.85

0.77

0.39

0.39

0.36

0.3

0.26

0.2

0.13

0.1

0

0.2

0.4

0.6

0.8

1

1.2

S.

Do

min

go

Plg

nd

ra

Gb

idm

Gm

un

d

Bai

ra

Hn

gsh

n

Sh

cao

zi

Sn

mn

xia

Ich

ari

Hsn

gli

n

Grn

sy

Gn

tng

Sfd

Ru

d

Och

i K

rgn

Reservoirs

LT

CR


calculated LTCR

critical LTCR

value of

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


144

0

5

10

15

20

25

30

35

Palg

an

ed

ra

Gm

un

d

San

to-

Do

min

go

Geb

idem

Bair

a

Hen

gsh

an

SB

R

Reservoir

Atkinson Value

Calculated Value

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.

587.0152.0

097.0015.00566.0036.0

/ ino

ff

VCL

ASTQSBR (4.1)

019.0019.0077.0

152.0028.0072.0

/ AQVC

STLLTCR

fino

f (4.2)

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.

Figure 4.7 Comparison between the given and calculated SBR values for foreign


145

0.0

0.2

0.4

0.6

0.8

1.0

1.2

Jammargal Talikna Dharabi Phalina Jabbi

Reservoir

LT

CR

Atkinson Value

Calculated Value

0.00

0.10

0.20

0.30

0.40

0.50

0.60

Talikna Jabbi Jammargal Dharabi Phalina

Reservoir

SB

R

Atkinson Value

Calculated Value

0.0

0.4

0.8

1.2

Pal

gane

dra

San

to-D

omin

go

Geb

idem

Gm

und

Bai

ra

Hen

gsha

n

LTC

R

Reservoir

Atkinson Value

Calculated Value

reservoirs

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.

Figure 4.9


146

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.


THROUGH RESERVOIRS USING SHARC 4.4.1 Baira Reservoir of India


147

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


148

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


149

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


150

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.


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


151

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.


152

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,


153

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.


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.


154

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


155

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.


156

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


157

Figure 4.25 Concentration leaving the Gmund Reservoir during flushing operation

Table 4.1 Comparison between Observed and Simulated Flushing Durations Using SHARC Model

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

Parameter Unit Baira Gebidem Gmund

Deposited sediments Mm3 Observed 0.450 0.270 0.0760

Simulated 0.452 0.271 0.0765

Flushed sediments Mm3 Observed 0.383 0.270 0.0650

Simulated 0.385 0.271 0.0655

Flushing duration hours Observed 31 96 168

Simulated 9.1 30.59 34.56

Flushing duration hrs/hrs Observed/Simulated 3.4 3.2 4.8

Average 3.8


158

(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.


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


159

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


160


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.


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.


161

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.


162

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


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.


163


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.


164

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.


165

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


166

y = 8.1328x

R2 = 0.9883

1

10

100

0.10 1.00 10.00

Sed

imen

t D

isch

arg

e, Q

s (T

/s)

Table 4.2 Comparison between Simulated and Observed Flushing Durations using HEC-RAS 4.1.0

4.6 MODELING SEDIMENT FLUSHING THROUGH RESERVOIRS USING TSINGHUA UNIVERSITY EQUATION

4.6.1 Modeling Sediment Flushing in Baira Reservoir

By plotting graph between Qs and (6.0

2.16.1

f

f

W

SQ ) the value of Erodibility Coefficient ()

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.


Deposited sediments Mm3 Observed 0.450 0.270 0.076

Simulated 0.450 0.270 0.076

Flushed sediments Mm3 Observed 0.383 0.270 0.065

Simulated 0.385 0.270 0.068

Flushing duration hours Observed 31 96 168

Simulated 32 102 180

Flushing duration hrs/hrs Simulated/ Observed 1.03 1.06 1.07

% Error 3 6 7

average 5

6.0

2.16.1

f

f

W

SQ


167

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

100 150 200 250 300 350 400 450 500

Flushing discharge (cumecs)

Flu

shin

g d

ura

tio

n (

hrs

)

Mf = 0.2 MCM

Mf = 0.383 MCM

Mf = 0.6 MCM

5

10

15

20

25

30

35

5 10 15 20 25 30 35

Observed Flushing duration (hrs)

Sim

ula

ted

Flu

shin

g D

ura

tio

n (

hrs

)

+10%

-10%

Figure 4.38 Determination of Erodibility Coefficient () for Baira Reservoir

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.

Figure 4.40 simulated flushing durations against flushing discharges for Baira Reservoir


168

y = 2.7774x

R2 = 0.9491

0.1

1.0

10.0

0.1 1.0

Sed

imen

t D

isch

arg

e, Q

os

(T/s

)

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

By plotting graph between Qs and (6.0

2.16.1

f

f

W

SQ ) the value of Erodibility Coefficient ()

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.

Figure 4.41 Determination of Erodibility Coefficient () for Gebidem Reservoir

6.0

2.16.1

f

f

W

SQ


169

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.

30

40

50

60

70

80

90

100

30 40 50 60 70 80 90 100

Observed Flushing duration (hrs)

Sim

ula

ted

Flu

sh

ing

Du

rati

on

(h

rs)

+10 %

-10 %

0.040.060.080.100.120.140.160.180.200.220.240.260.28

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)

Sim

ula

ted

Mas

s F

lush

ed (

MC

M)

+10%

-10%


170

0

200

400

600

800

1000

1200

1400

1600

0 5 10 15 20 25 30 35 40


Flu

shin

g d

ura

tio

n (

hrs

)

Mf = 0.27 MCM

Mf = 0.5 MCM

Mf = 1.0 MCM

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.

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

of determination (R2) 0.9638, which was determined by plotting correlation between Qs


171

y = 0.49x

R2 = 0.9638

0.1

1.0

0.1 1.0

Sed

imen

t D

isch

arg

e, Q

os

(T/s

)

0

100

200

300

400

500

600

25 30 35 40 45 50 55 60

Flu

shin

g D

ura

tio

n (

hrs

)

Flushing Discharge (cumecs)

Mf = 0.0654 MCM

Mf = 0.12 MCM

Mf = 0.18 MCM

and (6.0

2.16.1

f

f

W

SQ ). The value of (is low showing that water level during flushing was

higher eroding less sediments during flushing operation for Gmund Reservoir.

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.

.

Figure 4.46 Comparison between observed flushing duration and simulated flushing durations for Gmund Reservoir


172

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.

40

60

80

100

120

140

160

180

200

40 60 80 100 120 140 160 180 200

Observed Flushing Duration (hrs)

Sim

ula

ted

Flu

shin

g D

ura

tio

n

(hrs

) +10%

-10%


173

Table 4.3 Comparison Between Simulated And Observed Flushing Durations using Tsinghua University Equation

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.


flushed sediments Mm3 observed 0.383 0.270 0.065

simulated 0.401 0.280 0.0601

flushing duration hours observed 31 96 168

simulated 30 90 192

% Error 3 6 14

average 7


174

Table 4.4 Summary Of Results By three Models

RESERVOIR SCENARIO PARAMETER UNIT SHARC HEC-RAS TSINGHUA

EQUATION

BAIRA

Observed

Deposited

Sediments Mm3 0.45 0.45 -

Flushed

Sediments Mm3 0.383 0.383 0.383

Flushing

Duration Hours 31 31 31

Simulated

Deposited


Flushed

Sediments Mm3 0.385 0.385 0.401

Flushing

Duration Hours 9.1 34 30

Observed/Simulated Flushing Duration 3.4 0.9 1

GEBIDEM

Observed

Deposited


Flushed

Sediments Mm3 0.27 0.27 0.27

Flushing


Simulated

Deposited


Flushed

Sediments Mm3 0.271 0.266 0.28

Flushing


Observed/Simulated Flushing Duration 3.2 0.9 1.1

GMUND

Observed

Deposited


Flushed

Sediments Mm3 0.065 0.065 0.065

Flushing


Simulated

Deposited

Sediments Mm3 0.0765 0.076 -

Flushed

Sediments Mm3 0.0655 0.068 0.0601


175

Flushing


Observed/Simulated Flushing Duration 4.8 0.93 1


176

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.


177

0.90

0.84

0.81

0.79

0.78

0.67

0.66

0.63

0.56

0.53

0.47

0.41

0.35

0.33

0.30

0.28

0.27

0.26

0.25

0.22

0.00

0.25

0.50

0.75

1.00

1.25

Jam

mar

gal

Tal

ikn

a

Dh

arab

i

Ph

alin

a

Jab

bi

Jalw

al

Mia

l

Du

ng

i

Raw

al

Gh

azia

l

Pir

a F

ateh

al

Sal

ial

Tai

n P

ura

I

Leh

ri

Do

mel

i

Kh

ai

Saw

al

Jab

ba

Min

wal

Sh

ah H

abib

Reservoir

LT

CR

LTCR Critical LTCR

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.

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


178

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


179

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


180

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


181

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

flushing annual sediment deposition determined by the plot between Qs and (6.0

2.16.1

f

f

W

SQ)

was 2788.2. Higher value of (shows that sediments were easily eroded through the


182

0

5

10

15

20

25

30

35

40

45

0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Flushing Discharge (cumecs)

Flu

shin

g D

ura

tio

n (

hrs

)

Mf = 0.02 MCM

Mf = 0.0326 MCM

Mf = 0.04 MCM

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.

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.


183

0

30

60

90

120

150

180

210

240

270

300

1.5 2.25 3 3.75 4.5 5.25 6 6.75 7.5


Flu

sh

ing

du

rati

on

(h

rs)

Mf = 0.2 MCM

Mf = 0.326 MCM

Mf = 0.4 MCM

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

sediment deposition was determined by the plot between Qs and (6.0

2.16.1

f

f

W

SQ). The value

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.


184

Table 4.5 Modeling Results for Jabbi Reservoir

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.

Parameter Unit HEC-RAS Tsinghua

Flushing annual

sediments deposition

deposited sediments

Mm3 observed 0.0418

simulated 0.0418 -

flushed sediments Mm3 observed - -

simulated 0.0326 0.0326

flushing duration hours observed -

simulated 32 34

Flushing 10 years

sediments deposition

deposited sediments

Mm3 observed 0.418

simulated 0.4177 -

flushed sediments Mm3 observed - -

simulated 0.326 0.326

flushing duration hours observed -

simulated 96 96


185

0

1

2

3

4

5

6

7

8

0 30 60 90 120 150 180 210 240 270 300 330 360

time (days)

flo

w (

m3/

s)

hydrograph

flushing discharge

0

10

20

30

40

50

60

70

0 30 60 90 120 150 180 210 240 270 300 330 360

time (days)

Cum

nula

tive

flo

ws

(m3 /s)

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.

Figure 4.58 Average daily flows and minimum flushing discharge required for Jabbi Reservoir (year 1991-2000)

Figure 4.59 Flow mass curve for proposed flushing durations


186

0

1

2

3

4

5

6

0.1 1 10 100

Flus

hing

Dur

atio

n (d

ays)

Flushing Discharge (m3/s)

flushing after 1 year

flushing after 10 years

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.

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.

Figure 4.60 Flushing durations required to flush one year/10 years deposited sediments


187

0

1

2

3

4

5

6

7

8

370 372 374 376 378 380 382 384 386

Em

pty

ing

tim

e (h

rs)

Reservoir Level (m)

0

50

100

150

200

250

370 372 374 376 378 380 382 384 386

Reservoir Level (m)

Fil

iin

g T

ime

(day

s)

NO

L =

385

.6 m

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.

Figure 4.61 calculated reservoir emptying time

Figure 4.62 Re-filling time for Jabbi Reservoir

Refilling Time = 235 days


188

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0 2 4 6 8 10 12 14 16 18 20

River station

Mea

n V

eloci

ty (m

/s)

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).

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.


189

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 2 4 6 8 10 12 14 16 18 20

River Station

Mea

n V

elo

city

(m

/s)

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.


190

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

S. No.

Description Unit Values

1 Year Deposition

10 Years Deposition

1 Appropriate time to flush the reservoir month July,

August July,

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

8 Volume of water is required per flushing

Mm3 3.3 4.4

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.


191

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


192

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.

CHAPTER 5

191

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

CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS

192

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.

CHAPTER 5 CONCLUSIONS AND RECOMMENDATIONS

193

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.

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194

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