Simulation of Physical and Chemical Processes in Reservoirs

SIMULATION OF PHYSICAL AND CHEMIC AL PROCESSES IN RESERVOIRS: TWO CASE STUDIES

Selma L. Garcia Iturbe, B.A., M.S.

Dissertation Prepared for the Degree of

DOCTOR OF PHILOSOPHY

UNIVERSITY OF NORTH TEXAS

December 2005

APPROVED:

Miguel Acevedo, Major Professor Samuel F. Atkinson, Committee Member Kenneth Dickson, Committee Member William T. Waller, Committee Member Thomas La Point, Committee Member and Graduate Program Coordinator for Environmental Sciences Art Goven, Chair of the Department of Biological Sciences Sandra L. Terrell, Dean of the Robert B. Toulouse School of Graduate Studies García Iturbe, Selma L., Simulation of physical and chemical processes in reservoirs:

Two case studies. Doctor of Philosophy (Environmental Science), December 2005, 206 pp., 64

tables, 65 illustrations, references, 61 titles.

Managing water quality aspects requires the use of integrative tools that allow a holistic

approach to this problem. Water quality models coupled to hydrodynamic models are these tools.

This study presents the application of the water quality model WASP coupled to the

hydrodynamic model DYNHYD for two distinct reservoirs: Lake Texoma and Tocoma

Reservoir. Modeling the former included simulations of water velocities, water level, and four

chemical and physical compounds: chlorides, dissolved oxygen (DO), biochemical oxygen

demand (BOD), and total suspended solids (TSS); and validation of the results by comparing

with observed values during March – May, 1997. The latter is still under project status and the

simulation was performed in a prospective way. The analysis included simulations of water

velocities under current and for expected conditions, DO and BOD. Both models, DYNHYD and

WASP, fitted pretty well to observed conditions for Lake Texoma and for where Tocoma

Reservoir has been planned.

Considering management and decision support purposes, the role of boundary and

loading conditions also was tested. For Lake Texoma, controlling boundary conditions for

chlorides is a determinant factor for water quality of the system. However, DO and TSS in the reservoir are governed by additional process besides the condition of the boundary. Estimated

loadings for this system did not provided significant effects, even though the allocation of a load

for chlorides resulted in significant changes in the trend for expected chloride concentrations at

the Washita River Arm of Lake Texoma. For Tocoma Reservoir, the expected concentration of

DO all over the reservoir is going to driven by boundary conditions, as well as by the management of autochthonous BOD loadings provided by vegetation decomposition. These two factors will be determinant for the resulting water quality of the future reservoir.

Selma L. Garcia Iturbe

ACKNOWLEDGEMENTS

I would like to thank her dissertation adviser Dr. Miguel Acevedo for his guidance and support on this study. Many thanks are due Dr. William Thomas Waller for providing invaluable advice during all the stages of this work. I also wish to acknowledge Dr. Samuel Atkinson, Dr.

Kenneth Dickson and Dr. Thomas La Point, members of the committee, for their advice. I would also like to thank my fellow graduate student Alexandra Christina Upton for her friendship and support during most of the stages of this study.

Finally, I am grateful for the financial support of this research provided by CVG

EDELCA in Venezuela, the U.S. Environmental Protection Agency and the U.S. Army Corp of

Engineers.

iii

TABLE OF CONTENTS

Page

ACKNOWLEDGMENTS ...... iii

LIST OF TABLES...... vi

LIST OF ILLUSTRATIONS...... x

Chapters

1. INTRODUCTION ...... 1

2. HYDRODYNAMIC SIMULATION...... 7 Model Description ...... 9 Lake Texoma: Description of the Study Area ...... 10 Model Parameterization for Lake Texoma ...... 15 Hydrodynamic Modeling Results for Lake Texoma ...... 26 Tocoma Reservoir: Description of the Study Area...... 33 Model Parameterization for Tocoma Reservoir...... 37 Hydrodynamic Model Results for Tocoma Reservoir...... 44

3. RESERVOIR WATER QUALITY MODELING...... 50 Model Description ...... 51 Previous Water Quality Studies of Lake Texoma...... 54 Water Quality Model Parameterization for Lake Texoma ...... 56 Water Quality Modeling Results for Lake Texoma...... 78 Water Quality Studies for Tocoma Reservoir...... 95 Water Quality Model Parameterization for Tocoma Reservoir...... 98 Water Quality Modeling Results for Tocoma Reservoir ...... 108

4. ROLE OF BOUNDARY CONDITIONS AND LOADINGS IN WATER QUALITY SIMULATIONS...... 115 Sensitivity Analysis to Upstream Boundary Conditions ...... 115 Sensitivity Analysis for Loading Conditions...... 122

5. CONCLUSIONS...... 130

Appendices

A. GEOMETRY DATA FOR LAKE TEXOMA MODEL ...... 136

B. ESTIMATIONS OF TRAVEL TIME FOR LAKE TEXOMA MODEL ...... 139

C. GEOMETRY DATA FOR TOCOMA RESERVOIR MODEL...... 141

D. ESTIMATIONS OF HYDRAULIC TRAVEL TIME FOR TOCOMA RESERVOIR ...... 145

E. INPUT DATA FOR WATER QUALITY SIMULATION OF LAKE TEXOMA ...... 148

F. STATISTICAL ANALYSIS FOR DEVELOPING INITIAL CONDITIONS FOR LAKE TEXOMA WATER QUALITY MODEL...... 168

G. STATISTICAL ANALYSIS FOR DEVELOPING DAILY BOUNDARY CONDITIONS FOR LAKE TEXOMA WATER QUALITY MODEL ...... 174

H. VALUES FOR TESTING SENSITIVITY TO BOUNDARY CONDITIONS ...... 182

I. VALUES FOR TESTING SENSITIVITY TO LOADING CONDITIONS.... 193

REFERENCES ...... 201

LIST OF TABLES

Page

1. Some morphometric characteristics of Lake Texoma, Tocoma Reservoir, and their watersheds...... 6

2. Distribution of annual volumes in Lake Texoma for different usages (Compiled from TWDB 2003) ...... 10

3. Some morphometric characteristics of Lake Texoma and its basin ...... 11

4. Main creeks that drain directly into Lake Texoma ...... 15

5. Estimation of maximum time step (in seconds) for different conditions of water velocity and depth of the channels in the developed hydrodynamic network for Lake Texoma ...... 22

6. Time step values for different junction depths of the hydrodynamic network for Tocoma Reservoir...... 42

7. Predicted and measured velocities for Tocoma under river condition ...... 45

8. Relative change of water velocities for Caroni River when comparing river and reservoir condition at Tocoma site...... 46

9. General characteristics of segments used in WASP for Lake Texoma ...... 58

10. Relationship between water quality segments identification and station identification used by Atkinson et al. (1999) ...... 59

11. Some constants and rates used by WASP for water quality simulation ...... 60

12. Different loading conditions evaluated for Lake Texoma ...... 65

13. Identification of routine stations monitored by Atkinson et al. (1999) in Lake Texoma and their estimated location...... 66

14. Estimated initial concentrations of chlorides for Washita and Red River arms for WASP calibration ...... 69

15. Equations used to calculate daily concentrations for TSS at the boundary of the Washita River arm for Lake Texoma...... 73

16. Percentage distribution of solids per segment used in WASP...... 75

17. Equations for estimations of loadings of solids using estimations made by Upton (2004) ...... 77

18. EMCs selected for estimating loadings for Lake Texoma. Nitrogen values are from Upton (2004)...... 78

19. Estimated EMCs for each sub-watershed around Lake Texoma...... 79

20. Statistical analysis of predicted values for TSS concentrations in Lake Texoma using WASP ...... 87

21. Geographic coordinates for water quality sampling stations nearby Tocoma Dam site ...... 97

22. Annual average value for the main limnological characteristics of some water quality sampling stations at the lower Caroni River...... 99

23. Geometric data for water quality segments developed for the WASP network of Tocoma Reservoir...... 101

24. Time functions used for DO and BOD simulation of Tocoma Reservoir ...... 105

25. Constants and rates used by WASP for water quality simulation of Tocoma Reservoir ...... 105

26. BOD loading conditions for adjacent watershed of the Tocoma Reservoir ...... 106

27. Phytomass and degradation factors used for BOD loading estimates during the creation of Tocoma Reservoir...... 108

28. DO values for Guri Dam discharge channels and Tocoma Dam site measured during January and August, 2005...... 110

29. Estimated values used for DO balance at water quality segments at Necuima Canyon under river condition...... 112

30. Results of the sensitivity test for chlorides and DO showing percent change in daily, volume-averaged concentrations for some WASP segments of the Lake Texoma model compare to the base case in March-May, 1997...... 120

31. Results of the sensitivity test for solids 1, 2, and 3 showing percent change in daily, volume-averaged concentrations of TSS for some WASP segments of the Lake Texoma model compare to the base case in March-May, 1997...... 121

32. Output percent change for TSS concentrations at several segments of the water quality network of Lake Texoma during the sensitivity test to Solid 2 loading conditions ..... 124

33. Output percent change for chloride concentrations at several segments of the water quality network for Lake Texoma during the sensitivity test of the model to chloride loading conditions...... 125

34. Junction geometry data for Lake Texoma model ...... 137

vii

35. Channel geometry data for Lake Texoma model...... 138

36. Estimations of travel time per junction for Lake Texoma model using DYNHYD ..... 140

37. Junction geometry data for Tocoma Reservoir model...... 142

38. Channel geometry data for Tocoma Reservoir model ...... 143

39. Estimated travel time per segment in Tocoma Reservoir model ...... 146

40. Water quality segment definition for Lake Texoma model...... 149

41. System Data for Lake Texoma model ...... 150

42. Initial concentrations for chlorides and fractions of suspended solids for Lake Texoma model...... 151

43. Exchange functions defined for Lake Texoma model ...... 152

44. Settling and resuspension functions defined for solids type 1 (clay) in Lake Texoma model...... 152

45. Settling and resuspension functions defined for solids type 2 (silt) in Lake Texoma model ...... 155

46. Settling and resuspension functions defined for solids type 3 (sand) in Lake Texoma model...... 157

47. Red River boundary condition for calibration of WASP for Lake Texoma ...... 159

48. Washita River boundary condition for calibration of WASP for Lake Texoma ...... 160

49. Big Mineral Creek boundary condition for calibration of WASP for Lake Texoma ... 161

50. Water temperature function defined for calibration of WASP for Lake Texoma ...... 162

51. Print interval used in WASP for Lake Texoma water quality simulation ...... 162

52. Time step used in WASP for Lake Texoma water quality simulation ...... 162

53. Chloride loadings (Kg/day) for calibration of WASP for Lake Texoma during March to May, 1997 ...... 162

54. Solid 2 loadings for water quality simulation of Lake Texoma during March to May, 1997...... 163

55. BOD loadings for water quality simulation of Lake Texoma during March to May, 1997 ...... 166

viii

56. Values used to test change in 10% of boundary conditions of chlorides for Lake Texoma ...... 183

57. Values used to test change in 10% of boundary conditions of Solids 1 (Clay) for Lake Texoma ...... 185

58. Values used to test change in 10% of boundary conditions of Solids 2 (Silt) for Lake Texoma ...... 187

59. Values used to test change in 10% of boundary conditions of Solids 3 (Sand) for Lake Texoma ...... 189

60. Values used to test change in 30% of boundary conditions of DO for Lake Texoma ...... 192

61. Values of chloride loads at WASP segment WRT22 to test sensitivity of Lake Texoma model to changes in loading conditions...... 194

62. Values of TSS loads at several WASP segments to test sensitivity of Lake Texoma model to changes in plus 50% of loading conditions ...... 195

63. Values of TSS loads at several WASP segments to test sensitivity of Lake Texoma model to changes in minus 50 % of loading conditions ...... 197

64. BOD loadings for testing sensitivity to 50 % change of loading conditions in Tocoma Reservoir model...... 199

LIST OF ILLUSTRATIONS

Page

1. Lake Texoma indicating different zones in the lake based on water salinity and turbidity (Adapted from Atkinson et al. 1999) ...... 4

2. Tocoma Reservoir site in the Caroni River...... 5

3. Lake Texoma watershed (Adapted from Upton 2004) ...... 12

4. Lake Texoma and surrounding counties (Adapted from Upton 2004)...... 13

5. Image of the DEM file for Lake Texoma showing different depths. Lighter colors correspond to shallower areas (Adapted from: USGS 1999)...... 18

6. Junction network of the hydrodynamic model of Lake Texoma ...... 19

7. Channel segmentation of Lake Texoma for the hydrodynamic model...... 20

8. Variability of error in pool elevation estimations of Lake Texoma using different percentages of accumulated volume in a 24-hour period as inflow in Big Mineral Creek and applying DYNHYD ...... 21

9. Precipitation/Evaporation balance between March,1997-May,1997 using daily reported values for Denison Dam rain gage station ...... 23

10. Boxplot of mean daily total inflow in Lake Texoma during 1997 represented by month. The highest dispersion of values is observed in May and the lowest occurs in August ...... 24

11. Graphical comparison between observed and predicted elevations in Lake Texoma during March, 1997 and May,1997, using DYNHYD and estimated runoff for Big Mineral Creek provided by Upton (2004) ...... 26

12. Graphical comparison between observed and predicted elevations in Lake Texoma during March, 1997 and May,1997, using DYNHYD and a 50% of thedaily accumulated volume in the reservoir not explained by the Red and Washita rivers inflows based on mass balance estimations as a daily inflow of Big Mineral Creek ...... 27

13. Variation of water flow in different zones of Lake Texoma. 0 to 1 corresponds to the Red River Zone, 19 to 28 corresponds to the Red River Transition Zone, 0 to 20 corresponds to the entrance of Washita River to Lake Texoma, 27 to 28 represents Washita River Transition Zone, 28 to 29 and 31 to 0 represents the Main Lake Zone, 0 to 32 corresponds to the entrance of Big Mineral Creek to Lake Texoma, and 10 to 36 is the junction between the Big Mineral Creek and the Red River Transition Zone...... 29

14. Spatial distribution of predicted water velocities for Lake Texoma under the steady-state condition ...... 30

15. Graphical representation of travel time in Lake Texoma when a pulse of tracer is applied in the upper portion of the Red River arm ...... 31

16. Graphical representation of travel time in Lake Texoma when a pulse of a tracer is applied in the upper portion of the Washita River arm...... 32

17. Estimation of travel time for Lake Texoma using a combined loading at the Red and Washita River...... 33

18. Scheme of hydroelectrical development in the Lower Caroni River (Adapted from CVG EDELCA 2005b) ...... 34

19. Geographic location for the future Tocoma Reservoir, in the Caroni River ...... 35

20. Mean monthly turbine-used and spilled flow at Guri Dam discharge channels under average conditions ...... 36

21. Average monthly precipitation at Las Babas weather station, close to Tocoma Reservoir site (Period: 1986-2004) ...... 37

22. Schematic location for bathymetric cross-sections in the Caroni River for the Tocoma Reservoir area (Adapted from CVG EDELCA 1987) ...... 39

23. Bathymetric map for Tocoma Reservoir...... 40

24. Hydrodynamic network for Tocoma Reservoir ...... 41

25. Comparison between hydrodynamic network for river and reservoir condition for Caroni River at Tocoma Reservoir site...... 43

26. Spatial variation of simulated velocities for Tocoma Reservoir...... 47

27. Peak concentration of the tracer at segments close to the future dam, in Tocoma reservoir, when a pulse is applied at Guri Dam discharge channels...... 48

28. Temporal variation of a tracer concentration when applied at a) Cunaguaro River, and peak concentration of the tracer at b)the segment next to the future dam, in Tocoma reservoir ...... 49

29. Graphical representation of the configuration of water quality segments for the application of WASP for Lake Texoma...... 53

30. Graphical representation of the configuration of water quality segments for the application of WASP for Lake Texoma...... 54

31. Segmentation of Lake Texoma used in WASP6...... 57

32. Estimated water velocities in every segment of the water quality network developed for Lake Texoma using DYNHYD ...... 62

33. Settling velocities used for the water quality network developed for Lake Texoma using WASP ...... 63

34. Spatial variation of the average concentration of chlorides in the Red River arm, based on measurements made by Atkinson et al. (1999)...... 67

35. Spatial variation of the average concentration of chlorides in the Washita River arm, based on measurements made by Atkinson et al. (1999)...... 68

36. Local watershed defined by Upton (2004) for Lake Texoma...... 76

37. Temporal variation of chlorides concentration in some stations located in the Red River arm of Lake Texoma in comparison with simulated values for the period between March, 1997 through May, 1997...... 81

38. Temporal variation of chlorides concentration in some stations located in the Washita River arm and Main Lake Zone of Lake Texoma in comparison with simulated values for the period between March, 1997 through May, 1997...... 82

39. Temporal variation of total suspended solids concentration in some stations located in the Red River arm of Lake Texoma in comparison with simulated values for the period between March, 1997 through May, 1997...... 85

40. Temporal variation of total suspended solids concentration in some stations located in the Washita River arm of Lake Texoma in comparison with simulated values for the period between March, 1997 through May, 1997...... 86

41. Temporal variation of total suspended solids concentration in some stations located in the Main Lake Zone of Lake Texoma in comparison with simulated values for the period between March, 1997 through May, 1997...... 88

42. Temporal variability of particle resuspension velocity applied to solid type 2 in segment 27 of the water quality network developed for Lake Texoma using WASP, obtained by model calibration ...... 89

43. RMS of errors of TSS concentration for different water quality segments in a) the Red, and b) the Washita rivers arms of Lake Texoma using WASP ...... 91

44. Temporal variation of DO concentration in some stations located in the Red River arm of Lake Texoma in comparison with simulated values for the period between March, 1997 and May, 1997 ...... 92

45. Temporal variation of DO concentration in some stations located in the Washita River arm and Main Lake Zone of Lake Texoma in comparison with simulated values for the period between March, 1997 and May, 1997 ...... 93

xii

46. RMS of errors of DO concentrations for different water quality segments in the Red and Washita River arms of Lake Texoma using WASP...... 95

47. RMS of errors of DO concentrations for some water quality segments at the: a) Red River arm, and b) Washita River arm of Lake Texoma under different conditions of salinity and sediment oxygen demand...... 96

48. Spatial location of water quality measurement sites at the lower Caroni River considered for this study ...... 98

49. Correspondence between water quality and hydrodynamic networks developed for Tocoma Reservoir...... 102

50. Temporal variation of BOD loadings for two segments of the water quality network, while simulating the filling up process for Tocoma Reservoir...... 107

51. Spatial distribution of water quality segments with autochthonous BOD loadings ..... 110

52. Spatial distribution of DO concentrations for Caroni River between Guri Dam discharge channels and Tocoma Dam site ...... 111

53. Expected spatial distribution of DO for Tocoma Reservoir when simulating the reservoir a) with, and b) without BOD loadings...... 114

54. Spatial distribution of percent change of chloride concentrations in Lake Texoma when an increment of +10% of boundary condition for chlorides was applied...... 119

55. Spatial distribution of percent change obtained during the sensitivity test to changes in the boundary condition of DO for Tocoma Reservoir...... 122

56. Chloride concentrations simulated for WASP segment WRT22 while testing sensitivity of Lake Texoma model to changes in chloride loadings ...... 126

57. Comparative spatial variation of potential DO change during the flooding phase of Tocoma Reservoir by reducing autochthonous BOD loading in 50% in comparison with the effect of the total BOD load...... 128

58. Comparative expected spatial distribution of DO for Tocoma reservoir after flooding with 50% and 100% of autochthonous BOD loadings ...... 129

59. Linear regression curve between chloride concentrations and distance at the Washita River arm in Lake Texoma ...... 170

60. Linear regression curve between chloride concentrations and distance at the Red River arm in Lake Texoma ...... 171

61. Linear regression curve between chlorides and conductivity values measured at the gaging station at the Red River near Gainesville, TX ...... 173

xiii

62. Analysis of data distribution for conductivity values measured in the Washita River at the gage station near Dickson, OK ...... 176

63. Analysis of data distribution for conductivity values measured at Station 25 at Lake Texoma ...... 177

64. Linear regression curve between conductivity values measured at Washita River gaging station near Dickson, OK and chloride concentrations measured at Station 25 in Lake Texoma ...... 179

65. Linear regression curve between conductivity values and chloride concentrations measured at Station 25 in Lake Texoma...... 181

xiv CHAPTER 1

INTRODUCTION

Traditionally, monitoring water quality in rivers and lakes has been used as the primary source of information for making decisions and management of water bodies.

Empirical analysis of water quality data has resulted in useful descriptions of aquatic ecosystems (Hakanson and Peters 1995). This approach, however, has been considered insufficient for prediction and quantification of secondary effects, especially those associated to human activities and their effects on water bodies, and the sustainability of this important resource (Goodwing and Hardy 1999).

A new paradigm in water quality management is evolving, and a broad range of variables and relationships among them are considered before defining a management objective for water bodies. This approach requires extensive data and simulation tools to assist in the decision-making processes that managers and communities must make in order to select strategies that contribute to conservation of the considered water system

(Goodwing and Hardy 1999). Consequently, developing predictive models for rivers and lakes is a powerful tool in applied ecology and environmental management.

Water quality simulation is a representation of the natural ecosystem integrating the temporal and spatial variability of the physical, chemical, and biological characteristics of aquatic systems (Ford 1999). Aquatic ecosystem models, and particularly those applied to reservoirs, provide an integrated analysis of the morphometry, hydrology, ecology, and internal and external forcing functions acting on a water body, based on monitoring physical, chemical, and biological features of the water body (Tufford and McKellar 1999). Water quality measurements, normally made at a

1 finite number of specific localities during specified periods of time, provide the information needed to infer process dynamics and their effects over much broader spatial and temporal scales using water quality models. Thus, the decision-making process and water quality management are supported on more integrated and complete information, taking into account not only observed but also expected water quality conditions.

Using simulation tools to study reservoirs is particularly important because these impoundments may be more complex than natural lakes (Hakanson and Peters 1995,

Tufford and McKellar 1999). In a reservoir, river inflows determine a circulation pattern based on lake morphometry, defining advective transport and density gradients (Ford

1999). In addition, hydrology, land use, and weather establish temporal patterns and variations on water quality. As a consequence, the biotic component of the ecosystems respond, developing many different functional habitats (Kalff 2002). These processes can be better evaluated in their whole dimension (temporal and spatial) with a tool that allows their integration. Water quality simulation models represent this tool. When studying a reservoir with multiple uses, this approach can be particularly useful and desirable.

Modeling can also be used for prediction of impacts resulting from the creation of reservoirs. As a predictive tool, models provide a prospective vision of the processes that can result in the environment as a consequence of human developments (Canter 2002). In this study, the hydrodynamic model DYNHYD and the water quality simulation program

WASP have been used for providing a better understanding of physical, chemical, and biological processes developed in a created reservoir, Lake Texoma in the United States, and those expected for a new projected reservoir, the Tocoma Reservoir, in Venezuela.

2 Lake Texoma is a reservoir located on the border between States of Oklahoma and Texas (USA) at the confluence of the Red and Washita rivers. The U.S. Army Corps of Engineers created it in 1945 initially to generate hydroelectrical energy (Breeding

1997). However, at the present time it is also used for flood control, recreation, water supply, and fisheries, which is a very important source of revenue for the local economy

(USACE 2003a).

The Red and Washita rivers provide the main inflows to the lake. Variations of the mean monthly flow in the Red River, at the Gainesville gage station, in Texas, are between 36 and 226 m3/s (period: 1936-2004). In the Washita River, mean monthly flows

at Dickson, in Oklahoma, fluctuate between 18 and 117 m3/s (period: 1928-2004) (USGS

2005). These fluctuations are associated with variations in water quality of the reservoir,

especially during spring time (Work and Gophen 1999).

Analysis of data resulting from intensive monitoring of physical and chemical

characteristics of the lake reflects a spatial variation of conductivity and turbidity

(Atkinson et al. 1999, Waller et al. 2002). Four zones have been defined in those studies:

the Red River Arm and the Washita River Arm, considered as lotic zones, and the Red

River Transition Zone, the Washita River Transition Zone, and the Main Body,

considered as lentic zones (Figure 1). Other studies also consider the Big Mineral Creek

as an additional zone because of its differentiated characteristics (Mabe 2002).

Additional studies on water quality of the reservoir and its relationship with land

use of the shoreline and the watershed also show that there are biological gradients,

affecting biological productivity of the reservoir (Work and Gophen 1999, An et al. 2002,

3 An and Kampbell 2003), and the effects of additional human influence in the water body are still unknown.

Figure 1. Lake Texoma indicating different zones in the lake based on water salinity and turbidity (Adapted from Atkinson et al. 1999) Tocoma Reservoir will be created in Bolivar State (Venezuela) on the lower basin of the Caroni River. CVG EDELCA, a Venezuelan government-owned energy company that produces nearly 70% of the total energy consumed in the country, will create and operate this reservoir mainly for hydroelectrical generation. Located between two impoundments, Guri Reservoir and Caruachi Reservoir, the main source of water will come from the turbinated and spilled water of Guri Reservoir, on the Caroni River. Five tributaries to the Caroni River above the future dam site will contribute an additional, but small amount of flow. These tributaries are small, intermittent creeks, being the most important Cunaguaro River, flowing into the Caroni River close to the future dam site

(Figure 2).

C C

a a

r r

u u

a a

c c

h h

i i

Tocoma Dam Site

C a r o C unagua n ro Ri í ver Dam R iv Town k. e e re r C Principal river

a is Island u L a í Reservoir r a . M k erey Cree e El M k Direct Influence Area Limit e r C Rivers o p a r Permanent River a u G Guri Dam Intermittent River Unknown Flow Regime

GURI RESERVOIR

Figure 2.Tocoma Reservoir site in the Caroni River The reservoir will cover an area of 85 Km2, and it will impound a volume of water of 1.758 Km3. Considering an annual average inflow of 4,250 m3/s, theoretical residence time has been estimated in 4.8 days

Comparative values of some characteristics related with morphometry of both reservoirs and their watershed are presented in Table 1.

This document is organized into five chapters. The first chapter corresponds to this introduction. Chapters 2 and 3 provide a brief explanation of the characteristics of each reservoir, the calibration and simulation of hydrodynamic and water quality processes for both reservoirs. Then, in the fourth chapter, the role of loadings and boundary conditions for each of these water bodies is studied by means of sensitivity analysis. Lastly, the fifth chapter is dedicated to conclusions, as well as opportunities and needs of additional research.

5 Table 1. Some morphometric characteristics of Lake Texoma, Tocoma Reservoir, and their watersheds. Characteristics Texoma Lake Tocoma Reservoir Area (at normal pool elevation) 264,901,462 m2 (1) 85,000,000 m2 (3) Volume (at normal pool elevation) 3,064,601,914 m3 (1) 1,758,000,000 m3 (3) Mean depth 11.6 m 14.7 m Maximum depth 29.3 m 77 m Drainage basin area 87,498 km2 (2) 88,067 km2 Theoretical Residence Time 255 days 4.8 days Mean annual inflow 139 m3/s (1) 4,261 m3/s Mean annual outflow 168 m3/s (2) 4,261 m3/s (1) USACE 2003b (2) Atkinson et al. 1999 (3) CVG DELCA 2005

6 CHAPTER 2

HYDRODYNAMIC SIMULATION

Modeling and simulation of flow and water quality in rivers and lakes is a representation of the natural ecosystem integrating the temporal and spatial variability of the physical, chemical, and biological characteristics of aquatic systems (Ford 1999).

Aquatic ecosystem models, and particularly those applied to reservoirs, provide an integrated analysis of the morphometry, hydrology, ecology, and internal and external forcing functions acting on a water body, based on monitoring physical, chemical, and biological features of such a water body (Tufford and McKellar 1999). Water quality measurements, normally made at a finite number of specific localities during specified periods of time, provide the information needed to infer process dynamics and their effects over much broader spatial and temporal scales using water quality models. Thus, the decision-making process and water quality management are supported on more integrated and complete information, taking into account not only observed but also expected water quality conditions.

Using simulation tools for studying a reservoir is particularly important because these impoundments may be more complex than natural lakes (Hakanson and Peters 1995,

Tufford and McKellar 1999). In a reservoir, river inflows determine a circulation pattern based on lake morphometry, defining advective transport and density gradients (Ford

1999). In addition, hydrology, land use, and weather establish temporal patterns and variations on water quality. As a consequence, the biotic component of this ecosystem responds selecting many different functional habitats (Kalff 2002). Water quality models

7 allow the integration of these processes and their evaluation in their whole dimension.

Moreover, a preliminary step in this process is the understanding of hydrodynamic

processes through simulation. In the case of studying a reservoir with multiple uses, this

approach is particularly useful and desirable.

The U.S. Environmental Protection Agency (USEPA), in conjunction with the

Tulsa District of the U.S. Army Corps of Engineers (USACE), has been studying Lake

Texoma and its watershed. This dissertation is a contribution to the study and represents

one step in developing a decision support system, based in environmental modeling, to

manage the water quality of that reservoir. Hydrodynamic studies of Lake Texoma are

not available; consequently, in this study the hydrodynamics of Lake Texoma were

evaluated using a simple hydrodynamic model. The DYNHYD model was selected for its

flexibility and simplicity, and for the fact that it can be coupled with the Water Analysis

Simulation Program (WASP) (Ambrose et al. 1993).

On the other hand, creating a reservoir for hydroelectrical generation can require

exhaustive evaluations to establish the magnitude and intensity of environmental effects

of the project. In this sense CVG EDELCA, a state-owned company in charge of

approximately 70% of the electrical generation in Venezuela, has promoted the

evaluation of Tocoma Reservoir and its environmental impact. This study is a part of the

environmental impact evaluation of the project.

The general objective of this chapter is to describe the hydrodynamics of both

Lake Texoma and the future Tocoma Reservoir using the simulation model DYNHYD.

The specific objectives include calibrating and validating DYNHYD predictions of these

water bodies, and estimations of the hydraulic travel time and residence time in those

8 reservoirs. This information will also be used as an input for the analysis of physical, chemical and biological processes that determine the water quality of these reservoirs, using the water quality modeling components of WASP, presented in the following chapter.

Model Description

The model selected for this research is DYNHYD5, the hydrodynamic model of the WASP system. WASP (Water Analysis Simulation Program) is a system integrated by two independent, computational programs: DYNHYD5 and WASP6. Both of these are dynamic compartment models, very flexible, and are used to simulate water transport and fate and transport of water quality constituents and toxic compounds. Particularly,

DYNHYD5 simulates hydrodynamics, based on the momentum and mass conservation equations. The model system is developed and supported by the US Environmental

Protection Agency (USEPA) and it has been used to study a variety of issues in many different water bodies, such as rivers, lakes, reservoirs, and estuaries, including, but not limited to, physical and ecological characterization, evaluation of the effects of human activities, and development of watershed management strategies (Lang and Fontaine

1990, Lung and Larson 1995, Branch et al. 1996, De Smedt et al. 1998, Tufford and

McKellar 1999, Lee 2000).

To simulate the hydrodynamic of a water body, DYNHYD solves the equations of motion and continuity in a computational network integrated by junctions and channels, which are defined for the system to be simulated. The model provides volume and velocity in every junction and channel of the hydrodynamic network, respectively.

Channels define the movement of water between junctions, and the junctions hold the

9 volumes where chemical and biological components of the water systems are going to be distributed. These values will be also used as input for the water quality model.

Lake Texoma: Description of the Study Area

Lake Texoma is a man-made lake located on the border between Oklahoma and

Texas. It was created in 1945 at the confluence of the Red and Washita rivers by the U.S.

Army Corps of Engineers in order to generate hydroelectrical energy (Breeding 1997) and for flood control. However, other beneficial uses have been considered and at the present time, it is also used for recreation, water supply, and fisheries. These uses provide a very important source of revenue for the local economy (USACE 2003a). Only about

5.5% of the storage capacity of the reservoir is available for consumptive usages, and the rest is used for energy generation. The present distribution of water volumes by different usage is presented in Table 2. The rest of usages are a consequence of consumptive usage of the water of the reservoir.

Table 2. Distribution of annual volumes in Lake Texoma for different usages (Compiled from TWDB 2003).

USAGES ADJUDICATED TO VOL/YEAR (m3) Red River Authority of Texas 2,342,700 City of Denison 30,085,200 Greater Texoma Utility Authority 18,495,000 Municipal Purpose North Texas Municipal Water District 95,310,900 State of Oklahoma 16,029 SUB-TOTAL 146,249,829 State of Oklahoma 5,395,608 Irrigation Red River Authority of Texas 308,250 SUB-TOTAL 5,703,858 Greater Texoma Utility Authority 12,330,000 Texas Utilities 12,330,000 Industrial - Mining Red River Authority of Texas 123,300 SUB-TOTAL 24,783,300 TOTAL 176,736,987

10 Denison Dam was built to create Lake Texoma, and it is located on the Red River,

at river mile 725.9, five miles northwest of Denison in Grayson County, Texas (USACE

1996b). Two large arms depict the physiography of this reservoir: Red River and Washita

River arms. Lake Texoma has a very dendritic shape with a shoreline estimated to be

more than 970 Km. Table 3 summarizes some characteristics related with the

morphometry of the reservoir and its watershed.

Table 3. Some morphometric characteristics of Lake Texoma and its basin. Area (at normal pool elevation)(1) 264,901,462.4 m2 Volume (at normal pool elevation)(1) 3,064,601,913.8 m3 Mean depth 11.6 m Maximum depth(1) 28.8 m Length of the shoreline (at 620 ft a.s.l.) (4) 971.2 km Drainage basin area(2) 87,498 km2 Mean annual inflow (3) • Red River at Gainesville (1936-2000) 93 m3/s • Washita River near Dickson (1928-2000) 49 m3/s 3 • Total(1) 185 m /s Mean annual outflow (1) 168 m3/s (1) USGS 1999 (2) Atkinson et al. 1999 (3) USGS 2005 (4) TWDB 2003

Watershed Characteristics

The Lake Texoma watershed extends to the Red River headwaters in New

Mexico, and the Washita River headwaters, located in NW Oklahoma (Figure 3). About

70 million years ago, much of the western reaches of the Red River, draining into Lake

Texoma, were covered with a shallow saline sea. Consequently, soils and surface geology

of the watershed provide significant amounts of calcium carbonate, calcium sulfate, and

sodium chloride, which determine most of the ionic composition of the water in the

reservoir today (RRA 2003b).

11 OKLAHOMA

N TEXAS

Figure 3. Lake Texoma watershed (Adapted from Upton 2004) The local watershed of the reservoir encompasses Cooke and Grayson counties, in the Texas reach; and Carter, Love, Marshall, and Bryan counties in the Oklahoma reach

(Figure 4). Several small communities characterize much of the area of the local watershed of the reservoir. However, the area around the City of Denison has been reported as one of the fastest growing areas in Texas due to the overflow from the

Dallas/Fort-Worth area (RRA 2003a). Cities within the local watershed of Lake Texoma with a population over 10,000 include Denison and Gainesville. A significant number of farms that produce mainly hay, wheat, corn, peanuts and fruits are also located in the local watershed of Lake Texoma. Some farms in this area also raise beef cattle, poultry, and horses. Mining activity is also identified in this area and it includes oil and gas, sand, and gravel (RRA 2003a).

12 Figure 4. Lake Texoma and surrounding counties (Adapted from Upton 2004). Considering the geology of the region, adjacent watersheds of Lake Texoma are mainly located in the ecoregion of the Central Oklahoma-Texas Plain, integrated by sandstone and shale beds of the Triassic Age. Some Permian and Pennsylvania Age sandstone, shale, limestone, dolomite and gypsum are found in that ecoregion (Patrick

1994).

The topography surrounding Lake Texoma varies from gently sloping flats to rocky cliffs and steep, wooded hillsides. Many areas along the shoreline are forested, predominantly covered by different species of oak, hickory, American elm, and juniper.

Non-forested vegetation consists largely of many annual grasses, forbs, and wild legumes

(USACE 1996b).

13 Hydrology

The total drainage area of Lake Texoma is estimated to be 87,498 km2 (Atkinson et al. 1999). The surface area of the reservoir is 264.9 km2 at normal pool elevation (195 m a.s.l), and it holds a volume of approximately 3.03 km3 (USACE 2003a). The maximum depth has been estimated at 34 m (Atkinson et al. 1999), and the mean depth is calculated to be 11.6 m. The reservoir has a highly developed dendritic shoreline, with a length estimated to be more than 970 km.

The hydrodynamics of the reservoir are driven by two main inflows, from the Red and Washita rivers, and one main outflow at the Denison Dam. Daily inflow to Lake

Texoma coming from the Red River is measured near Gainesville, TX (USGS 07316000

- latitude 33o43’40”N, longitude 97o09’35”W) and the inflows of the Washita River are measured near Dickson, OK (USGS 11130303 – latitude 34o14’00”N, longitude

96o58’32”W). Variations in the mean monthly flow in the Red River, measured at

Gainesville gage station, are between 36 and 226 m3/s (period: 1936-2004). In the

Washita River, mean monthly flows at Dickson fluctuate between 18 and 117 m3/s

(period: 1928-2004) (USGS 2005). The highest variability of inflow to the reservoir occurs between May and June, and the lowest during August. These fluctuations are associated with variations in the water elevation and water quality of the reservoir, especially during spring time (Work and Gophen 1999). Other inflows come from numerous small tributaries draining to Lake Texoma that are not measured.

In addition, several small, intermittent creeks drain directly into the reservoir, providing additional inputs of water estimated at less than 10% of the total annual accumulated volume of the reservoir. The most important of the smaller inputs are

14 Hickory Creek and Big Mineral Creek. A list of some of the creeks draining directly to

Lake Texoma is presented in Table 4.

Table 4. Main creeks that drain directly into Lake Texoma Location Margin(1) Identified Creeks Hickory Creek Boggy Creek Wilson Creek Little Hauani Creek Left House Creek Red River arm Blair Creek Buncombe Creek Caney Creek Big Mineral Creek Right Mill Creek Little Mineral Arm Glasses Creek Washita River arm Right Little Glasses Creek (1): In reference to the downstream direction

Outflows of the reservoir are mainly at the dam. There, there are eight 20-ft diameter reinforced concrete conduits that are 800 ft long with an invert elevation of

523.0 ft. Five of them are dedicated for the delivery of hydroelectric power, and the other three are used for the required downstream releases and floodwater releases (USACE

1996b). Channel capacity below Denison Dam is approximately 1,700 m3/s. However, the mean annual outflow through the dam is estimated in 168 m3/s.

Model Parameterization for Lake Texoma

The simulation process involves parameterization or calibration of the selected model and validation process, including adjustment of initial conditions in the system to be simulated (Ford 1999). DYNHYD was set to describe water movement in the lake and periodic variations of pool elevations. Input parameters adjusted for DYNHYD to simulate the hydrodynamic of Lake Texoma included values associated with the

15 geometry of the reservoir, hydrology of the main inflows and outflows of the reservoir,

and meteorological information from weather stations located in the reservoir area.

The validation process was performed by estimation of the root mean square of the error (RMSE). The error is the difference between predicted and observed daily elevations of the reservoir, using reported values by USACE (2003b). The RMSE is a

measure of the differences between observed and predicted (Davis 2002):

2 ⎡ ⎛ ∧ ⎞ ⎤ ⎢∑⎜ yi − yi ⎟ ⎥ ⎢ ⎝ ⎠ ⎥ RMSE = ⎣ ⎦ , where: n

yi = Observed value

∧ yi = Predicted value

n = Number of paired values

The relative RMSE was calculated relating the RMSE with the average of the

observed values.

Calibration of hydrodynamic models is normally performed by adjusting

simulated and real values of water velocity (Ambrose et al. 1993). However, there were

no available measurements of water velocities for the reservoir. Consequently, calibration

of DYNHYD5 was performed by adjusting the estimated daily pool level of the reservoir to real pool level registers, generated under selected conditions of inflow and outflow.

The objective of this calibration process was to generate the best approximation between observed and simulated pool levels of the lake. A range of +/- 2.0 m. of the reported daily pool level value of a selected period of time was considered acceptable for this objective, using a dynamic condition for simulation. This number represents less than 10% of the

16 mean depth of the Main Lake Zone in Lake Texoma where water elevation of the

reservoir is measured. Similar studies have reported an RMSE less than 20 % of daily

pool elevation estimations (Crain et al. 2000, Tufford and McKellar 1999).

Parameterization of DYNHYD required considering parameters associated with

channel geometry, hydrology and meteorology (precipitation/evaporation balance).

Geometry of the reservoir was estimated using Geographic Information Systems (GIS).

Inflows were set to the measured values of daily mean discharge, using the information

from two USGS gage stations located at Gainesville, on the Red River, and at Dickson,

on the Washita River. Additional inflow was adjusted using runoff estimations for the

Big Mineral Creek sub-watershed (Upton 2004) or by a mass balance procedure.

Model Geometry

DYNHYD requires the definition of the network of channels and junctions for the

water body to be analyzed. The main objective of this segmentation process was to improve the understanding of the hydrodynamics in the reservoir and its relationship to

reported lake zonation. Atkinson et al. (1999), Clyde (2002), and Mabe (2003) have

reported the existence of five zones in Lake Texoma, determined by the chemistry of the

water. These zones are: the Red River arm, the Washita River arm (considered as lotic

zones), the Red River Transition Zone, the Washita River Transition Zone, and the Main

Body (lacustrine zone). The network of channels and junctions for the hydrodynamic modeling of Lake Texoma was developed taking into account this zonation. In addition, this segmentation was developed considering smaller sub-watersheds around the lake.

The bathymetry of the reservoir was used to develop the model segmentation needed for the hydrodynamic and water quality simulation models. The number of

17 segments that the system to be simulated can have is constrained, in part, by the scale at

which reasonable bathymetric estimates can be made (Tufford and McKellar 1999).

Some bathymetric surveys have been made in the reservoir recently (USGS 1999,

TWDB 2003). In the study performed by the USGS (1999), a Digital Elevation Model

(DEM) file of the lake and its surrounding area with a grid of six by six meters each cell

was produced. This file was used to define the network of channels and junctions for the

hydrodynamic simulations as well as determine the required segments parameters (Figure

5).

Figure 5. Image of the DEM file for Lake Texoma showing different depths. Lighter colors correspond to shallower areas (Adapted from: USGS 1999) Surface areas, volumes, and maximum depth for each segment designed for the

hydrodynamic network of the lake were adjusted to this source of information and were

calculated using GIS software ArcGIS 8.0TM (Environmental Systems Research Institute

Inc., Redlands, CA). In addition, because the model assumes every junction and channel to be a rectangular receptacle or conveyor, and because the geometry of the network

18 determines the time step for the simulation, consideration on water depths, length and surface area for each junction and channel were also made as criteria for segmentation of the reservoir.

After combining all mentioned constraints, the resulting network was set up in a one-dimensional framework and it resulted in 40 junctions and 39 channels (See Figures

6 and 7). Data on the geometry of each channel and junction are presented in Appendix A.

21 22 24 23

25 26 27 3 2 4 28 1 5 29 6 18 19 20 30 7 17 31 8 10 9 16 32 11 15 33 40 12 14 39 13 34 38 37 36 35

# = Junction Number

Figure 6. Junction network of the hydrodynamic model of Lake Texoma

Meteorological and Hydrologic Data

Three tributaries were defined in this simulation: Red River, Washita River, and

Big Mineral Creek. A review of the available literature showed that hydrological

19 measurements of the Washita and Red rivers have been performed for more than 60 years

continuously at Dickson, OK and Gainesville, TX, respectively (USGS 2005). These

locations represent the main inlets of water to the lake.

22 23 24 25 26 2 3 1 4 27 28 5 19 18 29 6 17 20 7 30 9 16 31 8 10 15 11 14 32 33 39 12 38 13 37 36 35

# = Channel Number

Channel

Figure 7. Channel segmentation of Lake Texoma for the hydrodynamic model Additional inputs for the reservoir were adjusted using estimated runoff for the

Big Mineral Creek sub-watershed (Upton 2004). Another estimation of additional inputs was made using the results of a mass balance of volumes in the reservoir (Tortorelli

personal communication), considering accumulated volume during a day period, reported

by USACE (2003b), and precipitation and evaporation data registered at Denison Dam weather station. During the calibration process it was determined that approximately one half of the accumulated volume during a 24-hour period, estimated from the reported

20 pool level by USACE and the lake stage-volume curve, and the consideration of

evaporation and precipitation daily data from Denison Dam station provided the lowest

error in pool level estimations of the reservoir and a good representation of the variability

of daily pool elevation of the reservoir (See Figure 8). The boundary downstream of the

system can be set both stipulating outflows and comparing resulting water elevations, or

defining surface elevation and using predicted velocities for validation. For this

simulation, the downstream boundary was defined specifying outflows in order to get

estimations of daily pool elevations and these numbers were used in the validation

process.

1.40 3.00

1.20 2.50

1.00 2.00

0.80 1.50 0.60 Absolute Error (m) RMS of Errors (m) 1.00 0.40

0.50 0.20

0.00 0.00 0 253040455060100 % additional inflow at Big Mineral Creek

RMS Max.Abs.Err.

Figure 8. Variability of error in pool elevation estimations of Lake Texoma using different percentages of accumulated volume in a 24-hour period as inflow in Big Mineral Creek and applying DYNHYD Meteorological data were also available for several years, in a daily frequency, for

some rain gage stations around the lake (NOAA 2003), as well as at Denison Dam

(USACE 2003b). A daily precipitation/evaporation balance was performed and was

21 included as an input of the model (Figure 9). Wind directions and wind velocities were not considered in this simulation.

The time step selected for this simulation was 60 seconds, based on the Courant condition (Ambrose et al. 1993). Considering this condition, the time step (Dt) should be less than the shortest channel length divided by the wave celerity, obtained by the equation:

L Δ=t i , where: ()gyii± U

Li = Length of channel i, m

yi = mean depth of the channel i, m

Ui = velocity in channel i, m/sec

g = acceleration of gravity (9.8 m/sec2)

The minimum length of the channels in the developed network was approximately

1,200 m, and the mean depth of the channels ranked between approximately 2 and 22 m.

Using different values of water velocity in the channels, various values for the time step were calculated, as shown in Table 5. The lowest calculated value for the time step was

85 sec, higher than the selected time step for this simulation.

Table 5. Estimation of maximum time step (in seconds) for different conditions of water velocity and depth of the channels in the developed hydrodynamic network for Lake Texoma

Water Velocity (m/s) Depth 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 2 270 270 269 269 268 267 267 266 5 171 171 171 170 170 170 170 169 10 121 121 121 121 121 120 120 120 15 99 99 99 99 99 98 98 98 20 86 86 86 85 85 85 85 85

22 March/1997

mm/day 5

0 1 2 3 4 5 6 7 8 9 10111213141516171819202122232425262728293031 -5

-10

-15 April/1997

mm/day 5

0 1 2 3 4 5 6 7 8 9 101112131415161718192021222324252627282930 -5

-10

-15 May/1997

mm/day 5

0 1 2 3 4 5 6 7 8 9 10111213141516171819202122232425262728293031 -5

-10

-15

Figure 9. Precipitation/Evaporation balance between March,1997-May,1997 using daily reported values for Denison Dam rain gage station

Two 92-days periods were simulated. One of them corresponded to March, 1997 to May, 1997, considered a period of high inflow variability (Figure 10); and the second one was between July, 1997 and September, 1997, when the main inflows were in the lowest hydrologic condition. These two ranges were selected because they provided the most complete, longest, and continuous set of physical, chemical and biological observations. Validation was performed for the interval between March, 1997 and May,

1997.

Figure 10. Boxplot of mean daily total inflow in Lake Texoma during 1997 represented by month. The highest dispersion of values is observed in May and the lowest occurs in August

24 Hydraulic Travel Time and Residence Time

To complete the analysis of the hydrodynamics of Lake Texoma an estimation of

the hydraulic travel time and the residence time was performed. The hydraulic travel time

is considered as the amount of time that it takes for some volume of water to move

between the segments of the hydrodynamic network, considering the flow. The residence

time is understood as the amount of time that it takes for a conservative component to

travel through the water body during a specific hydrologic condition (Tufford and

McKellar 1999).

Estimations of the theoretical hydraulic travel time can be performed using the

equation t = V/Qin, where V represents the volume of the reservoir at normal pool

elevation and Qin represent the annual average of the total inflow. However, it also can be

evaluated using hydrodynamic simulation performed in a steady-state condition and also

using the average flow for each river. Using this approach it is possible to identify portions of the considered water body where the highest hydraulic travel time is expected.

Residence and travel time were calculated using a hydrodynamic simulation under

steady-state setting at average hydrologic conditions for each river. Mean flows in each

channel were simulated and these values were related to volumes. Estimations of travel

time were performed using the water quality model and introducing a simulated pulse of

a tracer (i.e., a conservative constituent) as a point source load at the first segment of the

water quality model for each inflow on the first day of the simulation period. The

difference between the day of occurrence of the peak of tracer concentration in water

quality segment 31 (closest to the dam) and the first day of application of the pulse was

considered the travel time.

25 Hydrodynamic Modeling Results for Lake Texoma

Calibration of the hydrodynamic model using estimated runoff for Big Mineral

Creek resulted in a maximum difference between observed and predicted elevation of

2.03 m, with an overestimation of the reservoir level during the period of highest flow variability (Figure 11). The estimated RMSE in this prediction was 1.06 m.

Predicted Observed

Figure 11. Graphical comparison between observed and predicted elevations in Lake Texoma during March, 1997 and May,1997, using DYNHYD and estimated runoff for Big Mineral Creek provided by Upton (2004)

Setting Big Mineral Creek flows as a half of the daily accumulated volume in the reservoir which is not explained by the Red and Washita rivers inflows, in a mass balance estimation provided a better estimation of daily elevations of the reservoir, with a maximum difference between observed and predicted elevation of 1.93 m (Figure 12). In this case, the estimated RMSE was 0.94 m. During low flow conditions, both simulations provided similar results. This suggests that some additional losses should be included in the runoff model to adjust flows for the Big Mineral Creek sub-watershed during high precipitation condition. However, both methods provided results considered to be in the accepted range of lake elevation values for this simulation.

26 One aspect to take into account is the fact that these estimations assume negligible

seepage to groundwater and therefore the difference is attributed entirely to surface

inflows. This assumption was made to simplify the analysis and it does not affect the

resulting prediction of bulk movement of the water column.

Predicted Observed

Figure 12. Graphical comparison between observed and predicted elevations in Lake Texoma during March, 1997 and May,1997, using DYNHYD and a 50% of the daily accumulated volume in the reservoir not explained by the Red and Washita rivers inflows based on mass balance estimations as a daily inflow of Big Mineral Creek. In general, the model tracks daily variations of the elevation of the reservoir closely. Elevation of the reservoir directly responds to a combination of periods of

highest discharge and regulation of the outflows. This corresponds to the function of

flood control assigned to this dam. Precipitation/evaporation balance is also an important

factor in estimates of the reservoir elevation. If this factor is not considered,

overestimations of water elevation are expected. The effect of this parameter is similar

during both conditions, high variability of flow or lowest inflow with low variability.

The model also suggests the existence of a longitudinal gradient of velocity.

Water velocity suddenly declines once the Red River gets into the lake. This effect is not

observed in the Washita River, probably because of the effect of the Cumberland Pool,

located upstream of the boundary set for this simulation. Results of the steady-state

27 simulation seem to support the idea proposed by Atkinson et al. (1999), Clyde (2002) and

Mabe (2002). However, the identified zones in this hydrodynamic simulation are slightly different from those reported by Atkinson et al. (1999). A clear lotic zone is identified in

the proximity of the entrance of the Red River, and four lentic zones can be defined as the

Red River transition zone, the Washita River transition zone, the Main Lake zone, and

the Big Mineral Creek zone, with the lowest simulated velocities and even with some

backflow during peak flows of the Red River (Figure 13). A lotic zone for the Washita

River Arm is not clearly identified with this simulation.

Hydraulic Travel Time

Using the theoretical method to estimate the hydraulic travel time for Lake

Texoma, it resulted in approximated 255 days. Using the proposed hydrodynamic

segmentation, the largest travel time per segment is about 56 days, and it occurs in the

Big Mineral Creek area, where some back flows and lower velocities occur due to the

damming effect generated by Red River flows. Figure 14 presents a spatial zonation of

predicted water velocities for the reservoir. The average travel time is estimated around

15 days per segment, which is similar to the average hydraulic travel time in the

transition zones. Estimations of travel time in the Red River zone are the lowest and the

average is around 3 days. In transition zones the average travel time varies between 5 and

35 days, and in the Main Lake area the average travel time per segment is around 19 days.

Results of these estimations for each segment are presented in Appendix B.

28 Residence Time

A simulated pulse of load of 500 Kg/d of a tracer was applied in segment 1 during the first day of the simulation. It took 78 days to reach the maximum concentration in segment 31, using a dynamic condition with high flow variability. Dilution process and the effects of transport processes are also examined in this simulation (See Figure 15).

Figure 13. Variation of water flow in different zones of Lake Texoma. 0 to 1 corresponds to the Red River Zone, 19 to 28 corresponds to the Red River Transition Zone, 0 to 20 corresponds to the entrance of Washita River to Lake Texoma, 27 to 28 represents Washita River Transition Zone, 28 to 29 and 31 to 0 represents the Main Lake Zone, 0 to 32 corresponds to the entrance of Big Mineral Creek to Lake Texoma, and 10 to 36 is the junction between the Big Mineral Creek and the Red River Transition Zone.

Figure 14. Spatial distribution of predicted water velocities for Lake Texoma under the steady-state condition The same procedure was applied using a simulated pulse loading of 150 Kg/d of a tracer in segment 20, at the boundary in the Washita River Arm (See Figure 16).

Residence time in this case resulted shorter, considering also the longitude of this portion of the lake. The length of the Washita River Arm was estimated to be 30.8 Km, while the length of the Red River Arm is 54.9 Km, so the path of the tracer in this portion of the reservoir was almost a half of the path through the Red River Arm. It took about 48 days for the peak of concentration of the tracer to reach segment 31.

30 Figure 15. Graphical representation of travel time in Lake Texoma when a pulse of tracer is applied in the upper portion of the Red River arm A combined loading of a tracer next to both boundaries Washita and Red River was also tested and there is an overlap of the effect of both loadings. However the maximum peak is reached approximately 77 days after the occurrence of loading in segment 1. This suggests that residence time during high-variable-flow conditions is mainly driven by hydrologic conditions in the Red River (See Figure 17).

31 Figure 16. Graphical representation of travel time in Lake Texoma when a pulse of a tracer is applied in the upper portion of the Washita River arm

32 Figure 17. Estimation of travel time for Lake Texoma using a combined loading at the Red and Washita River

Tocoma Reservoir: Description of the Study Area

Tocoma Reservoir has been projected between miles 368 and 379 in the Caroni

River, Venezuela. Caroni River is a blackwater river, right-sided tributary in the lower reach of the Orinoco River, in Venezuela, with a total length of 431 miles approximately.

It flows to the Orinoco River in a North-South direction and its watershed is about 92,170

Km2. Among the very last 62 miles of the Caroni River, the most important hydroelectrical company in Venezuela, CVG EDELCA, has built three reservoirs for hydroelectrical generation: Guri, Macagua and Caruachi. Tocoma Reservoir is going to be the fourth and it will be located between two of these three reservoirs: Guri and

Caruachi, as presented in Figure 18. Consequently, the very last portion of river conditions in the lower Caroni will be flooded once the reservoir is created.

33 GURI DAM

250

200 TOCOMA CARUACHI 150 DAM MACAGUA 100 DAM Orinoco Elevation (masl) Elevation (masl) 50 River

0 Caroní River

100 90 80 70 60 50 40 30 20 10 0

Distance from the Orinoco River (km)

Figure 18. Scheme of hydroelectrical development in the Lower Caroni River (Adapted from CVG EDELCA 2005b)

Hydrograph and Hydrology

The total length to be flooded for the construction of the reservoir extends from

Guri Dam discharge channels to the La Pollera Island, a place located 17 Km downstream of Guri Dam, on the Caroni River (Figure 19). Under current conditions, water flows from Guri Dam for approximately 6 Km in a very straight pattern through a narrow canyon named Nekuima Canyon, approximately 200 to 500 m wide. Then, the river opens widely, reaching approximately 7 Km wide, as a result of a geologic fault.

Consequently, numerous islands and rocks emerge at this portion of the river, developing

rapids and small falls. Once the fault area ends, the river gets narrower again, with

approximately 3.5 Km wide. At this point a 6,250 m length concrete dam is going to be

erected to create a reservoir with a total area of 85 Km2.

The total drainage area of the future reservoir is 88,064 Km2, and the immediate

drainage area of the new reservoir has been estimated on 400 Km2. Other tributaries of

34 Tocoma Reservoir will be Cunaguaro River, and other three small creeks: El Merey,

Maria Luisa, and Guarapo. RESERVOIR CARUACHI

Tocoma Dam Site

C Cun aguaro ar River o n í R iv e r

. k e e r C Dam

a s i Principal river u L erey Creek Future flooded area a El M í r a . Island M k e e r Reservoir C Rivers Permanent River Intermittent River

Guarapo Guri Dam Unknown Flow Regime

GURI RESERVOIR

Figure 19. Geographic location for the future Tocoma Reservoir, in the Caroni River The main source of water for the new reservoir will come from the discharge of

Guri Reservoir. Since 1986, this reservoir controls the discharges of the lower portion of

the Caroni River. Therefore, most of the time there is no seasonal flow fluctuation, except

for those years with high precipitation in the watershed. The mean annual flow

downstream of Guri Dam is 4,630 m3/s (Period: 1986-2004) (CVG EDELCA 2005a), and

it is almost constant during the year. Only for very rainy years, the mean daily flow can ascend to 12,500 m3/s. A comparison of mean monthly flows discharged through the

turbines and the spillway during a typical year is presented in Figure 20.

35 4000

3500

3000

2500

2000 Flow (cms)

1500

1000

500

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Turbines Spillway

Figure 20. Mean monthly turbine-used and spilled flow at Guri Dam discharge channels under average conditions However, the flow of those small tributaries draining directly to the Tocoma

Reservoir area fluctuates considerably during the rainy season, with monthly daily mean

values ranging between 1.2 and 18 m3/s (CVG EDELCA 2005a). The annual inflow of these small creeks directly draining to Tocoma Reservoir has been estimated on 11 m3/s

(Acevedo and Sherman 2002).

Mean annual rainfall has been estimated at 1,270 mm. The highest precipitation

occurs between June and August, and the lowest between March and April, as presented

in Figure 21. Differently from what happens to those small rivers draining directly to the

future reservoir, the main inflows would not vary significantly during the rainy season

because of the flow regulation that Guri Dam exerts at the lower portion of the watershed.

36 250

200

150

Rainfall (mm)

100

0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 21. Average monthly precipitation at Las Babas weather station, close to Tocoma Reservoir site (Period: 1986-2004)

Model Parameterization for Tocoma Reservoir

Because the Tocoma reservoir has not been built yet, the hydrodynamic model

was examined as an expected quantitative prospective of potential water flow behavior.

For Tocoma Reservoir, model parameterization was based on validations of DYNHYD results describing river conditions at this portion of the Caroni River, as described by

Acevedo (2002) and Acevedo et al. (2002). Simulated velocities were compared with

measured values. Then, using topographic information for the area to be flooded by the

reservoir, and some bathymetric cross-sections of the Caroni River, a digital elevation model (DEM) was built for the new reservoir and a new hydrodynamic network was created. These results provided the parameters associated with channel geometry for describing the hydrodynamic network of the new reservoir. Inflows were set to the

37 monthly mean flow discharged by Guri Dam, using the information from the

hydrological data base of CVG EDELCA (CVG EDELCA 2005a). Additional parameters for the hydrodynamic simulation, such as Manning roughness coefficient, came from the literature review (Maidment 1992), considering topography and vegetation information.

Model results were evaluated for continuity and they should be compared with real values once the reservoir is completed and the area flooded.

Model Geometry

Based on the model network developed by Acevedo (2002) for river conditions of

the Caroni, a new water network was created, but this time considering the total area to

be flooded by the reservoir. According to project specifications, water level for the

simulation was set constant at 127 m.a.s.l. The approach used for developing the

hydrodynamic network described the flow in a quasi two-dimensional manner. This is to

say, the flow was considered to have a predominant one-dimension condition. However,

because of the topography of the zone, and the existence of multiple islands, some lateral flow components were also considered, resulting in a two-dimension network. Smaller sub-watersheds around the reservoir were also considered for defining location of junctions of the network.

The bathymetry of the new reservoir was built based on topographic maps, available at 1:10,000 scale, with 5-m contours. A digitalization of these data was

performed and a Digital Elevation Model (DEM) file for the new reservoir was created

using GIS software ArcGIS 9.0TM (Environmental Systems Research Institute Inc.,

Redlands, CA). This information was complemented by cross-section bathymetric data of the river acquired in previous studies (CVG EDELCA, 1987). The location of the cross

38 sections are indicated in Figure 22, and the resulting bathymetric model of the reservoir is presented in Figure 23.

In similar manner to the Lake Texoma parameterization previously described, additional criteria for developing the hydrodynamic network for Tocoma Reservoir were: homogeneous water depths, length, and surface area for each junction and channel of the resulting network. However, by combining these criteria, junction areas ranged between

267,973 and 667,290 m2.

1501 1501 16

872000

1701 1701

1702 1702 19 18

1902 1901 1904 1903 1905

BATHYMETRIC CROSS-SECTION

Figure 22. Schematic location for bathymetric cross-sections in the Caroni River for the Tocoma Reservoir area (Adapted from CVG EDELCA 1987)

39 The hydrodynamic network for Tocoma Reservoir resulted in 52 junctions and 66

channels, approximately twice than those described for Acevedo (2002) for this portion of the Caroni River functioning as a river. The resulting network is presented in Figure 24,

and a visual comparison of both, river and reservoir networks, is presented in Figure 25.

Data on geometry of each channel and junction are included in Appendix C. Values for

channel length and width also came from the analysis using ArcGIS9.0TM (Environmental

Systems Research Institute Inc., Redlands, CA).

Figure 23. Bathymetric map for Tocoma Reservoir

Hydrologic Data

The main inflow for the reservoir will come from the discharge of Guri Dam. At

this dam, there are two power houses. These two function differently from each other. In

40 addition, they release their discharges in two channels, one natural and one excavated, separated from each other by a rocky promontory. Average monthly flows estimated as a function of the energy produced for each power house were used for setting up the inflow for the new reservoir, considering data from 1986, when Guri started to work under these conditions. Two additional inputs, representing El Merey Creek and Cunaguaro River, were also considered. A constant level of 127 m was set as reservoir water level, considering that the reservoir will function as a constant-level reservoir. Meteorological data were not included under this analysis.

Island

Figure 24. Hydrodynamic network for Tocoma Reservoir The time step was set up considering the Courant condition (Ambrose et al. 1993), as explained for the hydrodynamic model calibration of Texoma Reservoir. A 20-second

41 time step was used, taking into account that the minimum length of the channels in the hydrodynamic model network developed for Tocoma Reservoir was approximately 700 m, and the mean depth of the channels ranged between 4.5 and 49.5 m. The lowest calculated value for the time step was 25, as presented in Table 6.

Table 6. Time step values for different junction depths of the hydrodynamic network for Tocoma Reservoir Depth Water Velocities (m/s) (m) 0.1 0.5 1 2 3 4 5 6 4.5 104 98 91 81 73 60 60 55 5 98 93 87 78 70 64 58 54 10 70 67 64 59 54 50 47 44 15 57 55 53 49 46 43 41 39 20 50 48 47 44 41 39 37 35 25 44 43 42 40 37 36 34 32 30 41 40 39 37 35 33 32 30 35 38 37 36 34 32 31 30 29 40 35 34 34 32 31 29 28 27 45 33 33 32 30 29 28 27 26 50 31 31 30 29 28 27 26 25

The model was set up as a steady-state condition and two flow conditions were simulated: 4,250 and 12,000 m3/s. These two conditions were selected based on the average discharge for Guri Dam and the maximum discharged allowed for this dam while hydroelectrical projects downstream of Guri are under construction. Validations for river conditions were performed using the flow reported for those days when velocity measurements were made at Tocoma dam site.

42 Tocoma Dam 32 site

31 30

29 28 27 25 26 24 23 21 22 19 20 18 17 16 1214 13 15

11 9 8 10 7 6

1 2 Guri Dam

Tocoma Dam site

55 40 52 45 39 53 38 44 49 54 46 25 23 37 50 22 21 36 51 43 47 20 24 19 35 48 42 18 34 41 33 17 27 26 16 31 15 32 13 30 29 28 14 10 12 11 9 8 7 6 5

3 2 1 Guri Dam

Figure 25. Comparison between hydrodynamic network for river and reservoir condition for Caroni River at Tocoma Reservoir site

43 Hydraulic and Travel Time

Residence and travel time were calculated under the steady-state condition for

both, as a river and as a reservoir, considering two hydrologic conditions: average and high flow. Estimates for travel time were performed aided by the water quality model, introducing a conservative substance as a tracer at the first water quality segment of the network. The difference between the day of occurrence of the peak of tracer’s concentration at the lower boundary of the network and the day of application of the load was considered the travel time. Estimates for these conditions, both river and reservoir conditions, were compared to establish the net change resulting by the construction of the reservoir on the hydrodynamics of the system.

Hydrodynamic Model Results for Tocoma Reservoir

Water velocity data collected during 2002 and 2005 were used to assess the “fit” of the DYNHYD model under river condition to the portion of the Caroni River where

Tocoma Reservoir will be created. During this period of time, water discharges from Guri

Dam did not substantially change. Therefore, it was not possible to evaluate the performance of the model under high flow conditions.

Model estimates for water velocity for channel 38 of hydrodynamic network under river condition were compared with observed values, and the RMSE was used as a measure of model fit. An RMSE of 9.63% (less than 20%) was achieved when simulating river conditions, and it was considered acceptable for these estimations. These data are presented in Table 7.

44 Table 7. Predicted and measured velocities for Tocoma under river condition Date Flow Observed Velocity Predicted Velocity (m3/s) (m/s) (m/s) 09/05/2002 5,242 0.63 0.77 09/10/2002 4,794 0.77 0.71 09/24/2002 4,453 0.65 0.66 10/01/2002 4,226 0.66 0.63 10/05/2002 3,967 0.60 0.60 10/22/2003 4,165 0.62 0.62

As a result of the simulation for Tocoma Reservoir, estimates of flow velocity and volumes for each channel and junction of the hydrodynamic network were obtained. The model suggests the reduction of water velocities in at least one order of magnitude at

Nekuima Canyon as a reservoir, when compared with simulation results for river conditions with average inflow (See Table 8).

Highest water velocity values are expected in the central channel of the new reservoir. For those littoral areas associated inflows of creeks and small rivers, like

Cunaguaro and El Merey, the model indicates that water velocities will have very low values. A graphical representation of water velocities results is presented in Figure 26.

Hydraulic Travel Time

A theoretical estimation of the hydraulic travel time for Tocoma Reservoir resulted in 4.8 days, considering the average inflow and the total volume for the reservoir.

Using the proposed segmentation, the largest travel time is about 32 days per segment, and it occurs in the Cunaguaro River area, where some back flows are expected, considering the amount of flow in the main channel of the reservoir in comparison with the flow of this small river. The average travel time is estimated in 1.7 days per segment, approximately, with the lowest values in segments located in the Nekuima Canyon and

45 followed by those in the central channel of the reservoir. Results of these estimations are presented in Appendix D.

Table 8. Relative change of water velocities for Caroni River when comparing river and reservoir condition at Tocoma site

River Condition Reservoir Condition Simulated Simulated Percent of Segment ID Water Velocity Segment ID Water Velocity Change (%) (m/s) (m/s) 1 3.45 4 0.82 23.81 2 5.07 5 0.50 9.88 3 5.04 6 0.20 4.04 4 3.66 9 0.11 2.89 5 1.75 11 -12 0.07 3.77 6 0.96 28 0.04 4.63 7 1.02 13 0.03 3.27 8 2.21 9 -14 0.08 3.65 9 0.93 29 - 30 0.01 1.58 10 0.81 31 0.03 3.55 11 1.32 15 - 26 0.03 2.51 12 1.09 27 0.02 1.86 13 1.11 15 -16 0.03 3.12 14 1.01 16 0.03 2.51 15 1.07 33 0.01 0.70 16 0.82 41 0.04 4.55 17 0.53 19 0.02 3.82 18 0.88 18 0.03 2.96 19 0.52 24 0.02 3.60 20 0.65 42 0.05 7.02 21 0.49 47 0.01 2.21 22 0.6 28 - 51 0.03 4.97 23 0.53 50 0.01 2.34 24 0.78 43 0.05 6.08 25 0.38 46 0.01 3.51 26 0.44 49 0.01 3.09 27 0.73 44 0.05 6.81 28 0.6 45 0.04 6.69

46 Figure 26. Spatial variation of simulated velocities for Tocoma Reservoir

Residence Time

A simulated pulse of load was applied at the discharge channels for Guri Dam which represents the first segment of the water quality network. It took 2 to 5 days to register the concentration peak at segments next to the future dam when the treatment was applied to segment 1. A graphical representation of temporal variation of the loading at these segments is presented in Figure 27. These values represent the residence time for the future reservoir. When testing the application of load at Cunaguaro River, it took 23 days for registering a peak concentration of the tracer at the end of the water quality network, indicating a high residence time for this portion of the new reservoir. Figure 28 shows temporal variation of tracer concentration when applying the treatment at

47 Cunaguaro River area. The difference in magnitude of these two values can be understood as a measure of the environmental sensitivity that Cunaguaro River arm at the future reservoir will have.

Tracer (ppt)

Segment 1 Segment 41 Segment 48

Figure 27. Peak concentration of the tracer at segments close to the future dam, in Tocoma reservoir, when a pulse is applied at Guri Dam discharge channels.

48 a)

Figure 28. Temporal variation of a tracer concentration when applied at a) Cunaguaro River, and peak concentration of the tracer at b)the segment next to the future dam, in Tocoma reservoir.

49 CHAPTER 3

RESERVOIR WATER QUALITY MODELING

Water quality has become one of the topics of major environmental and health concern in recent years (De Villiers 2002). Traditionally, monitoring water quality in rivers and lakes has been used as the primary source of information for making decisions relating to the management of water bodies. Empirical analysis of water quality data has resulted in useful descriptions of aquatic ecosystems (Hakanson and Peters 1995). This approach, however, has been considered insufficient for prediction and quantification of secondary effects, especially those associated with human activities and their effects on water bodies, and the sustainability of these important resources (Goodwing and Hardy

1999). A broad range of variables and relationships among water resources and their usages are considered before defining a management objective for water bodies. This approach requires extensive data and simulation tools to assist in the decision-making process for managers and communities in order to select management strategies that support the conservation of the water system under consideration (Goodwing and Hardy

1999). Consequently, developing predictive models for rivers, reservoirs, and lakes is a powerful tool in practical ecology and environmental management.

Natural processes and anthropogenic effects in constructed impoundments have been better understood using simulation tools considering that these impoundments may be more complex than natural lakes (Hakanson and Peters 1995, Tufford and McKellar

1999). This is important to be considered when evaluating the environmental impact associated to a reservoir creation, like the Tocoma Reservoir. In the case of studying a

50 reservoir with multiple uses, such as Lake Texoma, this approach is particularly useful

and desirable.

The specific objectives of this chapter are to describe: the procedure used in the

calibration and validation of WASP6 for both reservoirs, Lake Texoma and Tocoma

Reservoir; and the evaluation of predictions for some chemical components for these two study cases. For Lake Texoma, predictions for chlorides, total suspended solids and dissolved oxygen were performed using the selected water quality simulation model; and dissolved oxygen-biochemical oxygen demand estimates were analyzed for Tocoma

Reservoir with the same model.

Model Description

The model selected for this research was the Water Analysis Simulation Program,

WASP (Ambrose et al. 1993). WASP is a dynamic compartment model, which is very flexible, and is used to simulate fate and transport of water quality constituents and toxic compounds. WASP6 simulates transformation of water quality constituents based on the mass conservation equation, using two kinetic sub-models: EUTRO and TOXI, the former is related to the problem of reduced dissolved oxygen content and eutrophication processes, and the latter is related to pollutants and toxic substances in the water

(Ambrose et al. 1993). The model system was developed and is supported by the US

Environmental Protection Agency (USEPA) and has been used to study a variety of issues in many different water bodies, such as rivers, lakes, reservoirs, and estuaries.

These studies typically provided physical and ecological characterization, evaluation of the effects of human activities, and development of watershed management strategies

51 (Lang and Fontaine 1990, Lung and Larson 1995, Branch et al. 1996, De Smedt et al.

1998, Tufford and McKellar 1999, Lee 2000).

Within TOXI, organic and inorganic chemicals, metals, and sediments can be modeled. A chemical tracer can also be analyzed with this sub-model of WASP by specifying no decay. In this study, the TOXI sub-model of WASP was used to simulate the processes associated with chlorides and total suspended solids concentrations in Lake

Texoma. The model was set to predict concentrations of these variables in the reservoir by simulating: the inputs of flow and loads of suspended solids and chlorides, the advective and dispersive transport of these components, and the processes of sediment deposition. Therefore, the sub-model TOXI was set to describe kinetic transport of one chemical without degradation (chlorides) and three types of solids (clay, silt, and sand) within multiple water quality segments and their corresponding benthic layers. A sub- benthic layer covering the entire bottom of the reservoir was also defined in this simulation (Figure 29).

In EUTRO, several processes determine the transport and fate of nutrients, phytoplankton, carbonaceous material, and dissolved oxygen in a water body. These processes can be physical, chemical and biological. EUTRO was used to simulate dissolved oxygen (DO) and biochemical oxygen demand (BOD) for Lake Texoma and

Tocoma Reservoir. Simulations may incorporate phosphorous and nitrogen cycle, and phytoplankton kinetics in a subsequent effort.

Four complexity levels are included in EUTRO to simulate carbonaceous biochemical oxygen demand (CBOD) and DO. In this dissertation the simplest complexity level of EUTRO was used considering that this was the first approach of

52 evaluation of water quality for these water bodies; that is to say, a system for dissolved

oxygen balance that solves the Streeter-Phelps BOD-DO equation in a slightly modified

form, allowing time-variable temperatures to be specified. Under this approach, sources

of oxygen are given by reaeration. The sinks are mainly driven by total biochemical demand. A salinity function for Lake Texoma was included considering the natural brackish condition of the system and its effects on oxygen solubility.

Configuration of the water quality network for Lake Texoma in EUTRO was the same used for TOXI. Water quality network for Tocoma Reservoir consisted on multiple water quality segments with a single benthic layer covering the entire bottom surface of the future reservoir (Figure 30).

Water Quality Segment B 1 2 3 Flow

Benthic Layer

Sub-benthic Layer

B = Boundary # = Water Quality Segment ID

Figure 29. Graphical representation of the configuration of water quality segments for the application of WASP for Lake Texoma

53 Inflow

Water quality B 123 Flow segments (Surface Water)

Sedimentation and Resuspension

Benthic Segment

B = Boundary # = Water quality segment ID Figure 30. Graphical representation of the configuration of water quality segments for the application of WASP for Lake Texoma

Previous Water Quality Studies of Lake Texoma

Studies on the water quality of Lake Texoma include a variety of issues.

Chemistry of the water has been one of the topics of major concern in this reservoir, considering the value of the reservoir as a source of water supply and the revenue associated with the production of striped bass (Morone saxatilis) (USACE 2003a).

Analysis of data resulting from intensive monitoring of physical and chemical characteristics of the reservoir reflects a spatial zonation based on its conductivity and turbidity (Atkinson et al. 1999, Waller et al. 2002). Four zones were defined in those studies: the Red River Arm and the Washita River Arm, considered as lotic zones, and the Red River Transition Zone, the Washita River Transition Zone, and the Main Body, considered as lentic zones. Other studies also consider the Big Mineral Creek as an additional zone because of its differentiated characteristics (Mabe 2002).

54 Additional studies on water quality of the reservoir and its relationship to land use

of the shoreline and the watershed also show that there are biological gradients, affecting

biological productivity of the reservoir (Work and Gophen 1999, An et al. 2002, An and

Kampbell 2003), and the effects of additional human influence in this water body are still

unknown.

Biological aspects that have been analyzed in the reservoir include effects of

pollution, biological productivity, ecology of fish population, zooplankton, algae, and

bacteria. Atkinson et al. (1999) define the reservoir as slightly eutrophic, based on

chlorophyll a values measured in the main body area. A more recent study (Mabe 2002) considers the possibility of mesotrophic status, based on lower chlorophyll a concentrations measured in the lake. Hunter and Carroll (1982) analyzed the concentration of herbicide residues in fish and sediment samples collected in Lake

Texoma, showing elevated concentrations of PCB in bottom-feeding fishes but less significant amounts of herbicide residues in the same population. Work and Gophen

(1999) studied the environmental factors in Lake Texoma and other U.S. reservoirs affecting the distribution of a cladoceran. They report the gradient of water conductivity of Lake Texoma as an important factor in determining the distribution of Daphnia

lumholtzi in the reservoir. An et al. (2002) identified a seasonal occurrence of E. coli and total coliforms at the marinas of Lake Texoma, and stated that it was possibly due to variations of anthropogenic stress or environmental conditions in those areas. A seasonal variation in chlorophyll a and nitrates is also reported by An and Kampbell (2003) at the

marinas of Lake Texoma.

55 Water Quality Model Parameterization for Lake Texoma

The simulation process involved parameterization, calibration of the selected

model, and validation of WASP for Lake Texoma, including adjustment of initial system

conditions and simulation time step (Ford 1999). The model was set in a one-dimension

condition in order to describe variations in the average concentration of total suspended

solids, chlorides, BOD, and DO in different sectors of Lake Texoma, during a period of

92 days, between March, 1997 to May, 1997. Selection of this period for the simulation

was done because of the availability of water quality data and the variability of the total

inflow to the reservoir and its influence on pool elevation. The WASP model was linked

to the hydrodynamic model DYNHYD, previously calibrated for the reservoir with a time

step adjusted to 30 seconds, as described in the previous chapter, covering the same

period of time. Consequently, time step for WASP was adjusted to 300 seconds which

corresponds to tenfold of the hydrodynamic model time step.

WASP Network Characteristics

In order to model the reservoir using WASP, it was necessary to divide the water

body into segments. In this simulation, every segment was assumed to be a completely

mixed reactor with uniform modeling parameters, such as surface area, volume, cross

sectional area and chemical or physical constants or rates. The WASP network utilized

the same segmentation as the hydrodynamic study. Every junction of the hydrodynamic

model was defined as a water quality segment, except the junctions at the very beginning

or end of the hydrodynamic network because these junctions were used to set boundary

conditions of the water quality model, as explained later. In addition, a benthic layer of

20-cm depth was defined for every water quality segment. This layer acted as a

56 convenient sink for settling BOD and its depth was selected to adequately reflect the thickness of the active layer that interacts with overlying water. Also, it was defined to reflect a reasonable time history in the sediment layer. Below this benthic layer, a sub- benthic layer covering the entire bottom of the reservoir was also defined in order to define a boundary for simulations of the dynamic of solids at the bottom of the reservoir.

The resulting network has 36 water quality segments, 36 benthic segments and one 50-cm- depth sub-benthic layer. Figure 31 shows the final segmentation used, while

Table 9 presents some general characteristics of each water quality segment.

B 20 22 21

23 24 25 2 1 3 26 B 4 27 5 17 18 19 28 6 16 29 7 9 8 15 30 10 14 31 36 11 13 35 12 B 34 33 32 B

# = Segment ID

Figure 31. Segmentation of Lake Texoma used in WASP6

57 Table 9. General characteristics of segments used in WASP for Lake Texoma Description Volume Mean Depth (m) Description Volume Mean Depth (m) (m3) (m3) RR1 10900000 4.17 Benthic1 67849.36 0.02 RR2 57000000 6.38 Benthic2 227809 0.02 RR3 62100000 7.11 Benthic3 172382 0.02 RR4 46500000 9.59 Benthic4 105700.4 0.02 RR5 38300000 9.73 Benthic5 85160.28 0.02 RR6 38600000 10.68 Benthic6 75636.02 0.02 RR7 55100000 11.63 Benthic7 100194.2 0.02 RR8 50100000 12.1 Benthic8 82174.78 0.02 RT9 53600000 11.51 Benthic9 96575.22 0.02 RT10 65200000 10.46 Benthic10 137206.4 0.02 RT11 62500000 21.22 Benthic11 145330.2 0.02 RT12 17500000 6.29 Benthic12 145747.9 0.02 RT13 1.03E+08 14.06 Benthic13 207399.2 0.02 RT14 1.5E+08 14.25 Benthic14 193493.2 0.02 RT15 1.76E+08 15.82 Benthic15 242573.1 0.02 RT16 1.63E+08 16.09 Benthic16 191682.2 0.02 ML17 2.41E+08 16.17 Benthic17 290756.5 0.02 ML18 1.2E+08 15.65 Benthic18 159279.8 0.02 ML19 1.3E+08 17.02 Benthic19 202149.3 0.02 WR20 59100000 9.5 Benthic20 128412.7 0.02 WR21 96500000 11.12 Benthic21 196998.6 0.02 WRT22 1.58E+08 11.27 Benthic22 279131.8 0.02 WRT23 1.33E+08 16.72 Benthic23 173223.5 0.02 WRT24 72000000 16.63 Benthic24 82775.06 0.02 WRT25 83000000 15.76 Benthic25 116072.2 0.02 WRT26 1.03E+08 14.5 Benthic26 138434 0.02 WRT27 1.37E+08 14.32 Benthic27 230113.4 0.02 ML28 3E+08 14.56 Benthic28 368154.9 0.02 ML29 3.11E+08 19.88 Benthic29 372119.1 0.02 ML30 1.37E+08 21.39 Benthic30 121963.5 0.02 ML31 1.58E+08 21.43 Benthic31 142306.1 0.02 BMC32 5610000 3.6 Benthic32 43163.36 0.02 BMC33 12300000 2.91 Benthic33 64745.04 0.02 BMC34 16200000 5.49 Benthic34 64745.04 0.02 BMC35 26800000 6.64 Benthic35 86326.74 0.02 BMC36 59400000 7.77 Benthic36 160437.3 0.02 Sub-benthic 1.42E+08 0.5

A correspondence between the segment identification in the model and the station identification number used in Atkinson et al. (1999) is presented in Table 10.

58 Boundaries

Those segments of the water quality model that import, export, or exchange water with the locations outside of the defined network for the reservoir were considered

“boundaries”. Therefore, four segments were defined as boundaries in this simulation:

o At the very beginning of the Red River arm, and represented by the gage

station located on the Red River near Gainesville, TX.

o At the very beginning of the Big Mineral Creek arm.

o At the very beginning of the Washita River arm, represented by the area of the

reservoir where Station 25 of Atkinson et al. study (1999) is located, and

o At the very end of the reservoir, in the Main Body zone next to the dam.

Table 10. Relationship between water quality segments identification and station identification used by Atkinson et al. (1999) Water Quality Segment Identification Station ID (1) Number of WASP WASP Segment ID Station No. Zone Segment 4 RR4 1 RRZ 8 RR8 3 RRZ 34 BMC34 7 RRTZ 11 RRT11 8 RRTZ 14 RRT14 9 RRTZ 31 ML31 17 MLZ 29 ML29 19 MLZ 27 WRT27 20 WRTZ 22 WRT22 22 WRTZ 20 WR20 24 WRZ Boundary Boundary WR 25 WRZ (1) According to Atkinson et al. (1999) RR = Red River ML = Main Lake BMC = Big Mineral Creek WRT = Washita River Transition RRT = Red River Transition WR = Washita River

59 Model Constants

Several constants, as well as physical and chemical rates were required for WASP calibration to Lake Texoma. A list of the required rates and constants included in the model for the variables simulated in this project is presented in Table 11.

Table 11. Some constants and rates used by WASP for water quality simulation. Constants & Rates Units Value Chlorides

• Dispersion coefficient m2/sec Segments 28 to 22 69 Segments 21 to 20 46 Total Suspended Solids • Settling velocity: m/day Fine-grained sediment (clay) 0.032 to 0.32 Medium-grained sediments (silt) 19 Coarse-grained sediments (sand) 94 • Resuspension velocity: m/day Fine-grained sediment (clay) 0 to 1.27 x 10-07 Medium-grained sediments (silt) 0 to 1.27 x 10-06 Coarse-grained sediments (sand) 0 3 • Solid density: g/cm Fine-grained sediment (clay) 2.7 Medium-grained sediments (silt) 2.0 Coarse-grained sediments (sand) 1.8

Dissolved Oxygen and Biochemical Oxygen Demand

• Dissolved Oxygen Oxygen to Carbon ratio mg O2/mg C 2.57 Reaeration rate day-1 Model calculated Reaeration temperature coefficient 1.04 • Biochemical Oxygen Demand BOD decay day-1 0.1 BOD decay temperature coefficient 1.04 Half saturation constant for oxygen limitation mg O2/L 1

A review of the literature and a calibration process were performed to obtain the corresponding values for dispersion coefficients, settling and resuspension velocities, and density of solids used for this simulation, as well as for constants and kinetic rates associated with DO and BOD (Tufford and McKellar 1999, Benaman et al. 1996, Schultz

2001, Wool et al. 2001). For level one complexity in EUTRO, only two constants had to

60 be set in the model: the reaeration rate and the deoxygenation rate. The reaeration rate helps to determine the rate of gas transfer of oxygen from the overlying atmosphere into the surface. Within EUTRO there were three options to define this rate, including its calculation by the model from water velocity, wind velocity (default set to 0.6 m/sec), water temperature, and air temperature (default set to 15oC). This option was the selected

for this simulation. The deoxygenation rate is set with the sediment oxygen demand and

they account for the rate at which BOD consumes oxygen to stabilize the organic matter

and for the effects of settling and oxidation on BOD in sediments. After a calibration

process, a value of sediment oxygen demand of 0.4 g/m2day was set for the entire

reservoir.

Initially, transport of the components in the water quality simulation was driven

by advection, described by the calibrated hydrodynamic model for Lake Texoma.

However, predictions of chloride concentrations in the Washita River arm did not agree with observed values. As a result, dispersive transport was also included for the transport

of chemical components in this portion of the reservoir. A dispersion function was

specified in the water quality model between segments 28 and 22 (69 m2/sec). Another

dispersion function was defined between segments 21 and 20 (46 m2/sec) after

considering gradients of conductivity reported for that portion of the reservoir (Upton

2004). These values also account for the action of the wind and exceed those reported for

the same parameter in a simulation made by Tufford and McKellar (1999) of Lake

Marion, a large reservoir in the South Carolina (USA) coastal plain, but are lower than

the values used by Benaman et al. (1996) in a water quality simulation in the Houston

ship channel (Texas, USA).

61 In order to set the density of each type of solid considered for this simulation, and

its corresponding settling and resuspension velocity, results of the hydrodynamic study

were considered. Velocity gradients can influence the sedimentation pattern in a water

body. Heavier particles are expected to settle faster than lighter material (Allen et al.

1999). Consequently, settling and resuspension velocities were adjusted taking into account different water velocity gradients in each arm of Lake Texoma. Higher settling velocity values were assigned to larger particles in the Washita River arm, where water

velocities were lower, while the lowest settling velocities were assigned to suspended

particles in the Red River arm (Figure 32 and Figure 33). In addition, the diameter and

density of each type of solid were considered in selecting settling velocity values, as

recommended by Wool et al. (2001).

0.040

0.035

0.030

0.025

0.020

Velocity (m/s) 0.015

0.010

0.005

0.000

2 5 1 R4 R6 10 16 23 3 RR2 R R RR8 T T1 T L18 R T29 L M ML20 W RT2 R M ML33 RR RR RRT14 RR W WRT27 W Water Quality Segment ID

Red River Zone Red River Transition Zone Main Lake Zone Washita River Transition Zone

Figure 32. Estimated water velocities in every segment of the water quality network developed for Lake Texoma using DYNHYD

62 35.00

30.00

25.00 y

20.00

15.00 Settling Velocity (m/da Velocity Settling

10.00

5.00

0.00

1 3 7 1 7 R1 T9 1 2 23 35 R RR3 RR5 RR7 L R T L29 L31 RR RT1 RT15 M ML19 W M M R RRT1 R WR WRT25 WRT2 BMC33 BMC Water Quality Segment ID Red River Zone Red River Transition Zone Main Lake Zone Washita River Transition Zone Big Mineral Creek Zone

Figure 33. Settling velocities used for the water quality network developed for Lake Texoma using WASP Resuspension velocities also were site- and time-dependent. However, in most of

the cases the same value was used for both river arms and no resuspension was defined

for the lacustrine area. The ranges of the selected values are presented in Table 11.

Model Calibration

The calibration process for the water quality model required determining constants and rates for several physical and chemical processes involved in the

sedimentation and resuspension rate and transport processes, as well as those processes

that affect dissolved oxygen concentration. Degradation rates were considered for the

simulation of DO and BOD. Since chloride is a very conservative substance, it was not

necessary to estimate degradation rates for the chloride simulation. All rates and constants utilized were obtained from the literature and adjusted during the calibration.

63 A calculation of the RMSE of the predicted values for chlorides and total

suspended solids concentrations was performed in order to select the constant or rate value that provided the best estimation for these concentrations. Chlorides were evaluated using specific conductance values, which are highly correlated with the concentration of this ion in Lake Texoma (R2 = 95.51%, from Atkinson et al. 1999).

Initial conditions for each cell were set to the measured values of the constituent

concentrations in the lake. A selected period from previous studies made by Atkinson et al. (1999) was used during this process. Additional constituent loadings were set using

the results of the simulation of the local watersheds, based on results provided by Upton

(2004).

The validation process for chlorides, DO, and total suspended solids was

performed using the relative RMSE, a two-sample Kolmogorov-Smirnov test, and a two-

sample t-test for those sets of values with homogeneous variances. One sample corresponded to simulated values of the analyzed variable, and the second sample

corresponded to field measured values reported in previous studies (Atkinson et al. 1999,

Waller et al. 2002).

Model Runs

Data used to adjust the model for the simulated conditions between March 1997

and May 1997 are presented in Appendix E. In this study, the average loading condition

was considered for chlorides and BOD, while two different loading conditions for total

suspended solids were evaluated: the predicted actual condition and the maximum

expected condition, considering estimates made by Upton (2004) using a calibration

based on the Event Mean Concentration method for the local watersheds of Lake

64 Texoma. The highest estimation of total suspended solids reported by Upton (2004) was considered in this simulation as the maximum expected condition. No variations of loadings for chlorides were analyzed in this report since it was assumed that most of the source of this ion is natural. Table 12 summarizes conditions evaluated with WASP in this report.

Table 12. Different loading conditions evaluated for Lake Texoma

Case Boundary Condition Loading Chlorides TSS 1 Measured or simulated Simulated to average Simulated to average to average 2 Measured or simulated Simulated to average Maximum simulated to average

Data Sets

Water quality data were needed to calibrate the model and validate the outputs of the simulation. A review of the available literature shows that hydrological measurements of the Washita and Red rivers have been done continuously for more than 60 years at

Dickson, OK and Gainesville, TX, respectively (USGS 2005). However, water quality in the lake and in the rivers has not been measured as continuously as flow. As a consequence, the availability of water quality data was used to establish the period of time to be simulated.

Based on a review of different studies reported for Lake Texoma, the period of time considered for water quality simulation corresponded to the hydrological year 1996-

1997. During this time, an intensive survey was conducted for the reservoir, and results of this study were included in a database available on the web (Atkinson et al. 1999).

Atkinson et al. (1999) conducted physical, chemical, and biological sampling at

11 routine stations in order to develop a baseline of water quality data for Lake Texoma

65 (August 1996 through September 1997). Also, five random stations were sampled to get

additional information on the spatial distribution of water quality in the reservoir. A list

with routine stations and their locations is presented in Table 13.

Chlorides

Selection of values for chlorides included:

o The analysis of reported data for setting boundary conditions and the initial

concentration for every water quality segment in the model, and

o Identification of concentrations to perform validation of predicted values.

Table 13. Identification of routine stations monitored by Atkinson et al. (1999) in Lake Texoma and their estimated location. Lake Latitude (N) Longitude (W) Station Zone Degrees Minutes Seconds Degrees Minutes Seconds 1 33 54 45 96 54 9 Red River 3 33 52 26 96 50 2 Big Mineral Creek 7 33 47 54 96 48 15 Red River 8 33 49 38 96 46 15 Transition 9 33 50 7 96 42 18 17 33 49 28 96 35 26 Main Lake 19 33 53 22 96 35 10 Washita River 20 33 55 49 96 33 21 Transition 22 34 0 3 96 37 36 Washita 24 34 1 43 96 34 10 River 25 34 2 38 96 34 16

Reported values for chlorides show a general pattern consistent with historic data.

The highest concentration of chlorides is found in the Red River Zone, with a decreasing pattern toward the Main Lake. In the Washita River arm, chloride concentrations decrease from the Main Lake Zone toward the Washita River (Atkinson et al. 1999); the

highest numbers are in the Red River Zone, with decreasing values in the direction of the

66 flow. Instantaneous measurements did not reflect this pattern when the flow of the Red

River, measured at Gainesville gaging station, increases. The spatial variation of the concentration of chlorides in each arm of Lake Texoma during March through May, 1997 is shown in Figure 34 (Red River arm) and Figure 35 (Washita River arm).

Considering this spatial pattern, a linear regression between distance from the boundary of the water quality network and chlorides concentration was performed to estimate the initial concentration for each water quality segment of the model network.

The R2 for the Red River arm was 0.82 (p-value < 0.05), and 0.90 for the Washita River arm regression (p-value < 0.01). Statistical results are included in Appendix F and the resulting concentrations using the regression model are presented in Table 14.

500

450

400

350 Station 17 Main Lake Zone Chlorides (mg/l) 300 Station 4 Red River Zone 250

200 0 10000 20000 30000 40000 50000 60000 Distance (m)

03/09-12/97 04/13-16/97 05/04-07/97 05/18-21/97

Figure 34. Spatial variation of the average concentration of chlorides in the Red River arm, based on measurements made by Atkinson et al. (1999)

67 300

250

200

150 Station 17 Main Lake Zone Chlorides (mg/l) 100

Station 25 50 Washita River Zone

0 0 5000 10000 15000 20000 25000 30000 35000 Distance (m)

03/09-12/97 04/13-16/97 05/04-07/97 05/18-21/97

Figure 35. Spatial variation of the average concentration of chlorides in the Washita River arm, based on measurements made by Atkinson et al. (1999) In order to set boundary conditions for chlorides, a regression between the historic

values of chlorides and conductivity reported by the USGS (2005) for the Gainesville

gaging station, on the Red River during the 1996-1997 hydrologic year, was performed.

This analysis showed that approximately 98% of the variation in chloride concentration

could be explained by its relationship with conductivity during the period considered,

with a p-value of 6.19 x 10-9. Consequently, this relationship was used to transform daily conductivity measurements made at Gainesville gaging station in the Red River, into chloride concentrations. A daily function for chlorides was defined for the Red River boundary using this method. Results of the regression analysis for chlorides and conductivity at Gainesville gage station are included in Appendix G.

68 Table 14. Estimated initial concentrations of chlorides for Washita and Red River arms for WASP calibration WASP Segment ID Corresponding Water Distance from the Estimated Chloride Quality Station ID(1) boundary (m) Concentration (mg/l) RR1 1020.5 360.91 RR2 2286.5 358.21 RR3 4353.5 353.80 RR4 1 6105.5 350.06 RR5 8021.5 345.97 RR6 9514.5 342.79 RR7 11033.5 339.54 RR8 3 12651.5 336.09 RRT9 14600.5 331.93 RRT10 17241.5 326.30 RRT11 8 18929.5 322.69 RRT12 21527.5 317.15 RRT13 24474.5 310.86 RRT14 9 28603.5 302.05 RRT15 31582.5 295.69 RRT16 34438.5 289.60 ML17 36397.5 285.42 ML18 38464.5 281.01 ML19 40625.5 276.40 WR20 24 1148 96.60 WR21 3065 109.18 WRT22 22 5826 127.29 WRT23 8019 141.67 WRT24 11033 161.45 WRT25 12668 172.17 WRT26 14322 183.02 WRT27 20 16920 200.07 ML28 21947 233.04 ML29 19 22951 239.63 ML30 25823 258.47 ML31 17 27560 269.86 (1) as indicated by Atkinson et al. (1999)

In the case of the Washita River boundary, values obtained during water quality sampling at Station 25 in the Atkinson et al. (1999) survey were used to define the boundary condition. A limitation of these measurements was there were only four values for the period considered for the simulation. In order to improve the definition of this boundary condition, a Welch two-sample t-test was performed between chloride concentrations measured by Atkinson et al. (1999) at Station 25 and those measured by

69 the USGS at Dickson gaging station in the Washita River. No significant differences were found between these two stations. Consequently, a regression between chloride concentrations measured at Station 25 in Lake Texoma, and conductivity values measured at the gage station near Dickson, was performed. This analysis showed that about 85% of the variation of chlorides at Station 25 in Lake Texoma could be explained by its relationship with conductivity measurements at Dickson gage station in the

Washita River. Consequently, using this regression, a daily function for chlorides was defined for the boundary in the Washita River, using daily conductivity measurements obtained at the gaging station at Dickson. Statistics are included in Appendix G.

Dissolved Oxygen and Carbonaceous Biochemical Oxygen Demand

The analysis of available data for DO and BOD was performed in order to determine concentrations for these two variables in order to:

o Set the boundary condition at Red River, Washita River, and Big Mineral

Creek

o Set initial conditions for each segment of the water quality network developed

for the simulation, and

o Perform validation of predicted values

Values of monthly measurements of DO corresponding to the simulated period were used to define a monthly function for the boundary condition at Red River, Washita

River, and Big Mineral Creek. Values reported by the USGS for stations 07316000, at the

Red River near Gainesville, and 07331000, at the Washita River near Dickson, were used to set the boundary condition in those two rivers, respectively. Values reported by the

70 Red River Authority for station 15320, located at Big Mineral Creek at FM 901 North of

Sadler, were used to set the boundary condition for Big Mineral Creek (RRA 2003a).

The model requires the definition of the boundary condition for BOD as the ultimate BOD. Consequently, BOD5 values reported by the USGS for the gaging station

USGS 07316000 at the Red River near Gainesville, TX, during the period of time

between 1978 and 1986 were used to define monthly average concentrations for that

river. These values were transformed using an empirical relationship proposed by Weijers

-1 (1999) for waters with low BOD decay rate (KBOD § 0.16 day ). There were no data for

BOD at the gaging station USGS 07331000 at the Washita River near Dickson, OK. So,

in order to set the boundary condition for BOD at the Washita River, reported values by

the USGS of COD at the mentioned gaging stations were analyzed to establish average

monthly concentrations of COD and then transformed into BOD using a factor of 0.7,

considering that between 60 and 80% of biodegradable matter measured as COD in

natural waters is considered biologically degradable (Grady et al. 1999).

The selected level of complexity for the simulation of DO and BOD also

considered the effects of salinity on DO concentrations. Consequently, a boundary

condition for salinity was also defined. Considering the relationship between chlorides

and conductivity, and conductivity and salinity, chloride concentrations defined for the simulation of chlorides in TOXI were also used to set the boundary condition for salinity in EUTRO, but expressed in parts per thousand (ppt).

Only initial conditions for DO and salinity were set for the simulation. The initial concentration of DO for each segment represented the average value of the measurements reported by Atkinson et al. (1999) for each sampling station and the same number was

71 used for all the segments located in the same zone of the corresponding sampling station.

Estimated initial chlorides concentrations developed for TOXI were used as initial

conditions for salinity at each segment of the network, expressed in ppt.

Total suspended solids.

An analysis of the available data on total suspended solids (TSS) was performed

in order to establish the boundary conditions and initial concentrations for every water

quality segment in WASP. Reported values of TSS were less numerous than those of

chlorides. For the Red River, at the gaging station near Gainesville, no determination of

TSS was available for the period of time between March, 1997 to May, 1997. In the case

of the Washita River, only four values were reported by Atkinson et al. (1999) for Station

25 during the same period of time selected for the calibration of the water quality model.

Consequently, it was necessary to use historical data in order to develop a daily function

for the boundary condition in these two points using empirical methods.

The sediment rating curve method (James et al. 2001, Jain 2001) was selected to

develop daily concentrations for the boundaries of the system. For the two gaging stations

(Gainesville in the Red River and Dickson in the Washita River) time series of river discharge and sediment concentration were downloaded from the web server of the

USGS. Data for September, 1930 to August, 1995 were chosen for calibration of Dickson gaging station, and data for October, 1966 to September, 1986 were chosen for the calibration of the Gainesville gaging station. Using the relationship between the recorded

sediment concentration and discharge of the river given by the equation:

LaQ= b , where:

72 L = load; Q = flow; a = Event Mean Concentration (EMC); and b = washout

coefficient. It was assumed that the EMC was equal to the average of the sediment

concentration (C) recorded for each month considered in the calibration process while the

washout coefficient (b), defined as the first-order rate coefficient that relates the rate of

removal of a particle from the soil as a consequence of the runoff to its concentration,

was estimated performing a linear regression of the logC on logQ. Once the coefficient b was obtained, the load of sediments for the period between March, 1997 through May,

1997 was calculated. Finally, a daily concentration of TSS was estimated using the calculated load and the observed mean daily discharge at each gaging station. The washout coefficient for the Red River was estimated to be 0.0267 and for the Washita

River it was 0.9912.

An additional procedure of interpolation of the concentrations of TSS reported by

Atkinson et al. (1999) at Station 25 for the period of time between March and May, 1997

was used to estimate daily concentrations of TSS for the boundary condition at the

Washita River. Equations developed to calculate daily concentrations for each period of

time using this method are included in Table 15. This second procedure provided better

results for predictions in the Washita River Arm, according to the RMS of errors for

selected water quality segments of this portion of the network implemented for WASP6.

Table 15. Equations used to calculate daily concentrations for TSS at the boundary of the Washita River arm for Lake Texoma Simulation Time Period Equation (d = days of simulation) 1 to 32 TSS = -1.3548d + 67.355 32 to 62 TSS = 0.2d + 17.6 62 to 92 TSS = -.0667d + 34.133

73 Another adjustment required for this calibration was the discrimination of three

different types of solids for the estimated TSS concentrations at each boundary.

Historical values were downloaded from the USGS web site, but they were scarce for the

purpose of this simulation. Consequently, an approximate distribution was set based on estimations found in the literature (Schultz 2001) and historic values were only used to compare to the selected ranges for these parameters.

The initial concentration defined for every segment in the water quality network was established using a liner regression of the distance of each arm of the reservoir and the average of observed concentrations, using values reported by Atkinson et al. (1999).

Because of the lack of sediment distribution studies in the reservoir, a calibration process

was used to define the distribution of different types of solids in every water quality

segment of the network. Distribution of solids was also based on estimates found in the

literature (Schultz 2001) and by taking into consideration that sedimentation rates of

suspended particles in Lake Texoma are influenced to some degree by the amount of total

dissolved solids, which in turn are directly related to chloride concentrations (Schroeder

and Toro 1996). As a consequence, different proportions of each fraction of solids

considered in the model were applied to different segments of the water quality network

in the model. The distribution of solids in every segment of the water quality network in

the model is presented in Table 16.

74 Table 16. Percentage distribution of solids per segment used in WASP

%SolidsType 3 %SolidsType 1 %SolidsType 2 WASP Segment (Sand + Fine (Clay) (Silt) Sand) 1 29.22 46.76 24.02 2 29.90 47.85 22.25 3 31.09 49.75 19.15 4 32.20 51.51 16.29 5 33.47 53.55 12.99 6 34.55 55.29 10.16 7 35.71 57.14 7.14 8 37.04 59.26 3.70 9 38.79 61.21 0.00 10 41.43 58.57 0.00 11 43.29 56.71 0.00 12 46.55 53.45 0.00 13 50.92 49.08 0.00 14 58.55 41.45 0.00 15 65.70 34.30 0.00 16 74.29 25.71 0.00 17 81.70 18.30 0.00 18 91.24 8.76 0.00 19 100.00 0.00 0.00 20 5.00 95.00 0.00 21 5.00 95.00 0.00 22 5.00 95.00 0.00 23 5.00 95.00 0.00 24 5.00 95.00 0.00 25 5.00 95.00 0.00 26 5.00 95.00 0.00 27 5.00 95.00 0.00 28 100.00 0.00 0.00 28 100.00 0.00 0.00 29 100.00 0.00 0.00 29 100.00 0.00 0.00 30 100.00 0.00 0.00 30 100.00 0.00 0.00 31 100.00 0.00 0.00 31 100.00 0.00 0.00 Loading

Initially, no loads were considered for the calibration process performed to establish the distribution of different fractions of solids. Once calibrated, estimated loads defined by Upton (2004) for each local watershed around Lake Texoma, as presented in

Figure 36, were included in some segments of the water quality network.

75 Figure 36. Local watershed defined by Upton (2004) for Lake Texoma Estimates of daily runoff and mean event concentrations in fifteen sub-watersheds defined by Upton (2004) were used to define daily loads for some water quality segments of the reservoir. Because these estimates were performed for larger local sub-watersheds than those considered in the water quality segmentation, an equal amount of loading was included in those segments corresponding to a larger sub-watershed. In other cases it was necessary to add estimates made for local sub-watersheds located at the left and right banks of the water quality segment. The algorithms used for calculations of loadings for each water quality segment, using estimations made by Upton (2004), are included in

Table 17. Considering that the largest amount of solids found in the literature for the

76 runoff corresponded to silt, all estimated loads in the water quality model were assumed to be type 2 solids. Daily values of loading for TSS in the model are included in

Appendix E.

Table 17. Equations for estimations of loadings of solids using estimations made by Upton (2004) WASP Segment Algorithm 1 to 8 Load = (NS-RR)/8 + (SS-RR)/8 9 Load = NS-RRT 17 Load = NS-ML 20 Load = (WS-WR2)+ (ES-WR2) 21 Load = (WS-WR)/2 + (ES-WR)/3 22 Load = WS-WRT + (ES-WR)/3 27 Load = ES-WRT 30 Load = SS-ML 34 Load = SS-RRT NS-RR = North side Red River sub-watershed SS-RR = South side Red River sub-watershed NS-RRT = North side Red River transition sub-watershed NS-ML = North side Main Lake sub-watershed SS-ML = South side Main Lake WS-WR = West side Washita River sub-watershed WS-WR2= West side Washita River sub-watershed 2 ES-WR= East side Washita river sub-watershed ES-WR2= East side Washita river sub-watershed 2 SS-RRT = South side Red River transition sub-watershed ES-WRT = East side Washita River transition sub-watershed

The loading estimate approach developed by Upton (2004) for TSS was used to obtain loading estimates for BOD and COD. In other words, COD and BOD Event Mean

Concentrations (EMC) were multiplied by the runoff estimate of each sub-watershed around Lake Texoma.

The Event Mean Concentration (EMC) data for COD and BOD were estimated by compiling literature and water quality data related to the 17 land use categories described by Upton (2004). For each land use, the lowest value of the range reported in the literature was selected. The results are summarized in Table 18. A weighted average of the EMC based on proportions of land use for each sub-watershed was calculated to estimate the corresponding value for each sub-watershed. A summary of the estimated values of EMC for COD and BOD for each sub-watershed is included in Table 19.

Loadings used for each sub-watershed are included in Appendix E.

77 Table 18. EMCs selected for estimating loadings for Lake Texoma. Nitrogen values are from Upton (2004). Event Mean Concentrations (mg/l) Land Use Category BOD COD Min Max Min Max Open water 3.5b 18.0b Low Intensity Residential 7.7b 25.5c 47.4b 54.3c High Intensity Residential 8.8c 9.1b 54.3c 54.3c Commercial 6.6c 23.0c 38.0c 116.0e Barren Rock 3.8d 13.0f 18.0b Quarry 3.5b 28.9d 18.0b Deciduous Forest 3.8b 6.0f 20.0b Evergreen Forest 3.8b 6.0f 20.0b Mxed Forest 3.8b 6.0f 20.0b Shrubland 0.5e 6.0f 20.0b Grass Herbaceous 0.5e 6.0f 20.0b Pasture, Hay 0.5e 6.0f 20.0b Row Crop 4.0e 18.3d 23.9b Small Grains 4.0e 18.3d 23.9b Urban/Recreational Grasses 3.0d 4.1b 21.9b Woody Wetland 3.5b 6.0f 18.0b Herbaceous Wetland 3.5b 6.0f 18.0b

a. Upton 2004 b. Calculated according to Osborne 2002 c. NCTCOG 2001 d. McConnel 1999 e. Baird et al. 1996 f. Naranjo 1997

Water Quality Modeling Results for Lake Texoma

Simulation of chlorides, DO, and total suspended solids of Lake Texoma using

WASP provided important insights of the spatial and temporal variations of these variables in a complex system. The integration of hydrodynamics with other transport processes and sedimentation was evaluated on a daily basis and results of these simulations follow.

78 Table 19. Estimated EMCs for each sub-watershed around Lake Texoma Estimated EMCs by Sub-watershed (mg/l) Sub-watershed Nitrogen BOD COD ESWR 0.99 2.9 21.1 ESWR2 0.97 2.4 21.1 ESWRT 1.38 3.2 22.1 NSML 0.90 2.6 21.5 NSRR 1.01 2.8 21.5 NSRRT 1.15 3.0 21.6 SSML 1.39 3.4 22.9 SSRR 0.98 2.6 20.9 SSRRT 1.95 3.4 23.4 WSWR 0.80 2.7 20.8 WSWR2 0.87 2.4 20.7 WSWRT 1.08 2.3 21.7

Chlorides

As a result of the geology of the watershed, the Red River carries a high

concentration of chlorides (Patrick 1994). Chloride is the most abundant anion in Lake

Texoma (Atkinson et al. 1999), and many of the chemical, physical, and biological

processes are influenced by this ion (Lerfeste et al. 1971, cited by Atkinson et al. 1999).

Considering the conservative properties of chlorides, it was used as a tracer in the water

quality simulation, providing additional information for validation of hydrodynamic

simulation. Initially, only those loads at the boundary of the system provided by the rivers

were included. No additional loads from the local watersheds of Lake Texoma were considered. Results of the simulation showed that the variation of chlorides in the Red

River arm is driven mainly by advection. As shown in , there was a good agreement between the trend followed by measured values of chloride concentrations and simulated values for the period comprised during March, 1997 and May, 1997.

However, in the Washita River arm, predicted values did not agree with observed values during the simulated period when transport was defined mainly by advection

79 forces. It was necessary to define a dispersion function, considering the gradient of

concentration between the Red and Washita rivers. In addition, given the information on

land use, a load was added at segment 22 of the water quality network using estimated

runoff for this sub-watershed (Upton 2004) and the average concentration reported for

chlorides for smaller creeks located in this area. Once these adjustments were applied,

correspondence between predicted and observed values improved. A representation of the

results is presented in Figure 38.

Some temporal variations of chloride concentrations that occur in the reservoir as a consequence of variations in the inflows of the rivers, as well as in the concentration of chlorides in those water bodies, are not accounted for by monthly measurements.

However, the model was able to reproduce those changes, as can be seen in the graphical

representation of predicted values for the Red River Zone (WASP Segments RR4 and

RR8) (Figure 37). These changes do not affect the trend followed by the concentration of

chlorides in the Main Lake Zone of the reservoir, as shown in Figure 38 for WASP

Segments ML29 and ML31. The presence of a large transition zone in the Washita River arm was also sustained by the pattern shown by simulated values for chlorides in WASP

Segments 22 through 27, even though graphical comparison was only performed with the

results obtained for the WASP Segment WRT27, as presented in the same Figure.

The relative RMSE for predictions for the Washita River arm was below 25%,

with a lowest value at WASP segment WRT27 (9%). In the Main Lake zone, the relative

RMSE of the predictions was around 7%. For the Red River arm, the relative RMSE of the predictions was around 10%.

80 WASPWASP Se gmSegment e nt RR4 RR4 WASPWASP Segment Segment RR8 RR8

800 700

700 600 600 ))) /// 500 500 400 400 300 300 Chlorides (mg (mg (mgChlorides Chlorides Chlorides Chlorides (mg/l) (mg/l) (mg/l) Chlorides Chlorides Chlorides ChloridesChloridesChlorides (mg/l (mg/l (mg/l 200 Chlorides (mg/l) (mg/l) (mg/l) Chlorides Chlorides Chlorides 200 100 100 0 0 7 7 7 7 7 7 7 7 7 7 7 99 997 /199 199 199 199 199 199 199 1 /1997 199 2/ 9/ 6/199 7/1 1/ /6/1997 12 19/ 26/ 4/ 4/ 1 23/ 5/ 14/ 11/1997 4/ 4/8 15/1997 29/1997 5 /13/1997 3/ 3/ 3/ 4/ 4/ 4/30/199 5/ 3/ 3/18/1997 3/25/1997 4/ 4/22/ 4/ 5

Predicted (mg/l) Observed(Top) Observed (Bottom) Predicted (mg/l) Observed(Top) Observed (Bottom)

WASP Segment Segment RRT11 RRT11 WASPWASP Se gme Segment nt RRT14 RRT14

600 600 500 500

))) 400 400

300 300 200 ChloridesChloridesChlorides (mg/l (mg/l (mg/l

Chlorides (mg/l) (mg/l) (mg/l) Chlorides Chlorides Chlorides 200 ChloridesChloridesChlorides (mg/l) (mg/l) (mg/l) Chlorides (mg/l) (mg/l) (mg/l) Chlorides Chlorides Chlorides 100 100 0 0 7 7 7 7 7 7 7 7 9 9 9 7 7 7 97 97 9 97 99 9 99 9 99 99 99 99 9 9 1 /1 /199 1 /1 /1 /1 19 /19 1/ 1 5/19 2 9 0 8/1 2/1 /6 3/1 1 4/1/ 4/8/1997 1 2 2 5/6/ 2 4/1/1997 4/8/ /15/1997 /2 /29/1997 5 /1 / / / / / 3/11 3/1 3/25 4 4 4 5 3 3/18/1997 3/25/ 1997 4 4 4 5/13/ 1997 5

Predicted (mg/l) Observed(Top) Observed (Bottom) Predicted (mg/l) Observed(Top) Observed (Bottom)

Figure 37. Temporal variation of chlorides concentration in some stations located in the Red River arm of Lake Texoma in comparison with simulated values for the period between March, 1997 through May, 1997

81 WASPWASP Segment Segment WR20 WR20 WASPWASP Segment Segment WRT2 WRT4277

180 300 160 250 140 ))) 120 200

100 150 80 100 Chlorides (mg/l) (mg/l) (mg/l) Chlorides Chlorides Chlorides

ChloridesChloridesChlorides (mg/l (mg/l (mg/l 60 ChloridesChloridesChlorides (mg/l) (mg/l) (mg/l) Chlorides (mg/l) (mg/l) (mg/l) Chlorides Chlorides Chlorides 40 50

20 0 0 97 7 97 97 7 7 7 7 7 7 7 7 7 7 9 - -9 97 9 9 9 9 9 9 9 9 9 r- y y - r- r- r- r- r- r- - - - ar-97 pr- ar a a a p p p ay ay ay -Mar- M -Ma -A -Ma -Ma M M M M A A A M M M 1 1 0 0 0 - 1- 1- 1- 0- 0- 0- - - - 11- 21-Mar-97 3 1 20-Apr-97 30-Apr-97 1 2 30-May-97 1 1 2 3 1 2 3 10 20 30

Predicted (mg/l) Observed(Top) Observed (Bottom) Predicted (mg/l) Observed(Top) Obs e r ve d ( Bot t om)

WASPWASP SegmSegment ent ML29 ML29 WASPWASP SeSegment gme nt ML31 ML31

300 350

250 300

lll 250 200 200 150 150 ChloridesChloridesChlorides (mg/ (mg/ (mg/ 100 ChloridesChloridesChlorides (mg/l) (mg/l) (mg/l)

Chlorides (mg/l) (mg/l) (mg/l) Chlorides Chlorides Chlorides 100 Chlorides (mg/l) (mg/l) (mg/l) Chlorides Chlorides Chlorides

50 50

0 0 7 7 7 7 7 7 7 7 7 7 9 97 9 9 9 9 9 -9 - -9 r- r- - y r- r- y a a a ar-97 a ar-97 pr ay-97 ay-97 a -M M -M M -A -Apr-97 M M -M -M -Mar-97 -Mar- -Ap -Apr M -May-9 1 1- 1 0 0 1 1 1 0 0 1 2 31- 1 20-Apr-9 30 10- 20- 3 11-Mar-97 2 3 1 20-Apr-97 30 10- 2 30-May-97

Predicted (mg/l) Observed(Top) Obs e r ve d (Bottom ) Predicted (mg/l) Observed(Top) Obs e r ve d ( Bot t om)

Figure 38. Temporal variation of chlorides concentration in some stations located in the Washita River arm and Main Lake Zone of Lake Texoma in comparison with simulated values for the period between March, 1997 through May, 1997

82 The largest errors were found in those portions of the reservoir where a clear difference between concentration at the top and at the bottom of the water column was evident. This can be explained by the configuration used in this simulation. With the

arrangement used during this project, the model does not account for vertical differences

in the concentration of the chemicals. Vertical segmentation of the water column could be

a future improvement to develop in the implementation of WASP as a management tool

for Lake Texoma.

Total Suspended Solids

The water quality model calibration for total suspended solids demonstrated the

capability of WASP to simulate the spatial heterogeneity of this variable in Lake Texoma.

Predictions in the Red River arm depict seasonal and spatial variation of the

concentration of total suspended solids quite well (see Figure 39). This cannot be said for

estimations made for the Washita River arm and the Main Lake zone where the model

does not fully represent the average concentration of suspended solids (Figure 39 and

Figure 40). Information about the distribution of particles in these portions of the water

system and multiple simulations where boundary conditions and initial concentrations for

every segment of the water quality network were adjusted could improve the accuracy of

these predictions.

Statistical tests were used to examine differences between observed TSS values

for every water quality monitoring station in Atkinson et al. (1999) and predicted values

using WASP. A Kolmogorov-Smirnov test was performed for all the stations listed in

Table 10, and a Welch two-sample t-test was performed in those sets of values with

83 homogenous variances (as evaluated with a F test). Results of these analyses are

summarized in Table 20.

The Kolmogorov-Smirnov test shows that there is not enough evidence for

significant differences between predicted and observed values for all the stations.

Variances of predicted and observed values for TSS concentrations were homogeneous for most of the sets of values tested. WASP segments 4, 22, 29 and 31 were found to be non-homogeneous and therefore the t-test was not applied. The lowest p-value was obtained for WASP segment 20. However, when performed, results of the t-test also supported that there is no evidence of statistically significant differences between observed and predicted values for TSS concentrations during the simulated period for most of the segments in the transition zones of the reservoir.

Results for WASP segment 4 and 20, close to the boundaries were affected by estimates that defined boundary conditions on a daily basis. These estimates were made because of the lack of information for the simulated period. In the transition zone, the relationship between changes in TDS and the amount of suspended solids reported for

Lake Texoma (Schroeder and Toro 1996) and its effect in settling velocities can also represent a source of uncertainty in this simulation.

The model was able to simulate the temporal variability of TSS in Lake Texoma,

and many predictions are closer to values reported for the surface layer of the water column than bottom values. This is particularly noticeable in those conditions when there is a large difference between observed values for TSS concentrations at the top and at the bottom of the water column.

84 WASP Segment RR4 WASP Segment RR8

200 35 30 150 25 20 100 15 TSS (mg/l)TSS (mg/l) 50 TSS (mg/l)TSS (mg/l) 10 5 0 0 1-Mar-97 31-Mar-97 30-Apr-97 30-May-97 1-Mar-97 31-Mar-97 30-Apr-97 30-May-97

Predicted Observed(Top) Observed (Bottom) Predicted Observed(Top) Observed (Bottom)

WASP Segment RT11 WASP Segment RT14

20 25

15 20 15 10 10 TSS (mg/l) TSS (mg/l) TSS (mg/l) TSS (mg/l) 5 5 0 0 1-Mar-97 31-Mar-97 30-Apr-97 30-May-97 1-Mar-97 31-Mar-97 30-Apr-97 30-May-97

Predicted Observed(Top) Observed (Bottom) Predicted Observed(Top) Observed (Bottom)

Figure 39. Temporal variation of total suspended solids concentration in some stations located in the Red River arm of Lake Texoma in comparison with simulated values for the period between March, 1997 through May, 1997

85 WASP Segment WR20 WASP Segment WRT22

50 20

40 15 30 10 20 TSS (mg/l)TSS (mg/l) TSS (mg/l)TSS (mg/l) 10 5 0 0 1-Mar-97 31-Mar-97 30-Apr-97 30-May-97 1-Mar-97 31-Mar-97 30-Apr-97 30-May-97

Predicted Observed(Top) Observed (Bottom) Predicted Observed(Top) Observed (Bottom)

WASP Segment WRT27

10 8 6 4 TSS (mg/l)TSS (mg/l) 2 0 1-Mar-97 31-Mar-97 30-Apr-97 30-May-97

Predicted Observed(Top) Observed (Bottom)

Figure 40. Temporal variation of total suspended solids concentration in some stations located in the Washita River arm of Lake Texoma in comparison with simulated values for the period between March, 1997 through May, 1997.

86 The distribution of particles and the settling and resuspension velocities applied for different segments seem to explain these results. However, it can be said that the general temporal and spatial pattern provided by simulations fits closely to the observed values for the two large arms of Lake Texoma.

It was necessary to set different settling and resuspension rates for every type of solid simulated in the model in order to improve predictions. This can be explained by varying temporal and spatial hydrodynamic conditions reported for the reservoir using simulation tools described earlier in this report. In a reservoir with longitudinal velocity gradients it can be expected to find differences in net settling rates among locations in the reservoir. This was considered in the simulation by adjusting different settling and resuspension rates not only by location, but also temporally.

Table 20. Statistical analysis of predicted values for TSS concentrations in Lake Texoma using WASP

WASP K-S test F-test t-test Location Segment p-value F p-value t-value p-value 4 Red River Zone 0.2286 25.33 0.0259 * * 8 Red River Zone 1 0.43 0.5099 0.2003 0.8488 11 Red River Transition Zone 0.7714 0.22 0.2454 0.1124 0.9156 14 Red River Transition Zone 0.7714 1.31 0.8307 1.3559 0.2248 20 Washita River Zone 0.2286 1.26 0.852 2.7192 0.0352 22 Washita River Transition Zone 0.2286 0.0905 0.0791 * * 27 Washita River Transition Zone 0.7714 0.5727 0.6584 0.5205 0.6227 29 Main Lake Zone 0.2286 30.6479 0.0189 * * 31 Main Lake Zone 0.6994 14.3698 0.0552 * *

* = Variances are not homogeneous

In general, settling velocities obtained by calibration increase as velocity declines across the reservoir. The same trend is found on a temporal basis, where reductions in the flow and in the velocity of the water promote higher settling velocities. Consequently, higher temporal variations in the concentration of total suspended solids in the reservoir were more remarkable in the river zones and in the transitions zones rather than in the

87 Main Lake Zone. In the Main Lake Zone, variations in water velocity were less

noticeable and the temporal pattern of total suspended solids in this portion of the

reservoir was more stable (See Figure 41).

WASP Segment ML29

TSS (mg/l) TSS 5

0 1-Mar-97 31-Mar-97 30-Apr-97 30-May-97

Predicted Observed(Top) Observed (Bottom)

WASP Segment ML31

8 7 6 5 4 3 TSS (mg/l) TSS 2 1 0 1-Mar-97 31-Mar-97 30-Apr-97 30-May-97

Predicted Observed(Top) Observed (Bottom)

Figure 41. Temporal variation of total suspended solids concentration in some stations located in the Main Lake Zone of Lake Texoma in comparison with simulated values for the period between March, 1997 through May, 1997 Resuspension velocities were also dependant on predictions of water velocity in

different portions of the reservoir. In those areas where water velocities were found to be

very low, no resuspension velocities were applied. In Figure 42 temporal variation of

resuspension velocities applied in WASP6 for the calibration of solids type 2 in segments

20 and 27 is presented as an example. This means that there was a threshold water velocity affecting resuspension and settling velocities, and, consequently, the spatial distribution of suspended solids in the reservoir.

88 1.40E-06

1.20E-06

1.00E-06

8.00E-07

6.00E-07

4.00E-07 Resusupension VelocitiesResusupension (m/day)

2.00E-07

0.00E+00

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 Simulation Day

Figure 42. Temporal variability of particle resuspension velocity applied to solid type 2 in segment 27 of the water quality network developed for Lake Texoma using WASP, obtained by model calibration

89 Distribution of the size of the particles in different portions of the reservoir is

another important factor considered in the calibration process. Using only one type of solid does not account for the temporal variation of TSS concentrations in different locations in the reservoir.

During events of rapid washout, the distribution of particles changes and the

amount of total suspended solids in the reservoir is heavily affected, especially close to

the boundaries of the system. Rapid washout also affects predictions among different

locations in the reservoir, but by a smaller magnitude in absolute terms.

The RMSE was estimated for every water quality segment, and it was found that this value decreases in the direction of the flow (Figure 43). Close to the boundaries,

there is a clear differentiation between top and bottom concentration for TSS in the water

column. Vertical variation is not accounted for in the arrangement used for the model in

this simulation. However, lack of field data impeded the development of a more detailed

simulation.

The model also does not account for the effect of turbulence generated by wind, especially in shallow areas. This can be improved in future additional simulations of this variable for Lake Texoma. Another important factor to be improved in subsequent

simulations of this variable is the effect of the TDS gradient reported for Lake Texoma

and its relationship with the sedimentation of particles, mainly silt and clay.

DO and BOD

The model was successfully executed with loadings, flows, and parameters explained in previous sections of this report, using the Streeter-Phelps equation, but

representing a dynamic condition, as presented in Figure 44 and Figure 45. This approach

90 allowed a more realistic calibration to observed data. In addition, the sensitivity of the

model to boundary conditions, salinity, and sediment oxygen demand was also

investigated. Further research including nutrients and phytoplankton in the dynamic of oxygen for Lake Texoma could provide additional insights of this variable in the system.

45.00 a) 40.00 35.00 30.00 25.00 20.00 15.00 10.00 5.00 0.00 RMS of Errors for(mg/l) TSS Seg.4 Seg.8 Seg.11 Seg.14 WASP Segment

b) 12.00 10.00 8.00 6.00 4.00 2.00 0.00 RMS of Errors for TSS (mg/l) TSS for Errors of RMS Seg.20 Seg.22 Seg.27 Seg.29 Seg.31 WASP Segments

Figure 43. RMS of errors of TSS concentration for different water quality segments in a) the Red, and b) the Washita rivers arms of Lake Texoma using WASP. The model accounts for temporal and spatial variations of the average

concentration of dissolved oxygen in the reservoir. An overestimation of DO concentrations occurred during the first 15 days of the simulation, especially in some segments at the Red River Arm. It seems that the lack of simulation of other processes associated with oxygen consumption is affecting the results. Another possibility is related with the estimations made in order to set the boundary condition.

91 WASP Segment RR4 WASP Segment RR8

12 12 10 10 8 8 6 6 4 4 DO (mg/l) DO DO (mg/l) DO DO (mg/l) (mg/l) DO DO 2 2 0 0 3/1/19973/1/1997 3/8/19973/8/1997 4/5/19974/5/1997 5/3/19975/3/1997 3/1/19973/1/1997 3/8/19973/8/1997 4/5/19974/5/1997 5/3/19975/3/1997 3/15/1997 3/22/1997 3/29/1997 4/12/1997 4/19/1997 4/26/1997 5/10/1997 5/17/1997 5/24/1997 5/31/1997 3/15/1997 3/22/1997 3/29/1997 4/12/1997 4/19/1997 4/26/1997 5/10/1997 5/17/1997 5/24/1997 5/31/1997 3/15/19973/15/1997 3/22/19973/22/1997 3/29/19973/29/1997 4/12/19974/12/1997 4/19/19974/19/1997 4/26/19974/26/1997 5/10/19975/10/1997 5/17/19975/17/1997 5/24/19975/24/1997 5/31/19975/31/1997

Predicted Observed (Top) Observed (Bottom) Average Predicted Observed (Top) Observed (Bottom) Average

WASP Segment RRT11 WASP Segment RRT14

12 12 10 10 8 8 6 6 4 4 DO (mg/l) (mg/l) DO DO 2 (mg/l) (mg/l) DO DO 2 0 0 3/1/19973/1/1997 3/8/19973/8/1997 4/5/19974/5/1997 5/3/19975/3/1997 3/1/19973/1/1997 3/8/19973/8/1997 4/5/19974/5/1997 5/3/19975/3/1997 3/15/19973/15/1997 3/22/19973/22/1997 3/29/19973/29/1997 4/12/19974/12/1997 4/19/19974/19/1997 4/26/19974/26/1997 5/10/19975/10/1997 5/17/19975/17/1997 5/24/19975/24/1997 5/31/19975/31/1997 3/15/19973/15/1997 3/22/19973/22/1997 3/29/19973/29/1997 4/12/19974/12/1997 4/19/19974/19/1997 4/26/19974/26/1997 5/10/19975/10/1997 5/17/19975/17/1997 5/24/19975/24/1997 5/31/19975/31/1997

Predicted Observed (Top) Observed (Bottom) Average Predicted Observed (Top) Observed (Bottom) Average

Figure 44. Temporal variation of DO concentration in some stations located in the Red River arm of Lake Texoma in comparison with simulated values for the period between March, 1997 and May, 1997

92 WASP Segment WR20 WASP Se gme nt WRT 27

12 14 10 12 8 10 8 6 6 4 4 DO (mg/l) (mg/l) DO DO (mg/l) (mg/l) DO DO 2 2 0 0 3/1/19973/1/1997 3/8/19973/8/1997 4/5/19974/5/1997 5/3/19975/3/1997 3/1/19973/1/1997 3/8/19973/8/1997 4/5/19974/5/1997 5/3/19975/3/1997 3/15/19973/15/1997 3/22/19973/22/1997 3/29/19973/29/1997 4/12/19974/12/1997 4/19/19974/19/1997 4/26/19974/26/1997 5/10/19975/10/1997 5/17/19975/17/1997 5/24/19975/24/1997 5/31/19975/31/1997 3/15/19973/15/1997 3/22/19973/22/1997 3/29/19973/29/1997 4/12/19974/12/1997 4/19/19974/19/1997 4/26/19974/26/1997 5/10/19975/10/1997 5/17/19975/17/1997 5/24/19975/24/1997 5/31/19975/31/1997

Predicted Observed (Top) Observed (Bottom) Average Predicted Observed (Top) Observed (Bottom) Average

WASP Segment ML29 WASP Segment ML31

Predicted Observed (Top) Observed (Bottom) Average Predicted Observed (Top) Observed (Bottom) Average

Figure 45. Temporal variation of DO concentration in some stations located in the Washita River arm and Main Lake Zone of Lake Texoma in comparison with simulated values for the period between March, 1997 and May, 1997

93 However, the average of the relative RMSE did not exceed 20% (approximately 1.6 mg

O2/L) of the average concentration of DO in the reservoir. A representation of the absolute

values for the RSME for each water quality segment is presented in Figure 46. The highest

discrepancy between observed and predicted values was detected at WASP segment 11,

corresponding to the water quality station No. 8 at the Red River Zone. This is attributed to

divergences in magnitude, though the pattern followed by the simulation fitted pretty well the

observed pattern of temporal variation. The lowest RMS of errors was obtained for the

simulations of DO concentrations at the Main Lake Zone. Considering these results, it seems that the average condition for oxygen in the reservoir can be described taking into account physical processes only. In order to make a distinction between different strata in the reservoir it should

also be necessary to consider biochemical processes.

The effects of salinity and sediment oxygen demand for DO simulations in Lake Texoma

were compared by activating and deactivating the salinity function using two different conditions

for sediment oxygen demand in the model. It was found that, under conditions of high sediment

oxygen demand, simulated values are closer to the average of observed values when the salinity

function is activated. The magnitude of changes is more remarkable in segments at the Washita

River Arm, where salinity values are lower, and it is expected to be more sensitive to changes in

this variable. Using a lower sediment oxygen demand, like the one applied for simulations of DO

in Lake Texoma (0.4 g/m2-day), the salinity function does not exert any detectable effect in the

results (Figure 47). In all cases, a reduction in sediment oxygen demand remarkably improved

simulation results

94 Red River Arm

1.80 1.60 1.40 1.20 1.00 0.80 0.60 0.40 0.20

RMS of Errors for DO (mg/L) DO for Errors of RMS 0.00 Seg.4 Seg.8 Seg.11 Seg.14

Washita River Arm

1.40 1.20 1.00 0.80 0.60 0.40 0.20

RMS of Errors for DO (mg/L) DO for Errors of RMS 0.00 Seg.20 Seg.22 Seg.27 Seg.29 Seg.31

Figure 46. RMS of errors of DO concentrations for different water quality segments in the Red and Washita River arms of Lake Texoma using WASP

Water Quality Studies for Tocoma Reservoir

Physical, chemical, and biological characteristics of the Caroni River in the portion where

Tocoma Reservoir has been planned are driven by the characteristics of the water drained from

Guri Reservoir. These are, in general sense, very similar to those reported for the lower Caroni

River.

95 a) 25.00

20.00

) S-P+High SOD 15.00 SP+High SOD+Salinity SP+Low SOD 10.00 RMSE (% SP+Low SOD+Salinity

5.00

0.00 Seg.4 Seg.8 Seg.11 Seg.14 Aver.

b) 35.00 30.00

25.00

) S-P+High SOD 20.00 SP+High SOD+Salinity

15.00 SP+Low SOD RMSE (% SP+Low SOD+Salinity 10.00

5.00

0.00 Seg.20 Seg.22 Seg.27 Seg.29 Seg.31 Aver.

Figure 47. RMS of errors of DO concentrations for some water quality segments at the: a) Red River arm, and b) Washita River arm of Lake Texoma under different conditions of salinity and sediment oxygen demand The Caroni River, a blackwater river as a result of the high content of humic substances, is poor in electrolytes (Paolini 1986). Published studies for the lower Caroni River and Guri

Reservoir report a very low conductivity, poor nutrient contents, low concentrations for total suspended solids, and high content of dissolved organic carbon for this portion of the river

(Paolini 1986, Weibezahn 1992, Latorraca 1999, CVG EDELCA 1999).

However, considering the proximity that Tocoma Reservoir will have with respect of

Guri Reservoir, it was necessary to evaluate not only water quality characteristics for this

96 reservoir in a water quality measurement site close to the dam, but also some characteristics of the dam itself, to gain information about specific water quality conditions developed for this portion of the river.

In this sense, five water quality stations were considered during the study, as represented in Figure 48. A list of the geographic location for each water quality station is included in Table

21, and the annual average value for the main limnological characteristics of these places is included in Table 22.

Table 21. Geographic coordinates for water quality sampling stations nearby Tocoma Dam site Water Quality Station ID (1) UTM Coordinates Sector 20 – La Canoa N E Guri Reservoir at Boya 800 805676 500130 Guri Dam discharge channel 1 859673 500153 Guri Dam discharge channel 2 859642 500398 El Merey Creek 862018 503136 Caroni River at Tocoma site 872069 494140 (1) = as CVG EDELCA 2004

Dissolved oxygen content in the water column of the sampling station located at Guri

Reservoir close to the dam, is around 5 mg/l as an annual average. However, during May to

November, the reservoir is stratified and dissolved oxygen concentration close to the bottom is around 3 mg/l. The water drained through the power house No 1 at Guri Dam comes from this stratum close to the bottom of the reservoir, approximately 80 to 100 m deep. On the other hand, the power house No 2 at Guri Dam draws water from the stratum close to the surface, with higher dissolved oxygen content. This condition explains differences found in the dissolved oxygen content for each discharge channel of Guri Dam. Downstream, at Tocoma Dam site the water recovers the amount of dissolved oxygen required for supporting aquatic life, with values closer to the saturation condition (CVG EDELCA 2005b).

97 Tocoma Site C A Cuanaguaro R O N I R Cunaguaro I River V E R

El Merey Qda. El Merey

Discharge Discharge Channel 2 Channel 1

Boya 800

GURI RESERVOIR

Limnological sampling stations

Figure 48. Spatial location of water quality measurement sites at the lower Caroni River considered for this study Other limnological variables show that this portion of the river has soft waters with a

very low buffer capacity, considering total hardness and alkalinity values. Redox potential describes an oxidative environment. Total suspended solid contents are low along the system, as well as turbidity values. Biologically, the system is oligotrophic, with very low phytoplanton biomass, low nutrient contents, and low chlorophyll a values.

Water Quality Model Parameterization for Tocoma Reservoir

Due to the low value of DO found at the inflow of Tocoma, the major question for the model was the impact of impoundment on DO and whether it recovers at the future dam site.

Therefore, in contrast to Lake Texoma, water quality simulation for Tocoma Reservoir only

98 described the DO-BOD processes. Then, parameterization of the water quality model for the future Tocoma Reservoir mainly consisted on adjustments of boundary conditions, loadings, and several physical time functions describing the expected real system for these two processes.

Table 22. Annual average value for the main limnological characteristics of some water quality sampling stations at the lower Caroni River LIMNOLOGICAL Water Quality Sampling Stations VARIABLES Boya 800 Boya 800 Guri Dam Guri Dam Tocoma (surface) (bottom) Channel 1 Channel 2 Temperature (oC) 27.4 27.1 26.4 27.3 27.5 Dissolve Oxygen 5.2 1.9 2.4 4.0 7.4 (mg/l) Transparency (m) 2.35 - - - 2.19 Turbidity (NTU) 2.21 22.2 2.10 2.05 2.08 Real Color (Co-Pt 46.18 38.02 41.30 32.87 43.07 Units) Redox Potential (mv) 198 205 200 197 210 TSS (mg/l) 1.86 45.96 1.85 1.31 1.85 pH 5.89 5.66 5.60 5.89 5.89 Total Alkalinity (mg 3.03 3.03 3.04 2.99 3.02 CaCO3/l) Total Hardness (mg 2.65 2.60 2.53 2.64 2.67 CaCO3/l) Specific Conductivity 8.93 9.35 8.44 8.54 8.21 at 25oC (mS/cm) Ca (mg/l) 0.47 0.41 0.46 0.50 0.55 Mg (mg/l) 0.22 0.19 0.20 0.23 0.24 Na (mg/l) 0.73 0.96 0.79 0.78 0.80 K (mg/l) 0.40 0.41 0.41 0.41 0.42 TSP (mg/l) 0.009 0.020 0.027 0.010 0.012 Nitrites (mg/l) ND ND ND ND ND Nitrates (mg/l) 0.18 0.51 0.48 0.50 0.44 Chlorides (mg/l) 0.62 1.53 0.56 2.64 0.64 Chlorophyll a (mg/l) 0.269 - 0.185 0.294 0.44 TSP = Total soluble phosphorus ND = Non detectable

While evaluating water quality for Tocoma Reservoir, a water quality network for the reservoir was adapted from the hydrodynamic network previously described in this document. In addition, water quality model was set up for describing the reservoir in a one-dimension condition. This means that model estimates represent the average condition for every water

99 quality segment and do not reflect differences in the water column as a consequence of

stratification, if any. A time period of 40 days was established as simulation time, considering

that the system stabilizes in a long period of time for the Cunaguaro River area, based on

estimations of hydraulic and travel time for this portion of the future reservoir. For the rest of the

system, hydrodynamic simulation demonstrated that stability is reached in a short period of time.

The WASP model was linked to hydrodynamic results of the DYNHYD model. Consequently,

time step for water quality simulations was set at 198 seconds, which is approximately 10 times

the hydrodynamic time step.

WASP network for Tocoma Reservoir

Water quality network for the reservoir used the hydrodynamic network prepared for the

simulation of the reservoir with DYNHYD, described in the previous chapter of this document.

Every hydrodynamic junction corresponded with one water quality segment, and inflow and

seaward boundaries defined for DYNHYD became boundaries for the water quality network.

Therefore, the WASP network for Tocoma Reservoir has five boundary segments: one for every discharge channel of Guri Dam, and the other three for El Merey Creek, Cunaguaro River and at the end of the network, next to the future dam. It also has 50 water quality segments. A 20-cm depth benthic segment was defined for describing bottom conditions. This value responds to the need of developing enough “memory” for describing processes at the bottom of the reservoir,

such as oxygen consumption, as well as bottom-water interactions. As presented previously in

this chapter, Figure 30 shows the schematic representation of the water quality network

developed for Tocoma Reservoir. The correspondence between junctions of the hydrodynamic

network developed for DYNHYD and water quality segments for WASP is presented in Figure

100 49. Geometric data for water quality segments used for simulation of the future Tocoma

Reservoir are included in Table 23.

Table 23. Geometric data for water quality segments developed for the WASP network of Tocoma Reservoir WASP Volume Mean Depth WASP Volume Mean Depth Segment ID (m3) (m) Segment ID (m3) (m) 1 3,500,000 14.0 27 57,000,000 34.5 2 8,260,000 18.1 28 64,700,000 23.9 3 13,500,000 22.5 29 57,000,000 19.2 4 8,190,000 28.1 30 4,640,000 11.1 5 6,350,000 17.6 31 22,600,000 12.2 6 32,400,000 30.0 32 19,600,000 9.7 7 26,700,000 27.1 33 11,900,000 8.7 8 18,600,000 34.9 34 5,690,000 9.0 9 60,400,000 31.9 35 24,800,000 12.1 10 41,000,000 37.0 36 22,800,000 22.0 11 39,200,000 25.9 37 81,200,000 37.7 12 57,700,000 24.7 38 86,900,000 38.9 13 71,300,000 24.2 39 59,900,000 40.9 14 18,000,000 19.4 40 67,000,000 43.2 15 51,000,000 24.5 41 85,400,000 44.5 16 69,300,000 26.2 42 74,100,000 42.0 17 12,700,000 12.9 43 57,100,000 38.9 18 27,500,000 14.7 44 73,000,000 37.8 19 43,600,000 15.3 45 48,600,000 45.6 20 95,500,000 34.4 46 80,500,000 32.4 21 14,800,000 12.7 47 50,900,000 38.8 22 40,000,000 34.7 48 42,500,000 35.3 23 92,000,000 37.2 49 24,000,000 21.0 24 25,500,000 33.5 50 21,200,000 15.6 25 29,200,000 25.3 Benthic 16,954,281 0.2 26 61,600,000 20.4 Segment

101 Tocoma Dam site

DYNHYD Junctions Network Guri Dam

Tocoma Dam site

WASP Segments Network

Guri Dam

Figure 49. Correspondence between water quality and hydrodynamic networks developed for Tocoma Reservoir

102 Boundary and Initial Conditions

Water quality measurements were used as the source of data for defining boundary

conditions for the system. Water quality sampling sites at discharge channels of Guri Dam were considered for establishing the boundary condition at this portion of the network. A simple mass balance for these two channels was performed for calculating DO concentration at this boundary.

Therefore,

()[]DO × Q + ()[]DO × Q []DO = 1 1 2 2 , where Q1 + Q2

[DO1] = average DO concentration at discharge channel 1

Q1 = annual average water flow at discharge channel 1

[DO2] = average DO concentration at discharge channel 2

Q2 = annual average water flow at discharge channel 2

Using 2 mg/l for dissolved oxygen concentration at discharge channel 1 and 4 mg/l for

dissolved oxygen concentration at discharge channel 2, the resulting concentration for the boundary condition was 3.3 mg DO/l, considering that the average flow for these channels is

1,500 and 3,000 m3/s, respectively.

Boundary concentrations for El Merey Creek and Cunaguaro River came from the annual average of dissolved oxygen at the sampling stations located in these two water bodies. A value of 6 mg/l was used for both Cunaguaro River and El Merey Creek.

There were no measured data for BOD concentrations. Consequently, considering land use of the immediate watershed, BOD concentrations were set at zero value for the boundary condition at water quality segments 1 and 2, representing Guri Dam discharges.

103 Model Constants

The simplest complexity level for describing DO-BOD dynamic for Tocoma Reservoir was used for this simulation. The Streeter-Phelps equation described this process as indicated below:

o For BOD:

∂C ⎛ C ⎞ v (1 − f ) BOD (T −20) ⎜ DO ⎟ oms DS = −k Dθ D ⎜ ⎟ − C BOD ∂t ⎝ K1/ 2 + C BOD ⎠ D

o For DO:

∂C ⎛ C ⎞ SOD DO (T −20) ⎜ DO ⎟ (T −20) = k R (Cs − C DO ) − k Dθ D ⎜ ⎟C BOD − θ s , where: ∂t ⎝ K BOD + C BOD ⎠ D

CBOD = Biochemical oxygen demand concentration (mg/l)

CDO = Dissolved oxygen concentration (mg/l)

o -1 KD = Deoxygenation rate at 20 C (day )

qD = Deoxygenation temperature coefficient

T = Temperature (oC)

KBOD = Half-saturation constant for limited oxygen (mg O2/l)

voms = Organic matter settling velocity (m/day)

fDS = Dissolved BOD fraction

D = Surface water column depth (m)

-1 kR = Reaeration rate (day )

Cs = Dissolved oxygen saturation (mg/l)

SOD = Sediment oxygen demand at 20oC (g/m2day)

qs = Temperature coefficient

104 This simulation only considered reaeration processes and sediment oxygen demand for

describing variations on dissolved oxygen concentrations in the reservoir. Using this approach,

oxygen gets into the system by reaeration, driven by hydraulic processes and wind action forces;

and it is consumed by BOD decomposition and sediment oxygen demand. Based on mean wind

velocity registered at Las Babas weather station (CVG EDELCA 2005a), close to Guri Dam,

wind velocity was set at 3 m/s for the entire system.

In addition, WASP was allowed to make spatial and temporal adjustment of the

reaeration rate, considering differences between water quality segment depths in the network.

Then, two time functions were defined, as presented in Table 24.

Table 24. Time functions used for DO and BOD simulation of Tocoma Reservoir Time Function Value Water velocity 1 m/s Water temperature 27oC

Oxygen demand considered the oxidation process. Equations that govern oxidation in the

DO-BOD balance for WASP include phytoplankton anaerobic decomposition and anaerobic decomposition of benthic organic carbon. The latter fraction was the only one considered for this simulation of Tocoma Reservoir. As a result of the calibration process, constants and rates selected for Tocoma Reservoir are presented in Table 25, considering values recommended by

Ambrose et al. (1993b) and Acevedo (2002).

Table 25. Constants and rates used by WASP for water quality simulation of Tocoma Reservoir Constants and Rates Units Value Sediment oxygen demand g/m2day 1.4 • Temperature correction factor 1.0477 BOD degradation rate day-1 0.75 • Temperature correction factor 1.04

105 Loadings

Because the model describes water quality of the system once the reservoir is created, it

was necessary to consider two conditions to define loading conditions:

o Loadings provided by land use. In this case, values recommended by Acevedo (2002)

were included, considering that there have been few or no changes in land use of the

watershed since then, and

o Loadings resulting by flooding of vegetation as a consequence of the elevation of

water level for creating the new reservoir.

For the first condition, values considered for BOD loadings of each watershed around the future reservoir are presented in Table 26. For those loads considered as a consequence of the flooding process, a time function was developed to adjust the simulation process to the flooding plan of the reservoir. According to this, the reservoir will be filled up in two main phases: from elevation 94 to elevation 98 m.a.s.l., in a period of time of time of 16 days; and from this level to elevation 127 m.a.s.l., in 45 days since the 35th day of the filling period. A representation of the

time function developed for two of these segments is presented in Figure 50.

Table 26. BOD loading conditions for adjacent watershed of the Tocoma Reservoir Watershed WASP Segment ID Time period of loading Value receiving the load(1) application (Kg/day)(2) El Merey Creek 5 700 Maria Luisa Creek 26 200 Cunaguaro River 17 03/01/2012 to 07/01/2012 1,800 Nameless 30 140 Guarapo Creek 9 150 (1) = Numbers correspond to water quality network under reservoir condition

(2) = Values according to Acevedo and Sherman (2002)

Loading estimations included during the filling up process for the new reservoir required

consideration of the area and types of vegetation that will be flooded during this process.

106 Specific degradation factors for each type of vegetation were also considered for establishing the amount of BOD that will be incorporated into the system as a consequence of the flooding process.

BOD loadings resulting from this estimation were spatially distributed in the water quality network considering the types of vegetation present in each segment, based on the vegetation map developed for that area (CVG EDELCA 2005b). Consequently, the time function for BOD loadings was practically developed for most of the segments of the water quality network while simulating the filling process of the new reservoir, as presented in Figure 51.

90.0

80.0

70.0

60.0

50.0

40.0

BOD Loading (Kg/day) Loading BOD 30.0

20.0

10.0

0.0

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 /2 /2 /2 /2 /2 /2 /2 /2 /2 /2 /2 /2 /2 /2 /2 /2 /2 /2 /1 /8 5 2 9 /5 2 9 6 /3 0 7 4 1 /7 4 1 8 3 3 /1 /2 /2 4 /1 /1 /2 5 /1 /1 /2 /3 6 /1 /2 /2 3 3 3 4 4 4 5 5 5 5 6 6 6

Seg. 5 - El Merey Creek Seg. 17 - Cunaguaro River

Figure 50. Temporal variation of BOD loadings for two segments of the water quality network, while simulating the filling up process for Tocoma Reservoir

107 Model Runs

Once calibrated, the EUTRO sub-model of WASP was used for simulating expected DO

and BOD concentrations of the new reservoir during the filling up and operation time.

Simulation period was of 120 days, corresponding to the period between March and July/2012, considering that the new reservoir will be created during those dates.

Table 27. Phytomass and degradation factors used for BOD loading estimates during the creation of Tocoma Reservoir Type of Vegetation Phytomass Phytomass Degradable Floodable Content Leaf Fraction 2 Area (m ) (Ton/Ha) Fraction (%) (%) Gallery Forest 3,700,000 80 River Forest 9,200,000 95 10 40 Medium Forest 2,260,000 80 Savanna with woody elements 20,070,000 22 Savanna w/woody elements and 630,000 38 bushes Savanna w/woody elements and 80,000 48.6 medium forest Low savanna 2,670,000 1.7 Low savanna with bushes 5,340,000 22 Low savanna with thicket 2,510,000 1.7 Low savanna with floodable 80,000 22 100 100 grassland Low savanna and savanna with 2,180,000 22 woody elements Floodable grassland 60,000 1.7 Bushes 1,270,000 48.6 Bushes with low forest 70,000 48.6 Thicket 430,000 38 Thicket with low forest 40,000 48.6 Thicket with low savanna 20,000 22

Water Quality Modeling Results for Tocoma Reservoir

The simulation of potential future dissolved oxygen and biochemical oxygen demand for

Tocoma Reservoir using WASP provided a better comprehension and understanding of the role

of hydrodynamic and aerodynamic components of the reaeration process for this water body. In

108 addition, the role of BOD loadings on water quality conditions for the new reservoir, resulting of vegetation decomposition, was also stated.

Dissolved Oxygen and Biochemical Oxygen Demand

Model runs included testing of the calibrated model for river condition of this portion of

the Caroni River, as proposed by Acevedo (2002), and two different scenarios for the reservoir

condition. The first simulation under reservoir condition considered absence of autochthonous

BOD loadings, with oxygen demand provided only by external loadings resulting from land use

around in the immediate watershed and oxygen demand from sediments; the second scenario for

simulating reservoir condition included the effect of vegetation decomposition assuming no

removal of existing vegetation in the area before filling up the reservoir.

WASP was previously calibrated for simulating river conditions of the Caroni River for

the portion that entails Guri Dam discharge channels and Tocoma Dam site. By that time, there

were no DO measurements for this entire portion of the river and boundary conditions for

calibration were based on estimations of expected DO concentrations at Guri Dam discharge

channels assuming DO concentrations similar to those measured in the surface layer of Guri

Reservoir (Acevedo 2002). Under this assumption, DO concentrations at the boundary of the

system were set at 6 mg/l and it did not account for the effect of turbinated water coming from deeper layers of Guri Reservoir, with low oxygen content, and its effect on the resulting DO concentration at Guri Dam discharge channels.

Consequently, for understanding the DO dynamic at this portion of the river and under future reservoir conditions, water quality measurements, including DO monitoring, were performed at Guri Dam discharge channels and at Tocoma Dam site between January and

August, 2005. These values were used for estimating model boundary conditions using the mass

109 balance approach described earlier in this chapter and for validation of this extended DO simulation for this portion of the Caroni River. DO data collected in a monthly interval during this period of time are presented in Table 28.

Islands Segments with BOD loading

Figure 51. Spatial distribution of water quality segments with autochthonous BOD loadings Table 28. DO values for Guri Dam discharge channels and Tocoma Dam site measured during January and August, 2005 Dissolved Oxygen (mg/l) Date Guri Dam discharge Guri Dam discharge Tocoma Dam site channel 1 channel 2 01//2005 1.4 4.0 7.2 02//2005 1.5 4.1 6.5 03//2005 1.8 4.4 6.8 04//2005 NM NM 7.7 05//2005 3.8 4.7 7.2 06//2005 3.1 4.9 7.8 07//2005 2.7 4.1 7.4 08//2005 (1) 7.4 4.6 NM NM = Not measured (1) = By the time of the sampling, the spillway was open.

110 Using DO data measured from January to July, 2005, DO concentration at the boundary

resulted in approximately 3.3 mg/l. The model accounted for spatial variations of average

concentration of DO in the river. Simulation of the system under river condition reflected a low increment of DO in the water while it flows through the Necuima Canyon and an important gain

of oxygen by the water once it reaches the widest portion of the river, as presented in Figure 52.

DO (mg/L) Min=3.3 Max=6.51

Tocoma Dam site

Guri Dam

Figure 52. Spatial distribution of DO concentrations for Caroni River between Guri Dam discharge channels and Tocoma Dam site

111 When analyzing the reaeration process that explains this pattern of DO spatial

distribution, it was found that DO gain at Necuima Canyon is low, even though reaeration coefficients at that portion of the river are high as a result of high water velocities (3 to 5 m/s approximately) and low depth (4 m average depth). The hydraulic travel time of the water at this

portion of the river explains this behavior.

As presented in Table 29, travel time at those segments at Necuima Cayon is short.

Consequently, a rapid flush of the water occurs and there is not enough time for diffusion of atmospheric oxygen through the deeper layers of the water column. Then, low oxygenated waters coming from Guri Reservoir, as indicated by the boundary condition, do not have enough time for reaeration and the resulting average of DO concentration for segments at this portion of the network remains low.

Table 29. Estimated values used for DO balance at water quality segments at Necuima Canyon under river condition WASP Length Flow Water Hydraulic Depth Reareation DO DO Segment (m) (m3/s) Velocity Travel (m) Coefficient Upstream Downstream ID (1) (m/s) Time at 27oC (mg/l) (mg/l) (day) (day-1) 1 1570 1333 2.96 0.0061 4 1.51 2.0 2.06 2 1570 2666 2.33 0.0078 3 1.79 4.0 4.06 3 1500 4000 5.23 0.0033 3 3.86 3.39 3.45 4 1200 4000 4.89 0.0028 4 2.80 3.45 3.48 5 1500 4000 5.17 0.0034 4 3.64 3.48 3.54 Total 0.0234 (1) as developed by Acevedo (2002) for Tocoma under river condition

Those segments located in the wider portion of the network also had high reaeration

coefficients, and because these coefficients are the most important hydrodynamic components

for determining the reaeration process simulated by the model (hk = 3 to 9 day-1, wk = 0.15 to

0.30 day-1), the DO concentration increased. However, their surface areas were high enough and

the hydraulic travel time got smaller in comparison with values obtained for segments at the

Necuima Canyon portion of the network. Therefore, oxygen diffusion to deeper layers of the

112 water column was more efficient and the resulting average DO concentration was higher, and

even close to saturation condition. The RMSE of errors for DO estimations for water quality

segment 28 was 11.7%, when comparing calibrated model results for river condition with DO

measured values at Tocoma Dam site in the Caroni River. WASP tended to under-predict DO

concentrations probably because those effects associated with turbulent flow were not included

in the simulation.

Simulations for Tocoma under reservoir condition showed not only spatial but also

temporal differences for expected DO concentrations in the reservoir. For both cases, with and

without BOD loadings, the model accounted for spatial variations of DO concentrations, as presented in Figure 53. Expected DO concentrations ranged between 3.3 and 6.5 mg/l when simulating the new reservoir without BOD loadings. The highest values corresponded to those segments closer to boundary segments with high DO initial concentrations. Simulated DO concentrations ranked between 0 and 4.5 mg/l when BOD loadings resulting from decomposition of flooded vegetation were included, with the exception for those segments closer to boundary segments with high initial DO concentrations.

The model reflected that the lowest DO concentrations for the new reservoir will be distributed along the central axis of this water body when simulating the system without loadings.

This pattern responds to hydrodynamic components of the simulation. The largest portion of practically deoxygenated waters coming from Guri Reservoir will flow through this area of the new reservoir. However, littoral zones will be shallower and oxygen diffusion will be easier at these portions of the reservoir.

This spatial pattern substantially changes when BOD loadings are incorporated for the simulation. An important DO depletion occurs along the entire future reservoir, with the

113 exception of those arms where boundary conditions allow the incorporation of oxygenated water, reducing the effect of the load in terms of dissolved oxygen concentrations for these areas.

Deeper areas closer to the dam receive not only deoxygenated water from Guri Reservoir, but also loading effects will be importantly affected considering that oxygen diffusion toward deeper layers of these water quality segments will be difficult.

a) DO (mg/l) with BOD loadings b) DO (mg/l) without BOD loadings

TocomaDam Tocoma Dam

Guri Dam Guri Dam

Figure 53. Expected spatial distribution of DO for Tocoma Reservoir when simulating the reservoir a) with, and b) without BOD loadings

114 CHAPTER 4

ROLE OF BOUNDARY CONDITIONS AND LOADINGS IN WATER QUALITY

SIMULATIONS

A sensitivity analysis was performed for testing the role of upstream boundary conditions and loadings for simulating some water quality variables in both of these systems, Lake Texoma and Tocoma Reservoir. A sensitivity analysis of a model allows evaluating the response of the model to variations in input values. If a change in an input value causes significant changes in output values (i.e. constituent concentrations), the model is said to be sensitive to that input. The sensitivity analysis was conducted by changing the magnitude of boundary conditions and loadings within reasonable limits while keeping all other parameters unchanged. All combinations of changes of boundary conditions and loadings used in Lake Texoma and Tocoma

Reservoir models were not evaluated because of the large number of simulations required. Only single changes of the boundary condition for chlorides, TSS, and DO; and simultaneous changes for chlorides and TSS loadings were tested for the Lake Texoma model. DO and BOD for

Tocoma Reservoir were considered for each of the tests described below. The model output for each of these simulations was compared to the original model output presented in the previous chapter, and the percent change was calculated. This analysis provided important insights of the role of these two elements of water quality simulations for determining the behavior of water quality conditions in both systems, Lake Texoma and Tocoma Reservoir.

Sensitivity Analysis to Upstream Boundary Conditions

Simulation of water quality for Lake Texoma and Tocoma Reservoir using WASP involved the definition of inflow boundaries. Chemical and physical boundary conditions

115 included in the Lake Texoma model were chlorides, total suspended solids, dissolved oxygen,

and carbonaceous biochemical oxygen demand. For the Tocoma Reservoir, dissolved oxygen

and carbonaceous biochemical oxygen demand were considered for defining chemical inflow

boundaries of the system.

Consequently, the sensitivity analyses of boundary conditions for Lake Texoma

comprised simultaneous changes of all boundary values for each simulated variable: chlorides,

TSS, and DO. This is to say, when evaluating sensitivity to changes of boundary conditions for

chlorides, daily functions defined for all the boundaries of the system were simultaneously

changed, and similarly was done for the rest of the variables.

For the Tocoma Reservoir, isolated changes of each boundary defined for the system

were tested for DO and BOD considering not only the differences found in the hydrodynamics of

the central body of the future reservoir with respect to the water movement at Cunaguaro River

arm, but also the role that DO and BOD exert on the water quality of this reservoir.

Sensitivity to Boundary Conditions for Lake Texoma

Three upstream boundaries were defined for Lake Texoma: Red River, Washita River,

and Big Mineral Creek boundaries. The sensitivity analysis for upstream boundaries of the Lake

Texoma model was performed by changing more or less certain percent of values used as boundary conditions for chlorides, DO, and TSS. The percent difference between the original predicted values and resulting values after changing the boundary condition was calculated, and the average of these values during the simulated period of time was used to establish the sensitivity of the model to the applied change.

Boundary conditions for chlorides in Lake Texoma model involved the development of a statistical regression to transform daily conductivity measurements made at Gainesville gaging

116 station in the Red River, into daily chloride concentrations, using the entire historical data available. For the Washita River boundary a numerical regression between chloride concentrations measured at Station 25 of Atkinson et al. study (1999) and conductivity values

measured at the gage station near Dickson, OK, was performed to develop a daily function for

chlorides. For Big Mineral Creek, an exponential function was found between chlorides

concentrations and flow, using historical data from USGS station 07316200, at a location near

Saddler, TX. Then, flow estimates using HEC-HMS simulations were converted into chlorides

concentrations using the function previously mentioned. Changes of +/- 10% were applied to

these three boundary conditions to test sensitivity of model results to boundary condition

changes.

Three different types of solids were considered for defining the boundary condition for

simulating TSS for Lake Texoma. After defining the function for developing daily

concentrations for TSS concentrations at the boundary, an additional function was used for

defining the fraction that each type of solids represented at the boundary. The sensitivity test was

performed by simultaneously changing by +/- 10% the concentration of each type of solids used

in the original simulation.

For testing sensitivity to changes in DO boundary conditions, those values estimated for

the original simulation were changed in more or less 30%, but taking care of not exceeding the

saturation concentration based on water temperature.

All values used for these sensitivity tests as boundaries are included in Appendix H, and

the results of the tests in terms of percent of changes are shown in Table 30 and Table 31.

117 The simulation results reflected that the system is very sensitive to changes of the

boundary condition for chlorides, but returned less intensity to changes in the boundary condition

of solids type 1 (clay), dissolved oxygen, and solids type 2 (silt) and 3 (sand), respectively.

However a spatial pattern of sensitivity was identified in this system. As reflected in

Figure 54, the Red River arm is the most sensitive to changes to the boundary condition,

especially for chlorides. This response becomes less significant as the water flows through the

system. Transition zones for the Red and Washita River arms reflected a lower sensitivity to this

change, and in the Main Lake zone, the effect was almost negligible. It can be considered that the

travel time could have affected these results, attenuating the effect of boundary condition

changes in the middle and terminal segments of the reservoir. Another factor to be considered is

the diffusion process, which was included in the simulation through a function defined for the

Washita River arm. The magnitude ascribed to this function could have overlapped the effect of

boundary condition changes at this portion of the reservoir. Consequently, the value of the

diffusion rate is another important factor that deserves to be tested for sensitive of the system to

changes in the inflow of chlorides.

When evaluating sensitivity for the Lake Texoma model to changes in the boundary

condition of DO, other factors, such as: reaeration processes and biochemical oxygen demand might have a definite effect on sensitivity to these changes. As shown in Table 30, changes in the boundary condition of dissolved oxygen did not represent a significant change in most of the areas of the reservoir. Only a relatively significant effect was detected at the proximities of the boundaries, and this effect was more remarkable for the Red River arm than for the Washita

River arm. At the Big Mineral Creek area the effect was negligible. The model reflected that vertical diffusion of oxygen is not affected at the shallower areas of the reservoir, and the

118 resulting average concentration for those water quality segments located at the arms of the

reservoir is relatively high, no matter the condition of the boundary. In future research, further

analysis of this situation can be performed by setting up the model in two-dimensions.

Figure 54. Spatial distribution of percent change of chloride concentrations in Lake Texoma when an increment of +10% of boundary condition for chlorides was applied In terms of TSS, the model showed an important response to changes in the boundary

condition for solids type 1, but with less intensity to changes in boundary condition for solids type 2 and 3, as can be understood from values presented in Table 31. Factors such as settling

velocities and resuspension velocities associated to each type of solids could be overwhelming

the observed results. However, when changes of the amount of each type of solid at the boundary

condition were applied, a change in the composition of the total amount of solids incorporating

into the system also occurred. Consequently, a closer look at the effect that these changes in the

119 proportions of each type of solid at the boundaries of the system exert on TSS results is

recommended.

Table 30. Results of the sensitivity test for chlorides and DO showing percent change in daily, volume-averaged concentrations for some WASP segments of the Lake Texoma model compare to the base case in March-May, 1997 Chlorides Dissolved Oxygen WASP Segment Input (Percent Output (Percent Output (Percent Output (Percent change) Change) Change) Change) RR4 + 10 10 + 30 8 - 10 -10 - 30 -8 RR8 + 10 9 + 30 5 - 10 -8 - 30 -5 RRT11 + 10 7 + 30 2 - 10 -6 - 30 -2 RRT14 + 10 6 + 30 1 - 10 -5 - 30 -1 WR20 + 10 5 + 30 9 - 10 -4 - 30 -9 WR22 + 10 3 + 30 3 - 10 -2 - 30 -3 WRT27 + 10 2 + 30 1 - 10 -1 - 30 -1 ML29 + 10 2 + 30 0.4 - 10 -1 - 30 -0.4 ML31 + 10 1 + 30 0.2 - 10 0 - 30 -0.2 BMC34 + 10 3 + 30 0.4 - 10 -3 - 30 -0.4

Sensitivity to Boundary Conditions for the Tocoma Reservoir

For the Tocoma Reservoir, three boundaries were set for DO-BOD simulations of the reservoir based on water quality measurements made at those sites. However, the sensitivity analysis of upstream boundary conditions was performed by changing ≤50% the average concentration of DO measured at Guri Dam discharge channels. The value originally simulated was 3.3 mg/l. The model output of each of these simulations was compared to the original model output and the relative difference was calculated. The spatial distribution of these differences

120 was graphically displayed, as shown in Figure 55, to establish whether there was a pattern for the

calculated differences.

Table 31. Results of the sensitivity test for solids 1, 2, and 3 showing percent change in daily, volume-averaged concentrations of TSS for some WASP segments of the Lake Texoma model compare to the base case in March- May, 1997 WASP Solids 1 Solids 2 Solids 3 Segment Input Output TSS Input Output TSS Input Output TSS (Percent concentration (Percent concentration (Percent concentration change) (Percent change) (Percent change) (Percent change) change) change) RR4 + 10 5.1 + 10 4.5 + 10 0.4 - 10 -4.9 - 10 -4.4 - 10 -0.3 RR8 + 10 5.4 + 10 3.6 + 10 0.4 - 10 -4.7 - 10 -3.0 - 10 0.2 RRT11 + 10 5.1 + 10 2.2 + 10 0.2 - 10 -4.7 - 10 -1.8 - 10 0.2 RRT14 + 10 4.6 + 10 1.0 + 10 4.6 - 10 -4.5 - 10 -0.9 - 10 0.1 WR20 + 10 2.0 + 10 4.9 + 10 1.0 - 10 -2.0 - 10 -4.9 - 10 -1.0 WRT22 + 10 2.6 + 10 3.1 + 10 0.2 - 10 -2.6 - 10 -3.1 - 10 -0.2 WRT27 + 10 2.6 + 10 1.4 + 10 0.01 - 10 -2.6 - 10 -1.4 - 10 -0.01 WRT29 + 10 2.0 + 10 1.1 + 10 0 - 10 -2.0 - 10 -1.1 - 10 0 ML31 + 10 1.5 + 10 1.1 + 10 0 - 10 -1.5 - 10 -1.1 - 10 0 BMC34 + 10 9.4 + 10 0.02 + 10 0 - 10 -9.4 - 10 -0.02 - 10 0

The effect of changing the boundary for DO was remarkable in the central portion of the

future reservoir. For those segments located in the path of the main flow of turbinated waters

coming from Guri Dam and flowing through the future Tocoma reservoir, DO concentrations

practically responded in the same magnitude of the change. The average DO concentration for

the entire reservoir increased in approximately 25% by increasing the boundary condition by

50%, and decreased in the same magnitude when the value at the boundary was reduced. Only

121 for those segments located at the confluence the tributaries to the main water body, the effect of

changes in the boundary condition was negligible. With this simulation, the role of vertical diffusion of oxygen to the deeper layers of the reservoir cannot be appreciated because the model estimated an average of the concentration for each water segment and as a consequence of the important effect that the boundary condition exerts over the entire system.

Figure 55. Spatial distribution of percent change obtained during the sensitivity test to changes in the boundary condition of DO for Tocoma Reservoir

Sensitivity Analysis for Loading Conditions

Loading conditions for TSS and chlorides were tested in the Lake Texoma model. For the

Tocoma Reservoir model, estimated allochthonous and autochthonous loadings of BOD were

tested. For both models, Lake Texoma and Tocoma Reservoir, loadings were varied by ≤50%

and the concentrations results were compared to original output of the models to quantify the

numerical difference after incorporating these changes. All values used for setting new loading

conditions are included in Appendix I.

122 Sensitivity to Loadings for Lake Texoma

All defined loadings for TSS were simultaneously changed by ≤50% for testing the sensitivity of the model to these changes. According to the simulation process, explained in the previous chapter of this dissertation, loadings for TSS were estimated based on simulation results of all sub-watersheds around Lake Texoma. TSS loadings were defined for 16 segments around the lake. Two runs were performed for testing the effect of increasing or decreasing in 50% inputs of solids type 2 (silt) from the surrounding watersheds on the resulting concentrations. For chlorides, the same procedure was applied to the unique load defined for the system, located at the Washita River Arm. An additional run was performed in this case, by eliminating the load, considering the magnitude of the results obtained during the sensitivity test. All results are presented in Table 32 and Table 33.

These results showed that the concentration of TSS estimated by Lake Texoma model is practically unaffected by important changes in loadings for solids type 2. Even though the magnitude of change for values of daily loading was remarkable and it represented significant amounts of solids entering into the system, the resulting TSS concentrations estimated for most of the water quality segments did not change. This might be the result of the application of high settling velocities in the model, which were related to low water velocities through the water body.

The evaluation of changes in the amount of solids accumulated in benthic layers defined in the calibration of WASP for Lake Texoma might provide important insights of the overall effect that these changes in TSS loadings produce in this reservoir.

Sensitivity to changes in loadings for chlorides was tested only at the Washita River Arm.

In this case, the results showed the effect to a lower extent. Percent changes of simulated

123 chloride concentrations were close to zero for almost all segments tested. However, an additional test was performed by totally removing the load at segment WRT22 and there was a noticeable effect in temporal trend of chlorides concentrations described by the model for the segment receiving the load (WRT22), as presented in Figure 56, and those downstream of the loading.

Table 32 . Output percent change for TSS concentrations at several segments of the water quality network of Lake Texoma during the sensitivity test to Solid 2 loading conditions WASP Input (Percent Output (Percent Segment change of boundary) change) +50 0.1 RR4 -50 - 0.1 +50 0.4 RR8 -50 - 0.4 +50 0.2 RRT11 -50 - 0.2 +50 0.1 RRT14 -50 - 0.1 +50 0.4 WR20 -50 - 0.4 +50 0.5 WRT22 -50 - 0.5 +50 0.4 WRT27 -50 - 0.4 +50 0.5 WRT29 -50 - 0.5 +50 1.7 ML31 -50 - 1.7 +50 0.03 BMC34 -50 - 0.03

These results showed that the permanent incorporation of a dissolved conservative substance, like chlorides, in this portion of the system, as described by the model for Lake

Texoma, is responsible of the temporal trend of the concentration of that substance at the

Washita River arm, and a lower influence is produced by the magnitude of the load. Diffusion processes defined in the model could be influencing these results and a deeper analysis of this process at this portion of the reservoir is recommended.

124 Table 33. Output percent change for chloride concentrations at several segments of the water quality network for Lake Texoma during the sensitivity test of the model to chloride loading conditions WASP Input (Percent Output (Percent Segment change of boundary) change) +50 5.6 x 10-4 WR20 -50 - 6.6 x 10-4 +50 6.8 x 10-4 WR21 -50 - 7.6 x 10-4 +50 9.8 x 10-4 WRT22 -50 - 9.3 x 10-4 +50 9.1 x 10-4 WRT23 -50 - 8.6 x 10-4 +50 7.4 x 10-4 WRT24 -50 - 6.3 x 10-4 +50 6.2 x 10-4 WRT25 -50 - 5.6 x 10-4 +50 4.8 x 10-4 WRT26 -50 - 4.6 x 10-4 +50 3.7 x 10-4 WRT27 -50 - 3.5 x 10-4 +50 2.3 x 10-4 WRT28 -50 - 2 x 10-4 +50 2.2 x 10-4 ML29 -50 - 1.4 x 10-4 +50 1.7 x 10-4 ML30 -50 - 1.2 x 10-4 +50 1.6 x 10-4 ML31 -50 - 1.2 x 10-4

Sensitivity to Loadings for Tocoma Reservoir

Testing sensitivity to loadings for Tocoma Reservoir included the analysis of

simultaneous changes to non-point BOD loadings defined for some water quality segments, as a

consequence of land use around the future reservoir, based on estimations found in the literature;

and changes of autochthonous BOD loadings as a consequence of decomposition of the

vegetation present in the reservoir area by flooding. Changes of ≤100% of loadings were applied

for testing allochthonous BOD loadings and changes of ≤50% of loadings were applied for testing changes of autochthonous BOD loadings. The metric used to measure sensitivity in this

125 case was the average of percent change of DO concentrations when comparing calibrated model results with those results obtained after applying the sensitivity test.

WASP Segment WRT22

180

160

140

120

100

Chlorides (mg/l) 60

7 7 7 7 99 /1997 /1 199 199 8/1997 /7 /2/1997 /9/1997 6 3/ 0/ 2 3 /28/1997 4/4/1997 /11/1997 /18/1997 /25/199 5 5 /1 /2 /3 2/ 3/14/1997 3/21/1997 3 4 4 4 5 5 5

Predicted w/o Load Predicted with Load

Figure 56. Chloride concentrations simulated for WASP segment WRT22 while testing sensitivity of Lake Texoma model to changes in chloride loadings The Tocoma Reservoir model did not reflect any change when increments or reductions

of non-point allochthonous BOD loadings were applied. The magnitude of the load and hydrodynamic conditions contributed to dissipate the effect of these loadings.

However, when changing autochthonous loading conditions, the results were different.

This difference was particularly remarkable when analyzing those values associated with the potentially most critical condition of DO concentration in this system. This is to say, when the progress of the reservoir flooding was simulated.

According to these simulation results, the potential reduction of DO expected for the

Tocoma Reservoir would occur in a lower overall intensity by removing 50% of the

126 autochthonous BOD provided by vegetation decomposition (See Figure 57). However, the final

condition expected for this reservoir would not significantly change by removing the load resulting of the mentioned decomposition (See Figure 58). This can be explained by the important effect that the DO boundary condition exerts over the entire system, as mentioned in a previous section of this chapter.

These results would be useful for managing the resulting environmental impact that the flooding plan could produce not only at the flooding area of the new reservoir, but also over the system of reservoirs located downstream of this new project.

127

Figure 57. Comparative spatial variation of potential DO change during the flooding phase of Tocoma Reservoir by reducing autochthonous BOD loading in 50% in comparison with the effect of the total BOD load

128 Figure 58. Comparative expected spatial distribution of DO for Tocoma reservoir after flooding with 50% and 100% of autochthonous BOD loadings

129 CHAPTER 5

CONCLUSIONS

DYNHYD, the hydrodynamic model coupled to the WASP system was calibrated for two

different systems: Lake Texoma and Tocoma Reservoir. For Lake Texoma, results were

validated with observed water elevation values of the reservoir and they showed a good correspondence between predicted and measured pool water elevation for the reservoir.

Validation of DYNHYD results for Tocoma Reservoir is still pending, considering that the

reservoir is still in project status. However, the performance of the model for describing river

conditions where Tocoma Reservoir is planned to be built was tested, showing a good agreement

between predicted and observed average water velocities when describing the river system.

This simulation also provided some insights of the different hydrodynamic conditions

developed in these reservoirs as a consequence of the hydrology of the main rivers that feed the

reservoirs and the morphology of the reservoirs. A calibrated model for each reservoir was used

to estimate the residence time of a simulated tracer in these reservoirs. A period of 78 days was

estimated as the longest residence time for Lake Texoma, considering the time that it took the

tracer to flow through the Red River Arm. On the other hand, 23 days was the longest travel time

estimated for Tocoma Reservoir, specifically at the Cunaguaro River arm. In the central portion

of this projected reservoir, the travel time was estimated to be 4.8 days.

Five different hydrodynamic areas were identified for Lake Texoma based on the

hydrodynamic results of DYNHYD. One of these areas is acting as an enlarged river (Red River

Zone), and the rest of the areas present clear lentic characteristics (Washita River Transition

130 Zone, Red River Transition Zone, Main Lake Zone, and Big Mineral Creek Zone). Back flow conditions and the largest residence time occur in the Big Mineral Creek Zone.

For Tocoma Reservoir, hydrodynamic results of DYNHYD showed that a clear central

flow is expected to be developed in the future reservoir, with several areas with practically

standing water, as a consequence of the shape of the reservoir and the bed channel.

These differences in the hydrodynamic of the evaluated water bodies were coupled to

water quality observations, and provided additional explanations of the functioning of physical

and chemical processes to be expected in these reservoirs. Also, this information can be used as

an input for the estimation of the assimilative capacity and as a tool for the explanation of the

biological productivity of these reservoirs in a future research.

The WASP model was capable of simulating longitudinal gradients in the physical

processes in these reservoirs. In Lake Texoma, the average of temporal and spatial variability of

chlorides in the reservoir was clearly depicted by the simulation, especially during those periods

of the year when there was not a marked stratification of chloride concentrations. DO

concentrations were used for understanding of temporal and spatial variability of a chemical compound in Tocoma Reservoir. Reaeration was the physical process driving DO concentrations in this reservoir

Transport and dilution processes determine the variations of chloride concentrations in

Lake Texoma. In the Red River arm, advection is the primary driver in the transport of chlorides, whereas in the Washita River arm advection and dispersion processes are both involved in the transport of chlorides. The main source of chlorides in the reservoir is the Red River. However, in the Washita River arm it was necessary to consider additional loads of chlorides for the area around Glasses Creek, in order to adjust predicted values. This was justified based on field

131 measurements of increased conductivity in this area of the reservoir that was not caused by the

Washita River (as measured at the gage station near Dickson) or by the Main Lake zone, which has lower conductivity. Additional studies are required to identify the sources of this additional load of chlorides in this portion of the reservoir, but oil and gas production appear to be likely sources.

The model predicts short-term variability of chloride concentrations in both the river and transition zones for Lake Texoma. However, monthly measurements do not account for this variability. On the other hand, chemical stratification reported for the reservoir is not accounted for in this depth-averaged arrangement of the model. In those areas where a clear difference between concentrations at the top and at the bottom of the water column was reported, predictions of the model underestimated the observed conditions because they are expected to represent an average of the concentration.

In terms of DO, physical processes considered in the Streeter-Phelps approach for DO simulations seems to be good enough for describing the average concentration of DO in Lake

Texoma, and for understanding the potential distribution of DO in the Tocoma Reservoir.

The model fits pretty well the temporal variation of the average concentration for this variable in the different zones of Lake Texoma, even though the patterns in these zones are different. However, some discrepancies were found. These differences between observed and predicted values of DO mainly occur in two ways:

Model results diverge at one point in a otherwise close correspondence to the

average

Model results diverge in magnitude but the temporal pattern is generally correct.

132 Consequently, these discrepancies may reflect the occurrence of other processes that the

model is not considering in the way it was implemented.

Using this approach, reaeration processes, sediment oxygen demand, and boundary

conditions are the most important factors to run the model in a way that describes the DO dynamics for both reservoirs. For Lake Texoma the role of boundary conditions for BOD was one of the most important factors to run the model in a way that describes DO dynamic in this reservoir. Other intervening factors are water velocity and surface area of water quality segments defined in the network.

Streeter-Phelps model does not account for oxygen consumption as a part of the Nitrogen cycle or planktonic respiration, just to mention some of the processes involved in DO dynamic for aquatic environments. An improvement in the results can be gained by including nutrients and algal activity, particularly in those places and periods of time where the model overestimates the results. In addition, a distinction between epilimnetic and hypolimnetic strata should be considered in future modeling efforts in order to improve the understanding of production in the reservoir.

For the Tocoma Reservoir, the model accounted for important temporal and spatial variations of DO and BOD concentrations while simulating a prospective condition of the water quality. Spatial variations responded to differences in the reaeration process among different areas of the new reservoir. Temporal variations resulted from the incorporation of BOD loadings provided by the decomposition of the vegetation present in the area nowadays, when assuming no removal of the vegetation prior to filling up the reservoir.

The boundary condition defined for DO of the future Tocoma Reservoir determines to a great extent the average concentration of this variable in the reservoir, considering that the

133 diffusion process of atmospheric oxygen to deeper layers in the future reservoir is going to be

affected by a short residence time. However, the effect of turbulence caused by the wind was not

fully included in these simulations.

The TOXI component of the WASP model was only implemented in this dissertation for

Lake Texoma simulations. It provided an explanation for the seasonal and spatial variability of

total suspended solids in some areas of that reservoir. However, variations in the distribution of

solids close to the boundaries affected predictions; consequently, the model did not fully

accommodate the estimates of TSS close to the boundaries.

Predictions for areas close to the boundaries were heavily affected by variations in the amount of solids associated with changes in the load carried by the rivers, as well as by the variations in the distribution of solids as a consequence of rapid washout effects. In addition,

these two factors are associated with significant differences between top and bottom

concentrations of total suspended solids and these differences are not accounted for in this

simulation.

The estimated distribution of solids in the reservoir as a resultant of changes in water

velocities across the reservoir partially explains temporal and spatial variation of suspended

solids in both the transitional and main lake areas for Lake Texoma. In those areas with lower

water velocities, the amount of coarse particles suspended in the water column is practically

nonexistent and only those very fine-grained particles remain in suspension.

Water velocities also affect settling and resuspension processes in the reservoir.

Adjustment of these parameters as a function, not only of particle size, but also of water velocity

for every reservoir segment, improved model predictions. Settling velocities increase as water

134 velocity declines across the reservoir and no resuspension is observed in those areas where water

velocities are too low.

Another factor to be considered in future simulations is the relationship reported between

conductivity and settling processes of very fine-grained particles (silt, clay, and algal “particles”,

mainly). This could have affected predictions for the Washita River Transition Zone, where

chloride concentrations, heavily related to conductivity in this reservoir, are driven by more

complex transport processes in comparison with the Red River arm transport processes.

Additional studies relating the distribution of particle size in the water column and chloride

gradients in the reservoir are recommended in order to improve these predictions.

The same predictive tool was applied for both systems, allowing a preliminary analysis of

the role of boundary conditions and loadings for each reservoir. These results provide guidance

to future more comprehensive monitoring and modeling studies of these water bodies. For Lake

Texoma, the role of the boundary at the Red River arm is determinant for most of the processes

simulated with the WASP system. For the Washita River arm, loadings for chlorides represent an

important issue to test. Consequently, besides all those aspects mentioned before, a monitoring

effort should be considered in order to improve the understanding of these two factors. For the

future Tocoma Reservoir, the Cunaguaro River arm would represent an important area to

evaluate. Therefore, studies on this water body, previous to the flooding would reduce uncertainties associated to the prospective simulations included in this dissertation. For both systems, further efforts in environmental simulation should consider a full two-dimension approach.

135

APPENDIX A

GEOMETRY DATA FOR LAKE TEXOMA MODEL

136 Table 34. Junction geometry data for Lake Texoma model

Bottom Junction Initial Head Surface Area of Elevation Number (m a.s.l.) Junction (m2) (m) Channel Number entering the Junction 1 189.4 3383433 187.3 1 0 0 0 0 0 2 189.4 3392468 186.2 1 2 0 0 0 0 3 189.4 11390449 184.4 2 3 0 0 0 0 4 189.4 8619099 182.2 3 4 0 0 0 0 5 189.4 5285018 180.6 4 5 0 0 0 0 6 189.4 4258014 180.4 5 6 0 0 0 0 7 189.4 3781801 179.2 6 7 0 0 0 0 8 189.4 5009710 178.4 7 8 0 0 0 0 9 189.4 4108739 177.2 8 9 0 0 0 0 10 189.4 4828761 178.3 9 10 0 0 0 0 11 189.4 6860322 179.9 10 11 39 0 0 0 12 189.4 7266509 180.8 11 12 0 0 0 0 13 189.4 7287395 187 12 13 0 0 0 0 14 189.4 10369958 179.5 13 14 0 0 0 0 15 189.4 9674660 173.9 14 15 0 0 0 0 16 189.4 12128654 174.9 15 16 0 0 0 0 17 189.4 9584109 172.4 16 17 0 0 0 0 18 189.4 14537825 172.8 17 18 0 0 0 0 19 189.4 7963988 174.3 18 19 0 0 0 0 20 189.4 10107464 176.5 19 20 0 0 0 0 21 189.4 4719782 184.8 21 0 0 0 0 0 22 189.4 6420636 180.2 21 22 0 0 0 0 23 189.4 9849929 179.6 22 23 0 0 0 0 24 189.4 13956592 178.1 23 24 0 0 0 0 25 189.4 8661176 174.1 24 25 0 0 0 0 26 189.4 4138753 172 25 26 0 0 0 0 27 189.4 5803608 175.1 26 27 0 0 0 0 28 189.4 6921698 174.5 27 28 0 0 0 0 29 189.4 11505671 177.5 28 29 0 0 0 0 30 189.4 18407743 173.1 20 29 30 0 0 0 31 189.4 18605957 172.7 30 31 0 0 0 0 32 189.4 6098174 167 31 32 0 0 0 0 33 189.4 7115307 167.2 32 33 0 0 0 0 34 189.4 6090772 173.2 33 0 0 0 0 0 35 189.4 2158168 186 34 0 0 0 0 0 36 189.4 2158168 186.8 34 35 0 0 0 0 37 189.4 3237252 185.6 35 36 0 0 0 0 38 189.4 3237252 184.4 36 37 0 0 0 0 39 189.4 4316337 183.2 37 38 0 0 0 0 40 189.4 8021866 182 38 39 0 0 0 0

137 Table 35. Channel geometry data for Lake Texoma model

Initial Mean Length of Width of Manning Roughness Velocity in the Connecting Channel # Channel (m) Channel (m) Coefficient (sec m-1/3) Channel (m/sec) Junctions 1 2041 1059 0.03 0.001 1 2 2 1266 1674 0.03 0.001 2 3 3 2067 3002 0.03 0.001 3 4 4 1752 2896 0.03 0.001 4 5 5 1916 1162 0.03 0.001 5 6 6 1493 1269 0.03 0.001 6 7 7 1519 1160 0.03 0.001 7 8 8 1618 1530 0.03 0.001 8 9 9 1949 1532 0.03 0.001 9 10 10 2641 1807 0.03 0.001 10 11 11 1688 1929 0.03 0.001 11 12 12 2598 2581 0.03 0.001 12 13 13 2947 2501 0.03 0.001 13 14 14 4129 2393 0.03 0.001 14 15 15 2979 3098 0.03 0.001 15 16 16 2856 3558 0.03 0.001 16 17 17 1959 5350 0.03 0.001 17 18 18 2067 3457 0.03 0.001 18 19 19 2161 3480 0.03 0.001 19 20 20 5520 4959 0.03 0.001 20 30 21 2296 2276 0.03 0.001 21 22 22 1917 3210 0.03 0.001 22 23 23 2761 2051 0.03 0.001 23 24 24 2193 4006 0.03 0.001 24 25 25 3014 2240 0.03 0.001 25 26 26 1635 2175 0.03 0.001 26 27 27 1654 2160 0.03 0.001 27 28 28 2598 2535 0.03 0.001 28 29 29 5027 3193 0.03 0.001 29 30 30 1004 4723 0.03 0.001 30 31 31 2872 3390 0.03 0.001 31 32 32 1737 3101 0.03 0.001 32 33 33 2126 3116 0.03 0.001 33 34 34 1700 900 0.03 0.001 35 36 35 1700 900 0.03 0.001 36 37 36 1700 1300 0.03 0.001 37 38 37 1700 1700 0.03 0.001 38 39 38 1700 2100 0.03 0.001 39 40 39 1700 2500 0.03 0.001 11 40

138

APPENDIX B

ESTIMATIONS OF TRAVEL TIME FOR LAKE TEXOMA MODEL

139 Table 36. Estimations of travel time per junction for Lake Texoma model using DYNHYD

DYNHYD Velocity Mean Depth Travel Time Travel Time Channel (m/s) Volume(m3) (m) (seg) (day) 2 0.035 3,610,000 2.2 40111.11 0.46 3 0.009 24,300,000 4.4 270000.00 3.13 4 0.005 29,600,000 5.7 328888.89 3.81 5 0.007 23,100,000 6.9 256666.67 2.97 6 0.009 19,100,000 7.8 212222.22 2.46 7 0.008 19,700,000 8.7 218888.89 2.53 8 0.007 28,500,000 9.8 316666.67 3.67 9 0.006 25,100,000 10.3 278888.89 3.23 10 0.006 43,400,000 9.5 482222.22 5.58 11 0.006 51,500,000 8.4 572222.22 6.62 12 0.005 48,500,000 6.1 510526.32 5.91 13 0.008 6,760,000 4.5 71157.89 0.82 14 0.004 81,700,000 9.7 860000.00 9.95 15 0.003 126,000,000 12.5 1326315.79 15.35 16 0.002 147,000,000 14.0 1547368.42 17.91 17 0.001 149,000,000 14.9 1568421.05 18.15 18 0.001 278,000,000 15.0 2926315.79 33.87 19 0.002 109,000,000 13.6 1147368.42 13.28 20 0.002 116,000,000 13.1 1221052.63 14.13 22 0.002 128,000,000 19.2 948148.15 10.97 23 0.002 148,000,000 19.3 1096296.30 12.69 24 0.001 58,900,000 14.8 1472500.00 17.04 25 0.001 66,600,000 13.9 1665000.00 19.27 26 0.001 83,100,000 12.4 2077500.00 24.05 27 0.001 106,000,000 12.5 2650000.00 30.67

140

APPENDIX C

GEOMETRY DATA FOR TOCOMA RESERVOIR MODEL

141 Table 37. Junction geometry data for Tocoma Reservoir model

Bottom Junction Initial Head Surface Area of Elevation Number (m a.s.l.) Junction (m2) (m) Channel Number entering the Junction 1 127 349706 114 1 0 0 0 0 0 2 127 366855 114 2 0 0 0 0 0 3 127 267973 114 1 2 3 0 0 0 4 127 485827 110 3 4 0 0 0 0 5 127 614245 105 4 5 0 0 0 0 6 127 282489 98 5 7 8 0 0 0 7 127 591342 121.6 6 0 0 0 0 0 8 127 920797 120.1 6 7 9 0 0 0 9 127 925045 92 8 9 10 11 0 0 10 127 1090780 102.5 11 14 0 0 0 0 11 127 503946 90 10 12 13 0 0 0 12 127 2262330 100.3 12 15 0 0 0 0 13 127 1107330 90 13 16 0 0 0 0 14 127 1702800 104 14 17 18 0 0 0 15 127 2365870 102.6 18 19 0 0 0 0 16 127 2711970 100.7 19 20 66 0 0 0 17 127 1393670 114.1 20 21 0 0 0 0 18 127 2033010 101.9 66 21 22 0 0 0 19 127 2816940 102.4 22 26 27 0 0 0 20 127 1675120 112.6 23 0 0 0 0 0 21 127 1281760 117.1 23 24 0 0 0 0 22 127 1636350 110.2 24 25 0 0 0 0 23 127 3049050 112.7 25 26 36 0 0 0 24 127 2580050 90 27 53 54 0 0 0 25 127 1391600 116.4 36 57 0 0 0 0 26 127 1080340 90 17 28 0 0 0 0 27 127 2486520 90 16 28 29 0 0 0 28 127 688855 90 15 30 31 0 0 0 29 127 1118640 100.9 31 32 0 0 0 0 30 127 3667290 110.2 32 33 0 0 0 0 31 127 1539360 90 30 34 35 0 0 0 32 127 2812020 104 33 34 37 0 0 0 33 127 2297050 102.2 37 38 40 0 0 0 34 127 2321160 125 38 39 0 0 0 0 35 127 3224230 120 39 40 41 43 0 0 36 127 1662000 115.2 41 42 0 0 0 0 37 127 1529630 119.2 42 43 44 45 0 0 38 127 1093600 121.8 44 47 0 0 0 0 39 127 2159450 115.5 45 46 0 0 0 0 40 127 1431190 111.1 46 47 48 0 0 0 41 127 2135810 89 29 35 49 0 0 0 42 127 2286640 89 49 50 0 0 0 0 43 127 1427290 85 50 51 0 0 0 0

142 Table 37. Junction geometry data for Tocoma Reservoir model (Continuation)

Bottom Junction Initial Head Surface Area of Elevation Number (m a.s.l.) Junction (m2) (m) Channel Number entering the Junction 44 127 1596340 85 51 52 0 0 0 0 45 127 1817250 80 48 52 61 62 64 0 46 127 1764620 85 58 61 0 0 0 0 47 127 1503380 89 55 58 0 0 0 0 48 127 1920230 89 53 55 0 0 0 0 49 127 1033030 80 59 62 0 0 0 0 50 127 1916070 85 56 59 0 0 0 0 51 127 1339940 89 54 56 0 0 0 0 52 127 1429330 97.3 63 65 0 0 0 0 53 127 1333170 109 60 63 0 0 0 0 54 127 1298120 110.7 57 60 0 0 0 0 55 127 1435020 75 64 65 0 0 0 0

Table 38. Channel geometry data for Tocoma Reservoir model

Initial Mean Length of Width of Manning Roughness Velocity in the Connecting Channel # Channel (m) Channel (m) Coefficient (sec m-1/3) Channel (m/sec) Junctions 1 1000 200 0.03 0.1 1 3 2 1000 200 0.03 0.1 2 3 3 1460 200 0.03 0.1 3 4 4 1386 350 0.03 0.1 4 5 5 1020 375 0.03 0.1 5 6 6 1540 550 0.04 0.1 7 8 7 1250 375 0.04 0.1 6 8 8 1350 650 0.03 0.1 6 9 9 1056 735 0.04 0.1 8 9 10 825 530 0.03 0.1 9 11 11 1467 775 0.03 0.1 9 10 12 1062 700 0.03 0.1 11 12 13 2035 325 0.03 0.1 11 13 14 941 1200 0.03 0.1 10 14 15 1520 800 0.03 0.1 12 28 16 1814 1300 0.03 0.1 13 27 17 1987 500 0.03 0.1 26 14 18 1431 875 0.03 0.1 14 15 19 1317 1425 0.03 0.1 15 16 20 1328 1175 0.04 0.1 16 17 21 1490 575 0.04 0.1 17 18 22 1192 1800 0.03 0.1 18 19 23 1906 475 0.04 0.1 20 21 24 2108 550 0.04 0.1 21 22

143 Table 38. Channel geometry data for Tocoma Reservoir model (Continuation)

Initial Mean Length of Width of Manning Roughness Velocity in the Connecting Channel # Channel (m) Channel (m) Coefficient (sec m-1/3) Channel (m/sec) Junctions 25 1892 775 0.04 0.1 22 23 26 1149 2000 0.04 0.1 19 23 27 1911 1375 0.03 0.1 19 24 28 1118 1435 0.03 0.1 27 26 29 1221 1575 0.03 0.1 27 41 30 1419 500 0.03 0.1 28 31 31 970 500 0.03 0.1 28 29 32 2232 525 0.04 0.1 29 30 33 1188 2375 0.04 0.1 30 32 34 1835 675 0.03 0.1 31 32 35 1384 675 0.03 0.1 31 41 36 2385 2325 0.04 0.1 23 25 37 1338 1575 0.04 0.1 32 33 38 1901 750 0.04 0.1 33 34 39 910 1625 0.04 0.1 34 35 40 1673 1375 0.04 0.1 33 35 41 1847 500 0.04 0.1 35 36 42 987 1550 0.04 0.1 36 37 43 1448 1125 0.04 0.1 35 37 44 1323 750 0.04 0.1 37 38 45 1192 1325 0.03 0.1 37 39 46 1754 1000 0.03 0.1 39 40 47 860 1125 0.04 0.1 38 40 48 1375 775 0.03 0.1 40 45 49 1625 1550 0.03 0.1 41 42 50 1552 1375 0.03 0.1 42 43 51 1272 1300 0.03 0.1 43 44 52 1312 1125 0.03 0.1 44 45 53 1755 875 0.03 0.1 24 48 54 1336 750 0.03 0.1 24 51 55 1640 1200 0.03 0.1 48 47 56 1253 875 0.03 0.1 50 51 57 1000 1500 0.04 0.1 25 54 58 1322 1050 0.03 0.1 46 47 59 1220 950 0.03 0.1 50 49 60 984 1500 0.04 0.1 53 54 61 1490 675 0.03 0.1 45 46 62 1492 625 0.03 0.1 45 49 63 976 1500 0.04 0.1 52 53 64 1285 1000 0.03 0.1 45 55 65 1502 1175 0.03 0.1 52 55 66 1375 1500 0.03 0.1 16 18

144

APPENDIX D

ESTIMATIONS OF HYDRAULIC TRAVEL TIME FOR TOCOMA RESERVOIR

145 Table 39. Estimated travel time per segment in Tocoma Reservoir model

Mean Travel WASP Velocity Depth Time Segment (m/s) Volume(m3) (m) (day) 1 1.022 3,500,000 14.0 0.01 2 0.821 8,260,000 18.1 0.02 3 0.501 13,500,000 22.5 0.03 4 0.204 8,190,000 28.1 0.02 5 0.051 6,350,000 17.6 0.12 6 0.106 32,400,000 30.0 0.08 7 0.075 26,700,000 27.1 0.15 8 0.080 18,600,000 34.9 0.09 9 0.052 60,400,000 31.9 0.51 10 0.033 41,000,000 37.0 0.45 11 0.056 39,200,000 25.9 0.22 12 0.044 57,700,000 24.7 0.49 13 0.025 71,300,000 24.2 0.61 14 0.010 18,000,000 19.4 1.15 15 0.026 51,000,000 24.5 0.43 16 0.020 69,300,000 26.2 0.59 17 0.015 12,700,000 12.9 14.84 18 0.010 27,500,000 14.7 32.34 19 0.011 43,600,000 15.3 1.25 20 0.019 95,500,000 34.4 1.14 21 0.015 14,800,000 12.7 0.42 22 0.023 40,000,000 34.7 0.66 23 0.020 92,000,000 37.2 0.6 24 0.044 25,500,000 33.5 0.21 25 0.019 29,200,000 25.3 1.27 26 0.010 61,600,000 20.4 2.68 27 0.029 57,000,000 34.5 0.59 28 0.005 64,700,000 23.9 2.62 29 0.008 57,000,000 19.2 2.31 30 0.004 4,640,000 11.1 1.26 31 0.013 22,600,000 12.2 0.91 32 0.011 19,600,000 9.7 2.18 33 0.013 11,900,000 8.7 0.48 34 0.007 5,690,000 9.0 1.07 35 0.015 24,800,000 12.1 1.28 36 0.011 22,800,000 22.0 0.92 37 0.037 81,200,000 37.7 0.33 38 0.046 86,900,000 38.9 0.35 39 0.048 59,900,000 40.9 0.24 40 0.050 67,000,000 43.2 0.27 41 0.040 85,400,000 44.5 0.24 42 0.013 74,100,000 42.0 1.81 43 0.011 57,100,000 38.9 1.39 44 0.012 73,000,000 37.8 1.78 45 0.014 48,600,000 45.6 1.14

146 Table 39. Estimated travel time per segment in Tocoma Reservoir model (Continuation)

Mean Travel WASP Velocity Depth Time Segment (m/s) Volume(m3) (m) (day) 46 0.013 80,500,000 42.4 1.88 47 0.015 50,900,000 38.8 1.19 48 0.009 42,500,000 35.3 1.22 49 0.013 24,000,000 21.0 0.69 50 0.017 21,200,000 15.5 0.61

147

APPENDIX E

INPUT DATA FOR WATER QUALITY SIMULATION OF LAKE TEXOMA

148 Table 40. Water quality segment definition for Lake Texoma model Volume Velocity Depth Segment Bottom Description (m3) Multiplier Multiplier Type Segment RR1 10900000 29.54 4.17 Surface Benthic1 RR2 57000000 9.91 6.38 Surface Benthic2 RR3 62100000 8.1 7.11 Surface Benthic3 RR4 46500000 17.73 9.59 Surface Benthic4 RR5 38300000 14.84 9.73 Surface Benthic5 RR6 38600000 13.69 10.68 Surface Benthic6 RR7 55100000 11.55 11.63 Surface Benthic7 RR8 50100000 9.67 12.1 Surface Benthic8 RT9 53600000 9.55 11.51 Surface Benthic9 RT10 65200000 9.57 10.46 Surface Benthic10 RT11 62500000 -3.42 21.22 Surface Benthic11 RT12 17500000 13.26 6.29 Surface Benthic12 RT13 103000000 6.13 14.06 Surface Benthic13 RT14 150000000 5.87 14.25 Surface Benthic14 RT15 176000000 4.33 15.82 Surface Benthic15 RT16 163000000 4.09 16.09 Surface Benthic16 ML17 241000000 4.47 16.17 Surface Benthic17 ML18 120000000 4.47 15.65 Surface Benthic18 ML19 130000000 -1.54 17.02 Surface Benthic19 WR20 59100000 3.9 9.5 Surface Benthic20 WR21 96500000 5.14 11.12 Surface Benthic21 WRT22 158000000 4.68 11.27 Surface Benthic22 WRT23 133000000 3.19 16.72 Surface Benthic23 WRT24 72000000 3.31 16.63 Surface Benthic24 WRT25 83000000 3.66 15.76 Surface Benthic25 WRT26 103000000 3.78 14.5 Surface Benthic26 WRT27 137000000 3.21 14.32 Surface Benthic27 ML28 300000000 2.84 14.56 Surface Benthic28 ML29 311000000 5.88 19.88 Surface Benthic29 ML30 137000000 5.83 21.39 Surface Benthic30 ML31 158000000 5.93 21.43 Surface Benthic31 BMC32 5610000 4.64 3.6 Surface Benthic32 BMC33 12300000 4.38 2.91 Surface Benthic33 BMC34 16200000 1.69 5.49 Surface Benthic34 BMC35 26800000 1.22 6.64 Surface Benthic35 BMC36 59400000 1 7.77 Surface Benthic36 Benthic1 67849.36 0 0.02 Surface benthic Subsurface benthic Benthic2 227808.98 0 0.02 Surface benthic Subsurface benthic Benthic3 172381.98 0 0.02 Surface benthic Subsurface benthic Benthic4 105700.36 0 0.02 Surface benthic Subsurface benthic

149 Table 40. Water quality segment definition for Lake Texoma model (Continuation)

Volume Velocity Depth Segment Bottom Description (m3) Multiplier Multiplier Type Segment Benthic5 85160.28 0 0.02 Surface benthic Subsurface benthic Benthic6 75636.02 0 0.02 Surface benthic Subsurface benthic Benthic7 100194.2 0 0.02 Surface benthic Subsurface benthic Benthic8 82174.78 0 0.02 Surface benthic Subsurface benthic Benthic9 96575.22 0 0.02 Surface benthic Subsurface benthic Benthic10 137206.44 0 0.02 Surface benthic Subsurface benthic Benthic11 145330.18 0 0.02 Surface benthic Subsurface benthic Benthic12 145747.9 0 0.02 Surface benthic Subsurface benthic Benthic13 207399.16 0 0.02 Surface benthic Subsurface benthic Benthic14 193493.2 0 0.02 Surface benthic Subsurface benthic Benthic15 242573.08 0 0.02 Surface benthic Subsurface benthic Benthic16 191682.18 0 0.02 Surface benthic Subsurface benthic Benthic17 290756.5 0 0.02 Surface benthic Subsurface benthic Benthic18 159279.76 0 0.02 Surface benthic Subsurface benthic Benthic19 202149.28 0 0.02 Surface benthic Subsurface benthic Benthic20 128412.72 0 0.02 Surface benthic Subsurface benthic Benthic21 196998.58 0 0.02 Surface benthic Subsurface benthic Benthic22 279131.84 0 0.02 Surface benthic Subsurface benthic Benthic23 173223.52 0 0.02 Surface benthic Subsurface benthic Benthic24 82775.06 0 0.02 Surface benthic Subsurface benthic Benthic25 116072.16 0 0.02 Surface benthic Subsurface benthic Benthic26 138433.96 0 0.02 Surface benthic Subsurface benthic Benthic27 230113.42 0 0.02 Surface benthic Subsurface benthic Benthic28 368154.88 0 0.02 Surface benthic Subsurface benthic Benthic29 372119.12 0 0.02 Surface benthic Subsurface benthic Benthic30 121963.48 0 0.02 Surface benthic Subsurface benthic Benthic31 142306.14 0 0.02 Surface benthic Subsurface benthic Benthic32 43163.36 0 0.02 Surface benthic Subsurface benthic Benthic33 64745.04 0 0.02 Surface benthic Subsurface benthic Benthic34 64745.04 0 0.02 Surface benthic Subsurface benthic Benthic35 86326.74 0 0.02 Surface benthic Subsurface benthic Benthic36 160437.32 0 0.02 Surface benthic Subsurface benthic Sub-benthic 142455531 0 0.5 Subsurface benthic None Table 41. System Data for Lake Texoma model Density Loading System (g/cm3) Conversion Factor Chemical 1 (Chlorides) 1.00 1 Solids 1 (Clay) 2.70 1 Solids 2 (Silt) 2.00 1 Solids 3 (Sand) 1.80 1

150 Table 42. Initial concentrations for chlorides and fractions of suspended solids for Lake Texoma model Solids 1 Solids 2 Solids 3 Segment # [Cl-] (mg/l) (mg/l) (mg/l) (mg/l) 1 360.91 5 8 4.11 2 358.21 5 8 3.72 3 353.8 5 8 3.08 4 350.06 5 8 2.53 5 345.97 5 8 1.94 6 342.79 5 8 1.47 7 339.54 5 8 1 8 336.09 5 8 0.5 9 331.93 5 7.89 0 10 326.3 5 7.07 0 11 322.69 5 6.55 0 12 317.15 5 5.74 0 13 310.86 5 4.82 0 14 302.05 5 3.54 0 15 295.69 5 2.61 0 16 289.6 5 1.73 0 17 285.42 5 1.12 0 18 281.01 5 0.48 0 19 276.4 4.8 0 0 20 96.6 0.99 18.84 0 21 109.18 0.84 15.88 0 22 127.29 0.66 12.51 0 23 141.67 0.55 10.42 0 24 161.45 0.43 8.2 0 25 172.17 0.38 7.25 0 26 183.02 0.34 6.44 0 27 200.07 0.28 5.4 0 28 233.04 2.2 0 0 29 239.63 0 0 0 30 258.47 0 0 0 31 269.86 0 0 0 32 224.33 0.72 10.8 2.88 33 224.33 0.72 10.8 2.88 34 224.33 0.72 10.8 2.88 35 224.33 0.72 10.8 2.88 36 224.33 0.72 10.8 2.88

151 Table 43. Exchange functions defined for Lake Texoma model

SALINITY 1 Segment 1 Segment 2 Area Distance Date Time Value RT10 BMC36 21125 1700 3/1/1997 12:00:00 AM 69 BMC36 BMC35 14280 1700 5/31/1997 12:00:00 AM 69 BMC35 BMC34 9520 1700 ML28 WRT27 45021.3 5027 WRT27 WRT26 33969 2598 WRT26 WRT25 31536 1654 WRT25 WRT24 34473.75 1635 WRT24 WRT23 36624 3014 WRT23 WRT22 53279.8 2193

SALINITY 2 Segment 1 Segment 2 Area Distance Date Time Value WRT22 WRT21 21638.05 2761 3/1/1997 12:00:00 AM 46 WRT21 WR20 30495 1917 5/31/1997 12:00:00 AM 46

SALINITY BMC Segment 1 Segment 2 Area Distance Date Time Value RRT10 BMC36 21125 1700 3/1/1997 12:00:00 AM 1.7 BMC36 BMC35 14280 1700 5/31/1997 12:00:00 AM 1.7 BMC35 BMC34 9520 1700 BMC34 BMC33 5720 1700 BMC33 BMC32 2880 1700 BMC32 Boundary 2700 1700

Table 44. Settling and resuspension functions defined for solids type 1 (clay) in Lake Texoma model

Settling ML Fraction From To of Flow Date Time Value ML28 Benthic 28 18401869 3/1/1997 12:00:00 AM 3.20x10-03 ML29 Benthic 29 18600020 5/31/1997 12:00:00 AM 3.20x10-03 ML30 Benthic 30 6096228 ML31 Benthic 31 7113037 Settling WR From To Frac. of Flow Date Time Value WRT20 Benthic 20 5632149 3/1/1997 12:00:00 AM 3.20x10-06 WRT21 Benthic 21 8640307 5/31/1997 12:00:00 AM 3.20x10-06 WRT22 Benthic 22 13028374 WRT23 Benthic 23 8085143 WRT24 Benthic 24 3863496 WRT25 Benthic 25 5417624 WRT26 Benthic 26 6461354 WRT27 Benthic 27 10740457

152 Table 44. Settling and resuspension functions defined for solids type 1 (clay) in Lake Texoma model (Continuation)

Settling BMC From To Fraction of Flow Date Time Value BMC32 Benthic 32 2063267 3/1/1997 12:00:00 AM 3.20x10-06 BMC33 Benthic 33 3094901 5/31/1997 12:00:00 AM 3.20x10-06 BMC34 Benthic 34 3094091 BMC35 Benthic 35 4126535 BMC36 Benthic 36 7669121 Settling From To Fraction of Flow Date Time Value RR1 Benthic 1 2561333 3/1/1997 12:00:00 AM 3.20x10-05 RR2 Benthic 2 8599855 5/31/1997 12:00:00 AM 3.20x10-05 RR3 Benthic 3 6507470 RR4 Benthic 4 3990219 RR5 Benthic 5 3214825 RR6 Benthic 6 2855282 RR7 Benthic 7 3782360 RR8 Benthic 8 3305530 RT9 Benthic 9 4616426 RT10 Benthic 10 6558654 RT11 Benthic 11 6946979 RT12 Benthic 12 6966947 RT13 Benthic 13 9913960 RT14 Benthic 14 9249236 RT15 Benthic 15 11595321 RT16 Benthic 16 9581051 ML17 Benthic 17 14533186 ML18 Benthic 18 7961447 ML19 Benthic 19 10104239 Resuspension Rivers From To Fraction of Flow Date Time Value Benthic 1 RR1 2561333 3/1/1997 12:00:00 AM 1.27x10-10 Benthic 2 RR2 8599855 5/31/1997 12:00:00 AM 1.27x10-10 Benthic 3 RR3 6507470 Benthic 4 RR4 3990219 Benthic 5 RR5 3214825 Benthic 6 RR6 2855282 Benthic 7 RR7 3782360 Benthic 32 BMC32 2063267 Benthic 33 BMC33 3094901 Benthic 34 BMC34 3094091

153 Table 44. Settling and resuspension functions defined for solids type 1 (clay) in Lake Texoma model (Continuation)

Resuspension WRT From To Fraction of Flow Date Time Value Benthic 20 WR20 5632149 3/1/1997 12:00:00 AM 0 Benthic 21 WR21 8640307 4/9/1997 12:00:00 AM 1.27x10-06 Benthic 22 WRT22 13028374 4/29/1997 12:00:00 AM 1.27x10-06 Benthic 23 WRT23 8085143 4/30/1997 12:00:00 AM 0 Benthic 24 WRT24 3863496 5/19/1997 12:00:00 AM 1.27x10-07 Benthic 25 WRT25 5417624 5/31/1997 12:00:00 AM 1.27x10-07 Benthic 26 WRT26 6461354 Benthic 27 WRT27 10740457 Resuspension RRT-ML From To Frac. of Flow Date Time Value Benthic 8 RR8 3305530 3/1/1997 12:00:00 AM 0 Benthic 9 RT9 4616426 5/31/1997 12:00:00 AM 0 Benthic 10 RT10 6558654 Benthic 11 RT11 6946979 Benthic 12 RT12 6966947 Benthic 13 RT13 9913960 Benthic 14 RT14 9249236 Benthic 15 RT15 11595321 Benthic 16 RT16 9581051 Benthic 17 ML17 14533186 Benthic 18 ML18 7961447 Benthic 19 ML19 10104239 Benthic 28 ML28 18401869 Benthic 29 ML29 18600020 Benthic 30 ML30 6096228 Benthic 31 ML31 7113037 Benthic 35 BMC35 4126535 Benthic 36 BMC36 7669121

154 Table 45. Settling and resuspension functions defined for solids type 2 (silt) in Lake Texoma model

Settling WR Fraction of From To Flow Date Time Value WRT20 Benthic 20 5632149 3/1/1997 12:00:00 AM 1.93x10-3 WRT21 Benthic 21 8640307 5/31/1997 12:00:00 AM 1.93x10-3 WRT22 Benthic 22 13028374 WRT23 Benthic 23 8085143 WRT24 Benthic 24 3863496 WRT25 Benthic 25 5417624 WRT26 Benthic 26 6461354 WRT27 Benthic 27 10740457 BMC32 Benthic 32 2063267 BMC33 Benthic 33 3094901 BMC34 Benthic 34 3094091 Settling RR Fraction of From To Flow Date Time Value RR1 Benthic 1 2561333 3/1/1997 12:00:00 AM 1.93x10-3 RR2 Benthic 2 8599855 5/31/1997 12:00:00 AM 1.93x10-3 RR3 Benthic 3 6507470 RR4 Benthic 4 3990219 RR5 Benthic 5 3214825 RR6 Benthic 6 2855282 RR7 Benthic 7 3782360 RR8 Benthic 8 3305530 RT9 Benthic 9 4616426 RT10 Benthic 10 6558654 RT11 Benthic 11 6946979 RT12 Benthic 12 6966947 RT13 Benthic 13 9913960 RT14 Benthic 14 9249236 RT15 Benthic 15 11595321 RT16 Benthic 16 9581051 ML17 Benthic 17 14533186 ML18 Benthic 18 7961447 ML19 Benthic 19 10104239 ML28 Benthic 28 18401869 ML29 Benthic 29 18600020 ML30 Benthic 30 6096228 ML31 Benthic 31 7113037 BMC35 Benthic 35 4126535 BMC36 Benthic 36 7669121

155 Table 45. Settling and resuspension functions defined for solids type 2 (silt) in Lake Texoma model (Continuation)

Resuspension Rivers Fraction of From To Flow Date Time Value Benthic 1 RR1 2561333 3/1/1997 12:00:00 AM 1.27x10-10 Benthic 2 RR2 8599855 5/31/1997 12:00:00 AM 1.27x10-10 Benthic 3 RR3 6507470 Benthic 4 RR4 3990219 Benthic 5 RR5 3214825 Benthic 6 RR6 2855282 Benthic 7 RR7 3782360 Benthic 20 WR20 5632149 Benthic 21 WR21 8640307 Benthic 22 WRT22 13028374 Benthic 23 WRT23 8085143 Benthic 24 WRT24 3863496 Benthic 25 WRT25 5417624 Benthic 26 WRT26 6461354 Benthic 27 WRT27 10740457 Benthic 32 BMC32 2063267 Benthic 33 BMC33 3094901 Benthic 34 BMC34 3094091 Resuspension RRT-ML Fraction of From To Flow Date Time Value Benthic 8 RR8 3305530 3/1/1997 12:00:00 AM 0 Benthic 9 RT9 4616426 5/31/1997 12:00:00 AM 0 Benthic 10 RT10 6558654 Benthic 11 RT11 6946979 Benthic 12 RT12 6966947 Benthic 13 RT13 9913960 Benthic 14 RT14 9249236 Benthic 15 RT15 11595321 Benthic 16 RT16 9581051 Benthic 17 ML17 14533186 Benthic 18 ML18 7961447 Benthic 19 ML19 10104239 Benthic 28 ML28 18401869 Benthic 29 ML29 18600020 Benthic 30 ML30 6096228 Benthic 31 ML31 7113037 Benthic 35 BMC35 4126535 Benthic 36 BMC36 7669121

156 Table 46. Settling and resuspension functions defined for solids type 3 (sand) in Lake Texoma model

Settling From To Fraction of Flow Date Time Value RR1 Benthic 1 2561333 3/1/1997 12:00:00 AM 9.40x10-03 RR2 Benthic 2 8599855 5/31/1997 12:00:00 AM 9.40x10-03 RR3 Benthic 3 6507470 RR4 Benthic 4 3990219 RR5 Benthic 5 3214825 RR6 Benthic 6 2855282 RR7 Benthic 7 3782360 RR8 Benthic 8 3305530 RT9 Benthic 9 4616426 RT10 Benthic 10 6558654 RT11 Benthic 11 6946979 RT12 Benthic 12 6966947 RT13 Benthic 13 9913960 RT14 Benthic 14 9249236 RT15 Benthic 15 11595321 RT16 Benthic 16 9581051 ML17 Benthic 17 14533186 ML18 Benthic 18 7961447 ML19 Benthic 19 10104239 WR20 Benthic 20 5632149 WR21 Benthic 21 8640307 WRT22 Benthic 22 13028374 WRT23 Benthic 23 8085143 WRT24 Benthic 24 3863496 WRT25 Benthic 25 5417624 WRT26 Benthic 26 6461354 WRT27 Benthic 27 10740457 ML28 Benthic 28 18401869 ML29 Benthic 29 18600020 ML30 Benthic 30 6096228 ML31 Benthic 31 7113037 BMC32 Benthic 32 2063267 BMC33 Benthic 33 3094901 BMC34 Benthic 34 3094091 BMC35 Benthic 35 4126535 BMC36 Benthic 36 7669121

157 Table 46. Settling and resuspension functions defined for solids type 3 (sand) in Lake Texoma model (Continuation)

Resuspension From To Fraction of Flow Date Time Value Benthic 1 RR1 2561333 3/1/1997 12:00:00 AM 9.40x10-03 Benthic 2 RR2 8599855 5/31/1997 12:00:00 AM 9.40x10-03 Benthic 3 RR3 6507470 Benthic 4 RR4 3990219 Benthic 5 RR5 3214825 Benthic 6 RR6 2855282 Benthic 7 RR7 3782360 Benthic 8 RR8 3305530 Benthic 9 RT9 4616426 Benthic 10 RT10 6558654 Benthic 11 RT11 6946979 Benthic 12 RT12 6966947 Benthic 13 RT13 9913960 Benthic 14 RT14 9249236 Benthic 15 RT15 11595321 Benthic 16 RT16 9581051 Benthic 17 ML17 14533186 Benthic 18 ML18 7961447 Benthic 19 ML19 10104239 Benthic 20 WR20 5632149 Benthic 21 WR21 8640307 Benthic 22 WRT22 13028374 Benthic 23 WRT23 8085143 Benthic 24 WRT24 3863496 Benthic 25 WRT25 5417624 Benthic 26 WRT26 6461354 Benthic 27 WRT27 10740457 Benthic 28 ML28 18401869 Benthic 29 ML29 18600020 Benthic 30 ML30 6096228 Benthic 31 ML31 7113037 Benthic 32 BMC32 2063267 Benthic 33 BMC33 3094901 Benthic 34 BMC34 3094091 Benthic 35 BMC35 4126535 Benthic 36 BMC36 7669121

158 Table 47. Red River boundary condition for calibration of WASP for Lake Texoma

RR1 Solids Solids Solids Solids Solids Solids Solids Solids Solids Date Cl 1 2 3 Date Cl 1 2 3 Date Cl 1 2 3 3/1/1997 256.1 5.95 29.75 23.80 4/1/1997 811.45 7.94 47.66 23.83 5/1/1997 271.14 7.19 35.93 28.74 3/2/1997 285.6 5.94 29.71 23.76 4/2/1997 798.99 7.95 47.70 23.85 5/2/1997 300.97 7.25 36.24 28.99 3/3/1997 323.34 5.96 29.81 23.84 4/3/1997 810.7 7.95 47.70 23.85 5/3/1997 335.18 7.29 43.71 21.86 3/4/1997 406.56 5.98 35.86 17.93 4/4/1997 666.77 7.87 47.23 23.61 5/4/1997 390.41 7.31 43.83 21.92 3/5/1997 445.65 5.99 35.93 17.96 4/5/1997 559.38 7.87 47.20 23.60 5/5/1997 436.92 7.34 44.02 22.01 3/6/1997 479.14 6 36.01 18.00 4/6/1997 611.89 7.91 47.43 23.72 5/6/1997 447.28 7.37 44.20 22.10 3/7/1997 474.88 6.02 36.11 18.05 4/7/1997 742.95 7.6 38.02 30.41 5/7/1997 498.8 7.38 44.27 22.13 3/8/1997 482.5 6.03 36.15 18.08 4/8/1997 919.14 7.44 37.19 29.75 5/8/1997 570.79 7.41 44.48 22.24 3/9/1997 482.38 6.04 36.25 18.13 4/9/1997 630.21 7.48 37.42 29.93 5/9/1997 503.94 7.37 44.21 22.11 3/10/1997 455.28 6.05 36.32 18.16 4/10/1997 464.76 7.56 37.81 30.24 5/10/1997 346.43 7.32 43.90 21.95 3/11/1997 465.52 6.07 36.41 18.20 4/11/1997 340.4 7.56 37.78 30.22 5/11/1997 460.4 7.18 35.92 28.73 3/12/1997 480.37 6.08 36.49 18.25 4/12/1997 271.17 7.43 37.14 29.71 5/12/1997 327.67 7.2 35.98 28.78 3/13/1997 436.46 6.1 36.58 18.29 4/13/1997 273.45 7.33 36.66 29.33 5/13/1997 292.02 7.27 43.63 21.81 3/14/1997 438.03 6.11 36.68 18.34 4/14/1997 257.32 7.29 36.47 29.17 5/14/1997 378.99 7.34 44.02 22.01 3/15/1997 533.68 6.13 36.76 18.38 4/15/1997 347.63 7.35 36.76 29.41 5/15/1997 420.49 7.37 44.21 22.11 3/16/1997 598.86 6.15 36.89 18.45 4/16/1997 270.77 7.42 37.12 29.69 5/16/1997 441.64 7.4 44.39 22.19 3/17/1997 599.2 6.16 36.97 18.49 4/17/1997 258.37 7.53 45.19 22.60 5/17/1997 477.59 7.4 44.40 22.20 3/18/1997 691.04 6.17 37.03 18.52 4/18/1997 263.22 7.55 45.31 22.65 5/18/1997 543.72 7.4 44.41 22.20 3/19/1997 732.14 6.17 37.04 18.52 4/19/1997 288.68 7.56 45.37 22.68 5/19/1997 599.56 7.42 44.49 22.25 3/20/1997 686.22 6.18 37.08 18.54 4/20/1997 322.58 7.58 45.46 22.73 5/20/1997 548.23 7.4 44.38 22.19 3/21/1997 683.09 6.19 37.14 18.57 4/21/1997 359.15 7.6 45.60 22.80 5/21/1997 509.87 7.32 43.91 21.95 3/22/1997 736.29 6.2 37.21 18.61 4/22/1997 394.59 7.62 45.71 22.86 5/22/1997 471.62 7.26 43.54 21.77 3/23/1997 745.25 6.21 37.24 18.62 4/23/1997 416.72 7.64 45.83 22.91 5/23/1997 169.47 7.3 43.81 21.90 3/24/1997 697.95 6.22 37.33 18.66 4/24/1997 438.42 7.67 46.04 23.02 5/24/1997 207.37 7.35 44.12 22.06 3/25/1997 6.23 37.36 18.68 4/25/1997 318.88 7.66 45.97 22.99 5/25/1997 325.12 7.39 44.32 22.16 3/26/1997 706.16 6.23 37.37 18.68 4/26/1997 409.6 7.6 45.58 22.79 5/26/1997 465.84 7.41 44.47 22.24 3/27/1997 706.16 6.24 37.42 18.71 4/27/1997 384.78 7.36 36.81 29.44 5/27/1997 628.75 7.44 44.66 22.33 3/28/1997 705.66 6.24 37.45 18.73 4/28/1997 414.78 7.21 36.05 28.84 5/28/1997 699.97 7.47 44.81 22.41 3/29/1997 708.56 6.25 37.51 18.76 4/29/1997 356.26 7.17 35.87 28.69 5/29/1997 758.31 7.49 44.91 22.46 3/30/1997 717.79 6.27 37.61 18.81 4/30/1997 273.11 7.23 36.13 28.90 5/30/1997 746.03 7.45 44.70 22.35 3/31/1997 753.54 6.28 37.69 18.85 5/31/1997 658.15 7.34 44.05 22.03

159 Table 48. Washita River boundary condition for calibration of WASP for Lake Texoma

WR20 Solids Solids Solids Solids Solids Solids Solids Solids Solids Date Cl 1 2 3 Date Cl 1 2 3 Date Cl 1 2 3 3/1/1997 45.14 6.60 39.6 19.80 4/1/1997 127.28 2.40 14.40 7.20 5/1/1997 50.08 3.00 18.00 9.00 3/2/1997 52 6.46 38.79 19.39 4/2/1997 124.52 2.42 14.52 7.26 5/2/1997 50.93 2.99 17.96 8.98 3/3/1997 55.82 6.33 37.97 18.99 4/3/1997 86.91 2.44 14.64 7.32 5/3/1997 59.52 2.99 17.92 8.96 3/4/1997 64.83 6.19 37.16 18.58 4/4/1997 76.87 2.46 14.76 7.38 5/4/1997 69.8 2.98 17.88 8.94 3/5/1997 73.18 6.06 36.35 18.17 4/5/1997 7.85 2.48 14.88 7.44 5/5/1997 81.53 2.97 17.84 8.92 3/6/1997 80.14 5.92 35.54 17.77 4/6/1997 5.66 2.50 15.00 7.50 5/6/1997 95.42 2.97 17.80 8.90 3/7/1997 89.61 5.79 34.72 17.36 4/7/1997 9.09 2.52 15.12 7.56 5/7/1997 102.88 2.96 17.76 8.88 3/8/1997 96.45 5.65 33.91 16.95 4/8/1997 10.39 2.54 15.24 7.62 5/8/1997 71.74 2.95 17.72 8.86 3/9/1997 102.55 5.52 33.10 16.55 4/9/1997 8.62 2.56 15.36 7.68 5/9/1997 75.81 2.95 17.68 8.84 3/10/1997 107.68 5.38 32.28 16.14 4/10/1997 7.9 2.58 15.48 7.74 5/10/1997 72.5 2.94 17.64 8.82 3/11/1997 113.08 5.25 31.47 15.74 4/11/1997 10.23 2.60 15.60 7.80 5/11/1997 84.32 2.93 17.60 8.80 3/12/1997 95.19 5.11 30.66 15.33 4/12/1997 9.49 2.62 15.72 7.86 5/12/1997 107.45 2.93 17.56 8.78 3/13/1997 128.18 4.97 29.85 14.92 4/13/1997 25.95 2.64 15.84 7.92 5/13/1997 128.78 2.92 17.52 8.76 3/14/1997 137.18 4.84 29.03 14.52 4/14/1997 44.5 2.66 15.96 7.98 5/14/1997 103.9 2.91 17.48 8.74 3/15/1997 143.56 4.70 28.22 14.11 4/15/1997 53.22 2.68 16.08 8.04 5/15/1997 94.93 2.91 17.44 8.72 3/16/1997 147.72 4.57 27.41 13.70 4/16/1997 35.73 2.70 16.20 8.10 5/16/1997 104.61 2.90 17.40 8.70 3/17/1997 150.02 4.43 26.59 13.30 4/17/1997 35.93 2.72 16.32 8.16 5/17/1997 117.33 2.89 17.36 8.68 3/18/1997 150.16 4.30 25.78 12.89 4/18/1997 46.02 2.74 16.44 8.22 5/18/1997 124.13 2.89 17.32 8.66 3/19/1997 150.41 4.16 24.97 12.48 4/19/1997 83.21 2.76 16.56 8.28 5/19/1997 119.36 2.88 17.28 8.64 3/20/1997 137.71 4.03 24.16 12.08 4/20/1997 97.57 2.78 16.68 8.34 5/20/1997 85.61 2.87 17.24 8.62 3/21/1997 133.77 3.89 23.34 11.67 4/21/1997 100.96 2.80 16.80 8.40 5/21/1997 80.27 2.87 17.20 8.60 3/22/1997 52.05 3.75 22.53 11.26 4/22/1997 97.69 2.82 16.92 8.46 5/22/1997 98.97 2.86 17.16 8.58 3/23/1997 0.65 3.62 21.72 10.86 4/23/1997 64.95 2.84 17.04 8.52 5/23/1997 109.49 2.85 17.12 8.56 3/24/1997 3.03 3.48 20.90 10.45 4/24/1997 47.19 2.86 17.16 8.58 5/24/1997 93.64 2.85 17.08 8.54 3/25/1997 29.47 3.35 20.09 10.05 4/25/1997 50 2.88 17.28 8.64 5/25/1997 114.98 2.84 17.04 8.52 3/26/1997 138 3.21 19.28 9.64 4/26/1997 76.13 2.90 17.40 8.70 5/26/1997 39.11 2.83 17.00 8.50 3/27/1997 135.02 3.08 18.47 9.23 4/27/1997 68.55 2.92 17.52 8.76 5/27/1997 10.24 2.83 16.96 8.48 3/28/1997 68.58 2.94 17.65 8.83 4/28/1997 64.03 2.94 17.64 8.82 5/28/1997 36.57 2.82 16.92 8.46 3/29/1997 91.71 2.81 16.84 8.42 4/29/1997 73.53 2.96 17.76 8.88 5/29/1997 96.34 2.81 16.88 8.44 3/30/1997 128.67 2.67 16.03 8.01 4/30/1997 79.04 2.98 17.88 8.94 5/30/1997 106.57 2.81 16.84 8.42 3/31/1997 126.74 2.54 15.21 7.61 5/31/1997 45.52 2.80 16.80 8.40

160 Table 49. Big Mineral Creek boundary condition for calibration of WASP for Lake Texoma

BMC32 Solids Solids Solids Solids Solids Solids Solids Solids Solids Date Cl 1 2 3 Date Cl 1 2 3 Date Cl 1 2 3 3/1/1997 40.74 101.71 4.07 97.64 4/1/1997 130.00 0.00 0.00 0.00 5/1/1997 141.73 0.00 0.00 0.00 3/2/1997 73.56 0.00 0.00 0.00 4/2/1997 130.00 0.00 0.00 0.00 5/2/1997 130.00 0.00 0.00 0.00 3/3/1997 40.09 101.71 4.07 97.64 4/3/1997 130.00 0.00 0.00 0.00 5/3/1997 130.00 0.00 0.00 0.00 3/4/1997 22.21 321.31 12.85 308.46 4/4/1997 130.00 0.00 0.00 0.00 5/4/1997 130.00 0.00 0.00 0.00 3/5/1997 27.43 209.27 8.37 200.90 4/5/1997 191.30 0.00 0.00 0.00 5/5/1997 130.00 0.00 0.00 0.00 3/6/1997 38.37 101.71 4.07 97.64 4/6/1997 64.14 0.00 0.00 0.00 5/6/1997 130.00 0.00 0.00 0.00 3/7/1997 52.25 72.94 2.92 70.02 4/7/1997 57.34 0.00 0.00 0.00 5/7/1997 130.00 0.00 0.00 0.00 3/8/1997 95.64 0.00 0.00 0.00 4/8/1997 101.95 0.00 0.00 0.00 5/8/1997 130.00 0.00 0.00 0.00 3/9/1997 203.37 0.00 0.00 0.00 4/9/1997 175.50 0.00 0.00 0.00 5/9/1997 461.24 0.00 0.00 0.00 3/10/1997 273.57 0.00 0.00 0.00 4/10/1997 119.94 0.00 0.00 0.00 5/10/1997 245.89 0.00 0.00 0.00 3/11/1997 201.80 0.00 0.00 0.00 4/11/1997 55.84 0.00 0.00 0.00 5/11/1997 307.49 0.00 0.00 0.00 3/12/1997 260.45 0.00 0.00 0.00 4/12/1997 25.42 249.65 9.99 239.66 5/12/1997 555.40 0.00 0.00 0.00 3/13/1997 311.63 0.00 0.00 0.00 4/13/1997 21.88 335.01 13.40 321.61 5/13/1997 738.24 0.00 0.00 0.00 3/14/1997 173.95 0.00 0.00 0.00 4/14/1997 28.28 197.78 7.91 189.87 5/14/1997 390.28 0.00 0.00 0.00 3/15/1997 217.56 0.00 0.00 0.00 4/15/1997 51.37 72.94 2.92 70.02 5/15/1997 348.58 0.00 0.00 0.00 3/16/1997 392.97 0.00 0.00 0.00 4/16/1997 108.76 0.00 0.00 0.00 5/16/1997 220.87 0.00 0.00 0.00 3/17/1997 834.72 0.00 0.00 0.00 4/17/1997 130.00 0.00 0.00 0.00 5/17/1997 239.71 0.00 0.00 0.00 3/18/1997 825.06 0.00 0.00 0.00 4/18/1997 593.74 0.00 0.00 0.00 5/18/1997 340.95 0.00 0.00 0.00 3/19/1997 439.77 0.00 0.00 0.00 4/19/1997 328.09 0.00 0.00 0.00 5/19/1997 630.78 0.00 0.00 0.00 3/20/1997 130.00 0.00 0.00 0.00 4/20/1997 426.47 0.00 0.00 0.00 5/20/1997 200.22 0.00 0.00 0.00 3/21/1997 130.00 0.00 0.00 0.00 4/21/1997 130.00 0.00 0.00 0.00 5/21/1997 152.41 0.00 0.00 0.00 3/22/1997 130.00 0.00 0.00 0.00 4/22/1997 373.14 0.00 0.00 0.00 5/22/1997 193.34 0.00 0.00 0.00 3/23/1997 130.00 0.00 0.00 0.00 4/23/1997 200.73 0.00 0.00 0.00 5/23/1997 306.32 0.00 0.00 0.00 3/24/1997 130.00 0.00 0.00 0.00 4/24/1997 244.70 0.00 0.00 0.00 5/24/1997 259.61 0.00 0.00 0.00 3/25/1997 254.31 0.00 0.00 0.00 4/25/1997 404.35 0.00 0.00 0.00 5/25/1997 221.69 0.00 0.00 0.00 3/26/1997 135.58 0.00 0.00 0.00 4/26/1997 240.61 0.00 0.00 0.00 5/26/1997 101.43 0.00 0.00 0.00 3/27/1997 169.54 0.00 0.00 0.00 4/27/1997 67.09 0.00 0.00 0.00 5/27/1997 29.08 185.51 7.42 178.09 3/28/1997 306.23 0.00 0.00 0.00 4/28/1997 32.78 141.83 5.67 136.16 5/28/1997 25.08 258.68 10.35 248.33 3/29/1997 649.63 0.00 0.00 0.00 4/29/1997 38.33 101.71 4.07 97.64 5/29/1997 32.84 141.83 5.67 136.16 3/30/1997 130.00 0.00 0.00 0.00 4/30/1997 69.08 0.00 0.00 0.00 5/30/1997 33.69 141.83 5.67 136.16 3/31/1997 130.00 0.00 0.00 0.00 5/31/1997 25.61 240.24 9.61 230.63

161 Table 50. Water temperature function defined for calibration of WASP for Lake Texoma

Date Time Value 2/1/1997 12:00:00 AM 12 3/1/1997 12:00:00 AM 12 4/1/1997 12:00:00 AM 19 5/31/1997 12:00:00 AM 20 Table 51. Print interval used in WASP for Lake Texoma water quality simulation

Date Time Value 3/1/1997 12:00:00 AM 1 5/31/1997 12:00:00 AM 1 Table 52. Time step used in WASP for Lake Texoma water quality simulation

Date Time Value 3/1/1997 12:00:00 AM 0.02 5/31/1997 12:00:00 AM 0.02 Table 53. Chloride loadings (Kg/day) for calibration of WASP for Lake Texoma during March to May, 1997

WRT22 Date Value Date Value Date Value 3/1/1997 1.91x10+02 4/5/1997 1.11 5/7/1997 0 3/2/1997 1.38x10+02 4/6/1997 1.35 5/8/1997 5.39x10-02 3/3/1997 1.61x10+03 4/7/1997 2.14x10-1 5/9/1997 3.38x10-01 3/4/1997 3.64x10+03 4/8/1997 0 5/10/1997 1.82x10-01 3/5/1997 7.65x10+02 4/9/1997 4.08x10-02 5/11/1997 2.74x10-02 3/6/1997 3.05x10+01 4/10/1997 1.02x10-01 5/12/1997 9.91x10-04 3/7/1997 0 4/11/1997 2.08 5/13/1997 0 3/9/1997 0 4/12/1997 9.06x10+02 5/14/1997 0 3/10/1997 2.37x10-01 4/13/1997 4.42x10+02 5/15/1997 2.59x10-03 3/11/1997 8.95x10-02 4/14/1997 2.87x10+01 5/16/1997 3.18x10-02 3/12/1997 2.97x10-02 4/15/1997 4.59x10-01 5/17/1997 7.74x10-02 3/13/1997 2.88x10-01 4/16/1997 0 5/18/1997 1.61x10-02 3/14/1997 5.07x10-01 4/19/1997 0 5/19/1997 6.39x10-04 3/15/1997 1.02x10-01 4/20/1997 1.02x10-02 5/20/1997 1.04x10-01 3/16/1997 4.03x10-03 4/21/1997 1.63x10-01 5/21/1997 3.29x10-01 3/17/1997 0 4/22/1997 5.30x10-01 5/22/1997 1.03x10-01 3/24/1997 0 4/23/1997 1.11 5/23/1997 1.05x10-02 3/25/1997 5.36x10-01 4/24/1997 2.34x10-01 5/24/1997 2.26x10-03 3/26/1997 1.78 4/25/1997 3.36x10-01 5/25/1997 2.49x10-02 3/27/1997 3.66x10-01 4/26/1997 1.27 5/26/1997 1.42x10-01 3/28/1997 1.45x10-02 4/27/1997 1.68x10+02 5/27/1997 7.32x10-01 3/29/1997 0 4/28/1997 1.71x10+03 5/28/1997 4.77x10-01 4/2/1997 0 4/29/1997 4.69x10+02 5/29/1997 1.02x10-01 4/3/1997 7.34x10-02 4/30/1997 1.96x10+01 5/30/1997 2.78x10-01 4/4/1997 6.80x10-01 5/1/1997 0 5/31/1997 5.02x10-01

162 Table 54. Solid 2 loadings for water quality simulation of Lake Texoma during March to May, 1997

Seg. 1 to 8 Seg. 9 Seg. 17 Seg. 20 Seg. 21 Seg. 22 Seg. 27 Seg. 30 Seg. 32 Date High Aver. High Aver. High Aver. High Aver. High Aver. High Aver. High Aver. High Aver. High Aver. 03/01/97 4749 2695 156 91 5 3 78639 46546 15 10 4118 2316 119 68 0 0 20789 11663 03/02/97 1991 1127 97 57 0 0 30771 18243 720 448 3460 1975 0 0 336 186 1749 981 03/03/97 2510 1497 5064 2969 5530 3193 30848 17302 4749 2955 37816 21463 6059 3469 10955 6052 22236 12475 03/04/97 18185 10923 24522 14375 27917 16121 85690 44557 8154 5073 84016 47590 35163 20131 27301 15081 263978 148099 03/05/97 15564 8925 2111 1238 1841 1063 55730 32379 379 236 16724 9423 3463 1982 1556 860 109043 61177 03/06/97 10975 6277 1929 1131 2 1 28296 16101 558 347 1213 715 3674 2103 3105 1715 26714 14987 03/07/97 4780 2714 124 72 0 0 7863 4609 7 5 7 5 258 148 31 17 7331 4113 03/08/97 2030 1148 0 0 0 0 2711 1566 62 39 62 39 225 129 0 0 582 327 03/09/97 753 426 0 0 0 0 2029 1012 161 100 161 100 1063 609 0 0 25 14 03/10/97 0 0 1 0 1 1 2 1 0 0 5 3 1 1 4 2 7 4 03/11/97 2 1 1 1 0 0 12 6 0 0 2 1 0 0 5 3 26 14 03/12/97 2 1 0 0 0 0 18 9 0 0 1 0 0 0 0 0 9 5 03/13/97 1 1 0 0 2 1 14 7 0 0 6 4 1 0 3 2 4 2 03/14/97 2 1 2 1 8 5 14 7 1 0 11 6 4 2 8 4 48 27 03/15/97 3 2 0 0 1 0 15 7 0 0 2 1 0 0 0 0 19 10 03/16/97 2 1 0 0 0 0 10 5 0 0 0 0 0 0 0 0 2 1 03/17/97 1 0 0 0 0 0 5 2 0 0 0 0 0 0 0 0 0 0 03/18/97 0 0 0 0 0 0 2 1 0 0 0 0 0 0 0 0 0 0 03/19/97 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 1 1 03/20/97 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 03/21/97 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 03/22/97 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 03/23/97 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 03/24/97 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 03/25/97 0 0 2 1 5 3 2 1 1 0 12 7 2 1 13 7 10 5 03/26/97 5 3 8 5 22 13 12 6 1 1 39 22 9 5 32 18 135 76 03/27/97 11 6 0 0 1 1 22 11 0 0 8 4 1 0 0 0 53 30 03/28/97 8 4 0 0 0 0 17 8 0 0 0 0 0 0 0 0 4 2 03/29/97 3 2 0 0 0 0 8 4 0 0 0 0 0 0 0 0 0 0 03/30/97 1 1 0 0 0 0 3 1 0 0 0 0 0 0 0 0 0 0 03/31/97 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 04/01/97 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0

163 Table 54. Solid 2 loadings for water quality simulation of Lake Texoma during March to May, 1997 (Continuation)

Seg. 1 to 8 Seg. 9 Seg. 17 Seg. 20 Seg. 21 Seg. 22 Seg. 27 Seg. 30 Seg. 32 Date High Aver. High Aver. High Aver. High Aver. High Aver. High Aver. High Aver. High Aver. High Aver. 04/02/97 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 04/03/97 0 0 0 0 0 0 0 0 0 0 2 1 0 0 0 0 0 0 04/04/97 0 0 0 0 0 0 4 2 0 0 15 8 0 0 0 0 0 0 04/05/97 2 1 5 3 14 8 7 4 1 1 24 14 4 2 29 16 32 18 04/06/97 14 8 5 3 20 11 37 19 1 1 29 17 8 4 29 16 3106 1742 04/07/97 17 10 0 0 1 1 41 20 0 0 5 3 0 0 0 0 4965 2786 04/08/97 9 5 0 0 0 0 23 11 0 0 0 0 0 0 0 0 446 250 04/09/97 4 2 0 0 0 0 10 5 0 0 1 0 0 0 0 0 46 26 04/10/97 3 1 0 0 0 0 7 3 0 0 2 1 0 0 0 0 226 127 04/12/97 1130 693 9 5 7627 4404 4571 2636 1068 664 20532 11610 9586 5488 12761 7049 149925 84112 04/13/97 4999 3066 2 1 2186 1262 27253 15065 861 536 10364 5880 8967 5134 7290 4027 281166 157742 04/14/97 983 603 0 0 75 43 30141 15797 12 7 629 354 592 339 77 42 95915 53811 04/15/97 32 19 0 0 0 0 15977 7903 0 0 10 6 1 1 0 0 7871 4416 04/16/97 2 1 0 0 0 0 6507 3107 0 0 0 0 0 0 0 0 340 191 04/17/97 1 0 0 0 0 0 2640 1225 0 0 0 0 0 0 0 0 0 0 04/18/97 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 0 0 04/19/97 1 1 0 0 0 0 3 1 0 0 0 0 0 0 2 1 3 2 04/20/97 1 1 0 0 0 0 2 1 0 0 0 0 0 0 0 0 1 1 04/21/97 1 0 0 0 0 0 2 1 0 0 4 2 0 0 0 0 0 0 04/22/97 1 1 1 0 3 2 8 4 0 0 12 7 1 1 4 2 2 1 04/23/97 5 3 3 2 12 7 22 11 1 0 24 14 4 2 9 5 26 15 04/24/97 10 6 0 0 1 0 28 14 0 0 5 3 0 0 1 0 11 6 04/25/97 8 5 1 1 0 0 19 9 0 0 7 4 0 0 0 0 1 1 04/26/97 10 6 5 3 6 3 22 11 1 1 28 16 2 1 19 10 12 7 04/27/97 41 24 8 5 34 20 90 50 42 26 3656 2059 2921 1672 1264 698 2573 1444 04/28/97 1129 686 3 2 2441 1410 6009 3285 2884 1793 39593 22437 15185 8693 6820 3768 51685 28997 04/29/97 998 577 0 0 281 162 18165 8500 62 39 10143 5708 1292 739 90 50 26848 15063 04/30/97 725 410 0 0 0 0 20733 9225 0 0 421 237 3 2 0 0 2276 1277 05/01/97 326 184 0 0 0 0 11862 5123 0 0 0 0 0 0 0 0 112 63 05/02/97 137 77 0 0 0 0 5439 2322 0 0 0 0 0 0 0 0 0 0 05/03/97 51 29 0 0 0 0 2472 1047 0 0 0 0 0 0 0 0 0 0 05/04/97 19 11 0 0 0 0 1097 462 0 0 0 0 0 0 0 0 0 0

164

Table 54. Solid 2 loadings for water quality simulation of Lake Texoma during March to May, 1997 (Continuation)

Seg. 1 to 8 Seg. 9 Seg. 17 Seg. 20 Seg. 21 Seg. 22 Seg. 27 Seg. 30 Seg. 32 Date High Aver. High Aver. High Aver. High Aver. High Aver. High Aver. High Aver. High Aver. High Aver. 05/05/97 7 4 0 0 0 0 485 202 0 0 0 0 0 0 0 0 0 0 05/06/97 1 1 0 0 0 0 223 93 0 0 0 0 0 0 0 0 0 0 05/07/97 0 0 0 0 0 0 98 41 0 0 0 0 0 0 0 0 0 0 05/08/97 0 0 0 0 0 0 1 1 1 0 1 1 0 0 0 0 0 0 05/09/97 1 0 1 0 3 2 18 10 2 1 8 4 2 1 9 5 1 0 05/10/97 8 5 3 2 15 9 45 23 1 1 4 2 8 5 21 11 11 6 05/11/97 18 10 0 0 1 1 42 20 0 0 1 0 1 0 0 0 4 2 05/12/97 12 7 0 0 0 0 24 11 0 0 0 0 0 0 0 0 0 0 05/13/97 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 05/14/97 0 0 0 0 0 0 6 3 0 0 0 0 0 0 0 0 2 1 05/15/97 0 0 0 0 0 0 5 3 0 0 0 0 0 0 2 1 3 1 05/16/97 1 0 1 0 1 0 4 2 0 0 1 0 1 0 4 2 17 10 05/17/97 1 1 0 0 4 2 3 2 0 0 2 1 3 1 2 1 12 7 05/18/97 1 0 0 0 0 0 3 1 0 0 0 0 0 0 0 0 3 2 05/19/97 0 0 0 0 0 0 2 1 0 0 0 0 0 0 0 0 0 0 05/20/97 3 2 4 3 4 3 2 1 0 0 2 1 1 1 33 18 26 15 05/21/97 15 8 3 2 20 12 20 10 1 0 7 4 5 3 17 9 83 46 05/22/97 18 10 0 0 4 2 40 21 0 0 2 1 1 0 2 1 31 17 05/23/97 10 5 0 0 0 0 28 14 0 0 0 0 0 0 2 1 4 2 05/24/97 4 2 1 1 0 0 13 6 0 0 0 0 1 0 5 3 9 5 05/25/97 3 2 0 0 3 2 7 3 0 0 1 0 5 3 3 2 17 10 05/26/97 54 33 1 1 4 2 7 4 1 0 3 2 1 0 21 11 455 255 05/27/97 6843 4197 4 2 5 3 505 298 1 1 16 9 17 10 6621 3657 85322 47868 05/28/97 4469 2741 3 2 1553 897 4155 2456 4 2 13 8 4085 2339 12289 6788 158786 89084 05/29/97 772 473 0 0 274 158 3597 2127 40 25 42 26 505 289 2716 1500 51326 28795 05/30/97 2576 1580 2 1 4963 2866 6014 3582 796 495 796 495 3431 1964 18185 10046 46068 25845 05/31/97 5272 3210 377 221 43956 25383 23237 13548 2733 1700 2319 1441 24578 14071 33552 18534 145391 81569

165 Table 55. BOD loadings for water quality simulation of Lake Texoma during March to May, 1997

RR1 to RR8 RRT9 ML17 WR20 WR21 WRT22 WRT27 ML30 BMC32 Date Value Date Value Date Value Date Value Date Value Date Value Date Value Date Value Date Value 3/1/1997 133 3/1/1997 4 3/1/1997 0 3/1/1997 2047 3/1/1997 0 3/1/1997 89 3/1/1997 3 3/1/1997 0 3/1/1997 516 3/2/1997 56 3/2/1997 3 3/2/1997 0 3/2/1997 843 3/2/1997 23 3/2/1997 111 3/2/1997 0 3/2/1997 9 3/2/1997 43 3/3/1997 76 3/3/1997 131 3/3/1997 156 3/3/1997 1032 3/3/1997 149 3/3/1997 1057 3/3/1997 155 3/3/1997 290 3/3/1997 552 3/4/1997 558 3/4/1997 633 3/4/1997 785 3/4/1997 2421 3/4/1997 257 3/4/1997 2233 3/4/1997 898 3/4/1997 722 3/4/1997 6549 3/5/1997 444 3/5/1997 55 3/5/1997 52 3/5/1997 1445 3/5/1997 12 3/5/1997 382 3/5/1997 88 3/5/1997 41 3/5/1997 2705 3/6/1997 311 3/6/1997 50 3/6/1997 0 3/6/1997 744 3/6/1997 18 3/6/1997 67 3/6/1997 94 3/6/1997 82 3/6/1997 663 3/7/1997 134 3/7/1997 3 3/7/1997 0 3/7/1997 203 3/7/1997 0 3/7/1997 1 3/7/1997 7 3/7/1997 1 3/7/1997 182 3/8/1997 57 3/8/1997 0 3/8/1997 0 3/8/1997 73 3/8/1997 2 3/8/1997 6 3/8/1997 6 3/8/1997 0 3/8/1997 14 3/9/1997 21 3/9/1997 0 3/9/1997 0 3/9/1997 55 3/9/1997 5 3/9/1997 15 3/9/1997 27 3/9/1997 0 3/9/1997 1 3/10/1997 0 3/10/1997 0 3/10/1997 0 3/10/1997 0 3/10/1997 0 3/10/1997 0 3/10/1997 0 3/10/1997 0 3/10/1997 0 3/11/1997 0 3/11/1997 0 3/11/1997 0 3/11/1997 0 3/11/1997 0 3/11/1997 0 3/11/1997 0 3/11/1997 0 3/11/1997 1 3/12/1997 0 3/12/1997 0 3/12/1997 0 3/12/1997 0 3/12/1997 0 3/12/1997 0 3/12/1997 0 3/12/1997 0 3/12/1997 0 3/13/1997 0 3/13/1997 0 3/13/1997 0 3/13/1997 0 3/13/1997 0 3/13/1997 0 3/13/1997 0 3/13/1997 0 3/13/1997 0 3/14/1997 0 3/14/1997 0 3/14/1997 0 3/14/1997 0 3/14/1997 0 3/14/1997 0 3/14/1997 0 3/14/1997 0 3/14/1997 1 3/15/1997 0 3/15/1997 0 3/15/1997 0 3/15/1997 0 3/15/1997 0 3/15/1997 0 3/15/1997 0 3/15/1997 0 3/15/1997 0 3/16/1997 0 3/16/1997 0 3/16/1997 0 3/16/1997 0 3/16/1997 0 3/16/1997 0 3/16/1997 0 3/16/1997 0 3/16/1997 0 3/17/1997 0 3/17/1997 0 3/17/1997 0 3/17/1997 0 3/17/1997 0 3/17/1997 0 3/17/1997 0 3/17/1997 0 3/17/1997 0 3/18/1997 0 3/18/1997 0 3/18/1997 0 3/18/1997 0 3/18/1997 0 3/18/1997 0 3/18/1997 0 3/18/1997 0 3/18/1997 0 3/19/1997 0 3/19/1997 0 3/19/1997 0 3/19/1997 0 3/19/1997 0 3/19/1997 0 3/19/1997 0 3/19/1997 0 3/19/1997 0 3/20/1997 0 3/20/1997 0 3/20/1997 0 3/20/1997 0 3/20/1997 0 3/20/1997 0 3/20/1997 0 3/20/1997 0 3/20/1997 0 3/21/1997 0 3/21/1997 0 3/21/1997 0 3/21/1997 0 3/21/1997 0 3/21/1997 0 3/21/1997 0 3/21/1997 0 3/21/1997 0 3/22/1997 0 3/22/1997 0 3/22/1997 0 3/22/1997 0 3/22/1997 0 3/22/1997 0 3/22/1997 0 3/22/1997 0 3/22/1997 0 3/23/1997 0 3/23/1997 0 3/23/1997 0 3/23/1997 0 3/23/1997 0 3/23/1997 0 3/23/1997 0 3/23/1997 0 3/23/1997 0 3/24/1997 0 3/24/1997 0 3/24/1997 0 3/24/1997 0 3/24/1997 0 3/24/1997 0 3/24/1997 0 3/24/1997 0 3/24/1997 0 3/25/1997 0 3/25/1997 0 3/25/1997 0 3/25/1997 0 3/25/1997 0 3/25/1997 0 3/25/1997 0 3/25/1997 0 3/25/1997 0 3/26/1997 0 3/26/1997 0 3/26/1997 1 3/26/1997 0 3/26/1997 0 3/26/1997 1 3/26/1997 0 3/26/1997 1 3/26/1997 3 3/27/1997 0 3/27/1997 0 3/27/1997 0 3/27/1997 0 3/27/1997 0 3/27/1997 0 3/27/1997 0 3/27/1997 0 3/27/1997 1 3/28/1997 0 3/28/1997 0 3/28/1997 0 3/28/1997 0 3/28/1997 0 3/28/1997 0 3/28/1997 0 3/28/1997 0 3/28/1997 0 3/29/1997 0 3/29/1997 0 3/29/1997 0 3/29/1997 0 3/29/1997 0 3/29/1997 0 3/29/1997 0 3/29/1997 0 3/29/1997 0 3/30/1997 0 3/30/1997 0 3/30/1997 0 3/30/1997 0 3/30/1997 0 3/30/1997 0 3/30/1997 0 3/30/1997 0 3/30/1997 0 3/31/1997 0 3/31/1997 0 3/31/1997 0 3/31/1997 0 3/31/1997 0 3/31/1997 0 3/31/1997 0 3/31/1997 0 3/31/1997 0 4/1/1997 0 4/1/1997 0 4/1/1997 0 4/1/1997 0 4/1/1997 0 4/1/1997 0 4/1/1997 0 4/1/1997 0 4/1/1997 0 4/2/1997 0 4/2/1997 0 4/2/1997 0 4/2/1997 0 4/2/1997 0 4/2/1997 0 4/2/1997 0 4/2/1997 0 4/2/1997 0 4/3/1997 0 4/3/1997 0 4/3/1997 0 4/3/1997 0 4/3/1997 0 4/3/1997 0 4/3/1997 0 4/3/1997 0 4/3/1997 0 4/4/1997 0 4/4/1997 0 4/4/1997 0 4/4/1997 0 4/4/1997 0 4/4/1997 0 4/4/1997 0 4/4/1997 0 4/4/1997 0 4/5/1997 0 4/5/1997 0 4/5/1997 0 4/5/1997 0 4/5/1997 0 4/5/1997 1 4/5/1997 0 4/5/1997 1 4/5/1997 1 4/6/1997 0 4/6/1997 0 4/6/1997 1 4/6/1997 1 4/6/1997 0 4/6/1997 1 4/6/1997 0 4/6/1997 1 4/6/1997 77 4/7/1997 0 4/7/1997 0 4/7/1997 0 4/7/1997 1 4/7/1997 0 4/7/1997 0 4/7/1997 0 4/7/1997 0 4/7/1997 123 4/8/1997 0 4/8/1997 0 4/8/1997 0 4/8/1997 0 4/8/1997 0 4/8/1997 0 4/8/1997 0 4/8/1997 0 4/8/1997 11 4/9/1997 0 4/9/1997 0 4/9/1997 0 4/9/1997 0 4/9/1997 0 4/9/1997 0 4/9/1997 0 4/9/1997 0 4/9/1997 1 4/10/1997 0 4/10/1997 0 4/10/1997 0 4/10/1997 0 4/10/1997 0 4/10/1997 0 4/10/1997 0 4/10/1997 0 4/10/1997 6 4/11/1997 0 4/11/1997 0 4/11/1997 0 4/11/1997 0 4/11/1997 0 4/11/1997 1 4/11/1997 0 4/11/1997 1 4/11/1997 138

166 Table 55. BOD loadings for water quality simulation of Lake Texoma during March to May, 1997 (Continuation)

RR1 to RR8 RRT9 ML17 WR20 WR21 WRT22 WRT27 ML30 BMC32 Date Value Date Value Date Value Date Value Date Value Date Value Date Value Date Value Date Value 4/12/1997 36 4/12/1997 0 4/12/1997 215 4/12/1997 187 4/12/1997 34 4/12/1997 521 4/12/1997 245 4/12/1997 337 4/12/1997 3720 4/13/1997 158 4/13/1997 0 4/13/1997 61 4/13/1997 719 4/13/1997 27 4/13/1997 286 4/13/1997 229 4/13/1997 193 4/13/1997 6976 4/14/1997 31 4/14/1997 0 4/14/1997 2 4/14/1997 692 4/14/1997 0 4/14/1997 14 4/14/1997 15 4/14/1997 2 4/14/1997 2380 4/15/1997 1 4/15/1997 0 4/15/1997 0 4/15/1997 345 4/15/1997 0 4/15/1997 0 4/15/1997 0 4/15/1997 0 4/15/1997 195 4/16/1997 0 4/16/1997 0 4/16/1997 0 4/16/1997 135 4/16/1997 0 4/16/1997 0 4/16/1997 0 4/16/1997 0 4/16/1997 8 4/17/1997 0 4/17/1997 0 4/17/1997 0 4/17/1997 53 4/17/1997 0 4/17/1997 0 4/17/1997 0 4/17/1997 0 4/17/1997 0 4/18/1997 0 4/18/1997 0 4/18/1997 0 4/18/1997 0 4/18/1997 0 4/18/1997 0 4/18/1997 0 4/18/1997 0 4/18/1997 0 4/19/1997 0 4/19/1997 0 4/19/1997 0 4/19/1997 0 4/19/1997 0 4/19/1997 0 4/19/1997 0 4/19/1997 0 4/19/1997 0 4/20/1997 0 4/20/1997 0 4/20/1997 0 4/20/1997 0 4/20/1997 0 4/20/1997 0 4/20/1997 0 4/20/1997 0 4/20/1997 0 4/21/1997 0 4/21/1997 0 4/21/1997 0 4/21/1997 0 4/21/1997 0 4/21/1997 0 4/21/1997 0 4/21/1997 0 4/21/1997 0 4/22/1997 0 4/22/1997 0 4/22/1997 0 4/22/1997 0 4/22/1997 0 4/22/1997 0 4/22/1997 0 4/22/1997 0 4/22/1997 0 4/23/1997 0 4/23/1997 0 4/23/1997 0 4/23/1997 1 4/23/1997 0 4/23/1997 1 4/23/1997 0 4/23/1997 0 4/23/1997 1 4/24/1997 0 4/24/1997 0 4/24/1997 0 4/24/1997 1 4/24/1997 0 4/24/1997 0 4/24/1997 0 4/24/1997 0 4/24/1997 0 4/25/1997 0 4/25/1997 0 4/25/1997 0 4/25/1997 0 4/25/1997 0 4/25/1997 0 4/25/1997 0 4/25/1997 0 4/25/1997 0 4/26/1997 0 4/26/1997 0 4/26/1997 0 4/26/1997 1 4/26/1997 0 4/26/1997 1 4/26/1997 0 4/26/1997 0 4/26/1997 0 4/27/1997 1 4/27/1997 0 4/27/1997 1 4/27/1997 5 4/27/1997 1 4/27/1997 82 4/27/1997 75 4/27/1997 33 4/27/1997 64 4/28/1997 35 4/28/1997 0 4/28/1997 69 4/28/1997 338 4/28/1997 91 4/28/1997 1065 4/28/1997 388 4/28/1997 180 4/28/1997 1282 4/29/1997 29 4/29/1997 0 4/29/1997 8 4/29/1997 374 4/29/1997 2 4/29/1997 223 4/29/1997 33 4/29/1997 2 4/29/1997 666 4/30/1997 20 4/30/1997 0 4/30/1997 0 4/30/1997 401 4/30/1997 0 4/30/1997 9 4/30/1997 0 4/30/1997 0 4/30/1997 56 5/1/1997 9 5/1/1997 0 5/1/1997 0 5/1/1997 222 5/1/1997 0 5/1/1997 0 5/1/1997 0 5/1/1997 0 5/1/1997 3 5/2/1997 4 5/2/1997 0 5/2/1997 0 5/2/1997 101 5/2/1997 0 5/2/1997 0 5/2/1997 0 5/2/1997 0 5/2/1997 0 5/3/1997 1 5/3/1997 0 5/3/1997 0 5/3/1997 45 5/3/1997 0 5/3/1997 0 5/3/1997 0 5/3/1997 0 5/3/1997 0 5/4/1997 1 5/4/1997 0 5/4/1997 0 5/4/1997 20 5/4/1997 0 5/4/1997 0 5/4/1997 0 5/4/1997 0 5/4/1997 0 5/5/1997 0 5/5/1997 0 5/5/1997 0 5/5/1997 9 5/5/1997 0 5/5/1997 0 5/5/1997 0 5/5/1997 0 5/5/1997 0 5/6/1997 0 5/6/1997 0 5/6/1997 0 5/6/1997 4 5/6/1997 0 5/6/1997 0 5/6/1997 0 5/6/1997 0 5/6/1997 0 5/7/1997 0 5/7/1997 0 5/7/1997 0 5/7/1997 2 5/7/1997 0 5/7/1997 0 5/7/1997 0 5/7/1997 0 5/7/1997 0 5/8/1997 0 5/8/1997 0 5/8/1997 0 5/8/1997 0 5/8/1997 0 5/8/1997 0 5/8/1997 0 5/8/1997 0 5/8/1997 0 5/9/1997 0 5/9/1997 0 5/9/1997 0 5/9/1997 1 5/9/1997 0 5/9/1997 0 5/9/1997 0 5/9/1997 0 5/9/1997 0 5/10/1997 0 5/10/1997 0 5/10/1997 0 5/10/1997 1 5/10/1997 0 5/10/1997 0 5/10/1997 0 5/10/1997 1 5/10/1997 0 5/11/1997 1 5/11/1997 0 5/11/1997 0 5/11/1997 1 5/11/1997 0 5/11/1997 0 5/11/1997 0 5/11/1997 0 5/11/1997 0 5/12/1997 0 5/12/1997 0 5/12/1997 0 5/12/1997 0 5/12/1997 0 5/12/1997 0 5/12/1997 0 5/12/1997 0 5/12/1997 0 5/13/1997 0 5/13/1997 0 5/13/1997 0 5/13/1997 0 5/13/1997 0 5/13/1997 0 5/13/1997 0 5/13/1997 0 5/13/1997 0 5/14/1997 0 5/14/1997 0 5/14/1997 0 5/14/1997 0 5/14/1997 0 5/14/1997 0 5/14/1997 0 5/14/1997 0 5/14/1997 0 5/15/1997 0 5/15/1997 0 5/15/1997 0 5/15/1997 0 5/15/1997 0 5/15/1997 0 5/15/1997 0 5/15/1997 0 5/15/1997 0 5/16/1997 0 5/16/1997 0 5/16/1997 0 5/16/1997 0 5/16/1997 0 5/16/1997 0 5/16/1997 0 5/16/1997 0 5/16/1997 0 5/17/1997 0 5/17/1997 0 5/17/1997 0 5/17/1997 0 5/17/1997 0 5/17/1997 0 5/17/1997 0 5/17/1997 0 5/17/1997 0 5/18/1997 0 5/18/1997 0 5/18/1997 0 5/18/1997 0 5/18/1997 0 5/18/1997 0 5/18/1997 0 5/18/1997 0 5/18/1997 0 5/19/1997 0 5/19/1997 0 5/19/1997 0 5/19/1997 0 5/19/1997 0 5/19/1997 0 5/19/1997 0 5/19/1997 0 5/19/1997 0 5/20/1997 0 5/20/1997 0 5/20/1997 0 5/20/1997 0 5/20/1997 0 5/20/1997 0 5/20/1997 0 5/20/1997 1 5/20/1997 1 5/21/1997 0 5/21/1997 0 5/21/1997 1 5/21/1997 0 5/21/1997 0 5/21/1997 0 5/21/1997 0 5/21/1997 0 5/21/1997 2 5/22/1997 0 5/22/1997 0 5/22/1997 0 5/22/1997 1 5/22/1997 0 5/22/1997 0 5/22/1997 0 5/22/1997 0 5/22/1997 1 5/23/1997 0 5/23/1997 0 5/23/1997 0 5/23/1997 1 5/23/1997 0 5/23/1997 0 5/23/1997 0 5/23/1997 0 5/23/1997 0 5/24/1997 0 5/24/1997 0 5/24/1997 0 5/24/1997 0 5/24/1997 0 5/24/1997 0 5/24/1997 0 5/24/1997 0 5/24/1997 0 5/25/1997 0 5/25/1997 0 5/25/1997 0 5/25/1997 0 5/25/1997 0 5/25/1997 0 5/25/1997 0 5/25/1997 0 5/25/1997 0 5/26/1997 2 5/26/1997 0 5/26/1997 0 5/26/1997 0 5/26/1997 0 5/26/1997 0 5/26/1997 0 5/26/1997 1 5/26/1997 11 5/27/1997 217 5/27/1997 0 5/27/1997 0 5/27/1997 13 5/27/1997 0 5/27/1997 0 5/27/1997 0 5/27/1997 175 5/27/1997 2117 5/28/1997 142 5/28/1997 0 5/28/1997 44 5/28/1997 108 5/28/1997 0 5/28/1997 1 5/28/1997 104 5/28/1997 325 5/28/1997 3939 5/29/1997 24 5/29/1997 0 5/29/1997 8 5/29/1997 96 5/29/1997 1 5/29/1997 4 5/29/1997 13 5/29/1997 72 5/29/1997 1273 5/30/1997 82 5/30/1997 0 5/30/1997 140 5/30/1997 211 5/30/1997 25 5/30/1997 75 5/30/1997 88 5/30/1997 481 5/30/1997 1143 5/31/1997 165 5/31/1997 10 5/31/1997 1236 5/31/1997 765 5/31/1997 86 5/31/1997 219 5/31/1997 628 5/31/1997 887 5/31/1997 3607

167

APPENDIX F

STATISTICAL ANALYSIS FOR DEVELOPING INITIAL CONDITIONS FOR LAKE

TEXOMA WATER QUALITY MODEL

168 Simple linear regression between distance and chloride concentrations to estimate initial

concentrations for water quality segments of WASP in Lake Texoma

a.- Washita River Arm:

DistWR ClWR 1 0.0 63.33 2 2296.0 90.70 3 8070.5 183.33 4 16769.0 217.33 5 24099.0 247.50 6 29771.0 263.67

Coefficients: (Intercept) DistWR 89.7436 0.00656

Call: lm(formula = ClWR ~ DistWR, data = ChlorWR.df)

Residuals: 1 2 3 4 5 6 -25.7444 -13.4366 41.3114 18.2473 0.3309 -20.7086

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 89.074359 18.605054 4.788 0.00873 ** DistWR 0.006560 0.001069 6.139 0.00357 ** --- Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1

Residual standard error: 28.77 on 4 degrees of freedom Multiple R-Squared: 0.904, Adjusted R-squared: 0.8801 F-statistic: 37.69 on 1 and 4 DF, p-value: 0.003570

169 Figure 59. Linear regression curve between chloride concentrations and distance at the Washita River arm in Lake Texoma b.- Red River Arm:

distRR ClRR 1 9042.0 364.50 2 14646.5 332.67 3 19950.0 292.00 4 35459.0 291.67 5 48170.0 247.50 6 53842.0 263.67

Call: lm(formula = ClRR ~ distRR, data = ChlorRR.df)

Coefficients: (Intercept) distRR 363.090431 -0.002134 Residuals: 1 2 3 4 5 6 20.7074 0.8388 -28.5122 4.2579 -12.7837 15.4917

Coefficients:

170 Estimate Std. Error t value Pr(>|t|) (Intercept) 3.631e+02 1.708e+01 21.26 2.90e-05 *** distRR -2.134e-03 4.941e-04 -4.32 0.0125 * --- Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1

Residual standard error: 20.4 on 4 degrees of freedom Multiple R-Squared: 0.8235, Adjusted R-squared: 0.7793 F-statistic: 18.66 on 1 and 4 DF, p-value: 0.01245

Figure 60. Linear regression curve between chloride concentrations and distance at the Red River arm in Lake Texoma

Simple linear regression analysis between chloride concentrations and conductivity for the Red

River at the gage station near Gainesville, TX to estimate boundary condition.

Cond Cl 1 3470 760 2 3420 730 3 6030 1400

171 4 5330 1300 5 3110 630 6 2390 392 7 2730 507 8 3710 729 9 3740 739 10 2670 515 11 5500 1180

Call: lm(formula = Cl ~ Cond, data = Clcond.df)

Coefficients: (Intercept) Cond -221.2123 0.2688

Cond Cl Min. :2390 Min. : 392.0 1st Qu.:2920 1st Qu.: 572.5 Median :3470 Median : 730.0 Mean :3827 Mean : 807.5 3rd Qu.:4535 3rd Qu.: 970.0 Max. :6030 Max. :1400.0

Call: lm(formula = Cl ~ Cond, data = Clcond.df)

Residuals: Min 1Q Median 3Q Max -77.0381 -37.0764 0.5123 25.2991 88.6532

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -221.21229 51.53747 -4.292 0.00201 ** Cond 0.26877 0.01287 20.888 6.19e-09 *** --- Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1

Residual standard error: 50.41 on 9 degrees of freedom Multiple R-Squared: 0.9798, Adjusted R-squared: 0.9775 F-statistic: 436.3 on 1 and 9 DF, p-value: 6.186e-09

172

Figure 61. Linear regression curve between chlorides and conductivity values measured at the gaging station at the Red River near Gainesville, TX

173

APPENDIX G

STATISTICAL ANALYSIS FOR DEVELOPING DAILY BOUNDARY CONDITIONS FOR

LAKE TEXOMA WATER QUALITY MODEL

174 Simple linear regression analysis between chloride concentrations and conductivity and chloride

concentrations for Station 25 (Atkinson et al. (1999)) and for conductivity and chloride

concentrations for the Washita River at the gage station near Dickson, OK, to estimate boundary

condition

a.- Linear regression between conductivity values measured at Station 25 (Atkinson et al. 1999) and conductivity values measured in the Washita River at Dickson gaging station (USGS 2003):

st25 wr 1 833 1145 2 840 817 3 981 1005 4 1079 1032 5 864 792 6 927 1206 7 1128 1518 8 1520 1910

Call: lm(formula = st25 ~ wr, data = cond.df)

Coefficients: (Intercept) wr 387.3114 0.5383

Residuals: Min 1Q Median 3Q Max -170.67 -84.72 31.62 65.65 136.16

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 387.3114 144.7937 2.675 0.03678 * wr 0.5383 0.1178 4.570 0.00381 ** --- Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1

Residual standard error: 116.8 on 6 degrees of freedom Multiple R-Squared: 0.7768, Adjusted R-squared: 0.7396 F-statistic: 20.88 on 1 and 6 DF, p-value: 0.003811

> summary(wr) Min. 1st Qu. Median Mean 3rd Qu. Max. 792 958 1089 1178 1284 1910

175

Figure 62. Analysis of data distribution for conductivity values measured in the Washita River at the gage station near Dickson, OK > summary(st25) Min. 1st Qu. Median Mean 3rd Qu. Max. 833 858 954 1022 1091 1520

176 Figure 63. Analysis of data distribution for conductivity values measured at Station 25 at Lake Texoma b.- Welch two-samples t-test results:

b.1.-F test to compare two variances data: wr and st25 F = 2.6808, num df = 7, denom df = 7, p-value = 0.2166 alternative hypothesis: true ratio of variances is not equal to 1 95 percent confidence interval: 0.5367064 13.3903515 sample estimates: ratio of variances 2.6808

b.2.- Welch Two Sample t-test data: wr and st25

177 t = 1.0084, df = 11.584, p-value = 0.3338 alternative hypothesis: true difference in means is not equal to 0 95 percent confidence interval: -183.127 496.377 sample estimates: mean of x mean of y 1178.125 1021.500

c.- Regression analysis between conductivity values at Dickson gaging station in the Washita River, and chlorides measured at Station 25 (Atkinson et al. 1999):

cl25 condD 1 63.33 1145 2 55.70 817 3 94.50 1005 4 93.62 1032 5 61.59 792 6 41.64 1206 7 77.50 1518 8 178.67 1910

Call: lm(formula = cl25 ~ condD, data = reg25D)

Coefficients: (Intercept) condD -15.14866 0.08358

Residuals: Min 1Q Median 3Q Max -44.009 -21.471 6.554 23.299 34.181

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -15.14866 38.71432 -0.391 0.7091 condD 0.08358 0.03150 2.654 0.0378 * --- Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1

Residual standard error: 31.24 on 6 degrees of freedom Multiple R-Squared: 0.54, Adjusted R-squared: 0.4633 F-statistic: 7.042 on 1 and 6 DF, p-value: 0.03784

178

Figure 64. Linear regression curve between conductivity values measured at Washita River gaging station near Dickson, OK and chloride concentrations measured at Station 25 in Lake Texoma c.- Regression analysis between conductivity values and chlorides measured at Station 25 (Atkinson et al. 1999)

clst25 condst25 1 63.33 833 2 55.70 840 3 94.50 981 4 93.62 1079 5 61.59 864 6 41.64 927 7 77.50 1128 8 178.67 1520

179

Call: lm(formula = clst25 ~ condst25, data = clcond25)

Coefficients: (Intercept) condst25 -92.5731 0.1722

Residuals: Min 1Q Median 3Q Max -25.407 -5.739 4.512 10.253 18.155

Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -92.57309 30.22850 -3.062 0.02216 * condst25 0.17219 0.02896 5.945 0.00101 ** --- Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.' 0.1 ` ' 1

Residual standard error: 17.55 on 6 degrees of freedom Multiple R-Squared: 0.8549, Adjusted R-squared: 0.8307 F-statistic: 35.35 on 1 and 6 DF, p-value: 0.001012

180 Figure 65. Linear regression curve between conductivity values and chloride concentrations measured at Station 25 in Lake Texoma

181

APPENDIX H

VALUES FOR TESTING SENSITIVITY TO BOUNDARY CONDITIONS

182 Table 56. Values used to test change in 10% of boundary conditions of chlorides for Lake Texoma

Red River Washita River Big Mineral Creek Date Plus 10% Minus 10% Plus 10% Minus 10% Plus 10% Minus 10% 3/1/1997 281.633 230.427 49.654 40.626 44.8159435 36.6675901 3/2/1997 314.083 256.977 57.2 46.8 80.9137521 66.2021608 3/3/1997 355.597 290.943 61.402 50.238 44.1020431 36.0834898 3/4/1997 447.128 365.832 71.313 58.347 24.432743 19.9904261 3/5/1997 490.116 401.004 80.498 65.862 30.1734926 24.687403 3/6/1997 526.955 431.145 88.154 72.126 42.2120863 34.5371616 3/7/1997 522.269 427.311 98.571 80.649 57.476599 47.0263083 3/8/1997 530.651 434.169 106.095 86.805 105.202572 86.074832 3/9/1997 530.53 434.07 112.805 92.295 223.702756 183.029527 3/10/1997 500.709 409.671 118.448 96.912 300.926104 246.212267 3/11/1997 511.973 418.887 124.388 101.772 221.984424 181.62362 3/12/1997 528.308 432.252 104.709 85.671 286.499414 234.408611 3/13/1997 480.018 392.742 140.998 115.362 342.795166 280.468772 3/14/1997 481.745 394.155 150.898 123.462 191.343372 156.553668 3/15/1997 586.949 480.231 157.916 129.204 239.313674 195.802097 3/16/1997 658.636 538.884 162.492 132.948 432.267267 353.673218 3/17/1997 659.01 539.19 165.022 135.018 918.193693 751.249385 3/18/1997 760.023 621.837 165.176 135.144 907.562242 742.550926 3/19/1997 805.233 658.827 165.451 135.369 483.748225 395.794002 3/20/1997 754.721 617.499 151.481 123.939 0 0 3/21/1997 751.278 614.682 147.147 120.393 0 0 3/22/1997 809.787 662.553 57.255 46.845 0 0 3/23/1997 819.643 670.617 0.715 0.585 0 0 3/24/1997 767.613 628.047 3.333 2.727 0 0 3/26/1997 776.655 635.445 32.417 26.523 279.741514 228.879421 3/27/1997 776.655 635.445 151.8 124.2 149.135073 122.019605 3/28/1997 776.105 634.995 148.522 121.518 186.495476 152.587207 3/29/1997 779.284 637.596 75.438 61.722 336.850958 275.605329 3/30/1997 789.437 645.903 100.881 82.539 714.595311 584.668891 3/31/1997 828.762 678.078 141.537 115.803 0 0 4/1/1997 892.463 730.197 139.414 114.066 0 0 4/2/1997 878.746 718.974 140.008 114.552 0 0 4/3/1997 891.638 729.522 136.972 112.068 0 0 4/4/1997 733.326 599.994 95.601 78.219 0 0 4/5/1997 615.219 503.361 84.557 69.183 0 0 4/6/1997 672.969 550.611 8.635 7.065 210.427334 172.167819 4/7/1997 817.113 668.547 6.226 5.094 70.5531584 57.7253114 4/8/1997 1010.9 827.1 9.999 8.181 63.0776141 51.608957 4/9/1997 693.121 567.099 11.429 9.351 112.144833 91.7548632 4/10/1997 511.137 418.203 9.482 7.758 193.053336 157.95273 4/11/1997 374.363 306.297 8.69 7.11 131.932598 107.944853 4/12/1997 298.221 243.999 11.253 9.207 61.4235437 50.2556267 4/13/1997 300.718 246.042 10.439 8.541 27.9652821 22.8806853 4/14/1997 282.986 231.534 28.545 23.355 24.0676191 19.6916884 4/15/1997 382.305 312.795 48.95 40.05 31.1117528 25.4550705 4/16/1997 297.77 243.63 58.542 47.898 56.508676 46.2343713

183 Table 56. Values used to test change in 10% of boundary conditions of chlorides for Lake Texoma (Continuation)

Red River Washita River Big Mineral Creek Date Plus 10% Minus 10% Plus 10% Minus 10% Plus 10% Minus 10% 4/17/1997 284.141 232.479 39.303 32.157 119.633455 97.8819175 4/18/1997 289.476 236.844 39.523 32.337 0 0 4/19/1997 317.471 259.749 50.622 41.418 653.11661 534.368135 4/20/1997 354.761 290.259 91.531 74.889 360.902728 295.28405 4/21/1997 394.988 323.172 107.327 87.813 469.114664 383.821089 4/22/1997 433.961 355.059 111.056 90.864 0 0 4/23/1997 458.304 374.976 107.459 87.921 410.455261 335.827032 4/24/1997 482.174 394.506 71.445 58.455 220.803071 180.657058 4/25/1997 350.68 286.92 51.909 42.471 269.165393 220.226231 4/26/1997 450.461 368.559 55 45 444.78137 363.91203 4/27/1997 423.17 346.23 83.743 68.517 264.666256 216.545119 4/28/1997 456.17 373.23 75.405 61.695 73.7949143 60.3776571 4/29/1997 391.809 320.571 70.433 57.627 36.0596056 29.5033137 4/30/1997 300.355 245.745 80.883 66.177 42.1617914 34.4960112 5/1/1997 298.188 243.972 86.944 71.136 75.9867869 62.1710074 5/2/1997 330.99 270.81 55.088 45.072 155.900496 127.554951 5/3/1997 368.621 301.599 56.023 45.837 0 0 5/4/1997 429.363 351.297 65.472 53.568 0 0 5/5/1997 480.524 393.156 76.78 62.82 0 0 5/6/1997 491.92 402.48 89.683 73.377 0 0 5/7/1997 548.581 448.839 104.962 85.878 0 0 5/8/1997 627.759 513.621 113.168 92.592 0 0 5/9/1997 554.235 453.465 78.914 64.566 0 0 5/10/1997 380.985 311.715 83.391 68.229 507.35944 415.112269 5/11/1997 506.341 414.279 79.75 65.25 270.483904 221.305012 5/12/1997 360.36 294.84 92.752 75.888 338.244031 276.745116 5/13/1997 321.145 262.755 118.195 96.705 610.939076 499.859244 5/14/1997 416.801 341.019 141.658 115.902 812.065365 664.417117 5/15/1997 462.451 378.369 114.29 93.51 429.303117 351.248004 5/16/1997 485.705 397.395 104.423 85.437 383.442469 313.725657 5/17/1997 525.25 429.75 115.071 94.149 242.955085 198.781433 5/18/1997 597.982 489.258 129.063 105.597 263.68576 215.742894 5/19/1997 659.406 539.514 136.543 111.717 375.040722 306.8515 5/20/1997 602.943 493.317 131.296 107.424 693.852852 567.697788 5/21/1997 560.758 458.802 94.171 77.049 220.246038 180.201304 5/22/1997 518.683 424.377 88.297 72.243 167.646815 137.165576 5/23/1997 186.351 152.469 108.867 89.073 212.67199 174.004355 5/24/1997 228.041 186.579 120.439 98.541 336.951418 275.687524 5/25/1997 357.555 292.545 103.004 84.276 285.569242 233.647562 5/26/1997 512.325 419.175 126.478 103.482 243.863517 199.524695 5/27/1997 691.504 565.776 43.021 35.199 111.569198 91.2838891 5/28/1997 769.835 629.865 40.227 32.913 31.9930455 26.1761282 5/29/1997 834.009 682.371 105.974 86.706 27.5845707 22.5691942 5/30/1997 820.501 671.319 117.227 95.913 36.119667 29.5524548 5/31/1997 723.844 592.236 50.072 40.968 37.0636175 30.324778

184 Table 57. Values used to test change in 10% of boundary conditions of Solids 1 (Clay) for Lake Texoma

Red River Washita River Big Mineral Creek Minus Date Plus 10% 10% Plus 10% Minus 10% Plus 10% Minus 10% 3/1/1997 6.545 5.355 7.26 5.94 105.456268 86.2824015 3/2/1997 6.534 5.346 7.106 5.814 32.167731 26.3190526 3/3/1997 6.556 5.364 6.963 5.697 108.914892 89.1121844 3/4/1997 6.578 5.382 6.809 5.571 356.887726 291.999049 3/5/1997 6.589 5.391 6.666 5.454 233.529512 191.069601 3/6/1997 6.6 5.4 6.512 5.328 118.936275 97.311498 3/7/1997 6.622 5.418 6.369 5.211 63.9608405 52.3315968 3/8/1997 6.633 5.427 6.215 5.085 18.9807733 15.5297236 3/9/1997 6.644 5.436 6.072 4.968 4.16741664 3.40970452 3/10/1997 6.655 5.445 5.918 4.842 2.2964073 1.8788787 3/11/1997 6.677 5.463 5.775 4.725 4.23249889 3.46295363 3/12/1997 6.688 5.472 5.621 4.599 2.53470111 2.07384636 3/13/1997 6.71 5.49 5.467 4.473 1.76747849 1.44611876 3/14/1997 6.721 5.499 5.324 4.356 5.70474962 4.66752242 3/15/1997 6.743 5.517 5.17 4.23 3.63908566 2.97743372 3/16/1997 6.765 5.535 5.027 4.113 1.10904291 0.90739874 3/17/1997 6.776 5.544 4.873 3.987 0.24402362 0.19965569 3/18/1997 6.787 5.553 4.73 3.87 0.24980227 0.20438367 3/19/1997 6.787 5.553 4.576 3.744 0.88459284 0.72375778 3/20/1997 6.798 5.562 4.433 3.627 0 0 3/21/1997 6.809 5.571 4.279 3.501 0 0 3/22/1997 6.82 5.58 4.125 3.375 0 0 3/23/1997 6.831 5.589 3.982 3.258 0 0 3/24/1997 6.842 5.598 3.828 3.132 0 0 3/25/1997 6.853 5.607 3.685 3.015 2.65925679 2.17575555 3/26/1997 6.853 5.607 3.531 2.889 9.41340492 7.70187675 3/27/1997 6.864 5.616 3.388 2.772 6.00667625 4.9145533 3/28/1997 6.864 5.616 3.234 2.646 1.8307167 1.49785911 3/29/1997 6.875 5.625 3.091 2.529 0.40385849 0.33042967 3/30/1997 6.897 5.643 2.937 2.403 0 0 3/31/1997 6.908 5.652 2.794 2.286 0 0 4/1/1997 8.734 7.146 2.64 2.16 0 0 4/2/1997 8.745 7.155 2.662 2.178 0 0 4/3/1997 8.745 7.155 2.684 2.196 0 0 4/4/1997 8.657 7.083 2.706 2.214 0 0 4/5/1997 8.657 7.083 2.728 2.232 4.71260771 3.85576994 4/6/1997 8.69 7.11 2.75 2.25 42.3648389 34.662141 4/7/1997 8.36 6.84 2.772 2.268 53.0587053 43.4116679 4/8/1997 8.184 6.696 2.794 2.286 16.6932413 13.6581065 4/9/1997 8.228 6.732 2.816 2.304 5.6036575 4.58481069 4/10/1997 8.316 6.804 2.838 2.322 12.0424533 9.85291632 4/11/1997 8.316 6.804 2.86 2.34 55.9691388 45.7929317 4/12/1997 8.173 6.687 2.882 2.358 272.06493 222.598579 4/13/1997 8.063 6.597 2.904 2.376 367.851738 300.969604 4/14/1997 8.019 6.561 2.926 2.394 219.591657 179.665901 4/15/1997 8.085 6.615 2.948 2.412 66.1815743 54.1485608

185 Table 57. Values used to test change in 10% of boundary conditions of Solids 1 (Clay) for Lake Texoma (Continuation) Red River Washita River Big Mineral Creek Date Plus 10% Minus 10% Plus 10% Minus 10% Plus 10% Minus 10% 4/16/1997 8.162 6.678 2.97 2.43 14.6596438 11.994254 4/17/1997 8.283 6.777 2.992 2.448 0 0 4/18/1997 8.305 6.795 3.014 2.466 0.48388765 0.39590807 4/19/1997 8.316 6.804 3.036 2.484 1.59377782 1.30400004 4/20/1997 8.338 6.822 3.058 2.502 0.94092003 0.76984366 4/21/1997 8.36 6.84 3.08 2.52 0 0 4/22/1997 8.382 6.858 3.102 2.538 1.23065994 1.00690359 4/23/1997 8.404 6.876 3.124 2.556 4.27812989 3.5002881 4/24/1997 8.437 6.903 3.146 2.574 2.87340572 2.35096831 4/25/1997 8.426 6.894 3.168 2.592 1.04722607 0.85682133 4/26/1997 8.36 6.84 3.19 2.61 2.97241048 2.43197221 4/27/1997 8.096 6.624 3.212 2.628 38.7077232 31.6699553 4/28/1997 7.931 6.489 3.234 2.646 163.231979 133.553438 4/29/1997 7.887 6.453 3.256 2.664 119.221573 97.5449233 4/30/1997 7.953 6.507 3.278 2.682 36.4965528 29.860816 5/1/1997 7.898 6.462 3.3 2.7 8.61044657 7.04491083 5/2/1997 7.975 6.525 3.289 2.691 0 0 5/3/1997 8.019 6.561 3.289 2.691 0 0 5/4/1997 8.03 6.57 3.278 2.682 0 0 5/5/1997 8.074 6.606 3.267 2.673 0 0 5/6/1997 8.107 6.633 3.267 2.673 0 0 5/7/1997 8.118 6.642 3.256 2.664 0 0 5/8/1997 8.151 6.669 3.245 2.655 0 0 5/9/1997 8.107 6.633 3.245 2.655 0.80380606 0.65765951 5/10/1997 8.052 6.588 3.234 2.646 2.84532641 2.32799434 5/11/1997 7.898 6.462 3.223 2.637 1.81559578 1.48548746 5/12/1997 7.92 6.48 3.223 2.637 0.55336227 0.45275095 5/13/1997 7.997 6.543 3.212 2.628 0.31234352 0.25555379 5/14/1997 8.074 6.606 3.201 2.619 1.12448524 0.92003338 5/15/1997 8.107 6.633 3.201 2.619 1.41108824 1.15452674 5/16/1997 8.14 6.66 3.19 2.61 3.53030417 2.88843068 5/17/1997 8.14 6.66 3.179 2.601 2.99466407 2.45017969 5/18/1997 8.14 6.66 3.179 2.601 1.47533436 1.20709175 5/19/1997 8.151 6.669 3.168 2.592 0.42848739 0.35058059 5/20/1997 8.14 6.66 3.157 2.583 4.29990191 3.51810157 5/21/1997 8.052 6.588 3.157 2.583 7.44091044 6.08801763 5/22/1997 7.986 6.534 3.146 2.574 4.61318219 3.77442179 5/23/1997 8.03 6.57 3.135 2.565 1.82961997 1.49696179 5/24/1997 8.085 6.615 3.135 2.565 2.55132027 2.08744386 5/25/1997 8.129 6.651 3.124 2.556 3.50392522 2.86684791 5/26/1997 8.151 6.669 3.113 2.547 16.8667777 13.8000909 5/27/1997 8.184 6.696 3.113 2.547 207.604488 169.858217 5/28/1997 8.217 6.723 3.102 2.538 279.663553 228.815635 5/29/1997 8.228 6.732 3.091 2.529 162.686963 133.107515

186 Table 57. Values used to test change in 10% of boundary conditions of Solids 1 (Clay) for Lake Texoma (Continuation) Red River Washita River Big Mineral Creek Date Plus 10% Minus 10% Plus 10% Minus 10% Plus 10% Minus 10% 5/30/1997 8.195 6.705 3.091 2.529 154.467337 126.382367 5/31/1997 8.074 6.606 3.08 2.52 268.086872 219.343804

Table 58. Values used to test change in 10% of boundary conditions of Solids 2 (Silt) for Lake Texoma

Red River Washita River Big Mineral Creek Date Plus 10% Minus 10% Plus 10% Minus 10% Plus 10% Minus 10% 3/1/1997 32.714 26.766 29.4426 24.0894 4.21825074 3.45129606 3/2/1997 32.681 26.739 29.4129 24.0651 1.28670924 1.05276211 3/3/1997 32.791 26.829 29.5119 24.1461 4.35659568 3.56448738 3/4/1997 32.868 26.892 29.5812 24.2028 14.2755091 11.679962 3/5/1997 32.934 26.946 29.6406 24.2514 9.3411805 7.64278404 3/6/1997 33.011 27.009 29.7099 24.3081 4.75745102 3.89245992 3/7/1997 33.099 27.081 29.7891 24.3729 2.55843362 2.09326387 3/8/1997 33.143 27.117 29.8287 24.4053 0.75923093 0.62118894 3/9/1997 33.231 27.189 29.9079 24.4701 0.16669667 0.13638818 3/10/1997 33.297 27.243 29.9673 24.5187 0.09185629 0.07515515 3/11/1997 33.374 27.306 30.0366 24.5754 0.16929996 0.13851815 3/12/1997 33.451 27.369 30.1059 24.6321 0.10138804 0.08295385 3/13/1997 33.528 27.432 30.1752 24.6888 0.07069914 0.05784475 3/14/1997 33.627 27.513 30.2643 24.7617 0.22818999 0.1867009 3/15/1997 33.704 27.576 30.3336 24.8184 0.14556343 0.11909735 3/16/1997 33.814 27.666 30.4326 24.8994 0.04436172 0.03629595 3/17/1997 33.891 27.729 30.5019 24.9561 0.00976094 0.00798623 3/18/1997 33.946 27.774 30.5514 24.9966 0.00999209 0.00817535 3/19/1997 33.957 27.783 30.5613 25.0047 0.03538371 0.02895031 3/20/1997 33.99 27.81 30.591 25.029 0 0 3/21/1997 34.045 27.855 30.6405 25.0695 0 0 3/22/1997 34.111 27.909 30.6999 25.1181 0 0 3/23/1997 34.133 27.927 30.7197 25.1343 0 0 3/24/1997 34.221 27.999 30.7989 25.1991 0 0 3/25/1997 34.254 28.026 30.8286 25.2234 0.10637027 0.08703022 3/26/1997 34.254 28.026 30.8286 25.2234 0.3765362 0.30807507 3/27/1997 34.298 28.062 30.8682 25.2558 0.24026705 0.19658213 3/28/1997 34.331 28.089 30.8979 25.2801 0.07322867 0.05991436 3/29/1997 34.386 28.134 30.9474 25.3206 0.01615434 0.01321719 3/30/1997 34.485 28.215 31.0365 25.3935 0 0 3/31/1997 34.551 28.269 31.0959 25.4421 0 0 4/1/1997 43.692 35.748 39.3228 32.1732 0 0 4/2/1997 43.725 35.775 39.3525 32.1975 0 0 4/3/1997 43.725 35.775 39.3525 32.1975 0 0 4/4/1997 43.285 35.415 38.9565 31.8735 0 0

187 Table 58. Values used to test change in 10% of boundary conditions of Solids 2 (Silt) for Lake Texoma (Continuation) Red River Washita River Big Mineral Creek Minus Date Plus 10% 10% Plus 10% Minus 10% Plus 10% Minus 10% 4/5/1997 43.274 35.406 38.9466 31.8654 0.18850431 0.1542308 4/6/1997 43.472 35.568 39.1248 32.0112 1.69459356 1.38648564 4/7/1997 41.822 34.218 37.6398 30.7962 2.12234821 1.73646672 4/8/1997 40.898 33.462 36.8082 30.1158 0.66772965 0.54632426 4/9/1997 41.151 33.669 37.0359 30.3021 0.2241463 0.18339243 4/10/1997 41.591 34.029 37.4319 30.6261 0.48169813 0.39411665 4/11/1997 41.558 34.002 37.4022 30.6018 2.23876555 1.83171727 4/12/1997 40.843 33.417 36.7587 30.0753 10.8825972 8.90394315 4/13/1997 40.326 32.994 36.2934 29.6946 14.7140695 12.0387842 4/14/1997 40.117 32.823 36.1053 29.5407 8.78366629 7.18663605 4/15/1997 40.436 33.084 36.3924 29.7756 2.64726297 2.16594243 4/16/1997 40.832 33.408 36.7488 30.0672 0.58638575 0.47977016 4/17/1997 41.426 33.894 37.2834 30.5046 0 0 4/18/1997 41.536 33.984 37.3824 30.5856 0.01935551 0.01583632 4/19/1997 41.591 34.029 37.4319 30.6261 0.06375111 0.05216 4/20/1997 41.668 34.092 37.5012 30.6828 0.0376368 0.03079375 4/21/1997 41.8 34.2 37.62 30.78 0 0 4/22/1997 41.899 34.281 37.7091 30.8529 0.0492264 0.04027614 4/23/1997 42.009 34.371 37.8081 30.9339 0.1711252 0.14001152 4/24/1997 42.196 34.524 37.9764 31.0716 0.11493623 0.09403873 4/25/1997 42.141 34.479 37.9269 31.0311 0.04188904 0.03427285 4/26/1997 41.778 34.182 37.6002 30.7638 0.11889642 0.09727889 4/27/1997 40.48 33.12 36.432 29.808 1.54830893 1.26679821 4/28/1997 39.655 32.445 35.6895 29.2005 6.52927918 5.34213751 4/29/1997 39.446 32.274 35.5014 29.0466 4.76886292 3.90179693 4/30/1997 39.743 32.517 35.7687 29.2653 1.45986211 1.19443264 5/1/1997 39.512 32.328 35.5608 29.0952 0.34441786 0.28179643 5/2/1997 39.853 32.607 35.8677 29.3463 0 0 5/3/1997 40.073 32.787 36.0657 29.5083 0 0 5/4/1997 40.172 32.868 36.1548 29.5812 0 0 5/5/1997 40.359 33.021 36.3231 29.7189 0 0 5/6/1997 40.524 33.156 36.4716 29.8404 0 0 5/7/1997 40.579 33.201 36.5211 29.8809 0 0 5/8/1997 40.777 33.363 36.6993 30.0267 0 0 5/9/1997 40.535 33.165 36.4815 29.8485 0.03215224 0.02630638 5/10/1997 40.238 32.922 36.2142 29.6298 0.11381306 0.09311977 5/11/1997 39.501 32.319 35.5509 29.0871 0.07262383 0.0594195 5/12/1997 39.578 32.382 35.6202 29.1438 0.02213449 0.01811004 5/13/1997 39.996 32.724 35.9964 29.4516 0.01249374 0.01022215 5/14/1997 40.359 33.021 36.3231 29.7189 0.04497941 0.03680133 5/15/1997 40.535 33.165 36.4815 29.8485 0.05644353 0.04618107 5/16/1997 40.689 33.291 36.6201 29.9619 0.14121217 0.11553723 5/17/1997 40.7 33.3 36.63 29.97 0.11978656 0.09800719 5/18/1997 40.7 33.3 36.63 29.97 0.05901337 0.04828367

188 Table 58. Values used to test change in 10% of boundary conditions of Solids 2 (Silt) for Lake Texoma (Continuation) Red River Washita River Big Mineral Creek Date Plus 10% Minus 10% Plus 10% Minus 10% Plus 10% Minus 10% 5/18/1997 40.7 33.3 36.63 29.97 0.05901337 0.04828367 5/19/1997 40.777 33.363 36.6993 30.0267 0.0171395 0.01402322 5/20/1997 40.689 33.291 36.6201 29.9619 0.17199608 0.14072406 5/21/1997 40.249 32.931 36.2241 29.6379 0.29763642 0.2435207 5/22/1997 39.908 32.652 35.9172 29.3868 0.18452729 0.15097687 5/23/1997 40.15 32.85 36.135 29.565 0.0731848 0.05987847 5/24/1997 40.447 33.093 36.4023 29.7837 0.10205281 0.08349775 5/25/1997 40.623 33.237 36.5607 29.9133 0.14015701 0.11467392 5/26/1997 40.766 33.354 36.6894 30.0186 0.67467111 0.55200363 5/27/1997 40.942 33.498 36.8478 30.1482 8.30417952 6.7943287 5/28/1997 41.085 33.615 36.9765 30.2535 11.1865421 9.15262538 5/29/1997 41.162 33.678 37.0458 30.3102 6.50747853 5.32430062 5/30/1997 40.975 33.525 36.8775 30.1725 6.17869349 5.05529467 5/31/1997 40.381 33.039 36.3429 29.7351 10.7234749 8.77375217

Table 59. Values used to test change in 10% of boundary conditions of Solids 3 (Sand) for Lake Texoma

Red River Washita River Big Mineral Creek Date Plus 10% Minus 10% Plus 10% Minus 10% Plus 10% Minus 10% 3/1/1997 26.18 21.42 21.78 17.82 101.238018 82.8311054 3/2/1997 26.136 21.384 21.329 17.451 30.8810218 25.2662905 3/3/1997 26.224 21.456 20.889 17.091 104.558296 85.547697 3/4/1997 26.301 21.519 20.438 16.722 342.612217 280.319087 3/5/1997 26.345 21.555 19.987 16.353 224.188332 183.426817 3/6/1997 26.4 21.6 19.547 15.993 114.178824 93.4190382 3/7/1997 26.477 21.663 19.096 15.624 61.4024069 50.2383329 3/8/1997 26.51 21.69 18.645 15.255 18.2215424 14.9085347 3/9/1997 26.587 21.753 18.205 14.895 4.00071997 3.27331634 3/10/1997 26.631 21.789 17.754 14.526 2.20455101 1.80372355 3/11/1997 26.697 21.843 17.314 14.166 4.06319893 3.32443549 3/12/1997 26.763 21.897 16.863 13.797 2.43331306 1.9908925 3/13/1997 26.818 21.942 16.412 13.428 1.69677935 1.38827401 3/14/1997 26.895 22.005 15.972 13.068 5.47655964 4.48082152 3/15/1997 26.961 22.059 15.521 12.699 3.49352223 2.85833637 3/16/1997 27.049 22.131 15.07 12.33 1.06468119 0.87110279 3/17/1997 27.115 22.185 14.63 11.97 0.23426268 0.19166946 3/18/1997 27.159 22.221 14.179 11.601 0.23981018 0.19620833 3/19/1997 27.159 22.221 13.728 11.232 0.84920913 0.69480747 3/20/1997 27.192 22.248 13.288 10.872 0 0 3/21/1997 27.236 22.284 12.837 10.503 0 0 3/22/1997 27.291 22.329 12.386 10.134 0 0 3/23/1997 27.302 22.338 11.946 9.774 0 0 3/24/1997 27.368 22.392 11.495 9.405 0 0

189 Table 59. Values used to test change in 10% of boundary conditions of Solids 3 (Sand) for Lake Texoma (Continuation) Red River Washita River Big Mineral Creek Minus Minus Date Plus 10% 10% Plus 10% 10% Plus 10% Minus 10% 3/25/1997 27.401 22.419 11.055 9.045 2.55288652 2.08872533 3/26/1997 27.401 22.419 10.604 8.676 9.03686872 7.39380168 3/27/1997 27.445 22.455 10.153 8.307 5.7664092 4.71797116 3/28/1997 27.467 22.473 9.713 7.947 1.75748803 1.43794475 3/29/1997 27.511 22.509 9.262 7.578 0.38770415 0.31721248 3/30/1997 27.588 22.572 8.811 7.209 0 0 3/31/1997 27.643 22.617 8.371 6.849 0 0 4/1/1997 34.947 28.593 7.92 6.48 0 0 4/2/1997 34.98 28.62 7.986 6.534 0 0 4/3/1997 34.98 28.62 8.052 6.588 0 0 4/4/1997 34.628 28.332 8.118 6.642 0 0 4/5/1997 34.617 28.323 8.184 6.696 4.5241034 3.70153915 4/6/1997 34.782 28.458 8.25 6.75 40.6702454 33.2756553 4/7/1997 33.451 27.369 8.316 6.804 50.936357 41.6752012 4/8/1997 32.714 26.766 8.382 6.858 16.0255117 13.1117823 4/9/1997 32.923 26.937 8.448 6.912 5.3795112 4.40141826 4/10/1997 33.275 27.225 8.514 6.966 11.5607552 9.45879967 4/11/1997 33.242 27.198 8.58 7.02 53.7303732 43.9612145 4/12/1997 32.681 26.739 8.646 7.074 261.182332 213.694636 4/13/1997 32.263 26.397 8.712 7.128 353.137669 288.93082 4/14/1997 32.098 26.262 8.778 7.182 210.807991 172.479265 4/15/1997 32.351 26.469 8.844 7.236 63.5343113 51.9826184 4/16/1997 32.659 26.721 8.91 7.29 14.073258 11.5144838 4/17/1997 33.143 27.117 8.976 7.344 0 0 4/18/1997 33.231 27.189 9.042 7.398 0.46453214 0.38007175 4/19/1997 33.275 27.225 9.108 7.452 1.53002671 1.25184003 4/20/1997 33.33 27.27 9.174 7.506 0.90328323 0.73904991 4/21/1997 33.44 27.36 9.24 7.56 0 0 4/22/1997 33.517 27.423 9.306 7.614 1.18143355 0.96662745 4/23/1997 33.605 27.495 9.372 7.668 4.1070047 3.36027657 4/24/1997 33.759 27.621 9.438 7.722 2.75846949 2.25692958 4/25/1997 33.715 27.585 9.504 7.776 1.00533703 0.82254848 4/26/1997 33.418 27.342 9.57 7.83 2.85351406 2.33469332 4/27/1997 32.384 26.496 9.636 7.884 37.1594142 30.4031571 4/28/1997 31.724 25.956 9.702 7.938 156.7027 128.2113 4/29/1997 31.559 25.821 9.768 7.992 114.45271 93.6431264 4/30/1997 31.79 26.01 9.834 8.046 35.0366907 28.6663833 5/1/1997 31.614 25.866 9.9 8.1 8.26602871 6.7631144 5/2/1997 31.878 26.082 9.878 8.082 0 0 5/3/1997 32.054 26.226 9.856 8.064 0 0 5/4/1997 32.142 26.298 9.834 8.046 0 0 5/5/1997 32.285 26.415 9.812 8.028 0 0 5/6/1997 32.417 26.523 9.79 8.01 0 0 5/7/1997 32.461 26.559 9.768 7.992 0 0

190 Table 59. Values used to test change in 10% of boundary conditions of Solids 3 (Sand) for Lake Texoma (Continuation) Red River Washita River Big Mineral Creek Minus Minus Date Plus 10% 10% Plus 10% 10% Plus 10% Minus 10% 5/8/1997 32.615 26.685 9.746 7.974 0 0 5/9/1997 32.428 26.532 9.724 7.956 0.77165382 0.63135312 5/10/1997 32.197 26.343 9.702 7.938 2.73151335 2.23487456 5/11/1997 31.603 25.857 9.68 7.92 1.74297195 1.42606796 5/12/1997 31.658 25.902 9.658 7.902 0.53122778 0.43464091 5/13/1997 31.999 26.181 9.636 7.884 0.29984977 0.24533163 5/14/1997 32.285 26.415 9.614 7.866 1.07950583 0.88323204 5/15/1997 32.428 26.532 9.592 7.848 1.35464471 1.10834567 5/16/1997 32.549 26.631 9.57 7.83 3.389092 2.77289346 5/17/1997 32.56 26.64 9.548 7.812 2.8748775 2.3521725 5/18/1997 32.56 26.64 9.526 7.794 1.41632099 1.15880808 5/19/1997 32.626 26.694 9.504 7.776 0.4113479 0.33655737 5/20/1997 32.549 26.631 9.482 7.758 4.12790584 3.3773775 5/21/1997 32.197 26.343 9.46 7.74 7.14327402 5.84449692 5/22/1997 31.922 26.118 9.438 7.722 4.4286549 3.62344492 5/23/1997 32.12 26.28 9.416 7.704 1.75643517 1.43708332 5/24/1997 32.351 26.469 9.394 7.686 2.44926746 2.0039461 5/25/1997 32.505 26.595 9.372 7.668 3.36376821 2.75217399 5/26/1997 32.615 26.685 9.35 7.65 16.1921066 13.2480872 5/27/1997 32.747 26.793 9.328 7.632 199.300308 163.063889 5/28/1997 32.868 26.892 9.306 7.614 268.477011 219.663009 5/29/1997 32.934 26.946 9.284 7.596 156.179485 127.783215 5/30/1997 32.78 26.82 9.262 7.578 148.288644 121.327072 5/31/1997 32.307 26.433 9.24 7.56 257.363397 210.570052 5/9/1997 32.428 26.532 9.724 7.956 0.77165382 0.63135312 5/10/1997 32.197 26.343 9.702 7.938 2.73151335 2.23487456 5/11/1997 31.603 25.857 9.68 7.92 1.74297195 1.42606796 5/12/1997 31.658 25.902 9.658 7.902 0.53122778 0.43464091

191 Table 60. Values used to test change in 30% of boundary conditions of DO for Lake Texoma

Red River Washita River Big Mineral Creek Plus Minus Plus Minus Plus Minus Date 30% 30% Date 30% 30% Date 30% 30% 1/23/1997 15.99 8.61 1/31/1997 17.94 9.66 1/20/1997 14.911 8.029 2/19/1997 15.6 8.4 2/20/1997 12.74 6.86 3/11/1997 11.271 6.069 3/17/1997 16.25 8.75 3/19/1997 13.13 7.07 4/15/1997 11.856 6.384 4/23/1997 10.79 5.81 4/23/1997 6.11 3.29 5/23/1997 9.815 5.285 5/21/1997 9.49 5.11 5/20/1997 10.01 5.39 6/27/1997 13.195 7.105 6/18/1997 9.36 5.04 6/17/1997 9.88 5.32 7/18/1997 13.013 7.007 7/25/1997 10.01 5.39 7/23/1997 10.53 5.67 8/21/1997 18.915 10.185 8/6/1997 7.41 3.99 8/7/1997 9.88 5.32 9/26/1997 10.4 5.6 9/24/1997 10.79 5.81 9/25/1997 10.92 5.88 10/30/1997 11.518 6.202 10/21/1997 15.73 8.47 11/5/1997 12.61 6.79 11/30/1997 13.676 7.364 11/4/1997 15.86 8.54 12/10/1997 14.56 7.84 12/18/1997 13.039 7.021 12/10/1997 14.95 8.05

192

APPENDIX I

VALUES FOR TESTING SENSITIVITY TO LOADING CONDITIONS

193 Table 61. Values of chloride loads at WASP segment WRT22 to test sensitivity of Lake Texoma model to changes in loading conditions

Date Plus 50% Minus 50% Date Plus 50% Minus 50% 3/1/1997 7.035 2.345 4/16/1997 241.5 80.5 3/2/1997 0.0153 0.0051 4/17/1997 109.65 36.55 3/3/1997 360 120 4/18/1997 0.00765 0.00255 3/4/1997 2085 695 4/19/1997 0.05355 0.01785 3/5/1997 205.5 68.5 4/20/1997 0.07185 0.02395 3/6/1997 217.5 72.5 4/21/1997 0.05655 0.01885 3/7/1997 15.3 5.1 4/22/1997 0.174 0.058 3/8/1997 13.335 4.445 4/23/1997 0.5715 0.1905 3/9/1997 63 21 4/24/1997 0.888 0.296 3/10/1997 0.04005 0.01335 4/25/1997 0.729 0.243 3/11/1997 0.324 0.108 4/26/1997 0.627 0.209 3/12/1997 0.603 0.201 4/27/1997 1.326 0.442 3/13/1997 0.5175 0.1725 4/28/1997 116.55 38.85 3/14/1997 0.4425 0.1475 4/29/1997 732 244 3/15/1997 0.525 0.175 4/30/1997 990 330 3/16/1997 0.4275 0.1425 5/1/1997 616.5 205.5 3/17/1997 0.2235 0.0745 5/2/1997 291 97 3/18/1997 0.10125 0.03375 5/3/1997 135.3 45.1 3/19/1997 0.0459 0.0153 5/4/1997 60.75 20.25 3/20/1997 6.255E-06 2.085E-06 5/5/1997 27.6 9.2 3/21/1997 0.00004065 0.00001355 5/6/1997 12.81 4.27 3/22/1997 0.000054 0.000018 5/7/1997 5.61 1.87 3/23/1997 0.00003195 0.00001065 5/8/1997 0.012525 0.004175 3/24/1997 1.4175E-05 4.725E-06 5/9/1997 0.315 0.105 3/25/1997 0.02535 0.00845 5/10/1997 1.1445 0.3815 3/26/1997 0.3045 0.1015 5/11/1997 1.4835 0.4945 3/27/1997 0.777 0.259 5/12/1997 1.0155 0.3385 3/28/1997 0.735 0.245 5/13/1997 0.02205 0.00735 3/29/1997 0.384 0.128 5/14/1997 0.1437 0.0479 3/30/1997 0.1725 0.0575 5/15/1997 0.204 0.068 3/31/1997 0.07815 0.02605 5/16/1997 0.1635 0.0545 4/1/1997 0.03525 0.01175 5/17/1997 0.12705 0.04235 4/2/1997 0.01605 0.00535 5/18/1997 0.10455 0.03485 4/3/1997 0.00975 0.00325 5/19/1997 0.0696 0.0232 4/4/1997 0.0831 0.0277 5/20/1997 0.03675 0.01225 4/5/1997 0.10545 0.03515 5/21/1997 0.4125 0.1375 4/6/1997 0.765 0.255 5/22/1997 1.0245 0.3415 4/7/1997 1.221 0.407 5/23/1997 0.96 0.32 4/8/1997 0.876 0.292 5/24/1997 0.51 0.17 4/9/1997 0.438 0.146 5/25/1997 0.234 0.078 4/10/1997 0.264 0.088 5/26/1997 0.1575 0.0525 4/11/1997 0.2925 0.0975 5/27/1997 0.513 0.171 4/12/1997 33.3 11.1 5/28/1997 1.3335 0.4445 4/13/1997 355.5 118.5 5/29/1997 1.3245 0.4415 4/14/1997 666 222 5/30/1997 1.0065 0.3355 4/15/1997 505.5 168.5 5/31/1997 95.4 31.8

194 Table 62. Values of TSS loads at several WASP segments to test sensitivity of Lake Texoma model to changes in plus 50% of loading conditions

Plus 50 % of SST Loadings Date RT9 ML17 BMC32 ML30 WRT27 WR21 WRT22 RR1 to RR8 WR20 03/01/97 223 9 21301 0 177 22 6004 6126 231416 03/02/97 139 0 1792 646 0 1065 5065 2519 91393 03/03/97 7234 10045 22783 21065 9016 7029 55277 4478 83964 03/04/97 35031 50709 270471 52496 52326 12074 122746 33756 191990 03/05/97 3016 3343 111725 2992 5153 563 24400 21660 157799 03/06/97 2756 4 27371 5971 5467 836 1791 14993 76986 03/07/97 176 0 7511 60 384 11 11 6192 22666 03/08/97 0 0 597 0 335 94 94 2551 7639 03/09/97 0 0 25 0 1582 241 241 944 4133 03/10/97 1 2 7 7 1 0 8 0 5 03/11/97 1 0 26 9 0 0 3 2 26 03/12/97 0 0 9 0 0 0 1 3 34 03/13/97 1 3 4 6 1 1 9 2 24 03/14/97 3 15 49 15 6 1 16 3 26 03/15/97 0 1 19 0 0 0 3 4 27 03/16/97 0 0 2 0 0 0 0 2 16 03/17/97 0 0 0 0 0 0 0 1 7 03/18/97 0 0 0 0 0 0 0 0 3 03/19/97 0 0 1 0 0 0 0 0 1 03/20/97 0 0 0 0 0 0 0 0 1 03/21/97 0 0 0 0 0 0 0 0 2 03/22/97 0 0 0 0 0 0 0 0 1 03/23/97 0 0 0 0 0 0 0 0 0 03/24/97 0 0 0 0 0 0 0 0 0 03/25/97 2 9 10 26 3 1 17 0 5 03/26/97 11 41 138 62 13 2 57 7 27 03/27/97 1 3 54 1 1 0 11 14 40 03/28/97 0 0 5 0 0 0 0 9 25 03/29/97 0 0 0 0 0 0 0 4 11 03/30/97 0 0 0 0 0 0 0 1 4 03/31/97 0 0 0 0 0 0 0 0 2 04/01/97 0 0 0 0 0 0 0 0 1 04/02/97 0 0 0 0 0 0 0 0 0 04/03/97 0 0 0 0 0 0 2 0 1 04/04/97 0 0 0 0 0 1 22 0 9 04/05/97 7 26 33 56 6 2 36 3 19 04/06/97 7 36 3182 56 12 1 43 18 83 04/07/97 0 2 5088 1 1 0 7 22 79 04/08/97 0 0 457 0 0 0 0 12 38 04/09/97 0 0 47 0 0 0 1 5 16 04/10/97 0 0 231 0 0 0 3 3 13 04/11/97 3 12 5687 100 7 2 66 26 30 04/12/97 12 13853 153613 24537 14265 1600 29983 2337 14010 04/13/97 3 3971 288081 14017 13344 1290 15148 10348 69794 04/14/97 0 137 98274 148 882 18 917 2027 66730

195 Table 62. Values of TSS loads at several WASP segments to test sensitivity of Lake Texoma model to changes in plus 50% of loading conditions (Continuation) Plus 50 % of SST Loadings Date RT9 ML17 BMC32 ML30 WRT27 WR21 WRT22 RR1 to RR8 WR20 04/15/97 0 0 8065 0 2 0 14 62 30300 04/16/97 0 0 348 0 0 0 0 3 11134 04/17/97 0 0 0 0 0 0 0 1 4139 04/18/97 0 0 0 2 1 0 0 0 1 04/19/97 0 0 3 4 0 0 0 1 6 04/20/97 0 0 1 0 0 0 0 1 4 04/21/97 0 0 0 0 0 0 5 1 4 04/22/97 1 5 2 8 2 1 17 1 18 04/23/97 4 23 27 18 6 1 35 6 46 04/24/97 0 1 12 1 1 0 7 12 52 04/25/97 1 0 1 1 0 0 11 11 33 04/26/97 7 11 13 36 3 1 40 13 45 04/27/97 12 62 2636 2430 4347 63 5333 69 285 04/28/97 4 4434 52956 13115 22597 4321 57852 2234 18301 04/29/97 0 511 27508 174 1922 93 14793 1461 29302 04/30/97 0 1 2332 0 5 0 614 915 28221 05/01/97 0 0 115 0 0 0 0 408 14485 05/02/97 0 0 0 0 0 0 0 172 6347 05/03/97 0 0 0 0 0 0 0 64 2795 05/04/97 0 0 0 0 0 0 0 24 1215 05/05/97 0 0 0 0 0 0 0 9 512 05/06/97 0 0 0 0 0 0 0 2 233 05/07/97 0 0 0 0 0 0 0 0 102 05/08/97 0 0 0 0 0 1 2 0 4 05/09/97 1 6 1 16 3 3 11 1 43 05/10/97 4 27 11 40 12 1 6 11 94 05/11/97 0 2 4 0 1 0 1 23 74 05/12/97 0 0 0 0 0 0 0 15 36 05/13/97 0 0 0 0 0 0 0 0 2 05/14/97 0 0 2 0 0 0 0 0 12 05/15/97 0 0 3 3 0 0 0 0 9 05/16/97 1 1 18 8 1 0 1 1 6 05/17/97 1 7 13 4 4 0 3 1 6 05/18/97 0 0 3 0 0 0 1 1 4 05/19/97 0 0 0 0 0 0 0 1 2 05/20/97 6 8 27 64 2 1 4 4 4 05/21/97 5 37 85 32 8 1 11 19 45 05/22/97 1 7 31 4 1 0 3 22 84 05/23/97 0 0 5 4 0 0 0 12 52 05/24/97 1 0 9 10 1 0 0 5 20 05/25/97 1 5 18 6 7 1 1 4 12 05/26/97 2 7 467 40 1 1 5 109 15 05/27/97 6 8 87421 12731 26 2 24 14182 1471 05/28/97 4 2821 162692 23629 6080 5 20 9255 12199

196

Table 62. Values of TSS loads at several WASP segments to test sensitivity of Lake Texoma model to changes in plus 50 % of loading conditions (Continuation) Plus 50 % of SST Loadings RR1 to Date RT9 ML17 BMC32 ML30 WRT27 WR21 WRT22 RR8 WR20 05/29/97 1 497 52588 5222 752 59 62 1593 10614 05/30/97 3 9015 47201 34968 5105 1192 1192 5336 18906 05/31/97 539 79843 148967 64516 36575 4070 3474 10515 68834

Table 63. Values of TSS loads at several WASP segments to test sensitivity of Lake Texoma model to changes in minus 50 % of loading conditions

Minus 50% of TSS Loadings RR1 to Date RT9 ML17 BMC32 ML30 WRT27 WR21 WRT22 RR8 WR20 03/01/97 74 3 7100 0 59 7 2001 2042 77139 03/02/97 46 0 597 215 0 355 1688 840 30464 03/03/97 2411 3348 7594 7022 3005 2343 18426 1493 27988 03/04/97 11677 16903 90157 17499 17442 4025 40915 11252 63997 03/05/97 1005 1114 37242 997 1718 188 8133 7220 52600 03/06/97 919 1 9124 1990 1822 279 597 4998 25662 03/07/97 59 0 2504 20 128 4 4 2064 7555 03/08/97 0 0 199 0 112 31 31 850 2546 03/09/97 0 0 8 0 527 80 80 315 1378 03/10/97 0 1 2 2 0 0 3 0 2 03/11/97 0 0 9 3 0 0 1 1 9 03/12/97 0 0 3 0 0 0 0 1 11 03/13/97 0 1 1 2 0 0 3 1 8 03/14/97 1 5 16 5 2 0 5 1 9 03/15/97 0 0 6 0 0 0 1 1 9 03/16/97 0 0 1 0 0 0 0 1 5 03/17/97 0 0 0 0 0 0 0 0 2 03/18/97 0 0 0 0 0 0 0 0 1 03/19/97 0 0 0 0 0 0 0 0 0 03/20/97 0 0 0 0 0 0 0 0 0 03/21/97 0 0 0 0 0 0 0 0 1 03/22/97 0 0 0 0 0 0 0 0 0 03/23/97 0 0 0 0 0 0 0 0 0 03/24/97 0 0 0 0 0 0 0 0 0 03/25/97 1 3 3 9 1 0 6 0 2 03/26/97 4 14 46 21 4 1 19 2 9 03/27/97 0 1 18 0 0 0 4 5 13 03/28/97 0 0 2 0 0 0 0 3 8 03/29/97 0 0 0 0 0 0 0 1 4 03/30/97 0 0 0 0 0 0 0 0 1 03/31/97 0 0 0 0 0 0 0 0 1 04/01/97 0 0 0 0 0 0 0 0 0 04/02/97 0 0 0 0 0 0 0 0 0

197 Table 63. Values of TSS loads at several WASP segments to test sensitivity of Lake Texoma model to changes in minus 50 % of loading conditions (Continuation) Minus 50% of TSS Loadings RR1 to Date RT9 ML17 BMC32 ML30 WRT27 WR21 WRT22 RR8 WR20 04/03/97 0 0 0 0 0 0 1 0 0 04/04/97 0 0 0 0 0 0 7 0 3 04/05/97 2 9 11 19 2 1 12 1 6 04/06/97 2 12 1061 19 4 0 14 6 28 04/07/97 0 1 1696 0 0 0 2 7 26 04/08/97 0 0 152 0 0 0 0 4 13 04/09/97 0 0 16 0 0 0 0 2 5 04/10/97 0 0 77 0 0 0 1 1 4 04/11/97 1 4 1896 33 2 1 22 9 10 04/12/97 4 4618 51204 8179 4755 533 9994 779 4670 04/13/97 1 1324 96027 4672 4448 430 5049 3449 23265 04/14/97 0 46 32758 49 294 6 306 676 22243 04/15/97 0 0 2688 0 1 0 5 21 10100 04/16/97 0 0 116 0 0 0 0 1 3711 04/17/97 0 0 0 0 0 0 0 0 1380 04/18/97 0 0 0 1 0 0 0 0 0 04/19/97 0 0 1 1 0 0 0 0 2 04/20/97 0 0 0 0 0 0 0 0 1 04/21/97 0 0 0 0 0 0 2 0 1 04/22/97 0 2 1 3 1 0 6 0 6 04/23/97 1 8 9 6 2 0 12 2 15 04/24/97 0 0 4 0 0 0 2 4 17 04/25/97 0 0 0 0 0 0 4 4 11 04/26/97 2 4 4 12 1 0 13 4 15 04/27/97 4 21 879 810 1449 21 1778 23 95 04/28/97 1 1478 17652 4372 7532 1440 19284 745 6100 04/29/97 0 170 9169 58 641 31 4931 487 9767 04/30/97 0 0 777 0 2 0 205 305 9407 05/01/97 0 0 38 0 0 0 0 136 4828 05/02/97 0 0 0 0 0 0 0 57 2116 05/03/97 0 0 0 0 0 0 0 21 932 05/04/97 0 0 0 0 0 0 0 8 405 05/05/97 0 0 0 0 0 0 0 3 171 05/06/97 0 0 0 0 0 0 0 1 78 05/07/97 0 0 0 0 0 0 0 0 34 05/08/97 0 0 0 0 0 0 1 0 1 05/09/97 0 2 0 5 1 1 4 0 14 05/10/97 1 9 4 13 4 0 2 4 31 05/11/97 0 1 1 0 0 0 0 8 25 05/12/97 0 0 0 0 0 0 0 5 12 05/13/97 0 0 0 0 0 0 0 0 1 05/14/97 0 0 1 0 0 0 0 0 4 05/15/97 0 0 1 1 0 0 0 0 3 05/16/97 0 0 6 3 0 0 0 0 2 05/17/97 0 2 4 1 1 0 1 0 2

198 Table 63. Values of TSS loads at several WASP segments to test sensitivity of Lake Texoma model to changes in minus 50 % of loading conditions (Continuation) Minus 50% of TSS Loadings RR1 to Date RT9 ML17 BMC32 ML30 WRT27 WR21 WRT22 RR8 WR20 04/03/97 0 0 0 0 0 0 1 0 0 05/19/97 0 0 0 0 0 0 0 0 1 05/20/97 2 3 9 21 1 0 1 1 1 05/21/97 2 12 28 11 3 0 4 6 15 05/22/97 0 2 10 1 0 0 1 7 28 05/23/97 0 0 2 1 0 0 0 4 17 05/24/97 0 0 3 3 0 0 0 2 7 05/25/97 0 2 6 2 2 0 0 1 4 05/26/97 1 2 156 13 0 0 2 36 5 05/27/97 2 3 29140 4244 9 1 8 4727 490 05/28/97 1 940 54231 7876 2027 2 7 3085 4066 05/29/97 0 166 17529 1741 251 20 21 531 3538 05/30/97 1 3005 15734 11656 1702 397 397 1779 6302 05/31/97 180 26614 49656 21505 12192 1357 1158 3505 22945

Table 64. BOD loadings for testing sensitivity to 50 % change of loading conditions in Tocoma Reservoir model

50% minus 50% plus BOD Loading (Kg/day) by BOD Loading (Kg/day) by WASP Segment ID Days of Flooding # Segmento WASP Days of Flooding 1 to 15 35 al 82 1 al 15 35 al 82 5 8.8 32.6 5 26.3 97.8 6 0.7 3.1 6 2.2 9.4 7 3.0 12.8 7 8.9 38.4 8 0.3 1.4 8 1.0 4.1 9 3.6 15.3 9 10.7 45.9 10 2.6 11.2 10 7.8 33.7 11 2.7 11.5 11 8.0 34.5 12 3.7 16.0 12 11.2 48.0 13 13.7 59.1 13 41.2 177.2 14 5.5 23.8 14 16.6 71.4 15 10.9 46.9 15 32.7 140.7 16 11.8 50.9 16 35.5 152.8 17 9.7 41.8 17 29.1 125.4 18 10.2 43.7 18 30.5 131.0 19 2.7 11.8 19 8.2 35.4 20 4.9 20.9 20 14.6 62.8 21 6.2 26.5 21 18.5 79.6 22 1.0 4.4 22 3.1 13.1 23 1.2 5.0 23 3.5 15.1 24 0.4 1.9 24 1.3 5.6 25 1.7 7.4 25 5.2 22.3 26 29.8 128.2 26 89.4 384.6

199 Table 64. BOD loadings for testing sensitivity to 50 % change of loading conditions in Tocoma Reservoir model (Continuation) 50% minus 50% plus BOD Loading (Kg/day) by BOD Loading (Kg/day) by WASP Segment ID Days of Flooding # Segmento WASP Days of Flooding 1 to 15 35 al 82 1 al 15 35 al 82 27 1.5 6.2 27 4.4 18.7 28 8.7 37.4 28 26.1 112.3 29 8.5 36.6 29 25.6 109.9 30 9.6 41.5 30 28.9 124.5 31 13.7 58.7 31 41.0 176.1 32 7.4 31.7 32 22.1 95.1 33 6.8 29.2 33 20.4 87.5 34 4.9 20.9 34 14.6 62.6 35 9.6 41.2 35 28.7 123.6 36 6.3 27.3 36 19.0 81.9 37 0.7 2.9 37 2.0 8.7 38 0.4 1.5 38 1.1 4.6 45 0.3 1.4 45 1.0 4.2 46 0.6 2.6 46 1.8 7.8 47 0.4 1.8 47 1.3 5.4 48 9.8 41.9 48 29.3 125.6 49 5.9 25.4 49 17.7 76.3 50 5.8 24.8 50 17.3 74.3

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