Water Quality Modeling of Lake Waco Using CE-QUAL …tiaer.tarleton.edu/pdf/TR0114.pdfWater quality...

USDA: Lake Waco-Bosque RiverInitiative

Water Quality Modeling of LakeWaco Using CE-QUAL-W2

for Assessment of PhosphorusControl Strategies

Joan D. Flowers, Larry M. Hauck, and Richard L. Kiesling

TR0114

December 2001

Texas Institute for Applied Environmental ResearchTarleton State University •Box T0410 •Tarleton Station •Stephenville, Texas 76402V254.968.9567 • F254.968.9568

Water Quality Modeling of Lake Waco Using CE-QUAL-W2

Acknowledgments

The Texas Institute for Applied Environmental Research (TIAER) acknowledges the United States Department of Agriculture-Natural Resources Conservation Service (USDA-NRCS) for providing the funding for this research. Water quality modeling under this project was a collaborative effort between research scientists at TIAER and Texas A&M Blackland Research and Extension Center (BREC). Modeling of the Lake Waco-Bosque River watershed was performed by Chinnisamy Santhi under the direction of Jimmy Williams and William Dugas of BREC in Temple, Texas.

The authors would like to acknowledge the assistance of the developers of the CE-QUAL-W2 model, Thomas Cole, of the U.S. Army Corp of Engineers, Waterways Experiment Station and Edward Buchak, of J.E. Edinger and Associates, Inc. for their assistance and guidance in applying the model to Lake Waco.

TIAER would also like to recognize the input and assistance of numerous scientists and technical experts including Rajeev Jain of Edinger and Associates and members of the Technical Work Group (TWG) assembled for this project. The TWG, whose purpose was to provide technical oversight of the project, consisted of 19 members including: Clyde Bohmfalk (Chairman), Texas Natural Resource Conservation Commission (TNRCC); Jim Davenport (TNRCC); Larry Koenig (TNRCC); Allison Woodall (TNRCC); Mel Vargas (formerly of TNRCC); Larry Mitchell (TNRCC); Marie Knipfer (TNRCC); Hari Krishna (TNRCC); Charles Maddox (TNRCC); James Moore, Texas State Soil and Water Conservation Board (TSSWCB); Beade Northcut (TSSWCB); Richard Garrett, City of Waco; Mike Jones, City of Waco; Allan Colwick, Natural Resources Conservation Service (NRCS); George Ward, University of Texas Center for Research in Water Resources (UT-CRWR); Mike Meadows, Brazos River Authority (BRA); Tom Conry (BRA); Joan Glass, Texas Parks and Wildlife Department (TPWD); and Shawneille Campbell, Environmental Protection Agency, Region VI (EPA).

The authors would also like to recognize the assistance to Jim Rogers, computer programmer-analyst with TIAER for programming support and development of preprocessing, postprocessing and interface programs for the modeling effort. Although too numerous to mention by name, we also acknowledge the TIAER field crew, laboratory personnel, data analysts, and GIS specialists for their role in supplying the data necessary for the application and calibration of the lake model.

USDA2.2

2


Abstract

The Texas Institute for Applied Environmental Research (TIAER) conducted modeling assessments of alternative phosphorus (P) control strategies for the USDA Lake Waco-Bosque River Initiative. This report documents the results of CE-QUAL-W2 model calibration for Lake Waco and long-term (32-year) simulations assessing the impact of different scenarios on lake water quality.

A water quality target of 10 ppb PO4-P was recommended and adopted by the stakeholder committee for Lake Waco. The 10 ppb PO4-P target was established in terms of a mean concentration over the algal “growing” season defined as April through September. The lake target was defined for summer mean PO4-P concentrations within the surface water layer near the drinking water intake for the City of Waco. Alternative P control strategies were assessed based on long-term simulations of lake water quality relative to target attainment.

A two-phased approach was utilized for calibration of CE-QUAL-W2 for Lake Waco. CE-QUAL-W2 and SWAT were first calibrated independent of one another (i.e., the independent calibration phase). SWAT and CE-QUAL-W2 models were then integrated through an intermediate interface program, which translated stream flow and watershed loads predicted by SWAT into inflow boundary conditions required by CE-QUAL-W2. The second phase of calibration entailed fine-tuning of the integrated SWAT/CE-QUAL-W2 models to conditions observed in Lake Waco. Observational data collected over a two-year period from July 11, 1996 through July 29, 1998 were used in both the independent and integrated calibrations.

CE-QUAL-W2 calibration focused on the prediction of summer mean PO4-P concentrations in the surface layer of model segment 8, which corresponded to the location where the water quality target was defined. Inspection of daily time series output and summer mean concentrations predicted by CE-QUAL-W2 indicated acceptable agreement between observed and predicted data. The magnitude of temporal variations in response to seasonal changes and storm pulses were successfully reproduced by CE-QUAL-W2. Summer mean surface PO4-P concentrations predicted by CE-QUAL-W2 were within ± 21 of observed data. Calibration statistics computed for surface and water column average concentrations over the entire calibration period were within acceptable tolerance ranges and depending on the constituent, indicated the quality of the calibration ranged from fair to very good. Results indicated that the calibration quality with respect to surface PO4-P concentrations in segment 8 was very good for both phases of calibration.

Alternative P controls were applied for the two major sources of P in the watershed— municipal wastewater treatment plants (WWTPs) and dairy manure application fields. P controls were assessed individually and as combination scenarios relative to target attainment. P controls which targeted nonpoint source loads from dairy manure application fields (haul off, P-rate, and P-diet scenarios) had the greatest impact on summer mean PO4-P concentrations in Lake Waco. Individual dairy BMPs were predicted to reduce summer mean PO4-P concentrations by 15 to 39 percent from future conditions depending upon BMP evaluated. Effluent limits imposed on WWTPs were predicted to reduce summer mean PO4-P in Lake Waco by less than 10 percent even under the strictest limit of 0.5 mg/L total phosphorus. Three combination scenarios were assessed relative to the lake target of 10 ppb. Exceedence probability plots of combination scenarios indicated that even with the most aggressive implementation of P control measures in the watershed, the lake target would be exceeded roughly 35 to 40 percent of the years.

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4

Contents

Chapter 1 Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Chapter 2 Description of Study Area and Lake Waco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Lake Waco-Bosque River Watershed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Lake Waco . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16Target Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18Identification of Phosphorus Sources in the Watershed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Chapter 3 Description of Lake Model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19Model Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19Modeling Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20Model Input Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Physical Representation and Segmentation Scheme. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22Model State Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25Collection of In-Lake Calibration Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26Initial Conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28Boundary Conditions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28Hydraulic and Kinetic Parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Chapter 4 Model Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35Water Balance Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Independent Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37Integrated Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Hydrodynamic/DO/Temperature Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37Water Quality Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42Post-Audit of Effects of Conservation Pool Increase . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

Chapter 5 Evaluation of Phosphorus Control Strategies. . . . . . . . . . . . . . . . . . . . . . . . . . . . 49Description of Phosphorus Control Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

Dairy Best Management Practices (BMPs) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50WWTP Phosphorus Control Strategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51Combination Scenarios . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51Effect of Individual Control Measures on Lake Water Quality. . . . . . . . . . . . . . . . . . . . . . . 51

Effect of Combined Control Measures on Lake Water Quality & Target Attainment . . . . . . . 52

Chapter 6 Summary and Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57Major Accomplishments of the Modeling Effort . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

Appendix A Model Calibration Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

Appendix B CE-QUAL-W2 Calibration Plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

Appendix C Procedure for Generating Exceedence Probability Charts . . . . . . . . . . . . . . 75

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Tables

Table 1 Land uses within the Lake Waco-Bosque River watershed.. . . . . . . . . . . . . . . . . . . . . . . . . . . 13Table 2 Morphometric characteristics of Lake Waco at conservation pool level. . . . . . . . . . . . . . . . . 15Table 3 Estimated percent contribution of phosphorus by source to the Bosque watershed . . . . . . 18Table 4 Water quality parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28Table 5 Final calibration values for temperature input parameters. . . . . . . . . . . . . . . . . . . . . . . . . . . 35Table 6 Final calibration values for phytoplankton input parameters. . . . . . . . . . . . . . . . . . . . . . . . . 36Table 7 Final calibration values for water quality input parameters. . . . . . . . . . . . . . . . . . . . . . . . . . 36

Table 8 Calibration results for segment 8 (July 11, 1996 - July 29, 1998). . . . . . . . . . . . . . . . . . . . . . . 46Table 9 Phosphorus reduction scenarios. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50Table 10 Percent change in predicted loading to Lake Waco for 1966 through 1997 . . . . . . . . . . . . . . 53

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Figures

Figure 1 Lake Waco-Bosque River watershed. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Figure 2 Lake Waco bathymetry. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15Figure 3 Diagram of calibration process.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21Figure 4 Lake Waco segmentation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23Figure 5 Lake Waco computational grid. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24Figure 6 Location of Lake Waco sampling sites used in model calibration. . . . . . . . . . . . . . . . . . . . . . 27Figure 7 Main tributary gauging stations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Figure 8 Watershed delineations for Lake Waco model inputs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30Figure 9 Time-series plots of water balance calibration.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38Figure 10 Time-series plots of predicted to observed water temperatures.. . . . . . . . . . . . . . . . . . . . . . . 40Figure 11 Time-series plots of predicted to observed dissolved oxygen concentrations. . . . . . . . . . . . 41Figure 12 Time-series plot of predicted and observed surface PO4-P data for segment 8. . . . . . . . . . . 43Figure 13 Time-series plot of predicted and observed surface chlorophyll-α data . . . . . . . . . . . . . . . . 43Figure 14 Calibration results for summer mean chl-α concentration for surface layer . . . . . . . . . . . . . 45Figure 15 Calibration results for summer mean PO4-P concentration for surface layer . . . . . . . . . . . . 45Figure 16 Percent reductions in predicted mean summer PO4-P concentrations . . . . . . . . . . . . . . . . . . 52Figure 17 Exceedence probability chart for summer mean chlorophyll-α concentration. . . . . . . . . . . 54Figure 18 Exceedence probability chart for summer mean PO4-P concentration. . . . . . . . . . . . . . . . . . 55

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CHAPTER 1

Introduction

The Lake Waco-Bosque River Watershed Initiative (Initiative) was established in 1996 through a cooperative agreement between the Texas Institute for Applies Environmental Research (TIAER) and United States Department of Agriculture, Natural Resources Conservation Service (USDA-NRCS). The purpose of the Initiative was to develop a community based “bottom-up” approach for dealing with water quality problems associated with livestock which satisfies broad water quality objectives, on both a state and national level. Several components of the Initiative are pertinent to information contained in this report including the establishment of a water quality monitoring program in the basin for the assessment of current conditions, identification of pollutant sources, determination of suitable water quality targets, and finally the assessment of different management or control strategies in attaining desired water quality targets.

The Initiative employed a holistic, watershed approach to the entire Lake Waco drainage area and a watershed stakeholder process through the Bosque River Advisory Committee (BRAC) to address the specific issue of nutrient enrichment. The North Bosque River, which drains approximately three-quarters of the Lake Waco watershed, is currently listed on the State’s Section 303(d) list for nutrient enrichment (TNRCC, 2000). Although the lake itself is not listed by the State for impaired water quality, the general perception of local residents is that excessive nutrients are responsible for the unpleasant taste of their tap water and increased treatment costs to remedy the taste and odor problems.

This report documents the water quality modeling of Lake Waco that was performed for the Initiative. Although the bulk of this report documents the model calibration process and underlying assumptions, the more meaningful outcome of the modeling exercise is the assessment of various control strategies with respect to target attainment. The assessment results represented the culmination of the modeling effort and when condensed into exceedence probability plots were effective tools in conveying the “bottom-line” to members of the BRAC.

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Water Quality Monitoring of Lake Waco Using CE-QUAL-W2

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CHAPTER 2

Description of Study Areaand Lake Waco

Lake Waco-Bosque River WatershedThe Lake Waco-Bosque River watershed (Figure 1) encompasses 1,660 square miles, spanning portions of 6 counties in the Brazos River Basin. Four major tributaries are located in the watershed including the North, Middle and South Bosque Rivers and Hog Creek. Numerous water quality problems have been cited on the North Bosque including elevated nutrients and fecal coliform levels. The region’s most conspicuous nonpoint source contributor, the dairy industry, and municipal wastewater treatment plants have been targeted as the probable causes. The Middle and South Bosque Rivers, while receiving less attention, are not without problems. Elevated nitrogen levels associated with nonpoint source pollution from cropland areas threaten water quality in these watersheds (TNRCC, 1996).

Land use in the watershed (Table 1) is classified primarily as woodland or rangeland, comprising roughly 63 percent of the watershed area (McFarland and Hauck, 1999). Pasture and cropland areas comprise the next largest land use categories, covering 14 and 17 percent of the watershed, respectively. Pasture and cropland acreage receiving dairy waste were classified as a separate land use category. Dairy waste application fields and urban areas each comprised about two percent of the watershed.

Table 1 Land uses within the Lake Waco-Bosque River watershed. Source: McFarland and Hauck (1999)

Land Use Category Percent of the Watershed

Wood/range 63%

Pasture 14%

Row crop 15%

Non-row crop 2%

Dairy waste application 2%

Urban 2%

Water < 1%

Other < 1%

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Figure 1 Lake Waco-Bosque River watershed.

Lake WacoLake Waco1 is a highly productive, polymictic reservoir (Kimmel, 1969) located in McLennan County. The reservoir which was impounded in 1965, is relatively young, but has experienced accelerated eutrophication, responsible for the shift in it’s trophic status from mesotrophic (Lind, 1979 & 1986) to eutrophic (Roark & Lind, 1988). Designated uses for the reservoir, classified as Segment 1225, include contact recreation, high aquatic life, and public water supply, serving an estimated 140,000 residents of Waco and nearby communities. The reservoir is also operated as a flood control reservoir by the United States Army Corp of Engineers (USACE), which shares water rights with the Brazos River Authority (BRA) for the conservation storage.

1 Although technically a misnomer, this water body is commonly referred to as “Lake Waco” (a.k.a. Waco Reservoir) in much of the published literature and maps of the area (USGS, 1975) and its common usage is continued throughout this report.

Waco

0 32

Kilometers

Hamilton Co.

Comanche Co.

Bosque Co.

Coryell Co.

McLennan Co.

Somervell Co.

Erath Co.

Hico

LakeWaco

Stephenville

Dublin

Iredell

Meridian

Clifton

Cranfils Gap

Valley Mills

Crawford

McGregor

Hog Creek

North Bosque

Duffau C

reek

Green Creek

North Bosque

North Bosque

Sout

h Bo

sque

Middle Bosque

Neils Creek

Meridian Creek

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Chapter 2 Description of Study Area and Lake Waco

Lake Waco is relatively shallow, ranging in depth from approximately 3 meters in the riverine portions of the arms to 23 meters near the dam (Figure 2). A recent volumetric survey by Texas Water Development Board (Sullivan et al., 1995) determined that the volume had declined from its original capacity of 152,500 acre-feet in 1965 to 144,830 acre-feet in 1995. The TWDB survey reported a surface area of 7,194 acres at conservation pool elevation and a maximum depth of 76 feet near the dam. A sediment accumulation rate of 164.8 acre-feet per year was estimated for the 25 year period between 1970 to 1995. Table 2 lists some of the morphometric characteristics of Lake Waco.

Figure 2 Lake Waco bathymetry.

Table 2 Morphometric characteristics of Lake Waco at conservation pool level.(adapted from Sullivan et al., 1995)

Conservation pool elevation 138.7 m (455 ft.)Surface area 2911 ha (7194 Ac)Volume 179x106 m3 (144,830 Ac-ft.)Maximum depth 23 m (76 ft.)Mean depth 6 m (20 ft.)Length of shoreline 91 km (60 mi)

0 5 10

Kilometers

0-33-66-99-1212-1515-18>18

Islands

Depth below conservation pool(meters)

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One of the more prominent features of Lake Waco’s morphometry is due to its location at the confluence of the North Bosque River with the Middle and South Bosque Rivers resulting in a “L” shape with two branches oriented at almost right angles to each other. Inflows from the North Bosque River enter the northern most arm of the lake and converge with inflows from the Middle and South Bosque Rivers and Hog Creek, which form the southern arm.

A second feature of Lake Waco important in the understanding of reservoir behavior is the uneven distribution of flows and nutrients entering through each arm. This uneven distribution is due to marked differences in the size of the drainage basins along with differences in their soil and land use characteristics. As a result, the north arm of the reservoir dominates the inflow to the reservoir. For the period of Lake Waco monitoring used in this report (July 11, 1996 to July 29, 1998), approximately three-quarters of the flow entered through the north arm. The north arm is the major supplier of phosphorus to the lake ecosystem contributing an estimated 78 percent of the soluble phosphorus and 73 percent of the total phosphorus load to Lake Waco (McFarland and Hauck, 1999). The south arm contributes a significant portion of the nitrogen load to the lake due to the influence of row-crop agriculture. Row-crop agriculture was estimated to be the largest contributing source of total nitrogen (TN), suppling approximately 49 percent of the TN load. Land use estimates indicated that 93 percent of the row-crop agriculture in the watershed drains into the south arm of Lake Waco (McFarland and Hauck, 1999).

Literature ReviewA review of historical data and published literature pertaining to Lake Waco was performed to identify important limnologic processes to be modeled. Available literature indicated four dominant factors governing hydrodynamic and trophic response of the lake: wind driven mixing, light extinction by organic and inorganic turbidity, adsorption/desorption, equilibrium of clay-bound phosphorus, and water residence time. Previous studies of Lake Waco were also helpful in characterizing the phytoplankton community for specification of broad taxonomic or functional groupings within the model that would adequately mimic the real world community response. Published literature pertinent to parameterization of the lake model is summarized in the following section.

Historical data suggest a strong and persistent presence of bloom-forming diatoms and blue-green algae within the phytoplankton community of Lake Waco. Kimmel (1969) observed continual diatom dominance through November following a diatom bloom in August 1968. Species composition observed in May 1977 consisted mainly of diatoms and green algae (Wyrick, 1978). Lind (1979) noted a spring diatom bloom in mid-May 1977. Algal assays conducted at the time of the bloom indicated no nutrient limitations for the main body of the reservoir near the dam (Wyrick,1978). Numerical dominance of blue-green algae was observed in August 1981, with numbers exceeding 40 percent of the total (USGS, 1982).

To adequately represent algal dynamics, a minimum of three phytoplankton groups, (green algae, blue-green algae, and diatoms) would need to be simulated. The inclusion of blue-green algae in the model was further supported by growing concerns about taste and odor problems in the City of Waco’s water supply that may be caused by blooms of certain blue-green algae species. It is interesting to note that taste and odor problems were experienced in Lake Waco within the first few years following impoundment which led to the installation of artificial aerators in problem areas including one in the deepest region near the outlet works (Biederman and Fulton, 1971). Thermal stratification persisting from May to October was observed prior to installation of the aerators in 1970 (Biederman and Fulton, 1971). Following installation, stratification is occasionally observed during calm periods but is generally short-

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Chapter 2 Description of Study Area and Lake Waco

lived (Kimmel and Lind, 1972). The causative agent responsible for taste and odor problems in Lake Waco is unknown but the blue-green algae or cyanobacteria which are commonly blamed for these problems may only be indirectly responsible due to their ability to fix atmospheric nitrogen. Concomitant increases in actinomycetes following cyanobacteria blooms may produce the metabolites responsible for the problems (Lind and Katzif, 1988).

Turbulent mixing caused by steady southwesterly or northwesterly winds (Lind, 1971) contributes to both a shallow photic zone due to resuspension of inorganic sediments and a deep mixed layer, which within the main body of the reservoir often includes the entire water column. This high mixing depth to photic depth ratio can contribute to a condition termed “morphometric oligotrophy” (Kimmel & Lind, 1972).

The influence of wind mixing on Lake Waco is responsible for considerable resuspension of clays (Lind, 1986). Additionally, these clays were found to be the primary removal mechanism for orthophosphate through adsorption processes. The dynamic equilibrium established between soluble and solid-bound fractions of phosphorus is also capable of maintaining a relatively constant amount of soluble orthophosphate under well mixed conditions (Kimmel and Lind, 1970).

Light extinction by organic and inorganic turbidity was found to be most limiting of algal growth (Kimmel, 1969). High turbidity due to suspension of clays originating from tributary inflows along with strong wind action was responsible for limiting algal production. High turbidity within the south arm of the reservoir was due to inflows of the South Bosque River, which according to Kimmel (1969) is “the principal silt contributor to Waco Reservoir.” Marl and clays of the Black Prairie physiographic province within the South Bosque drainage basin are more readily transported in surface runoff than the Grand Prairie soils within the North Bosque watershed (Kimmel and Lind, 1972). As noted by Lind (1979), light demonstrated a greater governing role than did nutrients in spring phytoplankton populations. High inorganic turbidity in the lake was cited as a possible explanation for differences in the relationship between total phosphorus and chlorophyll-α concentration observed by Lind (1979), through either a reduction in light penetration or sorption of phosphorus onto clay particles. Preliminary testing of a phytoplankton production model suggests that the combined influences of physical factors outweighs that of chemical factors three to one (Lind, 1979).

Lind (1971) observed a relatively consistent relationship between soluble and total organic matter within the four main tributaries draining the Lake Waco watershed. This relationship indicated that the amount of organic matter in the dissolved form exceeded that in the particulate form roughly three to one. Periods of high flow, when daily discharge exceeded the mean daily discharge for the month by more that five times, were excluded in developing this relationship. It was noted that during these flood dates, a significantly greater proportion of the organic matter was present in the particulate form.

Water residence time also plays an important role in controlling phytoplankton productivity. Chlorophyll-α may be negatively related to reservoir inflows (Heiskary, 1995) particularly at high flows due to shorter residence times and cell washout (Walker, 1987). A poor relationship between phytoplankton production and chlorophyll-α in Lake Waco was observed during periods of severe drought (i.e., long residence times) such as that observed by Lind in 1978, which was characterized by low inflows and minimal external nutrient supply. During this time, the reservoir level dropped two meters and algal “production was more dependent on physical factors and recycling of internal nutrient stores” (Lind, 1979). During 1977, which was characterized by more normal rainfall patterns (both amounts and frequency), seasonal patterns in algal production tended to parallel chlorophyll concentrations (Lind, 1979).

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Artificial aeration at two sites in Lake Waco was initiated in 1970 to discourage chemical stratification and hypolimnetic anoxia. One aerator is located in the deepest portion of the lake near the outlet works and the other near the confluence of the North Bosque River. A study conducted by Roark and Lind (1988) concluded that the effects of the aerator were minor and limited to within 50 meters of the aerator.

Target DevelopmentTarget development for Lake Waco centered on identifying the nutrient most limiting to the phytoplankton community in the lacustrine portion of the lake and determining a range or threshold value for the limiting nutrient conducive of a healthy algal community. From bioassay studies conducted by Dr. Owen Lind, phosphorus was determined to be the nutrient most limiting to algal communities (Davalos-Lind and Lind, 1998). Relationships between chlorophyll-α and orthophosphate concentrations developed from TIAER monitoring data revealed a threshold effect with a Monod-type growth response (Kiesling et al., 2001). Relationships were established based on near-surface summer mean concentrations of orthophosphate and chlorophyll-α for stations located in model segment 8. The summer growing season for phytoplankton, defined as April through September, provided the strongest relationship between the response variable (chlorophyll-α) and the stimulus (PO4-P). Based on these relationships and recommendations by the Technical Work Group (TWG), a target range between 8 and 14 parts per billion (ppb) was proposed with a preliminary target for mean summer (April-September) orthophosphate concentration set at 10 ppb.

Identification of Phosphorus Sources in the WatershedPercent contribution of soluble (PO4-P) and total (TP) phosphorus in the Bosque watershed (Table 3) were estimated by McFarland and Hauck (1999). While comprising about 2 percent of the watershed area, dairy waste application fields were estimated to contribute 35 percent of the PO4-P and 21 percent of the TP within the watershed for the period examined. The wood and range land use category was also estimated to contribute a large percentage of the phosphorus load (22 percent of the PO4-P and 30 percent of the TP) but comprises roughly 63 percent of the watershed area. Other sources identified in the watershed include pasture, cropland, urban areas, and point source discharges from municipal wastewater treatment plants (WWTPs).

Table 3 Estimated percent contribution of phosphorus by source to the Bosque watershedfor November 1, 1995 through March 30, 1998. Source: McFarland and Hauck (1999)

Source PO4-P (%) TP (%) Land Use (%)

Dairy waste application 35 21 2

Row crop 11 17 15

Non-row crop 2 2 2

Pasture 10 16 14

Wood/range 22 30 63

WWTP 9 4 not applicable

Urban 11 10 2

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CHAPTER 3

Description of Lake Model

Model SelectionThe two-dimensional, laterally averaged, numerical model, CE-QUAL-W2, was selected for simulation of the hydrodynamics and water quality of Lake Waco and to assess the impact of alternate allocation scenarios on selected water quality constituents. CE-QUAL-W2, developed and supported by the U.S. Army Corp of Engineers Waterways Experiment Station (USACE-WES), is recognized as a state-of-the-art hydrodynamic/water quality model (Cole and Buchak, 1995). In addition to the ability to simulate 21 water quality state variables, CE-QUAL-W2 builds on the proven hydrodynamic solution techniques of its predecessors, the Laterally Averaged Reservoir Model (LARM) and the Generalized Longitudinal and Vertical Hydrodynamics and Transport (GLVHT) model. The capability of the model to predict changes in water quality in two dimensions (longitudinally and vertically) for multiple branches were among the characteristics which made CE-QUAL-W2 particularly suited to simulation of Lake Waco. The assumption of well-mixed conditions in the lateral direction is usually suitable to long, narrow reservoirs dominated by longitudinal flow through the water body. The configuration of Lake Waco with two relatively narrow branches that exhibit strong longitudinal gradients in flow and nutrient concentrations support the use of lateral averaging and the need for greater longitudinal resolution.

The model’s hydrodynamic sophistication, allowing for the addition or subtraction of layers and segments, and the use of an auto-stepping algorithm for greater numerical stability were suitable for modeling the changes in water level due to temporary storage and routing of flood flows through the reservoir. The emphasis placed on both hydrodynamic and water quality capabilities, favored the selection of CE-QUAL-W2 over simpler hydrodynamic approaches with comparable water quality capabilities such as the Water Quality Analysis Package (WASP). The model’s implicit calculation of vertical and horizontal velocities by layer was also an important consideration in the selection of CE-QUAL-W2 over WASP.

In addition to simulation of hydrodynamics and water temperature, the water quality constituents necessary for this application were associated with nutrient forms transported from the watershed and associated changes in the phytoplankton community. As discussed earlier, at least three algal groups would need to be specified in the model to adequately represent algal community dynamics of interest in Lake Waco. Since the currently released version of the model (version two) was limited to a single algal group, a prerelease of version three was obtained from USACE, which allowed for multiple algal groups. Initial testing of the beta version of CE-QUAL-W2 (V3) demonstrated that this version possessed the capabilities required for simulation of the unique hydrodynamics and water quality conditions in Lake Waco. Although a published manual for the prerelease version (V3) was not available, Cole and Tillman (1999) document the application of a similar prerelease version to Lake Monroe and discuss the capabilities and enhancements to the model since release of version two.

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Water Quality Modeling of Wake Waco Using CE-QUAL-W2

Modeling ApproachA complicating factor in the calibration process for this project was the need to integrate the CE-QUAL-W2 model with the Soil and Water Assessment Tool (SWAT) model as a means to predict the effects of various phosphorus control strategies in the watershed. This integration necessitated independent calibration of both SWAT and CE-QUAL-W2 followed by a calibration check of the integrated model output as depicted in Figure 3. A two-phase calibration approach was adopted for this project. The first phase consisted of calibration of SWAT and CE-QUAL-W2 independent of one another. During the independent calibration phase, data from TIAER’s stream monitoring network served dual purposes of calibration data for SWAT and input to CE-QUAL-W2. Following the initial calibration (referred to as independent calibration hereafter), the watershed and lake models were integrated through an intermediate interface program, which allowed the predicted stream quality output from SWAT to be used as input to CE-QUAL-W2. The second phase of calibration consisted of fine-tuning the integrated SWAT/CE-QUAL-W2 model (referred to as integrated calibration hereafter). The rational for using a two-phase calibration approach stemmed from the desire to account for the uncertainties associated with simulation of the landscape and receiving water body separately before lumping the uncertainties in the integrated modeling system.

The watershed loading model, SWAT, developed by the United States Department of Agriculture-Agriculture Research Service (USDA-ARS), was applied by Blackland Research and Extension Center (BREC) to simulate watershed processes. The SWAT model is physically based, uses readily available inputs, and is computationally efficient to operate on large basins in a reasonable time. SWAT is capable of simulating long periods for computing the effects of management changes. Major components of the model include hydrology, weather, erosion, soil temperature, crop growth, nutrients, pesticides and agriculture management. A complete description of SWAT model components may be found in Arnold et al. (1998). Santhi et al. (2001) provides documentation of SWAT calibration and application to the Bosque River watershed.

The approach adopted by the TWG for evaluation of alternative phosphorus (P) control strategies involved the use of long-term simulations to minimize the stochastic uncertainty associated with model predictions. Based on the availability of input data for CE-QUAL-W2, a 32-year simulation period was selected, which utilized historic weather data from 1966 through 1997 as representative climate data for simulations of future conditions. A 20-year planning horizon was adopted for projection of watershed conditions to future (year 2020) conditions. Future conditions in the watershed were estimated based on projected changes in land-use patterns, population, urban area and dairy herd size. For those sectors controlled by permit limitations, future conditions were represented by either the projected growth from current levels to year 2020 or fully permitted levels, whichever was greater. The efficacy of P control measures in improving water quality in Lake Waco would be assessed relative to target attainment under future conditions.

Future projections through the year 2020 also considered physical changes in the normal operating depth of Lake Waco and potential water quality changes that could result. In anticipation of increased water supply demand by the City of Waco, the USACE (1982) approved the reallocation of a portion of Lake Waco’s storage capacity from flood control to water supply. Dam alteration for the proposed reallocation was scheduled to begin in 1998 and upon completion would increase the conservation pool elevation by 2.1 meters (7 feet).

20

Chapter 3 Description of Lake Model

Figure 3 Diagram of calibration process.

The increased pool elevation could potentially impact water quality. Greater water depths could lead to greater anoxia in the bottom waters, both in extent and duration of exposure. Under anoxic conditions, PO4-P and NH3-N may be released from the sediments into the overlying water column further accelerating the eutrophication process. The rise in conservation pool elevation will also inundate new areas along the lake’s periphery. Terrestrial vegetation such as grasses, trees and shrubs once submerged will soon die and decay. The newly inundated areas along the lake’s periphery will serve as an additional organic nutrient source and exert an oxygen demand as it decays.

The TWG recommended, however, that the modeling evaluations be performed for current lake conditions and a post-audit model run be performed with the increased conservation pool elevation to examine model’s response to increased depths. Further, the TWG recommended that this post-audit be performed with the calibrated values, ignoring the effects of decaying vegetation in newly inundated areas. Based on the guidance of the TWG, except for a post-audit model exercise, all CE-QUAL-W2 calibration and subsequent applications occurred for lake conditions existing at the time of the study.

Model Input DataInput data for calibration of CE-QUAL-W2 were assembled from numerous sources which varied based on whether CE-QUAL-W2 was being calibrated with observed inflows or with inflows predicted by SWAT. Input data required for model setup may be divided into four broad categories, 1) physical and spatial characteristics of the water body defining the computational grid, 2) time-varying boundary conditions describing the meteorologic and hydrologic influences on the water body, 3) initial conditions, and 4) kinetic and hydrodynamic data characterizing the physical, chemical, and biological processes.

21


Physical Representation and Segmentation SchemeBathymetric data were required for specification of the computation grid defining the longitudinal and vertical segmentation of the water body and cell widths. The computational grid defined for Lake Waco was based on the desired longitudinal and vertical resolution, the branch configurations, and the reservoir bathymetry.

The reservoir was discretized into a finite-difference grid consisting of three branches. The main body of the reservoir was designated as branch 1, the North Bosque Arm (North Arm) as branch 2, and the Middle and South Bosque Arm (South Arm) as branch 3. Each branch was divided into segments approximately 500 meters in length. Segments were divided into 1-meter thick vertical layers. The segmentation of Lake Waco and the associated computational grid are depicted in Figures 4 and 5, respectively. The computational grid consisted of 47 segments and 37 layers with 311 active cells at the conservation pool elevation of 455 feet (139 meters). The computational grid was defined up to the maximum design water surface elevation (elevation of 505 ft or 154 m), which is well above lake levels observed during the lake’s history.

The CE-QUAL-W2 model requires specification of boundary segments at the upstream and downstream boundaries of each branch. Segments 1, 9, 10, 28, 29, and 47 are boundary segments. The model also requires boundary layers at the surface and bottom of the reservoir. Layer 1 is the surface boundary layer. The bottom boundary layer varies by segment depending on depth. Boundary grid cells are depicted in Figure 5.

Bathymetric data for the lake were used to assign volumes to each cell in the numerical grid. Reservoir bathymetry was obtained in digital form from Baylor University’s Department of Geology from the most recent volumetric survey of Lake Waco (Sullivan et al., 1995). These data were used to determine grid characteristic below the conservation pool elevation (layers 17-36). Topographic data from the USGS 7.5 minute Digital Elevation Model (DEM) were used to determine volumes for cells above the conservation pool elevation (layers 2-16). Layer 1 is a boundary layer required by the model. Boundary cells are required at the top and bottom and at the upstream and downstream boundaries.

Two branch inflows and five tributary inflows were defined in the computational grid (Figure 4). The North Bosque River enters segment 11 at the uppermost segment of the north arm and constitutes the inflow to branch 2. The Middle Bosque River enters at segment 30 and forms the inflow to the south arm (branch 3). Two main tributaries, the South Bosque River and Hog Creek enter branch 3 at segments 32 and 35, respectively. Three small ungauged areas are located adjacent to the lake and enter at segments 27, 39, and 45.

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Figure 4 Lake Waco segmentation.

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Model State VariablesThe variables modeled (or state variables) in this application include:

1. Hydrodynamics (water level, horizontal velocity, and vertical velocity)

2. Conservative tracer

3. Water temperature

4. Dissolved oxygen

5. Four organic matter pools

6. Ammonium

7. Nitrate+nitrite

8. Orthophosphorus

9. Three phytoplankton groups

Organic matter (OM) in the model is partitioned into four pools based on classification of physical state (dissolved versus particulate) and into one of two classes (labile or refractory) characterizing the mineralization (decay) rate of the organic compound to inorganic nutrients. Labile OM is more readily mineralized (i.e., faster decay rates), whereas refractory OM is less readily mineralized (i.e., slower decay rates). The four OM pools are labile dissolved organic matter (LDOM), labile particulate organic matter (LPOM), refractory dissolved organic matter (RDOM), and refractory particulate organic matter (RPOM).

Organic matter (OM), as specified in CE-QUAL-W2, was not directly measured by TIAER and had to be estimated based on organic phosphorus (Org-P) measurements. Org-P was computed as total phosphorus (TP) minus orthophosphate phosphorus (PO4-P). Two CE-QUAL-W2 input parameters, ORGN and ORGP, govern the breakdown of OM into Organic N (Org-N) and Organic P (Org-P) fractions. By definition, ORGN is the fraction of N in OM and ORGP is the fraction of P in OM used by CE-QUAL-W2. Published literature and model default values suggested a typical value of ORGN equal to 0.08 (8 percent of OM is Org-N). The fraction of Org-P in OM (ORGP) was estimated relative to ORGN assuming that the ratio of ORGN to ORGP was similar to the mass ratio of Org-N to Org-P loads to the Lake Waco (equation 1).

1)

Monitoring data from stream stations on major tributaries indicated that Org-N to Org-P ratios were variable both temporally and spatially. Restrictions in the model allowed only a single value of ORGN and ORGP to be used within a simulation; therefore, cumulative organic loads to the lake over the period of interest were computed and used to estimate the value of ORGP. This method would ensure that the total organic N and P load to the lake would be realized. Time-series generated for OM would reflect the temporal variation in Org-P; however, Org-N loads would be under or overestimated on a daily basis but the cumulative load would be correct. Considering the importance of P as the nutrient most limiting of algal production and the focus of water quality targets selected for Lake Waco, it was important to accurately reflect the Org-P loads.

Cumulative organic N and organic P loads to Lake Waco for the calibration period were estimated based on measured stream flow data to be 2,548,045 kg and 507,670 kg, respectively. Rearranging equation 1 and assuming ORGN = 0.08, yields an ORGP value of 0.016 (equation 2), which was used in CE-QUAL-W2.

ORGNORGP------------------

ΣOrg-N LoadΣOrg-P Load------------------------------------=

25


2)

Consequently, OM was back computed from Org-P timeseries data (equation 3).

3)

The same procedure was used for both the observational data and SWAT predictions. An ORGP of 0.016 was used for calibration and for long-term simulations of P control scenarios. The ORGN values was set at 0.08 for the independent calibration. For the integrated model calibration and long-term simulations, ORGN was adjusted based on the cumulative loads predicted by SWAT. Estimation of organic matter was performed for initial conditions and specification of inflow boundary conditions.

The phytoplankton community of Lake Waco was represented in CE-QUAL-W2 by three algal groups based on temperature and light preferences and competitive ability under nutrient limited conditions. Algal group one was loosely defined as diatoms, although other types of algae such as flagellates and cryptomonads may also be included in this group. Algae in this group were considered to be spring and fall dominants with optimal temperatures below that of the other two groups. Alga of this group were also characterized by slightly higher growth rates and considerably higher settling rates compared to the others groups due to their siliceous frustrules. It should be noted at this point that silica was not modeled in this application. There is some evidence that under bloom conditions, silica may limit diatom growth in Lake Waco; however, the primary focus being on phosphorus along with the lack of data characterizing inflowing silica concentrations and the inability of SWAT to predict these concentrations, precluded the simulation of dissolved silica concentrations in Lake Waco.

Specification of the other two algal groups were based primarily on their ability to compete for limited resources under nutrient limited conditions. Algal group two, broadly defined as green algae, would prevail under phosphorus limited conditions, whereas, group three, characterized by blue-green algae, would prevail when nitrogen was limiting.

Collection of In-Lake Calibration DataWater quality data collected at eight sites within Lake Waco (Figure 6) were used for the purpose of model calibration. Monthly monitoring began in June 1996 except for biweekly sampling, which was initiated for the summer 1998 sampling period to better characterize algal community changes. The duration of algal bloom conditions are generally short, lasting days to weeks before available resources are depleted. Residual water quality effects indicating the occurrence of an algal bloom may only be apparent for a several weeks following the bloom and could go undetected based on a monthly sampling frequency.

Data collected through July 29, 1998 were used in model calibration. Comparisons of model predictions to observed data focused on model segment 8, which is the area of the lake where the phosphorus target was defined. Four sampling sites were established within segment 8 to characterize water quality conditions near important features such as the confluence of Landon Branch, the reservoir outlet structure (riser), and spillway. Water quality data collected at these four sites were averaged to provide observed data comparable to the lateral average predicted by CE-QUAL-W2. During the first portion of the model calibration period (through March 1998), observational data for segment 8 were represented as a mean values with error bars representing the range of values observed during that sampling date. Latter in

ORGP 0.08 507670( )2548045

--------------------------------- 0.016==

OM Org-P 0.016⁄=

26


the calibration period (April 1998 to July 1998), the number of sampling sites in segment 8 was reduced to a single site (LW013) to accommodate an increase in sampling frequency from monthly to biweekly monitoring. Four additional sites (LW070, LW012, LW015, and LW060) were used for model comparisons with respect to longitudinal gradients along the north and south arms of Lake Waco.

The water quality parameters listed in Table 4 were determined either directly or indirectly from TIAER sampling data. At each lake monitoring site, water temperature and dissolved oxygen (DO) profile measurements were taken at one-meter increments from the surface through the water column. Organic and inorganic nutrients and total suspended solids concentrations were determined for surface, mid-depth and bottom samples collected at each site. A Secchi depth measurement was also made at each site. Chlorophyll-α samples representing depth composites of three samples taken between the surface and secchi depths were used to estimate algal biomass. All laboratory and field methods were performed according to the EPA-approved quality assurance project plan developed for this project (Easterling, 1996 and 1997).

Figure 6 Location of Lake Waco sampling sites used in model calibration.

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Initial ConditionsSpecification of initial water quality conditions in the lake was based on the first monthly sampling conducted by TIAER on June 14, 1996. Initial water quality conditions generally exhibited both longitudinal and vertical gradients and were therefore specified as vertical profiles for each segment. Observed data were interpolated vertically but not longitudinally. Segments were assigned constituent concentrations based on the nearest station sampled on June 14, 1996.

A single concentration was specified for constituents with uniform concentrations throughout the reservoir. This method of initialization was used for the conservative tracer and organic matter concentrations. The conservative tracer concentration was set uniformly to 1.0 mg/L.

The organic matter (OM) concentrations were estimated from organic phosphorus (Org-P) concentrations (Equation 3). Org-P was computed as TP minus PO4-P.

On June 14, 1996, the TP and PO4-P concentrations throughout the lake were below method detection limits (MDLs), therefore the Org-P concentration was computed as one-half the MDL for TP minus one-half the MDL for PO4-P and partitioned according to the following breakdown: 30 percent LDOM, 60 percent RDOM, 5 percent LPOM, and 5 percent RPOM.

Initial algal biomass concentrations for each of the three phytoplankton groups were estimated from chlorophyll-α data collected on June 14, 1996 and the algal biomass to chlorophyll-α ratios specified for the three groups. Total chlorophyll-α was partitioned based on the assumption that 10 percent was contributed by group 1 (diatoms), 60 percent by group 2 (green algae), and 30 percent by group 3 (blue-green algae).

Boundary ConditionsTime series data describing stream flow, meteorologic conditions, and spillway releases were required to define the model boundary conditions.

Table 4 Water quality parameters.a Water Quality Parameters Abbreviation Computation

Water temperature —

Dissolved oxygen DO

Ammonia nitrogen NH3-N

Nitrate+nitrite nitrogen NO2+NO3-N NO2-N + NO3-N

Orthophosphate phosphorus PO4-P

Organic nitrogen Org-N Total Kjeldahl nitrogen - NH3-N

Organic phosphorus Org-P Total phosphorus - PO4-P

Total phosphorus TP

Chlorophyll-α chl-αTotal suspended solids TSS

Secchi depth Zsd

a. TIAER’s monitoring program includes additional parameters, however, only those used in the modeling analysis are listed here. See Easterling (1996, 1997) for a complete list of parameters and sites monitored.

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Inflow Boundary Conditions

Boundary conditions must be specified for each inflow to the reservoir describing the time-varying flow, temperature, and constituent concentrations for each branch and tributary. Inflow boundary conditions were initially generated from observational data for an independent calibration of the lake model. A second set of inflow boundary conditions were generated based on the calibrated output predicted by the SWAT model. Calibration of CE-QUAL-W2 was further refined based on the use of predicted inflow boundary conditions.

Inflow Boundary Specification for Independent Model Calibration

An extensive stream monitoring network was established in the Bosque River watershed and maintained for the duration of the project. TIAER’s monitoring network provided stream flow and water quality measurements for calibration of SWAT and CE-QUAL-W2. Daily stream flow and waterborne constituent loads were computed from TIAER monitoring data collected at the most downstream station on each of the four main tributaries: site BO100 on the North Bosque River, site HC060 on Hog Creek, site MB060 on the Middle Bosque River and sites SB050/SB060 on the South Bosque River (Figure 7). Daily loads were computed based on stream flow data determined on 5-minute or 15-minute intervals and water quality samples collected during biweekly and storm event monitoring (McFarland and Hauck, 1998; Pearson and McFarland, 1999). The contributions from intervening areas between the gauge sites and the lake were estimated based on a drainage area ratio of the total drainage area to the gauged area.

Daily flows and loads from wastewater treatment plant (WWTP) discharges to ungauged sections of the tributaries were computed based on weekly monitoring of WWTP effluents and monthly self-reporting flow data. WWTP contributions from Valley Mills and Crawford were added into the North and Middle Bosque Rivers, respectively.

Figure 7 Main tributary gauging stations.

Ungauged area contributions immediately adjacent to the lake (Figure 8) were estimated based on drainage area adjustments to flow and loads measured for Hog Creek. These areas included an ungauged area draining into the north arm of the lake, input at segment 27, and an ungauged area draining into the south arm, input at segment 45. A third ungauged area

29


representing an adjacent urban area of the City of Waco was input on the south arm at segment 39. Flow contributions from the urban area were estimated based on drainage area adjustments to Hog Creek flows, and waterborne constituents were based on volume-weighted mean concentrations observed for a gauged urban watershed in Stephenville. In total, the ungauged areas draining into Lake Waco made up a small faction of the watershed area. Only 12 percent (50,357 ha) of the 426,780 ha watershed were ungauged.

Figure 8 Watershed delineations for Lake Waco model inputs.

Flow-weighted mean concentrations were specified for organic matter (LDOM, LPOM, RDOM, RPOM), NH3-N, NO2+NO3-N, and PO4-P, algae, and DO at the inflow boundaries. NH3-N, NO2+NO3-N, and PO4-P concentrations were determined directly from the analytes monitored.

Organic matter (OM), as specified in CE-QUAL-W2, was not directly measured by TIAER and had to be estimated based on organic phosphorus (Org-P) measurements (Table 4). It was estimated that the fraction of Org-P in OM was 0.016 (equation 3). Division of OM among the four pools were based on analysis of TIAER water quality data, published results of a study of Lake Waco’s OM budget (Lind, 1971), and on previous model applications of CE-QUAL-W2. An analysis of TIAER data collected at a site on the North Bosque River suggested that approximately 40 percent of the OM was in particulate form. This percentage was also supported by historical data published by Lind (1971). A majority of OM (75 percent) was assumed to be refractory while only 25 percent was assumed to be labile. The partitioning of dissolved and particulate fractions varied between two conditions based on inflowing sediment loads. Under low and base flow conditions, when total suspended solids (TSS) concentrations were below 100 mg/L, the majority (60 percent) of OM was assumed to be in

30


the dissolved form. Under storm flow conditions with high sediment loads (i.e., TSS concentrations at or above 100 mg/L), the majority (75 percent) of OM was assumed to be particulate in nature.

It was assumed that riverine algae species upon entering Lake Waco would not generally be viable in the lacustrine environment. Therefore, concentrations of algal biomass at inflow boundaries were set to zero. Since measurements of Org-N and Org-P at inflow boundaries did not distinguish living algal OM from dead (detrital) OM, estimates of OM pools based on Org-P accounted for the additional organic nutrients released due to algal mortality.

Inflow temperatures were estimated from air temperatures using an algorithm similar to the one used in the SWAT model (Stefan and Preudhomme, 1993). Dissolved oxygen concentrations were set to a fraction of the saturated value estimated from observed data at each inflow boundary.

A conservative tracer was simulated to check for numerical stability through conservation of mass. The tracer concentration was initialized at 1.0 mg/L with inflow concentrations also at 1.0 mg/L. When run for the simulation period, without evaporation included in the water balance, any significant deviation of in-lake tracer concentration from 1.0 mg/L would indicate numerical instabilities.

Inflow Boundary Specification for Integrated Model Calibration

Integration of the SWAT and CE-QUAL-W2 models was accomplished through the use of an interface program, which transformed predicted flow and watershed loads from SWAT into variables required for CE-QUAL-W2 inflow boundary conditions. SWAT subbasin output corresponding to the outlet of basins defined in Figure 8 were used to generate inflow boundary conditions. Daily reach file output from SWAT subbasins 40, 27, and 33 were used to compute inflow boundaries at the North, Middle, and South Bosque Rivers, respectively. SWAT subbasin 25 represented inflow from Hog Creek. The small, ungaged areas surrounding the lake were represented by subbasins 35, 37, 38, and 39.

Daily reach file output from SWAT included predictions of flow in cubic meters per second, sediment in metric tons, organic N, organic P, mineral N, and soluble P in kilograms. The sediment variable predicted by SWAT was equivalent to measurements of total suspended solids (TSS). Sediment predictions were used in a manner similar to TSS to partition the OM into the four pools as described for the independent calibration. Organic N and organic P predictions from SWAT were equivalent to measured Org-N and Org-P values. Soluble P predictions from SWAT represented PO4-P concentrations. SWAT predictions of mineral N included all forms of inorganic N—nitrate+nitrite nitrogen (NO2+NO3-N) and ammonia nitrogen (NH3-N). Partitioning of SWAT mineral N predictions into NO2+NO3-N and NH3-N was accomplished through a cumulative mass ratio of NH3-N to NO2+NO3+NH3-N from observational data at major tributary inflow sites over the period of interest.

Water temperature, algal biomass, and conservative tracer concentrations were specified in the same manner as discussed for independent calibration.

Surface Boundary Conditions

Surface boundary conditions govern the processes that occur at the reservoir surface (air-water interface) such as heat exchange, solar radiation adsorption, wind stress, and gas exchange. Time-varying boundary conditions at the surface of the reservoir were defined based on meteorologic observations and atmospheric samples collected at nearby sites. The

31


same surface boundary conditions were used for both the independent and integrated model calibrations.

Meteorologic data was obtained from National Oceanic and Atmospheric Administration, National Climatic Data Center (NOAA-NCDC) for the Waco Madison Cooper Airport, situated at the northeast corner of the dam, within a mile of the lake (NOAA, 1997; USDOC et al., 1993). Meteorologic data required for specification of CE-QUAL-W2 surface boundary conditions included precipitation, air temperature, dew point temperature, wind speed, wind direction, cloud cover, and solar radiation. With the exception of precipitation, the meteorological variables were input on an hourly basis. Precipitation data was input on a daily basis. Daily rainfall amounts were accumulated from the hourly data.

Atmospheric contributions were determined from rainfall samples collected via a wet depositional sampler located at the Lake Waco dam. Concentrations of organic matter (LDOM, LPOM, RDOM, RPOM), NH3-N, NO2+NO3-N, and PO4-P were determined from analytes as described previously for inflow boundary conditions. Volume-weighted mean concentrations were computed from the available data and input during rainfall events. Rainfall temperatures were set to mean daily air temperatures and dissolved oxygen concentrations were computed assuming 100 percent saturation.

Outflow Boundary Conditions

Boundary conditions must also be specified for each point where water exits the reservoir. CE-QUAL-W2 allows specification of two types of outflow boundary conditions 1) downstream releases through spillway structures such as sluice gates or tainter gates, and 2) withdraws of water for allocation of water rights. The three outflow boundaries defined for Lake Waco were downstream releases through the outlet structure, emergency flood releases through the spillway tainter gates, and withdraws for raw drinking water supplies. Daily flow rates were specified for each outflow boundary. Specification of outflow boundary conditions differed for the two calibration phases. The independent lake model calibration utilized observational data whereas the integrated model calibration relied on a reservoir operating rule to determine the magnitude of downstream releases.

Outflow Boundary Specification for Independent Model Calibration

Hydrologic characteristics at outflow boundaries were specified based on the type of control structure, dimensions, and elevation (or depth). The other water quality state variables (algae and nutrient concentrations) were predicted by the model. Daily withdraws by the City of Waco for raw drinking water supplies were obtained from pump meter summaries provided by the city and USACE. Daily reservoir releases from the outlet structure were obtained from the USACE. Additional information on the limited historical operation of the tainter gates for flood releases were obtained from verbal communications with USACE personnel at the Ft. Worth District office. Because greater confidence was given to reservoir inflows and reservoir water level than outflows, the independent calibration process involved adjustment of reservoir outflow.

Outflow Boundary Specification for Integrated Model Calibration

The use of the integrated SWAT/CE-QUAL-W2 models required a method of predicting the amount of water released through the reservoir outlet structure. Tributary inflows predicted by SWAT differed in timing and magnitude from measured inflows. These differences necessitated an approach other than direct adjustment of reservoir releases. An operating rule was developed for predicting daily reservoir releases based on water surface elevation and incorporated into the model code. The model operating rule was based on a simplified set of

32


rules governing releases from Lake Waco (USACE, 1971). Actual operation of Lake Waco by the USACE is dependent on factors other than water level such as inflow rates and downstream flow conditions on the Brazos River, which were not incorporated in the operating rule. The operating rule was only used to predict downstream releases through the outlet structure. Observational data for drinking water withdraws and emergency spillway releases remained the same for both phases of calibration.

Hydraulic and Kinetic ParametersValues describing the hydraulic and kinetic functions within the model were specified as input parameters in the CE-QUAL-W2 control file. Hydraulic parameters governing horizontal dispersion and bottom friction were set to default values recommended by the model developers (Cole and Buchak, 1995).

A number of kinetic parameters affecting constituent kinetics were required by the model. Initially, kinetic coefficients were set based on default or literature values and subsequently adjusted during water quality calibration. Kinetic coefficients were adjusted within acceptable ranges based on data in published literature. Reputable sources were consulted for selection of appropriate ranges for the values (USEPA, 1985; Thomann and Mueller, 1987; Cole, 1994; Reynolds, 1984). Site specific data were preferable but rarely available. With the exception of algal growth rate and half-saturation constants, kinetic coefficients were based on literature values.

Dose response studies conducted by Dr. Owen Lind (Lind and Dávalos, 1999) provided site specific data for specification of algal growth rates and phosphorus half-saturation constants for the three algal groups simulated. The dose response experiments represented overall algal community response rather than species or functional group response. Growth rates and half-saturation constants were assigned to the algal groups based on estimates of dominant phytoplankton groups present in the lake samples when the dose response studies were conducted (TIAER, unpublished data).

33


34

CHAPTER 4

Model Calibration

The calibration process consisted of comparisons of predicted in-lake concentrations to those observed during the monitoring period, which extended from June 14, 1996 through July 29, 1998. The relatively short time span of observational data (approximately 2 years) and the atypical climate conditions during this period resulted in a decision to use all available data for model calibration without reserving some period of data for model verification. The first month of the calibration simulation was considered as a start-up period wherein the model was adjusting to initial condition specification and was excluded from the calibration period. Therefore, the time span considered for comparison of predicted to observed data was July 11, 1996 through July 29, 1998.

Model calibration was achieved through adjustment of parameter values within the specified ranges such that model predictions mimicked observed data. Final calibration values for temperature, phytoplankton and water quality input parameters are listed in Tables 5 through 7, respectively.

Calibration of CE-QUAL-W2 was first accomplished with measured inflow data then further refined based on the inflows predicted by the SWAT model. Model calibration consisted of a three-step process: 1) water balance calibration, 2) hydrodynamic/thermal calibration, and finally 3) water quality calibration. The calibration results for both the independent calibration with measured inflows and the final integrated model calibration with SWAT inflows are presented in this chapter for each of the three calibration steps.

Table 5 Final calibration values for temperature input parameters.

Parameter Description Units Value

AX Horizontal eddy viscosity m2 s-1 1

DX Horizontal eddy diffusivity m2 s-1 1

CHEZY Bottom frictional resistance m1/2 s-1 70

BETA Solar radiation fraction absorbed at the water surface - 0.45

EXH2O Solar radiation extinction - water m-1 0.25

EXOM Solar radiation extinction - detritus m-1 0.40

EXA(1) Solar radiation extinction - algal group 1 m-1 0.25



WSC Wind sheltering coefficient - 1.0

35


Table 6 Final calibration values for phytoplankton input parameters.

Value for Algal Groups

Parameter Description Units 1 (Diatoms) 2 (Greens) 3 (Bluegreens)

AG Growth rate day-1 2.1 1.3 0.9

AR Dark respiration rate day-1 0.02 0.02 0.02

AE Excretion rate day-1 0.02 0.02 0.02

AM Mortality rate day-1 0.09 0.05 0.02

AS Settling rate day-1 0.25 0.09 0.03

AHSP Phosphorus half-saturation coefficient g m-3 0.034 0.026 0.042

AHSN Nitrogen half-saturation coefficient g m-3 0.140 0.110 0.002

ASAT Light saturation W m-2 150 125 100

AT1 Lower temperature for minimum algal rates ° C 3.0 5.0 3.0

AT2 Lower temperature for maximum algal rates ° C 20.0 25.0 25.0

AT3 Upper temperature for maximum algal rates ° C 25.0 35.0 35.0

AT4 Upper temperature for minimum algal rates ° C 35.0 40.0 40.0

AK1 Lower temperature rate multiplier for minimum algal rates - 0.2 0.1 0.1

AK2 Lower temperature rate multiplier for maximum algal rates - 0.99 0.99 0.99

AK3 Upper temperature rate multiplier for maximum algal rates - 0.99 0.99 0.99

AK4 Upper temperature rate multiplier for minimum algal rates - 0.1 0.1 0.1

ALGP Phosphorus to biomass ratio - 0.015 0.015 0.015

ALGN Nitrogen to biomass ratio - 0.08 0.08 0.08

ALGC Carbon to biomass ratio - 0.45 0.45 0.45

ACHLA Algae to chlorophyll-α ratio - 94 64 87

Table 7 Final calibration values for water quality input parameters.

Parameter Description Units Value

PO4R Sediment release rate of phosphorus fraction of SOD 0.002

ORGP Fraction of phosphorus in organic matter - 0.016

ORGN Fraction of nitrogen in organic matter - 0.08

NO3DK Nitrate decay rate day-1 0.05

NO3T1 Lower temperature for nitrate decay ° C 5.0

NO3T2 Upper temperature for nitrate decay ° C 25.0

NO3K1 Lower temperature rate multiplier for nitrate decay - 0.1

NO3K2 Upper temperature rate multiplier for nitrate decay - 0.99

NH4DK Ammonium decay rate day-1 0.12

NH4R Sediment release rate of ammonium fraction of SOD 0.04

NH4T1 Lower temperature for ammonium decay ° C 5.0

NH4T2 Upper temperature for ammonium decay ° C 25.0

NH4K1 Lower temperature rate multiplier for ammonium decay - 0.1

NH4K2 Upper temperature rate multiplier for ammonium decay - 0.99

36

Chapter 4 Model Calibration

Water Balance Calibration

Independent CalibrationThe water balance calibration consisted of closure of the reservoir water balance such that water levels predicted by CE-QUAL-W2 matched those reported by the USACE Ft. Worth District office. The water balance was performed initially using observational data, independent of SWAT predictions for the watershed. Observational data included tributary inflow data from TIAER’s stream monitoring network, estimated spillway releases from the USACE and daily pumpage rates from the City of Waco. Based on evaluation of the relative accuracy of tributary inflow and reservoir release data, the decision was made that the tributary inflow data were the more accurate; therefore, reservoir releases were adjusted to balance the reservoir water budget. Results of water level comparisons for the independent calibration with measured inflows are presented in Figure 9a.

Two large storm events occurred during the model calibration period and are evident in the lake level data presented in Figure 9. The first occurred in February 1997. Flood storage from this storm caused lake levels to increase 4.5 meters above the normal conservation pool elevation. Lake level crested at 143 meters on March 3, 1997 and remained elevated for several weeks before returning to the normal conservation pool elevation. A second intense storm event occurred in March 1998, which generated peak stream flows in excess of 91,000 cfs on the North Bosque River at Valley Mills (BO100) on March 17, 1998. Lake Waco crested the following day (March 18, 1998) at an elevation of 142 meters. Unlike the February1997 storm, lake levels returned to normal conservation pool within a week following the March 1998 storm.

Integrated CalibrationCalibration of the integrated SWAT/CE-QUAL-W2 models utilized an operating rule to predict downstream releases through the outlet structure. Results of the water balance calibration for the integrated SWAT/CE-QUAL-W2 models are depicted in Figure 9b. With SWAT inflows and the model operating rule, observed water levels were reasonably predicted. Differences between observed and predicted water level rises generally reflect under or overprediction of storm runoff by SWAT, while differences during periods when reservoir water levels are below the conservation elevation (138.7 m) reflect under or overprediction of base flow by SWAT.

Hydrodynamic/DO/Temperature CalibrationHydrodynamic calibration is typically performed by examining longitudinal and vertical concentration gradients for a conservative constituent. The only truly conservative constituent that has historically been used for hydrodynamic calibration is salinity; however, salinity is not typically measured in fresh water bodies. Dissolved solids are nonconservative and are not a good substitute for salinity. Of the suite of measurements typically collected in water quality studies, temperature is most commonly used for calibration of the hydrodynamics. Cole and Wells (2000) recommend the use of temperature gradients as a first step in hydrodynamic calibration followed by examination of dissolved oxygen (DO) gradients. Previous applications of CE-QUAL-W2 have shown DO to be a better indicator of proper hydrodynamic calibration than either temperature or salinity (Cole and Wells, 2000). DO is

37


much more dynamic then temperature due to the response to wind induced mixing such as seiching. The observed displacement of anoxic zones in response to hydrodynamics can also aid in the calibration process.

Figure 9 Time-series plots of water balance calibration.a) independent calibration with measured inflows b) integrated calibration check with SWAT inflows.

Hydrodynamic calibration is typically accomplished through comparisons of seasonal thermal stratification and mixing within the reservoir (i.e., the onset, strength, and breakdown of stratification, depth of thermocline development, etc.). Lake Waco; however, does not typically stratify. The well-mixed nature of Lake Waco prevents persistent thermal stratification from developing with only an occasional oxycline apparent during quiescent summer periods. The CE-QUAL-W2 zero-order sediment oxygen demand (SOD) option was used in the simulation of water column DO in Lake Waco. The primary focus on simulation of DO was in predicting the occurrences of anoxia due to the potential for release of phosphorus

137

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b)

38


from the bottom sediments under these conditions. SOD values for each segment were initially set to literature values and adjusted during calibration to produce bottom DO conditions similar to those in the observational data.

Model predicted vertical temperature and DO profiles were compared to observed values. Time-series plots of predicted and observed water temperatures at segment 8 are presented in Figure 10 for surface, mid-depth, and near-bottom. The observed value represents a mean of multiple measurements taken at different sites within segment 8 and a vertical bar represents the range of observed values for that date. Model predicted temperatures conform well to seasonal patterns and show good agreement with observed data.

Time-series plots of predicted and observed DO concentrations at surface, mid-depth and near-bottom within segment 8 are presented in Figure 11. The predicted daily DO concentrations are superimposed over the observed mean and range of values from TIAER’s sampling program. Predicted DO concentrations correspond well to observed concentrations at each of the three depths, capturing both the seasonal fluctuations and the periodic hypolimnetic oxygen depletion. Two areas where the model deviates somewhat from the observed values are in the surface layers in late summer when the model underpredicts DO concentrations and the prediction of bottom anoxia occurring earlier than observed in 1998. Underpredictions in the surface layer during late summer are probably a consequence of declining algal populations predicted during this time which will be discussed in the following section. The prediction of bottom anoxia during the first two weeks of June 1998 occurs roughly one month earlier than the observed data would suggest; however, due to a malfunction in the field equipment during the June 3, 1998 sampling, this period of anoxia falls between two sampling dates and while suspect, cannot be ruled out.

The decreased spatial resolution of observed data may account for some of the deviations between model predictions and observed datapoints. Observed DO data showed a high degree of variability between different stations in segment 8 particularly during the summer months (May-September), and this variation tended to increase with depth. Following the reduction in sampling stations in mid April 1998, observed datapoints were characterized by a single site.

39


Figure 10 Time-series plots of predicted to observed water temperatures.a) surface b) mid-depth c) near-bottom of segment 8

40


Figure 11 Time-series plots of predicted to observed dissolved oxygen concentrations.a) surface b) mid-depth c) near-bottom of segment 8

41


Water Quality CalibrationAlthough comparisons were made at all model segments corresponding to in-lake monitoring stations, segment 8 was the primary focus of calibration and results presented in this report. Segment 8 is located at the deepest point in the reservoir where thermocline and oxycline development would be most pronounced and contains the intake structure for the City of Waco’s drinking water supply. The water quality target for Lake Waco as recommended by the TWG for this project and adopted by the watershed stakeholder group was defined in terms of a summer mean orthophosphate concentration near the drinking water intake (i.e., segment 8). Calibration of the water quality components of the CE-QUAL-W2 model focused on the two state variables important to target development and target attainment, namely orthophosphate phosphorus (PO4-P) and chlorophyll-α (chl-α). The CE-QUAL-W2 model predicts algal biomass for each of the three algal groups simulated. Measures of algal biomass and relative abundance of the individual algal groups were not available for the calibration period. Calibration of algal kinetics was performed for chl-α with the algal group dynamics assessed on best professional judgement.

Predicted concentrations for each water quality state variable were output on a daily basis for the five segments used in model calibration. During the first portion of the model calibration period (through March 1998), observational data was available for multiple sites within segment 8 and characterized the degree of spatial variability (laterally) within the segment. Observational data collected through March 1998 were represented graphically as mean values with error bars depicting the range of values observed during the sampling date. Latter in the calibration period (April 1998 to July 1998), observational data were characterized by a single sampling site within segment 8. Calibration plots presented for segment 8 depict mean concentration values for the multiple sites with error bars depicting the observed ranges for observational data from July 11, 1996 through March 17, 1998. Observational data collected after March 17, 1998 represent sampling at a single station within segment 8 and calibration plots depict individual measurements from LW013 without error (range) bars.

Time-series plots of segment 8 for predicted and observed concentrations are presented in Figure 12 for PO4-P and Figure 13 for chl-α. Seasonal trends in the data are well represented by both the independent and integrated model predictions. Trends in the data represent the cyclic response of algae to water temperature defining their “growing season” as well as nutrient responses following storm pulses.

In-lake concentrations of PO4-P and chl-α varied by an order of magnitude in response to storm pulses. PO4-P varied from concentrations below analytical method detection limits (MDL) to over 0.10 mg/L following the February 1997 and March 1998 storms. chl-α concentrations displayed a similar degree of variability but lagged the storm pulses. chl-α concentrations below the MDL were observed during the passage of storm water flows due to dilution and washout of algae. Peak chl-α concentrations lagged PO4-P by several weeks. The range of values and magnitude of peak concentrations predicted by CE-QUAL-W2 conformed well to the observed data.

42


Figure 12 Time-series plot of predicted and observed surface PO4-P data for segment 8.

Figure 13 Time-series plot of predicted and observed surface chlorophyll-α datafor segment 8.

Since the integrated CE-QUAL-W2/SWAT model would be used in a predictive mode to compare alternative scenarios with respect to target attainment, water quality calibration focused on the model’s ability to predict summer mean concentration of PO4-P within the surface layer of segment 8. Comparisons of summer mean chl-α and PO4-P are depicted in Figures 14 and15, respectively. Individual summer means were computed for 1996, 1997, and 1998 growing seasons as well as a grand mean of all summer months combined and the grand mean of all months simulated (July 1996 - July 1998). Individual summer means displayed greater deviations between the predicted and observed values than did the grand summer mean or calibration mean.

0.00

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4-P

Con

cent

ratio

n (m

g/L)

Predicted (Measured Inflows) Observed Mean and Range Predicted (SWAT Inflows)

0

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43


It was difficult to assess model performance to predict individual summer means due to several factors:

1. Short time-span of calibration data (two years),

2. 1996 and 1998 summer means were computed from partial records characterizing averages over a portion of the growing season,

3. Extreme hydrologic conditions were experienced in February 1997 and March 1998 and although they proceed the defined growing season (April-September), PO4-P concentrations remained elevated well into the growing season.

4. Low summer concentrations of PO4-P regularly fell below the analytical method detection limit, requiring an assumed concentration of 1/2 the MDL to be used in statistical analysis.

Observed summer mean chl-α concentrations for the surface layer of segment 8 ranged between 16 and 25 ppb (Figure 14). With the exception of the 1996 independent model prediction, predicted summer means agreed well with the observed data. The grand summer mean predicted for both the independent and integrated calibration were slightly underpredicted but within 15 percent of the measured value.

Observed summer mean PO4-P concentrations for the surface layer of segment 8 (Figure 15) ranged from 8 to 17 ppb for individual summer means. No systematic over or under prediction was evident in the simulation of summer mean concentrations by either the independent of integrated calibrations. Grand summer mean PO4-P concentrations predicted during the independent and integrated calibrations were within 21 and 16 percent of the observed mean, respectively. The grand summer mean PO4-P concentration was overpredicted during the independent calibration with measured inflow and underpredicted by the integrated SWAT/CE-QUAL-W2 model; however, both calibrations were within acceptable limits (±25 percent of observed grand summer mean).

Calibration results for segment 8 are provided in Table 8 for each model state variable. The results include comparisons of predicted and observed concentrations for the surface layer and average of the water column. Calibration performance was assessed based on recommended tolerance values (Donigian, 2001). Without the use of tolerance factors or other definitive rules, judging the degree of calibration is often subjective. Based on the tolerances recommended by Donigian (2001), the independent calibration was considered very good for all constituents except surface NH3-N, which was considered good and profile PO4-P and NH3-N considered fair. The integrated model calibration quality was deemed good or very good except for surface Org-P, which was fair.

44


Figure 14 Calibration results for summer mean chl-α concentration for surface layerof segment 8. Bars represent the range of possible summer means due to spatial variability (laterally) within segment 8 and observed concentrations less than MDL.

Figure 15 Calibration results for summer mean PO4-P concentration for surface layerof segment 8. Bars represent the range of possible summer means due to spatial variability (laterally) within segment 8 and observed concentrations less than MDL.

0

5

10

15

20

25

30

35

1996 SummerMean

1997 SummerMean

1998 SummerMean

Grand Mean,Summer Months

Grand Mean,(7/96-7/98)

Mea

n Su

rfac

e ch

l-α

Con

cent

rati

on (p

pb)

Observed Predicted with Measured Inflow Data Predicted with SWAT Inflows

0

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15

20

25

1996 SummerMean

1997 SummerMean

1998 SummerMean

Grand Mean,Summer Months

Grand Mean,(7/96-7/98)

Mea

n Su

rfac

e PO

4-P

Con

cent

rati

on (p

pb)

Observed Predicted with Measured Inflow Data Predicted with SWAT Inflows

45


Table 8 Calibration results for segment 8 (July 11, 1996 - July 29, 1998).

Calibration results for all of the water quality parameters at each of the five locations—segment 8 (LW013, LW020, LW030, LW040), segment 24 (LW070), segment 27 (LW012), segment 40 (LW015), and segment 43 (LW060) are presented in tabular form in Appendix A. Tables A–1 and A–2 list the results of comparisons performed for the independent calibration of CE-QUAL-W2. Tables A–3 and A–4 summarize similar information for the CE-QUAL-W2 integrated calibration. Appendix B contains plots comparing the predicted vertical mean concentrations to those observed at segment 8.

In conclusion, the calibration process produced an integrated modeling system of SWAT and CE-QUAL-W2 with sufficient accuracy to allow evaluation of reservoir water quality response to phosphorus control strategies. Based on the defined target location, the focus of CE-QUAL-W2 calibration was on the surface layer of segment 8. Both the independent and integrated calibrations provided acceptable predictions of summer season and annual mean concentrations of chl-α and PO4-P. Based on the tolerance ranges of Donigian (2001), both these constituents provided very good predictions of mean surface concentrations over the calibration period.

Post-Audit of Effects of Conservation Pool IncreaseTo assess the impact of the proposed increase in the conservation pool elevation in Lake Waco by 2.1 meters (7 ft), two long-term (32-year) simulations were performed for existing and future conditions. The existing conditions scenario represents the current level of watershed loading with respect to dairy herd size, municipal WWTP discharges, runoff from urban areas, and other land uses such as rangeland, forest, and row crop. The future condition scenario represents the watershed loadings accounting for future conditions in dairy herd size, municipal WWTP discharges, and urban runoff for the year 2020 (see Easterling, 2000).

Comparisons of model output for the current conservation pool elevation of 138.7 meters (455 ft) to the simulation based on the proposed increase to 140.8 meters (462 ft) revealed only minor changes in water quality due to the increased depth. It is important to keep in mind that

Independent Calibration

Integrated Calibration


IntegratedCalibration


Integrated Calibration

Surface TEMP, C 29 21.4 21.7 21.6 1.3% 1.1% very good very goodSurface DO, mg/L 29 8.50 8.11 8.09 -4.6% -4.9% very good very goodSurface Chl-α, µg/L 31 16.9 14.8 16.4 -12.6% -3.1% very good very goodSurface Org-P, mg/L 30 0.107 0.096 0.075 -11.0% -30.1% very good fairSurface PO4-P, mg/L 30 0.018 0.021 0.021 13.1% 14.9% very good very goodSurface TP, mg/L 30 0.125 0.116 0.096 -7.0% -23.2% very good goodSurface NO2+NO3-N, mg/L 30 0.478 0.430 0.391 -10.0% -18.2% very good goodSurface NH3-N, mg/L 30 0.062 0.049 0.059 -20.7% -4.7% good very goodSurface Org-N, mg/L 30 0.546 0.479 0.508 -12.2% -6.9% very good very goodSurface TN, mg/L 30 1.085 0.958 0.958 -11.7% -11.7% very good very goodAvg. profile TEMP, C 29 20.9 21.1 21.2 0.9% 1.1% very good very goodAvg. profile DO, mg/L 30 7.17 7.39 7.41 3.0% 3.4% very good very goodAvg. profile Org-P, mg/L 30 0.102 0.098 0.077 -3.7% -24.9% very good goodAvg. profile PO4-P, mg/L 30 0.020 0.025 0.023 25.6% 16.6% fair goodAvg. profile TP, mg/L 30 0.121 0.123 0.100 1.9% -17.5% very good goodAvg. profile NO2+NO3-N, mg/L 30 0.487 0.435 0.393 -10.7% -19.1% very good goodAvg. profile NH3-N, mg/L 30 0.081 0.053 0.062 -34.7% -22.8% fair goodAvg. profile Org-N, mg/L 30 0.558 0.492 0.518 -11.9% -7.2% very good very goodAvg. profile TN, mg/L 30 1.125 0.979 0.974 -13.0% -13.5% very good very good1 Tolerance ranges from Donigian (2001). For water quality/nutrients, <15% is very good, 15-25% is good, and 25-35% is fair. For water temperature, <7% is very good, 8-12% is good, and 13-18% is fair.

Percent Deviation of Simulated Mean from

Observed Mean

Observed

Calibration Quality1

Variable N

Mean

Predicted

46


the increased depth simulations were performed with current calibration values, ignoring the increased organic decay which is likely along the periphery of the lake due to submergence of terrestrial vegetation.

A legitimate concern associated with the proposed depth increase is whether the increase would cause the reservoir to exhibit thermal stratification during the summer months exacerbating hypolimnetic anoxia. The model results showed no increased propensity toward thermal stratification and modest increases in the degree of hypolimnetic anoxia. Under existing loading conditions and current conservation pool elevation, it was predicted that anoxia (defined by dissolved oxygen levels below 0.5 mg/L) occurred on 1,201 days of the 11,687 day simulation (roughly 10 percent). Following the 2.1 meter increase in depth, the model predicted that anoxia occurred on 1,695 days of the simulation or 15 percent of the time — a 50 percent increase in predicted days of anoxia. A similar increase in hypolimnetic anoxia was observed for the baseline conditions run where the number of days of anoxia increased from 1,401 days (12 percent) to 1,819 days (16 percent) — a 33 percent increase in predicted days of anoxia.

47


48

CHAPTER 5

Evaluation of Phosphorus ControlStrategies

Long-term simulations representing a 32-year period of record for climatic data from 1966 through 1997 were used to evaluate various phosphorus control strategies. Only the weather data were representative of historical conditions. Watershed management and loading rates were predicted by SWAT and represented the scenario under evaluation. The purpose of performing long-term simulations with historical weather data was to account for one of the two forms of uncertainty associated with the model predictions. Two types of uncertainties are inherent in model predictions, stochastic uncertainty and knowledge uncertainty. While knowledge uncertainty, associated with an incomplete knowledge of the process, models, or parameters, may be minimized through research and data collection; stochastic uncertainty cannot be eliminated. Stochastic uncertainty is associated with the inherent randomness of natural processes such as precipitation and flood events. Although stochastic uncertainty cannot be eliminated, the impact of variability of climatic and hydrologic conditions on model predictions can be examined through long-term simulations. The length of the simulation is somewhat irrelevant as long as the data represents a reasonable range of climatic and hydrologic conditions that may be encountered in nature. The length of simulations is generally dictated by the availability of input data for the particular model.

Lake model simulations were performed for the time period representing climatic conditions from June 1, 1965 through December 31, 1997. A 7-month period from June 1, 1965 through December 31, 1965 was used to initialize the model and was excluded from the final analysis. Although SWAT simulations were performed for a somewhat longer timeframe (1960-1998), the lake simulations were restricted to the period following construction when the lake was initially filled to the conservation pool.

Description of Phosphorus Control ScenariosIn addition to the scenarios for existing and future (2020) conditions, a number of phosphorus control scenarios were suggested by the stakeholder committee. These scenarios are described in Table 9. Based on discussion of the Bosque River Advisory Committee (BRAC), the control measures focused on dairies and municipal wastewater treatment plants (WWTPs). While other sources of phosphorus were recognized, the BRAC considered dairies and WWTPs as two of the major sources which could be controlled through best management practices (BMPs) or effluent limitations. Future growth conditions within the watershed were projected for urban land area, dairy herd size, row crop, and municipal WWTPs through the year 2020 (Easterling, 2000).

49


Dairy Best Management Practices (BMPs)Three scenarios addressed the effect of BMP implementation on dairies—a haul off scenario, a phosphorus rate scenario, and phosphorus diet scenario. Each of these scenarios was simulated under future growth conditions. The haul off scenario placed restrictions on the amount of solid dairy manure applied in the watershed. Under this scenario, it was assumed that 100 percent of the solid manure from the projected herd size of 67,000 cows would be hauled out of the watershed and liquid forms of dairy manure retained in wastewater lagoons would be applied at the nitrogen agronomic rate. This scenario would account for a removal of 88 percent of the nutrients associated with dairy manure from the watershed.

The phosphorus rate (P-rate) scenario assumed a reduction in the manure application rate to disposal fields. The application rate would be reduced from one that satisfies the nitrogen requirements of the crop (N-agronomic rate or N-rate) to one that satisfies the phosphorus requirements (P-agronomic rate or P-rate) of the particular crop. The balance of the nitrogen required by the crop would be supplied by commercial fertilizer. Application of manure at the P-rate increased the amount of acreage of disposal fields by about seven times the amount used at the N-rate.

The phosphorus diet (P-diet) scenario assumed a reduction in the amount of phosphorus ingested by the dairy cattle from their feed ration. A reduction in dietary phosphorus levels was assumed to result in a proportionate decrease in the amount of phosphorus in dairy manure. Under this scenario, the manure was applied at the N-rate, but contained about 30 percent less phosphorus (Keplinger, 1999).

Table 9 Phosphorus reduction scenarios.

Scenario Type of BMPWWTP Flow and Dairy Herd Size

Condition

WWTP P Limit

Dairy Manure Application Rate

Reduced P in Dairy Feed

Portion of Solid Manure Hauled Out

of Watershed

ExistingCurrent

conditions Existing Median conc.Between N&P

rates No No

FutureProjected

conditions Future Median conc. N rate No NoHaul off Dairy Future Median conc. N rate No YesP-Rate Dairy Future Median conc. P rate No NoP-Diet Dairy Future Median conc. N rate Yes No

WWTP - 0.5 mg/L WWTP Future 0.5 mg/L N rate No No

WWTP - 1.0 mg/L WWTP Future 1.0 mg/L N rates No No



Scenario I Combined Existing 1.0 mg/L P rate Yes NoScenario II Combined Future 1.0 mg/L P rate Yes NoScenario III Combined Future 1.0 mg/L P rate Yes Yes

50

Chapter 5 Evaluation of Phosphorus Control Strategies

WWTP Phosphorus Control StrategiesFour scenarios imposed various levels of effluent limitations on municipal wastewater treatment plants in the watershed. The scenarios set limits on the concentration of total phosphorus (TP) discharged to 2.0 mg/L, 1.5 mg/L, 1.0 mg/L, and 0.5 mg/L. Under the future and existing scenarios, the concentration of TP in the WWTP effluent was determined from TIAER monitoring data. Under the WWTP scenarios, all of the WWTP effluents were reduced to the proposed TP limit. If the WWTP was currently discharging at a concentration below the imposed limit, the concentration remained at current levels (i.e., the concentration was not increased to the TP limit).

Combination ScenariosThe combination BMP scenarios presented here (Scenarios I-III) include either three or four BMPs—three that target dairy manure application fields and one that reduces phosphorus loads from municipal WWTPs (Table 9). The dairy BMPs involve a reduction in the amount of phosphorus in dairy feed rations which consequently reduces the phosphorus content of the manure by about 30 percent (Keplinger, 1999) and a reduction in manure application rates from a nitrogen to a phosphorus agronomic rate. A third dairy BMP limits the amount of available dairy waste application fields and composts excess manure for haul off from the watershed. The control measure considered for WWTPs imposes phosphorus removal technology and a 1,000 ppb (1.0 mg/L) effluent limit for total phosphorus.

Scenario I represented existing dairy herd size and discharges from WWTPs to determine the short-term effects of BMP implementation on PO4-P concentrations in the watershed. Scenario II represented BMP implementation effects at future or year 2020 growth conditions, which takes into account full permitted dairy herd numbers and WWTP permitted discharge rates, with additional future growth estimates for urban and industrial growth projected to the year 2020. Scenario III was a modification of Scenario II with an added restriction imposed on manure disposal fields, which limited their aerial extent to that which is currently specified in dairy permits. Excess manure was assumed to be composted and hauled out of the watershed under Scenario III.

Effect of Individual Control Measures on Lake Water QualityThe effects of the individual control measures on water quality in Lake Waco were represented as average percent reduction in summer mean PO4-P concentrations predicted for the surface layer of segment 8. Percent reductions in mean summer PO4-P concentrations from those predicted under the future conditions scenario are depicted in Figure 16. Lake Waco showed greater sensitivity to nonpoint source controls targeting dairy manure application fields than to point source controls for WWTPs. This response is typical of lake and reservoirs, which due to their nature, act as flow integrators and capture the effects of infrequent albeit significant pulses of phosphorus transported during storm runoff.

The greatest impact on lake water quality was predicted for the haul off scenario, which was predicted to reduce summer mean PO4-P concentrations by 39 percent from future conditions. P-rate and P-diet scenarios were predicted to reduce summer mean PO4-P in Lake Waco by 20 and 15 percent, respectively. Lake water quality was less responsive to effluent limits imposed on WWTPs. Even under the strictest effluent limit of 0.5 mg/L TP, summer mean PO4-P concentrations were reduced by less than 10 percent.

51


Figure 16 Percent reductions in predicted mean summer PO4-P concentrationsfor individual BMPs using future conditions as the reference.

Effect of Combined Control Measures on Lake Water Quality & Target Attainment

This section focuses on three combination BMP scenarios (Table 9), which were evaluated with respect to target attainment and through comparisons to future and existing conditions. First the changes in nutrient and sediment loadings to the reservoir as predicted with SWAT will be shown, and then the CE-QUAL-W2 results for Lake Waco will be presented.

Percent reduction in annual watershed loads predicted by SWAT for each of the combination scenarios are listed in Table 10. Percent reductions are relative to the future conditions. Scenario I provided the greatest PO4-P load reductions (39 percent), though it, imposed the control measures to existing loading conditions, which were already reduced 25 percent from future loads. Scenario I provided an indication of expected water quality results in the near future if control measures were implemented. Scenario II represented conditions at the end of the 20-year planning horizon and indicated roughly a 30 percent decrease in watershed PO4-P loads into Lake Waco. Scenario III was the most aggressive P control strategy applied to future conditions, and it reduced inflow PO4-P loads to Lake Waco by 36 percent.

0

5

10

15

20

25

30

35

40

45

Existing Haul off P-rate P diet reduction WWTP-0.5 mg/l WWTP-1.0 mg/l WWTP-1.5 mg/l WWTP-2.0 mg/l

Scenario

Red

uct

ion

of s

um

mer

mea

n P

O4-

P c

once

ntra

tion

(%)

52


The results of the CE-QUAL-W2 simulations were presented in the form of exceedence probability charts to aid in comparison between different scenarios and assess the degree of target attainment. Exceedence probability charts depicted results of the 32-year simulation and the probability of exceeding the proposed target. Exceedence plots were generated from the summer mean PO4-P concentrations predicted for the surface layer of segment 8. Daily model output was used to compute summer mean PO4-P concentrations for the months of April through September during the simulation period (1966 to 1997). Summer mean PO4-P concentrations predicted for each of the 32 years were ordered from highest to lowest and plotted as a cumulative probability function. These plots also provide information on the probability of achieving a particular summer mean PO4-P concentration (that is 1.0 minus probability of exceedence). This type of analysis provided the BRAC with a more realistic assessment of the likelihood of success in implementing a particular BMP. Exceedence plots were also used to identify relevant BMPs to achieve the desired water quality target. The procedure used to generate exceedence probability charts from model output is outlined through an example included in Appendix C.

Results of scenario evaluations are presented as exceedence probability charts for chl-α and PO4-P in Figure 17 and Figure 18, respectively. Figures 17 and 18 depict the exceedence probability for each year of the simulation (x-axis) plotted against the corresponding summer mean concentration (y-axis). Scenarios I through III represent reductions in watershed loading and respondes expected with a reduction in predicted summer mean chl-α concentrations. It should be noted that the annual values are ranked in descending order according the summer mean concentration and the placement of years is not always consistent across scenarios such that the highest summer mean concentrations depicted in Figure 17 may not be the same calendar year for each scenario. In general the scenarios responded as expected with lower concentrations that are fairly linear except for the extremes (i.e., very high and very low concentrations), which occur infrequently due to extreme hydrometeorological conditions such as floods and droughts. For this reason, the exceedence probability plots generally show greater departure between the scenarios for extreme conditions at low exceedence probabilities.

Under existing conditions, the summer mean chl-α concentration was predicted to range between 8 and 30 ppb with a median value (0.5 probability) of about 19 ppb. Increased watershed loading under future conditions was predicted to cause an increase in chl-α concentrations with a range between 11 and 41 ppb and a median concentration of 25 ppb. Evaluation of predicted chl-α concentrations with respect to the TNRCC screening level of 20 ppb (TNRCC, 1998), which was the level at the time of the study, indicates that for future conditions the screening level would be exceeded approximately 70 percent of simulated

Table 10 Percent change in predicted loading to Lake Waco for 1966 through 1997using future conditions as the reference.

Scenario Sediment(%)

Organic Nitrogen(%)

Organic Phosphorus(%)

Soluble Nitrogen -Nitrate+Nitrite +Ammonium

(%)

Orthophosphate Phosphorus

(%)

Existing -2.0 -4.8 -9.0 -8.8 -24.7

Scenario I -2.4 -4.3 -12.7 -6.2 -38.8

Scenario II -1.8 -0.2 -9.2 +4.4 -29.8

Scenario III -0.5 -2.2 -8.3 -2.9 -35.5

53


years. The model predicts that the screening level would be exceeded about 30 percent of the years under the existing watershed loading. Results of the combined scenarios indicated that even with P control measures implemented, the summer mean chl-α concentrations in Lake Waco would still exceed the TNRCC screening level during some years. Under the most stringent controls (Scenario III), roughly 25 percent of the years would be characterized by chl-α concentrations in excess of the TNRCC screening level.

The exceedence probabilities predicted for summer mean PO4-P with respect to the preliminary target of 10 ppb are depicted in Figure 18. The combination scenarios exhibited a narrow range of probabilities (Figure 18). For future conditions, model predictions indicated that the PO4-P target would be exceeded for over half the years (p=0.55). For existing conditions, the predictions were for just under half the years exceeding the target (p=0.45). The increased loading under future conditions increased the probability of exceeding the lake water quality target from 0.45 to 0.55. Exceedence probabilities ranged from 0.32 to 0.42 for the three scenarios and indicated that even under the most stringent control measures (Scenario III) the lake water quality target would be exceeded roughly one-third of the years. Through the implementation of P control measures outlined in Scenario III, it was predicted that the probability of exceeding the lake water quality target decreased from 0.55 for future conditions to 0.32.

Figure 17 Exceedence probability chart for summer mean chlorophyll-α concentration.

0

5

10

15

20

25

30

35

40

45

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Probability of Exceedence

Chl

orop

hyll-

α C

once

ntra

tion

(ppb

)

Future Existing Scenario I Scenario II Scenario III

TNRCC Screening

Levelfor Reservoirs

54


Figure 18 Exceedence probability chart for summer mean PO4-P concentration.

0

2

4

6

8

10

12

14

16

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

Probability of Exceedence

Dis

solv

ed O

rtho

phos

phat

e Ph

osph

orus

C

once

ntra

tion

(ppb

)

Future Existing Scenario I Scenario II Scenario III

TargetRange

Preliminary Target

55


56

CHAPTER 6

Summary and Conclusions

Evaluations of phosphorus control strategies were accomplished through the collaborative research of TIAER and BREC. The watershed loading model, Soil and Water Assessment Tool (SWAT) was used to predict the nutrient loads from the watershed which were then used as input to the CE-QUAL-W2 model of Lake Waco. CE-QUAL-W2, a two-dimensional, laterally averaged, numerical model, was selected for simulation of the water quality of Lake Waco. Both SWAT and CE-QUAL-W2 were independently calibrated against measured water quality data. A calibration check of the integrated model was also performed to ensure that the model predictions were within acceptable error bounds given the additional uncertainty introduced by integration of models. Following model calibration, simulated outputs from SWAT such as flow, sediment, and nutrients were used to develop input to the CE-QUAL-W2 model to study the phytoplankton biomass response in Lake Waco.

The integrated modeling system of SWAT and CE-QUAL-W2 proved to be a successful approach to evaluate P control measures for the Lake Waco-Bosque River watershed. The modeling system was successfully applied to evaluate various scenarios that considered BMPs for dairies and control technologies for municipal WWTPs.

Major Accomplishments of the Modeling Effort1. An integrated modeling approach was employed to examine water quality issues in the Lake Waco-Bosque River watershed. Through the integration of a watershed model (SWAT) with a receiving water model (CE-QUAL-W2), the impact of alternative management scenarios was assessed based on predicted water quality in Lake Waco.

2. The impact of nutrient enriched stream flow from the North Bosque River on Lake Waco was characterized through monitoring studies and through calibration of CE-QUAL-W2. Calibration of the lake model was performed using inflow boundary conditions (loads and flows) from two sources. The first set of inflow boundary conditions was estimated from monitoring data collected in the watershed. A second set of inflows was predicted using the Soil and Water Assessment Tool (SWAT), which was calibrated for the watershed (Santhi et al., 2001).

3. The future status of water quality in Lake Waco was predicted based on projections through year 2020 or permit limits using whichever was greater. The predicted future water quality under status quo (i.e., no additional P control measures in place) represented a worst case scenario and provided a baseline to judge the efficacy of different phosphorus control strategies based on improvement in key water quality indicators.

4. Several phosphorus control strategies were considered to evaluate their ability to achieve water quality targets. Water quality targets were derived from empirical relationships between chl-α and PO4-P concentrations observed in Lake Waco intake (Kiesling et al., 2001). A water quality target of 10 ppb PO4-P was selected by the BRAC, under scientific advisement of the Technical Work Group (TWG). The target (10 ppb PO4-P) was established in terms of a

57

Water Quality Monitoring of Lake Waco Using CE-QUAL-W2

summer mean concentration (April through September). Water quality targets were set for Lake Waco at a region near the dam where the City of Waco’s drinking water intake is located. Application of the lake target was further restricted to near-surface conditions corresponding to the top layer in the CE-QUAL-W2 model.

5. Long-term model simulations were performed to account for stochastic uncertainty. Simulations were performed for a 32-year period from 1966 through 1997, which represented a reasonable range of climatic and hydrologic conditions observed in nature. Long-term simulation results were used to generate exceedence probability charts characterizing the probability that the water quality targets would be exceeded under various nutrient control strategies.

6. In coordination with the BRAC, several strategies for controlling phosphorus and achieving lake water quality targets were recommended. The strategies represent a combination of point and nonpoint source reductions imposed on dairies and municipal WWTPs. Long-term simulations of CE-QUAL-W2 integrated with SWAT predicted the impact of these combination scenarios on lake water quality. No combination of control strategies allowed the phosphorus target to be obtained in all simulated years, though the number of years of exceedence could be appreciably reduced.

58

References

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Cole, G.A. 1994. Textbook of Limnology, 4th edition. Waveland Press, Inc., Prospect Heights, IL.

Cole, T.M. and E.M. Buchak. 1995. CE-QUAL-W2: A Two-Dimensional, Laterally Averaged, Hydrody-namic and Water Quality Model, Version 2.0, User Manual. Instruction Report EL-95-1. U.S. Army Corps of Engineers, Waterways Experiment Station, Vicksburg, MS.

Cole, T.M. and D.H. Tillman. 1999. Water Quality Modeling of Lake Monroe Using CE-QUAL-W2. Mis-cellaneous Paper EL-99-1, January 1999. US Army Corps of Engineers, Waterways Experiment Sta-tion. Vicksbury, M.S.

Cole, T.M. and S.A. Wells. 2000. CE-QUAL-W2: A Two-Dimensional, Laterally Averaged, Hydrodynamic and Water Quality Model, Version 3.0, User Manual. Instruction Report EL-00-1. U.S. Army Corps of Engineers, Waterways Experiment Station, Vicksburg, MS.

Dávalos-Lind, L.D. and O.T. Lind. 1998. The Algal Growth Potential of and Growth-Limiting Nutrients in Lake Waco and its Tributary Waters. An Interim report to TIAER. Limnology Laboratory, Depart-ment of Biology, Baylor University. Waco, Texas.

Donigian, T. 2001. Watershed Modeling for TMDL Development. Presentation at Water Environment Federation TMDL Science Issues Conference. March 4-7, 2001. St. Louis MO.

Easterling, N. 2000. Future Growth Projections for the Lake Waco-Bosque River Watershed. Texas Institute for Applied Environmental Research, Tarleton State University, Stephenville, Texas. TIAER Work-ing Paper Series, WP00-05 (May 2000).

Easterling, N. 1997. Quality Assurance Project Plan for the United States Department of Agriculture Bosque River Initiative, Revision 1. Texas Institute for Applied Environmental Research, Tarleton State University, Stephenville, Texas.

Easterling, N. 1996. Quality Assurance Project Plan for the United States Department of Agriculture Bosque River Initiative. Texas Institute for Applied Environmental Research, Tarleton State University, Stephenville, Texas.

Heiskary, S.A. 1995. Establishing a Chlorophyll α Goal for Runoff the River Reservoir. Lake and Res-ervoir Management. 11:67-76.

Keplinger, Keith. 1999. Cost Savings and Environmental Benefits of Dietary P Reductions for Dairy Cows in the Bosque River Watersheds. Texas Institute for Applied Environmental Research, Tarleton State University, Stephenville, Texas. (in preparation).

Kiesling, R.L., A.M.S. McFarland, and L. Hauck. 2001. Nutrient Targets for Lake Waco and North Bosque River: Developing Ecosystem Restoration Criteria. Texas Institute for Applied Environmental Re-search, Tarleton State University, Stephenville, Texas. TIAER Technical Report Series, TR01-07.

Kimmel, Bruce L. 1969. Phytoplankton Production in a Central Texas Reservoir. Masters Thesis, Dept. of Biology, Baylor University. Waco, TX.

Kimmel, Bruce L. and Owen T. Lind. 1972. Factors Affecting Phytoplankton Production in a Eutrophic Reservoir. Archives of Hydrobiology. 71: 124-141.

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Kimmel, Bruce L. and Owen T. Lind. 1970. Factors Influencing Orthophosphate Concentration De-cline In the Water of Laboratory Mud-Water Systems. The Texas Journal of Science 21:439-445.

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Lind, Owen T. 1979. Reservoir Eutrophication: Factors Governing Primary Production. U.S. Dept. Interior, Office of Water Resources Research, Research Project Completion Report, B-210-TEX.

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Lind, Owen T. and Samuel D. Katzif. 1988. Nitrogen and the Threshold Odor Number Produced by an Actinomycete Isolated From Lake Sediments. Water Science Technology. 20:8/9, pp. 185-191.

McFarland, A. and L. Hauck. 1999. Existing Nutrient Sources and Contributions to the Bosque River Wa-tershed. Texas Institute for Applied Environmental Research, Tarleton State University, Stephen-ville, Texas, PR99-11 (September 1999).

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Santhi, C., J.R. Williams, and W.A. Dugas. 2001. USDA Lake Waco/Bosque River Initiative: Water Quality Modeling Using SWAT for the Assessment of Phosphorus Control Strategies. BREC Report No. 01-34, June 2001. Texas A&M Blackland Research Center, Temple, Texas.

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61


62

APPENDIX A

Model Calibration Results

Calibration Statistics

The mean error (ME) is simply the difference between the predicted mean and the observed mean. A positive ME indicates that the predicted mean is greater than the observed mean (i.e., the value is overpredicted). A negative ME indicates that the value tends to be underpredicted. ME was computed as defined in equation A-1.

A - 1)

The absolute mean error (AME) gives an indication of how close on either side of the observed values the predicted values lie. For example, an AME of 0.2 indicates that, on average, the predicted values are within ±(mg/L or °C) of the observed value. The AME was computed as defined in equation A-2.

A - 2)

The root mean square error (RMSE) is a measure of the variability between observed and predicted concentrations. For example, a RMSE of 0.2 indicates that the predicted values are within ±0.2(mg/L or °C) of the observed value 67 percent of the time. The RMSE was computed as defined in equation A-3.

A - 3)

ME Predicted Mean Observed Mean–=

AME Σ predicted observed–number of observations--------------------------------------------------------=

RMSE Σ predicted observed–( )2

number of observations------------------------------------------------------------=

63


Table A–1 Calibration results with measured inflows for surface layer water quality.

Units N Obs Sim Obs Sim

Surface TP mg/L 30 0.125 0.116 0.100 0.043 -0.009 0.065 0.086Surface TEMP C 29 21.4 21.7 7.7 7.3 0.3 0.8 0.9Surface DO mg/L 29 8.50 8.11 1.22 1.55 -0.39 0.75 0.98Surface PO4-P mg/L 30 0.018 0.021 0.019 0.029 0.002 0.014 0.024Surface NO2+NO3-N mg/L 30 0.478 0.430 0.481 0.296 -0.048 0.210 0.270Surface NH3-N mg/L 30 0.062 0.049 0.051 0.016 -0.013 0.035 0.051Surface Org-N mg/L 30 0.546 0.479 0.122 0.121 -0.066 0.151 0.186Surface TN mg/L 30 1.085 0.958 0.510 0.362 -0.127 0.208 0.284Surface Org-P mg/L 30 0.107 0.096 0.099 0.024 -0.012 0.064 0.092Surface Chl-α mg/L 31 0.017 0.015 0.009 0.008 -0.002 0.007 0.010

Surface TP mg/L 26 0.119 0.129 0.098 0.049 0.010 0.063 0.090Surface TEMP C 25 21.1 20.9 8.3 7.6 -0.2 1.0 1.2Surface DO mg/L 25 8.92 7.90 1.45 1.84 -1.02 1.25 1.51Surface PO4-P mg/L 26 0.022 0.028 0.022 0.036 0.006 0.022 0.034Surface NO2+NO3-N mg/L 26 0.321 0.403 0.296 0.216 0.082 0.164 0.184Surface NH3-N mg/L 26 0.046 0.051 0.030 0.019 0.005 0.026 0.033Surface Org-N mg/L 26 0.565 0.503 0.200 0.123 -0.062 0.163 0.210Surface TN mg/L 26 0.927 0.957 0.417 0.263 0.031 0.267 0.310Surface Org-P mg/L 26 0.081 0.100 0.082 0.025 0.020 0.053 0.078Surface Chl-α mg/L 27 0.024 0.014 0.012 0.008 -0.010 0.012 0.016

Surface TP mg/L 29 0.132 0.112 0.099 0.036 -0.019 0.067 0.090Surface TEMP C 29 21.7 21.6 7.9 7.3 -0.2 0.9 1.2Surface DO mg/L 29 8.66 7.99 1.34 1.66 -0.67 0.90 1.18Surface PO4-P mg/L 29 0.016 0.019 0.018 0.026 0.003 0.013 0.022Surface NO2+NO3-N mg/L 29 0.376 0.391 0.408 0.250 0.015 0.211 0.254Surface NH3-N mg/L 29 0.052 0.048 0.036 0.015 -0.004 0.030 0.040Surface Org-N mg/L 29 0.536 0.468 0.187 0.110 -0.068 0.138 0.201Surface TN mg/L 29 0.947 0.947 0.476 0.476 0.000 0.000 0.000Surface Org-P mg/L 29 0.084 0.093 0.063 0.022 0.009 0.044 0.060Surface Chl-α mg/L 29 0.020 0.016 0.010 0.008 -0.004 0.007 0.010

Surface TP mg/L 30 0.097 0.112 0.070 0.038 0.022 0.054 0.065Surface TEMP C 29 21.2 21.3 7.7 7.2 0.1 0.8 1.1Surface DO mg/L 29 8.36 7.85 2.07 1.79 -0.52 1.03 1.31Surface PO4-P mg/L 30 0.023 0.018 0.024 0.026 -0.004 0.017 0.026Surface NO2+NO3-N mg/L 30 0.771 0.443 0.814 0.357 -0.311 0.449 0.659Surface NH3-N mg/L 30 0.058 0.047 0.060 0.013 -0.008 0.033 0.060Surface Org-N mg/L 30 0.500 0.473 0.202 0.122 -0.032 0.144 0.185Surface TN mg/L 30 1.363 0.963 0.874 0.430 -0.387 0.530 0.740Surface Org-P mg/L 30 0.068 0.095 0.056 0.024 0.033 0.050 0.059Surface Chl-α mg/L 28 0.018 0.015 0.011 0.008 -0.003 0.008 0.011

Surface TP mg/L 29 0.089 0.113 0.066 0.038 0.024 0.054 0.067Surface TEMP C 29 21.5 21.4 7.5 7.3 -0.1 0.8 1.0Surface DO mg/L 29 8.21 7.95 1.80 1.74 -0.27 0.78 1.04Surface PO4-P mg/L 29 0.023 0.018 0.024 0.026 -0.005 0.016 0.024Surface NO2+NO3-N mg/L 29 0.601 0.431 0.680 0.331 -0.170 0.314 0.479Surface NH3-N mg/L 29 0.052 0.047 0.040 0.014 -0.005 0.030 0.045Surface Org-N mg/L 29 0.456 0.473 0.138 0.119 0.017 0.136 0.175Surface TN mg/L 29 1.119 0.951 0.770 0.399 -0.168 0.366 0.534Surface Org-P mg/L 29 0.064 0.094 0.051 0.024 0.030 0.048 0.060Surface Chl-α mg/L 29 0.017 0.016 0.009 0.008 -0.002 0.007 0.009

Segment 8 - LW013, LW020, LW030, LW040

AbsoluteMeanError

RootMean

SquareErrorVariable (units)

Std. Dev.

MeanError

Mean

Segment 24 - LW070

Segment 27 - LW012

Segment 40 - LW015

Segment 43 - LW060

64

Appendix A Model Calibration Results

Table A–2 Calibration results with measured inflows for vertical mean water quality.


Average Water Column TP mg/L 30 0.121 0.123 0.085 0.047 0.002 0.065 0.078Average Water Column TEMP C 29 20.9 21.1 7.5 7.2 0.2 0.6 0.7Average Water Column DO mg/L 30 7.17 7.39 2.14 1.97 0.22 0.74 0.99Average Water Column PO4-P mg/L 30 0.020 0.025 0.018 0.029 0.005 0.016 0.026Average Water Column NO2+NO3-N mg/L 30 0.487 0.435 0.469 0.286 -0.052 0.204 0.265Average Water Column NH3-N mg/L 30 0.081 0.053 0.052 0.019 -0.028 0.041 0.056Average Water Column Org-N mg/L 30 0.558 0.492 0.102 0.133 -0.066 0.136 0.166Average Water Column TN mg/L 30 1.125 0.979 0.479 0.367 -0.146 0.201 0.280Average Water Column Org-P mg/L 30 0.102 0.098 0.083 0.027 -0.004 0.061 0.078

Average Water Column TP mg/L 26 0.129 0.140 0.091 0.065 0.010 0.067 0.094Average Water Column TEMP C 25 20.6 20.4 8.3 7.6 -0.2 0.9 1.0Average Water Column DO mg/L 25 7.72 7.43 2.03 2.09 -0.29 0.79 0.95Average Water Column PO4-P mg/L 26 0.024 0.031 0.023 0.038 0.007 0.021 0.030Average Water Column NO2+NO3-N mg/L 26 0.323 0.409 0.283 0.226 0.086 0.157 0.172Average Water Column NH3-N mg/L 26 0.067 0.055 0.041 0.025 -0.012 0.028 0.035Average Water Column Org-N mg/L 26 0.596 0.542 0.185 0.182 -0.055 0.154 0.192Average Water Column TN mg/L 26 0.983 1.006 0.373 0.317 0.022 0.233 0.266Average Water Column Org-P mg/L 26 0.092 0.108 0.079 0.036 0.017 0.060 0.081

Average Water Column TP mg/L 29 0.127 0.119 0.084 0.045 -0.008 0.061 0.079Average Water Column TEMP C 29 21.3 21.3 7.8 7.3 0.0 0.8 1.0Average Water Column DO mg/L 29 7.85 7.69 1.70 1.77 -0.16 0.00 0.72Average Water Column PO4-P mg/L 29 0.018 0.022 0.018 0.029 0.004 0.015 0.024Average Water Column NO2+NO3-N mg/L 29 0.365 0.390 0.380 0.242 0.025 0.188 0.224Average Water Column NH3-N mg/L 29 0.061 0.049 0.032 0.018 -0.012 0.027 0.035Average Water Column Org-N mg/L 29 0.536 0.485 0.139 0.137 -0.051 0.126 0.164Average Water Column TN mg/L 29 0.947 0.947 0.437 0.437 0.000 0.000 0.000Average Water Column Org-P mg/L 29 0.087 0.097 0.067 0.027 0.010 0.000 0.068

Average Water Column TP mg/L 30 0.112 0.119 0.084 0.045 0.007 0.059 0.080Average Water Column TEMP C 29 20.7 20.9 7.5 7.3 0.2 0.6 0.8Average Water Column DO mg/L 29 7.49 7.48 2.24 1.95 -0.01 0.83 0.99Average Water Column PO4-P mg/L 30 0.022 0.021 0.023 0.028 -0.002 0.018 0.027Average Water Column NO2+NO3-N mg/L 30 0.777 0.463 0.824 0.319 -0.314 0.443 0.653Average Water Column NH3-N mg/L 30 0.059 0.049 0.043 0.016 -0.009 0.030 0.042Average Water Column Org-N mg/L 30 0.499 0.490 0.189 0.132 -0.009 0.162 0.205Average Water Column TN mg/L 30 1.378 1.003 0.871 0.399 -0.376 0.489 0.713Average Water Column Org-P mg/L 30 0.085 0.098 0.075 0.026 0.013 0.055 0.072

Average Water Column TP mg/L 29 0.096 0.114 0.055 0.038 0.019 0.047 0.058Average Water Column TEMP C 29 20.8 21.1 7.5 7.3 0.3 0.7 0.9Average Water Column DO mg/L 29 7.61 7.62 2.01 1.86 0.01 0.67 0.84Average Water Column PO4-P mg/L 29 0.024 0.019 0.025 0.026 -0.005 0.016 0.025Average Water Column NO2+NO3-N mg/L 29 0.622 0.433 0.690 0.302 -0.189 0.323 0.487Average Water Column NH3-N mg/L 29 0.053 0.048 0.032 0.015 -0.005 0.028 0.038Average Water Column Org-N mg/L 29 0.461 0.478 0.129 0.118 0.017 0.141 0.171Average Water Column TN mg/L 29 1.158 0.959 0.772 0.367 -0.199 0.367 0.537Average Water Column Org-P mg/L 29 0.071 0.095 0.041 0.024 0.025 0.040 0.050

Segment 24 - LW070


Mean

Segment 43 - LW060

Segment 40 - LW015

Segment 27 - LW012

Variable (units)

Std. Dev.

MeanError

AbsoluteMeanError

RootMean

SquareError

65


Table A–3 Calibration results with SWAT inflows for surface layer water quality.


Surface TP mg/L 30 0.125 0.096 0.100 0.043 -0.029 0.074 0.107Surface TEMP C 29 21.4 21.6 7.7 7.1 0.2 0.8 1.0Surface DO mg/L 29 8.50 8.09 1.22 1.58 -0.41 0.77 1.01Surface PO4-P mg/L 30 0.018 0.021 0.019 0.027 0.003 0.014 0.022Surface NO2+NO3-N mg/L 30 0.478 0.391 0.481 0.331 -0.087 0.277 0.384Surface NH3-N mg/L 30 0.062 0.059 0.051 0.017 -0.003 0.039 0.050Surface Org-N mg/L 30 0.546 0.508 0.122 0.147 -0.038 0.149 0.181Surface TN mg/L 30 1.085 0.958 0.510 0.416 -0.127 0.280 0.385Surface Org-P mg/L 30 0.107 0.075 0.099 0.022 -0.032 0.071 0.105Surface Chl-α mg/L 31 0.017 0.016 0.009 0.009 -0.001 0.009 0.011

Surface TP mg/L 26 0.119 0.096 0.098 0.044 -0.023 0.070 0.090Surface TEMP C 25 21.1 20.8 8.3 7.4 -0.3 1.1 1.3Surface DO mg/L 25 8.92 7.86 1.45 1.80 -1.06 1.19 1.51Surface PO4-P mg/L 26 0.022 0.024 0.022 0.029 0.002 0.019 0.026Surface NO2+NO3-N mg/L 26 0.321 0.388 0.296 0.291 0.067 0.188 0.249Surface NH3-N mg/L 26 0.046 0.060 0.030 0.019 0.013 0.031 0.036Surface Org-N mg/L 26 0.565 0.485 0.200 0.140 -0.080 0.193 0.242Surface TN mg/L 26 0.927 0.933 0.417 0.376 0.006 0.300 0.369Surface Org-P mg/L 26 0.081 0.072 0.082 0.021 -0.009 0.046 0.075Surface Chl-α mg/L 27 0.024 0.014 0.012 0.007 -0.011 0.012 0.016

Surface TP mg/L 29 0.132 0.093 0.099 0.040 -0.038 0.081 0.106Surface TEMP C 29 21.7 21.5 7.9 7.2 -0.2 1.0 1.3Surface DO mg/L 29 8.66 7.95 1.34 1.64 -0.71 0.94 1.23Surface PO4-P mg/L 29 0.016 0.020 0.018 0.025 0.003 0.015 0.023Surface NO2+NO3-N mg/L 29 0.376 0.365 0.408 0.310 -0.012 0.236 0.318Surface NH3-N mg/L 29 0.052 0.058 0.036 0.016 0.006 0.031 0.037Surface Org-N mg/L 29 0.536 0.497 0.187 0.139 -0.039 0.188 0.235Surface TN mg/L 29 0.947 0.947 0.476 0.476 0.000 0.000 0.000Surface Org-P mg/L 29 0.084 0.074 0.063 0.021 -0.011 0.046 0.062Surface Chl-α mg/L 29 0.020 0.017 0.010 0.009 -0.003 0.009 0.011

Surface TP mg/L 30 0.097 0.098 0.070 0.049 0.001 0.060 0.071Surface TEMP C 29 21.2 21.3 7.7 7.0 0.1 0.9 1.1Surface DO mg/L 29 8.36 7.80 2.07 1.79 -0.56 1.12 1.38Surface PO4-P mg/L 30 0.023 0.021 0.024 0.030 -0.001 0.017 0.024Surface NO2+NO3-N mg/L 30 0.771 0.401 0.814 0.331 -0.369 0.509 0.768Surface NH3-N mg/L 30 0.058 0.058 0.060 0.016 0.001 0.034 0.054Surface Org-N mg/L 30 0.500 0.519 0.202 0.167 0.019 0.199 0.232Surface TN mg/L 30 1.363 0.979 0.874 0.428 -0.384 0.586 0.783Surface Org-P mg/L 30 0.068 0.077 0.056 0.025 0.009 0.048 0.059Surface Chl-α mg/L 28 0.018 0.017 0.011 0.010 -0.001 0.009 0.011

Surface TP mg/L 29 0.089 0.096 0.066 0.046 0.007 0.053 0.069Surface TEMP C 29 21.5 21.4 7.5 7.1 -0.1 0.9 1.1Surface DO mg/L 29 8.21 7.91 1.80 1.73 -0.30 0.84 1.10Surface PO4-P mg/L 29 0.023 0.020 0.024 0.028 -0.003 0.015 0.023Surface NO2+NO3-N mg/L 29 0.601 0.381 0.680 0.325 -0.220 0.388 0.583Surface NH3-N mg/L 29 0.052 0.058 0.040 0.016 0.006 0.034 0.042Surface Org-N mg/L 29 0.456 0.511 0.138 0.162 0.054 0.156 0.205Surface TN mg/L 29 1.119 0.950 0.770 0.414 -0.169 0.415 0.577Surface Org-P mg/L 29 0.064 0.076 0.051 0.024 0.011 0.039 0.055Surface Chl-α mg/L 29 0.017 0.017 0.009 0.009 0.000 0.007 0.009

Segment 43 - LW060


Segment 24 - LW070

Segment 27 - LW012

Segment 40 - LW015

AbsoluteMeanError

RootMean

SquareErrorVariable (units)

Std. Dev.

MeanError

Mean

66

Appendix A Model Calibration Results

Table A–4 Calibration results with SWAT inflows for vertical mean water quality.


Average Water Column TP mg/L 30 0.121 0.100 0.085 0.043 -0.021 0.071 0.091Average Water Column TEMP C 29 20.9 21.2 7.5 7.1 0.2 0.7 0.8Average Water Column DO mg/L 30 7.17 7.41 2.14 1.98 0.24 0.65 0.90Average Water Column PO4-P mg/L 30 0.020 0.023 0.018 0.026 0.003 0.015 0.022Average Water Column NO2+NO3-N mg/L 30 0.487 0.393 0.469 0.325 -0.093 0.268 0.379Average Water Column NH3-N mg/L 30 0.081 0.062 0.052 0.019 -0.018 0.039 0.053Average Water Column Org-N mg/L 30 0.558 0.518 0.102 0.150 -0.040 0.136 0.173Average Water Column TN mg/L 30 1.125 0.974 0.479 0.408 -0.152 0.288 0.385Average Water Column Org-P mg/L 30 0.102 0.077 0.083 0.022 -0.025 0.066 0.089

Average Water Column TP mg/L 26 0.129 0.099 0.091 0.045 -0.031 0.071 0.088Average Water Column TEMP C 25 20.6 20.4 8.3 7.5 -0.2 1.0 1.1Average Water Column DO mg/L 25 7.72 7.35 2.03 2.13 -0.37 0.77 0.96Average Water Column PO4-P mg/L 26 0.024 0.024 0.023 0.027 0.000 0.017 0.022Average Water Column NO2+NO3-N mg/L 26 0.323 0.379 0.283 0.271 0.056 0.186 0.240Average Water Column NH3-N mg/L 26 0.067 0.061 0.041 0.017 -0.006 0.031 0.040Average Water Column Org-N mg/L 26 0.596 0.507 0.185 0.170 -0.090 0.208 0.241Average Water Column TN mg/L 26 0.999 0.947 0.356 0.358 -0.053 0.253 0.316Average Water Column Org-P mg/L 26 0.095 0.075 0.081 0.025 -0.020 0.054 0.079

Average Water Column TP mg/L 29 0.127 0.096 0.084 0.041 -0.031 0.069 0.088Average Water Column TEMP C 29 21.3 21.3 7.8 7.2 0.0 0.9 1.0Average Water Column DO mg/L 29 7.85 7.63 1.70 1.78 -0.22 0.55 0.72Average Water Column PO4-P mg/L 29 0.018 0.020 0.018 0.026 0.002 0.015 0.022Average Water Column NO2+NO3-N mg/L 29 0.366 0.365 0.380 0.308 -0.001 0.221 0.305Average Water Column NH3-N mg/L 29 0.061 0.059 0.032 0.016 -0.003 0.024 0.031Average Water Column Org-N mg/L 29 0.536 0.508 0.139 0.147 -0.027 0.162 0.199Average Water Column TN mg/L 29 0.970 0.970 0.411 0.411 0.000 0.000 0.000Average Water Column Org-P mg/L 29 0.091 0.075 0.069 0.022 -0.016 0.050 0.071

Average Water Column TP mg/L 30 0.114 0.097 0.084 0.043 -0.017 0.066 0.087Average Water Column TEMP C 29 20.7 21.0 7.5 7.2 0.3 0.6 0.8Average Water Column DO mg/L 29 7.49 7.40 2.24 1.97 -0.08 0.85 1.01Average Water Column PO4-P mg/L 30 0.022 0.021 0.023 0.026 -0.002 0.016 0.024Average Water Column NO2+NO3-N mg/L 30 0.778 0.393 0.824 0.321 -0.385 0.514 0.797Average Water Column NH3-N mg/L 30 0.059 0.058 0.043 0.016 0.000 0.033 0.041Average Water Column Org-N mg/L 30 0.499 0.519 0.189 0.152 0.020 0.167 0.211Average Water Column TN mg/L 30 1.388 0.970 0.860 0.401 -0.418 0.550 0.798Average Water Column Org-P mg/L 30 0.086 0.077 0.074 0.023 -0.009 0.056 0.076

Average Water Column TP mg/L 29 0.101 0.096 0.057 0.042 -0.005 0.048 0.059Average Water Column TEMP C 29 20.8 21.2 7.5 7.2 0.4 0.7 0.9Average Water Column DO mg/L 29 7.61 7.55 2.01 1.86 -0.06 0.69 0.87Average Water Column PO4-P mg/L 29 0.024 0.020 0.025 0.026 -0.004 0.016 0.025Average Water Column NO2+NO3-N mg/L 29 0.623 0.377 0.690 0.322 -0.246 0.414 0.628Average Water Column NH3-N mg/L 29 0.053 0.058 0.032 0.016 0.005 0.029 0.035Average Water Column Org-N mg/L 29 0.461 0.512 0.129 0.153 0.052 0.150 0.193Average Water Column TN mg/L 29 1.166 0.948 0.763 0.401 -0.218 0.422 0.616Average Water Column Org-P mg/L 29 0.074 0.076 0.046 0.023 0.002 0.039 0.048

Segment 43 - LW060

Std. Dev.

Variable (units)

Root Mean

Square Error

Absolute Mean Error


Segment 24 - LW070

Segment 27 - LW012

Segment 40 - LW015

Mean Error

Mean

67


68

APPENDIX B

CE-QUAL-W2 Calibration Plots -Time-series plots of mean water

column concentrations forsegment 8

69


Figure B–1 Time-series plot of mean water column PO4-P concentration.

Figure B–2 Time-series plot of mean water column Org-P concentration.

Water Column Average at Segment 8

0.00

0.05

0.10

0.15

0.20

9-Jul-96

7-Sep-96

6-Nov-96

5-Jan-97

6-Mar-97

5-May-97

4-Jul-97

2-Sep-97

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31-Dec-97

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30-Apr-98

29-Jun-98

PO4-P

Con

cent

ratio

n (m

g/L

)



0.00

0.10

0.20

0.30

0.40

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0.60

9-Jul-96

7-Sep-96

6-Nov-96

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Org

anic

P C

once

ntra

tion

(mg/

L)


70

Appendix B CE-QUAL-W2 Calibration Plots - Time-series plots of mean water column concentra-

Figure B–3 Time-series plot of mean water column TP concentration.

Figure B–4 Time-series plot of mean water column NH3-N concentration.


0.00

0.10

0.20

0.30

0.40

0.50

0.60

9-Jul-96

7-Sep-96

6-Nov-96

5-Jan-97

6-Mar-97

5-May-97

4-Jul-97

2-Sep-97

1-Nov-97

31-Dec-97

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30-Apr-98

29-Jun-98

Tot

al P

Con

cent

ratio

n (m

g/L

)



0.000.100.200.300.400.500.600.700.80

9-Jul-96

7-Sep-96

6-Nov-96

5-Jan-97

6-Mar-97

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4-Jul-97

2-Sep-97

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31-Dec-97

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29-Jun-98

NH

3-N

Con

cent

ratio

n (m

g/L

)


71


Figure B–5 Time-series plot of mean water column NO2+NO3-N concentration.

Figure B–6 Time-series plot of mean water column Org-N concentration.


0.00

0.50

1.00

1.50

2.00

2.50

9-Jul-96

7-Sep-96

6-Nov-96

5-Jan-97

6-Mar-97

5-May-97

4-Jul-97

2-Sep-97

1-Nov-97

31-Dec-97

1-Mar-98

30-Apr-98

29-Jun-98

NO

2+

NO

3-N

Con

cent

ratio

n (m

g/L

)



0.00

0.50

1.00

1.50

2.00

2.50

3.00

9-Jul-96

7-Sep-96

6-Nov-96

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4-Jul-97

2-Sep-97

1-Nov-97

31-Dec-97

1-Mar-98

30-Apr-98

29-Jun-98

Org

anic

N C

once

ntra

tion

(mg/

L)


72

Appendix B CE-QUAL-W2 Calibration Plots - Time-series plots of mean water column concentra-

Figure B–7 Time-series plot of mean water column TN concentration.


0.000.501.001.502.002.503.003.50

9-Jul-96

7-Sep-96

6-Nov-96

5-Jan-97

6-Mar-97

5-May-97

4-Jul-97

2-Sep-97

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Tot

al N

Con

cent

ratio

n (m

g/L

)


73


74

APPENDIX C

Procedure for GeneratingExceedence Probability Charts

75

Lake Waco-Bosque River Initiative

Figu

re C

–1E

xam

ple

of e

xcee

den

ce p

roba

bilit

y d

eter

min

atio

n.

76

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