Application of SWAT for Water Quality Modeling of the Caddo Lake Watershed, TXKendra Riebschleager, M.S., E.I.T.August 5, 2009

Espey Consultants, Inc.3809 S. 2nd Street, B-300Austin, TX 78704512.326.5659

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Outline Description of Study Area Scope of Work Methodology

Project Setup/Watershed Delineation Edit Inputs Initial Results Calibration Procedures

Manual Sensitivity Analysis Auto-Calibration

Next Steps Coordinate with Lake Model Alternatives Analysis

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Description of Study Area

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Description of Study Area Population ~570,000 (10 counties, 1 parish) Employment – Services, wholesale/retail, and

manufacturing Agriculture – Livestock 64%, crop sales

22.5% Natural Resources – oil and gas industry,

lignite

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Description of Study Area Topography

Gently rolling to hilly dissected by flat floodplains and terraces

Avg. Elevation 200 to 500 ft msl

Major Drainages Big Cypress Creek Little Cypress Creek Black Cypress Bayou James Bayou Frazier Creek

Soils Bowie-Cuthbert-Kirvin

Associations gently sloping to steep, well

drained to moderately well drained, loamy and gravelly

Darco Association gently sloping to

moderately steep, well drained, sandy soils

Cuthbert-Redsprings Association Strongly sloping to steep,

well drained, gravelly soils.

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Description of Study Area Vegetative Cover

Pine and mixed Pine-hardwood Forests Cultivated or Pasture land Bottomland hardwood forest and Cypress Swamp

Climatology Avg. Annual Air Temperature 64-67ºF Precipitation

Storm events 60 days/yr ~33 inches

Evaporation Monthly Range ~10” in summer, 3” in winter

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Description of Study Area

Photo: Bruce Moring, USGS

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Scope of Work Model the watershed influences on Caddo Lake

Extent of Study: Cypress Basin downstream of Lake O’Pines

Parameters of Interest: Nutrients, DO, Bacteria

Develop hydrodynamic and water quality model for Caddo Lake

Perform Alternatives Analysis in support of WPP

Watershed ModelingMethodology

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Watershed Modeling Cypress Creek Basin encompasses 110 mi2

over two-thirds rural significant amount of agricultural lands

SWAT provides a wide variety of agricultural surface treatments enabling simulation of structural and non-structural BMP's for evaluation of alternative strategies for runoff control from agricultural areas.

Currently not including bacteria in SWAT model SELECT approach Load Duration Curves

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Watershed - SWAT Model Setup Watershed Delineation Hydrologic Response Unit (HRU) Definition

Unique areas with similar hydrologic characteristics to “lump” model runs

Input Data Files Weather data (precip, temp) PS and NPS Pollutant Loadings

Permitted Discharges Fertilizer/Pesticide Applications

Land Practices Ponds and Reservoirs

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SWAT - WatershedDelineation Digital Elevation Model

NED 30-m NHD Plus Streams

Define Points of Interest

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SWAT – Watershed Delineation Things to Consider

Calibration data Water Quality Station Locations USGS Flow Gage Locations

Point Sources Direct discharges from WWTP Expected Loading from CAFOs

Reservoirs and Large Ponds Lake O’Pines controlled discharge National Inventory of Dams (storage > 15,000 ac-ft)

Lake Gilmer 2001

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SWAT – Delineated Watersheds

Lake O’ Pines Watershed

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SWAT - HRU Definition Inputs Include: Land use

NLCD 2001 verified by aerial photography Soils

NRCS SSURGO Slope

SWAT derives from the DEM Four Classes

0-1%, 1-3%, 3-9%, >9%

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SWAT – Edit Input Data Files Weather data (precip, temp, etc.)

Is data sufficient to spatially and temporally describe watershed? SWAT assigns weather data from nearest station location to sub-watershed

How can we improve the data? Interpolation Techniques

Point Source and Non-Point Source Pollutant Loadings Permitted Discharges Fertilizer Applications Livestock Grazing Septic Systems

Land Practices Pond data

storage > 15,000 ac-ft Along stream network

Reservoir data Volume Construction date

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Run the SWAT Model Route watershed surface pollutants through

catchments Rainfall/Runoff Mechanisms

Simulated Weather Events SCS Curve Number Method

Growth and Decay Plant Uptake

Route pollutants from catchments to streams Incorporates QUAL-2E model for routing and predicting

stream concentrations

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SWAT – Initial RunAverage Monthly Flow at USGS 07346045 Black Cypress Bayou

SWAT Subbasin 89

0

10

20

30

40

50

60

70

80

3/1997 7/1998 12/1999 4/2001 9/2002 1/2004 5/2005 10/2006 2/2008 7/2009

Date

Flow

(cm

s)

SWAT FlowUSGS Gauge Flow

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SWAT Calibration Process1. Hydrology

Daily Predicted vs. Measured at gauged locations2. Sediments

Total amounts (tons/acre) TSS concentrations

LOADEST for monthly load predictions3. Water Quality

Depends on reliable hydrology/sediment predictions Available SWQM data

Nitrogen (Total N, TKN, NH4, NO3, NO2) Phosphorus (Total P, Ortho-P)

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SWAT - Model Calibration Sensitivity Analysis Determine which input parameters have greatest

influence on model results Use both measured and literature values for

fine tuning model inputs Incorporate Stakeholder Suggestions based on

their knowledge of the watershed

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SWAT Calibration - Hydrology Key Considerations

Water Balance Overall Amount Distribution among hydrologic components

Storm Sequence Time lag or shifts

Time of concentration, travel time Shape of Hydrograph

Peak Recession Consider antecedent conditions

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SWAT Calibration - Hydrology Initial Run

Graphically compare prediction to flow data at gauged locations Manual Calibration

Adjust model parameters to more appropriately describe watershed Primarily Graphical Exercise

Sensitivity Analysis Which parameters have the greatest influence on the model

predictions? Auto-Calibration

Based on manual adjustment and modifying most sensitive parameters to meet model objectives

Saves time, increases efficiency Statistical Optimization Techniques

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SWAT – Initial RunAverage Monthly Flow at USGS 07346045 Black Cypress Bayou

SWAT Subbasin 89

0

10

20

30

40

50

60

70

80

3/1997 7/1998 12/1999 4/2001 9/2002 1/2004 5/2005 10/2006 2/2008 7/2009

Date

Flow

(cm

s)

SWAT FlowUSGS Gauge Flow

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Manual Calibration Procedure Peak Flows CN, canopy cover, soil available water capacity,

soil evaporation Groundwater (baseflows) Minimum flow, revap => Increase Deep Aquifer

Recharge Time of Peak Overland and Channel Roughness (n values) Surface lag coefficient

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Manual CalibrationDaily Flow at USGS 07346045 / Subbasin 89 Black Cypress Bayou

0

100

200

300

400

500

600

1/1/2000 2/20/2000 4/10/2000 5/30/2000 7/19/2000 9/7/2000 10/27/2000 12/16/2000

Date

Flow

(cm

s)

0.0

20.0

40.0

60.0

80.0

100.0

120.0

140.0

160.0

180.0

200.0

USGS Gauge FlowSWAT Daily Flow Sim 16SWAT Daily Flow Sim 1Precip at Jefferson

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Manual Calibration

Flow Duration Curve

0

20

40

60

80

100

120

140

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Percent Exceedance

Flow

(cm

s)

SWAT Sim 16Black Cypress Bayou Gauge

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Sensitivity Analysis Estimate the rate of change in the output of a model

with respect to changes in model inputs Parameters important to have more accurate values Understand the behavior of the system being modeled Evaluate applicability of the model

Rank Name Description Location

1 ESCO Soil evaporation compensation factor *.hru

2 ALPHA_BF Baseflow alpha factor [days] *.gw

3 GWQMN Threshold water depth in the shallow aquifer for flow (mm) *.gw

4 CN2 Initial SCS CNII value *.mgt

5 CH_N2 Manning’s n value for main channel *.rte

6 CH_K2 Channel effective hydraulic conductivity [mm/hr] *.rte

7 SURLAG Surface runoff lag time [days] *.bsn

8 SOL_AWC Available soil water capacity [mm H2O/mm soil] *.sol

9 CANMX Maximum canopy storage [mm] *.hru

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Auto-Calibration – In Progress Automation of calibration requires the formulation of

“closeness” measures (objective functions) Automatic Optimization

Algorithms that optimize an objective function by systematically searching the parameter space according to a fixed set of rules

Simplified by Sensitivity Analysis – determine parameters to optimize Manual Calibration – narrow optimization space for parameters

SWAT-CUP SUFI-2

Local Optimization Nash-Sutcliffe Objective Function

Next Steps

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Calibration Re-run model with calibrated hydrology

parameters Begin Sediment Calibration Nutrient Calibration

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Lake Modeling Watershed model output (SWAT results) will be

input to the lake model Pollutant loadings reaching the lake based on watershed

characteristics and practices Special Considerations for Caddo Lake

Lack of temperature stratification Off-channel wetland areas Shallow depths Invasive Species (Macrophytes)

Giant Salvinia Hyacinth

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Lake Modeling EC Determined the Appropriate Model based

on Caddo Lake’s Unique Environment WASP7 (Water Quality Analysis Simulation

Program) Determine through scenario analysis the

impact of watershed NPS load Percent Reduction Goal Relative to other sources of pollutants

(resuspension, lake discharges, etc.)

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SWAT - Alternatives Analysis Modify Watershed Practices Fertilizer Application CAFO waste management Structural BMPs Wastewater Treatment Options

Consolidate or Replace septic systems Additional Regionalized Collection Systems

Resultant Change in Pollutant Loadings Feed to lake model

34Espey Consultants, Inc.3809 S. 2nd Street, B-300Austin, TX 78704

Questions or Comments Phone 512-326-5659 Kendra Riebschleager

kriebschleager@espeyconsultants.com David Harkins dharkins@espeyconsultants.com Tim Osting tosting@espeyconsultants.com Ernest To eto@espeyconsultants.com

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Extra Slides

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SELECT – Potential Bacteria Loading Spatially Explicit Load Enrichment Calculation Tool

Distributes Sources of Bacteria Livestock, Wildlife, Septic Systems, WWTPs

Within the appropriate habitat Livestock in pastures/grassland Wildlife in forested and non-developed lands Septic Systems in residential areas without municipal services

Considering other factors Magnitude of fecal production and bacteria concentration Soil type Distance to streams and waterbodies

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Lake Modeling - WASP