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Presentation II 1

Overview of Models for Estimating

Pollutant Loads & Reductions

(Handbook Chapter 8.3–8.5)

Texas Watershed Planning Short Course

Tuesday, Nov. 5, 2013Larry Hauck

hauck@tiaer.tarleton.edu

Valley Mills (BO100) Monthly Average daily streamflow

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40

60

80

100

120

Ap

r-93

Oct-9

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Ap

r-94

Oct-9

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Ap

r-95

Oct-9

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Ap

r-96

Oct-9

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Ap

r-97

Oct-9

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Month

m3 /s

measured predicted

E = 0.66%E = - 12.0M = 14.4P = 12.7

Upper Oyster Creek - Lower portion (8/9/2004) Mainstem

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0.05.010.015.020.025.0

DO (mgO2/L) Avg(P) DO (mgO2/L) Avg(O) DO (mgO2/L) Min(P)DO (mgO2/L) Max(P) DO (mgO2/L) Min(O) DO (mgO2/L) Max(O)DO sat(P)

A Humorous View or a Reality Check?Watershed Monitoring and Modeling is

the Art and Science of

• Collecting data in systems we cannot adequately sample • Using methods developed by committees of technically

unqualified participants • For organisms and pollutants we know very little about• In order to form concepts about processes we do not

fully understand • That we represent as mathematical abstractions that we

cannot precisely analyze • To determine their responses to indeterminate stresses

we cannot accurately predict now let alone in the future • All in such a way that society at large is given no reason

to suspect the extent of our ignorance.

Adapted from a slide by Dr. Thom Hardy, Texas State Univ.

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Why Use Watershed Modeling?

“Models provide another approach, besides monitoring data and export coefficients, for

estimating loads, providing source load estimates, and evaluating various

management alternatives.”

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What Are Models?

Mathematical models are analytical abstractions of the real world and as such represent an approximation of the real system.

In the context of watershed planning, mathematical models are computer based, simplified representations of landscape and water quality processes that govern the fate and transport of one or more pollutants.

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• Empirical Model

• Mechanistic Model

Two Basic Types of Models

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• A model where the structure is determined by the observed relationship among experimental data.

• These models can be used to develop relationships for forecasting and describing trends.

• These relationships and trends are not necessarily mechanistically relevant.

Source: EPA website, glossary of frequently used modeling terms.

Empirical Model:

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• Investigating the relationship of inflowing nutrients in a lake to algal biomass production (eutrophication).

• Most early (circa 1970) lake eutrophication models based on statistical relationships between mass loading of nutrients and average algal biomass (e.g., Vollenweider models with numerous adaptations by others)

• Applied to PL-566 reservoirs in North Bosque River Watershed

An Example of an Empirical Model:

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Annual mean summer chlorophyll-a concentration as a function of predicted total-P for years 1993-1998 from PL-566 reservoirs. N=25

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• A model that has a structure that explicitly represents an understanding of biological, chemical, and/or physical processes.

• These models attempt to quantify phenomena by their underlying casual mechanisms.

Source: EPA website, glossary of frequently used modeling terms and our Handbook

Mechanistic Model:

Presentation II 10

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• HSPF – Hydrologic Simulation Program - FORTAN

• SWAT – Soil & Water Assessment Tool

• Both models are comprehensive watershed loading, hydrologic, water quality models.

• Both models are intensive in use of resources and skills to operate the models.

• More on these a little later.

Two Common Mechanistic Models Used in Watershed Modeling in Texas:

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• Steady State Model

• Dynamic Model

Types of Mechanistic Models

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A mathematical model of fate and transport of waterborne constituents using constant input parameters to predict constant values of receiving water quality concentrations (typically under low-flow conditions)

Source: Our Handbook

Steady-State Models:

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An Example: QUAL2K

QUAL2K is a stream water quality model. It is one-dimensional* and operates under steady-state flow.

All water quality variables are simulated on a diurnal (24-hour) time scale, including dissolved oxygen.

*fully-mixed in the vertical and lateral directions

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Upper Oyster Creek - Lower Reach - May 2004 Verification Case (5/7/2004) Mainstem

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0.05.010.015.020.025.0

DO (mgO2/L) Avg(P) DO (mgO2/L) Avg(O) DO (mgO2/L) Min(P) DO (mgO2/L) Max(P)DO (mgO2/L) Min(O) DO (mgO2/L) Max(O) DO sat(P)

Portion of Oyster Creek – Dissolved Oxygen (DO) Results for Verification Period

1207712075

12074

17690

Presentation II 16

A mathematical model of fate and transport of waterborne constituents formulated to describe the physical behavior of a system and its temporal variability

Dynamic Models:

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SWAT Prediction for a Subbasin of North Bosque River

(Streamflow)SC020 Monthly Average daily streamflow

0.0

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0.6

Jan-95 Jun-95 Nov-95 Apr-96 Sep-96 Feb-97 Jul-97 Dec-97

month

m3 /s

measured predicted

E = 0.30%E = - 29.0M = 0.11P = 0.08

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SWAT Prediction for a Subbasin of North Bosque River(Total Phosphorus)

SC020 Monthly Total TP

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400

600

800

1,000

1,200

1,400

1,600

Jan-95 Jun-95 Nov-95 Apr-96 Sep-96 Feb-97 Jul-97 Dec-97month

kg

s

measured predicted

E = -8.7%E = 199M = 88P = 263

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• Both supported by EPA BASINS (Better Assessment Science Integrating Point & Nonpoint Sources)

• Include processes of:

• Rainfall/runoff

• Erosion & sediment transport

• Pollutant loading

• Stream transport

• Management practices

HSPF & SWAT – Two Dynamic Watershed Models:

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Model efficiency (E) for measured and predicted daily (d) and monthly (m) flow, total suspended solids (TSS), and nutrient loadings during (a) calibrations and (b) verification, at the outlet of the UNBRW.

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• Both models are complete watershed and water quality models with several state variables (runoff, streamflow, bacteria, nutrients, dissolved oxygen, toxics).

• HSPF has an edge in predicting hydrology• SWAT more user friendly• SWAT more powerful in representing

agricultural practices• HSPF more powerful in urban environment• HSPF has an edge with instream fate and

transport processes

HSPF & SWAT General Impressions by Presenter

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• Field-scale: application focused at the subbasin or smaller level; often on a single land use

• EPIC – Environmental Policy Integrated Climate

• APEX – Agricultural Prediction Policy/Environmental eXtender

Developer: Dr. Jimmy Williams, BREC

EPIC & APEX – Two Dynamic Field-Scale Models:

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Field plot and rain gauge locations within the Goose Branch microwatershed.

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APEX Application: Measured versus predicted storm runoff volume for a) all values and b) for values less than 50 cubic meters per hectare.

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APEX ApplicationComparison of measured and predicted storm loads of total nitrogen.

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APEX predicted soil extractable P levels (0-15 cm) for Coastal bermudagrass field.

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• Not a model

• Method of data organization and presentation that assists in understanding and refining water quality assessments (data requirements: streamflow and pollutant data)

• Applicable to stream systems

• Explained in previous session.

Load Duration Curves

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Load duration curves including MOS, historical data, and flow regimes - Station 10938, West Fork Trinity River

─ 75th percentile─ geo. mean

1.E+08

1.E+09

1.E+10

1.E+11

1.E+12

1.E+13

1.E+14

1.E+15

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

Percent of days loading exceeded

E.

coli

(cf

u/d

ay)

Allowable Load Geometric Mean (CFU / Day) Allowable Load Single Sample (CFU / Day)Measured Load Geomean75th Percentile

─ 75th percentile ─ geo. mean

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• Will management actions achieve desired goals?

• Which sources are the main contributors to pollutant load?

• What are the loads associated with individual sources?

• Which combination of management actions will most effectively meet identified goals?

• When does impairment occur?

• Will the loading or impairment get worse under future conditions?

• How can future growth be managed?

What Questions Can Models Assist in Answering?

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Examples of questions models can answer from

North Bosque River (SWAT)

Upper Oyster Creek (QUAL2K)

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SWAT Model Subbasins forNorth Bosque

River Watershed

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Land Use and Soluble-P SourcesNorth Bosque River at Clifton

PO4-P Source Contribution

Dairy Waste Appl.49%

Pasture8%

WWTP10%

Crop 5%

Urban6%

Wood/Range22%

Land Uses

Dairy Waste Appl.4%

Pasture14%

Crop9%

Wood/Range71%

Urban2%

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Scenarios Evaluated

P R A C T I C E S

Scen

Filter strip

WWTP 1 mg/l

New lagoon

50% manure

NRCS New PL-566

Microgy remed.

HWAF remed.

75% manure

WWTP .5 mg/l

Red. graze

A x X x x x

B x x x x x x

C x x x x x x x

D x x x x x x x x

E x x x x x x x x

F x x x x x x x x

G x x x x x x x x x

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Exceedance

Probability Graph

for the North

Bosque River at

Valley Mills

50,505 44,549

36,20128,977

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120,000

140,000

160,000

180,000

200,000

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Exceedance Probability

PO

4 lo

ad (

kg)

full-permitted baseline full-permitted baseline avg

1990s baseline 1990s baseline avg

future A future A avg

future G future G avg

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Upper Oyster Creek -Lower Reach

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Lower Reach Summer Critical High Temperature - Upper Oyster Cr. (Preliminary Results)

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0510152025

Location, km

Dis

solv

ed O

xyge

n, m

g/L

Present Permit LimitsCriteriaBOD=5 mg/L, NH3=2 mg/L, DO=6 mg/L

Stafford Run

Hwy 6

Steep Bank Cr.

Dam 3

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Complex Watershed Modeling Systems:

• Reservoirs

• Tidal & Estuarine Systems

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• The Soil and Water Assessment Tool (SWAT)

• applied to the Bosque River Watershed to predict In-stream PO4-P (or Soluble P) concentrations

• Used to assess various P Control Strategies

Integrated Modeling Approach for Watershed and Reservoir

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Integrated Modeling Approach

CE-QUAL-W2, a two- dimensional, laterally averaged, hydrodynamic and water quality model

• developed by U.S. Army Corps of Engineers, Waterways Experiment Station, Vicksburg, MS

• applied to Lake Waco to predict in-lake PO4-P concentrations and algal response

• Used to assess various P Control Strategies

Modeling System LinkageSWAT: Watershed & CE-QUAL-W2: Lake

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Presentation II 41Lake Waco-Bosque River Watershed

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CE-QUAL-W2 Segmentation of Lake Waco

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CE-QUAL-W2 Calibration

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0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0Probability of Exceedence

Sol

uble

Pho

spho

rus

Con

cent

ratio

n

(ppb

)

Baseline Existing Scenario A Scenario B Scenario C

TargetRange

Preliminary Target

Exceedence Probability for Lake Waco

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• Link watershed model to appropriate hydrodynamic and water quality models

• Watershed: HSPF, SWAT, etc.

• Water Quality: for example, Water Quality Analysis Simulation Program (WASP)

• Hydrodynamic model: for example, DYNHYD, RMA2, Environmental Fluid Dynamics Computer Code (EFDC; Public Domain Code)

• Complete H / WQ models: EFDC (Proprietary Code), CE-QUAL_W2

Tidal Streams & Estuaries

Soil and Water Assessment Tool (SWAT)• Watershed model with stream component• Predicts loadings into CE-QUAL-W2• Predicts changes in loadings from control

measuresCE-QUAL-W2• A two- dimensional model of Tidal Segment• Predicting dissolved oxygen & bacteria

Integrated Modeling System for Watershed and Arroyo Colorado Tidal

Dissolved Oxygen & Bacteria

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Arroyo Colorado

(Tidal segment

has depressed dissolved oxygen)

SWAT Subbasin Delineation

Presentation II 48Source: Dr. Jaehak Jeong, BREC, Temple, TX

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Agricultural BMPs Simulated with SWAT 

Source: Dr. Jaehak Jeong, BREC, Temple, TX

Segmentation – Top View

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Reason for CE-QUAL-W2:

• Represent tidal influences & salt wedge

• Two-dimensional representation required

• DO variations within a day required

(Lower DO, higher salinity)

(Mixed surface layer, higher DO, lower salinity)

Urban Dominated Watersheds

• SWMM – Storm Water Management Model; hydraulic and water quality capabilities

• P8-UCM – Urban Catchment Model for evaluating nonpoint source controls

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SWMM Modeling Example

Subbasins 30 & 32 at Site 8

Two Subbasin SWMM Modeling & BMP

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SWMM Calibration for Stormwater Runoff

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10/8/2011 10/9/2011 10/10/2011 10/11/2011 10/12/2011

Run

off (

CF

S)

Time (dd/mm/yy)

Monitoring Site 8 (10/08/11 06:00 AM to 10/12/11 23:45 PM) Validation

Measured Runoff SWMM

Simulated Volume (ac-ft):Precipitation: 407.42Infiltration:347.93Runoff: 58.01Surface storage: 3.08

Total Volume (ac-ft):Measured: 75.46Simulated: 58.01

Typical Rain Event 1‐yr Event 5‐yr Event

TSS/BOD 55% 62% 49%

TP/TN 29% 45% 29%

 Percent Removed (%) at BMP 2 System

Load Reduction – BMP

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Oh, No!

No Model Does Close to What Is Needed

Try load duration curve approach

or

Modify and adapt an existing model

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An Example:

Modifying APEX to Better Accommodate Situations in

Forestry

Work by Drs. Jimmy Williams, BREC & Ali Saleh, TIAER

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Modifications of APEX for forestry conditions.

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Simulated and measured sediment losses for (a) SHR and (b) CON treatments during 1980-1985 (average of three replications).

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1. Relevance: Is the approach appropriate to your situation?

2. Credibility: Has the model been shown to give valid results?

3. Usability: Is the model easy to learn and use? (If not, is the expertise available to apply a sophisticated model?) Are data available to support the model?

4. Utility: Is the model able to predict the water quality changes based on anticipated management changes?

Four Considerations That Assist in Defining Your Model Application

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• See our Handbook, pages 8-18 through 8-27

• EPA. 1997. Compendium of Tools for Watershed Assessment and TMDL Development. EPA841-B-97-006

• EPA’s Council on Regulatory Environmental Modeling (http://cfpub.epa.gov/crem/)

Available Models & Model Capabilities

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Presentation II 63Source: Handbook, page 8-26

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Questions?