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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

Wednesday, Jan 14, 2009Larry Hauck

hauck@tiaer.tarleton.edu

Valley Mills (BO100) Monthly Average daily streamflow

0

20

40

60

80

100

120

Apr-93

Oct-93

Apr-94

Oct-94

Apr-95

Oct-95

Apr-96

Oct-96

Apr-97

Oct-97

Month

m3 /s

measured predicted

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

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

02468

10121416182022

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, Utah State Univ.

Presentation II 3

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:

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Presentation II 11

• 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

Presentation II 15

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

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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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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

kgs

measured predicted

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

4

Presentation II 19

• 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:

Presentation II 20

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. c

oli

(cfu

/day

)

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?

Presentation II 30

Examples of questions models can answer fromNorth Bosque River (SWAT)

Upper Oyster Creek (QUAL2K)

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Presentation II 31

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%

Presentation II 32

Scenario WWTPFlow

WWTPP Limit

DairyManureApp. Rate

DairyFeedP Diet

DairyManureHaul off

Existing 1997-98 Median Btw N&PRate

No No

Baseline 2020 Median N rate Yes NoScenario A 1997-98 1 mg/l P rate Yes NoScenario B 2020 1 mg/l P rate Yes NoScenario C 2020 1 mg/l P rate Yes Yes

BMPs Imposed on Selected Scenarios

Presentation II 33

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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

Solu

ble

Phos

phor

us C

once

ntra

tion

(ppb

)

Baseline Existing Scenario A Scenario B Scenario C

Preliminary Target

Exceedence Probability for the North Bosque River at Valley Mills

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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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0510152025Location, km

Dis

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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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Presentation II 37

• The Soil and Water Assessment Tool (SWAT)

• applied to the Bosque River Watershed to predict In-stream PO4-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

Presentation II 39Lake Waco-Bosque River Watershed Presentation II 40

CE-QUAL-W2 Segmentation of Lake Waco

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CE-QUAL-W2 Calibration

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Solub

le Ph

osph

orus

Con

cent

ratio

n (p

pb)

Baseline Existing Scenario A Scenario B Scenario C

TargetRange

Preliminary Target

Exceedence Probability for Lake Waco

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Presentation II 43

• 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• Complete H / WQ models: for example,

Environmental Fluid Dynamics Computer Code (EFDC)

Tidal Streams & Estuaries

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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

Presentation II 46Source: Handbook, page 8-26

Presentation II 47

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.Presentation II 50

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

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An Example Model Selection & Application

North Bosque River Watershed

Presentation II 52Land Use – Lake Waco Watershed

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North Bosque River Watershed with SWAT subbasin delineation

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Measured X-sectional AreasX-sectional areas measured at over 10 sites along the North Bosque and its tributaries.

BO095 Cross-Section

50.00

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70.00

80.00

90.00

100.00

0.00 100.00 200.00 300.00 400.00

Tagline (ft)

Elev

atio

n (ft

)

10

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Model ValidationThe validation process consists of model calibration and

verification.

Calibration - model parameters are adjusted within allowable limits until model output for a given time period matches measured output within some predetermined measure of model performance.

Verification - refers to running the calibrated model (i.e., holding adjustment parameters constant) during a different time period and comparing model output to measured values.

Note: Definitions & terms vary from those in Handbook.

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Model Validation

Calibration and verification increases confidence that the model will accurately simulate watershed conditions for different management scenarios.

This process gives confidence that the model output during management scenarios will reasonably reflect what the true measured values would be.

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Calibration and Verification Periods

VerificationCalibration

TimeSite 1993 1994 1995 1996 1997 1998 1999 2000SF020NF009NF020NF050SF075BO020 MB040BO040IC020AL040SC020GC100SP020BO070 (Hico) BO090 (Clifton)NC060BO100 (VM)

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Measures of Model PerformanceNash-Suttcliffe goodness of fit (E)

% error (% E)

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Measures of Model PerformancePercent Error ( + %E )

> 70> 55Unsatisfactory40 - 7030 - 55Satisfactory25 - 4015 - 30Good

< 25< 15Very GoodP, NSediment

Moriasi et al. (2007)

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Calibration of Total Streamflow

Hico Monthly (BO070) Average daily streamflow

01020304050607080

Apr-93

Sep-93

Feb-94

Jul-94

Dec-94

May-95

Oct-95

Mar-96

Aug-96

Jan-97

Jun-97

Nov-97

Month

m3 /s

measured predicted

E = 0.86%E = -8.5M = 3.45P = 3.16

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Calibration: Average daily loads

BO070 average daily load 1994-1997

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OrgN OrgP NO3 MinP TP TN

kgs BO070 sim

BO070 meas

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Verification Period(flow)

Valley Mills (BO100) Monthly Average daily streamflow

01020304050607080

Jan-98 Jul-98 Jan-99 Jul-99

Month

m3 /s

measured predicted

E = 0.70%E = 11.9M = 6.1P = 6.8

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Verification: Average daily loads

BO070 average daily load 1998-1999

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OrgN OrgP NO3 MinP TP TN

kgs BO070 sim

BO070 meas

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Scenarios Evaluated

xxxxxxxxxGxxxxxxxxF

xxxxxxxxExxxxxxxxD

xxxxxxxCxxxxxxB

xxxXxA

Red. graze

WWTP .5 mg/l

75% manure

HWAF remed.

Microgyremed.

New PL-566

NRCS50% manure

New lagoon

WWTP 1 mg/l

Filter stripScen

SECITCARP

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1990s and Future Baseline and Scenario A and G Annual daily-average PO4 conc. and annual PO4 loadings

At Valley Mills(PRELIMINARY RESULTS SUBJECT TO CHANGE)

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Exceedance Probability

PO4 c

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(ppb

)

full-permitted baseline 1990s baselinefuture A future G

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Exceedance Probability

PO4 l

oad

(kg)

full-permitted baseline 1990s baselinefuture A future G

Presentation II 66

Land Uses of North Bosque River Watershed with dairy waste application fields

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Presentation II 67

Questions?