Model Assessment of the Effects Model Assessment of the Effects of Landuse Change on of Landuse Change on Hydrologic ResponseHydrologic Response

Kellie B. VacheKellie B. VacheDepartment of BioengineeringDepartment of Bioengineering

February 11, 2003February 11, 2003

• Rationale / Research GoalsRationale / Research Goals

• Part 1 – The application of a watershed scale water quality modelPart 1 – The application of a watershed scale water quality model• Study Design – future scenariosStudy Design – future scenarios• ResultsResults• LimitationsLimitations

• Part 2 – WET_Hydro – a model of watershed hydrologyPart 2 – WET_Hydro – a model of watershed hydrology• Introduction to the modelIntroduction to the model• CalibrationCalibration• VerificationVerification• Utility of additional model derived criteria Utility of additional model derived criteria

• Part 3 – Simulation of landuse change using WET_Hydro as part of Part 3 – Simulation of landuse change using WET_Hydro as part of a decision support systema decision support system

• New DirectionsNew Directions

Talk Overview

AcknowledgementsAcknowledgements John BolteJohn Bolte Committee MembersCommittee Members

Mary Santelmann - GeosciencesMary Santelmann - Geosciences Jeff McDonnell – Forest EngineeringJeff McDonnell – Forest Engineering John Selker - BioengineeringJohn Selker - Bioengineering Dan Sullivan – Crop and Soil ScienceDan Sullivan – Crop and Soil Science Richard Cuenca - BioengineeringRichard Cuenca - Bioengineering

The students in the BAG research GroupThe students in the BAG research Group Mindy and Johnny CrandallMindy and Johnny Crandall

The Overall RationaleThe Overall Rationale Watershed restoration is occurringWatershed restoration is occurring

Often in the absence of a watershed contextOften in the absence of a watershed context

The process of prioritizing restoration sites is The process of prioritizing restoration sites is complicated and can benefit from a scientific complicated and can benefit from a scientific understanding understanding

In a perfect world, measurement based studies would be used In a perfect world, measurement based studies would be used in this processin this process

But cost of measurements and scale of the problem requires the But cost of measurements and scale of the problem requires the use of models to fill in where measurements are not availableuse of models to fill in where measurements are not available

The Overall Rationale The Overall Rationale ContinuedContinued

Watershed scale modeling tools existWatershed scale modeling tools exist

But as increasing amounts of But as increasing amounts of • Data become available andData become available and• Computational capacity increases andComputational capacity increases and• Measurement provide additional process understanding Measurement provide additional process understanding

Modeling methods need to be revisitedModeling methods need to be revisited

Research QuestionsResearch QuestionsWatershed Restoration PlanningWatershed Restoration Planning

• What are the current modeling tools available to What are the current modeling tools available to managers focused on developing responses to managers focused on developing responses to TMDLs?TMDLs?

• What are some current limitations of those What are some current limitations of those modeling tools?modeling tools?

• Can we develop methods to increase the utility of Can we develop methods to increase the utility of modeling designed to inform the planning modeling designed to inform the planning process?process?

Research QuestionsResearch QuestionsHydrologic SciencesHydrologic Sciences

How can visual analysis of distributed model How can visual analysis of distributed model calculations improve modeling practices?calculations improve modeling practices?

Can GIS data be used more directly in hydrologic Can GIS data be used more directly in hydrologic models?models?

Are there model derived criteria that can Are there model derived criteria that can complement the standard discharge based complement the standard discharge based calibration process?calibration process?

Can we develop modeling exercises which Can we develop modeling exercises which facilitate interactions between the hydrologist facilitate interactions between the hydrologist and the landuse planner?and the landuse planner?

Part 1 –Part 1 – The SWAT ModelThe SWAT Model

The Soil Water Assessment Tool (SWAT) model

Developed by USDA, beginning in 1995, as a distributed, physically based model designed to:

“predict the impact of land management practices on water, sediment and agricultural chemical yields in large complex watersheds with varying soils, landuse and management conditions over long periods of time.”

The Iowa ProjectThe Iowa ProjectAn Application of the SWAT ModelAn Application of the SWAT Model

Designed to evaluate the potential for Designed to evaluate the potential for restoration benefits in two Iowa watershedsrestoration benefits in two Iowa watersheds

Utilized a set of three potential future scenarios Utilized a set of three potential future scenarios designed with the following objectives:designed with the following objectives: Production (Scenario 1)Production (Scenario 1) Water Quality (Scenario 2)Water Quality (Scenario 2) Biodiversity (Scenario 3)Biodiversity (Scenario 3)

Walnut Creek, IowaWalnut Creek, Iowa

Maps courtesy of Dennis White at the EPA

Present Landuse

Scenario 1

Walnut Creek, IowaWalnut Creek, Iowa

Maps courtesy of Dennis White at the EPA

Scenario 2

Scenario 3

SWAT Simulation ResultsSWAT Simulation Results

Yearly Sediment Yearly Sediment LoadingLoading Each box represents 7 Each box represents 7

years of simulated resultsyears of simulated results We learned:We learned:

Sediment loading is higher Sediment loading is higher in Buck Creek Watershed in Buck Creek Watershed than in Walnut Creek than in Walnut Creek WatershedWatershed

Potential for ~30 % Potential for ~30 % reduction in sediment reduction in sediment loading, given the loading, given the alternative landscapealternative landscape

Informing the planning Informing the planning process through scenario process through scenario design and evaluation is design and evaluation is feasiblefeasible

But the SWAT model is:But the SWAT model is:1)1) OverparameterizedOverparameterized

• At least 70 individual parameters related to hydrologyAt least 70 individual parameters related to hydrology• No way to quantify parameter uncertaintyNo way to quantify parameter uncertainty• Potential for the right model, but wrong reasonsPotential for the right model, but wrong reasons

2)2) Relies on the SCS Curve Number method Relies on the SCS Curve Number method • Other options may be more usefulOther options may be more useful

3)3) Not clearly sensitive to all restoration optionsNot clearly sensitive to all restoration options• Example: The model does not specifically incorporate the effects of Example: The model does not specifically incorporate the effects of

riparian buffersriparian buffers

4)4) Reliant on a standard “90’s” model architectureReliant on a standard “90’s” model architecture• preprocessor in combination with older FORTRAN codepreprocessor in combination with older FORTRAN code

5)5) Scenarios represent static endpointsScenarios represent static endpoints

Requirements for Watershed Requirements for Watershed Scale Restoration AnalysisScale Restoration Analysis

1)1) A minimum number of parametersA minimum number of parameters

2)2) Explicitly incorporate important restoration activitiesExplicitly incorporate important restoration activities

3)3) Develop model units that are small enough in size to Develop model units that are small enough in size to reflect site scale restoration activitiesreflect site scale restoration activities

4)4) Methods to quickly generate alternative restoration Methods to quickly generate alternative restoration plansplans

5)5) Direct utilization of readily available GIS dataDirect utilization of readily available GIS data

Part 2 – The WET_Hydro Model Part 2 – The WET_Hydro Model An OverviewAn Overview

1)1) Define the modelDefine the model• AlgorithmsAlgorithms• Data requirementsData requirements

2)2) Establish success of the code Establish success of the code developmentdevelopment

3)3) Analyze parameter spaceAnalyze parameter space• Various basinsVarious basins• Various hydrologic regimesVarious hydrologic regimes

4)4) Verify model operationsVerify model operations5)5) Use additional model criteria to further Use additional model criteria to further

characterize model operationscharacterize model operations

Key Model ProcessesKey Model Processes Upslope ModelUpslope Model

V = volumeV = volume P = Precip RateP = Precip Rate SS = Subsurface flow rateSS = Subsurface flow rate SOF = Surface flow rateSOF = Surface flow rate G = groundwater loss rateG = groundwater loss rate ET = Evapotranspiration rateET = Evapotranspiration rate

Instream ModelInstream Model Kinematic WaveKinematic Wave

SummationSummation

outoutinin SOFSSGETSOFSSPdt

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Groundwater Loss

Groundwater Loss

Key Data InputsKey Data Inputs Required Required

A description of spaceA description of space Two ESRI shapefilesTwo ESRI shapefiles

StreamsStreams Corresponding watershedsCorresponding watersheds

Meteorological dataMeteorological data

Optional Optional Distributed parametersDistributed parameters

SoilsSoils Landuse/landcoverLanduse/landcover

The Bear The Bear Creek Creek

WatershedWatershed 13877 polygons 13877 polygons

in the watershedin the watershed 3150 model units3150 model units

351 reaches in 351 reaches in

the networkthe network

74 sq km total 74 sq km total areaarea

Corvallis

Willamette Basin

¯

Landuse

Agriculture

Forestry

Wetlands

Natural Vegetation

Water

Roads

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0 1 2 3 40.5

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Territorial Highway

Highway 36

Bear Creek Watershed – 10/1/93 to 4/20/94

Animation represents Volumetric Soil Water Content and Discharge

Clock

Parameter EstimationParameter EstimationStudy AreasStudy Areas

Maimai watershed M8Maimai watershed M8, new Zealand, new Zealand 3.8 ha, 20 min rainfall/runoff time series3.8 ha, 20 min rainfall/runoff time series Dominated by subsurface runoffDominated by subsurface runoff

Data courtesy of Jeff McDonnellData courtesy of Jeff McDonnell

San Jose watershedSan Jose watershed, Chile, Chile 726 ha, 15 min rainfall/runoff time series726 ha, 15 min rainfall/runoff time series Flashy hydrology dominated by overland Flashy hydrology dominated by overland

flowflow Data courtesy of David Rupp and John SelkerData courtesy of David Rupp and John Selker

Parameter IdentificationParameter Identification Monte Carlo simulationsMonte Carlo simulations

25,000 individual model 25,000 individual model runsruns

Initial parameter uncertainty Initial parameter uncertainty expressed as an acceptable expressed as an acceptable range of valuesrange of values

Parameters selected from a Parameters selected from a uniform distributionuniform distribution

For each model run, we For each model run, we calculate an efficienycalculate an efficieny

Efficiency – A measure of Efficiency – A measure of how well the model how well the model simulates the discharge simulates the discharge hydrographhydrograph

Nash Sutcliffe defined Nash Sutcliffe defined efficiency as:efficiency as:

Ranges from 1 (perfect) to Ranges from 1 (perfect) to –infinity (bad) –infinity (bad)

We specify a lower We specify a lower efficiency cutoff of 0. efficiency cutoff of 0.

Simulations below that Simulations below that are not included in any are not included in any additional analysisadditional analysis

nt

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Observed Variance

Parameter IdentificationParameter Identification

Where (in Where (in parameter space) parameter space) can we identify can we identify parameter values parameter values providing good fit?providing good fit?

A well identified A well identified parameterparameter

A poorly A poorly identified identified parameterparameter

Parameter IdentificationParameter Identification

San Jose – May 28 – May 30, 2001

Parameter Estimation ResultsParameter Estimation Results

Model appears to perform reasonably well Model appears to perform reasonably well across a range of basin scales and across a range of basin scales and hydrologic regimeshydrologic regimes

Parameter m (a rate constant related to Parameter m (a rate constant related to soil conductivity) and the initial conditions soil conductivity) and the initial conditions appear as the most important parametersappear as the most important parameters Could not generally identify meaningful minima Could not generally identify meaningful minima

in most other parameters, which demonstrates:in most other parameters, which demonstrates: ““equifinality” of the model structure.equifinality” of the model structure. This could not be determined with SWAT This could not be determined with SWAT

too many parameterstoo many parameters

The Value Of Additional The Value Of Additional CriteriaCriteria

An Example Using the San JoseAn Example Using the San Jose

An example of An example of an additional an additional criterion:criterion: Percent new Percent new

water in a water in a storm storm hydrographhydrograph

The Value Of Additional The Value Of Additional CriteriaCriteria

An Example Using the San JoseAn Example Using the San Jose

Red dotsRed dots = % new = % new water water > 50> 50

Black dots = % new Black dots = % new water < 50water < 50

Identifies Identifies parameter sets that parameter sets that produce the produce the “efficient” results “efficient” results for the wrong for the wrong reasonsreasons

The WET_Hydro ConceptThe WET_Hydro ConceptA SummaryA Summary

Visualization of spatial and temporal changes in Visualization of spatial and temporal changes in distributed models facilitates model distributed models facilitates model understandingunderstanding

Not all parameters display identifiable minimaNot all parameters display identifiable minima

Model derived values of Percent New Water in Model derived values of Percent New Water in storm runoff provides information that can be storm runoff provides information that can be used to reject certain model structuresused to reject certain model structures

Part 3 – Watershed Scale Landuse Change

Goal: use the hydrologic model to provide estimates of the effect of landuse change on water quality, at the watershed scale.

Proposed solution: 1) Implement the Universal Soil Loss Equation (USLE)2) Integrate simulations with the RESTORE decision support

system (DSS) to generate scenarios3) Develop a set of simple models to characterize effects of

restoration on sediment export at the site scale 4) Run simulations of the scenarios to quantify the cumulative

effects of watershed scale landuse plans on water quality (measured as sediment export)

The RESTORE DSS A spatially explicit software tool for assisting

watershed councils and other landscape users identifying where in a watershed restoration activities should focus

Developed by researches in the Biosystems Analysis Group at OSU

Models Rules

Thanks to France Lamy for this slide!

The RESTORE DSS Concept

1) Work with users to identify important objectives and issues

2) Develop “expert” rules that relate restoration strategies, site-based landscape features, and objectives/subobjectives

3) Use the rules in a spatially explicit landscape generator to rationally allocate restoration strategies on the landscape, according to site features and objective “dials”

4) Evaluate the resulting landscape using watershed-scale evaluative tools

A conceptual example –Watershed scale sensitivity to site level landuse change

Consider sediment export and riparian buffer systems What is the site scale response?

A decrease?

What is the watershed scale response? Depends on the site scale responses and the

placement of the buffers Given this uncertainty, allow the user to simply

specify the site scale response and ask the following question:

If individual riparian buffers reduce sediment movement by X percent, what is the cumulative effect of a distributed buffer system?

Watershed Scale Landuse Change Conceptual Example Continued…

Buffers on all 1st order streams

Run multiple twoweek simulations overa Winter period

specify a buffer reduction of 5 – 35 percent for each buffered reach

Evaluate sediment export at the basin outlet

Blue areas represent riparian buffers

Watershed Scale Landuse Change Conceptual Example Continued…

Buffers on all higher order streams

Run multiple twoweek simulations overa Winter period

specify a buffer reduction of 5 – 35 percent for each buffered reach

Evaluate sediment export at the basin outlet

Blue areas represent riparian buffers

Watershed Scale Landuse Change Conceptual Example Continued…

Buffers on all 1st order streams

Buffers on all other streams

81 km of buffer

63 km of buffer

DSS Derived Scenarios

Temperature Objective

Water Quality Objective

Riparian Buffer

Wetlands

Increase Late Summer Flow

BMPs

DSS Derived Scenarios

Habitat Objective

All Objectives

Riparian Buffer

Wetlands

Increase Late Summer Flow

BMPs

Simulation Results The habitat objective resulted in a significant increase in

wetland areas This scenario is simulated to produce the largest decrease in

sediment export The model and the rules complement one another

Water Quality Focus

Habitat Focus

Temperature Focus

General Focus

Summer Simulations Winter Simulations

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Overall Conclusions

The clarity of the HYDROLOGIC model is improved by– Visualization of spatial output– Characterization of parameter space– Model derived criteria

Simple models of SITE scale landuse effects on sediment export in combination with the detailed HYDROLOGIC model provide WATERSHED scale insights

The integration of the above model and the RESTORE DSS– Feasible– Provides a useful summary of distributed scenarios

New Directions

Hydrologic modeling– Use of distributed datasets

to further constrain models– Formalize the incorporation

of multi-criteria measures in model analyses

– Component based modeling framework

Potential to improve the model/modeler interaction

Watershed planning– Include additional state

variables and restoration activities in the model

– Formalize the relationship between the DSS and the model.

– Establish direct feedback between the model and DSS

Use in an optimization sense

Part 4 – New Directions

SWAT – Instream ProcessingSWAT – Instream Processing

SWAT SWAT CalibratioCalibrationn

Average Monthly ValuesAverage Monthly Values Relatively close fit Relatively close fit Buck CreekBuck Creek

RR22 = 0.64 = 0.64 Walnut CreekWalnut Creek

RR22 = 0.67 = 0.67

State Variable 2 – Stream Discharge– Instream Routing Model - Partial Differential Equation from First Principles

Implicit Finite Difference Solution Procedure

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Conservation of Mass Conservation of Momentum

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Overall VerificationOverall Verification

No. Basin Parameters likelihood

IS m n kEff phi kdepth Reff R2 D RMSE

1 Wiley 0.77 12.6 0.10 1.7 0.3 0.0001 0.45 0.71 0.85 3.926

2 Wiley 0.77 12.6 0.10 1.7 0.3 0.0001 0.61 0.64 0.89 9.331

3 Schaefer 0.77 19.5 0.13 0.28 0.3 0.0001 0.15 0.56 0.85 0.248

4 Schaefer 0.77 19.5 0.13 0.28 0.3 0.0001 0.35 0.55 0.83 0.463

5 Maimai 0.97 9.8 0.01 1.1 0.3 0.0001 0.89 0.91 0.97 0.002

6 Maimai 0.97 9.8 0.01 1.1 0.3 0.0001 0.89 0.96 0.98 0.002

7 San Jose 0.95 3.9 0.02 0.6 0.3 0.0001 0.14 0.41 0.76 0.198

8 San Jose 0.92 3.9 0.02 0.6 0.3 0.0001 0.57 0.58 0.86 0.521

Verification Verification - San Jose - San Jose

07/07/87 – 07/09/8707/07/87 – 07/09/87

Developed using parameter Developed using parameter vector with the highest URS vector with the highest URS efficiencyefficiency

RReff eff = 0.35= 0.35

Study AreasStudy Areas Maimai Watershed M8Maimai Watershed M8, New Zealand, New Zealand

3.8 ha, 20 min rainfall/runoff time series3.8 ha, 20 min rainfall/runoff time series San Jose WatershedSan Jose Watershed, Chile, Chile

726 ha, 15 min rainfall/runoff time series726 ha, 15 min rainfall/runoff time series Flashy hydrology dominated by overland Flashy hydrology dominated by overland

flowflow

Also applied at:Also applied at: Schaefer Creek WatershedSchaefer Creek Watershed, South Santiam River Basin, , South Santiam River Basin,

OROR 305 ha, monitored by USGS (daily)305 ha, monitored by USGS (daily) Forested, complicated by snowfallForested, complicated by snowfall

Wiley Creek WatershedWiley Creek Watershed, South Santiam River Basin, OR, South Santiam River Basin, OR 16107 ha (161km2), monitored by USGS16107 ha (161km2), monitored by USGS Forest/Ag mixForest/Ag mix

The value of additional criteriaThe value of additional criteria

A virtual tracer experimentA virtual tracer experiment Indicates dominant flowpathsIndicates dominant flowpaths

newold

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Part 2 – A Hydrologic Model Part 2 – A Hydrologic Model

A comparison with SWAT A comparison with SWAT

SWATSWAT WET_HydroWET_Hydro> 75 Hydrologic > 75 Hydrologic ParametersParameters

Minimum of 6 Minimum of 6 Parameters, maximum of Parameters, maximum of 1010

SCS equationSCS equation Conceptual, physical Conceptual, physical basisbasis

FORTRANFORTRAN OOP C++; OOP C++; model/interface model/interface integrationintegration

Daily Time StepDaily Time Step Variable Runge-Kutta Variable Runge-Kutta based time stepbased time step

VerificatioVerification n

- Maimai - Maimai

Maimai Maimai 10/28/87 – 11/1/8710/28/87 – 11/1/87

Developed using parameter Developed using parameter vector with the highest URS vector with the highest URS efficiencyefficiency

RReff eff = 0.89= 0.89