A Comparison of a SWAT A Comparison of a SWAT model for the model for the

Cannonsville Watershed Cannonsville Watershed with and without Variable with and without Variable

Source Area HydrologySource Area HydrologyJosh WoodburyJosh Woodbury

Christine A. ShoemakerChristine A. ShoemakerDillon CowanDillon Cowan

Zachary EastonZachary Easton

OutlineOutline

SWAT2005 vs SWAT-VSASWAT2005 vs SWAT-VSA Calibration Calibration Corn analysisCorn analysis ConclusionConclusion Questions ?Questions ?

SWAT2005 and SWAT-SWAT2005 and SWAT-VSAVSA

The current SWAT2005 version is a replication of the The current SWAT2005 version is a replication of the SWAT 2000 model developed by Bryan TolsonSWAT 2000 model developed by Bryan Tolson

Dillon Cowan replicated the SWAT 2000 files as Dillon Cowan replicated the SWAT 2000 files as closely as possible to create the current SWAT2005 closely as possible to create the current SWAT2005 versionversion This included creating the same number of subbasins with This included creating the same number of subbasins with

similar HRUssimilar HRUs Much time was spent insuring that the corn and pasture areas Much time was spent insuring that the corn and pasture areas

in each model are identicalin each model are identical Although corn is only a small percentage of the watershed, it Although corn is only a small percentage of the watershed, it

accounts for a significant percentage of the phosphorous loading accounts for a significant percentage of the phosphorous loading to the reservoirto the reservoir

Meticulous attention to corn area is important in order to create Meticulous attention to corn area is important in order to create an accurate model replicationan accurate model replication

Very time consuming since the watershed delineation did not Very time consuming since the watershed delineation did not create the correct amount of corn area because of the small create the correct amount of corn area because of the small percentagepercentage

SWAT2005 and SWAT-VSA

Source code changes done in the 2000 version (Tolson and Shoemaker, 2007, Jn of Hydrology) were also done in the 2005 source code Includes modifications to manure

spreading, plant growth, flow in/on frozen soils, and monthly subbasin temperatures

Tolson showed that these changes produce a better model

SWAT-VSA model

The SWAT-VSA model incorporates the model and file changes in the SWAT2005 model, as well as Variable Source Area Hydrology VSA hydrology is incorporated into the model

using the same techniques used to create the Town Brook VSA model

Meticulously accounted for corn and pasture areas between the SWAT2005 and SWAT-VSA models

SWAT-VSA uses 10 different wetness classes

Why bother with VSA hydrology?

The VSA model will make different predictions concerning the spatial distribution of the nutrient transport than a non-VSA model

If we know where the runoff is coming from, we can make judgments about the best nutrient placement

Apply management practices to the model and see how this changes future predictions

We can compare the future predictions of SWAT2005 and SWAT-VSA to see if careful placement of nutrients changes nutrient loading to the reservoir

OutlineOutline

SWAT2005 vs SWAT-VSASWAT2005 vs SWAT-VSA CalibrationCalibration Corn analysisCorn analysis ConclusionConclusion Questions ?Questions ?

CalibrationCalibration

Both of the models are calibrated first for Both of the models are calibrated first for flow, then sediment and finally flow, then sediment and finally phosphorousphosphorous

The calibration period is from Jan. 1994 The calibration period is from Jan. 1994 to Dec. 1999to Dec. 1999

Auto-calibration and manual calibration Auto-calibration and manual calibration techniques are used to get the best fittechniques are used to get the best fit

Parameters used are based upon a Parameters used are based upon a sensitivity analysis done by Ryan Flemingsensitivity analysis done by Ryan Fleming

CalibrationCalibration

Firstly the models are calibrated using an Firstly the models are calibrated using an algorithm called DDSalgorithm called DDS DDS is a simple stochastic single-solution based DDS is a simple stochastic single-solution based

heuristic global search algorithm designed for heuristic global search algorithm designed for automatic calibration of watershed models automatic calibration of watershed models (Tolson and Shoemaker, WRR, 2007)(Tolson and Shoemaker, WRR, 2007)

DDS is used with a weighted Sum of DDS is used with a weighted Sum of Squared Error objective functionSquared Error objective function

SedSedFlow SSESSEOF

Sed

FlowSed SSE

SSE

CalibrationCalibration

Once flow and sediment are calibrated, Once flow and sediment are calibrated, Total Dissolved Phosphorous (TDP) and Total Dissolved Phosphorous (TDP) and Particulate Phosphorous (PP) are Particulate Phosphorous (PP) are calibrated using DDScalibrated using DDS

PPTDP SSESSEOF

Manual calibration techniques are then Manual calibration techniques are then used to slightly improve the modelsused to slightly improve the models

Calibration Many different attempts where made in

order to find the best way to calibrate for more than one output at a time The problem is that the SSE values for each of

the outputs vary by orders of magnitude By simply summing all the outputs, some of the

outputs are weighted more heavily than others This problem has plagued users trying to auto-

calibrate SWAT Most papers addressing the subject suggest using some

type of weighting scheme, either simple weighting factors, or complicated statistical weighting schemes

Calibration

Initially tried to calibrate for Flow, Sediment, PP and TDP at once Tried using weighting values, taking the natural

log of the data, and weighting the natural logs of the data in order to decrease the differences in magnitude

Eventually gave up on calibrating all four outputs at once and adopted the calibration method previously presented

This is still not the best way to auto-calibrate, as it still requires some manual calibration at the end

Results – Calibration Results – Calibration PeriodPeriod

SWAT2005 (Monthly)

Flow

Sediment

TDP PP

R – Squared 0.85 0.73 0.7 0.72

% Diff. 0.34 -3.3 -4.18 -6.22

SWAT-VSA (Monthly)

Flow

Sediment

TDP PP

R – Squared 0.84 0.73 0.72 0.53

% Diff. 0.44 -1.9 -4.77 -0.85

Calibration period: January 1994 to December 1999 Both models do well simulating the measured data Discrepancy in PP phosphorous results

SWAT model does better although both models do well with sediment

Results – Flow and Results – Flow and SedimentSediment

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Flow Calibrations are quite good, both models capture trends

Models tend to over predict high flows and under predict low flows

Sediment Models do well with average loads, but tend to under predict high loadings

Some of this error can be attributed to flow error

Flow

Sediment

Results – Phosphorous Results – Phosphorous

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TDP Both models do well with average loads, but tend to under predict high loads

Part of this error can be attributed to flow under predictionPP

SWAT2005 model does better than SWAT-VSA model

Interesting since PP is largely impacted by sediment, which is captured well by both models

TDP

PP

OutlineOutline

SWAT2005 vs SWAT-VSASWAT2005 vs SWAT-VSA Calibration Calibration Corn analysisCorn analysis ConclusionConclusion Questions ?Questions ?

Land Use Management Analysis

Since SWAT-VSA uses a combination of land use and wetness class to determine HRUs, we can look into the impact of moving different land uses

In this analysis, we looked at the impact of moving corn to low runoff generating areas, i.e. low wetness classes

Corn Analysis - Setup SWAT-VSA

All corn HRUs are changed to either wetness class 1 or 2

Turned corn wetness classes of 3 – 10 into hay or pasture

In order to keep total corn area constant, some hay and pasture wetness classes 1 and 2 were turned into corn

Meticulously kept track of each wetness class area as well as land use area

SWAT2005 All corn was turned into either hay or pasture of

the same soil type Only thing that can really be done with SWAT in

terms of land use

SWAT2005 Model without Corn

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Flow % Difference = 0.23

There is no difference because overall CN did not change

Sediment % Difference = -33.5

Peak sediment loads are nearly cut in half, shows the impact of corn on the sediment loading

SWAT2005 Model without Corn

TDP % Difference = -38.4

Shows the large impact that corn has on TDP loading

PP % Difference = -71

large impact on PP is due to removal of corn as a direct source as well as the decrease in sediment loading

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SWAT-VSA Model Corn Analysis

Flow % Difference = -0.07

Does not change because overall wetness class areas do not change

Sediment % Difference = -0.23

Does not change because decrease in sediment loading from corn is balanced by the increase in sediment loading from hay and pasture

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SWAT-VSA Model Corn Analysis

TDP % Difference = -27.5

substantial decrease in peak loadings shows the impact of moving corn to areas of lower runoff

PP % Difference = -49

Since the overall sediment loadings do not change, this change in PP is directly due to moving corn areas

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Corn Analysis - conclusion

From the previous analysis, it is apparent that the location of corn areas has a significant impact on Phosphorous runoff

Analysis results make physical sense This type of nutrient reduction would occur in the

watershed if all corn is moved to low-runoff areas Although this is a best case scenario in terms of

nutrient reduction, it may not be entirely practical Moving corn to low-runoff areas may also reduce corn

yeild Need to find some trade-off point

OutlineOutline

SWAT vs SWAT-VSASWAT vs SWAT-VSA Calibration Calibration Corn analysisCorn analysis ConclusionConclusion Questions ?Questions ?

ConclusionConclusion SWAT-VSA and SWAT 2005 Models produce SWAT-VSA and SWAT 2005 Models produce

similar results based on available calibration data similar results based on available calibration data for the large 1200 kmfor the large 1200 km22 Cannonsville watershed. Cannonsville watershed.

Flow distributions can have important Flow distributions can have important implications for nutrient managementimplications for nutrient management

Management scenarios in SWAT-VSA can include Management scenarios in SWAT-VSA can include specific nutrient placement based on flow specific nutrient placement based on flow distributionsdistributions

SWAT-VSA will predict decreases in phosphorous SWAT-VSA will predict decreases in phosphorous transport when corn is placed mostly in dry areas.transport when corn is placed mostly in dry areas.

OutlineOutline

SWAT vs SWAT-VSASWAT vs SWAT-VSA Calibration Calibration Corn analysisCorn analysis ConclusionConclusion Questions ?Questions ?