A Comparison of a SWAT model for the Cannonsville Watershed with and without Variable Source Area Hydrology Josh Woodbury Christine A. Shoemaker Dillon Cowan Zachary Easton

Outline SWAT2005 vs SWAT-VSA SWAT2005 vs SWAT-VSA Calibration Calibration Corn analysis Corn analysis Conclusion Conclusion Questions ? Questions ?

SWAT2005 and SWAT-VSA The current SWAT2005 version is a replication of the SWAT 2000 model developed by Bryan Tolson The current SWAT2005 version is a replication of the SWAT 2000 model developed by Bryan Tolson Dillon Cowan replicated the SWAT 2000 files as closely as possible to create the current SWAT2005 version Dillon Cowan replicated the SWAT 2000 files as closely as possible to create the current SWAT2005 version This included creating the same number of subbasins with similar HRUs This included creating the same number of subbasins with similar HRUs Much time was spent insuring that the corn and pasture areas in each model are identical Much time was spent insuring that the corn and pasture areas in each model are identical Although corn is only a small percentage of the watershed, it accounts for a significant percentage of the phosphorous loading to the reservoir Although corn is only a small percentage of the watershed, it accounts for a significant percentage of the phosphorous loading to the reservoir Meticulous attention to corn area is important in order to create an accurate model replication Meticulous attention to corn area is important in order to create an accurate model replication Very time consuming since the watershed delineation did not create the correct amount of corn area because of the small percentage Very time consuming since the watershed delineation did not create the correct amount of corn area because of the small percentage

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

Outline SWAT2005 vs SWAT-VSA SWAT2005 vs SWAT-VSA Calibration Calibration Corn analysis Corn analysis Conclusion Conclusion Questions ? Questions ?

Calibration Both of the models are calibrated first for flow, then sediment and finally phosphorous Both of the models are calibrated first for flow, then sediment and finally phosphorous The calibration period is from Jan to Dec The calibration period is from Jan to Dec Auto-calibration and manual calibration techniques are used to get the best fit Auto-calibration and manual calibration techniques are used to get the best fit Parameters used are based upon a sensitivity analysis done by Ryan Fleming Parameters used are based upon a sensitivity analysis done by Ryan Fleming

Calibration Firstly the models are calibrated using an algorithm called DDS Firstly the models are calibrated using an algorithm called DDS DDS is a simple stochastic single-solution based heuristic global search algorithm designed for automatic calibration of watershed models (Tolson and Shoemaker, WRR, 2007) DDS is a simple stochastic single-solution based heuristic global search algorithm designed for automatic calibration of watershed models (Tolson and Shoemaker, WRR, 2007) DDS is used with a weighted Sum of Squared Error objective function DDS is used with a weighted Sum of Squared Error objective function

Calibration Once flow and sediment are calibrated, Total Dissolved Phosphorous (TDP) and Particulate Phosphorous (PP) are calibrated using DDS Once flow and sediment are calibrated, Total Dissolved Phosphorous (TDP) and Particulate Phosphorous (PP) are calibrated using DDS Manual calibration techniques are then used to slightly improve the models Manual calibration techniques are then used 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 Period SWAT2005 (Monthly)FlowSedimentTDPPP R – Squared % Diff SWAT-VSA (Monthly)FlowSedimentTDPPP R – Squared % Diff 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 Sediment 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 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 prediction PP  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

Outline SWAT2005 vs SWAT-VSA SWAT2005 vs SWAT-VSA Calibration Calibration Corn analysis Corn analysis Conclusion Conclusion 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 Flow  % Difference = 0.23  There is no difference because overall CN did not change Sediment  % Difference =  Peak sediment loads are nearly cut in half, shows the impact of corn on the sediment loading

SWAT2005 Model without Corn TDP  % Difference =  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

SWAT-VSA Model Corn Analysis Flow  % Difference =  Does not change because overall wetness class areas do not change Sediment  % Difference =  Does not change because decrease in sediment loading from corn is balanced by the increase in sediment loading from hay and pasture

SWAT-VSA Model Corn Analysis TDP  % Difference =  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

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

Outline SWAT vs SWAT-VSA SWAT vs SWAT-VSA Calibration Calibration Corn analysis Corn analysis Conclusion Conclusion Questions ? Questions ?

Conclusion SWAT-VSA and SWAT 2005 Models produce similar results based on available calibration data for the large 1200 km 2 Cannonsville watershed. SWAT-VSA and SWAT 2005 Models produce similar results based on available calibration data for the large 1200 km 2 Cannonsville watershed. Flow distributions can have important implications for nutrient management Flow distributions can have important implications for nutrient management Management scenarios in SWAT-VSA can include specific nutrient placement based on flow distributions Management scenarios in SWAT-VSA can include specific nutrient placement based on flow distributions SWAT-VSA will predict decreases in phosphorous transport when corn is placed mostly in dry areas. SWAT-VSA will predict decreases in phosphorous transport when corn is placed mostly in dry areas.

Outline SWAT vs SWAT-VSA SWAT vs SWAT-VSA Calibration Calibration Corn analysis Corn analysis Conclusion Conclusion Questions ? Questions ?