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Effect of Soil Data on SWAT Modeling SSURGO, STATSGO, and SoLIM derived.

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Presentation on theme: "Effect of Soil Data on SWAT Modeling SSURGO, STATSGO, and SoLIM derived."— Presentation transcript:

1 Effect of Soil Data on SWAT Modeling SSURGO, STATSGO, and SoLIM derived

2 Objectives: Compare the accuracy of a SWAT hydrological model for the St. Joseph River Watershed using three soil datasets: – SSURGO 2.2 – STATSGO2 – SoLIM derived soil map (Soil Land Inference Model) 9/10/20092

3 St. Joseph River Watershed: – NE of Indiana, NW of Ohio, S of Michigan – HUC-8, 694,400 acres – 9 HUC-11 subwatersheds – NW boundary of Western Lake Erie Basin – Flows NE to SW – Rolling hills in Hillsdale, Williams, Noble, Steuben counties – Nearly flat plain in DeKalb and Allen counties – Parent material: dense glacial till – Texture: silt loam, silty clay loam, and clay loam – Udic moisture regime 9/10/20093

4 Data: 9/10/20094 – Watershed boundary – 1/3” NED – SSURGO 2.2 dataset – STATSGO2 dataset – Landuse/management data – Drainage network – Climatic data – Stream flow data – Soil scientist input

5 Methodology: 9/10/20095 – Build the SoLIM soil map – Setup SWAT similarly for the 3 different models – Only difference is soil data Will impact the number of HRUs and the soil parameters in each HRU – Run the three models, uncalibrated – Compare the streamflow output of each with actual – Expected results: Increased accuracy from STATGO2 -> SSURGO 2.2 -> SoLIM

6 Soil Types: 9/10/20096

7 Hydrologic group: 9/10/20097

8 Number of Layers: 9/10/20098

9 Texture: 9/10/20099

10 Texture: 9/10/200910

11 Watershed (1): 9/10/200911

12 Watershed (1): 9/10/200912

13 Watershed (2): 9/10/200913


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