Soil and Water Assessment Tool evaluation of soil and land use geographic information system data sets on simulated stream flow

Abstract

The integration of geographic information systems (GIS) and hydrologic models provides the user with the ability to simulate watershed-scale processes within a spatially digitized computer-based environment. Soil type and land use data are essential GIS data layers used in a wide array of government and private sector activities, including resource inventory, land management, landscape ecology, and hydrologic modeling. This investigation was conducted to evaluate the use of different combinations of Soil Survey Geographic (SSURGO) and State Soil Geographic (STATSGO) soil classification systems and the USDA National Agricultural Statistics Service (NASS) and national Gap Analysis Project (GAP) land use data sets and their effects on modeled stream flow using the Soil and Water Assessment Tool (SWAT2005). Performance of the model was tested on the Cedar Creek Watershed in northeastern Indiana, one of 14 benchmark watersheds in the USDA Agricultural Research Service Conservation Effects Assessment Project (CEAP) watershed assessment component. CEAP comprises two main components: (1) a national assessment that provides model estimates of conservation benefits for annual reporting and (2) a watershed assessment component aimed at quantifying the environmental benefits from specific conservation practices at the watershed scale. Model performance for daily, monthly, and annual uncalibrated stream flow responses in SWAT was assessed using the Nash-Sutcliffe efficiency coefficient (ENS), coefficient of determination (R²), root mean square error (RMSE), ratio of RMSE to the standard deviation of measured data (RSR), and percent bias (PBIAS). We found that the range of relative error (e.g., PBIAS) and ENS values for uncalibrated stream flow predictions in this study were similar to others that have been reported in the literature. Simulated stream flow values ranged from slight overestimations of approximately 5%, to underestimating stream flow by 25% to 41% depending on the combination of soil and land use input data sets. Overall, the NASS SSURGO data sets gave the best model performance for monthly stream flow having an ENS value of 0.58, R² of 0.66, RSR of 0.65, and PBIAS equal to 21.93. The poorest model performance results were obtained using the GAP SSURGO data sets that had an ENS value of -2.58, R² of 0.49, RSR of 1.89, and a PBIAS value of 27.92. The results of this study indicate that in using the SWAT model, several factors regarding GIS input data sets may affect stream flow simulations and, consequently, water quality assessment studies. In addition to the effect of GIS source data on model output (e.g., SSURGO, STATSGO, NASS, GAP), there is evidence shown in this study that the interaction, pre-processing, and aggregation of unique combinations of GIS input layers within SWAT also influence simulated stream flow output. Overall, results of the study indicate that the use of different land use GIS layers has a greater effect on stream flow estimates than different soil data layers.