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United States Department of Agriculture

Agricultural Research Service


item Chu, Tzyy-woei
item Sadeghi, Ali
item Shirmohammadi, A
item Montas, H

Submitted to: Annual International SWAT Conference
Publication Type: Abstract Only
Publication Acceptance Date: 6/15/2003
Publication Date: 7/1/2003
Citation: Chu, T., Sadeghi, A.M., Shirmohammadi, A., Montas, H. 2003. Application of SWAT Model to the Piedmont physiographic region of Maryland. Proceedings of the 2nd International SWAT Conference, Bari, Italy. Water Research Institute (IRSA-CNR), p. 55-58.

Interpretive Summary:

Technical Abstract: Nonpoint source pollution of surface and groundwater is an important environmental issue for agricultural production. Continuous water quality monitoring is expensive and spatially impractical in mixed land-use watersheds. Mathematical watershed scale models are among the best tools available for analyzing water resources (quantity and quality) issues in spatially diverse watersheds. Although existing watershed scale models provide some reasonable guidelines, their application without proper validation has resulted in some misconceptions about such models. A comprehensive watershed scale hydrologic water quality model, SWAT (Soil & Water Assessment Tool), was applied to a 340 ha watershed with mixed land-use in the piedmont physiographic region of Maryland (USA). Six years (1994-1999) of hydrologic and nutrient loading data were used to calibrate and validate the capability of SWAT. Preliminary simulation results showed that SWAT underestimated subsurface flow and total stream flow, especially during the wet season of 1996. Reasons for the model underestimation were explored. Overall, model validation concluded that SWAT is a reasonable watershed scale model on long-term simulations of the impact of different management practices on hydrologic and water quality response of mixed land-use watersheds. However, its use on a short-term or storm by storm basis, especially in wet regions, may require further water balance adjustment.

Last Modified: 10/20/2017
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