Spatial Distributions and Stochastic Parameter Influences on SWAT Flow and Sediment Predictions
Publication: Journal of Hydrologic Engineering
Volume 13, Issue 4
Abstract
The Soil and Water Assessment Tool (SWAT) was implemented in Northwest Arkansas to investigate flow and sediment predictive ability at multiple subbasin and hydrologic response units (HRU) distributions. The objectives of this study were to use SWAT and identify differences in annual predicted flow and sediment response of a watershed considering two subbasin delineations with six different HRU distributions each; quantify the uncertainty in SWAT output when sensitive model parameters are considered to have stochastic distribution; and evaluate the ability of the model to describe flow and sediment predictions of an ungauged watershed by stochastically validating the model. Flow results from the Single-Factor Between-Subjects Analysis of Variance test indicated that predicted flow means were not significantly different from each other for all SWAT subbasin/HRU combinations; however, predicted flow means and measured flow mean were significantly different. Sediment simulation results suggested significant differences were present amongst the different model subbasin/HRU delineations and measured values. The Monte Carlo simulation of the model, using curve number (CN), soil evaporation compensation factor (ESCO), groundwater revap coefficient (GW_REVAP), and peak rate adjustment factor for sediment routing in the subbasin (AMP) as uncertain parameters, indicated that generally ESCO induced most uncertainty in predicted flow. However, sediment prediction uncertainty was affected most by uncertainty in AMP. Results indicated that SWAT applications on ungauged watersheds should include small subbasin sizing ( of watershed area), HRUs that reflect actual land cover composition, a check to evaluate surface runoff and ground water contributions and modification of parameters as needed to reflect site conditions, sensitivity analysis, and uncertainty analysis that includes several sensitive parameters.
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Acknowledgments
The writers would like to acknowledge the University of Arkansas Division of Agriculture and the University of Florida Institute of Food and Agricultural Sciences for their support in conducting this research. The writers would like to thank the Arkansas Soil and Water Conservation Commission for funding the Watershed Modeling Laboratory and Steven Cole who assisted in development of the Monte Carlo VB programming. Excellent comments provided by three anonymous reviewers greatly improved the earlier version of this manuscript.
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Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 13 • Issue 4 • April 2008
Pages: 258 - 269
Copyright
© 2008 ASCE.
History
Received: Mar 28, 2006
Accepted: Jun 19, 2007
Published online: Apr 1, 2008
Published in print: Apr 2008
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