Parameter sensitivity and uncertainty in SWAT: A comparison across five USDA-ARS watersheds

T. L. Veith, M. W. Van Liew, D. D. Bosch, J. G. Arnold

Research output: Contribution to journalArticlepeer-review

47 Scopus citations


The USDA-ARS Conservation Effects Assessment Project (CEAP) calls for improved understanding of the strengths and weaknesses of watershed-scale water quality models under a range of climatic, soil, topographic, and land use conditions. Assessing simulation model parameter sensitivity helps establish feasible parameter ranges, distinguish among parameters having regional versus universal interactions, and ensure that one model process does not compensate for another due to poor parameter settings. The Soil and Water Assessment Tool (SWAT) parameter sensitivity and autocalibration module was tested on two northern and three southern USDA-ARS experimental watersheds. These previously calibrated watersheds represent a range of climatic, physiographic, and land use conditions present in the U.S. Sixteen parameters that govern basin, snow accumulation/melt, surface, and subsurface response in the model were evaluated. Parameters governing surface runoff due to rainfall were found most sensitive overall, while parameters governing groundwater were the least sensitive. Surface runoff parameters were found most sensitive for areas with high evaporation rates and localized thunderstorms. Parameters from all categories were important in areas where precipitation includes both rainfall and snowfall. Differences in model performance were noticeable on a climatic basis; SWAT generally predicted streamflow with less uncertainty in humid climates than in arid or semi-arid climates. Study findings can be used to determine appropriate parameter ranges for ungauged watersheds of similar characteristics.

Original languageEnglish (US)
Pages (from-to)1477-1486
Number of pages10
JournalTransactions of the ASABE
Issue number5
StatePublished - Sep 2010

All Science Journal Classification (ASJC) codes

  • Forestry
  • Food Science
  • Biomedical Engineering
  • Agronomy and Crop Science
  • Soil Science


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