Submitted to: American Society of Agricultural Engineers Meetings Papers
Publication Type: Abstract only
Publication Acceptance Date: 6/1/2002
Publication Date: 7/28/2002
Citation: O'Neal, M.R., Flanagan, D.C., Meyer, C.R. Evaluation of the WEPP model in response to changes in the CLIGEN stachostic weather generator. American Society of Agricultural Engineers Meetings Papers. 2002. Paper No. 02-2225. Interpretive Summary:
Technical Abstract: The purpose of this study was to determine the impact of changes to maximum precipitation intensity and the interpolation of daily climate data from monthly means in the CLIGEN climate generator on outputs of the Water Erosion Prediction Project (WEPP) model. Outputs examined were precipitation, runoff, and soil loss for 1386 stations across the United States. It was anticipated that changes would be small, but maximum absolute differences of 48.7% and 75.0% in average annual soil loss were found between interpolation schemes. Median percent difference in annual precipitation (preliminary results) was 2.2% for two versions of CLIGEN (one with much lower values of maximum 30-minute precipitation and making rainfall intensity appropriately responsive to latitude) and 0.3% among four interpolation schemes for weather data (no interpolation, linear interpolation between midmonths, linear interpolation to preserve monthly means, Fourier series). Median soil loss differences were 24.3% between the two versions and 2.0% among the four interpolation schemes. Median differences in runoff were 16.3% between CLIGEN versions and 1.9% among monthly to daily data interpolation schemes. Differences were greater in the drier Western states. The results suggest different predicted WEPP output values between versions of CLIGEN and interpolation types. Further work is needed before definitive conclusions can be drawn. This research impacts users of process-based erosion models that require daily climate input from a weather generator. Testing and development of a CLIGEN version that best reproduces natural climate is needed for the best predictions of runoff and soil loss.