Submitted to: Transactions of the ASAE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/18/1995
Publication Date: N/A
Citation: N/A Interpretive Summary: Soil erosion is a major factor causing crop yield reduction in many areas. To control soil loss effectively, a reliable assessment tool (model) must be developed. This paper is to evaluate the overall performance of the Water Erosion Prediction Project (WEPP) model (the new generation soil erosion prediction model) in predicting surface water runoff and soil loss. Measured data from eight different locations with 34 different cropping and management systems were used. The WEPP model input files including soil, slope, climate, and crop management were compiled based on measured data. Model predicted surface runoff and soil loss values were compared to measured values. Results showed the WEPP model predicted daily and annual surface runoff volumes reasonably well. Soil loss was also predicted well, but the accuracy and reliability of the predictions were improved when the model was used to predict long term average soil loss. Results also showed the WEPP model responded well to different cropping and management systems. This indicates that the WEPP model can be used as a assessment and planning tool for laying out the best conservation plan.
Technical Abstract: This study was undertaken to evaluate the overall performance of the WEPP hillslope model in predicting runoff and soil loss under cropped conditions as compared to the measured data from natural runoff plots. Data from 556 plot years with 34 cropping scenarios at eight locations were selected. Measured and predicted runoff and soil loss were compared on event, annual, and average annual basis. Results indicated that WEPP predicted event, annual, and average annual runoff volumes agreed well with the measured data on all sites and for all cropping scenarios. The model performed well in predicting soil loss for most cropping systems used in this study. The accuracy and reliability of the predictions were improved from an event to annual to average annual basis.