Submitted to: Soil and Water Conservation Society
Publication Type: Abstract only
Publication Acceptance Date: 2/24/2014
Publication Date: 7/27/2014
Citation: Ghazanfarpour, N., Gantzer, C.J., Baffaut, C. 2014. Comparison of WEPP and APEX runoff and erosion prediction at field scale in Goodwater Creek Experimental Watershed [abstract]. Soil and Water Conservation Society. P. 40. Interpretive Summary:
Technical Abstract: The Water Erosion Prediction Project (WEPP) and the Agricultural Policy/Environmental eXtender (APEX) are process-based models that can predict spatial and temporal distributions of erosion for hillslopes and watersheds. This study applies the WEPP model to predict runoff and erosion for a 35-ha field located in Goodwater Creek Experimental Watershed, in the claypan region of Missouri, using field specific climate, soil and topographic data. We will evaluate WEPP’s prediction for the actual crop and management during 1993-2002 by comparing model results with measured runoff and sediment data collected from a discharge flume located at the field outlet. Results will also be compared with the simulated results obtained with APEX for the same location and period and published by Mudgal et al. in 2010. This analysis will highlight capabilities of the WEPP model for predicting runoff and erosion and show the differences between the WEPP and APEX models in simulating runoff and erosion at field scale. Since WEPP simulates soil detachment, transport, and deposition based on rainfall intensity, runoff rate, and soil properties and APEX uses a variant of the Modified Universal Soil Loss Equation, this study examines erosional behavior in these two predictive models. The erosion and runoff results for individual hillslopes and channels within the field have significant implications for improving management and will allow land managers and conservationists to delineate critical areas based on them. Evaluation and comparison of the WEPP and APEX model will help the researchers and action agencies or other users select a model that meets their needs based on modeling objectives, amount of input information available, and the capabilities of the personnel in the organization.