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Title:   Author  item
                                            Van Pelt, Robert

                                                         - *Scott*item
                                            Zobeck, Teddyitem
                                            Potter, Kennethitem
                                            Stout, Johnitem
                                            Popham, Thomas
  Submitted to: International Conference on Aeolian Research
                                    Publication Type: Proceedings
                                    Publication Acceptance Date: 7/22/2002
                                    Publication  Date: 7/25/2002
                                    Citation: Van Pelt, R.S., Zobeck, T.M., Potter, K.N., Stout, J.E., Popham, T.W. 2002. Validation of the wind erosion stochastic simulator (WESS) and the revised wind erosion equation (RWEQ) for single events[abstract]. International Conference on Aeolian Research. July 22-25, 2002. Lubbock, Texas. p. 292-295.Interpretive Summary: The Wind Erosion Stochastic Simulator (WESS) is a stand-alone wind erosion module from the Environmental Policy Integrated Climate (EPIC) soil erosion and crop production model. The Revised Wind Erosion Model (RWEQ) was recently developed to replace the Wind Erosion Equation (WEQ) that the United States Department of Agriculture Natural Resources Conservation Service (USDA-NRCS) has used for over two decades to help agricultural producers comply with mandated maximum soil loss rates. Both of these models are mechanistic in nature, and with inputs of soil type, surface roughness, time since rain or irrigation, and observed wind speeds, can be used to predict wind erosion on an event-wise basis. The wind erosion prediction made by WESS were compared to measured erosion from 24 wind erosion events at Big Spring, Texas. The wind erosion predictions made by RWEQ were compared to measured wind erosion data from 41 events collected at seven locations around the United States (24 of the 41 events were measured at Big Spring, Texas). We found that WESS tended to overestimate small magnitude events and underestimate large magnitude events. From the range of measured erosion, it was determined that WESS would underestimate erosion on an annual basis. RWEQ also tended to underestimate wind erosion as reflected in soil loss for an event and also underestimated the maximum load of suspended sediment in the wind over the eroding field.
                                Technical Abstract: The Wind Erosion Stochastic Simulator (WESS) is a single event wind erosion model that is the core of the wind erosion submodel of the Environmental Policy Integrated Climate (EPIC) erosion model. WESS uses inputs of soil texture, erodible particle diameter, soil roughness, soil water content, crop residue, and 10 minute average wind speeds to predict the erosion at several user-selected distances within a given field. The Revised Wind Erosion Equation (RWEQ) makes annual or period estimates of wind erosion based on a single event wind erosion model that includes factors for wind and rainfall, soil roughness, the erodible fraction of soil, crusting, and surface residues. In this study, we compared estimates of wind erosion at multiple points in a field for 24 events at Big Spring, Texas, with the predictions of WESS and compared estimates of maximum sediment transport (Qmax), critical field length (S), and soil loss (SL) calculated from field measured data collected at six locations and 41 events with the predictions of RWEQ. Compared to observed estimates of erosion for the 24 events, WESS under-predicted 9 events, accurately predicted 8 events, and over-predicted 7 events. There was some overall inaccuracy noted with the results, however, as most the events WESS under-estimated were large magnitude storms, and most the events that WESS over-predicted were much smaller storms. Thus, overall, WESS would tend to under-estimate erosion on a period-wise or annual basis. RWEQ underestimated Qmax and SL. Simple linear regressions between observed and RWEQ predicted Qmax, and SL revealed significant (P<0.05) correlations with correlation coefficients of 0.70 and 0.62, respectively.


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