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United States Department of Agriculture

Agricultural Research Service


item Huang, Chi Hua
item Darboux, F
item Zartl, A

Submitted to: American Society of Agricultural Engineers
Publication Type: Proceedings
Publication Acceptance Date: 1/3/2001
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: In the Water Erosion Prediction Project (WEPP) model, erosion and deposition are calculated based on a predefined value of sediment transport capacity (Tc), estimated from flow hydraulics, slope and sediment properties. Under this model concept, net erosion or deposition is estimated by the difference between sediment load (qs) and Tc, i.e., erosion when qs < Tc and deposition when qs > Tc. Separate erosion and deposition equations are derived and an accurate assessment of erosion or deposition depends on how the Tc is estimated. Recent laboratory results from a multiple-box system challenged the Tc concept. Experimental observations suggest that erosion and deposition processes are occurring simultaneously and different surface, flow and rainfall conditions may trigger the dominance of one process over the other. Therefore, we propose an alternative, single sediment mass balance equation, which contains both erosion and deposition terms, to model sediment transport. As a first approximation, the erosion process is modeled by a first-order rate process similar to the WEPP model, and the deposition process is estimated to be proportional to qs. Separate rate constants are used for erosion and deposition processes. An analytic solution for the proposed erosion equation is derived and the solution is examined for conditions similar to the laboratory multiple box experiments. The analytic solution reproduced experimental data trends, in other words, it was able to simulate sediment mass balance scenarios ranging from deposition- to detachment- and transport-dominated regimes. This proposed modification may lead to a better understanding of erosion processes, and consequently, an improved erosion prediction model.

Last Modified: 06/21/2017
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