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ARS Home » Midwest Area » West Lafayette, Indiana » National Soil Erosion Research Laboratory » Research » Publications at this Location » Publication #302952
Title: Estimation of USLE K-values with a process-based approach

Author
item WU, QIUJU - Northwest Agriculture And Forestry University
item Flanagan, Dennis
item Huang, Chi Hua
item WU, FAQI - Northwest Agriculture And Forestry University

Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/1/2016
Publication Date: 2/28/2017
Citation: Wu, Q., Flanagan, D.C., Huang, C., Wu, F. 2017. Estimation of USLE K-values with a process-based approach. Transactions of the ASABE. 60(1):159-172.

Interpretive Summary: Soil erosion by rainfall and flowing water is a serious problem both within the United States and throughout the world. Erosion removes precious topsoil, damaging the productive capability of fields as well as producing sediment that can degrade off-site water quality. In order to assess how much soil loss occurs from fields at a particular location and on a specific soil, USDA scientists have developed both empirical soil erosion equations (the Universal Soil Loss Equation (USLE), the Revised Universal Soil Loss Equation (RUSLE), and process-based soil erosion models (Water Erosion Prediction Project (WEPP) model). The Natural Resources Conservation Service (NRCS) requested assistance in determining USLE/RUSLE soil erodibility (K) factors for some soils high in clay content. Typical approaches to experimentally determine K require ten or more years of observed data from natural runoff plots. However, in this study we conducted simple laboratory experiments on four different soils to determine input parameters for the WEPP model, ran long-term (100 year) WEPP model simulations for a USLE fallow plot situation, then used the WEPP results with the USLE/RUSLE rainfall-runoff erosivity factor to estimate the USLE/RUSLE K factor. Results showed that this new approach provided K factor values equivalent to those already in use for USLE/RUSLE that were based on expert opinion for these clayey soils, and which are considerably greater than those determined with the standard USLE nomograph. The significance of this research is the ability to provide scientific data support using the most advanced laboratory erodibility procedures and the process-based modeling technology to the historic empirical USLE model.

Technical Abstract: The susceptibility of a soil to detachment by erosive agents is commonly referred to as the soil erodibility. In the Universal Soil Loss Equation (USLE), the K – erodibility factor is defined as the rate of soil loss per unit of the erosivity index from a continuously tilled fallow plot 72.6 ft long on a 9% slope (unit plot). A soil erodibility nomograph, which allows rapid estimation of K values for different soils, was developed from both long-term natural runoff plots, as well as from rainfall simulation studies. However, the nomograph does not always provide good estimates for K, and this has been noted for soils with high clay contents. In this study, we utilize a combination of laboratory measurements and process-based Water Erosion Prediction Project (WEPP) erosion model simulations to back-calculate USLE K factors for four soils, a Miami silty loam from Indiana, an Opal clay from South Dakota, a Vergennes silty clay loam soil from Vermont and a Mexico silty clay loam from Missouri. Experiments included: 1) rainfall experiments to estimate WEPP interrill erodibility (Ki); 2) mini-flume experiments to estimate WEPP rill erodibility (Kr) and critical shear stress (tcr). Erosion measurements were made under three soil moisture conditions: initially dry, pre-wetted then drained, and pre-wetted and saturated. Results show average Ki ranged from 1.23 × 106 to 7.33 × 10*6 kg s m*-4; K**r ranged from 0.6 ×10*-3 to 6.9 ×10*-3 s m*-1; and t**cr ranged from 0.24 to 0.67 Pa for the four soils. The back-calculated USLE K values under initially dry and saturated soil moisture conditions were close to the RUSLE2 database K values for all soils. Saturated/drained soil moisture conditions resulted in the lowest back-calculated USLE K values for all soils. Using the laboratory measurements combined with the process-based WEPP model simulations to back-calculate USLE K factors indicated that the existing RUSLE2 database provided good estimations of K for the South Dakota and Vermont soils.