Predicting post-fire erosion at the hillslope scale: Efforts to validate RUSLE and Disturbed WEPP

High-severity wildfires can increase hillslope-scale erosion rates by several orders of magnitude. Accurate predictions of post-fire erosion are needed to guide management decisions and assess the potential impact of soil loss on site productivity and downstream aquatic resources. The models most commonly used to predict post-fire erosion rates are the Revised Universal Soil Loss Equation (RUSLE) and Water Erosion Prediction Project (WEPP), but neither model has been extensively tested against field data. Over the past five years we have measured post-fire sediment production rates from 95 hillslopes in 10 wild and prescribed fires of various ages in the Colorado Front Range. This paper: (1) compares these measured values against the predictions from RUSLE and Disturbed WEPP, and (2) evaluates how each model can be modified to better predict post-fire erosion rates. Both RUSLE and Disturbed WEPP generally over-predicted erosion rates when measured values were less than 1 Mg ha-1 yr-1, and under-predicted when measured rates exceeded 1 Mg ha-1 yr-1. The predicted values were poorly correlated with the measured values for RUSLE (r2=0.11), and only slightly better for Disturbed WEPP ( r2=0.25). Model performance was only marginally improved by increasing the soil erodibility (K) factor in RUSLE to account for post-fire soil water repellency (Miller et al., 2003), or by changing the sequence of vegetation recovery in Disturbed WEPP. Previous efforts to validate RUSLE and WEPP have reported substantially higher model performance for plots in agricultural areas, whereas studies on rangelands have reported similar results. The relatively poor performance of both models highlights the complexity of the various controlling factors, and the need to more accurately represent the underlying processes. The dilemma for land managers is that better accuracy will require more detailed, spatially-explicit input data, but these data will not be available without a much greater investment of time and resources.