Using CLIGEN to generate RUSLE climate inputs

Abstract

CLIGEN is a stochastic weather generator that produces continuous daily variables to drive process–based runoff and erosion prediction models such as WEPP. To test CLIGEN's ability to generate precipitation–related variables, which are particularly important to runoff and erosion prediction, algorithms were developed to compute the R–factor, its monthly distribution, and 10–year storm erosion index (EI) needed to apply the Revised Universal Soil Loss Equation (RUSLE). Measured R–factor and 10–year storm EI for 76 sites in the U.S. were used for calibration, and 89 additional sites were used for validation. It was found that the generated R–factor using CLIGEN is highly correlated with the measured R–factor for the calibration sites (r2 = 0.96), although the generated R–factor is systematically larger than the measured R–factor. The predicted R–factor for validation sites has a model efficiency (Ec) of 0.92 and a root mean squared error of around 600 MJ mm ha–1 h–1 year–1, or 24% of the average R–factor for the 89 sites. In addition, CLIGEN–generated precipitation data can also be used to predict 10–year storm EI (Ec = 0.75) and monthly distribution of rainfall erosivity for a wide range of climate environments (average discrepancy = 2.6%). This represents considerable improvement over existing methods to estimate R–factor and 10–year storm EI for locations with only monthly precipitation data, although the systematic over–estimation of the R–factor using CLIGEN–generated climate data suggests possible inadequacies in the assumed storm patterns in CLIGEN and WEPP.