Evaluation of Cligen Precipitation Parameters and Their Implication on Wepp Runoff and Erosion Prediction

The quality of synthesized daily weather data directly affects the output of hydrological and agricultural responsemodels. The objectives of this study were to evaluate the ability of the CLIGEN model to reproduce daily, monthly, and annualprecipitation amounts, extremes, and internal storm patterns (i.e., storm duration, relative peak intensity, and time to peak)and to assess further the impact of generated storm patterns on WEPP runoff and erosion prediction. Four Oklahoma stationswith more than 50 years of daily precipitation data and eight other sites across the U.S. with an average record of 10 yearsof measured storm patterns were used. Mean absolute relative errors for simulating daily, monthly, and annual precipitationacross the four Oklahoma stations were 4.7%, 1.7%, and 1.5% for the means and 3.7%, 6.7%, and 15% for the standarddeviations, respectively. Mean absolute relative errors for the alltime maxima of daily, monthly, and yearly precipitationwere 17.7%, 8.9%, and 6.5%, respectively. Storm pattern generation, especially storm duration, was determined to needimprovement for better prediction of runoff and soil erosion. The measured storm patterns showed positive linear correlationsbetween precipitation, duration, and relative peak intensity, but little correlation was shown for generated storm patterns.The CLIGENgenerated durations were generally too long for small storms and too short for large storms. Inaccurate stormpattern generation led to WEPP prediction errors as high as 35% for average annual runoff and 47% for annual sedimentyield on the test sites. To improve WEPP runoff and erosion prediction, storm duration generation should be reconsidered,and a distributionfree approach may be used to induce proper correlations between the input storm variables.