An assessment of uncalibrated CLIGEN in Australia
Daily weather data are required as climate input to many models that continuously simulate natural resources systems. CLIGEN is a stochastic weather generator to produce 10 daily weather variables to meet this need. CLIGEN has primarily been used to provide climate input for the process-based runoff and soil erosion prediction model WEPP (water erosion prediction project). Runoff and erosion prediction is particularly sensitive to the four precipitation variables generated by CLIGEN. Weather data for 43 sites representing all major climate zones around Australia were used to prepare input parameter files for CLIGEN to generate 100 years climate data for each of these sites. The quality of generated precipitation variables was assessed in terms of (1) simulated runoff and soil loss for each of three soils under bare fallow conditions with WEPP, (2) climate inputs for the revised universal soil loss equation (RUSLE), and (3) a comparison with published rainfall intensity maps for fixed average recurrence interval and duration. This paper shows that uncalibrated CLIGEN can generate the required climate data for WEPP for these sites. Model efficiency between predicted runoff and soil loss using CLIGEN-generated and observed precipitation data is in excess of 0.95. Generated rainfall erosivity for RUSLE is systematically higher than (about 50% for the R-factor and 25% for the 10-year storm erosivity index) and closely related to the measured erosivity for these sites (r2=0.88-0.94), a trend consistent with what is observed for sites in the United States. CLIGEN can also be used to predict the seasonal distribution of rainfall erosivity quite well for all climate zones in Australia. Detailed analysis of the observed 6 min intensity data for these sites shows that over-prediction of rainfall erosivity and rainfall intensity at short time scales in general is an outcome of the particular storm pattern adopted in CLIGEN for WEPP, not an intrinsic deficiency of CLIGEN per se.
Agricultural and Forest Meteorology: an international journal