A daily spatially explicit stochastic rainfall generator for a semi-arid climate

Abstract Many semi-arid regions of the world experience rainfall patterns characterized by high spatial variability. Accurate spatial representation of different types of rainfall will facilitate the application of distributed hydrological models in these areas. This study presents a daily, spatially distributed, stochastic rainfall generator based on a first-order Markov chain model, calibrated using 50 years of rainfall observations at 88 gages from 1967 through 2016 in the 148-km 2 Walnut Gulch Experimental Watershed. Three types of rainfall, including convective, frontal, and tropical depression storms, were simulated separately in the generator using biweekly parameterization. Convective storms were simulated based on an elliptical shape rain cell conceptual model, whereas frontal and tropical depression storms were simulated as uniform rainfall fields over the whole watershed with introduced random variability. The rainfall generator was evaluated by comparing the mean statistics of 30 sets of 50-year simulated data versus the 50-year rain gage observed data. Most individual storm statistics and aggregated seasonal rainfall statistics were similar to the measured rainfall observations. The long-term mean values of both summer and winter rainfall amount were statistically satisfactory. This model can serve as a guide for application in areas with convective, frontal, and tropical depression storms.