Development of a Spatially Explicit Stochastic Rainfall Generator in Southeast Arizona

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. The objective of this study was to develop 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-km2 Walnut Gulch Experimental Watershed, and then apply it in three different sized watersheds to see how spatially varied rainfall inputs will impact the hydrologic responses. 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 replicates 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. Afterwards, two rainfall sequence data generated by a single-site rainfall generator (CLIGEN) and the spatial rainfall generator were passed into Soil and Water Assessment Tool (SWAT) for runoff simulation. Statistics showed that single-site and multi-site rainfall generators gave similar results regarding to annual precipitation. However, the multi-site generator performed much better than the single-site generator in both mean summer flow and different return period flows. Single-site generator derived runoff was significantly overestimated in all three level watersheds, whereas multi-site generator performed satisfactorily in smaller watersheds and only did an overestimation in the largest watershed. It is indicated that in small to medium sized watersheds, the spatial variability of rainfall could still play an important role for hydrologic response, which made the application of multi-site rainfall generator become necessary.