Location: Southwest Watershed Research CenterTitle: A daily spatially explicit stochastic rainfall generator for a semi-arid climate Author
|Zhao, Y. - University Of Arizona|
|Guertin, D.p. - University Of Arizona|
Submitted to: Journal of Hydrology
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/1/2019
Publication Date: 4/19/2019
Citation: Zhao, Y., Nearing, M.A., Guertin, D. 2019. A daily spatially explicit stochastic rainfall generator for a semi-arid climate. Journal of Hydrology. 54:181-192. https://doi.org/10.1016/j.jhydrol.2019.04.006.
DOI: https://doi.org/10.1016/j.jhydrol.2019.04.006 Interpretive Summary: This study presents a new and practical tool for modeling spatially distributed rainfall. In semi-arid areas the rainfall that drives surface hydrologic response (runoff and erosion) is highly localized in both space and time. Storms tend to be relatively small. This leads to high variability, which can be problematic for accurately assessing runoff response in medium to large sized-watersheds. A great advantage that we had in developing this model was the USDA-ARS Walnut Gulch Experimental Watershed rainfall data set, which consists of 88 raingages on a 150 km2 area with data collected over a period of decades. As such we think that it serves as a strong indicator of reliability and potential applicability for modeling. This model will greatly improve our accuracy and ability to apply watershed scale hydrologic models in the semi-arid Western United States.
Technical 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-km2 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 longterm 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.