Spatiotemporal modeling of monthly precipitation in the upper Shiyang River watershed in west central Gansu, northwest China

Abstract

This paper outlines a methodology to estimate monthly precipitation surfaces at 1-km resolution for the Upper Shiyang River watershed (USRW) in northwest China. Generation of precipitation maps is based on the application of a four-variable genetic algorithm (GA) trained on 10 years of weather and ancillary data, i.e., surface air temperature, relative humidity, Digital Elevation Model-derived estimates of elevation, and time of year collected at 29 weather stations in west-central Gansu and northern Qinghai province. An observed-to-GA predicted data comparison of 10 years of precipitation collected at the 29 weather stations showed that about 84% of the variability in observed values could be explained by the trained GA, including variability in two independent datasets. Point-comparisons of observed and modeled precipitation along an elevation-rainfall gradient demonstrated near-similar spatiotemporal patterns. A precipitation surface for USRW for July, 2005, was developed with the trained GA and input surfaces of surface air temperature and relative humidity generated from Moderate Resolution Imaging Spectroradiometer sensor (MODIS) products of land surface temperature. Spatial tendencies in predicted maximum and minimum values of surface air temperature, relative humidity, and precipitation within a 2-km radius circle around selected weather stations were in close agreement with the values measured at the weather stations.