Spatio-temporal precipitation changes and their localized predictors in the Taihang Mountain region, North China

Spatial–temporal variations in precipitation significantly influence infiltration, runoff, and other hydrological processes; and thus, in turn, they influence the risk of natural disasters such as flooding, drought, and erosion. Knowledge of these processes is still limited in the Taihang Mountain region, which is a highly heterogeneous environment in northern China. In this study, annual precipitation data for 1968–2017 from 88 weather stations in the Taihang Mountain region were analyzed. The Mann–Kendall (M–K) test and precipitation-related indices (precipitation amount, Sen’s slope, Precipitation Concentration Index (PCI), and Coefficient of Variation (CV)) were used to analyze the spatial and temporal trends in precipitation in this region. Nine predictors (elevation, longitude, latitude, slope gradient, slope aspect, maximum temperature (Tmax), minimum temperature (Tmin), difference between Tmax and Tmin (DT), and evapotranspiration (ET)) were used to predict the precipitation and the related indices. The results reveal that the annual precipitation generally decreased from 1968 to 2017, but the M–K test indicates a nonsignificant trend. The precipitation decreased from southeast to northwest with significantly different spatial variations over the five decades investigated. The decrease in the PCI was not significant, and it generally decreased from northeast to southwest, suggesting a higher risk of flooding and drought in the northeast. The CV was 0.18–0.32, indicating a moderate spatial variation. In addition, the CV slightly decreased during the 50 years investigated. Multiple linear regression revealed that the amount of precipitation could be predicted from the latitude and longitude. The slope trend could be predicted based on latitude. PCI could be predicted based on longitude and elevation. CV could be predicted based on elevation, longitude, and Tmax. This suggests that the precipitation was mainly influenced by the geographical factors in the Taihang Mountain. This is useful information for the prediction of precipitation and for water management in this mountain region.