Spatiotemporal patterns of precipitation based on the Bayesian maximum entropy method in a typical catchment of the Heihe River watershed, northwest China

Abstract

Precipitation patterns are vital to water resource management and hydrological research, especially in the upper reaches of inland rivers in arid and semiarid areas. However, estimating spatiotemporal precipitation patterns at a basin scale is challenging due to limited observations. In this study, spatiotemporal patterns of precipitation amount, frequency, duration, and intensity at different time scales from 2014 to 2019 are estimated using the Bayesian maximum entropy method in the Tianlaochi catchment of the Heihe River watershed, northwest China. The study's results show that the annual average precipitation amount was 535.9 mm from 2014 to 2019, with precipitation amount between May and September accounting for 85.9% of the annual precipitation amount. For daily precipitation, the average frequency rate of light precipitation is highest at 59.55%, however, the average contribution rate of moderate precipitation is highest at 50.33%. The spatial distribution of precipitation is characterized by high-value areas concentrated in the central valley and low-value areas located at the catchment's outlet. The most important driving factors of precipitation patterns are elevation, relative humidity, and wind direction. These outcomes can be used to establish accurate hydrological models in the catchment and provide support for water resource management in the Heihe River watershed.

Keywords

• geostatistics
• precipitation patterns
• spatial estimation
• cross-validation
• grey relational analysis