Quantitative Precipitation Estimation in the Tianshan Mountains Based on Machine Learning

Abstract

Precipitation in the Tianshan Mountains is abundant, and the quantitative estimation of precipitation in mountainous areas is important to the application and evaluation of regional water resources. With remote sensing technology, satellite inversion of precipitation can estimate precipitation in mountainous areas. However, the Tianshan Mountain terrain is complex, and the spatiotemporal variation in precipitation is large, so the accuracy of satellite precipitation inversion is not high. Here, precipitation data from around 1000 automatic weather stations in the Tianshan Mountains are used to study the correction technology of the Integrated Multisatellite Retrievals for the Global Precipitation Measurement (GPM) mission’s (IMERG) monthly precipitation products using stepwise regression (STEP), geographically weighted regression (GWR), and random forest (RF). First, geographic information system technology was used to extract topographic variables from a digital elevation model, and vegetation indexes, which are important precipitation indicators, were introduced as explanatory factors to correct satellite precipitation data. Second, GPM IMERG precipitation was corrected by establishing the stepwise regression, the geographically weighted regression model, and the random forest model. The three correction methods can improve the GPM IMERG in terms of relative bias, root mean square error, correlation coefficient, and Nash–Sutcliffe efficiency, while the random forest method shows better corrections than the two traditional methods. For dense rainfall stations, the geographically weighted regression method is as effective as random forest. For different altitudes, the results show that RF has the best correction effect in the first three zones, but the correction effect in the last zone (over 3000 m) is worse than STEP. This study provides a practical reference method for estimating precipitation data in the non-rainfall observation area, which helps to deepen the scientific understanding of the water resource distribution in the Tianshan Mountains and provide scientific data support for regional hydrological and meteorological research.