Low accuracy and coarse spatial resolution are the two main drawbacks of satellite precipitation products. Therefore, calibration and downscaling are necessary before these products are applied. This study proposes a two-step framework to improve the accuracy of satellite precipitation estimates. The first step is data merging based on optimum interpolation (OI), and the second step is downscaling based on geographically weighted regression (GWR); therefore, the framework is called OI-GWR. An Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG) product is used to demonstrate the effectiveness of OI-GWR in the Tianshan Mountains, China. First, the original IMERG precipitation data (OIMERG) are merged with rain gauge data using the OI method to produce corrected IMERG precipitation data (CIMERG). Then, using CIMERG as the first guess and the normalized difference vegetation index (NDVI) as the auxiliary variable, GWR is utilized for spatial downscaling. The two-step OI-GWR method is compared with several traditional methods, including GWR downscaling (Ori_GWR) and spline interpolation. The cross-validation results show that (1) the OI method noticeably improves the accuracy of OIMERG, and (2) the 1-km downscaled data obtained using OI-GWR are much better than those obtained from Ori_GWR, spline interpolation, and OIMERG. The proposed OI-GWR method can contribute to the development of high-resolution and high-accuracy regional precipitation datasets. However, it should be noted that the method proposed in this study cannot be applied in regions without any meteorological stations. In addition, further efforts will be needed to achieve daily- or hourly-scale downscaling of precipitation.

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.

Mapping flood risk zone is an essential task in the arid region for sustainable water resources management. Due to the lack of hydrological and meteorological information and disaster event inventory in Xinjiang, China, storm flood disaster (SFD) risk zoning is an effective technique in investigating the potential impact of SFD. In this study, the statistics about natural, social, and risk related to SFD are collated. With the help of the compiled inventory data, a disaster risk assessment model for storm flood is proposed for the Xinjiang region based on the random forest (RF) algorithm. Randomly selected negative and positive samples from the historical SFD locations are composed of five different total samples. The overall prediction accuracy of the five sample groups attained 83.48%, indicating that the proposed RF model can well capture the spatial distribution of SFD in Xinjiang. It should also be noted that the spatial heterogeneity and complexity of SFD had a significant effect on its spatial distribution in Xinjiang. There are spatial distribution characteristics of lowland plains and high plateaus; the main mountainous regions, plains in the middle‐lower reaches of major rivers, and areas surrounding major lakes are prone to flooding. The variable importance RF indicates that the disaster risk is mainly affected by the following factors, including hazard factors, catastrophic intensity, population density, as well as economic development in the affected area. Besides, latitude, longitude, agricultural acreage, road density, distance from rivers, and the maximum monthly precipitation account for most of the increase in storm flooding disasters, and they are the main triggering point for SFD in Xinjiang. The proposed model provides some insight into the disaster in the mountainous region, and gives useful guidance for the national macro‐control of flood prevention and disaster reduction.