遥感技术的发展实现了降水观测的近实时和大范围覆盖，遥感降水作为全球气候变化和流域水文研究的重要数据源，是当前的一个热点研究方向。然而关于多卫星降水用于水文模拟的不确定性，以及遥感降水和地理背景场多尺度耦合关系的研究尚不明确。本项目选择黑河流域和淮河流域为典型研究区，开展五种常用遥感降水（GPM、TRMM、CMORPH、PERSIANN和CHIRPS）的水文模型验证和基于多信息集成的遥感降水降尺度研究。主要研究内容包括：（1）对比分析多卫星遥感降水的时空分异规律，解析误差的主要来源。（2）利用VIC水文模型量化多卫星遥感降水用于水文模拟的不确定性及其传播规律，提出水文应用对遥感降水的现实需求。（3）建立遥感降水和地理背景场空间异质性的尺度耦合关系，提出基于贝叶斯地理加权回归和Copula理论的多信息集成遥感降水降尺度方法体系，实现高精度和高时空分辨率的遥感降水制图，提高水文模型模拟预测能力。

Satellite-based precipitation providing near real-time and widely coverage of precipitation observations is an important data source for global climate change and hydrological research, and the research on satellite precipitation has been a hot spot in recent years. However, the uncertainty of multiple satellite precipitation for hydrological simulation and the multi-scale coupling relationship between satellite precipitation and geography field are still not clear. In the proposed study, two typical basins (Heihe River Basin and Huaihe River Basin) are selected to evaluate the performance of five commonly used satellite precipitation (GPM, TRMM, CMORPH, PERSIANN and CHIRPS) in hydrological model, and the spatial downscaling of satellite precipitation based on multi-information integration is also conducted. The main contents are as follows: (1) to compare temporal-spatial differentiation and analyze the main error sources of multiple satellite precipitation. (2) to quantify the uncertainty and its propagation law of multiple satellite precipitation for hydrological simulation using the VIC model , and to put forward the realistic demand of satellite precipitation. (3) to establish the scale coupling relationship on spatial heterogeneity between satellite precipitation and geography field, and to propose a multi-source data integrated downscaling strategy based on Bayesian Geography Weighted Regression (BGWR) and Copula function for obtaining precipitation with high accuracy and resolution to improve the capabilities of hydrological simulation and forecasting.