新一代多卫星遥感反演降水的流域水文模拟和预报能力研究

降水是水文模拟和预报的关键。作为全球降水观测计划（GPM）的前身，新一代多卫星遥感降水反演技术的出现使得低成本快速获取数据质量更好、时空分辨率更高、覆盖范围更广的实时连续降水资料成为可能，多卫星遥感降水与分布式水文模型的集成为大尺度流域（特别是无资料或少资料地区）的水文模拟和洪水预报提供了新的契机。本项目拟在中国南北两个典型流域各建立一个加密实验格网，并结合站点观测数据对三种国际上主流的多卫星遥感降水进行地面验证，从反演机理上探讨影响遥感降水精度的关键因素；将遥感降水与分布式垂向混合产流模型进行集成，定量评估三种主流遥感降水在典型流域的水文模拟和预报能力；解析不同空间分辨率遥感降水在洪水预报中的多维误差结构，揭示新传感器引入和关键算法更新对遥感降水的水文模拟和预报能力的影响。本项目研究成果可以为我国即将发射的降雨雷达星在洪涝灾害预报中的应用提供重要的技术借鉴和理论参考。

Precipitation is a critical variable to hydrologic simulation and flood prediction. As a prelude to NASA's planned Global Precipitation Measurement (GPM), current Multi-satellite Precipitation Retrievals are intended to provide the best real-time precipitation estimates with higher spatiotemporal resolution at quasi-global scale. The integration of multi-satellite precipitation estimates to distributed hydrological models provides hydrologists with an opportunity to improve hydrological process simulation and flood prediction capability for large river basins, especially in the remote regions where in-situ precipitation and stream gauge networks are sparse. In this proposal, two tested-grids (approx. 25km×25km) with dense ground observation networks for precipitation will be constructed within the semi-arid Laohahe Basin located in northeast China and the southern humid Mishui Basin, respectively. We intend to install some new in-situ instruments of tipping-bucket rain gauges within these two tested-grids to benchmark the satellite rainfall products. The overarching goal of this proposal is to investigate the accuracy and error of current high resolution real-time satellite-borne precipitation estimates (i.e., TMPA-RT, CMORPH, PERSIANN) and assess their hydrologic application. This will be evaluated using the observations from the previously described two instrumented basins. Further, we will investigate and identify the key factors that affect the accuracy of three mainstream satellite precipitation estimates from the perspective of precipitation retrievals. Then,the satellite precipitation estimates will be integrated into an improved distributed hybrid hydrologic model for hydrologic simulation at daily scale and flood prediction at 3-hourly scale over our study basins. Additionally, a kind of satellite rainfall error model (SREM2D) will be used to characterize the multidimensional error structure of satellite-driven flood prediction at different spatial resolutions. Finally, we will investigate how the introduce of several new sensors and crucial algorithm upgrades affect the potential of satellite precipitation in hydrologic simulation and prediction.The research results of this project can provide theory and technology references for the application of forthcoming Chinese Precipitation Radar Satellite in the flood prediction.