降水诱发型滑坡是我国常见与危害性很大的地质灾害类型，滑坡预报是滑坡灾害防治最有效的非工程措施之一。相比滑坡统计模型，动力物理模型能够考虑降水入渗与产汇流等流域水文过程对滑坡的响应，实现边坡失稳过程模拟，成为滑坡预报新的发展方向。然而，水文过程模拟不完整性、降水预报不确定性、气象-水文-滑坡尺度匹配及预报不确定性，成为制约提高滑坡预报精度与预见期的主要瓶颈。本项目基于WRF中尺度模式、SLIDE和TRIGRS模型，对滑坡预报三个关键方面进行研究：（1）研究面向滑坡预报的分布式混合产流水文模型及参数先验估计技术，建立耦合分布式水文模型的滑坡物理模型；（2）研究WRF模式不确定性，建立高分辨率WRF集合预报模式；（3）建立WRF集合预报驱动，耦合分布式混合产流水文模型的滑坡集合预报模型，在滑坡灾害易发区域滑坡预报中评估预报性能与不确定性。项目旨在提高滑坡预报精度与预见期，为防灾减灾提供科学依据。

Rainfall-induced landslides is a frequent and heavily damaging natural geological hazard, and landslides forecasting is one of the most effective non-engineering measures for landslides disaster control. Compared with statistical model, physical dynamic model, which can take into consideration the physical understanding of how landslides respond to rainfall infiltration and runoff yield and concentration hydrological processes, and simulate precipitation-induced slope-failure processes, has become a new direction for landslides forecasting. However, hydrological processes simulation Incompleteness, precipitation forecasting uncertainty, scale match in atmospheric-hydrological-landslides model and its forecasting uncertainty, become a bottleneck to improve the precision and lead time of Rainfall-induced landslides forecasting. Based on the WRF mesoscale NWP model, slide and TRIGRS models, three key technologies of Rainfall-induced landslides forecasting will be studied. (1) A distributed conceptual hydrological model based on the mixed runoff generation model for landslides forecasting will be studied, and the model parameterization scheme is based on prior estimation method. A rainfall-induced landslides forecasting model by coupling with a distributed hydrological model will be developed.(2) With analyzing WRF model's uncertainty, WRF ensemble prediction model with high resolution will be developed.(3) Ensemble rainfall-induced landslides forecasting model by coupling with a distributed hydrological model, which driven by WRF ensemble prediction model , will be evaluated and its uncertainty will be analyzed in the rainfall-induced landslides prone areas. This project aims to improve the precision and lead time of rainfall-induced landslides forecasting, and provide scientific basis for flood control decision-making.