水文数据同化方法及遥感数据在水文数据同化中的应用进展  

Methods for hydrological data assimilation and advances of assimilating remotely sensed data into rainfall-runoff models

作　　者：王文[1] 寇小华[1] 

机构地区：[1]河海大学水文水资源与水利工程科学国家重点实验室,江苏南京210098

出　　处：《河海大学学报（自然科学版）》2009年第5期556-562,共7页Journal of Hohai University(Natural Sciences)

基　　金：国家自然科学基金(40771039);教育部博士点基金(20060294003);教育部长江学者创新团队(IRT0717)

摘　　要：多源信息的应用是未来水文科学发展的一个重要趋势,水文数据同化(尤其是遥感数据在流域降雨径流模拟中的同化)已经成为当前水文学研究的一个热点.数据同化有2个基本途径:一是全局拟合,以变分方法为代表;二是顺序同化,以各种卡尔曼滤波及粒子滤波方法为代表.数据同化技术已经被许多学者应用于降雨径流模拟与预报,但在遥感反演水文信息精度的改进、"最优"方法的选取、误差的定量描述、数据同化对象的选择,以及同化效果的评估等多方面,还有大量问题有待深入研究.The multi-source information application is a major trend in the development of hydrology science in the future. Hydrological data assimilation, especially assimilation of remotely sensed data into rainfall-runoff modeling, has become a hot spot in the field of hydrological research. There are two major approaches to data assimilation： the global fitting represented by the variational methods and the sequential assimilation represented by the various Kalman filters and particle filters. The data assimilation methods have been applied to the rainfall-runoff modeling by many researchers. However, deep researches are still required with regard to the following aspects： the improvement of the hydrological information retrieval from remotely sensed data, the choice of the optimum data assimilation method, the quantitative description of model errors, the choice of assimilated data and the evaluation of data assimilation effectiveness.

关 键 词：数据同化 降雨径流模型 遥感 土壤水分 水文预报 卡尔曼滤波 

分 类 号：P331[天文地球—水文科学]