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Summary: The aim of this paper is to examine the benefits of updating soil moisture of a distributed rainfall runoff model in forecasting large floods. The updating method uses Ensemble Kalman Filter concepts and involves an iterative similarity approach that avoids calculation of the Jacobian that relates the states and the observations. The soil moisture is updated based on observed runoff in a real-time mode, and is then used as an initial condition for the flood forecasts. The case study is set in the 622km2 Kamp catchment, Austria. The results indicate that the updating procedure indeed improves the forecasts substantially. The mean absolute normalised error of the peak flows of six large floods decreases from 25% to 12% (3h lead time), and from 25% to 19% (48h lead time). The Nash-Sutcliffe efficiency of forecasting runoff for these flood events increases from 0.79 to 0.92 (3h lead time), and from 0.79 to 0.88 (48h lead time). The flood forecasting system has been in operational use since early 2006. [Copyright &y& Elsevier]

• Remote-sensed soil moisture information is assimilated into a distributed model. • The influence of updating scheme on assimilation results is considered. • The assimilation improves simulated profile soil moisture and streamflow. Profile soil moisture (PSM), which represents soil moisture content over the whole soil layer depth, is a key variable to control plant growth, biological interactions, and streamflow generation, and its information plays a crucial role in hydrological analysis and agricultural water management. Recent studies have assimilated multi-source satellite PSM information into hydrological modelling to more accurately estimate real PSM. However, the PSM estimated from these studies are normally at coarse spatial resolution (i.e., larger than 25 × 25 km2). In this study, the high-resolution (1 × 1 km2) PSM are generated by assimilating multiple remote-sensed PSM data of coarse resolution into the Digital Elevation Model (DEM) based distributed rainfall-runoff model (DDRM) in three catchments (two humid catchments and one semiarid catchment) in China with the grid scale of 1 × 1 km2. The remote-sensed PSM data are pre-processed from two remote-sensed surface soil moisture datasets, i.e. the multi-satellite-retrieved soil moisture dataset released by the Europe Space Agency Climate Change Initiative (ESA CCI), and the soil moisture product from the Soil Moisture Active Passive (SMAP) satellite. The influence of remote-sensed datasets selection schemes (i.e. only ESA CCI, only SMAP, and ESA CCI and SMAP combined) on assimilation results are investigated. In the assimilation process, two updating schemes are considered, one is to only update the DDRM's state variable, i.e. PSM, and the other updating both the parameters and PSM variable of the DDRM. Thus, six assimilation scenarios are set in the study, whose performances are compared with the DDRM without assimilation for different time periods, including the whole period, the dormant period and the growing period. Results indicate that in any periods, for any of remote-sensed datasets used for assimilation, either the state update or the parameter-state update can improve the accuracy of high-resolution (1 × 1 km2) PSM simulations by the DDRM. Besides, assimilating the SMAP PSM dataset into the DDRM has the potential to improve streamflow simulations for the three catchments. This study has shown that, by assimilating multi-source remote-sensed PSM into a high spatial resolution distributed hydrological model, i.e. DDRM, estimation of PSM can be improved over both the original remote-sensed PSM and the DDRM-simulated PSM. [ABSTRACT FROM AUTHOR]