Data Assimilation of Satellite-Derived Surface Water Extent into a Global Rainfall-Runoff Model

Abstract:

In hydrological forecasting, data assimilation techniques are employed to improve estimates of initial conditions to update incorrect model states with observational data. However, the limited availability of continuous and up-to-date ground observational data is one of the main challenges for real-time applications such as global flood forecasting models. Remote sensing has been recognised as a valuable alternative source of observations of land surface hydrological fluxes and state variables due to its global coverage, open data policy and the advantage of being available at frequent temporal intervals and shortly after the satellite image retrieval.

In this study, we present the impact of assimilating remotely sensed surface water extent into the global hydrological LISFLOOD model using Ensemble Kalman Filter (EnFK) and its potential to improve the timing of the flood peak. We use the merged product from Global Flood Detection System (GFDS) that employs both AMSR-E (Advance Microwave Scanning Radiometer – Earth Observing System) and TRMM (Tropical Rainfall Measuring Mission) to derive water extent as used in the GFDS. This satellite-derived water extent signal is assimilated into LISFLOOD for selected catchments and results are compared to baseline initial conditions (without data assimilation). Validation is done based on ground-based discharge observations. Furthermore, we discuss the post-processing and data assimilation strategies of satellite data within a global hydrological model.