Soil moisture updating by Ensemble Kalman Filtering in real-time flood forecasting

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 622 km2 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% (3 h lead time), and from 25% to 19% (48 h lead time). The Nash-Sutcliffe efficiency of forecasting runoff for these flood events increases from 0.79 to 0.92 (3 h lead time), and from 0.79 to 0.88 (48 h lead time). The flood forecasting system has been in operational use since early 2006.