SATELLITE DATA ASSIMILATION TO GENERATE ENSEMBLE HYDROLOGICAL FORECASTS

In the article the novel implementation of bias correction and data assimilation using the ensemble Kalman filter (EnKF) were presented. These methods applied as a pre-processor suitable for integration into the conceptual rainfall-runoff HBV (Hydrologiska Byrans Vattenbalansavdelning) model were assessed for hydrological modelling based on seasonal hydrographs. The enhanced HBV model was able to generate an ensemble (interval) hydrographs and averaged streamflow simulation. The proposed system, which was constructed to examine the possibility of employing EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) data (e.g., precipitation and soil moisture), was shown to be useful in improving model updating and forecasting. Data from the Sola River mountain part of southern Poland, was used. A bias correction algorithm for a distribution derived transformation method was also developed by exploring Generalized Exponential (GE) theoretical distributions, along with Gamma (GA) and Weibull (WE) distributions on the different data used in this study. In order to remove biases in the ensemble of soil moisture the bias correction within the ensemble Kalman filter (EnKF-BC) was used as a post-processing technique. In terms of the precipitation and soil moisture satellite outputs, the EnKF-BC pre-processed HBV models significantly improved the accuracy of the simulated hydrographs in the summer season, while the positive effect of the corrected satellite precipitation was seen in the winter.