Abstract
Rainfall-runoff models play a very important role in flood forecasting. However, these models contain large uncertainties caused by errors in both the model itself and the input data. Data assimilation techniques are being used to reduce these uncertainties. The ensemble Kalman filter (EnKF) and the particle filter (PF) both have their own strengths. Research was carried out to a possible combination between both types of filters that will lead to a new type of filters that joins the strengths of both. The so called ensemble particle filter (EnPF) new combination is tested on flood forecasting problems in both the hindcast mode as well as the forecast mode. Several proposed combinations showed considerable improvement when a hindcast comparison on synthetic data was considered. Within the forecast comparison with field data, the suggested EnPF showed remarkable improvements compared to the PF and slight improvements compared to the EnKF.
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van Delft, G., El Serafy, G.Y. & Heemink, A.W. The ensemble particle filter (EnPF) in rainfall-runoff models. Stoch Environ Res Risk Assess 23, 1203–1211 (2009). https://doi.org/10.1007/s00477-008-0301-z
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DOI: https://doi.org/10.1007/s00477-008-0301-z