Performance of ensemble streamflow forecasts under varied hydrometeorological conditions
- 1Water Engineering and Management, Faculty of Engineering Technology, University of Twente, 7500 AE Enschede, the Netherlands
- 2Institute of Geophysics, Polish Academy of Sciences, 01-452 Warsaw, Poland
- 3Department of Water Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, 7500 AE Enschede, the Netherlands
- apresent address: Department of Water Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, 7500 AE Enschede, the Netherlands
Abstract. The paper presents a methodology that gives insight into the performance of ensemble streamflow-forecasting systems. We have developed an ensemble forecasting system for the Biała Tarnowska, a mountainous river catchment in southern Poland, and analysed the performance for lead times ranging from 1 to 10 days for low, medium and high streamflow and different hydrometeorological conditions. Precipitation and temperature forecasts from the European Centre for Medium-Range Weather Forecasts served as inputs to a deterministic lumped hydrological (HBV) model. Due to a non-homogeneous bias in time, pre- and post-processing of the meteorological and streamflow forecasts are not effective. The best forecast skill, relative to alternative forecasts based on meteorological climatology, is shown for high streamflow and snow accumulation low-streamflow events. Forecasts of medium-streamflow events and low-streamflow events under precipitation deficit conditions show less skill. To improve performance of the forecasting system for high-streamflow events, the meteorological forecasts are most important. Besides, it is recommended that the hydrological model be calibrated specifically on low-streamflow conditions and high-streamflow conditions. Further, it is recommended that the dispersion (reliability) of the ensemble streamflow forecasts is enlarged by including the uncertainties in the hydrological model parameters and the initial conditions, and by enlarging the dispersion of the meteorological input forecasts.