Performance of ensemble streamflow forecasts under varied hydrometeorological conditions
The paper presents a methodology that gives insight into the performance of ensemble streamflowforecasting 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 t...
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| Tác giả chính: | |
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| Định dạng: | BB |
| Ngôn ngữ: | eng |
| Thông tin xuất bản: |
2020
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| Chủ đề: | |
| Truy cập trực tuyến: | http://tailieuso.tlu.edu.vn/handle/DHTL/4885 |
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| Tóm tắt: | The paper presents a methodology that gives insight into the performance of ensemble streamflowforecasting 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 lowstreamflow 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 lowstreamflow 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. |
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