Ensemble forecasting system for the management of the Senegal River discharge: application upstream the Manantali dam
| Autor: | Soussou Sambou, Seïdou Kane, Samo Diatta, Didier Maria Ndione, Issa Leye, Moussé Landing Sane |
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| Rok vydání: | 2020 |
| Předmět: |
010504 meteorology & atmospheric sciences
Mean squared error 0208 environmental biotechnology Manantali dam 02 engineering and technology ARIMA 01 natural sciences Physics::Geophysics Senegal river lcsh:Water supply for domestic and industrial purposes Streamflow Statistics Autoregressive integrated moving average Physics::Atmospheric and Oceanic Physics 0105 earth and related environmental sciences Water Science and Technology Mathematics lcsh:TD201-500 Ensemble forecasting Stochastic process Affine kernel dressing Probabilistic logic 020801 environmental engineering Brier score Kernel (statistics) |
| Zdroj: | Applied Water Science, Vol 10, Iss 5, Pp 1-15 (2020) |
| ISSN: | 2190-5495 2190-5487 |
| Popis: | Providing useful inflow forecasts of the Manantali dam is critical for zonal consumption and agricultural water supply, power production, flood and drought control and management (Shin et al., Meteorol Appl 27:e1827, 2019). Probabilistic approaches through ensemble forecasting systems are often used to provide more rational and useful hydrological information. This paper aims at implementing an ensemble forecasting system at the Senegal River upper the Manantali dam. Rainfall ensemble is obtained through harmonic analysis and an ARIMA stochastic process. Cyclical errors that are within rainfall cyclical behavior from the stochastic modeling are settled and processed using multivariate statistic tools to dress a rainfall ensemble forecast. The rainfall ensemble is used as input to run the HBV-light to product streamflow ensemble forecasts. A number of 61 forecasted rainfall time series are then used to run already calibrated hydrological model to produce hydrological ensemble forecasts called raw ensemble. In addition, the affine kernel dressing method is applied to the raw ensemble to obtain another ensemble. Both ensembles are evaluated using on the one hand deterministic verifications such the linear correlation, the mean error, the mean absolute error and the root-mean-squared error, and on the other hand, probabilistic scores (Brier score, rank probability score and continuous rank probability score) and diagrams (attribute diagram and relative operating characteristics curve). Results are satisfactory as at deterministic than probabilistic scale, particularly considering reliability, resolution and skill of the systems. For both ensembles, correlation between the averages of the members and corresponding observations is about 0.871. In addition, the dressing method globally improved the performances of ensemble forecasting system. Thus, both schemes system can help decision maker of the Manantali dam in water resources management. |
| Databáze: | OpenAIRE |
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