Ensemble forecasting system for the management of the Senegal River discharge: application upstream the Manantali dam

Didier Maria Ndione,Seïdou Kane,Issa Leye,Samo Diatta,Soussou Sambou,Moussé Landing Sane

doi:10.1007/s13201-020-01199-y

Didier Maria Ndione, Seïdou Kane + Show 4 more

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https://doi.org/10.1007/s13201-020-01199-y

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Abstract

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.

Highlights

Synthetic precipitation time series can be used in forecasting hydrological variables, in producing likely scenarios preserving interchange of dry and wet frequencies
One can found some international organization promoting the use of ensemble forecasting systems, namely hydrological ensemble prediction experiment (HEPEX) (Schaake et al 2006)
Hydrological ensemble forecasting has been used at the USA in 2017, in order to provide rationally some short- to medium-range streamflow forecast through a combination of a meteorological ensemble forcing with a distributed hydrological ensemble forecast (Siddique and Mejia 2017; Sharma 2018)

Summary

Introduction

Synthetic precipitation time series can be used in forecasting hydrological variables, in producing likely scenarios preserving interchange of dry and wet frequencies. Hydrological ensemble forecasting has been used at the USA in 2017, in order to provide rationally some short- to medium-range streamflow forecast through a combination of a meteorological ensemble forcing with a distributed hydrological ensemble forecast (Siddique and Mejia 2017; Sharma 2018) Her et al (2016) studied uncertainties inherent to an ensemble forecast from multi-GCMs (multi-model) and uncertainties from an ensemble involving estimated multi-parameters of one hydrological modeling scheme under climate change effects; they found that uncertainties from multi-GCMs may be more important in magnitude and that attention should be paid in selecting hydrological input models. Pappenberger et al (2011) reveal that the National Hydrological Service produces long-term probabilistic flood forecasting using ensemble prediction from the European Flood Alert System (EFAS) runs every week upon ten years ahead They show that beyond the efficiency of the probabilistic forecast in comparison with classical methods, ensemble forecast is sensitive to the geographical position and to the considered catchment spread. In Addor et al (2011), the COSMO-7 deterministic model and the probabilistic COSMO-LEPS have been coupled with the PREVAH model to help managers of the Sihl River in decision making (Addor et al 2011)

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