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

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.

Water Constraints and Flood-Recession Agriculture in the Senegal River Valley

“…Instrumentation of other basins with limnimetric scales and other probes would provide information on a larger scale and take into account other parameters (rainfall, soil moisture, groundwater table), which may influence flooding at the local level. More could be done for a climatological service and for early warning of flood with the possibility of forecasting upstream inflow at Manantali dam [24]. To help predict years such as 2017, there is reliable skill in seasonal forecasting of a wet June/July/August precipitation in West Africa [49], which is relevant to this study.…”

“…As part of a program aimed at optimizing the reservoirs' management, several studies have been carried out on the propagation of flows, as well as on the satisfaction of the objectives assigned to the management of dams according to the available water resource [21,22]. Other studies focused on modeling of the rainfall-discharge relationship in the upper basin [23,24] and on the impacts of the Manantali dam on downstream production systems [8][9][10][11][12][13]. In the region, there is also a hydrological data sharing system that provides information on the river level of the day before and whether the level is rising or falling.…”

Synergies and Trade-Offs in Water Resources Management in the Bafing Watershed under Climate Change

“…It integrates relevant ecohydrological processes, sediment transport, and vegetation growth to study the effects of climate and land use change on hydrological systems and vegetation at a regional scale. The calibration procedure involves adjusting the parameters so that the simulated flows correspond to the observed flows [44]. The SWIM model was calibrated and validated manually at the Bafing Makana station.…”

“…Several studies have been carried out on the Senegal River. Some studies have focused on the impact of climate change or variability on water availability, while other studies have focused on the effects of dams on downstream water flow [28,31,35,[37][38][39][40][41][42][43][44]. Despite the amount of documentation and numerous projects on the Senegal River, a study on the potential impact of climate change on its hydropower potential has not been carried out.…”

Investigation of cross-entropy-based streamflow forecasting through an efficient interpretable automated search process

“…It should be mentioned that a categorical streamflow forecast differs from a probabilistic streamflow forecast in that the latter is still regarded as a regression problem. Ensemble model prediction, for example, is a sort of probabilistic rainfall forecast where the outputs are a set of combined regression forecasts produced from many models trained on the same training dataset (Ndione et al 2020).…”