The objectives of this research were to: (i) investigate different aspects of long-term trend detection in monthly, seasonal, and annual streamflow; (ii) assess climate change impacts on the upper Senegal River basin stream-flow; and (iii) evaluate data driven models to forecast daily stream at the upper Senegal River basin.
A preliminary study investigated long-term trends on stream-flow at the upper Senegal River basin. Results showed a trend of decreasing annual stream flow; but this was not significant at p < 0.05 for the whole period. However, when integrating various temporal breaking points, a decreasing trend was significant before the first breaking point (1976) but was reversed for the period of 1976 to 1993. For the monthly series, all months exhibit a non-significant decreasing trend except for the month of June that had an increasing trend. The seasonal series showed a decreasing trend that was significant (p <0.05) at MAMJ season. The extremely low and high daily streamflow had significant positive and negative trends, respectively.
This research used five General Circulation Models (CNRM, CSIRO, HadGEM2-CC, HadGEM2-ES, and MIROC5), two RCP (RCP4.5 and RCP8.5) scenarios, and the GR4J hydrological model to evaluate the impact of climate change on stream flow in the near future. The results showed that for all models, there were simulated increases in mean monthly temperature under the RCP 4.5 and RCP 8.5. Increases of temperature ranged from 0.54° C to 2.32° C under RCP 4.5 scenario and from 1.12° C to 2.78° C under RCP8.5. However, models were not consistently in agreement in the direction and magnitude of future precipitation changes for monthly rainfall. Some models predicted an increase while other a decrease. The multi-model ensemble projected a decrease of rainfall for all months except for September. The greatest precipitation increases were in September, at 15.6 mm and 10.1 mm with RCP4.5 and RCP8.5, respectively. The greatest precipitation decreases were between June to August for both scenarios with a maximum in July under RCP 4.5 (-9.53 mm) and RCP 8.5 (-20.1mm). Compared to the 1971 – 2000 reference period, results showed a decrease in annual stream-flow of 2.9% and 7.7% under RCP4.5 and RCP8.5 scenarios, respectively. Monthly variations showed a decrease of stream-flow in wet season for both scenarios.
This research verified the utility of the support vector regression (SVR) model and generalized regression neural network (GRNN) to predict one day ahead, the daily river flow in the upper Senegal River basin at the Bafing Makana station in West Africa. Two modeling scenarios were considered: (A): where only stream-flow data were used as an input via antecedent values and (B): where rainfall, evapotranspiration and stream-flow data are used. The results showed that the accuracy of the models varied by scenario. Combining the stream-flow data with rainfall and evapotranspiration can substantially improve the accuracy of the two models to predict one-day ahead stream-flow. A comparison of the optimal SVR and the GRNN models indicated that SVR model had superior performance compared to the GRNN.