JEERESD

Volume 4 · Issue 4 · 2026 · Pages 52–80 · Published online 1 October 2026

Original Article · English

Characterization and Forecasting of Future Drought in the Gambia River Basin at Kédougou Using Standardized Indices, Machine Learning, and Deep Neural Networks

Cheikh Faye · Ibrahima Faye

Department of Geography, Faculty of Science and Technology, Assane Seck University of Ziguinchor, Laboratory of Geomatics and Environment

DOI10.68305/jeeresd.v4i4.005

01Abstract

Background: Drought is one of the most complex and damaging hydroclimatic hazards, particularly in West Africa where water resources are highly sensitive to climate variability and change. This study analyzes the future evolution of meteorological, climatic, and hydrological droughts in the Gambia River basin controlled at the Kédougou gauging station in southeastern Senegal. Methods: The approach combines precipitation, potential evapotranspiration (PET), temperature, and simulated streamflow, and computes the SPI, SPEI, and SSI at 1-, 3-, 6-, and 12-month timescales. Results: Results reveal a major contrast between the precipitation signal and thermo-hydrological responses. Under SSP5-8.5, mean temperature increases from 29.67 °C in 2021–2040 to 33.99 °C in 2081–2100, annual PET rises from 1,574 to 1,770 mm, and mean streamflow decreases from 49.02 to 12.45 m³/s. Mean SSI-3 simultaneously deteriorates from −1.21 to −1.94, while the frequency of drought months increases from 67.5% to 91.7% and that of extreme drought months from 35.4% to 84.2%. Under SSP1-2.6, drying remains substantial but is less monotonic. Eight forecasting models are compared at 1-, 3-, and 6-month lead times: Random Forest, ExtraTrees, XGBoost, LightGBM, SVR, MLP/ANN, LSTM, and GRU. LightGBM performs best at one month (NSE = 0.867 under SSP1-2.6 and 0.718 under SSP5-8.5), whereas LSTM/GRU become competitive at longer lead times. Conclusion: The study highlights a risk of chronic hydrological drought by the end of the century, particularly under high emissions, and demonstrates the value of multimodel forecasting for early warning and adaptive water-resources management.

KeywordsHydrological drought; SSI; SPEI; SPI; Climate change; Gambia River

02Author contributions

Cheikh Faye

Ibrahima Faye

03References

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