From forecast to alert: an open-data AI flood early warning system with reservoir monitoring for the White Volta Basin

Flood early warning in the White Volta Basin of northern Ghana is complicated by unmonitored dam releases from Burkina Faso's Bagre Reservoir, which existing globally calibrated systems do not represent. We present an end-to-end AI-driven flood early warning system that integrates observed gauge discharge, CHIRPS satellite rainfall, ERA5-Land reanalysis forcing, and a novel JRC-derived Bagre reservoir storage proxy. An ensemble of Random Forest, XGBoost, and LSTM models trained on these inputs achieved Kling–Gupta Efficiency scores of 0.984, 0.974, and 0.957 at 1, 3, and 5 d lead times on an independent test period dominated by moderate flows (maximum 1206 m ³  s ⁻¹ , below the Orange alert threshold); higher-tier alert skill rests on leave-one-year-out cross-validation rather than held-out extreme events. Benchmarking against simple baselines shows the ensemble's advantage over a persistence forecast is marginal at 1 d lead but grows with lead time and concentrates on rising-limb and high-flow conditions, indicating that recent observed discharge carries most of the short-range skill while the ensemble's value lies at the longer horizons relevant to warning. For direct comparison, GloFAS v4 reanalysis discharge at Nawuni achieves KGE = −0.526 on the same test period, performing worse than a climatological mean predictor, primarily because LISFLOOD does not represent Bagre Dam release operations. A four-tier alert system calibrated to 30-year flood return periods achieved a cross-validated Red-tier probability of detection of 0.902 (false alarm ratio 0.134) at 1 d lead, declining to 0.762 at 5 d. Sentinel-1 SAR mapping provided spatial grounding for the discharge-based threshold framework, with Red-tier exceedance periods associated with floodplain inundation extents of 50 to 149 km ² across three validation seasons. The system integrates into Ghana's existing myDEWETRA-VOLTALARM platform without requiring new institutional infrastructure. The Bagre reservoir proxy, derived from freely available JRC Global Surface Water imagery and validated against independent satellite altimetry ( r  = 0.94), provides a measure of reservoir state. Because the reservoir fills to capacity in most wet seasons, this signal functions as an explicit model input and as seasonal situational awareness that the reservoir is capable of spilling, rather than as a standalone flood trigger.