Improvement of Streamflow Forecasting for Flood Management and Mitigation in the White Volta Basin, Northern Ghana

Abstract

Flooding in Ghana's White Volta basin has led to severe human displacement, fatalities, and extensive property damage. The region's heavy dependence on agriculture exacerbates these impacts, posing significant threats to food security and livelihoods. In Ghana, institutions such as the Ghana Meteorological Agency (GMet) and the Ghana Hydrological Authority (GHA) are mandated to provide flood forecasts. However, their forecast remains inadequate, prompting many communities to rely on traditional knowledge and informal coping mechanisms. This study qualitatively assesses the operational state of Flood Early Warning Systems (FEWS) in the White Volta basin, focusing on their effectiveness, limitations, and opportunities for improvement. Using semi-structured interviews with 18 key stakeholders, including representatives from government agencies, technical experts, and community leaders, the study analysed the institutional and technical dynamics of Ghana's FEWS through thematic analysis. Findings reveal that although the myDEWETRA-VOLTALARM platform offers 5-day flood forecasts through social media, SMS, and radio, its warnings are often mistrusted or inaccessible to rural populations. Thematic analysis identified four critical gaps: institutional fragmentation, exclusion of local knowledge, inadequate data infrastructure, and last-mile communication failures. These are complicated by the basin's unique environmental conditions, including transboundary dam releases, intense seasonal rainfall, flat terrain, and poor drainage. These findings suggest that the current FEWS framework remains insufficient for proactive flood risk governance. Strengthening institutional coordination, integrating community-based adaptation practices, and investing in localised data and communication infrastructure are essential to improving system legitimacy and resilience. The study contributes to broader discourses on early warning systems in resource-constrained settings.<br /> The study explored alternative data sources for building a robust and reliable FEWS in the White Volta basin. Satellite and reanalysis data were compared with ground-based observations in Northern Ghana. This surrogate data assumes prominence as an alternative predictor amid the scarcity of ground-based data for streamflow forecasting to manage and mitigate floods in the basin. Rainfall and mean temperature span from 1998 to 2019, and soil moisture from 2019 to 2019. Data were sourced from GMet, ISMN (ground-based), CHIRPS, PERSIANN-CDR, ERA5, ARC2, MERRA-2, TRMM, and CFSR (satellite and reanalysis). Using performance metrics, namely standard deviation, mean absolute error (MAE), and mean bias error (MBE), the accuracy of these datasets was thoroughly evaluated. The results revealed that CHIRPS and PERSIANN-CDR exhibited superior accuracy in rainfall simulation, with CHIRPS demonstrating particularly consistent congruence with observed data. ERA5 outperformed MERRA-2 and CFSR in predicting average temperatures. For soil moisture, both ERA5 and CFSR gave reliable results. Based on these findings, CHIRPS is recommended for rainfall, ERA5 for temperature, and either ERA5 or CFSR for soil moisture. These datasets are suitable for streamflow modelling, drought and flood forecasting, and managing water resources in Northern Ghana.<br /> The study also examines an operational Flood Early Warning System (FEWS) in the White Volta basin, aimed at delivering accurate streamflow forecasts critical for effective flood management and mitigation. For the first time, this research applies machine learning algorithms, specifically Long Short-Term Memory (LSTM) and Random Forest (RF), trained on rainfall, temperature, soil moisture, and evapotranspiration data to predict streamflow at 1-, 5-, and 10-day intervals within the basin. The study further used these models (RF and LSTM) to forecast future streamflow using CMIP6 SSP5-8.5 scenario data. The model's output was evaluated using Mean Absolute Error, Mean Bias Error, and Kling-Gupta Efficiency. The result showed high variability in the streamflow, and both models performed well in capturing these variabilities. LSTM showed superiority in capturing peak flows, and RF provided stable long-term predictions for up to 10 days. The future predictions also showed high variability in the streamflow, suggesting an increased risk of floods and droughts in the basin. Given that these models are able to capture the timings (seasonal patterns and peaks), they are well-positioned to provide accurate and reliable streamflow forecasts to support effective flood risk management and mitigation in the basin. The models can be extended to similar ungauged basins, offering a replicable and sustainable framework for proactive flood early warnings.