Communities living along Ghana’s White Volta Basin have long faced devastating floods triggered by water releases from the Bagre Dam in neighbouring Burkina Faso. Now, researchers are developing an innovative satellite-based flood forecasting system that could provide earlier warnings, helping vulnerable communities prepare for rising water levels and reduce the loss of lives, crops and livelihoods.
The Bagre Dam, located approximately 180 kilometres upstream in Burkina Faso, plays a major role in the region’s flood risk. During the West African monsoon season, heavy rainfall fills the reservoir and water releases can send powerful floodwaters downstream into Ghana within two to three days.
The recurring problem has caused significant damage over the years. The 2007 Bagre Dam spillage affected more than 307,000 people and destroyed over 12,000 hectares of farmland. In 2018 flooding claimed 17 lives. Despite repeated warnings and evacuation advisories, communities continue to receive limited time to prepare for major releases.
Why Existing Flood Warning Systems Are Failing
Researchers say one of the major weaknesses in Ghana’s existing flood forecasting system is its inability to account for water storage and release operations at the Bagre Dam.
Ghana’s myDEWETRA-VOLTALARM platform relies on the Global Flood Awareness System (GloFAS), which provides flood forecasts using global modelling data. However the system does not explicitly track the Bagre reservoir’s water levels or operational conditions.
As a result, it struggles to predict floods driven by decisions made at the dam across the international border, particularly when water releases combine with heavy local rainfall.
Institutional coordination is another major challenge, as there is no formal requirement for advance notification to downstream Ghanaian authorities before significant water releases.
New Satellite Based Technology Could Provide Earlier Warnings
To address these shortcomings, researchers have developed a forecasting approach that combines satellite observations, rainfall estimates and machine learning technology.
The system uses historical satellite records from the Joint Research Centre’s Global Surface Water dataset, which has monitored the Bagre reservoir since 1984. It also incorporates CHIRPS rainfall estimates, ERA5-Land atmospheric data and observed river discharge measurements.
Using these datasets, researchers trained machine learning models to forecast river discharge at Ghana’s Nawuni gauging station, a key monitoring point along the White Volta River.
The system provides forecasts for one, three and five days ahead. It also includes a seasonal reservoir monitoring feature that alerts downstream authorities when the Bagre Dam approaches or reaches its storage capacity.
During historical testing, the forecasting model identified approximately nine out of ten of the most severe flood days one day in advance. It also detected around three quarters of these severe events five days ahead.
Researchers reported that the new approach performed better than the existing global forecasting model in the historical assessment. However, the technology has not yet been deployed operationally and its real-world effectiveness still needs to be evaluated with Ghanaian disaster management authorities.
The proposed warning system includes four colour coded alert levels: green, yellow, orange and red. Each level is linked to specific preparedness and emergency response measures.
A yellow alert issued five days ahead would encourage communities to move livestock and harvested crops away from flood-prone areas, check on vulnerable residents and identify evacuation routes.
An orange alert three days before a potential flood would allow authorities to prepare emergency shelters and position relief supplies.
A red alert one day ahead would trigger evacuation measures to move residents away from dangerous areas.
The system also aims to deliver warnings in three languages: Gonja, Dagbani and English. This would make alerts more accessible to rural communities through local communication networks, community radio and disaster volunteer groups.
Community Participation and Regional Cooperation Remain Essential
Researchers emphasise that accurate forecasts alone cannot prevent disasters unless warnings reach people in time and communities trust the information they receive.
Many residents living along the Volta Basin remain unfamiliar with the Volta Basin Authority, the regional organisation responsible for coordinating water resource management across the basin.
To address this gap researchers are also developing the Household Experience Reports for Adaptive Local Disaster Response (HERALD) platform. It will allow flood-affected residents to anonymously report their experiences, including the severity of flooding and whether warnings reached them early enough.
The initiative aims to incorporate local knowledge into disaster planning and help authorities understand where existing warning systems are failing.
The White Volta Basin represents a wider challenge faced by communities across Africa, South Asia and Southeast Asia, where upstream dam operations can trigger devastating floods in downstream countries.
Researchers believe that combining satellite information, locally calibrated forecasting models and community based warning networks could significantly improve disaster preparedness in regions where conventional global forecasting systems have limitations.
The proposed technology can operate using publicly available data and standard computing equipment, potentially allowing national agencies to adopt it without expensive new infrastructure.
The initiative highlights the importance of combining scientific innovation, regional cooperation and community participation to ensure that flood warnings reach vulnerable populations before disaster strikes.