In the heart of Ethiopia, a monumental project is reshaping the landscape and the energy sector: the Grand Ethiopian Renaissance Dam (GERD). As the dam’s reservoir fills, a team of researchers led by Yilak Taye Bihon from Arba Minch University’s Faculty of Hydraulic & Water Resources Engineering has been meticulously tracking the changes in land use and land cover (LULC) in the GERD basin and reservoir areas. Their findings, published in the journal *Environmental Challenges* (translated to *Environmental Challenges*), offer a compelling narrative of transformation and a roadmap for sustainable management.
The study, which spans from 1986 to 2024, reveals a dramatic shift in the region’s landscape. Using Landsat and Sentinel-2 images, the team employed Random Forest machine learning in Google Earth Engine to analyze LULC changes. “The increasing water demand, limited water resources, and high soil erosion in the GERD basin necessitated this study,” Bihon explains. The results are striking: eucalyptus trees, built-up areas, water, agricultural land, and bare land have expanded significantly, while shrubs, forests, grassland, and wetlands have shrunk.
The implications for the energy sector are profound. The GERD, Africa’s largest hydroelectric dam, is a cornerstone of Ethiopia’s energy strategy. The reservoir’s annual water filling, detailed in the study, shows a steady increase in water volume, reaching its full supply level in 2025. This growth is not just about water storage; it’s about energy production and regional water management.
As the GERD reservoir fills, the Aswan High Dam (AHD) reservoir in Egypt has experienced excess water overflowing into the Toshka lakes. This interconnectedness underscores the need for regional cooperation and effective LULC management. “Effective LULC management that enhances ecosystem services and alleviates water stress on the GERD basin requires cooperation from all responsible stakeholders,” Bihon emphasizes.
The study also highlights the importance of clearing forests and shrubs in the GERD reservoir area before filling to prevent greenhouse gas emissions and water quality issues. This proactive approach could set a precedent for future dam projects, emphasizing the need for environmental considerations in energy infrastructure development.
The research also points to the potential of advanced tools and methods, such as Google Earth Engine and Random Forest machine learning, in enhancing classification accuracy. As Bihon notes, “Ensemble, coupling, and comparisons of tools, methods, and factors should be considered for better classification accuracy.”
This study is more than just a snapshot of change; it’s a call to action. As the GERD project continues to unfold, the insights from this research will be invaluable for policymakers, energy sector professionals, and environmental managers. It underscores the need for sustainable practices, regional cooperation, and advanced technological applications in managing our most precious resources.
In the ever-evolving landscape of the GERD basin, this research serves as a guiding light, illuminating the path towards a sustainable and energy-secure future. As the dam reaches its full potential, the lessons learned from this study will resonate far beyond the borders of Ethiopia, shaping the future of hydropower and water management in Africa and beyond.