SUSTAINDAM

Sustainable Management and Planning of Hydropower Generation in West Africa under Climate Change and Land Use/Land Cover Dynamics

Funds : Belmont Forum 2021 - 2022

Coordinator : Arona Diedhiou (IRD-IGE, France)

European Partners :
– IRD (National Research Institute for Sustainable Development), France
– Karlsruhe Institute of Technology, Germany
– Center for Development Research, Germany
– University of Würzburg, Germany

African Partners :
– National School of Agriculture, Senegal
– Centre of Excellence on Climate Change, Biodiversity and Sustainable Agriculture, Côte d’Ivoire
– The African Group on Earth Observations, Kenya
– University of Energy and Natural Resource, Ghana
– Centre of Excellence for Training and Research in Water Science and Technology, Energy and the Environment in West and Central Africa, Burkina Faso

Objectives :
The SUSTAINDAM project aims to contribute to sustainably manage and plan the hydropower generation in West Africa under climate uncertainties and LULC dynamics. This project aims to build communities of practices with HPG stakeholders (dam managers, local policy makers, representative of civil society, association of women, etc) to address the challenges, synergies and trade-off in the climate land energy water nexus in WA for a sustainable management and planning of HPG. This project will be focused on four (4) dams (pilot sites) in Ghana (Akosombo), Côte d’Ivoire (Buyo), Burkina Faso (Bagré) and Senegal (Manantali), located in different climates. At each pilot site, the specific objectives are :

  • To assess the past evolution of water-related activities near dams and the sensitivity of each hydropower plant to climate change and variability.
  • To assess LULCC near dams and propose trends scenarios of LULCC and drivers of water energy food nexus considering potential socio-economic development ;
  • To improve long term planning under uncertain effects of climate and LULC change in integrating vulnerability-based analysis and traditional risk-based assessment approaches using both top-down methods (Random Forest and dynamical downscaling) and bottom-up (Decision Scaling) methods.
  • To evaluate benefits in decision making for hydropower generation from seasonal forecasts with lead times up to 7 months ahead.

Mis à jour le 18 octobre 2022