Abstract While sustainable development goals are by their nature global, their achievement requires local action and thus targeting and monitoring sustainable solutions tailored to different social and ecological contexts. Ostrom stressed that there are no panaceas or universal solutions to environmental problems, and developed a social-ecological systems’ (SES) framework -a nested multi-tier set of variables- to help diagnose problems, identify complex interactions, and solutions tailored to each SES arena. The framework has been applied to over a hundred cases, typically reflecting in-depth analysis of local case studies, but with relatively small coverage in space and time. While case studies are context rich and necessary, it can be difficult to upscale their lessons to policy making realms. Here we develop a data driven method for upscaling Ostrom’s SES framework and apply it to a context where data is scarce, incomplete, but also where sustainable solutions are needed. The purpose of upscaling the framework is to create a tool that facilitates decision-making on data scarce contexts such as developing countries. We mapped SES by applying the SES framework to poverty alleviation and food security issues in the Volta River basin in Ghana and Burkina Faso. We found archetypical configurations of SES in space. Given data availability, we study their change over time, and discuss where agricultural innovations such as water reservoirs might have a stronger impact at increasing food security and therefore alleviating poverty and hunger. We conclude by outlining how the method can be used in other SES comparative studies.