HiClAP

Climate change poses a challenge to the livelihoods in many countries, with Sub-Saharan African nations being particularly affected. Adequate responses to climate risks are necessary at various levels and in different forms, including national and regional policy processes, as well as implementation at the local level. In order to steer investments in climate change adaptation, well-founded scientific forecasts on climate change are required. Despite extensive local scientific expertise, smaller countries like Rwanda face additional challenges. The spatial resolution of climate projections is suboptimal, relying on limited data with coarse resolution, limiting the possibilities of impact modeling and failing to capture Rwanda's complex topography. Standard climate projections are typically available at a resolution of 50 km, generated through downscaling data from global climate models. Projections from the CORDEX regional climate models used by Meteo Rwanda have a nearly identical spatial resolution. For such demanding regions, PIK has started applying the CHELSA approach (Climatologies at high resolution for the earth's land surface areas), a mechanistic downscaling method that adapts climate data to topography and produces climate projections at a resolution of 1 km. The proposed methods and impact models have the potential to provide insights into future climate changes and their socio-economic implications for agriculture, serving as a basis for adaptation planning and investments. The project aims to promote scientific collaboration between Rwandan and German researchers to strengthen climate science for improved adaptation planning.

The goals of the AGRICA_Rwanda project are: a) to provide high-resolution climate data in Rwanda; b) to utilize the high-resolution climate data for impact modeling, focusing on the agricultural sector; c) to translate climate forecasts into products for policy decision-makers; d) to support the implementation of the aforementioned methodology within a Rwandan institution, enabling local partners to apply it to various datasets and thereby ensuring the long-term impact of the project.

PIK will lead the overall project and is responsible for the final objectives and the respective results.