The project delivers a comprehensive comparison of different methodologies to model climate and crops at the subnational level in Germany at various temporal scales. It proposes an innovative integrated approach to better understand and characterize the impacts of compound extreme events. CROP generates new high-resolution data sets, based on best-performing hybrid approaches to bias-correct model simulations and downscale them to relevant spatio-temporal scales for the assessment of extreme weather impacts on crop yield in terms of variability and losses. A better understanding of how compound extremes influence crop production sets the basis for the development of an integrated seasonal crop yield forecasting system, an important and accurate tool to inform end-users. The results will be of relevance for climate change impact assessments, to guide extension services and for policy makers to reduce market volatility, avoid price spikes and support crop breeding efforts.

Website: CROP
Institution: University of Giessen
Contact: Dr. Elena Xoplaki