Forecasting localized extreme drought and heat impacts in Africa

Climate change will lead to an accumulation and intensification of various climate extremes. Drought and heat waves, as experienced repeatedly in the last decade, are expected to become more frequent in the future, as the corresponding persistent weather situations become more and more probable. The effects on various sectors are substantial, as could be seen for example from the effects on agriculture, inland waterways, and consequently nutrition and energy supply.

There is currently a lack of methods for assessing, in fine resolution, drought impact at the local level, as this requires downscaling from meteorological scales to sub-kilometer level using satellite data. The second gap is a lack of understanding of memory effects considering ecosystem dynamics after a drought event. A better understanding could be achieved with so-called hybrid dynamic models, which model the system partly with physical equations, partly with Machine Learning.

To this end, DeepCube – with the use of Artificial Intelligence on Big Earth Observation Data – aims to address key scientific needs such as the forecasting of long-term effects of drought and heat, the prediction of localized impacts given coarse scale meteorological information, the combination of physical approaches and Machine Learning to model the spatio-temporal drought impact, as well as the definition of spatial factors that yield impact susceptibility versus resilience to meteorological drought and heat waves, based on multivariate historical data analysis.

Can we predict localized impacts of meteorological drought and heat waves in sub-Saharan Africa based on multivariate Earth Observation and meteorological historical data analysis? What are the anticipated long-term effects of drought and heat, i.e. memory and lag effects?

Use Case Leader
Max Planck Institute for Biogeochemistry

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Markus Reichstein,
Nuno Carvalhais,