Inhaltspezifische Aktionen


CLImate INTelligence: Extreme Events Detection, Attribution and Adaptation Design using Machine Learning | duration: 07/2021 - 06/2024

  • Context and Objective

Within the CLINT project, the ZEU investigates potential development mechanisms of extreme weather events and focuses on so-called concurrent extremes. These events are a combination of at least two extreme weather events that influence each other within a certain time interval (e.g. heat plus drought, heavy rain plus flood, etc.) and can occur either at the same or at different locations. Another focus will be the investigation of so-called compound events, where compound is used to describe the (potentially also extreme) concomitant occurrence of events belonging to the extreme event. An example of this is the heatwave in Europe in the summer of 2018, in which the very high temperatures overlapped with a drought (that had prevailed since February), forest fires and the associated major agricultural and forestry damage. The extreme event, which was initially considered (heat wave), was then further intensified by these factors in terms of the environmental and socio-economic damage. The mentioned forms of extreme events cause large socio-economic damages every year and therefore receive an increasing interest in science because of their associated strong influence on society. 
For the consideration of the resulting damages of extreme weather events, the JLU together with various other institutions participates in the investigation of the influence of these extreme events on European supply systems of water, energy and food security (Water, energy and food security nexus; WEF nexus). For this purpose, the currently existing climate services (CS) will be further developed with the use of artificial intelligence in order to better predict or assess the forecasting of extreme weather events and the associated impacts on the sectors of the WEF nexus.  The goal of this initiative is to better understand the complex and dynamic interactions of these sectors to enable better coordinated management or optimized use of natural resources across sectors, and also to quantify the added value of the developed products over the CS currently provided.

JLU's focus here is on food supply within Europe and the JLU is working with the EU Commission's Joint Research Centre (EU-JRC) on the impact of extreme weather events on the food sector.

As described above, the steadily growing advances in artificial intelligence are used as a tool for the analysis and prediction of these extreme events - building on already applied or renowned prediction mechanisms - with the help of which one wants to further optimize the existing prediction systems so that a better forecasting system can be developed. The use of artificial intelligence is gaining an increasing interest in many branches of science due to the availability of large data sets and the increasing capacity of today's computers. There is an unprecedented availability of data in climate research - specifically because of the Copernicus Climate Service - so using artificial intelligence is an important step towards analysing this data and further establishing the use of these methods in future climate research.
After these forecasting methods have been developed, a critical evaluation of them is essential, as a statistical investigation might lead to a detection of a correlation between certain phenomena, but does not necessary imply a causal relationship between the latter and especially not (necessarily) a physical relationship between these. Here, JLU participates in the evaluation of the developed methods and hopes to better understand potential physical driving mechanisms behind the extreme events. The developed statistical and physical methods can then be used to better assess future trends in extreme events and to identify the human fingerprints in the latter in terms of climate change or global warming.
JLU's participation in the project will be completed by a case study of the water supply of Lake Como (Lombardy, Italy), which is of great interest to many agribusinesses or surrounding water supplies. Since this region is a hotspot for climate impact research due to the increasing intensity and number of heat waves, improved prediction of extreme events using the methods developed in CLINT can contribute to better balance agricultural water supplies, increased flood risk control, and other beneficial uses of water use systems in this region.


  • Principal Investigator & Institution

  • Staff

Center for International Development and Environmental Research (ZEU)

Justus-Liebig-University, Giessen (Germany)

Department of Geography

Postdoctoral Researcher at ZEU


Research Assistant and Doctoral Candidate at the ZEU

Justus-Liebig-University, Giessen




  • Project Partner Institutions
  • Activity Type
  • Website
Agencia Estatal Consejo Superior De Investigaciones Cientificas Research Organisations
Deutsches Klimarechenzentrum GmbH Research Organisations
E3-Modelling Ae Private for-profit entities
European Centre For Medium-Range Weather Forecasts Research Organisations
Fondazione Centro Euro Mediterraneosui Cambiamenti Climatici Research Organisations
Helmholtz-Zentrum Hereon GmbH Research Organisations
Hkv Lijn In Water Bv Private for-profit entities
Justus-Liebig-University, Giessen Higher Education Establishment
Open Geospatial Consortiom Europe Other
Politecnico Di Milano Higher Education Establishment
Stichting Ihe Delft Institute For Water Education Research Organisations
Sveriges Meteorologiska Och Hydrologiska Institut Public body
The Climate Data Factory Private for-profit entities
Universidad De Alcala Higher Education Establishment
Universidad Complutense De Madrid Higher Education Establishment


  • Acknowledgments

The project is funded by European Commission, European Climate, Infrastructure and Environment Executive Agency