Hydrologic extremes (floods and intense precipitations) are among Earth’s most common natural hazards and cause considerable loss of life and economic damage. Despite this, some of their key characteristics are still poorly understood at the global scale. The IPCC thus reports “a lack of evidence and thus low confidence regarding the sign of trend in the magnitude and/or frequency of floods on a global scale”. More generally, the space-time variability of hydrologic extremes is yet to be thoroughly described at the global scale. As a striking illustration, the recent initiative “23 unsolved problems in Hydrology that would revolutionise research in the 21st century” of the International Association of Hydrological Sciences includes questions such as: are the characteristics of extreme hydrologic events changing and if so why? How do extremes around the world teleconnect with each other and with other factors? Why do extreme-rich/poor periods exist?
It is vital to fill these knowledge gaps to inform design, safety and financial procedures and to improve hazard preparedness and response. The project’s ambition is hence to better understand the global space-time variability of hydrologic extremes, using a three-pillar research strategy based on methodological innovation, extensive data analysis and proof-of-concept case studies. The specific objectives are to:
1. Develop a statistical framework to describe the global-scale variability of extremes in relation to climate;
2. Analyse global precipitation/streamflow datasets with the aim of quantifying teleconnections, spatial clustering, trends and extreme-rich/poor periods, along with their climate drivers;
3. Explore practical applications such as global early warning systems allowing international disaster response organisations to trigger early actions.
Successful completion of the project will deliver new tools to analyse extremes at the global scale and will hence contribute to more efficient risk management.
Field of science
- /natural sciences/computer and information sciences/data science/data analysis
- /natural sciences/earth and related environmental sciences/physical geography/natural disaster
- /social sciences/sociology/governance/crisis management
Call for proposal
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