Data mining tools for abrupt climate change
Updating our knowledge on abrupt climate change
Coastal flooding due to tropical cyclones is one of the world’s most threatening hazards with damages up to hundreds of billions of euros per event. The risks of coastal floods are projected to increase in the future due to sea-level rise.
Policy-makers need accurate estimates of current and future flood risks in order to take informed decisions on disaster risk reduction and climate change adaptation. A major scientific challenge is to assess global flood risk with hydrodynamic models that have high resolution and accuracy. This can only be achieved by developing a novel approach that combines cutting-edge disciplinary science and eScience technologies. The aim of this project is developing and validating a computationally efficient, scalable, framework for large-scale flood risk assessment. This framework incorporates two major innovations:
1) We will simulate extreme sea levels for thousands of synthetic tropical cyclones – by using goal programming as a tool to reduce the computational costs and combine multiple tropical cyclones into one simulation
2) We will simulate flood inundation at high resolution by nesting local models within a global model – by coupling our models with the OMUSE software which allows for a multi-scale modelling approach.
Within the project, we will use the framework to test whether these improvements lead to more accurate estimates of extremes sea levels, inundation extent and flood risk. To this end, we select the North-Atlantic as a case study area. The novel framework is an important step towards improved global assessments of flood risk.