Climate change is expected to increase the frequency and intensity of hazard extremes in Europe such as floods and droughts. The risk from natural hazard will also increase due to the trends in population and assets in vulnerable areas (e.g. urbanisation in risky areas such as flood plains). However, risk is not only determined by dynamics in hazards and exposure, but also by the vulnerability of communities and how vulnerability changes over time and space. The CLIMAAX project will focus on this aspect, and aims to assess the dynamics of human vulnerability in Europe.
A key aspect of the project will be to unravel the driving factors of vulnerability to natural hazards. Using surveys, research has shown that socio-economic factors such as education level, wealth or income may determine the capacity of households to prepare and cope with extreme flood and droughts. Furthermore, demographic factors may play a role as, for example, elderly are less well prepared for evacuation to forecasted events. In addition, psychological factors may play a role such as risk perception, and communities who have recently experienced floods are mostly better prepared than those without such experience. A challenge, however, for the CLIMAAX project will be to upscale these local survey data to the European level, and develop a vulnerability model that enables to simulate vulnerability of communities to natural hazards over time (historic and into the future) for the whole continent, at a more aggregated resolution.
Such ambition requires a quantitative data-driven approach where local survey data and global data on vulnerability factors for Europe will be integrated and analysed to calibrate improved vulnerability metrics. Historic vulnerability data will be compared to data on flood and drought events, in order to see if these events have had an influence on vulnerability and risk. An important data processing approach is machine learning (e.g. Bayesian statistics) which can be used to sample data from different sources with the aim of merging these into a new vulnerability database. As the data will be often spatially explicit, GIS analyses can be important as well, as well as statistical data driven techniques to analyse time series of vulnerability factors into the future (for example using scenarios on demography and economy).
You will explore these data-driven methods to unravel the different factors of human vulnerability to natural hazards and to merge different data sources into vulnerability projections (2050 and perhaps 2100) for Europe. While this comes with uncertainty, multiple scenarios will be explored. You will work closely with Deltares and other organisations (e.g. IIASA) to derive data and to develop (future-) projections. You will work in a team of a postdoc, an assistant professor, and two professors at IVM. In the CLMIAAX project more than 25 researchers are involved, including multiple PhDs. Visits to other institutes in Europe is a possibility, dependent on the link to the proposed research.Your duties
- collect data on vulnerability factors at local and global/European scales
- integrate data from surveys and global databases on the drivers of vulnerability with statistical / machine learning methods such as Bayesian networks.
- develop historic and future scenarios of vulnerability for Europe and compare them with historic hazards events
- communicate modelling results with EU partners such as JRC and ECMWF