Projects

Goal of the Project

The goal of the project is to better understand flood risk in the Geul catchment. Traditionally, flood risk is calculated as a function of hazard, exposure, and vulnerability. This PhD project aims to work on all three aspects of flood risk. A new flood risk model will be developed, which calculates flood hazard, exposure and vulnerability. Additionally, also the effect of several flood risk adaptation measures will be investigated. This will include the effect of nature-based solutions (NBS), technical measures and households level adaptation measures.

Scientific Motivation

As explained before, flood risk is calculated as a combination of hazard, exposure and vulnerability. Flood hazard is generally calculated in two ways. The first option is to estimate flood probabilities based on extreme value statistics, which uses historical data. The second option is process based: hydrological and hydrodynamic models are coupled to simulate the process of flooding. Exposure is usually calculated by using satellite-derived land use maps, such as the CORINE Land Cover map. On small scales only a few studies use high resolution datasets with individual buildings, such as OpenStreetMap. Vulnerability is generally represented by ‘static’ stage-damage functions, which show the relationship between potential damage and flood depth. These functions, however, mainly focus on the vulnerability of buildings, i.e., physical vulnerability. In reality, vulnerability changes over time and space and is shaped by interactions between society and hazard.

In this research, we will couple a hydrological model (CWATM) and a hydrodynamic model (SFINCS) to be able to simulate floods in the Geul. We create a new exposure database with every building in the catchment, including its characteristics such as front door elevation, building type and the number of floors. We combine this information with newly derived vulnerability curves after the July 2021 flood. Once this model functions, we couple it to an agent-based model (ABM) to simulate households behavior. This way we can see how vulnerability changes over time and space. All this combined, improves our understanding of flood risk.

Case Study

The model will be developed for the entire Geul catchment (including the German and Belgian parts). Additionally, we set up the model in such a way that it can be set up anywhere in the world, if the required data is available.

Expected Outcomes

1. Exposure database for the Geul catchment
2. New flood risk model
3. An agent-based model to simulate dynamic vulnerability