In July 2021 severe flooding occurred in the South of Limburg, Belgium, and Germany due to heavy precipitation. Extreme precipitation events like this are expected to occur more often in our changing climate. Urbanization is thought to be another contributing factor to the July 2021 flood event. The Netherlands is expected to increase urbanization as a solution to its housing shortage. To make room for urbanization, while minimizing the effect of climate change, the government wants to make ”water and soil leading”, Water en Bodem Sturend, in the decision-making about the layout of the Netherlands.

Therefore, the goal of this research is to investigate the best suitable subcatchment for the construction of new residential houses within the Geul catchment, in terms of flooding. The July 2021 flood event is used as a reference. The first step was to investigate the hydrological response of the Geul catchment. Secondly, this hydrological response was modelled by the semi-distributed hydrological models HBV coupled to D-RR and by the distributed model Wflow_sbm. HBV and D-RR are set up in this research, while Wflow_sbm is adopted from Klein (2022) and Bouaziz (2022). The hydrological models are coupled to the Geul hydrodynamic model D-HYDRO of Hulsman, Weijers, Verstegen, and Goedbloed (2023). The building plans in the Geul catchment were investigated and scenarios were constructed. These scenarios were simulated in the hydrological models. This method resulted in a workflow that can be found in Idsinga (2024). The workflow can be applied on analyses of different land cover types.

The modelled hydrographs showed differences between the hydrological models. Each model better describes one part of the hydrological response compared to the other. HBV and D-RR better represent the subsurface flow and describe the hydrological response during consecutive precipitation events. Wflow_sbm represents the overland flow flux better and therefore describes the hydrological response during the July 2021 flood event. The modelled flood extents during the July 2021 flood event are also compared to the estimated extent by Slager, de Moel, and de Jong (2021). Wflow_sbm showed better similarity to the measured flood extent than HBV and D-RR. The Province of Limburg wants to build 18,730 new houses in the South of Limburg. This results in an increase of 6 km2 paved area. In this research, this increase is applied to different locations in the Geul catchment. Next, the impact of completely paved subcatchments was investigated. The relatively small 6 km² increase in paved area did not result in different discharge behaviour and the total area of the flood extent showed a small difference. However, it impacted the flooded paved area. Building far from the river on the hills resulted in no increase in the flooded paved area. New houses in the valleys, close to the river, are more exposed to flooding. In the Meerssen subcatchment, the added paved area was responsible for 95% of the total increase in the flooded paved area. This was also the case in the Gulp subcatchment, where about 90% of the increase in flooded paved area came from the added paved area.

The Meerssen subcatchment is the most vulnerable to flooding. This subcatchment contains the most paved area and more runoff will result in a more flooded paved area. A completely paved Gulp subcatchment results in a less flooded paved area than building 6 km2 close to the Geul in the Meerssen subcatchment. When the Belgians build new houses in the Sippenaeken subcatchment, the Netherlands will receive more water during an extreme event such as in July 2021.

The letter Water en Bodem Sturend states that new houses must be built in sensible locations. In this research, the location of new houses is found to be important for the hydrological response. Building close to the river results in a more flooded paved area than building far from the river. The Gulp subcatchment is the least vulnerable to flooding and can be considered the best building location for new houses among the three investigated subcatchments.

Understanding the propagation of a flood is crucial for effective emergency response measures. While traditional numerical models provide reliable flood simulations, their high computational costs pose significant limitations during emergencies. Deep learning models have recently demonstrated potential in accelerating hydrological calculations while preserving high accuracy. Although various deep learning flood models have been developed, many are limited to specific case studies or neglect the dynamic propagation of flood waves, constraining their application during emergencies. To address this, Bentivoglio et al., 2023 proposes the use of a physics-based surrogate model for spatio-temporal flood modelling; the shallow water equation graph neural network (SWE-GNN). The model demonstrates promising results on small virtual landscapes, showcasing strong generalizability to unseen breach locations and domains, while achieving computational speed ups. In this research, the real-world applicability of the SWE-GNN for time-sensitive situations is analyzed. Two dike rings in the Netherlands are selected as our case study areas. The model is trained and tested within the same domain to evaluate its application during a crisis. Performance is assessed using statistical metrics and practical evaluations, including direct and indirect damage models. The SWE-GNN model is able to correctly predict the spatio-temporal evolution of floods for unseen breach locations. The mean average errors in time are of 0.027 m and 0.029 m for water depth and of 0.007 m^2/s and 0.006 m^2/s on units discharge. The resulting flood maps prove viable for practical applicability, presenting good results for both direct as indirect damage assessment. Additionally, the SWE-GNN demonstrates a speedup of roughly 5 to 6 times for the test case areas compared to a traditional numerical model. In this project, we affirm that the SWE-GNN represents a promising innovation for a new approach to time-sensitive flood modeling, providing a reliable alternative to numerical models in situations with time constraints.