Network-Based Analysis ofCloud-Bursting Flood Impacts onUrban Road Networks : A Case study for city of Karlstad

Sammanfattning

Urban road networks are increasingly vulnerable to extreme flooding events caused by both intense rainfall and river inundation. Flood-induced disruptions can significantly reduce accessibility, alter traffic flow, and fragment transportation systems into disconnected subnetworks. This thesis presents a graph-theoretical framework for analysing the impacts of pluvial flooding on the urban road network of Karlstad, Sweden. The road network is represented as an undirected graph, where road intersections correspond to nodes and road segments correspond to edges. Flood impacts are modelled by removing inundated road segments from the graph according to predefined water-depth thresholds. The study primarily investigates two pluvial flood scenarios: a 100-year cloudburst event and a severe Copenhagen-style cloudburst scenario. These scenarios are then compared with 100-year and 10,000-year extreme river-flooding scenarios in order to highlight and contrast their impacts on the road network of Karlstad. Geographic Information System (GIS)-based flood datasets are integrated with MATLAB-based network analysis to evaluate how flooding modifies the structural and functional properties of the transportation network. The analysis focuses on graph connectivity and centrality-based measures, including degree centrality, closeness centrality, betweenness centrality, and network fragmentation. The results demonstrate that moderate flooding mainly produces localized reductions in accessibility and moderate redistribution of network traffic, whereas severe flooding causes non-linear system collapse and fragmentation into isolated network “islands.” Closeness centrality decreases substantially under extreme flooding, indicating widespread accessibility loss, while betweenness centrality becomes concentrated within a limited number of surviving corridors, revealing critical bottlenecks and vulnerable infrastructure points. Comparative analysis further shows that river flooding causes broader and more persistent system-wide disruption than cloudburst flooding due to its larger spatial extent and prolonged inundation. A graph-similarity analysis based on the edge-Jaccard index was also introduced to quantify structural differences between flood-affected transportation networks. The findings highlight the importance of network-based flood analysis for understanding urban infrastructure vulnerability and resilience. The proposed framework provides practical support for emergency planning, flood-risk management, and resilient urban transportation design, while also offering a transferable methodology for analysing flood-induced disruptions in other urban environments.