The escalating frequency of extreme rainfall events necessitates scalable and dynamic tools for urban flood risk assessment. The primary objective of this study is to demonstrate and evaluate how a machine-learning–derived building-level pluvial flood susceptibility index can be operationalised through multi-resolution aggregation using the H3 Hexagonal Discrete Global Grid System (DGGS). The study builds on the building-level Pluvial Flood Index ( PFI b ) developed in the InzureFlood project, which derives susceptibility from historical insurance damage patterns and geospatial predictors. In the proposed methodology, the analysis domain is partitioned using H3, and building-level scores are aggregated within hexagonal cells to produce a scalable hexagon-level index ( PFI h ). Results indicate that this grid-based approach substantially improves computational efficiency, reducing spatial query operations by approximately 98% compared to traditional geometry-based workflows. The resulting maps show spatial patterns that are qualitatively consistent with expected topographic controls, such as depressions and flow paths. However, quantitative analysis of resolution effects reveals a critical trade-off: while overall susceptibility levels are preserved, aggregation increasingly smooths local extremes. A Jaccard Similarity Index of 0.14 between street-level (R13) and neighborhood-level (R10) hotspots indicates that most fine-scale susceptibility hotspots are not visible at intermediate scales. These findings imply that coarser resolutions may support regional strategic screening, whereas high-resolution grids are necessary for safety-critical tasks such as emergency response and local mitigation. The framework provides a scalable and updateable representation that supports consistent multi-scale communication from micro-scale screening to macro-scale planning.
Svellingen et al. (Sun,) studied this question.