Abstract This study bridges the gap between geospatial risk screening and equitable adaptation finance allocation by developing a reproducible framework for flood risk mapping in data scarce cities. Urban flood risk is escalating globally due to climate change and rapid unplanned urbanization (Tellman et al , 2021). Our innovative approach integrates statistical hotspot analysis (Getis–Ord Gi* for spatial clustering) with terrain based “bluespot” depression modeling to identify flood prone areas where detailed hydrologic data are lacking. The model leverages openly available digital elevation data and remote sensingproxies of flooding to produce high resolution flood risk maps. Applying the approach to the Lower Indus Basin (Pakistan) as a case study demonstrates how the combined model delineates critical flood risk zones coinciding with vulnerable settlements. We generalize the methodology globally by comparing patterns across Sub-Saharan Africa, Southeast Asia, and Latin America. The resulting risk maps reveal that unplanned urban sprawl into low lying areas and informal neighborhoods significantly increase exposure, echoing findings that poorer communities face disproportionate flooding. Policy relevant insights include quantifying populations within high risk Combined Risk Zones (CRZs) and prioritizing these hotspots for resilience investments. The results align with global frameworks reducing disaster impacts (SDG 11.5), strengthening climate resilience (SDG 13.1, Sendai Framework), and answering COP28’s call for adaptation in vulnerable regions, emphasizing the need for participatory, equitable adaptation planning (UNDRR, 2023). Overall, the study concludes that low-cost, reproducible geospatial tools can guide targeted flood mitigation in data-poor contexts, helping policymakers allocate resources effectively and meet international climate resilience goals.
Baig et al. (Fri,) studied this question.