GIS-Based Identification of Upstream Blue–Green Infrastructure for Sustainable Stormwater Management in Sana'a, Yemen

Flash flooding poses an increasing threat to the historic city of Sana'a, Yemen, where rapid urban expansion, limited stormwater infrastructure, and changing land conditions have intensified surface runoff and increased the vulnerability of the UNESCO World Heritage Site of Old Sana'a. While previous studies have primarily focused on flood hazard assessment and conventional engineering solutions, limited attention has been given to the use of GIS and remote sensing for strategically identifying upstream locations suitable for Blue–Green Infrastructure (BGI) interventions. This study aims to develop a GIS-based decision-support framework for identifying priority upstream areas where BGI can be strategically implemented to support sustainable stormwater management and reduce downstream flood impacts on the historic city.

The research adopts a GIS- and remote-sensing-based spatial analysis approach integrating topographic and satellite-derived datasets. Hydrological analyses were conducted using a 12.5 m resolution ALOS PALSAR Digital Elevation Model (DEM) to delineate the upstream watershed draining toward Old Sana'a, covering approximately 804 km², and to identify drainage networks, flow direction, and flow accumulation patterns. Terrain characteristics and land-cover conditions were further evaluated using slope analysis and Sentinel-2-derived Normalized Difference Vegetation Index (NDVI) data to characterize the upstream catchments contributing runoff toward the historic city.

The analyses delineated the upstream drainage network across an elevation range of 2,200–3,190 m, revealing principal runoff pathways converging toward Old Sana'a from the surrounding highland terrain. NDVI values across the basin were predominantly low, indicating sparse vegetation cover typical of semi-arid terrain, with land cover characterized by a mix of cropland, bare/sparse vegetation, and built-up areas reflecting mixed agricultural–urban pressures on the catchment. These findings establish a robust spatial database that supports the subsequent identification and prioritization of upstream BGI intervention areas, demonstrating the potential of integrating GIS and remote sensing for evidence-based spatial planning in data-limited heritage cities.