In recent times, the swift and uncoordinated urban expansion in the medium sized city of Osogbo has increasingly encroached on the environmental sensitive areas. This uncoordinated exploitation of the ecological fragile areas for urban development has triggered urban flooding, which poses serious threat to human lives and properties in the city. This study therefore utilized geospatial technologies to generate flood hazard model for medium sized cities in developing countries in general and Nigeria in particular with a view to identifying hotspots and providing measures to forestall flood occurrences. The study used Shuttle Radar Topography Mission-Digital Elevation Model (SRTM-DEM), Landsat 7-2000 and Operational Land Imager 8 (OLI)-2015 images. Multi-criteria evaluation method was used to derive the physical characteristics of watersheds from the data to delineate the areas vulnerable to flooding. Flood inundation characteristics were simulated using Hydrologic Engineering Centre-River Analysis System (HEC-RAS) model with GIS interface and found discharge of over 500 m 3 /s for the 100-year profile period to be high, given the size of the analyzed basin (207.41 km 2). The flood hazard map showed that 3.92, 103.38 and 100.11 km 2 were least, moderately and highly vulnerable areas respectively. Most prone areas were built-ups with high impervious surface in the basin area where floodplain traversed high and moderate vulnerability zones. The study advocates consistent use of dynamic model within GIS for floodplain delineation and management planning to reduce the flood hazards.

Abstract:Identifying landscapes with similar hydrological characteristics is useful for the determination of dominant runoff process (DRP) and flood prediction. Several approaches used for DRP-mapping differ in respect to time and data requirement. Manual approaches based on field investigation and expert knowledge are time consuming and difficult to implement at regional scale. Automatic GIS-based approach on the other hand require simplification of data but are easier to implement and it is applicable on regional scale.