Assessment of Flood Early Warning and Response System in the Alau Dam Catchment Area, Borno State, Nigeria, using SRTM and GIS.

Abstract

The Flood Early Warning and Response System (FEWRS) was comprehensively evaluated in the 
Alau Dam catchment, Borno State, Nigeria, using an integrated geospatial and socio-institutional 
framework to assess flood vulnerability, early warning effectiveness, and community response 
capacity. The study was motivated by the recurrent downstream flooding that persistently 
affects the Maiduguri Metropolitan Council, Jere, and Konduga Local Government Areas. A 
mixed-methods research design was adopted, integrating Geographic Information Systems 
(GIS), Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), Multi-Criteria 
Decision Analysis (MCDA), and Sentinel-2 land use/land cover data with primary socio-economic 
information collected from 180 households and 40 institutional stakeholders through structured 
questionnaires, Key Informant Interviews (KIIs), and Focus Group Discussions (FGDs). 
Geospatial analysis revealed that the low-lying terrain (275–360 m above sea level) and 
predominantly gentle slopes (approximately 3°, covering 90% of the catchment) facilitate the 
natural concentration and downstream convergence of runoff into flood-prone plains. The 
integrated Multi-Criteria Evaluation (MCE) flood susceptibility model classified 28% of the 
catchment, primarily the urbanized Maiduguri area, as Very High flood risk, while an additional 
34% was categorized as High risk. Socio-institutional assessment showed that although 58% of 
households were aware of flood early warning information, 42% remained unreached by the 
warning system. Radio (44%) and traditional community leaders (27%) were identified as the 
principal channels for warning dissemination. Inferential statistical analyses established 
significant associations (p < 0.05) between educational attainment, household income, timely 
receipt of warnings, and proactive household response. Furthermore, binary logistic regression 
analysis (Nagelkerke R² = 0.61) identified early warning awareness (Exp(β) = 3.01) and education 
level (Exp(β) = 2.32) as the strongest predictors of appropriate flood response behaviour. The 
findings demonstrate that flood vulnerability within the Alau Dam catchment is a complex 
interaction of geomorphological controls, rapid urbanization, and institutional communication 
bottlenecks, highlighting the urgent need to transition from conventional warning practices to a 
people-centred, real-time, automated flood forecasting and early warning system capable of 
strengthening disaster preparedness, improving emergency response, and enhancing long-term 
community resilience to flood hazards.