Optimizing Flood Hazard Zonation and Planning Landscape‐Based Mitigation Measures in Gimba Sub Watersheds, Northeastern Ethiopia: A Comprehensive Approach

ABSTRACT Flooding remains one of the most critical natural hazards threatening livelihoods, infrastructure, and ecological systems in Ethiopia's highland landscapes. This study presents a rigorously integrated, multi‐criteria flood risk assessment that combines the Analytical Hierarchy Process (AHP) with GIS‐based spatial modeling to delineate, classify, and prioritize flood‐prone zones within the Gimba sub‐watershed. Eight biophysical flood‐generating factors stream density, rainfall, slope, elevation, land use/land cover (LULC), soil type, geology, and groundwater depth were systematically evaluated to derive a standardized and weighted Flood Hazard Index (FHI). Standardization was performed through min‐max normalization to ensure comparability across variables, while weighting was achieved using pairwise comparison matrices in AHP, allowing expert judgments to quantify the relative influence of each factor on flood susceptibility. The resulting FHI was subsequently integrated with exposure layers, including population density, built‐up intensity, and road network distribution, to compute a comprehensive Flood Risk Index (FRI). Model accuracy was assessed through spatial overlay with historical flood inventories and Receiver Operating Characteristic (ROC) curve analysis, generating an Area Under the Curve (AUC) value of 0.885, which confirms very strong predictive capability. Results reveal that approximately 34.6% of the Gimba sub‐watershed falls within high or very high flood hazard classes, with pronounced hotspots in downstream floodplains, mid‐slope flash‐flood corridors, and zones undergoing severe land degradation. A targeted mitigation suitability analysis further shows that 32.1% of these high‐risk zones are highly appropriate for nature‐based solutions such as agroforestry, check‐dam installation, terracing, and catchment‐scale reforestation. Importantly, participatory engagement through key informant interviews (KIIs) and focus group discussions (FGDs) played a direct role in refining hazard classifications and prioritizing intervention sites, ensuring that the spatial outputs aligned with community experience, local knowledge, and on‐the‐ground feasibility. This co‐production of knowledge enhanced both the accuracy and social legitimacy of the proposed measures. Overall, the study provides a transparent, adaptable, and policy‐relevant framework for evidence‐based flood risk management. By integrating biophysical hazard metrics, terrain suitability, and stakeholder insights, the approach supports Ethiopia's Climate Resilient Green Economy (CRGE) strategy and national Disaster Risk Management (DRM) initiatives. Moreover, the methodology demonstrates strong scalability and transferability, offering a robust decision‐support tool that can be applied across other data‐scarce, hazard‐prone watersheds to strengthen climate‐resilient landscape planning.