An exposure-based framework for morphometric flood susceptibility assessment: a comparative analysis of NMFI and CV methods

Traditional flood susceptibility assessments commonly rely on natural watershed boundaries, which can obscure the contributing terrain directly connected to exposed elements. This study evaluates the Normalized Morphometric Flood Index (NMFI) and Compound Value (CV) methods within an exposure-based sub-basin framework in which outlets are defined at settlement, agricultural, and road exposure points. A total of 170 sub-basins (35 settlement, 58 agricultural, and 77 road) in the Tohma River Basin (Türkiye) were analyzed using six morphometric parameters representing linear, areal, and relief controls on runoff generation. Six key morphometric parameters Bifurcation Ratio ( \:Rb ), Length Ratio ( \:Rₗ ), Drainage Density ( \:Dd ), Form Factor ( \:Ff ), Ruggedness Number ( \:Rₙ ), and Hypsometric Integral ( \:Hᵢ ) reflecting how basin topography and drainage network structure accelerate flood arrival times, were selected via correlation matrix analysis. The two methods produced distinct classification behaviors because CV transforms continuous morphometric information into ordinal ranks, whereas NMFI rescales parameter values to a dimensionless 0–1 range while preserving their relative positions and proportional spacing within the observed range. Validation against the 1865–2025 historical flood inventory revealed strongly context-dependent performance. In settlement sub-basins, NMFI achieved higher overall accuracy (68.6%), specificity (87.5%), precision (83.3%), F1-score (64.5%), and balanced accuracy (70.1%), while CV showed slightly higher sensitivity (57.9%). In agricultural sub-basins, NMFI achieved very high sensitivity (91.7%), whereas CV provided higher overall accuracy (60.3%) and balanced accuracy (68.8%) with better false-positive control. In road sub-basins, NMFI detected a larger proportion of historically flood-affected catchments (72.4% sensitivity) and reduced false negatives, whereas CV achieved higher specificity (70.8%) and slightly higher overall accuracy (63.6%). These results demonstrate that neither method is universally superior; rather, their relative performance depends on exposure type, geomorphological setting, and the classification error considered most critical. The exposure-based framework therefore provides a practical means of linking morphometric runoff-generation susceptibility to specific exposed receptors in data-scarce basins.