A Review of Methodological Approaches and Results Interpretations to Morphometric Analysis in Environmental Resource Management

Abstract

This comprehensive review examines methodological approaches and interpretative frameworks in morphometric analysis for environmental resource management, tracing its evolution from traditional cartographic techniques to advanced AI-driven applications. Morphometric analysis quantifies watershed characteristics through parameters such as drainage density, bifurcation ratio, and relief ratio, providing critical insights into hydrological processes, erosion susceptibility, groundwater potential, and flood risks. Methodological developments have progressed through four distinct phases: (1) manual cartographic methods (1940s1980s) establishing foundational quantitative principles; (2) GIS and Remote Sensing approaches enabling automated extraction from DEMs (SRTM, ASTER, LiDAR); (3) statistical and geostatistical techniques (PCA, regression, geostatistics) establishing predictive relationships; and (4) machine learning algorithms (Random Forest, SVM, ANNs) capturing complex non-linear dynamics. Despite technological advancements, significant challenges persist in result interpretation across diverse environmental contexts. High drainage density indicates erosion in semi-arid regions but groundwater recharge in humid tropics, while bifurcation ratios reflect tectonic controls in some settings and lithological influences in others. Methodological gaps include DEM resolution inconsistencies (causing up to 45% variation in drainage density estimates), limited AI adoption in developing regions (<5%  of global studies), scale dependency effects (60% parameter variation across spatial extents), and insufficient integration with hydrological-socioeconomic models. Interpretative limitations involve over-generalization across environments, over-reliance on single indices, neglect of temporal dynamics, and poor translation to policy frameworks (only 15% of watershed plans incorporate morphometrics). Future research priorities include high-resolution DEM accessibility, cloud-based AI platforms, longitudinal monitoring of morphometric evolution, and trans-disciplinary approaches bridging scientific analysis with governance frameworks. Addressing these gaps will enhance the contribution of morphometric analysis to sustainable watershed management, particularly in vulnerable regions like Africa where context-specific applications are urgently needed.