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Morphometric analysis, integrated with remote sensing (RS) and Geographic Information System (GIS), provides a critical framework for the assessment and prioritization particularly in data-scarce regions. The study aims to prioritize the five-micro-watersheds with in the 42.58 km 2 of Kagemaduvu sub-watershed of the Kaveri River Basin, Karnataka, India by integrating morphometric, hypsometric and land use land cover (LULC) information. The high-resolution CARTOSAT DEM (10 m) was used to delineate the drainage network, micro-watersheds and key morphometric parameters representing linear, areal and relief aspects were derived using ArcGIS 10.8.2. The sub-watershed exhibits a dendritic drainage pattern and the stream order ranges from first to fifth. Drainage density values ranged from 1.915 to 3.116 km/km², while HI values varied between 0.192 (old stage) and 0.383 (mature stage), indicating differences in geomorphic development and erosion susceptibility among the micro-watersheds. Unlike conventional Compound Factor (CF) studies, which report priority rankings without further checks, the CF-hypsometric ranking derived here was cross-validated against independent LULC evidence, and its sensitivity to the flow-accumulation threshold and equal-weighting assumptions was explicitly assessed — offering a replicable validation protocol for prioritization studies in ungauged basins. A compound factor based prioritization identified Arisinakunte and Kuthnagere micro-watersheds as the highest-priority micro-watersheds for soil and water conservation. The study highlights the value of morphometric analysis in the prioritization of sub-watersheds and supports the implementation of targeted management strategies such as afforestation, check dams and terracing. The findings offer critical insights for sustainable watershed development planning and can serve as a replicable model for similar ungauged basins. The integrated framework developed in this study provides a reliable basis for prioritizing erosion-prone micro-watersheds and supports informed decision-making for sustainable soil and water conservation.
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B. et al. (2026) studied this question.
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