Faculty Publications
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Item Comparative Analysis of Topographic Factor (LS Factor) Estimation Methods for Soil Erosion Risk Assessment in the Netravati Watershed, India(Springer Science and Business Media Deutschland GmbH, 2024) Makhdumi, W.; Shwetha, H.R.; Dwarakish, G.S.Soil erosion models are crucial for soil conservation planning and environmental assessments globally. The Universal Soil Loss Equation (USLE)-based models are most popular empirical models for estimating soil erosion. The topographic factor (LS factor), which combines slope length and slope steepness, significantly affects soil loss among the input parameters. This study aimed to estimate the LS factor for the Netravati watershed in Karnataka, India, which has a high elevation variation (0–1719 m) and diverse terrain, posing soil erosion and local landslide risks that require conservation planning. Three different methods were employed for dimensionless LS factor estimation: Wischmeier and Smith equation (Method I), Moore and Burch equation (Method II), and Desmet and Govers equation (Method III) along with Geographic Information System (GIS) techniques and a high-resolution (12.5 m) ALOS-PALSAR Digital Elevation Model (DEM). The LS factor values ranged from 0.06 to 1549.39 for Method I, 0 to 191.72 for Method II, and 0.03 to 149.09 for Method III. The results showed that Method III produced values that were evenly distributed throughout the spatial domain, with a mean value of 3.31 and a standard deviation of 4.87. Method I generated very high LS factor values along the flow path and almost uniform values for the rest of the study area. The mean LS factor values for Method I and Method II were 8.31 and 7.41, respectively, with standard deviations of 18.74 and 9.88. The findings of this research suggest that Method III is preferable approach for estimating LS factor values in a spatial domain due to its even distribution of values and low standard deviation. This study demonstrates that estimating the LS factor is impacted by the availability and accuracy of topographic data and the technique used. The findings can be used to support sustainable land management practices in the study area. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.Item Geospatial Techniques for Soil Erosion-Based Watershed Prioritisation: A Review(Springer Science and Business Media Deutschland GmbH, 2025) Makhdumi, W.; Suragamallika, R.; Shwetha, H.R.; Dwarakish, G.S.The degradation of the environment caused by anthropogenic has raised significant concerns about the sustainability of land, water, and energy resources. It is crucial to acknowledge the unique characteristics of each watershed and the variability in the impact of human and natural activities across regions. Soil erosion emerges as a major threat, which leads to degraded soil, reduced agricultural productivity, and water pollution. Effective watershed management is essential for preventing soil erosion and ensuring the sustainability of resources. A fundamental step in effective watershed management involves evaluating and identifying the most severely impacted sub-watersheds. This study focuses on soil erosion-based prioritisation studies in India, examining their main findings, models, and methodologies. Geospatial techniques, which include Remote Sensing (RS) and Geographic Information System (GIS), have proven effective for mapping and assessing soil erosion at different scales. These methods identify erosion-causing factors, including land use, slope, rainfall intensity, and soil characteristics. By integrating geospatial data, accurate assessments of soil erosion vulnerability can be made, supporting informed decision-making. Multi-Criteria Decision Analysis (MCDA) helps in prioritisation by evaluating multiple soil erosion criteria and assigning weights based on their relative importance. Geospatial tools facilitate comprehensive assessments of soil erosion vulnerability, aiding decision-making processes. The review offers insights for researchers to conduct reliable assessments and generate data on soil erosion. Integrating Land Use Land Cover Changes (LULCC) and socio-economic conditions in prioritisation studies is recommended. This paper can assist researchers generate reliable data on soil erosion, enabling policymakers to make informed decisions regarding adaptation and mitigation strategies. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.