Assessment of Climate Change on Soil Erosion Using Geospatial Techniques: A Review

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dc.contributor.authorPazhanivelan, Sen_US
dc.contributor.authorLad, SUen_US
dc.contributor.authorSelvakumar, Sen_US
dc.contributor.authorRavikumar, Ven_US
dc.contributor.authorRamesh, AVen_US
dc.contributor.authorKarale, OSen_US
dc.contributor.authorJadhav, RJ.en_US
dc.date.accessioned2025-08-13T11:23:50Z
dc.date.available2025-08-13T11:23:50Z
dc.date.issued2025-06
dc.description.abstractClimate change is accelerating soil erosion, presenting a significant threat to food security and ecosystem health globally. This review investigates the impact of climate change on soil erosion using advanced geospatial methods and the Revised Universal Soil Loss Equation (RUSLE). An analysis of over 100 recent peer-reviewed articles (including research from 1990 to 2024) explores how factors such as shifting precipitation patterns, rising temperatures, extreme weather events, and land-use changes influence erosion across various scales. Climate change-induced shifts in precipitation patterns and intensifying weather events significantly affect soil erosion rates. Heavy rainfall events can cause substantial soil displacement, while droughts dry out the soil, leaving it vulnerable to wind erosion. Rising temperatures further exacerbate the problem by altering soil moisture levels and influencing vegetation cover, a crucial factor in erosion control. Land-use changes, including deforestation, urbanization, and agricultural practices, disturb soil stability and increase erosion rates. Remote sensing, GIS, and AI-machine learning are increasingly combined with advanced RUSLE variations to identify erosion hotspots with greater precision. These technologies enable monitoring of spatiotemporal patterns and assessment of future risks under various climate scenarios. Remote sensing techniques allow for high-resolution mapping of erosion-prone areas, while AI and machine learning enhance predictive models, providing more targeted and effective adaptation strategies. Despite potential temporary reductions in erosion in regions experiencing initial increases in vegetation cover, projections indicate a significant global soil loss increase by mid-century (2050). This increase is driven by heavier precipitation, intensifying droughts, and more frequent and severe floods. The resulting erosion can have devastating effects on agricultural productivity, water quality, and biodiversity. Integrated soil conservation practices, such as reduced tillage, cover cropping, and revegetation, are essential for building resilience against this growing threat. These practices help stabilize soil, improve water retention, and enhance the land's overall health. In addition, landscape management techniques, including contour farming and agroforestry, can further mitigate erosion risks. Advancements in AI-machine learning-based erosion prediction models offer promising opportunities for more precise and timely interventions. By integrating these models with remote sensing data, researchers can develop more accurate risk assessments and design more efficient mitigation strategies. High-resolution remote sensing allows for continuous monitoring and evaluation of erosion patterns, enabling adaptive management approaches. As results emerging technologies and innovative management practices offer new tools and approaches to address this challenge. By investing in research and adopting advanced geospatial methods, policymakers and stakeholders can work together to develop more effective strategies for mitigating soil erosion and safeguarding global food security and biological systems.en_US
dc.identifier.affiliationsCentre for Water and Geospatial Studies, TNAU, Coimbatore, Tamil Nadu, Indiaen_US
dc.identifier.affiliationsDepartment of Soil and Water Conservation Engineering, AEC and RI, TNAU, Coimbatore, Tamil Nadu, Indiaen_US
dc.identifier.affiliationsCentre for Water and Geospatial Studies, TNAU, Coimbatore, Tamil Nadu, Indiaen_US
dc.identifier.affiliationsCentre for Water and Geospatial Studies, TNAU, Coimbatore, Tamil Nadu, Indiaen_US
dc.identifier.affiliationsDepartment of Agronomy, AC and RI, TNAU, Coimbatore, Tamil Nadu, Indiaen_US
dc.identifier.affiliationsDepartment of Soil and Water Conservation Engineering, IARI, New Delhi, Indiaen_US
dc.identifier.affiliationsDepartment of Civil Engineering, NIT, Warangal, Telangana, India.en_US
dc.identifier.citationPazhanivelan S, Lad SU, Selvakumar S, Ravikumar V, Ramesh AV, Karale OS, Jadhav RJ.. Assessment of Climate Change on Soil Erosion Using Geospatial Techniques: A Review . International Journal of Environment and Climate Change. 2025 Jun; 15(6): 191-209en_US
dc.identifier.issn2581-8627
dc.identifier.placeIndiaen_US
dc.identifier.urihttps://imsear.searo.who.int/handle/123456789/253777
dc.languageenen_US
dc.publisherMs. M. B. Mondal, Ph.D.en_US
dc.relation.issuenumber6en_US
dc.relation.volume15en_US
dc.source.urihttps://doi.org/10.9734/ijecc/2025/v15i64883en_US
dc.subjectSoil erosionen_US
dc.subjectremote sensingen_US
dc.subjectclimate changeen_US
dc.subjectRUSLEen_US
dc.titleAssessment of Climate Change on Soil Erosion Using Geospatial Techniques: A Reviewen_US
dc.typeJournal Articleen_US
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