{"title":"Assessment of soil erosion in the Beas Valley, Kullu, Himachal Pradesh: A study of Western Himalayan landscape, Northern India","authors":"Suraj Kumar Maurya, Vartika Singh, Kesar Chand, Prabuddh Kumar Mishra","doi":"10.37501/soilsa/185558","DOIUrl":null,"url":null,"abstract":"Soil erosion is a formidable global challenge with far-reaching consequences. It results in the depletion of soil nutrients, land degradation, decreased agricultural output, heightened runoff, and the exacerbation of geological hazards such as landslides and debris fl ows. This study focuses on the assessment of soil erosion in the Beas Valley region of Kullu, Himachal Pradesh, situated in the Western Himalaya landscape of Northern India. The research employs various datasets and a well-de fi ned methodology to analyze the complex interactions between climate, soil, topography, and land use in order to understand and mitigate soil erosion risks. The primary data sources utilized in this study include rainfall data from the Climate Research Unit at the University of East Anglia, soil data from the Food and Agriculture Organization, Digital Elevation Model (DEM) data from the Shuttle Radar Topography Mission, and satellite imagery from Landsat. The research methodology is based on the Revised Universal Soil Loss Equation (RUSLE), a widely accepted model for assessing soil erosion. The RUSLE equation (A= R·K·LS·C·P) incorporates several factors to quantify soil erosion rates. The R-factor, derived from monthly and annual rainfall data, is used to estimate erosivity. The K-factor, determined using soil type and composition, characterizes soil erodibility. The LS-factor considers slope and fl ow accumulation, while the C-factor is calculated based on the Normalized Difference Vegetation Index (NDVI) from satellite imagery. Lastly, the P-factor accounts for the effectiveness of conservation practices. This interdisciplinary approach provides valuable insights into the dynamics of soil erosion in the Beas Valley region. By leveraging cutting-edge data sources, fi led visit and a robust methodology, this study contributes to a better understanding of soil erosion processes in a fragile Himalaya ecosystem, facilitating informed land management decisions and environmental conservation efforts.","PeriodicalId":44772,"journal":{"name":"Soil Science Annual","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Soil Science Annual","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.37501/soilsa/185558","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"SOIL SCIENCE","Score":null,"Total":0}
引用次数: 0
Abstract
Soil erosion is a formidable global challenge with far-reaching consequences. It results in the depletion of soil nutrients, land degradation, decreased agricultural output, heightened runoff, and the exacerbation of geological hazards such as landslides and debris fl ows. This study focuses on the assessment of soil erosion in the Beas Valley region of Kullu, Himachal Pradesh, situated in the Western Himalaya landscape of Northern India. The research employs various datasets and a well-de fi ned methodology to analyze the complex interactions between climate, soil, topography, and land use in order to understand and mitigate soil erosion risks. The primary data sources utilized in this study include rainfall data from the Climate Research Unit at the University of East Anglia, soil data from the Food and Agriculture Organization, Digital Elevation Model (DEM) data from the Shuttle Radar Topography Mission, and satellite imagery from Landsat. The research methodology is based on the Revised Universal Soil Loss Equation (RUSLE), a widely accepted model for assessing soil erosion. The RUSLE equation (A= R·K·LS·C·P) incorporates several factors to quantify soil erosion rates. The R-factor, derived from monthly and annual rainfall data, is used to estimate erosivity. The K-factor, determined using soil type and composition, characterizes soil erodibility. The LS-factor considers slope and fl ow accumulation, while the C-factor is calculated based on the Normalized Difference Vegetation Index (NDVI) from satellite imagery. Lastly, the P-factor accounts for the effectiveness of conservation practices. This interdisciplinary approach provides valuable insights into the dynamics of soil erosion in the Beas Valley region. By leveraging cutting-edge data sources, fi led visit and a robust methodology, this study contributes to a better understanding of soil erosion processes in a fragile Himalaya ecosystem, facilitating informed land management decisions and environmental conservation efforts.
期刊介绍:
Soil Science Annual journal is a continuation of the “Roczniki Gleboznawcze” – the journal of the Polish Society of Soil Science first published in 1950. Soil Science Annual is a quarterly devoted to a broad spectrum of issues relating to the soil environment. From 2012, the journal is published in the open access system by the Sciendo (De Gruyter).