{"title":"喜马偕尔邦库卢比斯河谷水土流失评估:印度北部西喜马拉雅山地貌研究","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":"{\"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. 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引用次数: 0
摘要
水土流失是一项影响深远的全球性严峻挑战。它导致土壤养分枯竭、土地退化、农业减产、径流增加以及山体滑坡和泥石流等地质灾害的加剧。本研究的重点是评估位于印度北部西喜马拉雅地貌的喜马偕尔邦库鲁的比斯河谷地区的土壤侵蚀情况。研究采用了各种数据集和成熟的方法来分析气候、土壤、地形和土地利用之间复杂的相互作用,以了解和减轻土壤侵蚀风险。本研究使用的主要数据源包括东英吉利大学气候研究室提供的降雨量数据、粮食及农业组织提供的土壤数据、航天飞机雷达地形图任务提供的数字高程模型(DEM)数据以及大地遥感卫星提供的卫星图像。研究方法基于经修订的通用土壤流失方程(RUSLE),这是一个广为接受的土壤侵蚀评估模型。RUSLE 方程(A= R-K-LS-C-P)包含几个量化土壤侵蚀率的因子。根据月降雨量和年降雨量数据得出的 R 因子用于估算侵蚀率。K 系数根据土壤类型和成分确定,用于描述土壤的侵蚀性。LS 系数考虑了坡度和积土量,而 C 系数则根据卫星图像中的归一化植被指数 (NDVI) 计算得出。最后,P 因子考虑了保护措施的有效性。这种跨学科方法为了解比斯河谷地区的水土流失动态提供了宝贵的信息。通过利用最前沿的数据源、fi 主导的访问和稳健的方法,该研究有助于更好地了解喜马拉雅脆弱生态系统中的土壤侵蚀过程,促进明智的土地管理决策和环境保护工作。
Assessment of soil erosion in the Beas Valley, Kullu, Himachal Pradesh: A study of Western Himalayan landscape, Northern India
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).