Prioritising sub-watersheds using morphometric analysis, principal component analysis, and land use/land cover analysis in the Kinnerasani River basin, India

IF 1.5 Q4 WATER RESOURCES
Padala Raja Shekar, Aneesh Mathew
{"title":"Prioritising sub-watersheds using morphometric analysis, principal component analysis, and land use/land cover analysis in the Kinnerasani River basin, India","authors":"Padala Raja Shekar, Aneesh Mathew","doi":"10.2166/h2oj.2022.017","DOIUrl":null,"url":null,"abstract":"\n Due to the depletion of natural resources including land and water as a result of rapid population increase, industrialisation, and urbanisation, effective resource management is essential for long-term development. The Kinnerasani Watershed in Telangana State was chosen for the research based on morphological analysis, principal component analysis (PCA), and land use/land cover (LULC) analysis in this study. A catchment's morphometric characteristics, PCA, and LULC analysis can be estimated using geographic information system (GIS) and remote sensing (RS) approaches. The watershed generated 24 sub-watersheds (SWs) in all (SW1–SW24). SWs were ranked using morphometric features, PCA, and LULC features. To determine the final priority of SWs, several morphometric characteristics, including linear, shape, and relief aspects, have been estimated for each SW and given ranks based on compound parameter values. To prioritise SWs, the PCA was used to extract five parameters from morphometric characteristics. The LULC analysis used four characteristics to prioritise the SWs. SW3, SW9, and SW12 have been prioritised for morphometric analysis; SW2 and SW3 have been prioritised for PCA; and SW17, SW19, SW23, and SW24 have been prioritised for LULC analysis. The common SWs within each priority according to three different methodologies are SW4, SW6, SW10, SW13, SW15, and SW21. The results show that the high-priority locations have greater runoff and soil erosion issues, so it is essential to design and implement watershed management techniques such as check dams, construction of farm ponds, and construction of earthen embankments in these areas. The decision-making authorities might use the findings to plan and implement watershed management initiatives to minimise soil erosion in high-priority locations.","PeriodicalId":36060,"journal":{"name":"H2Open Journal","volume":null,"pages":null},"PeriodicalIF":1.5000,"publicationDate":"2022-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"H2Open Journal","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2166/h2oj.2022.017","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"WATER RESOURCES","Score":null,"Total":0}
引用次数: 8

Abstract

Due to the depletion of natural resources including land and water as a result of rapid population increase, industrialisation, and urbanisation, effective resource management is essential for long-term development. The Kinnerasani Watershed in Telangana State was chosen for the research based on morphological analysis, principal component analysis (PCA), and land use/land cover (LULC) analysis in this study. A catchment's morphometric characteristics, PCA, and LULC analysis can be estimated using geographic information system (GIS) and remote sensing (RS) approaches. The watershed generated 24 sub-watersheds (SWs) in all (SW1–SW24). SWs were ranked using morphometric features, PCA, and LULC features. To determine the final priority of SWs, several morphometric characteristics, including linear, shape, and relief aspects, have been estimated for each SW and given ranks based on compound parameter values. To prioritise SWs, the PCA was used to extract five parameters from morphometric characteristics. The LULC analysis used four characteristics to prioritise the SWs. SW3, SW9, and SW12 have been prioritised for morphometric analysis; SW2 and SW3 have been prioritised for PCA; and SW17, SW19, SW23, and SW24 have been prioritised for LULC analysis. The common SWs within each priority according to three different methodologies are SW4, SW6, SW10, SW13, SW15, and SW21. The results show that the high-priority locations have greater runoff and soil erosion issues, so it is essential to design and implement watershed management techniques such as check dams, construction of farm ponds, and construction of earthen embankments in these areas. The decision-making authorities might use the findings to plan and implement watershed management initiatives to minimise soil erosion in high-priority locations.
在印度Kinnerasani河流域使用形态计量分析、主成分分析和土地利用/土地覆盖分析对子流域进行优先排序
由于人口快速增长、工业化和城市化导致包括土地和水在内的自然资源枯竭,有效的资源管理对长期发展至关重要。本研究基于形态分析、主成分分析(PCA)和土地利用/土地覆盖(LULC)分析,选择特伦甘纳州的Kinnerasani流域进行研究。集水区的形态计量特征、主成分分析和LULC分析可以使用地理信息系统(GIS)和遥感(RS)方法进行估计。该流域总共产生了24个子流域(SW1–SW24)。使用形态计量学特征、主成分分析和LULC特征对SW进行排名。为了确定SW的最终优先级,已经估计了每个SW的几个形态测量特征,包括线性、形状和起伏方面,并基于复合参数值给出了等级。为了优先考虑SW,PCA用于从形态计量特征中提取五个参数。LULC分析使用了四个特征来确定SW的优先级。SW3、SW9和SW12已被优先用于形态计量分析;SW2和SW3已被优先用于PCA;以及SW17、SW19、SW23和SW24已被优先用于LULC分析。根据三种不同的方法,每个优先级内的公共SW是SW4、SW6、SW10、SW13、SW15和SW21。结果表明,高度优先的地区存在更大的径流和土壤侵蚀问题,因此设计和实施流域管理技术至关重要,如在这些地区修建拦水坝、农田池塘和土堤。决策当局可能会利用这些发现来规划和实施流域管理举措,以最大限度地减少高度优先地区的土壤侵蚀。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
H2Open Journal
H2Open Journal Environmental Science-Environmental Science (miscellaneous)
CiteScore
3.30
自引率
4.80%
发文量
47
审稿时长
24 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信