干旱区生态环境知识资源中心(Arid): Clustering of rainfall stations and distinguishing influential factors using PCA and HCA techniques over the western dry region of India

Arid

DOI	10.1002/met.1763
	Clustering of rainfall stations and distinguishing influential factors using PCA and HCA techniques over the western dry region of India
	Machiwal, Deepesh 1; Kumar, Sanjay 2,5; Meena, Hari M.3; Santra, Priyabrata 4; Singh, Ranjay K.4; Singh, Dharam V.3
通讯作者	Machiwal, Deepesh
来源期刊	METEOROLOGICAL APPLICATIONS
ISSN	1350-4827
EISSN	1469-8080
出版年	2019
卷号	26 期号:2 页码:300-311
英文摘要	This study used hierarchical cluster analysis (HCA) to delineate the spatial patterns of monthly, seasonal and annual rainfall by clustering 62 stations in the western arid region of India based on a 55 year (1957-2011) data set. The statistical properties of clusters were computed and box-whisker plots plotted. Furthermore, the relative influence of three geographical factors (longitude, latitude and altitude) and five statistical parameters (the mean, standard deviation (SD), co-efficient of variation (CV), and maximum and minimum rainfall) on mean rainfall was investigated using principal component analysis (PCA). The use of HCA resulted in four rainfall clusters geographically located at a distinct position. Cluster I, characterized by the lowest mean rainfall and highest CV, was located in the western portion, whereas mean rainfall was the highest for cluster IV situated in the eastern portion. Box-whisker plots revealed a slight skewness, although the monsoon and annual rainfall followed a normal distribution. The PCA results indicted two to three significant principal components (PCs) with eigenvalues > 1. In four clusters, two PCs explained the major variance, ranging from 69.41% (June) to 91.83% (August) in monthly rainfall, from 63.62% (monsoon) to 93.30% (post-monsoon) in seasonal rainfall, and from 71.48% to 90.73% in annual rainfall. In monthly and seasonal rainfall, first PC 1 is termed the mean rainfall component, which has strong to moderate associations with longitude, and is equally opposed by the CV. These findings are vital for planners and decision-makers to formulate strategies to manage unusual rainwater quantities.
英文关键词	arid region geographical factors hierarchical cluster analysis principal component analysis rainfall statistical parameters
类型	Article
语种	英语
国家	India
开放获取类型	Bronze
收录类别	SCI-E
WOS记录号	WOS:000463949000013
WOS关键词	PRINCIPAL COMPONENT ; CLASSIFICATION ; PATTERNS
WOS类目	Meteorology & Atmospheric Sciences
WOS研究方向	Meteorology & Atmospheric Sciences
来源机构	ICAR Central Arid Zone Research Institute
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/217585
作者单位	1.ICAR Cent Arid Zone Res Inst, Reg Res Stn, Bhuj, Gujarat, India; 2.ICAR CAZRI, Krishi Vigyan Kendra, Bhuj, India; 3.ICAR Cent Arid Zone Res Inst, Div Nat Resources, Jodhpur, Rajasthan, India; 4.ICAR Cent Arid Zone Res Inst, Div Agr Engn & Renewable Energy, Jodhpur, Rajasthan, India; 5.Banda Univ Agr & Technol, Coll Forestry, Banda 210001, Uttar Pradesh, India
推荐引用方式 GB/T 7714	Machiwal, Deepesh,Kumar, Sanjay,Meena, Hari M.,et al. Clustering of rainfall stations and distinguishing influential factors using PCA and HCA techniques over the western dry region of India[J]. ICAR Central Arid Zone Research Institute,2019,26(2):300-311.
APA	Machiwal, Deepesh,Kumar, Sanjay,Meena, Hari M.,Santra, Priyabrata,Singh, Ranjay K.,&Singh, Dharam V..(2019).Clustering of rainfall stations and distinguishing influential factors using PCA and HCA techniques over the western dry region of India.METEOROLOGICAL APPLICATIONS,26(2),300-311.
MLA	Machiwal, Deepesh,et al."Clustering of rainfall stations and distinguishing influential factors using PCA and HCA techniques over the western dry region of India".METEOROLOGICAL APPLICATIONS 26.2(2019):300-311.