DOI: 10.1002/joc.5335
论文题名: Spatial clustering of maximum 24-h rainfall over Urmia Lake Basin by new weighting approaches
作者: Dehghan Z. ; Eslamian S.S. ; Modarres R.
刊名: International Journal of Climatology
ISSN: 8998418
出版年: 2018
卷: 38, 期: 5 起始页码: 2298
结束页码: 2313
语种: 英语
英文关键词: attributes weighting
; correlation coefficients
; Iran
; principal component analysis
; Urmia Lake Basin
; Ward's clustering
Scopus关键词: Clustering algorithms
; Information management
; Lakes
; Rain
; Spatial variables measurement
; Attributes weighting
; Correlation coefficient
; Iran
; Lake basins
; Ward's clustering
; Principal component analysis
; cluster analysis
; correlation
; estimation method
; homogeneity
; principal component analysis
; rainfall
; regional climate
; spatial analysis
; Iran
; Lake Urmia
英文摘要: The lack of data in rainfall stations of Iran is one of the main problems in design and management of hydrologic systems. Moreover, the density of these stations network is not sufficient for estimation of rainfall at ungauged regions. Therefore, regionalization can be an essential tool to be applied for clustering the rainfall and spatial pattern analysis of homogeneous regions to quantify regional rainfall patterns. Homogeneous regions are usually defined based on different methods and with consideration of a category of attributes. Selection of attributes as representatives of the study region is an important aspect in clustering of a region, as is the importance degree (or determined weight) that each of these attributes can allocate to themselves. Consequently, the aim of this study is to select a broad category of climatic, geographical, and statistical attributes of the maximum 24-h rainfall of the Urmia Lake Basin for 63 selected stations for the period 1979–2008 and next to determine an appropriate weight for each of the attributes in each defined category. To investigate the weighting effect in regionalizing and to determine the appropriate weight for each defined attribute, respectively, Ward's clustering technique, principal component analysis, and correlation coefficients matrix methods were used. The homogeneity measure test showed that all identified clusters are homogeneous. The clustering results showed that based on the different attributes categories, different results can be presented in terms of the number of clusters, distribution of stations, and spatial pattern of clusters. Moreover, the performances of the proposed weighting approaches for spatial clustering analysis are better than no-weight scenario in most modes according to the spatial patterns and homogeneity measurements. © 2017 Royal Meteorological Society
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/117031
Appears in Collections: 气候减缓与适应
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作者单位: Department of Water Engineering, Faculty of Agriculture, Isfahan University of Technology, Iran; Department of Natural Resources, Isfahan University of Technology, Iran
Recommended Citation:
Dehghan Z.,Eslamian S.S.,Modarres R.. Spatial clustering of maximum 24-h rainfall over Urmia Lake Basin by new weighting approaches[J]. International Journal of Climatology,2018-01-01,38(5)