Spatial Patterns and Temporal Variability of Drought in Beijing-Tianjin-Hebei Metropolitan Areas in China

被引:18
作者
Cai, Wanyuan [1 ]
Zhang, Yuhu [1 ]
Chen, Qiuhua [2 ]
Yao, Yunjun [3 ]
机构
[1] Capital Normal Univ, Coll Resources Environm & Tourism, Beijing 100048, Peoples R China
[2] Capital Normal Univ, Sch Math Sci, Beijing 100048, Peoples R China
[3] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Coll Global Change & Earth Syst Sci, Beijing 100875, Peoples R China
关键词
PRINCIPAL COMPONENT; REGIONAL DROUGHT; FREQUENCY-ANALYSIS; INDEX RDI; PRECIPITATION; STREAMFLOW; VARIABLES; IRAN;
D O I
10.1155/2015/289471
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
Drought identification and assessment are essential for regional water resources management. In this paper, the spatiotemporal characteristics of drought were evaluated based on monthly precipitation data from 33 synoptic stations during the period of 1960-2010. The percent of normal precipitation was applied to illustrate the driest years in Beijing-Tianjin-Hebei metropolitan areas (BTHMA) (1965, 1997, and 2002). The modified Reconnaissance Drought Index (RDI) was applied to capture the drought patterns and to estimate the drought severity at 33 meteorological stations. Agglomerative hierarchical cluster analysis (AHCA) and principal component analysis (PCA) were used to identify three different drought subregions R1, R2, and R3 based on the monthly precipitation values in BTHMA, which is located in southeast, north, and south of BTHMA, respectively. The year 1965 was the driest and 1964 was the wettest during the observed period. The characteristics of drought were analyzed in terms of the temporal evolution of the RDI-12 values and the frequency of drought for the three identified regions. The percentage of years characterized by drought was 13.73% for R1, 16.50% for R2, and 15.53% for R3. 66.91% of drought belongs to the near normal drought category. The obtained results can aid to improve water resources management in the area.
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页数:14
相关论文
共 67 条
[1]   Principal component analysis [J].
Abdi, Herve ;
Williams, Lynne J. .
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS, 2010, 2 (04) :433-459
[2]  
Abramowitz M., 1965, Handbook of Mathematical Functions
[3]  
Anderberg M.R., 1973, Cluster analysis for applications
[4]  
[包云轩 BAO Yunxuan], 2011, [地理学报, Acta Geographica Sinica], V66, P599
[5]  
BARTLETT MS, 1954, J ROY STAT SOC B, V16, P296
[6]   SCREE TEST FOR NUMBER OF FACTORS [J].
CATTELL, RB .
MULTIVARIATE BEHAVIORAL RESEARCH, 1966, 1 (02) :245-276
[7]   REGIONAL FREQUENCY-ANALYSIS OF ANNUAL MAXIMUM STREAMFLOW DROUGHT [J].
CLAUSEN, B ;
PEARSON, CP .
JOURNAL OF HYDROLOGY, 1995, 173 (1-4) :111-130
[8]   Characteristics and trends in various forms of the Palmer Drought Severity Index during 1900-2008 [J].
Dai, Aiguo .
JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2011, 116
[9]   Principal Component Analysis on Spatial Data: An Overview [J].
Demsar, Urska ;
Harris, Paul ;
Brunsdon, Chris ;
Fotheringham, A. Stewart ;
McLoone, Sean .
ANNALS OF THE ASSOCIATION OF AMERICAN GEOGRAPHERS, 2013, 103 (01) :106-128
[10]   Selection of variables for the purpose of regionalization of Iran's precipitation climate using multivariate methods [J].
Dinpashoh, Y ;
Fakheri-Fard, A ;
Moghaddam, M ;
Jahanbakhsh, S ;
Mirnia, M .
JOURNAL OF HYDROLOGY, 2004, 297 (1-4) :109-123