Spatial-temporal analysis of urban water resource vulnerability in China

Because of urbanization and climate change, urban water environment faces a terrible vulnerability trend. This study aims to estimate the vulnerability of the urban water environment by quantifying the vulnerability indicators of urban water resources. This proposed indicator has two domains of deve...

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Bibliographic Details
Main Authors: Kato, T. (Author), Sun, M. (Author)
Format: Article
Language:English
Published: Elsevier B.V. 2021
Subjects:
Online Access:View Fulltext in Publisher
LEADER 02896nam a2200565Ia 4500
001 10.1016-j.ecolind.2021.108436
008 220427s2021 CNT 000 0 und d
020 |a 1470160X (ISSN) 
245 1 0 |a Spatial-temporal analysis of urban water resource vulnerability in China 
260 0 |b Elsevier B.V.  |c 2021 
856 |z View Fulltext in Publisher  |u https://doi.org/10.1016/j.ecolind.2021.108436 
520 3 |a Because of urbanization and climate change, urban water environment faces a terrible vulnerability trend. This study aims to estimate the vulnerability of the urban water environment by quantifying the vulnerability indicators of urban water resources. This proposed indicator has two domains of development pressure and management capability. Four province-level municipalities, Beijing, Tianjin, Shanghai, and Chongqing, and their neighboring provinces in China were selected as study sites. Regression analyses and vector autoregression models were conducted to study the temporal characteristics of the urban water vulnerability indicators. A comparison between the indicator values in the two domains distinguished three types of metropolises and revealed different levels of anthropogenic factors as the cause of water vulnerability. The combination of contemporaneous regression results and the Granger causality testing results using the vector autoregression models was used to identify different patterns of dependency between the metropolises and their neighboring provinces. Beijing and Tianjin have more obvious spatial -temporal relationships with neighborhood provinces. © 2021 The Authors 
650 0 4 |a anthropogenic effect 
650 0 4 |a Beijing [China] 
650 0 4 |a China 
650 0 4 |a China 
650 0 4 |a China 
650 0 4 |a Chongqing 
650 0 4 |a climate change 
650 0 4 |a Climate change 
650 0 4 |a Granger causality test 
650 0 4 |a Indicator indicator 
650 0 4 |a Metropolis 
650 0 4 |a Metropolis 
650 0 4 |a neighborhood 
650 0 4 |a Regression analysis 
650 0 4 |a Shanghai 
650 0 4 |a Spatial temporal analysis 
650 0 4 |a Spatial variables measurement 
650 0 4 |a Spatial–temporal analysis 
650 0 4 |a spatiotemporal analysis 
650 0 4 |a Tianjin 
650 0 4 |a Tianjin 
650 0 4 |a Two domains 
650 0 4 |a urban area 
650 0 4 |a Urban water environments 
650 0 4 |a Urban water resource vulnerability indicator 
650 0 4 |a Urban water resource vulnerability indicator 
650 0 4 |a Urban water resources 
650 0 4 |a urbanization 
650 0 4 |a Vector autoregression model 
650 0 4 |a Vector autoregression models 
650 0 4 |a vulnerability 
650 0 4 |a Vulnerability indicators 
650 0 4 |a water management 
650 0 4 |a water resource 
650 0 4 |a Water resources 
700 1 |a Kato, T.  |e author 
700 1 |a Sun, M.  |e author 
773 |t Ecological Indicators