Spatial-temporal assessment of agricultural virtual water and uncertainty analysis: The case of Kazakhstan (2000-2016)

被引:22
作者
Ding, Y. K. [1 ]
Li, Y. P. [1 ,2 ]
Liu, Y. R. [1 ]
机构
[1] Beijing Normal Univ, Sch Environm, Beijing 100875, Peoples R China
[2] Univ Regina, Inst Energy Environm & Sustainable Communities, Regina, SK S4S 0A2, Canada
关键词
Agriculture; Kazakhstan; Sustainable development; Uncertainty; Virtual water; Water scarcity; FOOTPRINT ASSESSMENT; CROP PRODUCTION; FUZZY; RESOURCES; TRADE; FLOWS; IMPACT; RIVER; OPTIMIZATION; TEMPERATURE;
D O I
10.1016/j.scitotenv.2020.138155
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In this study, a fuzzy-vertex-based virtual-water analysis method (FVAM) is developed for assessing the virtual water content (VWC) of main agricultural products, imports, and exports at a national scale. FVAM has advantages in quantifying state-level VWC with a bottom-up approach and reflecting uncertain parameters based on vertex analysis technique. FVAM is applied to a real case of Kazakhstan in Central Asia. Results reveal that (i) the VWC of Kazakhstan's agricultural products is between 55.61 and 83.98 billion m(3)/yr in 2000-2016, where wheat is the largest water consumer and the Kostanay state has the largest VWC; (ii) Kazakhstan is a net exporter of virtual water, most of which flows to neighboring countries such as Russia and Azerbaijan; (iii) uncertainties in crop coefficient (Kc), feed water requirement (FWR), drinking water requirement (DWR) and service water requirement (SWR) can affect the VWC assessment; (iv) the massive export of water-intensive products makes the water resources more severe in Kazakhstan, which further squeezes the local ecological water use. Therefore, reducing the export of virtual water should be the focus of future agricultural policies. The findings are useful for decision makers to optimize Kazakhstan's agricultural structure, mitigate the national water scarcity, and facilitate the regional sustainable development.
引用
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页数:15
相关论文
共 57 条
[11]   Numerical solute transport simulation using fuzzy sets approach [J].
Dou, CH ;
Woldt, W ;
Bogardi, I ;
Dahab, M .
JOURNAL OF CONTAMINANT HYDROLOGY, 1997, 27 (1-2) :107-126
[12]  
FAO, 2010, CROPWAT 80 MODEL
[13]   Risk and confidence analysis for fuzzy multicriteria decision making [J].
Fenton, Norman ;
Wang, Wei .
KNOWLEDGE-BASED SYSTEMS, 2006, 19 (06) :430-437
[14]   Water footprint of German agricultural imports: Local impacts due to global trade flows in a fifteen-year perspective [J].
Finogenova, Natalia ;
Dolganova, Iulia ;
Berger, Markus ;
Nunez, Montserrat ;
Blizniukova, Daria ;
Mueller-Frank, Andrea ;
Finkbeiner, Matthias .
SCIENCE OF THE TOTAL ENVIRONMENT, 2019, 662 :521-529
[15]  
Food and Agriculture Organisation of the United Nations (FAO), 2018, WORLD FOOD AGR STAT
[16]   A Factorial-based Dynamic Analysis Method for Reservoir Operation Under Fuzzy-stochastic Uncertainties [J].
Fu, D. Z. ;
Li, Y. P. ;
Huang, G. H. .
WATER RESOURCES MANAGEMENT, 2013, 27 (13) :4591-4610
[17]   Uncertainty of the Agricultural Grey Water Footprint Based on High Resolution Primary Data [J].
Gil, Rodrigo ;
Bojaca, Carlos Ricardo ;
Schrevens, Eddie .
WATER RESOURCES MANAGEMENT, 2017, 31 (11) :3389-3400
[18]   Embodied agricultural water use in China from 1997 to 2010 [J].
Guo, Shan ;
Shen, Geoffrey Qiping ;
Peng, Yi .
JOURNAL OF CLEANER PRODUCTION, 2016, 112 :3176-3184
[19]   Global water resources affected by human interventions and climate change [J].
Haddeland, Ingjerd ;
Heinke, Jens ;
Biemans, Hester ;
Eisner, Stephanie ;
Floerke, Martina ;
Hanasaki, Naota ;
Konzmann, Markus ;
Ludwig, Fulco ;
Masaki, Yoshimitsu ;
Schewe, Jacob ;
Stacke, Tobias ;
Tessler, Zachary D. ;
Wada, Yoshihide ;
Wisser, Dominik .
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2014, 111 (09) :3251-3256
[20]  
Hendy C., 1995, Livestock and the Environment: Finding a Balance: Interactions Between Livestock Production Systems and the Environment: Impact Domain: Concentrate Feed Demand