基于BWSI与GWSI的江苏省农业生产水资源压力评价

doi:10.11870/cjlyzyyhj201706008

长江流域资源与环境 >> 2017, Vol. 26 >> Issue (06): 856-864.doi: 10.11870/cjlyzyyhj201706008

基于BWSI与GWSI的江苏省农业生产水资源压力评价

操信春^1,2, 束锐¹, 郭相平^1,2, 邵光成¹, 王振昌¹

1. 河海大学南方地区高效灌排与农业水土环境教育部重点实验室, 江苏南京 210098;
2. 河海大学水利水电学院, 江苏南京 210098

收稿日期:2016-11-03 修回日期:2017-01-21 出版日期:2017-06-20
通讯作者: 郭相平,E-mail:xpguo@hhu.edu.cn E-mail:xpguo@hhu.edu.cn
作者简介:操信春(1986~),男,博士,讲师,主要从事水资源与水环境评价方面的研究.E-mail:caoxinchun@hhu.edu.cn
基金资助:
国家自然科学基金项目（51609065，51309080）；中央高校基本科研业务费专项资金资助（2015B11014）；江苏高校优势学科建设工程资助项目

WATER STRESSES EVALUATION OF AGRICULTURAL PRODUCTION IN JIANGSU PROVINCE USING BWSI AND GWSI

CAO Xin-chun^1,2, SHU Rui¹, GUO Xiang-ping^1,2, SHAO Guang-cheng¹, WANG Zhen-chang¹

1. Key Laboratory of Efficient Irrigation-Drainage and Agricultural Soil-Water Environment in Southern China of Ministry of Education, Hohai University, Nanjing 210098, China;
2. College of Water Conservancy and Hydropower, Hohai University, Nanjing 210098, China

Received:2016-11-03 Revised:2017-01-21 Online:2017-06-20
Supported by:
National Natural Science Foundation of China (51609065, 51309080);Fundamental Research Funds for the Central Universities (2015B11014);AProject Funded by thePriority Academic Program Development of Jiangsu Higher Education Institutions (PAPD)

摘要/Abstract

摘要： 为评价农业生产过程对区域水资源的影响，在核算江苏省农业广义水资源量和农作物生产水足迹的基础上，构建基于水资源消耗和水足迹的农业生产水资源压力指标BWSI和GWSI进行1999~2013农业生产水资源压力评价。结果显示，江苏省农业广义水资源约为1 034.6×10⁸ m³，绿水占70.4%，苏南地区相对丰富；农作物生产水足迹为1 069.5×10⁸ m³（5.9%蓝水、74.6%绿水、19.5%灰水），苏北地区占61.6%且有随时间增大趋势。全省BWSI和GWSI分别为2.60与1.09，水资源压力较大，且有随时间微弱增大的趋势；农业生产水资源压力由南向北呈增大态势，且在时间上呈现苏南减低、苏中稳定、苏北增大的整体态势。引江水缓解了全省及各分区水资源压力，对BWSI的影响大于GWSI。BWSI和GWSI可以用于区域农业生产水资源压力评价，BWSI能揭示缺水地区的水资源稀缺性，而GWSI适合全面反映水资源丰富地区的用水状况。

关键词: 蓝水, 绿水, 农业生产, 灰水足迹, 水资源压力

Abstract: The aim of this study is to evaluate water stress of agricultural production in Jiangsu Province of China. Two water stress assessment indices, i.e., BWSI and GWSI, was established in the blue-green water framework base on regional generalized agricultural water resources and crop water footprint quantification. Then, we analyzed crop-water relationship and water stress of agricultural production for Jiangsu Province and its three sub-regions, i.e., Northern Jiangsu, Central Jiangsu and Southern Jiangsu, during 1999–2013. Results showed that, the annual average of generalized agricultural water resources of the province in the observed period was 103.46 Gm³; green and blue water accounted for about 70.4% and 29.6% of the total water resources respectively; generalized agricultural water resources in the sub-region of Southern Jiangsu was more plentiful than the Northern Jiangsu and Central Jiangsu. Yearly water footprint for crop production was estimated to be 106.95 Gm³ during 1999–2013 and the proportion in total water footprint as a whole of blue, green and grey water footprint was 5.9%, 74.6% and 19.5%, respectively. Crop water footprint of the sub-region of Northern Jiangsu increased over time, and accounted for about 61.6% of the provincial value in the study period. The annual average of BWSI and GWSI in 1999–2013 of Jiangsu province were calculated about 2.60 and 1.09, respectively, revealing a severe water stress facing the province in agricultural production. Both of the indices BWSI and GWSI showed a slight increase trend in the latest fifty years. From the perspective of spatial pattern, water stress of agricultural production increased from south to north. Temporal trend of the three sub-regions were found that three was a decrease in Southern Jiangsu, stability in Central Jiangsu and an increase in North Jiangsu. About 18.52 Gm³ of water diversion, mainly from Yangze River, was supplied for agricultural production in Jiangsu province. For all that, the BWSI and GWSI was reduced from to 2.65 and 1.09 to 2.02 and 1.01 during the period of 2001–2013. In addition to cross-regional water diversion, efficient use of green water resources and water footprint regulation should be emphasized for regional water stress relief. BWSI and GWSI can be used to evaluate the regional water stress of agricultural production. In addition, BWSI can reveal water scarcity in water-scarce areas, while GWSI is more suitable for fully reflecting water use in water-rich areas compared to the conventional water stress index (WSI). The findings of this study can supply some implications for regional water resources management.

Key words: blue water, green water, agricultural production, grey water footprint, water stress

中图分类号:

TV213.4

操信春, 束锐, 郭相平, 邵光成, 王振昌. 基于BWSI与GWSI的江苏省农业生产水资源压力评价[J]. 长江流域资源与环境, 2017, 26(06): 856-864.

CAO Xin-chun, SHU Rui, GUO Xiang-ping, SHAO Guang-cheng, WANG Zhen-chang. WATER STRESSES EVALUATION OF AGRICULTURAL PRODUCTION IN JIANGSU PROVINCE USING BWSI AND GWSI[J]. RESOURCES AND ENVIRONMENT IN THE YANGTZE BASIN, 2017, 26(06): 856-864.

参考文献

[1] 操信春, 吴普特, 王玉宝, 等. 中国灌区水分生产率及其时空差异分析[J]. 农业工程学报, 2012, 28(13):1-7.[CAO X C, WU P T, WANG Y B, et al. Analysis on temporal and spatial differences of water productivity in irrigation districts in China[J]. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(13):1-7.]
[2] DALIN C, HANASKI N, QIU H G, et al. Water resources transfers through Chinese interprovincial and foreign food trade[J]. Proceedings of the National Academy of Sciences of the United States of America, 2014, 111(27):9774-9779.
[3] 操信春, 吴普特, 王玉宝, 等. 不同灌溉水分生产率指标的时空变异与相关关系[J]. 农业机械学报, 2014, 45(4):189-194.[CAO X C, WU P T, WANG Y B, et al. Spatial and temporal variation of three irrigation water productivity indexes in China[J]. Transactions of the Chinese Society for Agricultural Machinery, 2014, 45(4):189-194.]
[4] CAO X C, WU P T, WANG Y B, et al. Assessing blue and green water utilisation in wheat production of China from the perspectives of water footprint and total water use[J]. Hydrology and Earth System Sciences, 2014, 18(8):3165-3178.
[5] CAI X M, YANG Y C, RINGLER C, et al. Agricultural water productivity assessment for the Yellow River Basin[J]. Agricultural Water Management, 2011, 98(8):1297-1306.
[6] ZHUO L, MEKONNEN M M, HOEKSTRA A Y, et al. Inter-and intra-annual variation of water footprint of crops and blue water scarcity in the Yellow River basin (1961-2009)[J]. Advances in Water Resources, 2016, 87:29-41.
[7] 孙世坤, 王玉宝, 吴普特, 等. 小麦生产水足迹区域差异及归因分析[J]. 农业工程学报, 2015, 31(13):142-148.[SUN S K, WANG Y B, WU P T, et al. Spatial variability and attribution analysis of water footprint of wheat in China[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(13):142-148.]
[8] TUNINETTI M, TAMEA S, D'ODORICO P. Global sensitivity of high-resolution estimates of crop water footprint[J]. Water Resources Research, 2015, 51(10):8257-8272.
[9] ZHUO L, MEKONNEN M, HOEKSTRA A Y. Sensitivity and uncertainty in crop water footprint accounting:a case study for the Yellow River Basin[J]. Hydrology and Earth System Sciences, 2014, 18(6):2219-2234.
[10] 操信春, 吴普特, 王玉宝, 等. 水分生产率指标的时空差异及相关关系[J]. 水科学进展, 2014, 25(2):268-274.[CAO X C, WU P T, WANG Y B, et al. Temporal and spatial variation and correlativity of water productivity indexes in irrigated land of China[J]. Advances in Water Science, 2014, 25(2):268-274.]
[11] RODRIGUES G C, PEREIRA L S. Assessing economic impacts of deficit irrigation as related to water productivity and water costs[J]. Biosystems Engineering, 2009, 103(4):536-551.
[12] 王玉宝, 吴普特, 孙世坤, 等. 我国粮食虚拟水流动对水资源和区域经济的影响[J]. 农业机械学报, 2015, 46(10):208-215.[WANG Y B, WU P T, SUN S K, et al. Impact of virtual water flows of grain on water resources and regional economy in China[J]. Transactions of the Chinese Society for Agricultural Machinery, 2015, 46(10):208-215.]
[13] 韩宇平, 雷宏军, 潘红卫, 等. 基于虚拟水和广义水资源的区域水资源可持续利用评价[J]. 水利学报, 2011, 42(6):729-736.[HAN Y P, LEI H J, PAN H W, et al. Evaluation on water resources sustainable utilization based on virtual water and generalized water resources theory[J]. Journal of Hydraulic Engineering, 2011, 42(6):729-736.]
[14] 戚瑞, 耿涌, 朱庆华. 基于水足迹理论的区域水资源利用评价[J]. 自然资源学报, 2011, 26(3):486-495.[QI R, GENG Y, ZHU Q H. Evaluation of regional water resources utilization based on water footprint method[J]. Journal of Natural Resources, 2011, 26(3):486-495.]
[15] 史利洁, 吴普特, 王玉宝, 等. 基于作物生产水足迹的陕西省水资源压力评价[J]. 中国生态农业学报, 2015, 23(5):650-658.[SHI L J, WU P T, WANG Y B, et al. Assessment of water stress in Shaanxi Province based on crop water footprint[J]. Chinese Journal of Eco-Agriculture, 2015, 23(5):650-658.]
[16] ZENG Z, LIU J G, SAVENIJE H H G, et al. A simple approach to assess water scarcity integrating water quantity and quality[J]. Ecological Indicators, 2013, 34:441-449.
[17] ZHAO X, LIU J G, LIU Q Y, et al. Physical and virtual water transfers for regional water stress alleviation in China[J]. Proceedings of the National Academy of Sciences of the United States of America, 2015, 112(4):1031-1035.
[18] SUN S K, WANG Y B, ENGEL B A, et al. Effects of virtual water flow on regional water resources stress:A case study of grain in China[J]. Science of the Total Environment, 2016, 550:871-879.
[19] CAO X C, WANG Y B, WU P T, et al. et al. Water productivity evaluation for grain crops in irrigated regions of China[J]. Ecological Indicators, 2015, 55:107-117.
[20] ZHUO L, MEKONNEN M M, HOEKSTRA A Y. The effect of inter-annual variability of consumption, production, trade and climate on crop-related green and blue water footprints and inter-regional virtual water trade:A study for China (1978-2008)[J]. Water Research, 2016, 94:73-85.
[21] RASKIN P, GLEICK P, KIRSHEN P, et al. Water futures:assessment of long-range patterns and prospects[M]. Stockholm, Sweden:Stockholm Environment Institute, 1997.

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