基于LMDI模型的四川盆地作物水足迹时空演变及驱动因素分析
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(1.重庆师范大学地理与旅游学院,重庆 401331;2.重庆师范大学地理信息系统应用研究重庆市高校重点实验室,重庆 401331 )

作者简介:

阳君(1997—),男,硕士研究生,主要从事农业水资源研究。E-mail:yang_jaaa@163.com 通信作者:肖作林(1985—),男,教授,博士,主要从事地表生态过程研究。E-mail:xiaoll@cqnu.cn

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基金项目:

重庆市教委科学技术研究项目(KJQN202300558);重庆市自然科学基金创新发展联合基金项目(CSTB2023NSCQ-LZX0150);重庆师范大学基金项目(22XLB011)


Analysis of spatiotemporal evolution and driving factors of crop water footprint in the Sichuan Basin based on LMDI model
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(1.College of Geography and Tourism, Chongqing Normal University, Chongqing 401331, China;2.Chongqing Key Laboratory of GIS Application, Chongqing Normal University, Chongqing 401331, China)

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    摘要:

    为探究四川盆地农业水足迹演变特征及驱动因素,分析了2001—2021年四川盆地主要农作物蓝水、绿水、灰水和白水足迹时空演变特征。利用LMDI模型从技术、环境和社会维度识别了作物水足迹变化的驱动因素,并从全局与局部视角探讨了不同驱动因素的差异性。结果表明:2001—2021年四川盆地作物水足迹呈现先上升后下降的趋势;各市作物水足迹在空间上表现为北高南低的分布特征;从全局看,不同驱动因素对作物水足迹变化的影响程度由大到小依次为经济效应、人口变化效应、技术效应、水足迹强度效应、节水效应、生产效应,经济效应和人口变化效应是最强的促进和抑制因素,贡献值分别为82.90亿m3和-51.55亿m3;从局部看,技术效应与人口变化效应在空间上存在错位现象,生产效应、节水效应和水足迹强度效应在空间上表现为双向驱动作用。

    Abstract:

    To explore the evolution characteristics and driving factors of agricultural water footprint in the Sichuan Basin, the spatiotemporal evolution characteristics of blue water, green water, grey water, and white water footprint of major crops in the Sichuan Basin from 2001 to 2021 were analyzed. The LMDI model was used to identify the driving factors of crop water footprint change from the technical, environmental and social dimensions, and the differences of each driving factor were discussed from the global and local perspectives. The results showed that the crop water footprint in Sichuan Basin increased first and then decreased from 2001 to 2021. The spatial distribution characteristics of crop water footprint in each city were high in the north and low in the south. From the global perspective, the impact of different driving factors on changes in crop water footprint varies from large to small as follows:economic effect, population change effect, technological effect, water footprint intensity effect, water-saving effect, and production effect. The economic effect and population change effect were the strongest promoting and inhibiting factors, and the contribution values were 82.90×108 m3 and -51.55×108 m3, respectively. From the local perspective, there was a dislocation between the technical effect and the population change effect in space, and the production effect, water saving effect and water footprint intensity effect show a two-way driving effect in space.

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阳君,肖作林,徐伟峰.基于LMDI模型的四川盆地作物水足迹时空演变及驱动因素分析[J].水资源保护,2025,41(1):160-169.(YANG Jun, XIAO Zuolin, XU Weifeng. Analysis of spatiotemporal evolution and driving factors of crop water footprint in the Sichuan Basin based on LMDI model[J]. Water Resources Protection,2025,41(1):160-169.(in Chinese))

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  • 收稿日期:2024-05-20
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  • 在线发布日期: 2025-03-04
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