Huang Yun, Liu Jing, Zheng Bo-Fu, He Liu-Jie, Wu Shu-Yang, Zhang Ji-Hong, Liang Han, Wu Zhi-Jian, Zhu Jin-Qi, Wan Wei
School of Resources & Environment, Nanchang University, Nanchang 330031, China.
Engineering Research Center of Watershed Carbon Neutralization, Key Laboratory of Poyang Lake Environment and Resources Utilization, Ministry of Education, Jiangxi Institute of Ecological Civilization, Nanchang University, Nanchang 330031, China.
Huan Jing Ke Xue. 2025 Jun 8;46(6):3693-3707. doi: 10.13227/j.hjkx.202406084.
Water supply service is the basis of human survival and development， and mastering the dynamic balance of water resources is important for regional water resources management and high-quality development. Taking the Yangtze River Economic Belt as the study area， we use the InVEST model and the socio-economic development perspective to quantitatively assess the supply and demand of water supply services. Utilizing the ESDR and -score indexes， we reveal the spatial matching relationship of these services. Additionally， we construct a water supply service flow model by combining the D8 flow method and depth-first search. Through hotspot analysis and service flow spatial pattern， we categorize the study area into supply area， connection area， and demand area. We explore the influencing factors affecting the matching relationship between supply and demand of water supply service in each area from three perspectives： natural and social. The results show that： ① The water supply of the Yangtze River Economic Belt increased from 910 billion m in 2000 to 1 030 billion m in 2020， and the demand first rose and then fell. ② The ESDR of water supply service rose from 0.17 to 0.24， and the deficit range continued to contract. The spatial relationship between supply and demand was dominated by HL-type spatial mismatch clusters， and HL-type and LH-type spatial mismatch clusters transferred to HH-type and LL-type spatial match clusters. ③ The water supply service flow in the Yangtze River Economic Belt took the water system river network as the main flow path. The negative proportions of the flow in 2000， 2005， and 2010 were 1.2%， 1.7%， and 3.7%， respectively， and in 2015 and 2020 they were 2.2% and 1.3%， respectively， with the negative proportion increasing and then decreasing. ④ The dominant factor affecting the supply-demand matching relationship in the supply zone was precipitation （ = 0.44）， the contribution of the influence factors in the connectivity zone were all low， and the dominant factor affecting the demand zone was the share of construction land （ = 0.29）. The interaction between precipitation and other influencing factors was stronger in the supply and connection zones， while the interaction between socio-economic category factors was significantly stronger in the demand zone. The results of the study can provide scientific references for the management of water resources in the Yangtze River Economic Belt and the ecological compensation mechanism of the basin.
供水服务是人类生存和发展的基础，掌握水资源的动态平衡对于区域水资源管理和高质量发展至关重要。以长江经济带为研究区域，我们运用InVEST模型并从社会经济发展视角对供水服务的供需进行定量评估。利用ESDR和 -得分指标，我们揭示了这些服务的空间匹配关系。此外，我们结合D8流方法和深度优先搜索构建了供水服务流模型。通过热点分析和服务流空间格局，我们将研究区域划分为供应区、连接区和需求区。我们从自然和社会三个视角探讨了影响各区域供水服务供需匹配关系的影响因素。结果表明：①长江经济带的供水量从2000年的9100亿立方米增加到2020年的10300亿立方米，需求量先上升后下降。②供水服务的ESDR从0.17升至0.24，亏缺范围持续收缩。供需的空间关系以HL型空间错配集群为主，HL型和LH型空间错配集群向HH型和LL型空间匹配集群转移。③长江经济带的供水服务流以水系河网为主流路径。2000年、2005年和2010年的负流比例分别为1.2%、1.7%和3.7%，2015年和2020年分别为2.2%和1.3%，负流比例先增加后减少。④影响供应区供需匹配关系的主导因素是降水量（ = 0.44），连接区影响因素的贡献均较低，影响需求区的主导因素是建设用地占比（ = 0.29）。降水量与其他影响因素在供应区和连接区的相互作用更强，而社会经济类别因素在需求区的相互作用显著更强。研究结果可为长江经济带水资源管理和流域生态补偿机制提供科学参考。
