水资源承载力评价耦合模型的研究进展与干旱区应用

doi:10.13866/j.azr.2025.06.05

干旱区研究 ›› 2025, Vol. 42 ›› Issue (6): 1004-1020.doi: 10.13866/j.azr.2025.06.05 cstr: 32277.14.AZR.20250605

水资源承载力评价耦合模型的研究进展与干旱区应用

王艺璇¹^,²(), 邓晓红³, 范慧文青¹^,², 韩江哲⁴, 李宗省¹^,⁴()

1.中国科学院西北生态环境资源研究院，祁连山同位素生态水文与国家公园观测研究站，干旱区生态安全与可持续发展全国重点实验室，甘肃兰州 730000
2.中国科学院大学，北京 100049
3.兰州大学经济学院，甘肃兰州 730000
4.西北师范大学地理与环境科学学院，甘肃兰州 730070

收稿日期:2025-02-16 修回日期:2025-04-09 出版日期:2025-06-15 发布日期:2025-06-11
通讯作者: 李宗省. E-mail: lizxhhs@163.com
作者简介:王艺璇（1997-），女，博士研究生，主要从事干旱区地理经济研究. E-mail: wyxvv07@163.com
基金资助:
中国科学院基础与交叉前沿科研先导专项项目(XDB0720303);甘肃省拔尖领军人才项目;中国科学院青年交叉团队项目(JCTD-2022-18);甘肃省科技计划资助-优秀博士生项目(25JRRA533)

Research advances and arid zone applications of coupled models for water resources carrying capacity

WANG Yixuan¹^,²(), DENG Xiaohong³, FAN Huiwenqing¹^,², HAN Jiangzhe⁴, LI Zongxing¹^,⁴()

1. State Key Laboratory of Arid Region Ecological Security and Sustainable Development, Qilian Mountains Isotope Ecohydrology and National Park Observation and Research Station, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, Gansu, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. School of Economics, Lanzhou University, Lanzhou 730000, Gansu, China
4. College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, Gansu, China

Received:2025-02-16 Revised:2025-04-09 Published:2025-06-15 Online:2025-06-11

摘要/Abstract

摘要：

随着全球水资源短缺问题的日益加剧，准确评估水资源承载力已成为实现区域可持续水资源管理和应对气候变化挑战的关键。鉴于水资源-生态-社会系统的复杂性，单一评价方法难以全面揭示其多维交互与动态变化过程。本文综述了水资源承载力评价中耦合模型方法的应用现状与研究进展，通过系统分析法、综合评价法以及机器学习等主要评价方法，揭示其在水资源承载力评价中的优势与局限性，并特别探讨了这些方法在干旱区应用时面临的挑战。本文从动态反馈机制、非线性建模能力、数据驱动性以及适用性等方面对不同方法进行了横向对比，分析了各方法在干旱区应用的适用性与局限性，并探讨了耦合模型的可行性，为解决干旱区水资源承载力问题提供了新的思路。未来研究应聚焦于多模型集成与数据驱动优化，以提升模型的泛化能力和适用性，推动水资源管理从静态评价向动态模拟和精准预测转变，为干旱区乃至全球的水资源可持续利用提供科学支持。

关键词: 水资源承载力, 耦合模型, 系统动力学, 机器学习, 干旱区

Abstract:

As the scarcity of global water intensifies, accurate assessments of water resource carrying capacity (WRCC) have become essential for sustainably managing regional water resources and combating the adverse effects of climate change. However, the water resources-ecology-society system is highly complex, involving multidimensional interactions anddynamic internal changes that cannot be fully captured by a single evaluation method. This paper reviews the application status and research progress of coupled-model methods for WRCC evaluation. A systematic comparative analysis reveals the strengths and limitations of the major evaluation methods—systems analysis, comprehensive evaluation, and machine learning—in WRCC evaluation. Particular attention is devoted to the challenges of these methods in arid regions. The dynamic feedback mechanisms, nonlinear modeling capabilities, data-driven characteristics, and applicabilities of different methods are analyzed through a horizontal comparison study. The review also analyzes the suitabilities and limitations of each method in arid regions and explores the feasibility of coupled models, providing new insights for resolving WRCC issues in these areas. Multimodel integration and data-driven optimization will enhance the generalizability and applicability of models in future, facilitating the transition of water resource management from static evaluation to dynamic simulation and precise prediction. These developments will offer scientific support for sustainable water resource utilization in arid regions and worldwide.

Key words: water resources carrying capacity, coupling model, system dynamics, machine learning, arid regions

王艺璇, 邓晓红, 范慧文青, 韩江哲, 李宗省. 水资源承载力评价耦合模型的研究进展与干旱区应用[J]. 干旱区研究, 2025, 42(6): 1004-1020.

WANG Yixuan, DENG Xiaohong, FAN Huiwenqing, HAN Jiangzhe, LI Zongxing. Research advances and arid zone applications of coupled models for water resources carrying capacity[J]. Arid Zone Research, 2025, 42(6): 1004-1020.

图/表 6

图1

图2

表1

水资源承载力主要评价方法分类"

分类	评价方法	原理	优点	缺点	适用性
系统分析法	多目标决策分析法	通过分析和描述决策过程中的目标，以及冲突性的分解和理想点转移，来识别和选择最优解	能够结合定性和定量分析，简化复杂的决策问题	对主观因素的依赖较大，精度较低，且无法生成新的解决方案	侧重于在多个目标之间寻找最优平衡，其所需的指标通常涵盖水资源供给、需求、生态、社会经济等方面。适用于多个层级的分析，尺度可根据具体需求设定
	生态足迹法	通过计算一定人口消费的所有资源和吸纳这些人口产生的所有废弃物所需的生物生产土地总面积和水资源量，来评估人类对自然资源的需求与生态系统的供给能力	提供了一个直观的量化指标来评估资源消耗和环境影响	核算不完整性，结果可比性问题，方法标准性不规范	专注于评估生态承载力和人类活动的资源消耗，适用于长时间尺度和大范围空间尺度的水资源可持续性评估
	系统动力学法	模拟不同发展方案并预测决策变量，以确定最优发展策略，是中短期预测中常用的方法	可准确把握各承载因子之间的相互影响	系统参数变量存在偏差，对基础数据要求严苛	侧重于模拟水资源系统的动态行为，尤其适用于长期和复杂的水资源承载力问题分析。所需的指标涵盖水资源的供需动态、反馈机制以及社会经济、生态等因素的变化
综合评价法	模糊综合评价法	构建指标与评价等级间的模糊关系矩阵，利用隶属度函数对水资源承载力进行综合评估	将定性因素转化为定量指标，并综合考量各指标间的相互关系，可全面评估水资源承载力现状	确定隶属度函数时存在较大的主观性，相邻等级间的边界隶属度表现出模糊性	适用于处理多因素、多不确定性的情况，需考虑水资源的供给、需求、水质、生态等多个维度的指标，通常用于较大的空间和时间尺度，适用于各层级的综合评价
	主成分分析法	采用统计学原理，对多维变量进行线性变换以简化和整合，进而构建主成分分析模型，用以计算水资源承载力的综合评价指数	简化复杂数据，提升评价过程分析效率	主观性较强，容易忽略较为重要的影响因素	主要用于数据降维和提取主要影响因素，关注水资源供需、利用效率、污染物浓度等指标，适用于中小尺度的水资源分析
	投影寻踪法	将高维数据映射到低维空间，通过分析低维数据的分布特征来研究高维数据的特性	稳健型较好，且比较直观	计算量大，样本量过少时易产生误差	主要用于揭示复杂数据中的潜在模式，适用于大范围的水资源分析，特别是在动态变化较大的区域
机器学习	支持向量机	在特征空间中找到一个最优的超平面，这个超平面能够最大化地分开不同类别的数据点，即最大化两类数据点之间的间隔	有效的分类性能、核技巧允许在高维空间中找到复杂的决策边界、内存效率、鲁棒性以及适用于小样本数据	计算复杂度高、参数选择困难、不支持在线学习和解释性差	适用于分类和回归任务，能够处理非线性问题，通常用于中短期预测，适合较大空间尺度的水资源承载力评估
	随机森林	通过构建多个决策树并输出平均结果来提高预测准确性和控制过拟合	能有效处理高维数据、无需特征选择、速度快、抗过拟合能力强、对不平衡数据集有较好的处理能力	模型复杂度高、预测过程较慢、模型可解释性不佳、对噪声敏感	适用于高维数据的分类与回归，具有较强的鲁棒性和解释能力，适用于大范围和中长期的水资源承载力分析
	人工神经网络	通过模拟人脑神经元的组织方式，实现并行分布性处理、可学习性、鲁棒性和容错性，以及泛化能力	并行分布性处理、可学习性、鲁棒性和容错性以及泛化能力强	应用面不够宽阔、结果不够精确、训练速度不够高以及算法集成度不够高	主要应用于宏观和中观尺度的水文预测和水资源管理
	决策树	通过递归地选择最优特征进行数据集划分，构建树状模型以进行分类或回归分析	易于理解和解释、处理非线性关系、无需特征缩放、适用于混合数据类型	容易过拟合，尤其在数据较少或噪声较大的情况下	适用于分类和回归，具有较强的可解释性，适合短期预测和局部区域的水资源评估

表1

表2

图3

图4

参考文献 111

[1]	Yang X, Chen Z. Exploiting temporal features in water resource carrying capacity assessment based on extended VIKOR[J]. Sustainable Cities and Society, 2024, 114: 105801.
[2]	Wang T, Jian S, Wang J, et al. Dynamic interaction of water-economic-social-ecological environment complex system under the framework of water resources carrying capacity[J]. Journal of Cleaner Production, 2022, 368: 133132.
[3]	Abu-Zeid M. Water and sustainable development: The vision for world water, life and the environment[J]. Water Policy, 1998, 1(1): 9-19.
[4]	Li B, Zhang W, Long J, et al. Regional water resources security assessment and optimization path analysis in karst areas based on emergy ecological footprint[J]. Applied Water Science, 2023, 13(6): 142.
[5]	Mirzabaev A, Stringer L C, Benjaminsen T A, et al. Cross-Chapter Paper 3:Deserts, Semiarid Areas and Desertification[R]. Cambridge: Cambridge University Press, 2022: 2195-2231.
[6]	Chen F, Yuan Y, Trouet V, et al. Ecological and societal effects of Central Asian streamflow variation over the past eight centuries[J]. npj Climate and Atmospheric Science, 2022, 5(1): 1-8.
[7]	Zhou K. Comprehensive evaluation on water resources carrying capacity based on improved AGA-AHP method[J]. Applied Water Science, 2022, 12: 103.
[8]	李怀林. 基于模糊综合评价方法的干旱区水资源承载力研究[J]. 中国水运, 2014, 14(8): 206-208.
	[Li Huailin. Research on water resources carrying capacity in arid regions based on fuzzy comprehensive evaluation method[J]. China Water Transport, 2014, 14(8): 206-208.]
[9]	Zhu Q, Cao Y. Research on provincial water resources carrying capacity and coordinated development in China based on combined weighting TOPSIS model[J]. Scientific Reports, 2024, 14(1): 12497-12497. doi: 10.1038/s41598-024-63119-3 pmid: 38822005
[10]	Yan L, Jiao D, Yongshi Z. Evaluation of regional water resources carrying capacity in China based on variable weight model and grey-markov model: A case study of Anhui Province[J]. Scientific Reports, 2023, 13(1): 13490.
[11]	Song Q R, Wang Z C, Wu T H, et al. Risk analysis and assessment of water resource carrying capacity based on weighted gray model with improved entropy weighting method in the Central Plains region of China[J]. Ecological Indicators, 2024, 160: 111907.
[12]	Wu J H, Wang Z C, Dong L P, et al. Prediction and analysis of water resources demand in Taiyuan City based on principal component analysis and BP neural network[J]. AQUA-Water Infrastructure, Ecosystems and Society, 2021, 70(8): 1272-1286.
[13]	Yang X, Chen Z, Li Z. Regional water environmental carrying capacity: Changing trends and direction, obstacle factors, and implications[J]. Water Resources Management, 2024, 38: 3215-3234.
[14]	Wang X, Wang W, Yan C, et al. Driving effects of spatiotemporal evolution of the water resources carrying capacity in the Yellow River Basin (Henan Section)[J]. Scientific Reports, 2024, 14: 29340.
[15]	张蕊. 基于突变级数法的山西省水资源脆弱性评价[J]. 水电能源科学, 2019, 37(4): 29-32.
	[Zhang Rui. Evaluation of water resources vulnerability in Shanxi Province based on catastrophe progression method[J]. Science of Hydro-Energy, 2019, 37(4): 29-32.]
[16]	Djuwansyah R M. Environmental sustainability control by water resources carrying capacity concept: Application significance in Indonesia[J]. IOP Conference Series: Earth and Environmental Science, 2018, 118(1): 012027.
[17]	Harris J M, Kennedy S. Carrying capacity in agriculture: Global and regional issues[J]. Ecological Economics, 1999, 29(3): 443-461.
[18]	Del Monte-Luna P, Brook B W, Zetina-Rejón M J, et al. The carrying capacity of ecosystems[J]. Global Ecology and Biogeography, 2004, 13(6): 485-495.
[19]	Graefe A R, Vaske J J, Kuss F R. Social carrying capacity: An integration and synthesis of twenty years of research[J]. Leisure Sciences, 1984, 6(4): 395-431.
[20]	Cohen J. Population growth and earth’s human carrying capacity[J]. Science, 1995, 269(5222): 341-346. doi: 10.1126/science.7618100 pmid: 7618100
[21]	Park R E. Introduction to the Science of Sociology[M]. Chicago: University of Chicago Press, 1921.
[22]	Abernethy V R. Carrying capacity: The tradition and policy implications[J]. Ethics of Science and Environmental Politics, 2001, 1: 9-18.
[23]	Souro D J. Carrying capacities and standards as bases towards urban infrastructure planning in India: A case of urban water supply and sanitation[J]. Urban Infrastructure Planning in India, 1998, 22(3): 327-337.
[24]	UNESCO. Water Security and the Sustainable Development Goa-ls[M]. Paris: UNESCO, 2018.
[25]	闫业超, 孙希华, 李平. 水资源对区域社会经济发展的支撑能力研究——以济南市长清区为例[J]. 中国人口·资源与环境, 2005, 15(1): 135-140.
	[Yan Yechao, Sun Xihua, Li Ping. Study on the support capacity of water resources for regional socio-economic development: A case study of Changqing District, Jinan City[J]. China Population, Resources and Environment, 2005, 15(1): 135-140.]
[26]	Li Q S, Liu Z H, Yang Y H, et al. Evaluation of water resources carrying capacity in Tarim River Basin under game theory combination weights[J]. Ecological Indicators, 2023, 154: 110609.
[27]	王建华, 姜大川, 肖伟华, 等. 水资源承载力理论基础探析: 定义内涵与科学问题[J]. 水利学报, 2017, 48(12): 1399-1409.
	[Wang Jianhua, Jiang Dachuan, Xiao Weihua, et al. Exploration of the theoretical foundation of water resources carrying capacity: Definition, connotation, and scientific issues[J]. Journal of Hydraulic Engineering, 2017, 48(12): 1399-1409.]
[28]	惠泱河, 蒋晓辉, 黄强, 等. 二元模式下水资源承载力系统动态仿真模型研究[J]. 地理研究, 2001, 5(2): 191-198.
	[Hui Yanghe, Jiang Xiaohui, Huang Qiang, et al. Study on dynamic simulation model of water resources carrying capacity system under dual-mode[J]. Geographical Research, 2001, 5(2): 191-198.]
[29]	施雅风, 曲耀光. 乌鲁木齐河流域水资源承载力及其合理利用[M]. 北京: 科学出版社, 1992.
	[Shi Yafeng, Qu Yaoguang. Water Resources Carrying Capacity and Its Rational Utilization in the Urumqi River Basin[M]. Beijing: Science Press, 1992.]
[30]	王浩, 陈敏建, 何希吾, 等. 西北地区水资源合理配置与承载能力研究[J]. 中国水利, 2004, 55(22): 43-45.
	[Wang Hao, Chen Minjian, He Xiwu, et al. Research on rational allocation and carrying capacity of water resources in Northwest China[J]. China Water Resources, 2004, 55(22): 43-45.]
[31]	冯尚友, 刘国全. 水资源持续利用的框架[J]. 水科学进展, 1997, 8(4): 301-307.
	[Feng Shangyou, Liu Guoquan. Framework for sustainable utilization of water resources[J]. Advances in Water Science, 1997, 8(4): 301-307.]
[32]	阮本青, 沈晋. 区域水资源适度承载能力计算模型研究[J]. 土壤侵蚀与水土保持学报, 1998, 4(3): 58-62, 86.
	[Ruan Benqing, Shen Jin. Study on calculation model of regional water resources carrying capacity[J]. Journal of Soil and Water Conservation, 1998, 4(3): 58-62, 86.]
[33]	孙鸿烈. 中国资源科学百科全书[M]. 北京: 中国大百科全书出版社, 2000.
	[Sun Honglie. Encyclopedia of Resource Science in China[M]. Beijing: Encyclopedia of China Publishing House, 2000.]
[34]	许有鹏. 干旱区水资源承载能力综合评价研究——以新疆和田河流域为例[J]. 自然资源学报, 1993, 8(3): 229-237.
	[Xu Youpeng. Comprehensive evaluation of water resources carrying capacity in arid regions: A case study of the Hotan river basin in Xinjiang[J]. Journal of Natural Resources, 1993, 8(3): 229-237.]
[35]	夏军, 朱一中. 水资源安全的度量: 水资源承载力的研究与挑战[J]. 自然资源学报, 2002, 17(3): 262-269.
	[Xia Jun, Zhu Yizhong. Measurement of water resources security: Research and challenges of water resources carrying capacity[J]. Journal of Natural Resources, 2002, 17(3): 262-269.]
[36]	冯尚友, 刘国全, 梅亚东. 水资源生态经济复合系统及其持续发展[J]. 武汉水利电力大学学报, 1995, 28(6): 624-629.
	[Feng Shangyou, Liu Guoquan, Mei Yadong. The eco-economic composite system of water resources and its sustainable development[J]. Journal of Wuhan University of Hydraulic and Electric Engineering, 1995, 28(6): 624-629.]
[37]	姚治君, 王建华, 江东, 等. 区域水资源承载力的研究进展及其理论探析[J]. 水科学进展, 2002, 13(1): 111-115.
	[Yao Zhijun, Wang Jianhua, Jiang Dong, et al. Research progress and theoretical analysis of regional water resources carrying capacity[J]. Advances in Water Science, 2002, 13(1): 111-115.]
[38]	朱一中, 夏军, 谈戈. 关于水资源承载力理论与方法的研究[J]. 地理科学进展, 2002, 21(2): 180-188.
	[Zhu Yizhong, Xia Jun, Tan Ge. Research on the theory and method of water resources carrying capacity[J]. Progress in Geography, 2002, 21(2): 180-188.]
[39]	张永勇, 夏军, 王中根. 区域水资源承载力理论与方法探讨[J]. 地理科学进展, 2007, 26(2): 126-132.
	[Zhang Yongyong, Xia Jun, Wang Zhonggen. Discussion on the theory and method of regional water resources carrying capacity[J]. Progress in Geography, 2007, 26(2): 126-132.]
[40]	Li Yi, Yang Yanzhao, Yan Huimin, et al. Research methods of water resources carrying capacity: Progress and prospects[J]. Journal of Resources and Ecology, 2018, 9(5): 455-460. doi: 10.5814/j.issn.1674-764x.2018.05.001
[41]	段春青, 刘昌明, 陈晓楠, 等. 区域水资源承载力概念及研究方法的探讨[J]. 地理学报, 2010, 65(1): 82-90.
	[Duan Chunqing, Liu Changming, Chen Xiaonan, et al. Discussion on the concept and research methods of regional water resources carrying capacity[J]. Acta Geographica Sinica, 2010, 65(1): 82-90.]
[42]	翁文斌, 蔡喜明, 史慧斌, 等. 宏观经济水资源规划多目标决策分析方法研究及应用[J]. 水利学报, 1995, 26(2): 1-11.
	[Weng Wenbin, Cai Ximing, Shi Huibin, et al. Research and application of multi-objective analysis method for macro-economic water resources planning[J]. Journal of Hydraulic Engineering, 1995, 26(2): 1-11.]
[43]	张军, 张仁陟, 周冬梅. 基于生态足迹法的疏勒河流域水资源承载力评价[J]. 草业学报, 2012, 21(4): 267-274.
	[Zhang Jun, Zhang Renzhi, Zhou Dongmei. Evaluation of water resources carrying capacity in the Shule River Basin based on ecological footprint method[J]. Acta Prataculturae Sinica, 2012, 21(4): 267-274.]
[44]	Wang S, Yang F L, Xu L, et al. Multi-scale analysis of the water resources carrying capacity of the Liaohe Basin based on ecological footprints[J]. Journal of Cleaner Production, 2013, 53: 158-166.
[45]	程增辉, 陆宝宏, 熊丝, 等. 基于水足迹模型的新疆水资源承载力分析[J]. 水资源与水工程学报, 2016, 27(6): 54-59.
	[Cheng Zenghui, Lu Baohong, Xiong Si, et al. Analysis of water resources carrying capacity in Xinjiang based on water footprint model[J]. Journal of Water Resources and Water Engineering, 2016, 27(6): 54-59.]
[46]	Li J, Lei X, Fu Q, et al. Multi-scale research of time and space differences about ecological footprint and ecological carrying capacity of the water resources[J]. Applied Water Science, 2018, 8(1): 1-12.
[47]	Yue Z, Liu H, Xu Z, et al. Evaluation of sustainability in northern Xinjiang based on ecological footprint-planetary boundary system framework[J]. Ecological Indicators, 2023, 150: 110270.
[48]	王建华, 江东, 顾定法, 等. 基于SD模型的干旱区城市水资源承载力预测研究[J]. 地理学与国土研究, 1999, 15(2): 19-23.
	[Wang Jianhua, Jiang Dong, Gu Dingfa, et al. Prediction of water resources carrying capacity in arid region cities based on system dynamics model[J]. Geography and Territorial Research, 1999, 15(2): 19-23.]
[49]	李丽娟, 郭怀成, 陈冰, 等. 柴达木盆地水资源承载力研究[J]. 环境科学, 2000, 25(2): 20-23, 11.
	[Li Lijuan, Guo Huaicheng, Chen Bing, et al. Study on water resources carrying capacity in the Qaidam Basin[J]. Environmental Science, 2000, 25(2): 20-23, 11.]
[50]	陈冰, 李丽娟, 郭怀成, 等. 柴达木盆地水资源承载方案系统分析[J]. 环境科学, 2000, 25(3): 16-21.
	[Chen Bing, Li Lijuan, Guo Huaicheng, et al. System analysis of water resources carrying capacity in the Qaidam Basin[J]. Environmental Science, 2000, 25(3): 16-21.]
[51]	陈兴鹏, 戴芹. 系统动力学在甘肃省河西地区水土资源承载力中的应用[J]. 干旱区地理, 2002, 25(4): 377-382.
	[Chen Xingpeng, Dai Qin. Application of system dynamics in the water and soil resources carrying capacity in Hexi region of Gansu Province[J]. of Arid Land Geography, 2002, 25(4): 377-382.]
[52]	王薇, 雷学东, 余新晓, 等. 基于SD模型的水资源承载力计算理论研究——以青海共和盆地水资源承载力研究为例[J]. 水资源与水工程学报, 2005, 16(3): 11-15.
	[Wang Wei, Lei Xuedong, Yu Xinxiao, et al. Theoretical study of water resources carrying capacity based on SD model: A case study of Gonghe basin in Qinghai[J]. Journal of Water Resources and Water Engineering, 2005, 16(3): 11-15.]
[53]	何仁伟, 刘邵权, 刘运伟. 基于系统动力学的中国西南岩溶区的水资源承载力——以贵州省毕节地区为例[J]. 地理科学, 2011, 31(11): 1376-1382.
	[He Renwei, Liu Shaoquan, Liu Yunwei. Water resources carrying capacity in Karst area of Southwest China based on system dynamics: A case study of Bijie area in Guizhou Province[J]. Scientia Geographica Sinica, 2011, 31(11): 1376-1382.]
[54]	王西琴, 高伟, 曾勇. 基于SD模型的水生态承载力模拟优化与例证[J]. 系统工程理论与实践, 2014, 34(5): 1352-1360. doi: 10.12011/1000-6788(2014)5-1352
	[Wang Xiqin, Gao Wei, Zeng Yong. Simulation and optimization of water ecological carrying capacity based on SD model[J]. Systems Engineering-Theory & Practice, 2014, 34(5): 1352-1360.]
[55]	张礼兵, 胡亚南, 金菊良, 等. 基于系统动力学的巢湖流域水资源承载力动态预测与调控[J]. 湖泊科学, 2021, 33(1): 242-254.
	[Zhang Libing, Hu Yanan, Jin Juliang, et al. Dynamic prediction and regulation of water resources carrying capacity in chaohu basin based on system dynamics[J]. Journal of Lake Sciences, 2021, 33(1): 242-254.]
[56]	Wang G, Xiao C L, Qi Z W, et al. Development tendency analysis for the water resource carrying capacity based on system dynamics model and the improved fuzzy comprehensive evaluation method in the Changchun City, China[J]. Ecological Indicators, 2021, 122: 107232.
[57]	袁艳梅, 沙晓军, 刘煜晴, 等. 改进的模糊综合评价法在水资源承载力评价中的应用[J]. 水资源保护, 2017, 33(1): 52-56.
	[Yuan Yanmei, Sha Xiaojun, Liu Yuqing, et al. Application of improved fuzzy comprehensive evaluation method in water resources carrying capacity assessment[J]. Water Resources Protection, 2017, 33(1): 52-56.]
[58]	闵庆文, 余卫东, 张建新. 区域水资源承载力的模糊综合评价分析方法及应用[J]. 水土保持研究, 2004, 11(3): 14-16, 129.
	[Min Qingwen, Yu Weidong, Zhang Jianxin. Fuzzy comprehensive evaluation method and application for regional water resources carrying capacity[J]. Research of Soil and Water Conservation, 2004, 11(3): 14-16, 129.]
[59]	Zhang Y M, Gao Y, Zhang Y, et al. Assessment of agricultural water resources carrying capacity and analysis of its spatio-temporal variation in Henan Province, China[J]. Journal of Cleaner Production, 2023, 403: 136869.
[60]	周戎星, 陈梦璐, 金菊良, 等. 基于文献计量分析的投影寻踪法在水问题中应用的研究进展[J]. 灌溉排水学报, 2021, 40(4):137-146.
	[Zhou Rongxing, Chen Menglu, Jin Juliang, et al. Research progress of projection pursuit method in water issues based on bibliometric analysis[J]. Journal of Irrigation and Drainage, 2021, 40(4): 137-146.]
[61]	郑祖国, 刘大秀. 投影寻踪自回归和多维混合回归模型及其在大河长河段洪水预报中的应用[J]. 水文, 1994, 4(2): 6-10, 65.
	[Zheng Zuguo, Liu Daxiu. Projection pursuit autoregression and multidimensional mixed regression models and their application in flood forecasting of long river sections[J]. Hydrology, 1994, 4(2): 6-10, 65.]
[62]	任守德, 付强, 王凯. 基于宏微观尺度的三江平原区域农业水土资源承载力[J]. 农业工程学报, 2011, 27(2): 8-14.
	[Ren Shoude, Fu Qiang, Wang Kai. Agricultural water and soil resources carrying capacity of the Sanjiang plain region based on macro-micro scales[J]. Transactions of the Chinese Society of Agricultural Engineering, 2011, 27(2): 8-14.]
[63]	雍志勤, 张鑫. 基于投影寻踪模型的榆林市水资源承载能力研究[J]. 灌溉排水学报, 2019, 38(1): 101-107.
	[Yong Zhiqin, Zhang Xin. Study on water resources carrying capacity of Yulin city based on projection pursuit model[J]. Journal of Irrigation and Drainage, 2019, 38(1): 101-107.]
[64]	Markus R. What is principal component analysis[J]. Nature Biotechnology, 2008, 26(3): 303-304.
[65]	肖迎迎, 宋孝玉, 张建龙. 基于主成分分析的榆林市水资源承载力评价[J]. 干旱地区农业研究, 2012, 30(4): 218-223, 235.
	[Xiao Yingying, Song Xiaoyu, Zhang Jianlong. Evaluation of water resources carrying capacity in Yulin city based on principal component analysis[J]. Agricultural Research in the Arid Areas, 2012, 30(4): 218-223, 235.]
[66]	李高伟, 韩美, 刘莉, 等. 基于主成分分析的郑州市水资源承载力评价[J]. 地域研究与开发, 2014, 33(3): 139-142.
	[Li Gaowei, Han Mei, Liu Li, et al. Evaluation of water resources carrying capacity in Zhengzhou City based on principal component analysis[J]. Journal of Regional Research and Development, 2014, 33(3): 139-142.]
[67]	曹丽娟, 张小平. 基于主成分分析的甘肃省水资源承载力评价[J]. 干旱区地理, 2017, 40(4): 906-912.
	[Cao Lijuan, Zhang Xiaoping. Evaluation of water resources carrying capacity in Gansu Province based on principal component analysis[J]. Arid Land Geography, 2017, 40(4): 906-912.]
[68]	李燕, 张兴奇. 基于主成分分析的长江经济带水资源承载力评价[J]. 水土保持通报, 2017, 37(4): 172-178.
	[Li Yan, Zhang Xingqi. Evaluation of water resources carrying capacity in the Yangtze river economic belt based on principal component analysis[J]. Bulletin of Soil and Water Conservation, 2017, 37(4): 172-178.]
[69]	陈丽, 周宏. 基于模糊综合评价和主成分分析法的岩溶流域水资源承载力评价[J]. 安全与环境工程, 2021, 28(6): 159-173.
	[Chen Li, Zhou Hong. Evaluation of water resources carrying capacity in Karst basin based on fuzzy comprehensive evaluation and principal component analysis[J]. Safety and Environmental Engineering, 2021, 28(6): 159-173.]
[70]	Wu L, Su X L, Ma X Y, et al. Integrated modeling framework for evaluating and predicting the water resources carrying capacity in a continental river basin of Northwest China[J]. Journal of Cleaner Production, 2018, 204: 366-379.
[71]	Osman Z Y, Abdellatif E M. Improving accuracy of downscaling rainfall by combining predictions of different statistical downscale models[J]. Water Science, 2016, 30(2): 61-75.
[72]	Bu C, Zhang Z P. Research on overfitting problem and correction in machine learning[C]// Journal of Physics: Conference Series. IOP Publishing, 2020, 1693: 012100.
[73]	Jia Z, Zhang Q, Shi B, et al. Correction to: A new strategy for groundwater level prediction using a hybrid deep learning model under ecological water replenishment[J]. Environmental Science and Pollution Research, 2024, 31: 26341.
[74]	Zhi W, Appling A P, Golden H E, et al. Deep learning for water quality[J]. Nature Water, 2024, 2(3): 228-241.
[75]	许莉, 赵嵩正, 杨海光. 水资源承载力的BP神经网络评价模型研究[J]. 计算机工程与应用, 2008, 45(8): 217-219.
	[Xu Li, Zhao Songzheng, Yang Haiguang. Research on BP neural network evaluation model for water resources carrying capacity[J]. Computer Engineering and Applications, 2008, 45(8): 217-219.]
[76]	Cao R X, Zhang K X, Zeng W H, et al. Research on the early-warning method of water environment carrying capacity based on BP neural network: A case study of Beiyunhe River Basin[J]. Acta Scientiae Circumstantiae, 2021, 41(5): 2005-2017.
[77]	王学全, 卢琦, 李彬. 水资源承载力综合评价的RBF神经网络模型[J]. 水资源与水工程学报, 2007, 18(3): 1-5.
	[Wang Xuequan, Lu Qi, Li Bin. RBF neural network model for comprehensive evaluation of water resources carrying capacity[J]. Journal of Water Resources and Water Engineering, 2007, 18(3): 1-5.]
[78]	郭晓英, 陈兴伟, 陈莹, 等. 基于粗糙集和BP神经网络组合法的水资源承载力动态变化分析[J]. 南水北调与水利科技, 2015, 13(2): 236-240.
	[Guo Xiaoying, Chen Xingwei, Chen Ying, et al. Analysis of dynamic changes in water resources carrying capacity based on rough set and BP neural network combination method[J]. South-to-North Water Transfer and Water Science & Technology, 2015, 13(2): 236-240.]
[79]	Yu C X, Li Z Y, Yang Z F, et al. A feedforward neural network based on normalization and error correction for predicting water resources carrying capacity of a city[J]. Ecological Indicators, 2020, 118: 106724.
[80]	薛晴, 杨侃. 基于BP神经网络-系统动力学耦合模型的江苏省水资源承载力预测与调控研究[J]. 水利水电技术, 2022, 53(11):86-99.
	[Xue Qing, Yang Kan. Prediction and regulation of water resources carrying capacity in Jiangsu province based on BP neural network-system dynamics coupling model[J]. Water Resources and Hydropower Engineering, 2022, 53(11): 86-99.]
[81]	Kwon M, Kwon H H, Han D. A hybrid approach combining conceptual hydrological models, support vector machines and remote sensing data for rainfall-runoff modeling[J]. Remote Sensing, 2020, 12(11): 1801.
[82]	陈莹, 陈森发. 支持向量机在水资源研究中的应用[J]. 科技信息(科学教研), 2007, 28(16): 15.
	[Chen Ying, Chen Senfa. Application of support vector machines in water resources research[J]. Science & Technology Information (Science and Education), 2007, 28 (16): 15.]
[83]	Breiman L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32.
[84]	陆佳慧. 基于随机森林算法的湖北省水资源承载力评价模型及其应用[J]. 湖北农业科学, 2020, 59(13): 72-76.
	[Lu Jiahui. Evaluation model of water resources carrying capacity in Hubei Province based on random forest algorithm and its application[J]. Hubei Agricultural Sciences, 2020, 59(13): 72-76.]
[85]	LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
[86]	Ghobadi F, Kang D. Application of deep learning in water resources management: A systematic literature review[J]. Water, 2023, 15(4): 620.
[87]	张寒. 云南省水资源承载力时空变化研究[D]. 武汉: 中国地质大学, 2022.
	[Zhang Han. Study on Spatiotemporal Variation of Water Resources Carrying Capacity in Yunnan Province[D]. Wuhan: China University of Geosciences, 2022.]
[88]	Chen Y, Li Z, Xu J, et al. Changes and protection suggestions in water resources and ecological environment in arid region of northwest China[J]. Bulletin of Chinese Academy of Sciences, 2023, 38(3): 385-393.
[89]	方创琳, 孙心亮. 河西走廊水资源变化与城市化过程的耦合效应分析[J]. 资源科学, 2005, 27(2): 2-9.
	[Fang Chuanglin, Sun Xinliang. Analysis of the coupling effect between water resources changes and urbanization process in the Hexi Corridor[J]. Resources Science, 2005, 27(2): 2-9.]
[90]	Bie Q, Xie Y. The constraints and driving forces of oasis development in arid region: A case study of the Hexi Corridor in Northwest China[J]. Scientific Reports, 2020, 10: 17708. doi: 10.1038/s41598-020-74930-z pmid: 33077843
[91]	Sun Y S, Wang Y L, Zhang W, et al. Regional water resources carrying capacity in China based on analytic hierarchy process and system dynamics model: A case study of Golmud City[J]. Frontiers in Environmental Science, 2024, 12: 1450747.
[92]	贾琼, 宋孝玉, 宋淑红, 等. 基于LMDI-SD耦合模型的关中地区水资源承载力动态预测与调控[J]. 干旱区研究, 2023, 40(12):1918-1930. doi: 10.13866/j.azr.2023.12.05
	[Jia Qiong, Song Xiaoyu, Song Shuhong, et al. Dynamic prediction and regulation of water resources carrying capacity in Guanzhong region based on LMDI-SD coupling model[J]. Arid Zone Research, 2023, 40(12): 1918-1930.] doi: 10.13866/j.azr.2023.12.05
[93]	Zhang L Y, Yu Y, Guo Z K, et al. Investigating agricultural water sustainability in arid regions with Bayesian network and water footprint theories[J]. Science of the Total Environment, 2024, 951: 175544.
[94]	宫毓来, 马绍休, 刘伟琦. 机器学习与统计模型在石羊河流域气候降尺度研究中的适用性对比[J]. 中国沙漠, 2022, 42(1): 196-210. doi: 10.7522/j.issn.1000-694X.2021.00210
	[Gong Yulai, Ma Shaoxiu, Liu Weiqi. Comparison of the applicability of machine learning and statistical models in climate downscaling studies in the Shiyang River Basin[J]. Journal of Desert Research, 2022, 42(1): 196-210.] doi: 10.7522/j.issn.1000-694X.2021.00210
[95]	Sun W, Wu Z, Chen X. A dynamic feedback mechanism with attitudinal consensus threshold[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021, 30(5): 1287-1301.
[96]	Zhou Y, Gui Y, Zhou Q, et al. The study on spatial distribution of water ecological environment carrying capacity during extreme drought conditions[J]. Scientific Reports, 2024, 14: 11986. doi: 10.1038/s41598-024-62856-9 pmid: 38796635
[97]	Tu T B, Wang J H, Zhao G, et al. Scaling from global to regional river flow with global hydrological models: Choice matters[J]. Journal of Hydrology, 2024, 633: 130960.
[98]	Zhang L, Kang C, Wu C, et al. Optimization of drought limited water level and operation benefit analysis of large reservoir[J]. Water Resources Management, 2022, 36(14): 4677-4696.
[99]	Ren C F, Guo P, Li M, et al. An innovative method for water resources carrying capacity research-metabolic theory of regional water resources[J]. Journal of Environmental Management, 2016, 167: 139-146.
[100]	Jiang L, Yang Y, Shang S. Evaluation on irrigation efficiency of irrigation district in arid region based on evapotranspiration estimated from remote sensing data[J]. Agricultural Water Management, 2013, 29(20): 95-101.
[101]	Wu Y, Ding Q, Huang W, et al. Dynamic learning of synchronization in coupled nonlinear systems[J]. Nonlinear Dynamics, 2024, 112: 21945-21967.
[102]	Abbasi A, Kambali P N, Shahidi P, et al. Physics-informed machine learning for modeling multidimensional dynamics[J]. Nonlinear Dynamics, 2024, 112: 21565-21585.
[103]	Liao Z, Ren P, Jin M, et al. A system dynamics model to analyse the impact of environment and economy on scenic’s sustainable development via a discrete graph approach[J]. Journal of Difference Equations and Applications, 2016, 23(1-2): 275-290.
[104]	Ahmad S, Simonovic P S. Spatial system dynamics: New approach for simulation of water resources systems[J]. Journal of Computing in Civil Engineering, 2004, 18(4): 331-340.
[105]	Xu Z, Coors V. Combining system dynamics model, GIS and 3D visualization in sustainability assessment of urban residential development[J]. Building and Environment, 2011, 47: 272-287.
[106]	Zhou X, Wang F, Huang K, et al. System dynamics-multiple objective optimization model for water resource management: A case study in Jiaxing City, China[J]. Water, 2021, 13(5): 671.
[107]	Wang Z Y, Fu X T. Scheme simulation and predictive analysis of water environment carrying capacity in Shanxi Province based on system dynamics and DPSIR model[J]. Ecological Indicators, 2023, 154: 110862.
[108]	董婷, 任东, 邵攀, 等. 基于多源遥感数据和随机森林的综合旱情指标构建[J]. 农业机械学报, 2019, 50(8): 200-212.
	[Dong Ting, Ren Dong, Shao Pan, et al. Construction of comprehensive drought index based on multi-source remote sensing data and random forest[J]. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(8): 200-212.]
[109]	Li X, Li Z, Huang W, et al. Performance of statistical and machine learning ensembles for daily temperature downscaling[J]. Theoretical and Applied Climatology, 2020, 140(7): 571-588.
[110]	Alizamir M, Moghadam A M, Monfared H A, et al. Statistical downscaling of global climate model outputs to monthly precipitation via extreme learning machine: A case study[J]. Environmental Progress & Sustainable Energy, 2018, 37(5): 1853-1862.
[111]	Ghobadi F, Kang D. Application of machine learning in water resources management: A systematic literature review[J]. Water, 2023, 15(4): 620.

指标	定义	干旱区适用性权重设计依据	权重
动态反馈机制能力	评估模型在复杂动态系统中捕捉变量间反馈关系的能力	干旱区的水资源系统是动态变化的，需考虑反馈机制（如降水、灌溉与供需关系的反馈）	0.2
非线性建模能力	评估模型处理非线性关系的能力，如资源供需的不确定性、极端情况的应对能力	干旱区的水资源问题通常具有高度非线性（如极端干旱的影响）	0.15
数据驱动性	模型对高维、大规模数据的适应能力，特别是对干旱区数据稀缺问题的处理能力	干旱区数据通常稀缺或不完整，因此需要兼容性高的数据驱动特性，该能力在模型选择中是必要的	0.15
区域适用性	评估模型能否根据区域特性（气候、地貌、社会经济结构等）灵活调整参数并适配	区域适用性是干旱区适用性的前提	0.25
干旱区适用性	评估模型在干旱区具体环境中（如极端缺水、生态脆弱性）应用的适用性，包括模型能否适应特定问题场景的需求	直接决定模型在干旱区问题上的表现	0.25

水资源承载力评价耦合模型的研究进展与干旱区应用

Research advances and arid zone applications of coupled models for water resources carrying capacity

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 6

参考文献 111

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	林洲艳, 王霞迎, 夏元平. 基于多特征融合的面向对象冰川边界提取[J]. 干旱区研究, 2025, 42(6): 1032-1042.
[2]	张秀霞, 韩丽莎, 党星海, 汪孝贤, 林庆润, 邓灵芝, 杨明航, 张喜来. 西北干旱区近30 a植被覆盖的时空变化及其对气候因子的响应[J]. 干旱区研究, 2025, 42(6): 1067-1079.
[3]	李琪, 党国锋, 鱼腾飞, 张浪, 陈薇宇. 基于GEE的干旱区县域生态环境质量时空变化及驱动力分析——以阿拉善左旗为例[J]. 干旱区研究, 2025, 42(2): 360-371.
[4]	李双媛, 徐柱, 王玉刚, 孙金金. 干旱区自然资源地表基质细化分类体系构建与调查深度[J]. 干旱区研究, 2025, 42(1): 84-96.
[5]	吕壮壮, 乔庆庆, 董孙艺, 汪冬. 中中新世气候适宜期全球变暖背景下亚洲内陆干旱区古气候演化特征及驱动机制[J]. 干旱区研究, 2024, 41(8): 1309-1322.
[6]	王怡雯, 马瑶瑶, 史培军, 张钢锋. 干旱区光伏电站运营对局地生态环境的影响[J]. 干旱区研究, 2024, 41(8): 1423-1433.
[7]	龙威夷, 施建飞, 李双媛, 孙金金, 王玉刚. 流域绿洲土壤盐分多模型反演效果评估[J]. 干旱区研究, 2024, 41(7): 1120-1130.
[8]	叶虎, 裴浩, 姜艳丰, 那庆, 张立伟. 内蒙古半干旱区气溶胶散射特性及影响因素[J]. 干旱区研究, 2024, 41(5): 730-741.
[9]	雷菲亚, 李小双, 陶冶, 尹本丰, 荣晓莹, 张静, 陆永兴, 郭星, 周晓兵, 张元明. 西北干旱区藓类结皮覆盖下土壤多功能性特征及影响因子[J]. 干旱区研究, 2024, 41(5): 812-820.
[10]	洪国军, 谢俊博, 张灵, 范振岐, 喻彩丽, 付仙兵, 李旭. 基于多光谱影像的阿拉尔垦区棉田土壤盐分反演[J]. 干旱区研究, 2024, 41(5): 894-904.
[11]	袁萍, 韩欢, 赵红梅, 李从娟. 裸露与沙埋对极端干旱区凋落物分解和养分释放的影响[J]. 干旱区研究, 2024, 41(2): 293-300.
[12]	张寿川, 赵春涛, 安亚涛, 刘凯, 余冬梅, 陈亮, 李庆宽, 王建萍. 那棱格勒河流域氢氧同位素特征及其指示意义[J]. 干旱区研究, 2024, 41(11): 1853-1863.
[13]	汪翔, 吕海深, 朱永华, 郭晨煜. 两种河道洪水演进方法在新疆山区的应用比较[J]. 干旱区研究, 2023, 40(8): 1240-1247.
[14]	赵卓怡, 郝兴明. 基于Priestley-Taylor方法的中亚干旱区实际蒸散特征及归因[J]. 干旱区研究, 2023, 40(7): 1085-1093.
[15]	石建周, 刘贤德, 田青, 于澎涛, 王彦辉. 六盘山半干旱区华北落叶松林坡面土壤含水量的降雨响应[J]. 干旱区研究, 2023, 40(4): 594-604.