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

Abstract

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

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.

Figures/Tables 6

Fig. 1

Fig. 2

Tab. 1

Classification of main evaluation methods for water resources carrying capacity"

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

Tab. 1

Tab. 2

Fig. 3

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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

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

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