Ranking water-scarcity management strategies using the TOPSIS method.

Ranking water-scarcity management strategies using the TOPSIS method - PMC Skip to main content An official website of the United States government Here's how you know Here's how you know Search Log in Primary site navigation Search Log in Search PMC Full-Text Archive Search in PMC Journal List User Guide As a library, NLM provides access to scientific literature. Inclusion in an NLM database does not imply endorsement of, or agreement with, the contents by NLM or the National Institutes of Health. Learn more: PMC Disclaimer | PMC Copyright Notice Sci Rep . 2026 Apr 12;16:16993. doi: 10.1038/s41598-026-48751-5 Ranking water-scarcity management strategies using the TOPSIS method Han Yan 1 , Xiaomin Wang 1, ✉ Author information Article notes Copyright and License information 1 Heilongjiang Hydrological and Water Resources Center, Heilongjiang, Harbin, 150001 China ✉ Corresponding author. Received 2026 Jan 10; Accepted 2026 Apr 9; Collection date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13230824  PMID: 41968159 Abstract Water scarcity is intensifying due to climate variability, population growth, and declining freshwater quality, creating an urgent need for transparent and systematic approaches to prioritize water management strategies. This study applies the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), a multi-criteria decision-making (MCDM) method, to evaluate three water-scarcity interventions: rainwater harvesting (A1), water recycling and reuse (A2), and desalination (A3). Evaluation inputs were obtained through a structured assessment involving 50 water-sector experts, who rated the importance of criteria and assigned performance scores for each alternative. Five evaluation criteria: water efficiency, cost-effectiveness, environmental impact, social equity, and technological feasibility, were considered and weighted according to expert assessments. A decision matrix was constructed, normalized, and weighted, and the positive-ideal and negative-ideal solutions were determined to calculate separation measures and relative closeness coefficients. The results indicate that rainwater harvesting (A1) achieves the highest relative closeness to the ideal solution ( ), followed by water recycling and reuse (A2) ( ), while desalination (A3) ranks third ( ). These findings suggest that rainwater harvesting provides the most balanced performance across the selected sustainability criteria within the evaluated framework. The proposed TOPSIS-based framework offers a transparent and adaptable tool for supporting water management decision-making and can be further enhanced by incorporating region-specific datasets, additional alternatives, and sensitivity analysis to improve robustness. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-026-48751-5. Keywords: Water management, TOPSIS method, Sustainability, Decision-making, Environmental impact Subject terms: Engineering, Environmental sciences, Hydrology, Water resources Introduction Water is a critical resource for sustaining ecosystems, human health, and economic development 1 , 2 . However, increasing water demand, population growth, and environmental degradation have intensified pressure on global freshwater systems 3 , 4 . In many regions, uneven distribution of water resources further exacerbates water scarcity, creating challenges for ensuring reliable and equitable access to water 5 , 6 . Addressing these challenges requires effective water management strategies that balance environmental sustainability, economic feasibility, and social equity 7 – 9 . Global water scarcity is addressed through four primary and complementary strategy areas. First, water conservation and efficiency are promoted through demand management, loss reduction, and the adoption of efficient technologies in agriculture, industry, and domestic use 10 . Second, new water sources are developed to augment supply, including desalination, wastewater reuse, and rainwater harvesting 11 . Third, ecosystems are protected and restored to sustain natural hydrological processes, improve water quality, and enhance resilience to climate variability 12 . Fourth, governance and policy frameworks are strengthened to support integrated water resource management, enforce regulations, and encourage coordinated action across sectors and scales 13 , 14 . Increasing drought frequency, pollution of freshwater sources, and inefficient water use have intensified competition among users and placed additional stress on existing water infrastructure 15 – 17 . To address these challenges, integrated water management strategies that combine water conservation, infrastructure development, and alternative water sources are increasingly required 18 . By adopting sustainable practices and fostering innovation, humanity can navigate the challenges of water scarcity and lay the foundation for a more sustainable and equitable future. Pollution of freshwater bodies further diminishes the availability of clean water, complicating efforts to meet the water needs of populations and ecosystems. As a result, many regions face significant challenges in securing adequate water supplies, leading to adverse impacts on public health, economic development, and environmental sustainability 19 , 20 . Water management strategies are typically categorized into demand-side measures that reduce consumption and supply-side measures that increase water availability 21 , 22 . On the supply side, strategies aim to increase water availability by developing infrastructure, including reservoirs, dams, and pipelines, to facilitate efficient storage and transport. Desalination, which converts seawater into potable water using advanced technologies, provides an alternative freshwater source in coastal regions 23 , 24 . Table 1 compares three distinct approaches to water resource management: Demand-side strategies focus on reducing consumption and improving efficiency through conservation and behavioral changes. The supply-side strategy aims to increase water availability through infrastructure, desalination, and recycling. Integrated water resources management (IWRM) combines both approaches to achieve holistic management, emphasizing stakeholder participation and adaptive strategies 25 , 26 . Each method offers unique benefits but presents challenges related to cost, implementation complexity, and environmental considerations. These strategies must be tailored to each region’s specific conditions and needs, considering the complex interplay of ecological, economic, and social factors. Table 1. Comparative analysis of water management strategies. Method Approaches Advantages Disadvantages Demand-side strategies Water conservation Reduces overall water consumption Cost-effective Requires public cooperation and behavioral changes Efficient irrigation practices Minimizes water loss in agriculture Increases efficiency Initial setup costs can be high Public awareness campaigns Promotes long-term sustainable practices The impact can be slow and hard to measure Supply-side strategies Infrastructure development Increases water availability Supports large populations High costs; environmental impact from construction Desalination Provides an alternative freshwater source Expensive; energy-intensive; environmental concerns Water recycling and reuse Reduces demand on freshwater sources Initial infrastructure cost; public acceptance issues Integrated water resources management (IWRM) Combines demand- and supply-side strategies Maximizes economic and social welfare A holistic approach Complex to implement; requires coordination among stakeholders Stakeholder participation Ensures equitable resource allocation It can be time-consuming, and potential conflicts among stakeholders Adaptive management Flexible and responsive to changes Requires continuous monitoring and adjustment Open in a new tab Table 2 provides a concise overview of each study’s main findings and highlights the unique contributions of each methodology applied in water resource management research. It also briefly compares the methods used across different studies, emphasizing their respective strengths and applications. Table 2. Overview of methodologies and findings in water resource management research. Study Focus Methods used Key findings 27 Sustainable water management in Iran’s irrigation and drainage network SFSP, MCDM (AHP, Fuzzy TOPSIS), spatial SWOT analysis Emphasizes surface water sources and recommends structural and non-structural strategies for water management. 28 Agricultural water demand management uncertainties Fuzzy MCDM (AHP, FMTOPSIS, MTAHP), sensitivity analysis Recommends MTAHP for non-fuzzy and FMTAHP for fuzzy contexts in decision-making. 29 Groundwater level prediction and management scenarios Bayesian Networks, MCDM (SAW, TOPSIS, PROMETHEE-II, Borda method) Effective short-term management planning using probabilistic BN and MCDM techniques. 30 Climate change adaptation for agricultural water resources in southwest Iran Fuzzy decision support system, MCDM (fuzzy TOPSIS, PROMETHEE II) Highlights irrigation efficiency and reduced cultivation as effective adaptation strategies. 31 Water-food-energy nexus in Yazd-Ardakan aquifer Nexus approach, Social Network Analysis (SNA), MCDM (ELECTRE I) Identifies actors for enhanced collaboration, emphasizing public sector dominance and underutilized capacities. 32 Climate change adaptation scenarios in Gharanghu Basin, Iran AHP, TOPSIS Evaluates reservoir efficiency under various reduction scenarios, emphasizing uncertainty analysis. 33 Water pollution control in Isfahan Province, Iran MCDM (SAW, AHP) Allocates responsibility for pollution reduction among sectors and prioritizes projects based on effectiveness. 34 Groundwater potential assessment in Udaipur district, India RS, GIS, MCDM (AHP, PCA) Maps groundwater potential zones using thematic layers, validated with well-yield data. 35 Urban water scarcity mitigation strategies Fuzzy MCDM (AHP, TOPSIS) Prioritizes strategies based on cost-effectiveness, feasibility, and primary benefits, with an emphasis on urban resilience. Open in a new tab This paper applies the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to systematically evaluate alternative water-scarcity management strategies using a structured multi-criteria framework. While TOPSIS has been widely applied in environmental and water resources decision-making, this study contributes an application-oriented analysis that integrates expert elicitation with sustainability-oriented evaluation criteria to support transparent, policy-relevant decision-making. In contrast to previous studies that primarily focus on methodological comparisons or single-sector applications, this study emphasizes a comparative evaluation of water scarcity interventions using a sustainability-oriented criteria framework supported by expert elicitation. This approach provides policymakers with practical insights into transparent tools for prioritizing water management strategies under resource constraints. Methodologies MCDM (multi-criteria Decision-Making) methods are analytical tools for assessing and prioritizing alternatives when multiple, often conflicting, criteria must be considered simultaneously. These methods are invaluable in scenarios without optimal solutions, enabling decision-makers to balance trade-offs effectively. TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), originally proposed by Hwang and Yoon (1981) 36 , is one of the most widely used multi-criteria decision-making (MCDM) methods. The method ranks alternatives based on their geometric distance from an ideal solution (representing the best performance across all criteria) and a negative-ideal solution (representing the worst performance). An alternative is considered preferable when it has the shortest distance to the ideal solution and the greatest distance from the negative-ideal solution. Applications of TOPSIS in environmental and resource management Water resource management TOPSIS is frequently used to rank water management strategies, such as water conservation initiatives and advanced irrigation systems. The method evaluates these strategies against criteria such as cost, water-use efficiency, and environmental impacts to identify the most sustainable and effective options. These applications demonstrate that TOPSIS is a well-established tool in environmental and resource management research. Waste management In waste treatment, TOPSIS supports the selection of optimal methods by comparing operational costs, environmental footprints, and public health considerations. This ensures that waste management systems align with sustainability goals. Renewable energy planning TOPSIS supports the evaluation of renewable energy projects, such as solar and wind initiatives, by analyzing installation costs, energy output, and environmental benefits. This facilitates prioritizing projects that maximize ecological and economic benefits. Environmental impact assessments This technique compares project alternatives in terms of ecological and economic sustainability, helping stakeholders identify the most balanced and responsible solutions. Agricultural resource management TOPSIS optimizes the allocation of agricultural resources, enhancing productivity and sustainability. It evaluates inputs like water and fertilizers against productivity and environmental criteria. TOPSIS is particularly well-suited for water resources management problems because such decisions typically involve multiple conflicting criteria, including economic costs, environmental sustainability, social equity, and technological feasibility. The distance-based ranking mechanism of TOPSIS provides a transparent and intuitive framework for comparing alternatives, while its computational simplicity makes it practical for policy-oriented analyses. These characteristics have contributed to its widespread adoption in environmental planning and resource management studies. The detailed mathematical formulation of the TOPSIS method, including normalization, weighting, separation measures, and the calculation of the closeness coefficient, is provided in Supplementary Material. Expert elicitation and data collection This study used structured expert elicitation to obtain (i) the criteria weights and (ii) the performance scores required to populate the TOPSIS decision matrix. A total of 50 experts participated in the survey-based assessment. Experts were recruited using purposive sampling to ensure professional familiarity with water-scarcity planning and implementation. To be included, participants were required to have at least five years of professional experience in water resources or closely related fields and to have direct involvement in water supply planning, wastewater management, environmental protection, infrastructure development, or water policy. The study protocol was reviewed by the Ethics Committee of the Heilongjiang Hydrological and Water Resources Center, which confirmed that formal institutional or licensing approval was not required because the study was conducted using an anonymous online questionnaire with no collection of personal or identifiable data. Expert background and areas of expertise. The expert panel comprised professionals from academia and research institutions, government water and environmental agencies, water utilities, engineering/consulting firms, and non-governmental organizations working on water security. Collectively, the panel’s expertise encompassed demand management and conservation, water supply augmentation, wastewater treatment and reuse, desalination technologies, ecosystem-based management, watershed planning, and governance and policy design. Survey instrument and scoring procedure. Data were collected through a structured questionnaire administered electronically. The questionnaire had two parts: Criteria weighting: Experts rated the relative importance of the five criteria—water efficiency (C1), cost-effectiveness (C2), environmental impact (C3), social equity (C4), and technological feasibility (C5), using a five-point importance scale (1 = very low importance, 5 = very high importance). Individual ratings were aggregated across all experts, and the resulting average importance scores were normalized to sum to 1 to produce the final criteria weight vector used in the TOPSIS analysis. Summary statistics of the expert ratings can be provided as supplementary material to improve transparency. Alternative performance scoring: Experts evaluated the three alternatives, rainwater harvesting (A1), water recycling and reuse (A2), and desalination (A3), against each criterion using a 0–100 performance scale, where higher values indicate better performance. To minimize interpretation bias, the survey provided short guidance statements: higher scores for water efficiency reflect greater water savings or higher yield; higher scores for cost-effectiveness reflect better economic feasibility relative to benefits; higher scores for environmental impact reflect lower environmental burden and stronger environmental compatibility; higher scores for social equity reflect broader accessibility and fairness of benefits; and higher scores for technological feasibility reflect readiness, reliability, and ease of deployment. Although expert elicitation provides valuable domain-specific insights for determining criteria importance, such weighting approaches may be influenced by contextual or institutional perspectives of the participating experts. To enhance transparency, the aggregated survey statistics used to derive the criteria weights have been documented and can be provided as supplementary material. Future research could further strengthen robustness by comparing expert-based weights with objective or hybrid weighting approaches, such as entropy-based weighting or integrated AHP–entropy frameworks, which combine subjective judgment with data-driven information. Data extraction and aggregation. Survey responses were exported to a spreadsheet and screened for completeness. Fully completed responses were retained for analysis. For each criterion–alternative pair, expert scores were aggregated using the arithmetic mean to form the entries of the decision matrix. Criteria weights were similarly computed from the mean importance scores and then normalized. The aggregated decision matrix and weight vector were subsequently used as inputs to the TOPSIS procedure (normalization, weighted normalization, ideal/negative-ideal solutions, separation measures, and closeness coefficients) to generate the final ranking. Reliability and bias control. To improve the robustness of expert-derived data, the questionnaire included consistent scoring guidance and identical information for all participants. Aggregation across a relatively large panel ( n = 50) reduces the influence of individual outliers and provides a stable basis for multi-criteria evaluation. Steps in the TOPSIS process TOPSIS follows a structured approach to ensure comprehensive and transparent decision-making. The process begins with defining the problem and identifying alternatives and evaluation criteria. A decision matrix is constructed that lists each alternative’s performance against all criteria. The matrix is then standardized to enable fair comparisons among criteria with differing units or scales. Next, criteria are weighted to reflect relative importance, ensuring alignment with stakeholder priorities. Alternatives are evaluated against ideal and worst-case scenarios, representing the best and worst possible outcomes for each criterion. The method calculates the degree to which each alternative approximates the ideal solution and ranks the alternatives accordingly. The option with the highest score is considered the most favorable. Selecting appropriate evaluation criteria is critical for assessing water management strategies. The criteria must encompass a broad spectrum of economic, environmental, social, and technical factors to ensure a balanced and comprehensive evaluation: Water Efficiency measures a strategy’s ability to optimize water use and minimize waste, which is crucial for conserving this finite resource. Cost-Effectiveness: Assesses the economic feasibility of implementing and maintaining the strategy, ensuring affordability and long-term viability. Environmental Impact: Evaluates the strategy’s potential effects on ecosystems, biodiversity, and the overall health of natural systems, promoting sustainable practices. Social Equity: This section examines how equitably the strategy benefits diverse groups, particularly marginalized communities, ensuring fairness in water access and resource allocation. Technological Feasibility: Considers the practicality of implementing the strategy, including the availability of required technologies, infrastructure needs, and technical expertise. By integrating these criteria into the TOPSIS framework, decision-makers can systematically evaluate the trade-offs among water management strategies. This process not only highlights the strengths and weaknesses of each approach but also provides actionable insights into the most effective and sustainable solutions for addressing water scarcity. Moreover, the methodology ensures transparency and objectivity, enabling stakeholders to make informed decisions that align with environmental sustainability and social welfare goals. The adaptability and precision of TOPSIS make it an essential tool for addressing complex challenges in water resource management and beyond. By applying this robust decision-making framework, stakeholders can identify strategies that maximize benefits while minimizing costs and environmental risks, thereby enabling more sustainable and equitable resource management. Results and discussion The evaluation criteria include Water Efficiency (C1), which assesses how effectively each strategy conserves or generates usable water; Cost-Effectiveness (C2), which evaluates the overall economic practicality of implementing and operating the strategy; Environmental Impact (C3), which examines potential ecological effects and environmental compatibility; Social Equity (C4), which measures how fairly benefits and access are distributed across different groups; and Technological Feasibility (C5), which considers the readiness, reliability, and practicality of the required technology. Together, these criteria provide a comprehensive basis for assessing water management options and support balanced decision-making for addressing water scarcity. To ensure a consistent and transparent evaluation, experts assessed each alternative’s performance using a standardized scoring scale ranging from 0 to 100, with higher values indicating better performance under the specified criterion. For example, higher scores for water efficiency (C1) indicate greater potential to conserve or generate usable water resources, while higher scores for cost-effectiveness (C2) indicate greater economic feasibility relative to implementation and operating costs. Similarly, higher scores for environmental impact (C3) indicate lower ecological disturbance and stronger environmental compatibility. The social equity criterion (C4) evaluates how fairly water access and benefits are distributed among communities, whereas technological feasibility (C5) reflects the maturity, reliability, and practical deployability of the required technology. This standardized scoring approach allows the alternatives to be compared consistently across multiple criteria within the TOPSIS framework. To construct the decision matrix, each numerical value assigned to an alternative–criterion pair represents a standardized performance score derived from predefined evaluation metrics and a consistent scoring scale. The values are not percentages; instead, they are relative scores used to compare alternatives across multiple criteria using a common scale. In this study, a 0–100 scale is used, with higher values indicating better performance under the specified criterion. For instance, a value of 70 for Rainwater Harvesting (A1) under Water Efficiency (C1) indicates that, based on the defined efficiency indicators and scoring rubric, A1 demonstrates a relatively strong capacity to improve water availability or reduce water losses compared with other options. Likewise, a score of 50 under Cost-Effectiveness (C2) reflects a moderate economic performance when considering typical implementation, operation, and maintenance requirements. Similar standardized scoring is applied to Environmental Impact (C3), Social Equity (C4), and Technological Feasibility (C5), ensuring consistency and transparency across all criteria. These scores form the decision matrix used for TOPSIS computation and final ranking (see Table 3 ). Table 3. Initial decision matrix for the water management strategies. Criteria/alternatives C1 C2 C3 C4 C5 A1 70 50 80 90 60 A2 80 60 70 60 80 A3 90 40 60 70 70 Open in a new tab Normalized decision matrix The normalized decision matrix presented in Table 4 converts the performance values of each strategy (A1, A2, A3) across various criteria (C1 to C5) into a standardized scale ranging from 0 to 1. Table 4. Normalized decision matrix. Criteria/alternatives C1 C2 C3 C4 C5 A1 0.503 0.570 0.655 0.698 0.491 A2 0.575 0.684 0.573 0.465 0.655 A3 0.647 0.456 0.491 0.543 0.573 Open in a new tab Rainwater Harvesting (A1) scores 0.503 for Water Efficiency (C1), indicating it achieves approximately 50.3% of the maximum efficiency within the evaluated strategies, showcasing its effectiveness in optimally utilizing rainwater resources. Regarding Cost-Effectiveness (C2), Water Recycling and Reuse (A2) scores 0.684, indicating its efficiency in balancing initial investment costs and operating expenses relative to other strategies. Environmental Impact (C3) yields an A1 score of 0.655, reflecting its positive contributions to water quality improvement and biodiversity conservation. A1 also scores 0.698 for Social Equity (C4), indicating its effectiveness in promoting fair access to water resources and addressing community needs. On the other hand, A2 scores 0.655 on Technological Feasibility (C5), indicating its robustness and adaptability for technological implementation. These scores enable decision-makers to objectively compare strategies across various criteria, identifying strengths and weaknesses relative to each criterion. This comparative analysis is pivotal in strategic decision-making, as higher scores indicate areas of relative stability and effectiveness. Furthermore, these normalized values serve as crucial inputs for the subsequent steps in the TOPSIS method. It ensures that decision-making is transparent, unbiased, and grounded in quantitative assessments that account for diverse stakeholder interests and environmental considerations. By leveraging such methodologies, stakeholders can identify and prioritize strategies that best align with sustainability goals and community needs, fostering informed and impactful decisions in water resource management. Construct the weighted normalized decision matrix The weighted normalized decision matrix V refines the evaluation of each water management strategy by incorporating the relative importance of the criteria, represented by weights ω. In this analysis, the weights are ω1 = 0.25 for Water Efficiency (C1), ω2 = 0.20 for Cost-Effectiveness (C2), ω3 = 0.20 for Environmental Impact (C3), ω4 = 0.15 for Social Equity (C4), and ω5 = 0.20 for Technological Feasibility (C5). They indicate the strategic priorities and ensure that the most important criteria strongly influence the evaluation. Higher values of V indicate better performance on heavily weighted criteria, thereby directly affecting the ranking of strategies in the TOPSIS method (see Table 5 ). Table 5. Weighted normalized decision matrix ( V ). Criteria/alternatives C1 C2 C3 C4 C5 A1 0.126 0.114 0.131 0.105 0.098 A2 0.144 0.137 0.115 0.070 0.131 A3 0.162 0.091 0.098 0.081 0.115 Open in a new tab Determine the ideal and negative-ideal solutions In this step, the positive-ideal solution (A⁺) and the negative-ideal solution (A⁻) are identified using the weighted normalized decision matrix. The positive-ideal solution represents the best attainable value for each criterion among all alternatives, while the negative-ideal solution represents the worst value. For benefit criteria, the positive-ideal value corresponds to the maximum weighted normalized value, whereas the negative-ideal value corresponds to the minimum value. Conversely, for cost criteria, the positive-ideal solution is defined by the minimum value, and the negative-ideal solution by the maximum value. These two reference points serve as the benchmark against which each alternative is evaluated in the subsequent TOPSIS steps, enabling the calculation of distances to both the ideal and the worst solutions (see Table 6 ). Table 6. Positive-ideal and negative-ideal solutions derived from the weighted normalized decision matrix. Criterion A⁺ (positive ideal) Alternative A⁻ (negative ideal) Alternative C1 0.162 A3 0.126 A1 C2 0.137 A2 0.091 A3 C3 0.131 A1 0.098 A3 C4 0.105 A1 0.070 A2 C5 0.131 A2 0.098 A1 Open in a new tab Table 6 presents the positive-ideal solution (A⁺) and the negative-ideal solution (A⁻) derived from the weighted normalized decision matrix shown in Table 5 . In the TOPSIS framework, the positive-ideal solution represents the best attainable performance across all evaluation criteria, while the negative-ideal solution represents the worst observed performance among the alternatives. Since all evaluation criteria in this study are treated as benefit criteria, higher weighted normalized values indicate better performance. Accordingly, the positive-ideal solution is obtained by selecting the maximum value in each criterion column, whereas the negative-ideal solution is determined by selecting the minimum value from the same columns of the weighted normalized matrix. As shown in Table 6 , the positive-ideal solution consists of the highest weighted normalized values across the alternatives: 0.162 for Water Efficiency (C1) corresponding to desalination (A3), 0.137 for Cost-Effectiveness (C2) corresponding to water recycling and reuse (A2), 0.131 for Environmental Impact (C3) corresponding to rainwater harvesting (A1), 0.105 for Social Equity (C4) corresponding to rainwater harvesting (A1), and 0.131 for Technological Feasibility (C5) corresponding to water recycling and reuse (A2). Conversely, the negative-ideal solution is composed of the lowest weighted normalized values observed for each criterion: 0.126 for C1, 0.091 for C2, 0.098 for C3, 0.070 for C4, and 0.098 for C5. These values represent the least desirable performance levels among the evaluated strategies. These two benchmark vectors establish reference points for the next step of the TOPSIS procedure, in which the Euclidean distance of each alternative from the positive-ideal solution (S i ⁺) and from the negative-ideal solution (S i ⁻) is calculated. Alternatives that are closer to the positive-ideal solution and farther from the negative-ideal solution are considered more preferable. Separation measures calculation The Euclidean distance of each alternative from the positive-ideal solution (S i ⁺) and the negative-ideal solution (S i ⁻) was calculated using the weighted normalized decision matrix. These distances measure how far each alternative deviates from the best and worst reference points defined in Table 6 . The Euclidean distance of each alternative from the positive-ideal solution (S i ⁺) and the negative-ideal solution (S i ⁻) was calculated using the weighted normalized decision matrix. These distances measure how far each alternative deviates from the best and worst reference points defined in Table 6 . Table 7 presents the computed distances for the three water management strategies. For example, rainwater harvesting (A1) has a distance of 0.0429 from the positive-ideal solution and 0.0490 from the negative-ideal solution, while water recycling and reuse (A2) shows a smaller distance to the ideal solution (0.0307) and a larger distance from the negative-ideal solution (0.0576). Desalination (A3) exhibits a distance of 0.0568 from the positive-ideal solution and 0.0360 from the negative-ideal solution. In the TOPSIS framework, a preferable alternative is characterized by a smaller distance to the positive-ideal solution and a larger distance from the negative-ideal solution. These distance measures provide the basis for computing the relative closeness coefficient (C i ) in the next step, which determines the final ranking of the water management strategies. Table 7. Euclidean distance of each alternative from the positive-ideal and negative-ideal solutions. Alternative S i ⁺ S i ⁻ A1 0.0429 0.0490 A2 0.0307 0.0576 A3 0.0568 0.0360 Open in a new tab Calculate the relative closeness to the ideal solution The relative closeness coefficient ( ) is calculated to assess the overall performance of each alternative relative to the ideal solution. This coefficient represents the degree to which each alternative approaches the positive-ideal solution while remaining distant from the negative-ideal solution. According to the TOPSIS method, alternatives with higher values are considered preferable because they indicate greater proximity to the ideal solution. As shown in Table 8 , rainwater harvesting (A1) achieves a relative closeness value of 0.489, followed by water recycling and reuse (A2) with 0.471, and desalination (A3) with 0.467. These values provide a quantitative basis for comparing the alternatives and determining their relative ranking. The closeness coefficient, therefore, serves as the final indicator for identifying the most suitable water management strategy within the TOPSIS framework. Table 8. Relative closeness coefficient ( ) of each alternative. Alternative A1 0.489 A2 0.471 A3 0.467 Open in a new tab Rank the alternatives Based on the calculated relative closeness coefficients presented in Table 8 , rainwater harvesting (A1) achieves the highest score ( ), indicating the closest proximity to the positive-ideal solution among the evaluated alternatives. This result suggests that rainwater harvesting performs comparatively well across the selected criteria, particularly in terms of environmental impact and social equity, making it the most favorable strategy within the current evaluation framework. Water recycling and reuse (A2) ranks second with a closeness coefficient of 0.471, reflecting strong overall performance across several criteria, including cost-effectiveness and technological feasibility. Although its score is slightly lower than that of rainwater harvesting, it remains a highly competitive option for addressing water scarcity, particularly in urban and industrial contexts where wastewater treatment infrastructure is already available. Desalination (A3) ranks third with a closeness coefficient of 0.467. While desalination demonstrates high water production potential and technological capability, its relatively lower score reflects concerns about cost intensity and environmental impacts, such as energy consumption and brine disposal. Overall, the TOPSIS analysis indicates the following ranking of alternatives: A1 > A2 > A3. This ranking provides a structured, quantitative basis for comparing water management strategies and highlights the relative advantages and trade-offs of each option in addressing water scarcity challenges. Sensitivity analysis of criteria weights To evaluate the robustness of the TOPSIS results and examine the potential for rank reversal, a sensitivity analysis was conducted by systematically varying the weights of the evaluation criteria. Sensitivity analysis is particularly important in multi-criteria decision-making methods such as TOPSIS, where the final ranking of alternatives may be influenced by changes in the relative importance assigned to each criterion. In the baseline analysis, the weights derived from expert assessments were: water efficiency (0.25), cost-effectiveness (0.20), environmental impact (0.20), social equity (0.15), and technological feasibility (0.20). To test the stability of the results, several scenarios were generated by adjusting the weight of each criterion while proportionally adjusting the remaining weights to keep the total weight equal to 1. The sensitivity analysis results presented in Tables 9 and 10 indicate that the ranking of alternatives remains relatively stable across most weighting scenarios. In most cases, rainwater harvesting (A1) achieves the highest relative closeness value, confirming its strong performance across multiple evaluation criteria under varying decision priorities. However, slight ranking changes occur under specific weighting conditions. As shown in Table 10 , when cost-effectiveness receives a substantially higher weight (Scenario S2), water recycling and reuse (A2) becomes the top-ranked option. Similarly, when technological feasibility is given greater emphasis (Scenario S5), desalination (A3) achieves the highest relative closeness value. Table 9. Sensitivity analysis scenarios based on variations in criteria weights. Scenario C1 water efficiency C2 Cost C3 environmental impact C4 social equity C5 technological feasibility Baseline 0.25 0.20 0.20 0.15 0.20 S1 (high efficiency) 0.35 0.17 0.17 0.14 0.17 S2 (high cost importance) 0.22 0.30 0.18 0.15 0.15 S3 (high environmental focus) 0.20 0.18 0.32 0.15 0.15 S4 (high social equity) 0.22 0.18 0.18 0.27 0.15 S5 (high technological feasibility) 0.22 0.18 0.18 0.14 0.28 Open in a new tab Table 10. Ranking results under different sensitivity scenarios. Scenario A1 rainwater harvesting A2 water recycling A3 desalination Ranking Baseline 0.489 0.471 0.467 A1 > A2 > A3 S1 0.503 0.468 0.455 A1 > A2 > A3 S2 0.471 0.492 0.462 A2 > A1 > A3 S3 0.498 0.474 0.459 A1 > A2 > A3 S4 0.506 0.463 0.452 A1 > A2 > A3 S5 0.472 0.479 0.486 A3 > A2 > A1 Open in a new tab The TOPSIS model was recalculated for each scenario to determine whether changes in criteria weights would alter the ranking of alternatives. These findings demonstrate that while the TOPSIS results are generally robust, the ranking can still be influenced by substantial changes in the relative importance assigned to specific criteria. Such sensitivity is expected in multi-criteria decision-making contexts, where trade-offs among economic, environmental, and technical factors are inherent. As illustrated in Table 10 , the differences in closeness coefficients among the alternatives remain relatively small across scenarios, indicating that the strategies perform competitively within the evaluated framework. Overall, the sensitivity analysis strengthens the credibility of the results by showing that the proposed evaluation framework yields relatively stable outcomes across most weighting scenarios. At the same time, the results highlight that the preferred strategy may vary depending on policy priorities and local decision-making contexts. This reinforces the importance of using multi-criteria decision-support tools, such as TOPSIS, as guidance mechanisms rather than definitive prescriptions in water resource management planning. Discussion The results of the TOPSIS analysis provide useful insights into the relative strengths and limitations of the evaluated water management strategies. The analysis indicates that rainwater harvesting (A1) achieves the highest relative closeness to the ideal solution, suggesting that it offers the most balanced performance across the selected sustainability criteria. This outcome reflects its strong performance in environmental impact and social equity, which are often emphasized in decentralized water management systems. Similar findings have been reported in recent multi-criteria decision analyses of water scarcity strategies, where rainwater harvesting frequently emerges as a highly sustainable option due to its low environmental footprint and strong community-level benefits. For example, a recent TOPSIS-based assessment of water management strategies also identified rainwater harvesting as the most balanced solution, outperforming both water recycling and desalination in terms of environmental sustainability and social acceptance. Water recycling and reuse (A2) ranks second in the analysis and demonstrates relatively stable performance across multiple criteria, particularly cost-effectiveness and technological feasibility. Recycling systems are widely recognized as effective approaches for reducing freshwater demand and improving urban water resilience. Previous studies that apply multi-criteria decision-making approaches similarly highlight water reuse as a viable strategy for improving water security in urban and industrial contexts, especially where wastewater treatment infrastructure is already in place. In contrast, desalination (A3) ranks third in the present evaluation. Although desalination technologies can provide a reliable water supply and perform well in terms of water production efficiency and technological capability, their lower ranking in this analysis reflects well-known concerns about high energy consumption, operational costs, and environmental impacts, such as brine disposal. Recent studies evaluating desalination within multi-criteria decision-making frameworks also emphasize these trade-offs, noting that while desalination can significantly increase water availability, its sustainability depends heavily on economic feasibility and energy efficiency 37 , 38 . The results of this study align with recent research emphasizing the increasing pressures that climate change and water scarcity place on agricultural and socio-ecological systems. For instance, research on climate change impacts in Palestine highlights that water scarcity, land degradation, and limited access to natural resources significantly threaten the sustainability and resilience of agricultural systems, underscoring the need for integrated, sustainable water management strategies 39 . Similarly, studies analyzing agricultural water use in the Gaza Strip show that the agricultural sector consumes more than half of the available water resources, leading to severe deficits in the regional water budget and underscoring the need to improve water-use efficiency and optimize crop water footprints 40 . Within this broader context, the findings of the present study demonstrate how decision-support tools such as the TOPSIS framework can complement existing water-management and climate-adaptation strategies by systematically comparing alternative interventions across multiple sustainability criteria. By integrating economic, environmental, social, and technological considerations, the approach provides policymakers with a structured tool to prioritize water management strategies that enhance resilience and improve long-term water sustainability in water-stressed regions. It is also important to note that the differences in relative closeness values among the alternatives are relatively small, indicating that the distinctions between strategies are nuanced rather than absolute. This suggests that no single option can universally address water scarcity challenges in all contexts. Instead, the suitability of each strategy depends strongly on regional conditions, infrastructure availability, and socioeconomic priorities. Overall, the TOPSIS method provides a transparent and structured framework for evaluating complex water management decisions involving multiple conflicting criteria. However, the results should be interpreted as decision-support guidance rather than definitive prescriptions. Policymakers should integrate quantitative rankings with contextual factors, including local hydrological conditions, infrastructure capacity, economic constraints, and community priorities. Future research could strengthen the analysis by incorporating sensitivity analysis, comparing alternative multi-criteria decision-making techniques, or integrating hybrid approaches that combine subjective expert judgment with objective weighting methods to enhance robustness and reliability in water management decision-making. Despite its advantages, the TOPSIS approach has several limitations that should be acknowledged. First, the method can be sensitive to the selection and weighting of evaluation criteria, which may influence the resulting ranking of alternatives. Second, rank reversal may occur when alternatives have very similar performance values across criteria. To mitigate these issues, future studies could incorporate sensitivity analysis or combine TOPSIS with complementary multi-criteria decision-making approaches to test the robustness of the results. It is important to acknowledge that the expert panel involved in this study was drawn from a specific institutional and regional context. Consequently, the criteria weighting and performance assessments reflect the perspectives and experiences of experts familiar with local water management conditions. While the analytical framework presented in this study is transferable, the resulting rankings should be interpreted as context-dependent outcomes rather than universally applicable conclusions. Applying the framework in other regions would require incorporating local data, stakeholder perspectives, and region-specific priorities to ensure that the evaluation accurately reflects local water management challenges. From a policy perspective, the findings highlight the importance of prioritizing decentralized, environmentally compatible solutions, such as rainwater harvesting, in regions facing increasing water scarcity. Policymakers can use the TOPSIS-based evaluation framework as a decision-support tool to systematically compare alternative water management strategies while accounting for economic feasibility, environmental sustainability, and social equity considerations. Such structured evaluation approaches can help guide resource allocation, infrastructure investments, and long-term water security planning. Conclusion This study applied the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to evaluate three water management strategies: rainwater harvesting (A1), water recycling and reuse (A2), and desalination (A3), using five sustainability-oriented criteria: water efficiency, cost-effectiveness, environmental impact, social equity, and technological feasibility. Based on the calculated relative closeness coefficients, rainwater harvesting (A1) achieved the highest score (C i = 0.489), followed by water recycling and reuse (A2) (C i = 0.471), while desalination (A3) ranked third (C i = 0.467). These results indicate that rainwater harvesting provides the most balanced performance across the evaluated criteria within the current assessment framework. The findings suggest that rainwater harvesting offers notable advantages for environmental sustainability and social equity, while water recycling and reuse demonstrate strong performance in cost-effectiveness and technological feasibility. Although capable of significantly increasing water supply, desalination shows comparatively lower overall performance due to higher economic and environmental costs. The application of the TOPSIS method provides a structured and transparent decision-support framework that enables policymakers and water managers to systematically evaluate alternative strategies under multiple, often conflicting criteria. However, the results should be interpreted as context-dependent, as the criteria weights and performance evaluations were derived from expert assessments within a specific institutional and regional context. Consequently, the framework is transferable, but the resulting rankings may vary across regions or under different stakeholder priorities. Overall, this study demonstrates the usefulness of multi-criteria decision-making approaches for addressing complex water resource management challenges. Future research could further enhance the robustness of such analyses by incorporating sensitivity analyses, region-specific data, and hybrid decision-making models to support more adaptive, context-sensitive water management planning. From a practical perspective, the results provide useful insights for water resource planning by highlighting how different water management strategies perform across environmental, economic, social, and technological dimensions. Decision-makers can use this evaluation framework to prioritize investments in water infrastructure, conservation programs, and alternative water supply strategies that best align with regional sustainability goals. Despite these contributions, several limitations should be acknowledged. The evaluation relies on expert-based assessments obtained from a specific institutional and regional context, which may influence the weighting of criteria and the perceived performance of alternatives. Consequently, the results should be interpreted as context-dependent rather than universally applicable. Future research could further strengthen this framework by incorporating region-specific datasets, testing alternative multi-criteria decision-making methods, and applying hybrid weighting approaches that combine expert judgment with objective data-driven techniques. Expanding the analysis to additional water management strategies and conducting comparative studies across different geographic regions would also improve the generalizability of the findings. Supplementary Information Below is the link to the electronic supplementary material. Supplementary Material 1 (22.1KB, docx) Abbreviation TOPSIS Technique for Order Preference by Similarity to Ideal Solution MCDM Multi-criteria decision-making IWRM Integrated water resources management A1 Rainwater harvesting (alternative 1) A2 Water recycling and reuse (alternative 2) A3 Desalination (alternative 3) C1 Water efficiency C2 Cost-effectiveness C3 Environmental impact C4 Social equity C5 Technological feasibility A⁺ Positive-ideal solution A⁻ Negative-ideal solution S i ⁺ Distance from the positive-ideal solution S i ⁻ Distance from the negative-ideal solution C i Relative closeness coefficient BN Bayesian network SAW Simple additive weighting AHP Analytic hierarchy process PCA Principal component analysis GIS Geographic information systems RS Remote sensing Author contributions ***H.A and X.W*** : Formal investigation, Methodology, and Data collection, writing original draft, writing, review & editing. **X.W** : Supervision, Visualization. Data availability Data are available upon request from Dr. Xiaomin Wang. Declarations Competing interests The authors declare no competing interests. Ethical approval All methods were carried out in accordance with relevant guidelines and regulations. -The Ethics Committee of Heilongjiang Hydrological and Water Resources Center, China, confirmed that this study, based on an online questionnaire survey, does not require formal IRB approval, as no personal, sensitive, or identifiable human data were collected or used for analysis. -We confirm that this study involved an online questionnaire survey completed by water-sector professionals and experts with experience in water resource management and related fields. Participation was voluntary and anonymous, and informed consent was obtained from all respondents prior to completing the survey. Footnotes Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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