Global Water Security Research Articles

Innovative Approaches in Water Decontamination: A Critical Analysis of Biomaterials, Nanocomposites, and Stimuli-Responsive Polymers for Effective Solutions

In recent years, smart materials have emerged as a groundbreaking innovation in the field of water filtration, offering sustainable, efficient, and environmentally friendly solutions to address the growing global water crisis. This review explores the latest advancements in the application of smart materials—including biomaterials, nanocomposites, and stimuli-responsive polymers—specifically for water treatment. It examines their effectiveness in detecting and removing various types of pollutants, including organic contaminants, heavy metals, and microbial infections, while adapting to dynamic environmental conditions such as fluctuations in temperature, pH, and pressure. The review highlights the remarkable versatility of these materials, emphasizing their multifunctionality, which allows them to address a wide range of water quality issues with high efficiency and low environmental impact. Moreover, it explores the potential of smart materials to overcome significant challenges in water purification, such as the need for real-time pollutant detection and targeted removal processes. The research also discusses the scalability and future development of these materials, considering their cost-effectiveness and potential for large-scale application. By aligning with the principles of sustainable development, smart materials represent a promising direction for ensuring global water security, offering both innovative solutions for current water pollution issues and long-term benefits for the environment and public health.

Machine Learning and New-Generation Spaceborne Hyperspectral Data Advance Crop Type Mapping

Hyperspectral sensors provide near-continuous spectral data that can facilitate advancements in agricultural crop classification and characterization, which are important for addressing global food and water security issues. We investigated two new-generation hyperspectral sensors, Germany’s Deutsches Zentrum für Luft‐ und Raumfahrt Earth Sensing Imaging Spectrometer (DESIS) and Italy’s PRecursore IperSpettrale della Missione Applicativa (PRISMA), within California???s Central Valley in August 2021 focusing on five irrigated agricultural crops (alfalfa, almonds, corn, grapes, and pistachios). With reference data from the U.S. Department of Agriculture Cropland Data Layer, we developed a spectral library of the crops and classified them using three machine learning algorithms (support vector machines [SVM], random forest [RF], and spectral angle mapper [SAM]) and two philosophies: 1. Full spectral analysis (FSA) and 2. Optimal hyperspectral narrowband (OHNB) analysis. For FSA, we used 59 DESIS four-bin product bands and 207 of 238 PRISMA bands. For OHNB analysis, 9 DESIS and 16 PRISMA nonredundant OHNBs for studying crops were selected. FSA achieved only 1% to 3% higher accuracies relative to OHNB analysis in most cases. SVM provided the best results, closely followed by RF. Using both DESIS and PRISMA image OHNBs in SVM for classification led to higher accuracy than using either image alone, with an overall accuracy of 99%, producer’s accuracies of 94% to 100%, and user???s accuracies of 95% to 100%.

Uncertain multi‐objective programming approach for planning supplementary irrigation areas in rainfed agricultural regions

AbstractRainfed agriculture is crucial for ensuring global food and water security, and supplementary irrigation is an effective means of improving the economic benefits in rainfed agricultural regions. This study proposes a novel uncertain optimization approach to optimize supplementary irrigation areas in rainfed agricultural regions. The approach incorporates multi‐objective linear programming, interval linear programming, fuzzy goal programming and stochastic expected value programming. In the proposed interval multi‐fuzzy goal stochastic expected‐value programming, uncertainties are expressed in the form of discrete intervals, probability distributions and fuzzy goals. This approach, which considers the randomness of precipitation during the optimization process and allocates limited irrigation water resources to different subareas and crops, was applied to a case study of crop irrigation area planning in Guyuan City, Ningxia Hui Autonomous Region, northwestern China. The maximum economic benefits and the minimum sum of the Gini coefficients among the different subareas and crops were regarded as the planning objectives, and a series of optimal irrigation areas with different crops and subareas under different water levels were obtained. The optimization results revealed that vegetables and fruits consumed large amounts of irrigation water to increase their economic benefits. In addition, allocating a large amount of irrigation water to wheat is essential to decrease the Gini coefficient and meet food security constraints, particularly at extremely low water levels. Compared with current management, the optimized irrigation area decreased by 30%, the crop water deficit index increased by 9%, the economic benefits increased by 3% and the total Gini coefficient of crops decreased by 17%, indicating that the optimization approach could fairly and reasonably allocate irrigation water resources. Our research provides a mathematical approach for decision‐makers to plan supplementary irrigation areas in rainfed agricultural regions.

The Evolution of Potable Water Security: A Temporal Analysis of Key Indices and Trends

Water security is a critical global issue that has gained increasing attention from researchers, policymakers, and practitioners over the past few decades. This paper presents a comprehensive overview of the evolution of potable water security indices from the 1980s to the 2020s, focusing on the key research themes, trends, and influential factors in the field. We conducted a comprehensive search of the relevant literature and examined the development of water security indices to provide insights into the current state of research and identify future research directions. This paper will explore the historical context, key indicators, sector-specific challenges, climate change implications, governance and policy considerations, technological advancements, community engagement efforts, and future directions for research in this critical field. Researchers and policymakers will find this review valuable as it offers insights into the existing knowledge and the areas that require further exploration to address global water security challenges.

Gender inequality in global water security

Abstract Ensuring universal and equitable access to water and sanitation for all by 2030 (United Nations Agenda 2030, Sustainable Development Goal or SDG 6) is one of the most important goals for the world. 785 million people do not still have access to basic drinking water services. This burden disproportionately affects women, who are often responsible for collecting water from distant sources in regions with limited or no water supply systems. Understanding the spatial patterns of socio-demographic and hydrological factors is key to address the interlinked challenges of gender equality and water security under SDG 5 (‘Achieve gender equality’) and SDG 6. While the challenges women face in securing water are widely recognized, quantitative analysis of gender inequality in global water security has not previously been studied. Here, we present, to our knowledge the first global assessment of gender inequality in water security. We measured water security by examining both water access and water scarcity (hereafter, water stress), incorporating population data for working-age individuals and children. We integrated this hydrological and demographic information at a spatial resolution of approximately 10 km from 2000 to 2014 when the information is available. Our results showed that more working-age women than men experience poor water access, particularly in Africa. In addition, women in countries with high water insecurity, defined as high water stress and poor water access—such as Burkina Faso, Togo, and Somalia—primary conduct water collection. Our results indicate significant gender-based differences in water security, with women frequently remaining in rural areas characterized by poor water access. These inequalities could be further intensified by climate change and socio-economic factors.

Environmental safety performance dynamics and estimation of the deterioration of leachate containment elements: Implications for the lifespan of hazardous waste landfills

In the coming decades, the failure and expiration of over 100,000 landfills involving billions of tons of waste not only threaten global water security but also lead to a sudden increase in the global demand for solid waste disposal. Therefore, further research is needed on the ways in which landfill performance is degraded, their life expectancy, the factors that influence them and the ways in which they are influenced. A study estimated the performance degradation and expected lifespan of a modern hazardous waste landfill in China. The landfill experienced slow performance degradation in early years (0–12 years). But during mid-term (12–62 years) due to the rapid degradation of the liner material, the rate of leakage from the containment layer will increase by a factor of 4.8–27.1, leading to its earliest failure after about 38 years. This indicates the need for measures to prevent rapid performance degradation in the middle of the landfill's service life and to strengthen monitoring of performance indicators and environmental medium pollution in the later period (after 62 years) to respond to potential scrapping and pollutant leakage problems. Differences in construction quality and regional climate can cause differences in the lifespan of landfill facilities by up to 20 and 30 times, highlighting the importance of scenario-based failure and lifespan prediction for designing reasonable monitoring plans and preparing response measures to prevent potential groundwater pollution and to meet the sudden increase in demand for secondary waste disposal.

Enhancing water security: Statistical measurement and spatiotemporal analysis

Water security is essential for ensuring energy security, sustainable development, and human survival. However, due to a series of challenges, including rising water demand, environmental pollution, and water resource shortages, the global water security situation remains concerning and poses a threat to global sustainable development. To assess water security in China, this study uses data from 30 provinces in China from 2012 to 2021. A comprehensive evaluation method was applied to determine the level of water resource security in China. The Dagum Gini coefficient, Moran index, and spatial model were used to clarify regional differentiation characteristics and the driving factors. The results indicate that while China's water resource security is relatively low, it has shown steady improvement in recent years. Significant regional disparities exist in water resource security across China, with notable spatial characteristics, and socio-economic factors are the primary causes of these differences. Based on the above research, we put forward policy recommendations from the aspects of water resources management, public participation and inter-regional water resources cooperation, to provide reference for water resources security in developing countries.

IoT-based smart irrigation management system to enhance agricultural water security using embedded systems, telemetry data, and cloud computing

Agriculture, the key sector for food production, faces challenges exacerbated by the growing global demand for food and the scarcity of water resources. Traditional irrigation methods often lead to inefficient use of water, resulting in wastage. Moreover, unpredictable weather conditions and the need to adopt sustainable farming practices are driving us to develop advanced irrigation systems. This paper proposed a smart irrigation management system using new technology like 1) embedded systems, 2) Internet of Things (IoT), 3) telemetry data, 4) cloud computing, communication protocol, and 5) sensors to collect and process real-time data of the smart agriculture. This new technology and intelligent algorithm are used in this paper to improve agricultural practices. The architecture of the proposed scheme comprises three layers: 1) IoT devices, 2) ThingsBoard cloud, and 3) the dashboard, each playing a pivotal role in ensuring seamless data flow, secure communication, and enhanced user interaction. The algorithm orchestrates system operations, incorporating sensor data reading, JavaScript Object Syntax (JSON) payload creation, and secure telemetry data transmission via Hypertext Transfer Protocol (HTTP) protocol to the ThingsBoard cloud. Pump activation decisions are governed by predefined thresholds, preventing an over-irrigation system. The evaluation of system performance involves deploying temperature, humidity, moisture, and water level sensors in a testing field. Further, before data is sent to the cloud, sensor values are calibrated using a functional map. Tests carried out with temperature, humidity, and water level sensors demonstrate the system's dynamic efficiency. By visualizing and analyzing environmental information via ThingsBoard, the results provide real-time data on environmental parameters of the system proposed, improving the efficiency of water use and the sustainability of farming practices. In addition, the system features e-mail notifications for alerts. The integration of e-mail notifications reinforces monitoring and management practices by alerting farm owners and users. This study showcases the efficacy of the proposed paper in enhancing sustainability, water usage, and supporting smart agriculture practices. Further, this paper integrates embedded systems, IoT, cloud computing, and advanced algorithms to optimize and enhance crop productivity and contribute to global food and water security.

Evaluation of the Effects of Climate Variability on Water Resources and Agriculture: Insights from Regional Climate Models

Climate variability and change present significant challenges to global food and water security, particularly in vulnerable regions like East and Southern Africa. This review synthesizes current research on the impact of climate drivers such as temperature and precipitation variability—on water resources and agricultural productivity in this region. Analyzing over four decades of data and recent findings, this paper discusses the complex interactions between climate phenomena, including El Niño Southern Oscillation (ENSO), and their effects on regional agriculture and water systems. The review highlights the limitations of Regional Climate Models (RCMs) in accurately predicting climate patterns and emphasizes the need for improved modeling techniques. Recommendations for future research and policy interventions are provided to enhance climate resilience in these critical sectors.

Mapping a sustainable water future: Private sector opportunities for global water security and resilience

Water security remains a critical global development challenge, compounded by persistent public funding shortfalls. Society urgently needs to identify opportunities for innovative private sector engagement in water security solutions. To identify feasible and impactful solutions, quantitative tools are needed to delineate complex environmental and socioeconomic water challenges and prioritize private sector investment opportunity spaces to address these challenges. We introduce the first global and regional-scale maps showing where threats to water security coincide with private sector opportunities to address them. The successful deployment of water solutions is contingent upon the societal and governance landscape that underpins a nation’s capacity to support sustainable water threat interventions and water-related business activities. By delineating areas with substantial pressures on water resources and assessing nations’ enabling environments to support private sector investments, we find nearly two-thirds of the world’s population could benefit from private sector interventions today, with middle income countries realizing the greatest benefits. In the face of global economic development and climate change, such solutions will become increasingly essential in future decades.

Water neutrality: Concept, challenges, policies, and recommendations

Agriculture, urbanisation, and industrial growth have significantly increased water demand, leading to water pollution, necessitating the protection of rivers, wetlands, and ecosystems. Hence, a strategy is needed that is aligned with Sustainable Development Goal (SDG) 6, which includes ensuring safe and affordable drinking water (SDG 6.1), implementing integrated water resources management (SDG 6.5), and protecting and restoring water-related ecosystems (SDG 6.6). This review article introduces the concept of water neutrality (WN), a planning approach for new urban developments aimed at minimising impacts on water security. The peer-reviewed articles and governmental reports published between 2014 and 2024 provide an overview of the current water situation. It also explores the potential benefits of WN, evaluates existing policies and case studies, and offers actionable recommendations. The study reveals that implementing water-related policies often faces challenges, such as political commitment, insufficient funding, and weak monitoring systems, particularly in developing countries. In India, water policies primarily focus on irrigation, drinking water supply, and pollution control. However, these policies struggle due to weak enforcement and conflicting stakeholder interests. The study recommends significant policy reforms and the adoption of new approaches to achieve WN, which promote integrated water resource management, strengthen governance, pricing mechanisms, advanced pollution control technologies, climate-resilient infrastructure, and public awareness, all while aligning with SDG 6. Innovative solutions, such as smart irrigation systems and water recycling programs, offer promising paths to address water scarcity. Additionally, international collaborations can further support these goals. Integrating WN into SDG 6 has the potential to significantly enhance global water security and sustainability.

The occurrence of positive selection on BicA transporter of Microcystis aeruginosa

The rapid growth of cyanobacteria, particularly Microcystis aeruginosa, poses a significant threat to global water security. The proliferation of toxic Microcystis aeruginosa raises concerns due to its potential harm to human health and socioeconomic impacts. Dense blooms contribute to spatiotemporal inorganic carbon depletion, promoting interest in the roles of carbon-concentrating mechanisms (CCMs) for competitive carbon uptake. Despite the importance of HCO3− transporters, genetic evaluations and functional predictions in M. aeruginosa remain insufficient. In this study, we explored the diversity of HCO3− transporters in the genomes of 46 strains of M. aeruginosa, assessing positive selection for each. Intriguingly, although the Microcystis BicA transporter became a partial gene in 23 out of 46 genomic strains, we observed significant positive sites. Structural analyses, including predicted 2D and 3D models, confirmed the structural conservation of the Microcystis BicA transporter. Our findings suggest that the Microcystis BicA transport likely plays a crucial role in competitive carbon uptake, emphasizing its ecological significance. The ecological function of the Microcystis BicA transport in competitive growth during cyanobacterial blooms raises important questions. Future studies require experimental confirmation to better understand the role of the Microcysits BicA transporter in cyanobacterial blooms dynamics.

Coupled natural-social water system in a subwatershed of the Tennessee River Basin

Regional water cycle systems are increasingly characterized by the dual effect of natural and social processes, which have profound impacts on global water security. However, accurately interpreting the changes in the coupled natural-social water system and identifying the driving factors pose significant challenges. Here, we attempted to model a coupled natural-social water system in the East Fork Poplar Creek (EFPC) watershed of the Tennessee River, United States. The study area features two social water cycle components: a local water transfer project and the Oak Ridge Wastewater Treatment Facility (ORWTF). We conducted the Soil and Water Assessment Tool (SWAT) modeling in the open-source light-weight QGIS software, with the synthesis of various climate and land use change scenarios in both historical periods (1980–2016) and future periods (2017–2050). We achieved more accurate and realistic model simulations when considering the social water cycle components, indicating that the social water cycle accounted for 13–18 % of the observed streamflow. Climate variation/change dominates natural runoff changes. Though land use and cover change (LUCC) had minimal effect on natural runoff, it had a profound impact on the process of runoff generation, i.e., surface runoff (RS) and subsurface runoff (RSS). Specifically, LUCC would be responsible for 152 % and 45 % of the changes in RS and RSS, respectively, in future periods. This study highlights the significance of artificial water discharge and withdrawal impacts on the water cycle and emphasizes the need for water resources management measures that fully consider natural-social hydrological processes.

Turning global water security research into policy and action

Turning global water security research into policy and action

Selection of optimal draw solution recovery technology for forward osmosis desalination system using analytical hierarchy process

Water scarcity poses a significant threat to global food and water security, prompting a need for practical solutions. With 97% of Earth’s water situated in oceans, desalination emerges as a viable option. Among desalination technologies, forward osmosis (FO) using membrane-based technology stands out for its potential to reduce costs and energy requirements. The focus on energy consumption in FO has prompted an exploration of optimal technology selection through the Analytical Hierarchy Process (AHP), a multi-criteria decision-making method. Value judgments were collected through a questionnaire in consultation with two experts. Environmental aspects emerged as the most critical factor, weighted at 0.3963. The AHP analysis revealed nanofiltration (NF) as the optimal system, attaining a total weight of 0.2612. The NF scored highest in terms of environmental impact (C3), operating and maintenance costs (S6), and energy requirements (S4). Conversely, membrane distillation ranked as the least preferred alternative, with a total score of 0.1335, mainly due to lower maturity of technology (S3), higher capital costs (S5), and negative environmental impact (C3). Sensitivity analysis was conducted to investigate how changing weights for sub-criteria might affect the preferred technology. Notably, Reverse Osmosis became the most favored technology when efficiency (S1) and S3 weights were set at 0.3 and 0.2, respectively. Conversely, thermal separation gained preference when the weights for resistance to scaling and fouling (S2) and S5 were set at 0.3. Changes in S4, S6, and C3 have showed the most minor sensitivity.

Evaluating trends in groundwater quality of coastal alluvial aquifers of Eastern India for sustainable groundwater management.

Groundwater is a precious natural element which ensures global water, food, and environmental security in the twenty-first century. Systematic monitoring, sustainable utilization, preservation and remediation are critical aspects of efficient groundwater resource management. This study deals with the analysis of spatial variability and trend in groundwater chemistry as well as identification of possible contamination sources in a coastal alluvial basin of eastern India. Pre-monsoon season data of 14 groundwater-quality variables measured in 'leaky confined' and 'confined' aquifers were analyzed for ten years (2012-2021). Mann-Kendall (M-K) test with the Sen's Slope Estimator, Spearman Rank Order Correlation (SROC) and Innovative Trend Analysis (ITA) tests were employed to assess decadal (2012-2021) trends. The analysis of the results indicated that the 'critical' water-quality parameters exceeding the acceptable limits for drinking are TDS, EC, TH, pH, Mg2+, Na+, K+, Fe2+, HCO3-, Cl- and NO3-. Weak negative correlations between rainfall and groundwater elevation for both the aquifers reveal poor rainfall recharge into the aquifers. Therefore, a reduction in groundwater abstraction and augmentation of groundwater recharge is recommended. Trend analysis results indicated that the concentrations of TH, Mg2+ and Fe2+ exhibit significant increasing trends in the 'leaky confined aquifer'. In contrast, significant rising trends in TH, Mg2+, Na+, Fe2+, HCO3- and NO3- concentrations are identified in the 'confined aquifer'. Further, the SROC test could not detect the trends in groundwater quality in most blocks and for many parameters. On the other hand, the ITA test revealed significant trends in most of the parameters of the two aquifers in almost all the blocks. Trend magnitudes of the groundwater-quality parameters based on the Sen's Slope Estimator and the ITA test vary from -63.7 to 58.65mg/L/year for TDS, -14 to 39.07mg/L/year for TH, -1.49 to 4.83mg/L/year for Mg2+, -7.14 to 22.96mg/L/year for Na+, -0.32 to 0.44mg/L/year for Fe2+, -8.33 to 20.75mg/L/year for HCO3-, -26.52 to 31.01mg/L/year for Cl- and 1.29 to 3.76mg/L/year for NO3- over the study area. The results of M-K and ITA tests were found in agreement in all the blocks for both the aquifers. Groundwater contamination in both the aquifers can be attributed to weathering, geogenic processes, mineral dissolution, seawater intrusion, poor recharge pattern and injudicious anthropogenic activities. It is strongly recommended that concerned authorities urgently formulate efficient strategies for managing groundwater quality in the 'leaky confined' and 'confined' aquifers which serve as vital sources of drinking and irrigationwater suppliesin the study area.

Decarbonizing Water: The Potential to Apply the Voluntary Carbon Market toward Global Water Security.

The increase in global water insecurity is one of the first perceivable effects of climate change. Two billion people are now without access to safe drinking water, and four billion experience water stress at least once a year, primarily in low per-capita emission countries. This nexus between climate change and water insecurity has significant implications for the global economy, with the water sector contributing 10% of global emissions. Though traditionally a local issue, climate finance mechanisms like the voluntary carbon market (VCM) present opportunities for a global, sustainable, performance-based funding stream to address water insecurity. Since 2010, water-related carbon projects have yielded over 45 million emission reduction credits. Our analysis estimates a global potential of over 1.6 billion tCO2e per year across various water project subsectors. At a $10 per credit average, this could attract over $160 billion in investments over the next decade, enhancing global water security. However, barriers like high intervention costs and limited technologies hinder widespread implementation, creating a tension between standardized and bespoke credits. We present case studies, spanning drinking water initiatives to the wastewater treatment sector that illustrate VCM's role in channeling private sector capital for water security in climate-vulnerable regions.

Artificial Intelligence in Water Management for Sustainable Farming: A Review

Artificial Intelligence (AI) is capable of enhancing water management for sustainable farming. The growing demand for agricultural productivity and sustainability in the context of finite water resources and climate change drives the necessity for more efficient water management practices. AI technologies, through automated and precision irrigation systems, AI-based predictive models, and AI-driven water quality monitoring, offer significant improvements in water efficiency and agricultural output. These systems optimize irrigation scheduling based on real-time data, enhance the precision of water application, and ensure water quality, thus supporting sustainable agricultural practices. However, the implementation of AI in water management is not without challenges. Technical difficulties in adapting AI to diverse agricultural environments, data privacy and security concerns, ethical considerations, and barriers to adoption among small-scale farmers are critical issues that need addressing. This study addresses both the transformative impacts and the inherent challenges of integrating AI technologies. Furthermore, the review identifies a gap in research regarding AI’s adaptability to variable climates and its integration with socio-economic data, suggesting that future studies focus on these areas. Policy recommendations are also discussed, emphasizing the need for developing standards and best practices, increasing funding and incentives for AI research, promoting training and capacity building, and establishing robust regulatory frameworks for data management. By tackling these challenges and leveraging AI’s full potential, water management in agriculture can be significantly improved, leading to enhanced global water security and sustainability in farming practices. The review concludes that while AI presents a promising future for agricultural water management, strategic and thoughtful approaches are required to overcome obstacles and fully realize the benefits of this technology.

Drought Monitoring Using Moderate Resolution Imaging Spectroradiometer-Derived NDVI Anomalies in Northern Algeria from 2011 to 2022

Drought has emerged as a major challenge to global food and water security, and is particularly pronounced for Algeria, which frequently grapples with water shortages. This paper sought to monitor and assess the temporal and spatial distribution of drought severity across northern Algeria (excluding the Sahara) during the growing season from 2011 to 2022, while exploring the relationship between the normalized difference vegetation index (NDVI) anomaly and climate variables (rainfall and temperature). Temporal NDVI data from the Terra moderate resolution imaging spectroradiometer (MODIS) satellite covering the period 2000–2022 and climate data from the European Reanalysis 5th Generation (ERA5) datasets collected during the period 1990–2022 were used. The results showed that a considerable portion of northern Algeria has suffered from droughts of varying degrees of severity during the study period. The years 2022, 2021, 2016, and 2018 were the hardest hit, with 76%, 71%, 66%, and 60% of the area, respectively, experiencing drought conditions. While the relationship between the NDVI anomaly and the climatic factors showed variability across the different years, the steady decrease in vegetation health indicated by the NDVI anomaly corroborates the observed increase in drought intensity during the study period. We conclude that the MODIS-NDVI product offers a cost-efficient approach to monitor drought in data-scarce regions like Algeria, presenting a viable alternative to conventional climate-based drought indices, while serving as an initial step towards formulating drought mitigation plans.

Research on optimal allocation of soil and water resources based on water–energy–food–carbon nexus

Current population growth and rapid urbanization in China have intensified the demand for water, energy, and food. This is threatening the intertwined global water, energy, and food security. Therefore, it is necessary to formulate interconnected and interdependent water, energy, food, and carbon policies. In this study, based on the theory of water–energy–food–carbon nexus, a soil and water resource optimization allocation model was developed by establishing an optimization modeling framework to achieve coordination between various sectors of water allocation. The model is an effective decision-making tool that fully considers the close relationship within the nexus system, and multiple uncertainty conditions. It was applied on the Fen River Basin in Shanxi Province as the study area, and a series of optimized allocation schemes were obtained. The results indicated that the current water allocation to each sector is meeting its minimum water demand. The planting structure optimization of two crops, wheat and corn, reduces the regional agricultural water consumption. For example, in dry years, the area of wheat cultivation (which needs more irrigation and has lower net economic benefits) is reduced and that of corn (which needs less irrigation and consumes less water) is increased in each subdistrict. The wheat plantation area in the midstream and downstream is decreased by 2.08% and 26.25%, respectively. The water use structure of crops indicated that precipitation and groundwater resources significantly contribute to the production of both crops throughout the entire growth process. This model can be applied in other regions; the model's economic and ecological benefits are combined so that decision makers can consider the stability of the water–energy–food–carbon system and provide optimal resource allocation under various scenarios, contributing to achieving the goal of sustainable development.