Agricultural Water Management最新文献

{"title":"Identification of spatiotemporal changes and driving factors of ecological drought during 1982–2024 across the mainland China","authors":"Hexin Lai ,&nbsp;Shaofeng Yan ,&nbsp;Shikai Gao ,&nbsp;Ruyi Men ,&nbsp;Fei Wang ,&nbsp;Mengting Du ,&nbsp;Kai Feng ,&nbsp;Yanbin Li ,&nbsp;Wenxian Guo ,&nbsp;Haibo Yang","doi":"10.1016/j.agwat.2025.110079","DOIUrl":"10.1016/j.agwat.2025.110079","url":null,"abstract":"<div><div>Drought caused by long-term water shortage may lead to insufficient soil moisture and a decline in groundwater, affecting plant growth and species turnover, and thereby altering the structure and function of the ecosystem. The vast majority of regions in China are facing severe ecological water shortage pressure, which poses a challenge to the health of the ecosystem and the sustainable development of the economy and society. The standardized ecological water deficit index (SEWDI) adopts the actual water consumption of vegetation to characterize the ecosystem’s water supply, and calculates the ecological water deficit (EWD) as the difference between ecological water consumption (EWC) and ecological water requirements (EWR), thereby enabling dynamic assessment of regional drought stress status. Based on the SEWDI, this study used multi-source remote sensing data to reveal the dynamic changes and driving factors of ecological drought in China from 1982 to 2024, and the following main conclusions were obtained: (1) the changing trends of ecological drought in various regions of China were different, and the drought situation has been particularly severe since 2000. Except for the Huang-Huai-Hai Plain Region (HPR) and the Middle-Lower Yangtze Plain (MYP), SEWDI showed a downward trend in the other regions, indicating that ecological drought in China generally showed an increasing trend. (2) SEWDI had two seasonal mutation points, which occurred in January 2003 (confidence interval: December 2002 to March 2003) and April 2017 (confidence interval: June 2016 to March 2018). (3) The most severe ecological drought event occurred from July 2019 to April 2020, with a duration and intensity of 10 months and 9.15, respectively. The peak of the drought occurred in February 2020 (SEWDI= –1.21). (4) From spring to winter, the mean range of the grid trend feature <em>Z</em>_<em>s</em> of SEWDI was –1.12 (in winter) to 0.13 (in summer), suggesting that drought in summer showed a decreasing trend, while drought in spring, autumn and winter showed an increasing trend. (5) Under the combined influence of climate change and human activities, the three optimal variable factors driving changes in ecological drought in China were evapotranspiration, soil moisture and irrigation water. The research results aim to provide a reference for the identification of ecological drought and its driving factors, and to offer a scientific theoretical basis for China’s response to climate change and ecological environment protection.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"323 ","pages":"Article 110079"},"PeriodicalIF":6.5,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing deficit irrigation for climate resilience in the wheat–maize rotation of North China Plain","authors":"Xiangyu Fan,&nbsp;Niels Schütze","doi":"10.1016/j.agwat.2025.110038","DOIUrl":"10.1016/j.agwat.2025.110038","url":null,"abstract":"<div><div>Climate change significantly impacts agricultural production and water resources management, given crop growth’s dynamic response to weather changes and climate variables’ influence on irrigation supply and demand. Consequently, clarifying the effects of climate change on agricultural productivity and identifying adaptation strategies to support sustainable agriculture and livelihood security is essential. In this study, we assessed the effects of climate change on crop rotation management and water balance by simulating crop growth using the AquaCrop model under historical and projected climate scenarios (SSP2-4.5 and SSP5-8.5). Focusing on the winter wheat–summer maize rotation, the predominant cropping system in the North China Plain, we developed a computational framework to optimize irrigation practices and quantify the relationship between irrigation and yield across one year (i.e., corresponding to a complete hydrological cycle). Simulation results indicate that maize yields are projected to decline by approximately 10%, while wheat yields may increase slightly more than 20% due to the CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> fertilization effect. Although increased precipitation under climate change scenarios can reduce irrigation requirements, it may also lead to decreased yield stability, i.e., significantly lower yields in unfavorable years. Looking ahead, effective water demand management strategies, such as implementing water quotas, will be critical for sustainable agricultural water use. Considering constraints on irrigation water supply, total yield, and yield stability, deficit irrigation proved to be the most reliable scheduling strategy for this crop rotation system.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"323 ","pages":"Article 110038"},"PeriodicalIF":6.5,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Film mulching synergistically enhances water productivity and crop yield in maize-soybean intercropping","authors":"Jinwen Pang ,&nbsp;Hongji Zhang ,&nbsp;Ruotong Zhao ,&nbsp;Shixiong Ren ,&nbsp;Xiang Li ,&nbsp;Peng Zhang ,&nbsp;Hao Feng ,&nbsp;Qin’ge Dong","doi":"10.1016/j.agwat.2025.110072","DOIUrl":"10.1016/j.agwat.2025.110072","url":null,"abstract":"<div><div>Water scarcity and limited arable land availability constrain dryland agriculture, requiring optimized resource allocation to achieve sustainable production. Film mulching and intercropping are widely applied as solutions to water and land constraints in dryland farming, but their synergistic effects and underlying mechanisms remain poorly understood. Therefore, five treatments with different planting patterns were tested in this study: monoculture maize without mulching, monoculture maize with mulching, monoculture soybean without mulching, maize–soybean intercropping without mulching, and maize–soybean intercropping with mulching in the maize strip. The soil water dynamics, crop growth dynamics, root system characteristics and root–shoot coordination, water productivity (WP_c), land equivalent ratio (LER), and economic benefits were analyzed. Intercropping with mulching enhanced the resource use efficiency and reduced the risk due to yield variability. Intercropping significantly increased the soil water storage (at 120–200 cm) at harvest, lowering the drought risk compared with monoculture. The system adapted to late-growth water stress through increases in the deep-root density in maize and optimizing the maize–soybean root–shoot ratio, while mulching conserved moisture in the topsoil (0–40 cm) early. Their synergy enhanced WP_c by 4.66 % and LER was 1.46. Mulched intercropping also compensated for the yield losses under no mulching, increasing the total production by 10.65–27.75 % and economic returns by 8.10–29.07 %. Intercropping with plastic film mulching can optimize the use of soil and water resources and achieves synergies to establish a sustainable cropping system that facilitates water conservation, yield increases, and efficiency improvements in dryland farming areas.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"323 ","pages":"Article 110072"},"PeriodicalIF":6.5,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Soil salinity estimation based on satellite hyperspectral and synthetic aperture radar remote sensing image fusion","authors":"Nan Lin ,&nbsp;Xunhu Ma ,&nbsp;Yuanyuan Sui ,&nbsp;Ruifei Zhu ,&nbsp;Hanlin Liu ,&nbsp;Menghong Wu ,&nbsp;Ranzhe Jiang","doi":"10.1016/j.agwat.2025.110076","DOIUrl":"10.1016/j.agwat.2025.110076","url":null,"abstract":"<div><div>Soil salinization poses a significant threat to agricultural sustainability. High-precision remote sensing for mapping soil salinity is crucial for effective salinity management. Hyperspectral image (HSI) serve as essential data sources for regional-scale monitoring of soil salinity. However, the spectral response to soil salinity is highly susceptible to coupling effects from other soil physical properties. Synthetic Aperture Radar (SAR) remote sensing is highly sensitive to soil physical parameters and can effectively compensate for the limitations of HSI. This study proposes an HSI and SAR image fusion method based on a multi-scale, multi-depth Wasserstein Generative Adversarial Network with Gradient Penalty (MSD-WGAN-GP) to improve soil salinity estimation accuracy. A soil salinity prediction model was developed using a convolutional neural network based on 123 soil samples collected from Northeast China. The results show that: (1) HSI and SAR fusion can significantly improve the prediction accuracy of soil salinity. Compared with the prediction of soil salinity based on HSI, the <em>R</em>^<em>2</em> and <em>RPIQ</em> of the model increase by 0.22 and 1.13, respectively, and the <em>RMSE</em> is reduced by 2.68 ds·m⁻¹ . (2) Compared with the traditional image fusion method, the MSD-WGAN-GP model demonstrates superior performance in the fusion of HSI and SAR image, achieving a peak signal-to-noise ratio of 38.39 dB and a structural similarity index of 0.88. (3) The MSD-WGAN-GP model significantly improved the correlation between soil salinity and spectra, achieving an average increase of 0.32 in the correlation coefficient per spectral band, while effectively mitigating the prediction bias introduced by soil moisture and surface roughness. This study emphasizes the significance of integrating multi-source remote sensing data to comprehensively capture the multidimensional characteristics of soil, thereby enabling more accurate estimation of soil salinity.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"323 ","pages":"Article 110076"},"PeriodicalIF":6.5,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monitoring sub-canopy inundation dynamics in global croplands: An unexplored application of SWOT satellite data","authors":"Yongzhe Chen ,&nbsp;Shunlin Liang ,&nbsp;Huanjun Liu ,&nbsp;Phuping Sucharitakul ,&nbsp;Xuejing Leng ,&nbsp;Husheng Fang ,&nbsp;Wenyuan Li ,&nbsp;Han Ma ,&nbsp;Jianglei Xu ,&nbsp;Yichuan Ma ,&nbsp;Lichang Yin","doi":"10.1016/j.agwat.2025.110075","DOIUrl":"10.1016/j.agwat.2025.110075","url":null,"abstract":"<div><div>Inundation dynamics within croplands, particularly paddy fields, profoundly influence irrigation water consumption, greenhouse gas emissions, and crop yields. The traditional practice of continuous flooding irrigation in paddy fields has been abandoned in many regions worldwide, contributing to reduced water consumption and methane emissions. However, regional information on paddy field irrigation/inundation regime is typically limited to coarse estimates derived from meta-analyses or government reports, as no existing method enables spatiotemporally consistent monitoring of inundation beneath crop canopies worldwide. This limitation arises from the inability of optical sensors to detect water beneath dense canopies, substantial variability of SAR backscatter coefficients across crop growth stages, and the limited availability of full-polarization SAR data. Here, we develop the first method for effective year-round cropland inundation monitoring across diverse climate zones. Our method leverages coherent power (COP) rather than water level measurements from the Ka-band Radar Interferometer (KaRIn) aboard the SWOT satellite to distinguish between non-inundated, partially-inundated, and fully-inundated fields. By systematically mitigating and controlling confounding factors that can affect COP signals, including incidence angle, vegetation water content, wind speed variability and noise, we establish COP thresholds for different inundation statuses under a variety of conditions using Gaussian Mixture Models. Validated across four globally representative regions, the method's results are consistent with ground-truth photographs (17/18 match), farmer interviews, and published literature. A comparison with the CYGNSS-based Berkeley-RWAWC dataset, which spans 37.4°S<img>37.4°N, demonstrates a better performance of our SWOT-based method. The estimated cropland inundation dynamics can provide valuable support for improved agricultural water management worldwide.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"323 ","pages":"Article 110075"},"PeriodicalIF":6.5,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of extreme climate on hydrological dynamics in dryland apple orchards: a modeling study","authors":"Yumeng Yang ,&nbsp;Qi Liu ,&nbsp;Xiaodong Gao ,&nbsp;Xiaoya Shao ,&nbsp;Min Yang ,&nbsp;Xining Zhao","doi":"10.1016/j.agwat.2025.110074","DOIUrl":"10.1016/j.agwat.2025.110074","url":null,"abstract":"<div><div>In the past decades, extreme precipitation and drought have increased in both intensity and frequency in global drylands, threatening the sustainability of agricultural systems. To address this challenge, this study refined the process-oriented STEMMUS (simultaneous transfer of energy, mass, and momentum in unsaturated soil) model by introducing a dynamic leaf area index (LAI) development sub-module, and defined scenarios incorporating variations in precipitation amount, precipitation intensity, and temperature. These scenarios elucidate the response patterns of shallow and deep soil moisture and apple orchard evapotranspiration to climatic fluctuations. Key findings reveal that, at the interannual scale, increased growing-season precipitation significantly enhanced soil water storage in both shallow (0–200 cm) and deep (200–450 cm) layers. High-intensity precipitation partially increased soil water storage, particularly under reduced precipitation scenarios, though its contribution to deep soil remained limited. Compared to ambient temperature conditions, 2°C warming resulted in maximum reductions of 30.6 mm and 29.7 mm in shallow and deep soil water storage, respectively. Growing-season cumulative canopy transpiration (T), soil evaporation (E), and T/ET all increased significantly with greater precipitation. Conversely, high-intensity precipitation and 2°C warming reduced cumulative transpiration by 9.7–16.4 % and 7.2–18.3 %, respectively, while T/ET decreased by 4.0–9.4 % and 8.9–15.5 %. Notably, 2°C warming markedly amplified cumulative soil evaporation by 11.5–15.7 %, whereas high-intensity precipitation had no significant effect on soil evaporation. These findings provide a theoretical foundation for developing sustainable water management and climate adaptation strategies in dryland agroecosystems.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"323 ","pages":"Article 110074"},"PeriodicalIF":6.5,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Micro-sprinkler irrigation with optimal irrigation regimes maintain grain yields while increasing carbon emission efficiency and water productivity of winter wheat on the North China Plain” [Agric. Water Manag. 321 (2025) 109933]","authors":"Hao Zheng ,&nbsp;Chunlian Zheng ,&nbsp;Chitao Sun ,&nbsp;Yudong Zheng ,&nbsp;Caiyun Cao ,&nbsp;Anqi Zhang ,&nbsp;Junpeng Zhang ,&nbsp;Hongkai Dang","doi":"10.1016/j.agwat.2025.110065","DOIUrl":"10.1016/j.agwat.2025.110065","url":null,"abstract":"","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"323 ","pages":"Article 110065"},"PeriodicalIF":6.5,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated analytical framework for soil salinization assessment in the Yellow River Delta coastline: Spatiotemporal dynamics, future trends, and driving mechanisms (2003–2023)","authors":"Qizhuo Zhou ,&nbsp;Yan Zhou ,&nbsp;Danyang Wang ,&nbsp;Hongyan Chen ,&nbsp;Peng Liu","doi":"10.1016/j.agwat.2025.110057","DOIUrl":"10.1016/j.agwat.2025.110057","url":null,"abstract":"<div><div>Coastal salinization threatens arable land where marine influence, shallow groundwater, and cultivation interact. We develop an integrated framework that unifies spatiotemporal trend detection (Sen’s slope–Mann–Kendall), future trend persistence (Hurst exponent), and driver attribution with interactions (GeoDetector) to assess soil salinity content (SSC) in the Yellow River Delta from 2003 to 2023. SSC was mapped annually from Landsat using a random-forest model (validation RMSE 1.55 g kg⁻¹; RPD 1.60). Regional mean SSC declined from 2.46 to 2.20 g kg⁻¹(−10.6 %). Significant improvement covers 46.9 % of land, slight improvement 16.5 %, slight degradation 28.7 %, and significant degradation 3.0 %, indicating predominant improvement with localized relapse. Persistence analysis shows 46.9 % of the area is likely to sustain improvement, 16.5 % has potential to improve, about 29 % is at risk of renewed salinization, and 3 % exhibits persistent degradation; high-risk tracts cluster in southern Guangrao, eastern Wudi, western Zhanhua, and along the Yellow River. Driver attribution ranks the fallow-land ratio as the leading anthropogenic control, and distance to the sea, elevation, evapotranspiration, and silt content as dominant natural drivers; most two-factor combinations show nonlinear enhancement. We conclude that stabilizing cultivation and maintaining drainage underpin durable gains, while risk belts require targeted leaching, drain rehabilitation, and salt-tolerant rotations. By overcoming the separation between change diagnosis, future risk, and governance, this framework couples “where” and “how,” and supports management under different salinization conditions, future trajectories, and dominant drivers in coastal deltas.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"323 ","pages":"Article 110057"},"PeriodicalIF":6.5,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation of spatiotemporal variability in nitrate leaching from farmland in the vadose zone of coastal alluvial basin of Dagu River, China","authors":"Ke Yang ,&nbsp;Hui Peng ,&nbsp;Xiao Wang ,&nbsp;Meng Jiang","doi":"10.1016/j.agwat.2025.110063","DOIUrl":"10.1016/j.agwat.2025.110063","url":null,"abstract":"<div><div>Understanding the spatiotemporal dynamics of groundwater recharge and nitrate leaching driven by agricultural activities is essential for the sustainable management of groundwater resources in agricultural regions. In this study, we developed a regional-scale process-based modeling framework using Hydrus-1D to simulate long-term water flow and nitrate transport in the vadose zone of the Dagu River Basin in eastern coastal China. Groundwater recharge fluxes （21.21–832.31 mm） exhibited strong interannual variability and were significantly correlated with precipitation and irrigation. Nitrate leaching fluxes (9.33–1049.94 kg ha⁻¹ yr⁻¹) closely followed the temporal patterns of groundwater recharge, indicating that recharge is the dominant driver of nitrate leaching dynamics. Spatial variations in groundwater recharge and nitrate leaching were primarily controlled by land-use types and vadose zone thickness. In the vadose zone, water inputs were primarily supplied by precipitation (71.22 %) and irrigation (26.03 %), whereas water outputs were dominated by evapotranspiration (67.77 %) and groundwater recharge (21.40 %). Nitrogen inputs were overwhelmingly derived from chemical fertilizers (94.19 %), with major nitrogen losses occurring through nitrate leaching (29.50 %), root uptake (27.60 %), and denitrification (26.60 %). The deep vadose zone served as an important reservoir for nitrate storage and exerted a significant influence on groundwater quality. These findings provide critical insights for evaluating long-term nitrate contamination risks and developing sustainable nutrient and water management strategies in agricultural landscapes.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"323 ","pages":"Article 110063"},"PeriodicalIF":6.5,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731121","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sustainable exploitation of water resources in the framework of water-energy-food nexus in climate change conditions using new multi-objective optimization algorithm MOGWO-3D","authors":"Azar Darboei ,&nbsp;Arash Azari ,&nbsp;Ali Asghar Mirakzadeh","doi":"10.1016/j.agwat.2025.110025","DOIUrl":"10.1016/j.agwat.2025.110025","url":null,"abstract":"<div><div>Climate change exerts significant pressure on environmental and socio-economic systems, with water resources being among the most affected. Water, energy, and food form three interdependent pillars within the agricultural sector. Each of these pillars is influenced by climatic variability, and each also contributes to it. Because these components interact in complex ways, their management cannot be separated. So, achieving long-term sustainability requires integrated strategies that consider these interactions. In the Kermanshah Plain, the absence of such an integrated approach has resulted in the unsustainable use of natural resources and growing system instability. System instability denotes the vulnerability of enviroment to disruptions in WEF resourced availability. This research introduces a comprehensive framework for conjunctive surface–groundwater management under climate change conditions using the water-energy-food (WEF) nexus concept. Guided by projections from the Intergovernmental Panel on Climate Change Sixth Assessment Report (IPCC6), the framework simulates future changes in temperature and precipitation. It evaluates the resulting impacts on water availability and develops adaptive strategies for sustainable resource management. A coupled WEAP–MODFLOW model was used to dynamically simulate surface–groundwater interactions across the Kermanshah Plain. Based on these simulations, a multi-objective optimization model was developed using the three-dimensional Multi-Objective Gray Wolf Optimizer (MOGWO-3D). The optimization considered WEF interdependencies and sought the most efficient cropping pattern and resource allocation under projected climate scenarios. System performance was then assessed by comparing the optimized configuration with a reference SSP8.5Hy scenario. The WEF index analysis identified tomatoes, sugar beets, and potatoes as the most influential crops, with correlation coefficients of 0.70, 0.58, and 0.55, respectively. Implementation of the Optimized SSP8.5 Hybrid (Optimized SSP8.5Hy) scenario improved the reliability of meeting agricultural and water demands to 78.4–77.7 %, representing an increase of approximately 19–19.8 % compared with the baseline scenario. Moreover, the simulated groundwater decline was reduced by 9.5 m, indicating a 22 % improvement in subsurface resource stability. Overall, the optimization scenario demonstrated superior performance in maintaining reservoir storage levels, stabilizing groundwater, and sustaining water supply reliability across both dry and wet periods. In addition, the approach mitigated environmental risks associated with agricultural activities, including soil degradation, nutrient runoff, and greenhouse gas emissions. These findings confirm that the proposed simulation–optimization framework provides a robust basis for sustainable water management and agricultural resilience under changing climatic conditions.</div></div>","PeriodicalId":7634,"journal":{"name":"Agricultural Water Management","volume":"323 ","pages":"Article 110025"},"PeriodicalIF":6.5,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145731783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}