{"paper_id":"4ab36daf-de67-4b83-826e-9f377fa68aa6","body_text":"Spillover effects of upstream irrigation expansion on downstream water stress in transboundary river basins | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Spillover effects of upstream irrigation expansion on downstream water stress in transboundary river basins Jinwei Dong, Xi Chen, Xiaogang He, Dong Jiang, Yongqiang Zhang, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5351205/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract The expansion of irrigated cropland exacerbates water scarcity, while geopolitical environment further intensifies the spatial imbalance of water resources, particularly in transboundary rivers. However, little is known about the evolution of water stress in upstream and downstream regions within transboundary river basins and their potential interrelationships. Here, we find that 396 of 431 sub-basins (91.9%) experience increasing irrigation water stress (IWS) between 1901 and 2005, with the number of sub-basins facing irrigation water scarcity doubling from 51 to 118. Disparities in IWS between upstream and downstream regions widen in 92.4% of transboundary river basins, especially in South Asia, Central Asia, and Africa. The expansion of upstream irrigated areas (6 Mha·yr-1) and associated water withdrawals (20.4 km3·yr-1) exacerbate downstream IWS by 34.3 ± 3.5% from 1901 to 2005, with this spatial spillover effect projected to intensify through 2099. Our findings emphasize the urgent need for cooperative water management in transboundary basins. Earth and environmental sciences/Hydrology Scientific community and society/Agriculture Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Main Climate change and increasing human water withdrawal have escalated global water scarcity, posing significant challenges to sustainable development 1,2 . The expansion of irrigated croplands has emerged as a major contributor to water stress since the 20th century 3,4 , as agriculture accounts for the largest share of national water consumption, often exceeding half of the total in most countries 5 . Transboundary river basins, which cover nearly half of the global land area and support 40% of the population 6 , represent critical yet understudied regions regarding water management 5,7 . Geopolitical tension between upstream and downstream countries in these basins further intensifies regional water scarcity 8,9 . Thus, investigating how the expansion of irrigated croplands affects irrigation water stress (IWS) in upstream and downstream areas is crucial for informing policy aimed at sustainable water use and fostering cross-border cooperation. Several studies have examined the dynamics of water stress in transboundary basins, focusing on downstream dependence on upstream regions and the drivers of water scarcity 10-12 . However, these studies overlook the critical role of agriculture in influencing water stress across both upstream and downstream areas . Global and regional studies have shown that the expansion of irrigated cropland places significant pressure on freshwater resources 13-17 , with 52% of this expansion occurring in water-scarce regions by 2000 14 . This not only jeopardizes the sustainable replenishment of local water supplies but also exacerbates hydrological droughts due to agriculture's high water demand 16 . Moreover, variations in agricultural development and governance between countries further complicate water resource management in transboundary basins 18 . While previous studies have shown that uncontrolled upstream water use could lead to unsustainable downstream supplies 19,20 , the specific impacts of irrigated agriculture remain undocumented. Understanding the broader impacts of upstream agricultural practices requires considering their spillover effects on downstream areas. Spillover effects from transboundary interactions could present both benefits and risks, manifesting as synergies and trade-offs 21,22 . Trade-offs typically arise from competing demands for water resources 23 , while synergies could result from collaborative management of shared resources 24,25 . Addressing the spillover effects through effective watershed management benefits both upstream and downstream regions in achieving sustainable development goals. Although significant attention has been given to the impacts of water pollution 26,27 , drought 21 , and war 28,29 on transboundary water resources, the spillover effects of agricultural development on IWS in transboundary river basins remain less understood. Here, we address the following scientific questions: 1) is there a significant difference in irrigation water stress (IWS) between upstream and downstream sub-basins; 2) how do upstream irrigated croplands affect downstream IWS through spatial spillover effects; 3) where are the potential water conflict hotspots under IWS spillover effects; and 4) how will IWS and these spillover effects evolve under future climate scenarios? To address these questions, we used irrigation water withdrawal and water availability data from global hydrological models (GHMs) and global climate models (GCMs) to calculate the irrigation water stress index (IWSI). This allowed us to assess variations in IWS, focusing on downstream-upstream disparities from 1901 to 2099. Using multiple regression modeling (MRM) and structural equation modeling (SEM), we analyzed the direct and indirect impacts of upstream irrigated cropland expansion and water withdrawal on downstream IWS, focusing on spatial spillover effects. Furthermore, by integrating trend analysis of irrigation expansion with projected IWS changes under future climate scenarios, we identified potential changes in IWSI and spillover effects, emphasizing the risk of regional water conflicts. Our findings fill a knowledge gap regarding the spillover effects of upstream irrigated cropland expansion and shed light on sustainable water management in transboundary river basins. Results Changes in irrigation water stress (IWS) of upstream and downstream sub-basins During 1901–2005, 396 out of 431 sub-basins (92%) increasing IWS (Fig. 1 A–D). Also, the number of sub-basins facing irrigation water scarcity, which is defined as irrigation water stress index (IWSI) above 0.2, rises from 51 (12%) in the 1900s to 118 (27%) in the 2000s. Asia dominates the pattern of sub-basins in irrigation water scarcity in the 2000s, accounting for 64% of the global total, particularly in South Asia, where sub-basins in India, Pakistan, and Nepal—including the Ganges-Brahmaputra-Meghna Basin, the Indus Basin, and the Rann of Kutch Basin—are heavily impacted. Moreover, the number of sub-basins facing severe irrigation water scarcity (IWSI > 1), where irrigation water withdrawal exceeds local river discharge, increases significantly from 9 in the 1900s to 36 in the 2000s. The robustness of IWSI is supported by an uncertainty assessment we conducted, which is provided in the Supplemental Information. We compare changes between the upstream and downstream sub-basins and find that, out of the 218 downstream-upstream sub-basin pairs, 195 (89%) exhibit an increase in the downstream-upstream IWSI disparity (or, in basins with a middle stream, the difference between downstream and middle stream, and between middle stream and upstream) from the 1900s to the 2000s, with the most pronounced increases occurring in Asia and Africa (Fig. 1 E–H). The proportion of sub-basin pairs with significant widening differences (IWSI differences > 0.1 or < − 0.1) steadily rises, reaching half of the pairs, with figures at 27%, 35%, and 47% in the 1900s, 1950s, and 2000s, respectively. Notably, sub-basins where upstream IWS exceeds downstream IWS become more prevalent, accounting for 41%, 45%, and 48% of pairs in the 1900s, 1950s, and 2000s, respectively, and representing more than half (53%) of the sub-basin pairs with significant widening IWSI differences by the 2000s. Spillover effects of upstream irrigation on downstream IWS We conduct multiple regression models (MRMs) to analyze downstream IWS attribution using decadal averages from the 20th century. The variables considered include upstream irrigation areas, industrial water withdrawal, residential water withdrawal, water availability from the same years, and downstream-upstream IWSI differences from the previous year. Our findings indicate that upstream irrigated area has the largest impact, with downstream IWSI increasing by 0.87 ± 0.09% for each percentage point increase in upstream irrigated area (Table 1 ). This suggests that the expansion of upstream irrigated areas threatens downstream water security. Additionally, downstream-upstream IWSI differences from the previous year significantly influence downstream IWSI, which increases by 0.71 ± 0.04% for each percentage point of the previous year’s downstream-upstream IWSI differences. This highlights that downstream-upstream disparities adversely affect water use conditions in downstream basins. Downstream IWSI exhibits a significant negative correlation with upstream domestic water withdrawal ( p -value < 0.001), approximately double the positive impact of industrial water withdrawal: − 0.69 vs. 0.24. Upstream water availability has the least effect, accounting for only − 0.03% of downstream IWSI. These results highlight the substantial upstream spillover effects on downstream IWS, with irrigated cropland expansion contributing to 34.3 ± 3.5% of the total impacts. Furthermore, we replace the irrigation area with irrigation water withdrawal to redo the attribution analyses (see Supplementary Information), and the results confirm the previous findings. Table 1 Multiple linear regression model determining the effect of indicators to downstream irrigation water stress (IWS) from 1901 to 2005. Coefficient s.e. P values 95% Confidence Interval. irrArea up 0.873*** 0.045 0.000 0.089 IWSI dif 0.713*** 0.021 0.000 0.041 domWW up -0.690*** 0.052 0.000 0.102 indWW up 0.236*** 0.028 0.000 0.055 discharge up -0.033 0.020 0.098 0.039 Adjusted R² 0.466 P values 0.000 Asterisks denote the statistical significance based on P values: *P < 0.05, **P < 0.01, ***P < 0.001. IWSI dif indicates the previous year's downstream irrigation water stress index (IWSI) minus upstream IWSI difference. The subscripts \" up \" denote upstream. irrArea denotes the area under irrigated cropland. domWW and indWW represent water withdrawal from domestic and industrial sector, respectively. Discharge refers to the flow in the sub-basin after accounting for the removal of the environmental flow requirement (EFR), i.e., water availability. To understand the influence of upstream sub-basins on downstream IWS, we use structural equation modeling (SEM) to identify both direct and indirect pathways. We find that the direct effect of upstream IWSI (IWSI up ) is the highest (Fig. 2 and Table S1 ), suggesting that when upstream sub-basins experience irrigation water shortages, downstream sub-basins are similarly affected. As upstream IWS intensifies, downstream IWS correspondingly increases. The next largest total effects come from upstream irrigated acreage (irrArea up , 0.841) and irrigation water withdrawal (irrWW up , 0.808). This finding aligns with the multiple regression analysis, confirming that both upstream irrigated area and irrigation water withdrawal significantly impact downstream IWS. Upstream irrigated area not only has a direct impact but also indirectly affects downstream IWS by increasing upstream irrigation water withdrawal and consequently worsening upstream IWS, contributing to 95.6% of its total effect (Table S2). Although not directly affecting downstream IWS, upstream water withdrawal indirectly influences it by altering downstream discharge (0.002) and influencing upstream IWS (0.789). These results suggest that spatial spillover effects in transboundary basins mainly occur through changes in IWS driven by variations in upstream irrigated areas, which subsequently affect downstream IWS. Transboundary water conflict hotspots identified with upstream spillovers Transboundary water conflict hotspots identified with upstream spillovers We examine potential water conflict hotspots between upstream and downstream regions within transboundary river basins, identifying basins where irrigation expansion may intensify water conflicts to inform targeted water resource management strategies and enhance cooperative water resource measures across regions. We consider the basins with significant downstream-upstream IWS differences, substantial upstream irrigation expansion trends, and high IWS in the 2000s as current water stress conflict hotspots (Table S3). The top five typical water conflict basins across five continents are recognized: the Ganges-Brahmaputra-Meghna Basin in Asia, the Rio Grande Basin in North America, the Limpopo Basin in Africa, the Guadiana Basin in Europe, and the La Plata Basin in South America (Fig. 5 ). These basins have experienced rapid upstream irrigation expansion since the beginning of the 20th century. In the Ganges-Brahmaputra-Meghna Basin, which accounts for 36% of the world's unsustainable irrigation expansion 14 , for example, there has been significant expansion of irrigated croplands upstream (India) from the 1960s to the 2000s. Bangladesh is the downstream country of the basin. The IWS in Bangladesh exhibits a significant correlation with irrigated area, water stress, and irrigation water withdrawal in the upstream sub-basins (Table S4), implying that the over withdrawal of water for irrigation from the Ganges River have intensified pressure on downstream irrigation water use in Bangladesh. Potential IWS changes in future climate scenarios Under the future climate scenarios of RCP2.6 and RCP6.0 for the 21st century (Figs. S2 and 3), sub-basin IWS is projected to initially increase before gradually slowing down. From the 2010s to the 2050s, 68% of sub-basins are projected to experience rising stress, and 46% are projected to continue seeing increasing stress over the subsequent 50 years (Fig. 3 A–D). Overall, 55% of sub-basins are projected to exhibit an upward trend in the 21st century. Moreover, the proportion of basins with higher upstream IWS than downstream consistently rises to 48%, 52%, and 51% in the 2010s, 2050s, and 2090s, respectively (Fig. 3 E–H). Reflecting the overall change in water stress, a larger percentage of downstream-upstream pairs (78%) show an expanding difference between upstream and downstream IWSI from the 2010s to the 2050s, with more than half of these (57%) in areas where upstream IWS exceeds downstream IWS. Basins exhibiting significant trends in upstream irrigation expansion tend to experience greater future downstream IWS exacerbation trends, indicating increased spatial spillover effects (Fig. 4 ). Additionally, we identify 12 basins (Table S5), nine of which are in Asia, where potential future water conflict events may arise, by hierarchical ranking of trends in upstream irrigation expansion (1901–2005), downstream IWSI, and downstream-upstream differences between 1901 and 2099. Notably, four basins are identified as potentially emerged water conflict hotspots: the Don Basin in Europe, the Kura-Araks Basin and Irrawaddy Basin in Asia, and the La Plata Basin in South America. These regions will require enhanced cooperative water management and sustainable agricultural development in the future. Overall, these findings provide a global perspective on the potential for irrigation water conflicts within transboundary river basins. Discussion Spillover effects of upstream agricultural irrigation on downstream IWS While previous studies have primarily analyzed the impact of upstream water use on downstream water availability 30 , 31 from a purely water resources perspective, our study is the first to identify the spatial spillover effects of irrigation expansion in upstream basins on downstream water stress. By integrating IWS patterns with irrigated area data, we emphasize the significant role of agriculture in water stress 10 , 11 , 30 . Our study highlights the escalating water stress attributed to agriculture-driven irrigation water use in transboundary basins, where growing disparities between upstream and downstream IWS pose a heightened risk of future water conflicts. Our findings agree with previous global studies on agricultural water stress, indicating that over 80% of croplands are expected to face worsening water scarcity 32 , 33 , with more than half (52%) of current irrigation expansion occurring in regions already experiencing water stress as of 2000 14 . Our findings also indicate that 91.7% of sub-basins have experienced increased IWS since 1901, and this trend is expected to intensify in the future. This aligns with the global pattern of water stress 34 , 35 , which is projected to worsen in already stressed regions, with Central Asia, Southeast Asia, and northern Africa identified as hotspots. Policy implications for reconciling food and water securities in basins We found that upstream irrigation spillover effects exacerbate downstream irrigation water stress and we identified key conflict hotspots. Our findings underscore the importance of enhancing cooperation (SDG 17 \"Partnerships for the Goals\") to mitigate water scarcity (SDG 6 \"Clean Water and Sanitation\") and to address the increased pressure on irrigation water due to irrigation expansion (SDG 2 \"Zero Hunger\") in these regions. In sub-basins experiencing significant irrigated cropland expansion and increasing water scarcity, particularly in upstream sub-basins, governments can alleviate water stress by adopting water-saving irrigation practices, promoting sustainable agriculture, and optimizing water resource allocation. First, traditional surface irrigation can be replaced with water-saving systems such as drip irrigation under plastic film or mulch cover and sprinkler irrigation. These methods, supported by government policies and subsidies, have been shown to achieve significant water conservation in agricultural irrigation 36 , 37 . Second, implementing sustainable agricultural practices like crop rotation, fallowing, no-till farming, and organic fertilization helps maintain soil moisture, improving water retention while boosting crop productivity 38 , 39 . Third, optimizing water resource allocation to increase water availability can be achieved through targeted strategies such as augmenting water supply 40 , desalination 41 , 42 , reservoir reoperation 43 , inter-basin water transfers 44 , managed aquifer recharge 45 , and the sustainable use of renewable groundwater 46 . Fourth, constructing decentralized, small-scale water harvesting and storage facilities can offer cost-effective solutions, especially for small farmers, compared to large-scale dams and centralized irrigation systems 13 . Furthermore, establishing effective transboundary cooperation mechanisms is essential for sustainable water resources management and agricultural development. Improved treaties in transboundary basins can ensure equitable and sustainable water allocation through regulations on reservoir and dam use and upstream-downstream water allocation 47 . Typical solutions involve optimization methods 48 , game theory approaches 49 , 50 , and combining evolutionary game theory and system dynamics modeling 51 , 52 to find equilibrium outcomes for strategic scenarios in transboundary river basins. Such mechanisms can involve collaboration among countries, governments, non-governmental organizations, businesses, and communities, sharing information and resources, and developing standard action plans and strategies 53 . Limitations and uncertainties We combined four global hydrological models (GHMs) and four global climate models (GCMs) under the ISIMIP 2b protocol, using median values across the models to provide a more reliable estimation of IWS across regions. These multi-model predictions could significantly reduce uncertainty compared to previous studies relying on single-model projections 35 , 54 – 56 . The robustness of the method has been verified through interquartile range calculations and comparisons with existing water scarcity maps (see supplementary information). This multi-model approach updates prior water stress assessments 10 , 11 , 30 and mitigates projection uncertainties 57 . Comparisons reveal that models excluding the effects of rising CO 2 on crop water-use efficiency (e.g., H08, PCR-GLOBWB, CWatM) predict increased future irrigation water demand, whereas models accounting for these effects (e.g., LPJmL) do not 58 , 59 . This distinction may impact IWSI projections under future climate scenarios. This study assumes all water is withdrawn from surface rivers, which may underestimate water availability in regions relying on groundwater or other storage sources. Actual water shortages may be less severe than estimated. The Environmental Flow Requirements (EFR) assessment incorporates environmental protection principles in water resource management 60 , using the Variable Monthly Flow (VMF) method to align EFR calculations with periods of water abundance and depletion 61 . Our analysis employs water stress indicators to estimate water scarcity, which is directly related to water use but does not account for societal adaptive capacity to cope with stress 62 . Future research should incorporate changes in water quality 63 and seasonal variations in water stress 64 into the assessment. Methods Data We divided the data used for the study into three categories: (i) transboundary basin boundary data, (ii) water availability and withdrawal data, and (iii) irrigation cropland area data. We sourced the transboundary basin boundary data from Munia et al. 30 , which encompasses 246 basins and 886 sub-basins—433 upstream, 207 midstream, and 246 downstream (Fig. S5). They limited basin delineation to watersheds with a surface area greater than 10,000 km 2 , based on source data provided by the Transboundary Freshwater Disputes Database (TFDD), to maintain consistency with the 30-arc-minute resolution of the water availability and withdrawal data. A basin typically consists of a downstream sub-basin along with several midstream and upstream sub-basins. In our analysis of spatial spillover effects from upstream to downstream, we considered the difference between downstream and all upstream areas (DU) as the downstream-upstream difference when a basin lacked midstream sub-basins. Conversely, if a basin included midstream sub-basins, both the downstream-midstream (DM) and midstream-upstream (MU) differences were treated as downstream-upstream differences, resulting in two pairs of downstream-upstream differences within that basin. Using this pairing criterion, we divided the 246 basins into 326 pairs, comprising 166 DU sub-basin pairs, 80 MU sub-basin pairs, and 80 DM sub-basin pairs. In the results analysis, we referred to all these as pairs of downstream-upstream sub-basins. We used water availability and withdrawal data produced under the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) framework to calculate IWS. ISIMIP provided a comprehensive collection of state-of-the-art GHMs designed to capture water availability and human water use at a 0.5-degree grid resolution 54 . This international climate impact modeling framework offered a consistent view of the world under different climate change scenarios 65 – 67 . We investigated all GHMs under ISIMIP protocols that provided irrigation water withdrawal data. The ISIMIP 2b protocol provided bias-corrected outputs from CMIP5 and CMIP5-based impact models, covering historical and future periods with multiple Representative Concentration Pathways (RCPs) and Shared Socioeconomic Pathways (SSPs) to represent potential temporal trajectories of key climate change drivers 68 . Although the ISIMIP protocol had been updated to version 3, it had not yet been widely adopted for simulating irrigation water use. Therefore, we utilized four GHMs under protocol 2b: H08, LPJmL, PCR-GLOBWB, and CWatM. These models included the necessary metrics for calculating IWS: river flow (dis), irrigation water withdrawal (airrww), domestic water withdrawal (adomww), and industrial water withdrawal (aindww). We provided detailed descriptions of the selected metrics in Table S6, with validation references available in Table S7. We used four global climate models (GCMs) for general circulation modeling: GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5. For future climate and CO 2 concentration scenarios, all four GHMs considered two RCPs: RCP2.6 and RCP6.0. These models accounted for variations in water abstraction and land use based on SSP2 under both RCP2.6 and RCP6.0, while keeping dams and reservoirs fixed at their year-2005 levels. In models with fixed land use types, varying irrigation areas were also considered as part of the land use changes. We used irrigation cropland data Mehta, et al. 14 that provided the global area equipped for irrigation (AEI) at a resolution of 5 arcmin for the period 1901–2015, produced at 10-year intervals until 1980 and at 5-year intervals thereafter. This dataset, derived from recent subnational irrigation statistics from various official sources, was more compatible with long-term time-series analysis compared to other irrigated area datasets 69 – 71 . To reduce uncertainty, we averaged the two different data sets from various sources to the sub-basin scale, culminating in the dataset we used to analyze irrigation cropland expansion. IWSI calculation We derived the data used to calculate IWSI from a combination of four GHMs and four GCMs, resulting in 16 combinations. However, due to significant variations in the calculation methods among some models, certain metrics were represented in fewer than 16 combinations (see Table S6). For each indicator, we first calculated the ten-year synthetic means for each combination at the 0.5°-pixel scale. We then took the median of all combinations as the ensemble result. Finally, we aggregated these indicators to the sub-basin scale for IWSI calculations: where IWSI is the irrigation water stress index of the sub-basin (-), \\(\\:irrWW\\) is local irrigation water withdrawal (km 3 ·yr - 1 ), and WW is the total local water withdrawal (km 3 ·yr - 1 ), which includes irrigation ( irrWW ), domestic ( domWW ), and industrial ( indWW ) water withdrawal. The water stress index WSI (-) is calculated following established methods from existing studies 56 , 72 – 77 . The local discharge is Q (km 3 ·yr - 1 ), and EFR (km 3 ·yr - 1 ) is the environmental flow requirement. Q - EFR is the locally available water consumption. We consider an IWSI above 0.2 indicative of water scarcity and an IWSI above 1 indicates that withdrawal seriously exceeds the local water load. The uncertainty estimates of IWSI are analyzed in the Supplementary Information. We calculated the EFR using the Variable Monthly Flow (VMF) method 61 . When the monthly flow of the river was less than 0.4 times the decadal average, we considered it a dry month and allocated 60% of the flow to EFR. When the monthly flow was between 0.4 and 0.8 times the decadal average, it was a medium-flow month, with 45% allocated to EFR. When the monthly flow exceeded 0.8 times the decadal average, it was a high-flow month, and 30% was allocated to EFR 32 , 78 . Global applications have demonstrated that the VMF method effectively represents locally estimated EFRs 63 , 79 , 80 . We then summed the EFRs for each month to obtain the annual mean EFR for each grid. Spillover effects assessment We selected a total of 136 basins with irrigated cropland from 1901 to 2005 for analysis. To identify the upstream factors that most significantly impact downstream IWS, we processed upstream IWSI indicators, including domestic water withdrawal ( \\(\\:{domWW}_{up}\\) ), industrial water withdrawal ( \\(\\:{indWW}_{up}\\) ), irrigation water withdrawal ( \\(\\:{irrWW}_{up}\\) ), and water availability ( \\(\\:{discharge}_{up}\\) ). We also considered the irrigated area ( \\(\\:{irrArea}_{up}\\) ), which is closely related to irrigation water use. Given the strong correlation between irrigated areas and irrigation water withdrawal, we modeled these two variables separately to develop two MRMs and assess their respective impacts on downstream IWS. The difference in IWS between upstream and downstream sub-basins can indicate potential conflicts within the basin; therefore, we incorporated the previous year's IWSI difference ( \\(\\:{IWSI}_{dif}\\) ) to evaluate its effect on downstream IWS in the following year, thereby avoiding multicollinearity with other indicators. This approach resulted in six indicators for the MRM analysis (Eq. ( 4 )). $$\\:{IWSI}_{down}=\\left\\{\\begin{array}{c}\\alpha\\:\\:+\\:{\\beta\\:}_{1}{IWSI}_{dif}+\\:{{\\beta\\:}_{2}irrArea}_{up}{{+\\:\\beta\\:}_{3}domWW}_{up}{{+\\:\\beta\\:}_{4}indWW}_{up}+{{\\:\\beta\\:}_{5}discharge}_{up}\\\\\\:\\alpha\\:{\\prime\\:}\\:+\\:{\\beta\\:}_{1}{\\prime\\:}{IWSI}_{dif}+\\:{{\\beta\\:}_{2}{\\prime\\:}irrWW}_{up}{{+\\:\\beta\\:}_{3}{\\prime\\:}domWW}_{up}{{+\\:\\beta\\:}_{4}{\\prime\\:}indWW}_{up}+{{\\:\\beta\\:}_{5}{\\prime\\:}discharge}_{up}\\end{array}\\right.$$ 4 The subscripts “down”, “dif”, and “up” represent downstream, downstream-upstream differences, and upstream, respectively. \\(\\:\\alpha\\:\\) is a constant term, and \\(\\:{\\beta\\:}_{1}\\) ( \\(\\:{\\beta\\:}_{1}{\\prime\\:}\\) ) to \\(\\:{\\beta\\:}_{5}\\) ( \\(\\:{\\beta\\:}_{5}{\\prime\\:}\\) ) are coefficients to be estimated. All water withdrawals are in km 3 ·yr - 1 while irrigated area is provided in Mha. We applied Z-score standardization to eliminate the effects of magnitude differences between variables, enabling direct comparison within the regression model. The adjusted R-squared values obtained from the multiple regression analysis were 0.466 for irrigated area and 0.36 for irrigation water withdrawal. These p-values were less than 0.001, and Variance Inflation Factor (VIF) values were less than 10, indicating that the MRMs could effectively explain changes in downstream IWS. Additionally, we utilized SEM to understand each indicator's direct and indirect impacts on downstream IWS, analyzing the pathways through which upstream spillover effects are realized. Latent variables were not considered in this analysis. We constructed this SEM using AMOS software 81 – 83 . The goodness-of-fit statistics for the model were within expected ranges: the probability level of 0.001, indicating an extremely significant model fit; the Chi-square Ratio of Degrees of Freedom (CMIN/DF) of 2.203, which falls within the acceptable range of 1 to 3; the Root Mean Square Error of Approximation (RMSEA) of 0.029, meeting the requirement of being less than 0.1; and the Comparative Fit Index (CFI) of 0.999, the Normed Fit Index (NFI) of 0.998, and the Tucker-Lewis Index (TLI) of 0.995, all exceeding the threshold of 0.9. These indicators suggest an acceptable fit for the model, implying that this structural model can effectively represent the relationship between these variables and downstream IWS. Declarations Competing interests The authors declare no competing interests. Author Contributions J.D. and Q.G. conceptualized the study. X.C. collected and processed the data and constructed the figures. X.H., Y.Z., and S.S. improved experiments. D.J., Y.Q., Y.W., G.S., J. 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Environ Sci Technol 57:11520–11530 Tarhini A, Hone K, Liu X (2014) The effects of individual differences on e-learning users’ behaviour in developing countries: A structural equation model. Comput Hum Behav 41:153–163 Narayanan A (2012) A review of eight software packages for structural equation modeling. Am Stat 66:129–138 Sobaih AEE, Elshaer I, Hasanein AM, Abdelaziz AS (2021) Responses to COVID-19: The role of performance in the relationship between small hospitality enterprises’ resilience and sustainable tourism development. Int J Hospitality Manage 94:102824 Additional Declarations There is NO Competing Interest. Supplementary Files Supplementaryinformation.docx Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-5351205\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Article\",\"associatedPublications\":[],\"authors\":[{\"id\":379536866,\"identity\":\"70426be3-77cb-4183-99af-4384965e804b\",\"order_by\":0,\"name\":\"Jinwei 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Oklahoma\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Xiangming\",\"middleName\":\"\",\"lastName\":\"Xiao\",\"suffix\":\"\"},{\"id\":379536878,\"identity\":\"09cfab93-4530-4b21-8bbf-19e58373dc48\",\"order_by\":12,\"name\":\"Quansheng Ge\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Quansheng\",\"middleName\":\"\",\"lastName\":\"Ge\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2024-10-29 05:20:21\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-5351205/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-5351205/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":71844935,\"identity\":\"c8637986-190c-4d75-8d8c-3ffe4e10078f\",\"added_by\":\"auto\",\"created_at\":\"2024-12-19 06:20:53\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":655429,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eIrrigation water stress (IWS) and sub-basin differences in the 1900s, 1950s, and 2000s.\\u003c/strong\\u003e \\u003cstrong\\u003e(\\u003c/strong\\u003eA\\u003cstrong\\u003e–\\u003c/strong\\u003eC\\u003cstrong\\u003e)\\u003c/strong\\u003e Irrigation water stress index (IWSI) in sub-basin areas categorized into six levels, where values greater than 0.2 indicate water scarcity. \\u003cstrong\\u003e(\\u003c/strong\\u003eD\\u003cstrong\\u003e)\\u003c/strong\\u003e Distribution of sub-basins across continents for different IWSI categories. \\u003cstrong\\u003e(\\u003c/strong\\u003eE\\u003cstrong\\u003e–\\u003c/strong\\u003eG\\u003cstrong\\u003e)\\u003c/strong\\u003e Variation in IWSI between upstream and downstream sub-basins (or downstream\\u003cstrong\\u003e–\\u003c/strong\\u003emiddle stream, middle stream\\u003cstrong\\u003e–\\u003c/strong\\u003eupstream if the basin has a middle stream). Negative values indicate lower downstream IWSI compared to upstream, while positive values indicate higher downstream IWSI. \\u003cstrong\\u003e(\\u003c/strong\\u003eH\\u003cstrong\\u003e)\\u003c/strong\\u003eDistribution of sub-basins across continents for different categories of IWSI difference. Irrigation expansion led to an increase in the number of basins with IWSI differences across the three periods, resulting in varying total numbers of basins in each period: 162, 174, and 209, respectively.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5351205/v1/da842909e8666382e38785af.png\"},{\"id\":71844933,\"identity\":\"50630eaf-5ed5-48f2-9334-43b2aea25269\",\"added_by\":\"auto\",\"created_at\":\"2024-12-19 06:20:53\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":161835,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eStructural equation model (SEM) of the impact of indicators on downstream irrigation water stress (IWS).\\u003c/strong\\u003e Asterisks denote statistical significance based on P values: *P \\u0026lt; 0.05, **P \\u0026lt; 0.01, ***P \\u0026lt; 0.001. The numbers adjacent to the arrows represent standardized path coefficients, indicating how much the dependent variables change in standard deviations for each standard deviation change in the independent variables. Blue arrows represent positive effects, while red arrows represent negative effects. Dark arrows indicate direct effects\\u003cstrong\\u003e,\\u003c/strong\\u003eand light arrows indicate indirect effects on downstream IWS. The arrow thickness is proportional to the strength of the standardized path coefficient. The bar chart illustrates the direct and indirect effects, as well as the proportion of total effects, for the five indicators with total effects greater than 0.5. Detailed paths and the direct and indirect effects of other indicators are provided in Table S1 and Table S2.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5351205/v1/43eb9038988782258d7f937f.png\"},{\"id\":71844939,\"identity\":\"6d5f48c0-27b0-4a11-85ea-0e86d5f24674\",\"added_by\":\"auto\",\"created_at\":\"2024-12-19 06:20:54\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":661174,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eIWS and sub-basin differences in the 2010s, 2050s, and 2090s under future scenario RCP6.0.\\u003c/strong\\u003e \\u003cstrong\\u003e(\\u003c/strong\\u003eA\\u003cstrong\\u003e–\\u003c/strong\\u003eC\\u003cstrong\\u003e)\\u003c/strong\\u003eIWSI in sub-basin areas categorized into six levels, where values greater than 0.2 indicate water scarcity. \\u003cstrong\\u003e(\\u003c/strong\\u003eD\\u003cstrong\\u003e)\\u003c/strong\\u003e Distribution of sub-basins across continents for different IWSI categories. \\u003cstrong\\u003e(\\u003c/strong\\u003eE\\u003cstrong\\u003e–\\u003c/strong\\u003eG\\u003cstrong\\u003e)\\u003c/strong\\u003e Variation in IWSI between upstream and downstream sub-basins. Negative values indicate lower downstream IWSI compared to upstream, while positive values indicate higher downstream IWSI. \\u003cstrong\\u003e(\\u003c/strong\\u003eH\\u003cstrong\\u003e)\\u003c/strong\\u003eDistribution of sub-basins across continents for different categories of IWSI difference. Irrigation expansion led to an increase in the number of basins with IWSI differences across the three periods, resulting in varying total numbers of basins in each period: 218, 219, and 222, respectively. For the future, one of the RCP scenarios is shown here (see Fig. S2 for RCP2.6 scenarios).\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5351205/v1/5d9b2c2ca6e4915bf1887c96.png\"},{\"id\":71845868,\"identity\":\"35d4c34a-9c62-48b6-8916-65eb48835f46\",\"added_by\":\"auto\",\"created_at\":\"2024-12-19 06:28:53\",\"extension\":\"png\",\"order_by\":4,\"title\":\"Figure 4\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":910024,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eSpatial spillover effects of upstream irrigation expansion. \\u003c/strong\\u003eThe graded colors represent the downstream IWSI trend in the basin from 1901 to 2099, while the circle sizes indicate the corresponding trend magnitude of irrigated area expansion in the upstream region. Trend value grading is calculated based on interquartile spacing. The bar chart shows the number of basins with downstream IWSI trends relative to the trend magnitude of upstream irrigated cropland expansion.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5351205/v1/cfc845c9a4fea5028518f074.png\"},{\"id\":71844937,\"identity\":\"3f3eeb34-443e-4c13-9e27-9c970321de7e\",\"added_by\":\"auto\",\"created_at\":\"2024-12-19 06:20:53\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":348178,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eTypical transboundary basins where water conflicts occur on five continents.\\u003c/strong\\u003e (A)\\u003cstrong\\u003e \\u003c/strong\\u003eThe Rio Grande Basin in North America. (B)\\u003cstrong\\u003e \\u003c/strong\\u003eThe La Plata Basin in South America. (C)\\u003cstrong\\u003e \\u003c/strong\\u003eThe Guadiana Basin in Europe. (D)\\u003cstrong\\u003e \\u003c/strong\\u003eThe Limpopo Basin in Africa. (E), the Ganges-Brahmaputra-Meghna Basin in Asia. And changes in upstream and downstream irrigation water stress index (IWSI) and irrigation area (irrArea) during the 20th century.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5351205/v1/36aaefadc12732a41c21bf10.png\"},{\"id\":71846119,\"identity\":\"46875d0c-e39c-472e-85c1-cde3670d6242\",\"added_by\":\"auto\",\"created_at\":\"2024-12-19 06:36:55\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":3173159,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5351205/v1/7a63082c-a9da-4d01-9b82-634feb094eda.pdf\"},{\"id\":71844936,\"identity\":\"d2f6c5f4-0efe-4322-8f2c-0a9ea1128750\",\"added_by\":\"auto\",\"created_at\":\"2024-12-19 06:20:53\",\"extension\":\"docx\",\"order_by\":2,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":16267143,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"Supplementaryinformation.docx\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5351205/v1/33d2e42df5c2719393e22830.docx\"}],\"financialInterests\":\"There is \\u003cb\\u003eNO\\u003c/b\\u003e Competing Interest.\",\"formattedTitle\":\"Spillover effects of upstream irrigation expansion on downstream water stress in transboundary river basins\",\"fulltext\":[{\"header\":\"Main\",\"content\":\"\\u003cp\\u003eClimate change and increasing human water withdrawal have escalated global water scarcity, posing significant challenges to sustainable development\\u003csup\\u003e1,2\\u003c/sup\\u003e. The expansion of irrigated croplands has emerged as a major contributor to water stress since the 20th century\\u003csup\\u003e3,4\\u003c/sup\\u003e, as agriculture accounts for the largest share of national water consumption, often exceeding half of the total in most countries\\u003csup\\u003e5\\u003c/sup\\u003e. Transboundary river basins, which cover nearly half of the global land area and support 40% of the population\\u003csup\\u003e6\\u003c/sup\\u003e, represent critical yet understudied regions regarding water management\\u003csup\\u003e5,7\\u003c/sup\\u003e. Geopolitical tension between upstream and downstream countries in these basins further intensifies regional water scarcity\\u003csup\\u003e8,9\\u003c/sup\\u003e.\\u0026nbsp;Thus, investigating how the expansion of irrigated croplands affects irrigation water stress (IWS) in upstream and downstream areas is crucial for informing policy aimed at sustainable water use and fostering cross-border cooperation.\\u003c/p\\u003e\\n\\u003cp\\u003eSeveral studies have examined the dynamics of water stress in transboundary basins, focusing on downstream dependence on upstream regions and the drivers of water scarcity\\u003csup\\u003e10-12\\u003c/sup\\u003e. However, these studies overlook the critical role of agriculture in influencing water stress across both upstream and downstream areas\\u003cstrong\\u003e.\\u0026nbsp;\\u003c/strong\\u003eGlobal and regional studies have shown that the expansion of irrigated cropland places significant pressure on freshwater resources\\u003csup\\u003e13-17\\u003c/sup\\u003e, with 52% of this expansion occurring in water-scarce regions by 2000\\u003csup\\u003e14\\u003c/sup\\u003e. This not only jeopardizes the sustainable replenishment of local water supplies but also exacerbates hydrological droughts due to agriculture\\u0026apos;s high water demand\\u003csup\\u003e16\\u003c/sup\\u003e. Moreover, variations in agricultural development and governance between countries further complicate water resource management in transboundary basins\\u003csup\\u003e18\\u003c/sup\\u003e. While previous studies have shown that uncontrolled upstream water use could lead to unsustainable downstream supplies\\u003csup\\u003e19,20\\u003c/sup\\u003e, the specific impacts of irrigated agriculture remain undocumented.\\u003c/p\\u003e\\n\\u003cp\\u003eUnderstanding the broader impacts of upstream agricultural practices requires considering their spillover effects on downstream areas. Spillover effects from transboundary interactions could present both benefits and risks, manifesting as synergies and trade-offs\\u003csup\\u003e21,22\\u003c/sup\\u003e. Trade-offs typically arise from competing demands for water resources\\u003csup\\u003e23\\u003c/sup\\u003e, while synergies could result from collaborative management of shared resources\\u003csup\\u003e24,25\\u003c/sup\\u003e. Addressing the spillover effects through effective watershed management benefits both upstream and downstream regions in achieving sustainable development goals. Although significant attention has been given to the impacts of water pollution\\u003csup\\u003e26,27\\u003c/sup\\u003e, drought\\u003csup\\u003e21\\u003c/sup\\u003e, and war\\u003csup\\u003e28,29\\u003c/sup\\u003e on transboundary water resources, the spillover effects of agricultural development on IWS in transboundary river basins remain less understood.\\u003c/p\\u003e\\n\\u003cp\\u003eHere, we address the following scientific questions: 1) is there a significant difference in irrigation water stress (IWS) between upstream and downstream sub-basins; 2) how do upstream irrigated croplands affect downstream IWS through spatial spillover effects; 3) where are the potential water conflict hotspots under IWS spillover effects; and 4) how will IWS and these spillover effects evolve under future climate scenarios?\\u003c/p\\u003e\\n\\u003cp\\u003eTo address these questions, we used irrigation water withdrawal and water availability data from global hydrological models (GHMs) and global climate models (GCMs) to calculate the irrigation water stress index (IWSI). This allowed us to assess variations in IWS, focusing on downstream-upstream disparities from 1901 to 2099. Using multiple regression modeling (MRM) and structural equation modeling (SEM), we analyzed the direct and indirect impacts of upstream irrigated cropland expansion and water withdrawal on downstream IWS, focusing on spatial spillover effects. Furthermore, by integrating trend analysis of irrigation expansion with projected IWS changes under future climate scenarios, we identified potential changes in IWSI and spillover effects, emphasizing the risk of regional water conflicts. Our findings fill a knowledge gap regarding the spillover effects of upstream irrigated cropland expansion and shed light on sustainable water management in transboundary river basins.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec2\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eChanges in irrigation water stress (IWS) of upstream and downstream sub-basins\\u003c/h2\\u003e \\u003cp\\u003eDuring 1901\\u0026ndash;2005, 396 out of 431 sub-basins (92%) increasing IWS (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eA\\u0026ndash;D). Also, the number of sub-basins facing irrigation water scarcity, which is defined as irrigation water stress index (IWSI) above 0.2, rises from 51 (12%) in the 1900s to 118 (27%) in the 2000s. Asia dominates the pattern of sub-basins in irrigation water scarcity in the 2000s, accounting for 64% of the global total, particularly in South Asia, where sub-basins in India, Pakistan, and Nepal\\u0026mdash;including the Ganges-Brahmaputra-Meghna Basin, the Indus Basin, and the Rann of Kutch Basin\\u0026mdash;are heavily impacted. Moreover, the number of sub-basins facing severe irrigation water scarcity (IWSI\\u0026thinsp;\\u0026gt;\\u0026thinsp;1), where irrigation water withdrawal exceeds local river discharge, increases significantly from 9 in the 1900s to 36 in the 2000s. The robustness of IWSI is supported by an uncertainty assessment we conducted, which is provided in the Supplemental Information.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eWe compare changes between the upstream and downstream sub-basins and find that, out of the 218 downstream-upstream sub-basin pairs, 195 (89%) exhibit an increase in the downstream-upstream IWSI disparity (or, in basins with a middle stream, the difference between downstream and middle stream, and between middle stream and upstream) from the 1900s to the 2000s, with the most pronounced increases occurring in Asia and Africa (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003eE\\u0026ndash;H). The proportion of sub-basin pairs with significant widening differences (IWSI differences\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.1 or \\u0026lt; \\u0026minus;\\u0026thinsp;0.1) steadily rises, reaching half of the pairs, with figures at 27%, 35%, and 47% in the 1900s, 1950s, and 2000s, respectively. Notably, sub-basins where upstream IWS exceeds downstream IWS become more prevalent, accounting for 41%, 45%, and 48% of pairs in the 1900s, 1950s, and 2000s, respectively, and representing more than half (53%) of the sub-basin pairs with significant widening IWSI differences by the 2000s.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSpillover effects of upstream irrigation on downstream IWS\\u003c/h2\\u003e \\u003cp\\u003eWe conduct multiple regression models (MRMs) to analyze downstream IWS attribution using decadal averages from the 20th century. The variables considered include upstream irrigation areas, industrial water withdrawal, residential water withdrawal, water availability from the same years, and downstream-upstream IWSI differences from the previous year. Our findings indicate that upstream irrigated area has the largest impact, with downstream IWSI increasing by 0.87\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.09% for each percentage point increase in upstream irrigated area (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). This suggests that the expansion of upstream irrigated areas threatens downstream water security. Additionally, downstream-upstream IWSI differences from the previous year significantly influence downstream IWSI, which increases by 0.71\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.04% for each percentage point of the previous year\\u0026rsquo;s downstream-upstream IWSI differences. This highlights that downstream-upstream disparities adversely affect water use conditions in downstream basins. Downstream IWSI exhibits a significant negative correlation with upstream domestic water withdrawal (\\u003cem\\u003ep\\u003c/em\\u003e-value\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), approximately double the positive impact of industrial water withdrawal: \\u0026minus;\\u0026thinsp;0.69 vs. 0.24. Upstream water availability has the least effect, accounting for only \\u0026minus;\\u0026thinsp;0.03% of downstream IWSI. These results highlight the substantial upstream spillover effects on downstream IWS, with irrigated cropland expansion contributing to 34.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.5% of the total impacts. Furthermore, we replace the irrigation area with irrigation water withdrawal to redo the attribution analyses (see Supplementary Information), and the results confirm the previous findings.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eMultiple linear regression model determining the effect of indicators to downstream irrigation water stress (IWS) from 1901 to 2005.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eCoefficient\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003es.e.\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eP values\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e95% Confidence Interval.\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eirrArea\\u003c/b\\u003e\\u003csub\\u003e\\u003cb\\u003eup\\u003c/b\\u003e\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.873***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.045\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.089\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eIWSI\\u003c/b\\u003e\\u003csub\\u003e\\u003cb\\u003edif\\u003c/b\\u003e\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/td\\u003e 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colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.466\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eP values\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.000\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"5\\\"\\u003eAsterisks denote the statistical significance based on P values: *P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05, **P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01, ***P\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001. IWSI\\u003csub\\u003edif\\u003c/sub\\u003e indicates the previous year's downstream irrigation water stress index (IWSI) minus upstream IWSI difference. The subscripts \\\"\\u003csub\\u003eup\\u003c/sub\\u003e\\\" denote upstream. irrArea denotes the area under irrigated cropland. domWW and indWW represent water withdrawal from domestic and industrial sector, respectively. Discharge refers to the flow in the sub-basin after accounting for the removal of the environmental flow requirement (EFR), i.e., water availability.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eTo understand the influence of upstream sub-basins on downstream IWS, we use structural equation modeling (SEM) to identify both direct and indirect pathways. We find that the direct effect of upstream IWSI (IWSI\\u003csub\\u003eup\\u003c/sub\\u003e) is the highest (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e and Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e), suggesting that when upstream sub-basins experience irrigation water shortages, downstream sub-basins are similarly affected. As upstream IWS intensifies, downstream IWS correspondingly increases. The next largest total effects come from upstream irrigated acreage (irrArea\\u003csub\\u003eup\\u003c/sub\\u003e, 0.841) and irrigation water withdrawal (irrWW\\u003csub\\u003eup\\u003c/sub\\u003e, 0.808). This finding aligns with the multiple regression analysis, confirming that both upstream irrigated area and irrigation water withdrawal significantly impact downstream IWS.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eUpstream irrigated area not only has a direct impact but also indirectly affects downstream IWS by increasing upstream irrigation water withdrawal and consequently worsening upstream IWS, contributing to 95.6% of its total effect (Table S2). Although not directly affecting downstream IWS, upstream water withdrawal indirectly influences it by altering downstream discharge (0.002) and influencing upstream IWS (0.789). These results suggest that spatial spillover effects in transboundary basins mainly occur through changes in IWS driven by variations in upstream irrigated areas, which subsequently affect downstream IWS.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eTransboundary water conflict hotspots identified with upstream spillovers\\u003c/h3\\u003e\\n\\u003cdiv class=\\\"Heading\\\"\\u003eTransboundary water conflict hotspots identified with upstream spillovers\\u003c/div\\u003e \\u003cp\\u003eWe examine potential water conflict hotspots between upstream and downstream regions within transboundary river basins, identifying basins where irrigation expansion may intensify water conflicts to inform targeted water resource management strategies and enhance cooperative water resource measures across regions. We consider the basins with significant downstream-upstream IWS differences, substantial upstream irrigation expansion trends, and high IWS in the 2000s as current water stress conflict hotspots (Table S3).\\u003c/p\\u003e \\u003cp\\u003eThe top five typical water conflict basins across five continents are recognized: the Ganges-Brahmaputra-Meghna Basin in Asia, the Rio Grande Basin in North America, the Limpopo Basin in Africa, the Guadiana Basin in Europe, and the La Plata Basin in South America (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). These basins have experienced rapid upstream irrigation expansion since the beginning of the 20th century. In the Ganges-Brahmaputra-Meghna Basin, which accounts for 36% of the world's unsustainable irrigation expansion \\u003csup\\u003e\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u003c/sup\\u003e, for example, there has been significant expansion of irrigated croplands upstream (India) from the 1960s to the 2000s. Bangladesh is the downstream country of the basin. The IWS in Bangladesh exhibits a significant correlation with irrigated area, water stress, and irrigation water withdrawal in the upstream sub-basins (Table S4), implying that the over withdrawal of water for irrigation from the Ganges River have intensified pressure on downstream irrigation water use in Bangladesh.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e\\n\\u003ch3\\u003ePotential IWS changes in future climate scenarios\\u003c/h3\\u003e\\n\\u003cp\\u003eUnder the future climate scenarios of RCP2.6 and RCP6.0 for the 21st century (Figs. S2 and 3), sub-basin IWS is projected to initially increase before gradually slowing down. From the 2010s to the 2050s, 68% of sub-basins are projected to experience rising stress, and 46% are projected to continue seeing increasing stress over the subsequent 50 years (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eA\\u0026ndash;D). Overall, 55% of sub-basins are projected to exhibit an upward trend in the 21st century. Moreover, the proportion of basins with higher upstream IWS than downstream consistently rises to 48%, 52%, and 51% in the 2010s, 2050s, and 2090s, respectively (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003eE\\u0026ndash;H). Reflecting the overall change in water stress, a larger percentage of downstream-upstream pairs (78%) show an expanding difference between upstream and downstream IWSI from the 2010s to the 2050s, with more than half of these (57%) in areas where upstream IWS exceeds downstream IWS. Basins exhibiting significant trends in upstream irrigation expansion tend to experience greater future downstream IWS exacerbation trends, indicating increased spatial spillover effects (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eAdditionally, we identify 12 basins (Table S5), nine of which are in Asia, where potential future water conflict events may arise, by hierarchical ranking of trends in upstream irrigation expansion (1901\\u0026ndash;2005), downstream IWSI, and downstream-upstream differences between 1901 and 2099. Notably, four basins are identified as potentially emerged water conflict hotspots: the Don Basin in Europe, the Kura-Araks Basin and Irrawaddy Basin in Asia, and the La Plata Basin in South America. These regions will require enhanced cooperative water management and sustainable agricultural development in the future. Overall, these findings provide a global perspective on the potential for irrigation water conflicts within transboundary river basins.\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSpillover effects of upstream agricultural irrigation on downstream IWS\\u003c/h2\\u003e \\u003cp\\u003eWhile previous studies have primarily analyzed the impact of upstream water use on downstream water availability \\u003csup\\u003e\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e\\u003c/sup\\u003e from a purely water resources perspective, our study is the first to identify the spatial spillover effects of irrigation expansion in upstream basins on downstream water stress. By integrating IWS patterns with irrigated area data, we emphasize the significant role of agriculture in water stress \\u003csup\\u003e\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eOur study highlights the escalating water stress attributed to agriculture-driven irrigation water use in transboundary basins, where growing disparities between upstream and downstream IWS pose a heightened risk of future water conflicts. Our findings agree with previous global studies on agricultural water stress, indicating that over 80% of croplands are expected to face worsening water scarcity \\u003csup\\u003e\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e\\u003c/sup\\u003e, with more than half (52%) of current irrigation expansion occurring in regions already experiencing water stress as of 2000 \\u003csup\\u003e14\\u003c/sup\\u003e. Our findings also indicate that 91.7% of sub-basins have experienced increased IWS since 1901, and this trend is expected to intensify in the future. This aligns with the global pattern of water stress \\u003csup\\u003e\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e\\u003c/sup\\u003e, which is projected to worsen in already stressed regions, with Central Asia, Southeast Asia, and northern Africa identified as hotspots.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003ePolicy implications for reconciling food and water securities in basins\\u003c/h2\\u003e \\u003cp\\u003eWe found that upstream irrigation spillover effects exacerbate downstream irrigation water stress and we identified key conflict hotspots. Our findings underscore the importance of enhancing cooperation (SDG 17 \\\"Partnerships for the Goals\\\") to mitigate water scarcity (SDG 6 \\\"Clean Water and Sanitation\\\") and to address the increased pressure on irrigation water due to irrigation expansion (SDG 2 \\\"Zero Hunger\\\") in these regions.\\u003c/p\\u003e \\u003cp\\u003eIn sub-basins experiencing significant irrigated cropland expansion and increasing water scarcity, particularly in upstream sub-basins, governments can alleviate water stress by adopting water-saving irrigation practices, promoting sustainable agriculture, and optimizing water resource allocation. First, traditional surface irrigation can be replaced with water-saving systems such as drip irrigation under plastic film or mulch cover and sprinkler irrigation. These methods, supported by government policies and subsidies, have been shown to achieve significant water conservation in agricultural irrigation\\u003csup\\u003e\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e\\u003c/sup\\u003e. Second, implementing sustainable agricultural practices like crop rotation, fallowing, no-till farming, and organic fertilization helps maintain soil moisture, improving water retention while boosting crop productivity\\u003csup\\u003e\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e\\u003c/sup\\u003e. Third, optimizing water resource allocation to increase water availability can be achieved through targeted strategies such as augmenting water supply\\u003csup\\u003e\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e\\u003c/sup\\u003e, desalination\\u003csup\\u003e\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e\\u003c/sup\\u003e, reservoir reoperation\\u003csup\\u003e\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e\\u003c/sup\\u003e, inter-basin water transfers\\u003csup\\u003e\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e\\u003c/sup\\u003e, managed aquifer recharge\\u003csup\\u003e\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e\\u003c/sup\\u003e, and the sustainable use of renewable groundwater\\u003csup\\u003e\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e\\u003c/sup\\u003e. Fourth, constructing decentralized, small-scale water harvesting and storage facilities can offer cost-effective solutions, especially for small farmers, compared to large-scale dams and centralized irrigation systems\\u003csup\\u003e\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eFurthermore, establishing effective transboundary cooperation mechanisms is essential for sustainable water resources management and agricultural development. Improved treaties in transboundary basins can ensure equitable and sustainable water allocation through regulations on reservoir and dam use and upstream-downstream water allocation\\u003csup\\u003e\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e\\u003c/sup\\u003e. Typical solutions involve optimization methods\\u003csup\\u003e\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e\\u003c/sup\\u003e, game theory approaches\\u003csup\\u003e\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e\\u003c/sup\\u003e, and combining evolutionary game theory and system dynamics modeling\\u003csup\\u003e\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e\\u003c/sup\\u003e to find equilibrium outcomes for strategic scenarios in transboundary river basins. Such mechanisms can involve collaboration among countries, governments, non-governmental organizations, businesses, and communities, sharing information and resources, and developing standard action plans and strategies\\u003csup\\u003e\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eLimitations and uncertainties\\u003c/h3\\u003e\\n\\u003cp\\u003eWe combined four global hydrological models (GHMs) and four global climate models (GCMs) under the ISIMIP 2b protocol, using median values across the models to provide a more reliable estimation of IWS across regions. These multi-model predictions could significantly reduce uncertainty compared to previous studies relying on single-model projections\\u003csup\\u003e\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e,\\u003cspan additionalcitationids=\\\"CR55\\\" citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e\\u003c/sup\\u003e. The robustness of the method has been verified through interquartile range calculations and comparisons with existing water scarcity maps (see supplementary information). This multi-model approach updates prior water stress assessments\\u003csup\\u003e\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e\\u003c/sup\\u003e and mitigates projection uncertainties\\u003csup\\u003e\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e\\u003c/sup\\u003e. Comparisons reveal that models excluding the effects of rising CO\\u003csub\\u003e2\\u003c/sub\\u003e on crop water-use efficiency (e.g., H08, PCR-GLOBWB, CWatM) predict increased future irrigation water demand, whereas models accounting for these effects (e.g., LPJmL) do not\\u003csup\\u003e\\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e58\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e\\u003c/sup\\u003e. This distinction may impact IWSI projections under future climate scenarios.\\u003c/p\\u003e \\u003cp\\u003eThis study assumes all water is withdrawn from surface rivers, which may underestimate water availability in regions relying on groundwater or other storage sources. Actual water shortages may be less severe than estimated. The Environmental Flow Requirements (EFR) assessment incorporates environmental protection principles in water resource management\\u003csup\\u003e\\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e60\\u003c/span\\u003e\\u003c/sup\\u003e, using the Variable Monthly Flow (VMF) method to align EFR calculations with periods of water abundance and depletion\\u003csup\\u003e\\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e61\\u003c/span\\u003e\\u003c/sup\\u003e. Our analysis employs water stress indicators to estimate water scarcity, which is directly related to water use but does not account for societal adaptive capacity to cope with stress\\u003csup\\u003e\\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e62\\u003c/span\\u003e\\u003c/sup\\u003e. Future research should incorporate changes in water quality \\u003csup\\u003e\\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e\\u003c/sup\\u003eand seasonal variations in water stress\\u003csup\\u003e\\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e64\\u003c/span\\u003e\\u003c/sup\\u003e into the assessment.\\u003c/p\\u003e\"},{\"header\":\"Methods\",\"content\":\"\\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eData\\u003c/h2\\u003e \\u003cp\\u003eWe divided the data used for the study into three categories: (i) transboundary basin boundary data, (ii) water availability and withdrawal data, and (iii) irrigation cropland area data.\\u003c/p\\u003e \\u003cp\\u003eWe sourced the transboundary basin boundary data from Munia et al.\\u003csup\\u003e\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e\\u003c/sup\\u003e, which encompasses 246 basins and 886 sub-basins\\u0026mdash;433 upstream, 207 midstream, and 246 downstream (Fig. S5). They limited basin delineation to watersheds with a surface area greater than 10,000 km\\u003csup\\u003e\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e\\u003c/sup\\u003e, based on source data provided by the Transboundary Freshwater Disputes Database (TFDD), to maintain consistency with the 30-arc-minute resolution of the water availability and withdrawal data. A basin typically consists of a downstream sub-basin along with several midstream and upstream sub-basins. In our analysis of spatial spillover effects from upstream to downstream, we considered the difference between downstream and all upstream areas (DU) as the downstream-upstream difference when a basin lacked midstream sub-basins. Conversely, if a basin included midstream sub-basins, both the downstream-midstream (DM) and midstream-upstream (MU) differences were treated as downstream-upstream differences, resulting in two pairs of downstream-upstream differences within that basin. Using this pairing criterion, we divided the 246 basins into 326 pairs, comprising 166 DU sub-basin pairs, 80 MU sub-basin pairs, and 80 DM sub-basin pairs. In the results analysis, we referred to all these as pairs of downstream-upstream sub-basins.\\u003c/p\\u003e \\u003cp\\u003eWe used water availability and withdrawal data produced under the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) framework to calculate IWS. ISIMIP provided a comprehensive collection of state-of-the-art GHMs designed to capture water availability and human water use at a 0.5-degree grid resolution\\u003csup\\u003e\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e\\u003c/sup\\u003e. This international climate impact modeling framework offered a consistent view of the world under different climate change scenarios\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR66\\\" citationid=\\\"CR65\\\" class=\\\"CitationRef\\\"\\u003e65\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e67\\u003c/span\\u003e\\u003c/sup\\u003e. We investigated all GHMs under ISIMIP protocols that provided irrigation water withdrawal data. The ISIMIP 2b protocol provided bias-corrected outputs from CMIP5 and CMIP5-based impact models, covering historical and future periods with multiple Representative Concentration Pathways (RCPs) and Shared Socioeconomic Pathways (SSPs) to represent potential temporal trajectories of key climate change drivers\\u003csup\\u003e\\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e68\\u003c/span\\u003e\\u003c/sup\\u003e. Although the ISIMIP protocol had been updated to version 3, it had not yet been widely adopted for simulating irrigation water use. Therefore, we utilized four GHMs under protocol 2b: H08, LPJmL, PCR-GLOBWB, and CWatM. These models included the necessary metrics for calculating IWS: river flow (dis), irrigation water withdrawal (airrww), domestic water withdrawal (adomww), and industrial water withdrawal (aindww). We provided detailed descriptions of the selected metrics in Table S6, with validation references available in Table S7.\\u003c/p\\u003e \\u003cp\\u003eWe used four global climate models (GCMs) for general circulation modeling: GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5. For future climate and CO\\u003csub\\u003e2\\u003c/sub\\u003e concentration scenarios, all four GHMs considered two RCPs: RCP2.6 and RCP6.0. These models accounted for variations in water abstraction and land use based on SSP2 under both RCP2.6 and RCP6.0, while keeping dams and reservoirs fixed at their year-2005 levels. In models with fixed land use types, varying irrigation areas were also considered as part of the land use changes.\\u003c/p\\u003e \\u003cp\\u003eWe used irrigation cropland data Mehta, et al.\\u003csup\\u003e\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e\\u003c/sup\\u003e that provided the global area equipped for irrigation (AEI) at a resolution of 5 arcmin for the period 1901\\u0026ndash;2015, produced at 10-year intervals until 1980 and at 5-year intervals thereafter. This dataset, derived from recent subnational irrigation statistics from various official sources, was more compatible with long-term time-series analysis compared to other irrigated area datasets\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR70\\\" citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e69\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e71\\u003c/span\\u003e\\u003c/sup\\u003e. To reduce uncertainty, we averaged the two different data sets from various sources to the sub-basin scale, culminating in the dataset we used to analyze irrigation cropland expansion.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eIWSI calculation\\u003c/h2\\u003e \\u003cp\\u003eWe derived the data used to calculate IWSI from a combination of four GHMs and four GCMs, resulting in 16 combinations. However, due to significant variations in the calculation methods among some models, certain metrics were represented in fewer than 16 combinations (see Table S6). For each indicator, we first calculated the ten-year synthetic means for each combination at the 0.5\\u0026deg;-pixel scale. We then took the median of all combinations as the ensemble result. Finally, we aggregated these indicators to the sub-basin scale for IWSI calculations:\\u003c/p\\u003e \\u003c/div\\u003e\\u003cp\\u003e\\u003cimg src=\\\"https://myfiles.space/user_files/58894_9946feeafa4c1df7/58894_custom_files/img1734589064.png\\\" width=\\\"364\\\" height=\\\"136\\\"\\u003e\\u003c/p\\u003e\\u003cp\\u003ewhere IWSI is the irrigation water stress index of the sub-basin (-), \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:irrWW\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is local irrigation water withdrawal (km\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e\\u0026middot;yr\\u003csup\\u003e-\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u003c/sup\\u003e), and WW is the total local water withdrawal (km\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e\\u0026middot;yr\\u003csup\\u003e-\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u003c/sup\\u003e), which includes irrigation (\\u003cem\\u003eirrWW\\u003c/em\\u003e), domestic (\\u003cem\\u003edomWW\\u003c/em\\u003e), and industrial (\\u003cem\\u003eindWW\\u003c/em\\u003e) water withdrawal. The water stress index WSI (-) is calculated following established methods from existing studies \\u003csup\\u003e\\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e,\\u003cspan additionalcitationids=\\\"CR73 CR74 CR75 CR76\\\" citationid=\\\"CR72\\\" class=\\\"CitationRef\\\"\\u003e72\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR77\\\" class=\\\"CitationRef\\\"\\u003e77\\u003c/span\\u003e\\u003c/sup\\u003e. The local discharge is Q (km\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e\\u0026middot;yr\\u003csup\\u003e-\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u003c/sup\\u003e), and EFR (km\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e\\u0026middot;yr\\u003csup\\u003e-\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u003c/sup\\u003e) is the environmental flow requirement. Q - EFR is the locally available water consumption. We consider an IWSI above 0.2 indicative of water scarcity and an IWSI above 1 indicates that withdrawal seriously exceeds the local water load. The uncertainty estimates of IWSI are analyzed in the Supplementary Information.\\u003c/p\\u003e \\u003cp\\u003eWe calculated the EFR using the Variable Monthly Flow (VMF) method\\u003csup\\u003e\\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e61\\u003c/span\\u003e\\u003c/sup\\u003e. When the monthly flow of the river was less than 0.4 times the decadal average, we considered it a dry month and allocated 60% of the flow to EFR. When the monthly flow was between 0.4 and 0.8 times the decadal average, it was a medium-flow month, with 45% allocated to EFR. When the monthly flow exceeded 0.8 times the decadal average, it was a high-flow month, and 30% was allocated to EFR\\u003csup\\u003e\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR78\\\" class=\\\"CitationRef\\\"\\u003e78\\u003c/span\\u003e\\u003c/sup\\u003e. Global applications have demonstrated that the VMF method effectively represents locally estimated EFRs\\u003csup\\u003e\\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e63\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR79\\\" class=\\\"CitationRef\\\"\\u003e79\\u003c/span\\u003e,\\u003cspan citationid=\\\"CR80\\\" class=\\\"CitationRef\\\"\\u003e80\\u003c/span\\u003e\\u003c/sup\\u003e. We then summed the EFRs for each month to obtain the annual mean EFR for each grid.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSpillover effects assessment\\u003c/h2\\u003e \\u003cp\\u003eWe selected a total of 136 basins with irrigated cropland from 1901 to 2005 for analysis. To identify the upstream factors that most significantly impact downstream IWS, we processed upstream IWSI indicators, including domestic water withdrawal (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{domWW}_{up}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e), industrial water withdrawal (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{indWW}_{up}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e), irrigation water withdrawal (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{irrWW}_{up}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e), and water availability (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{discharge}_{up}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e). We also considered the irrigated area (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{irrArea}_{up}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e), which is closely related to irrigation water use.\\u003c/p\\u003e \\u003cp\\u003eGiven the strong correlation between irrigated areas and irrigation water withdrawal, we modeled these two variables separately to develop two MRMs and assess their respective impacts on downstream IWS. The difference in IWS between upstream and downstream sub-basins can indicate potential conflicts within the basin; therefore, we incorporated the previous year's IWSI difference (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{IWSI}_{dif}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e) to evaluate its effect on downstream IWS in the following year, thereby avoiding multicollinearity with other indicators. This approach resulted in six indicators for the MRM analysis (Eq.\\u0026nbsp;(\\u003cspan refid=\\\"Equ1\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e)).\\u003cdiv id=\\\"Equ1\\\" class=\\\"Equation\\\"\\u003e\\u003cdiv format=\\\"TEX\\\" class=\\\"mathdisplay\\\" id=\\\"FileID_Equ1\\\" name=\\\"EquationSource\\\"\\u003e\\n$$\\\\:{IWSI}_{down}=\\\\left\\\\{\\\\begin{array}{c}\\\\alpha\\\\:\\\\:+\\\\:{\\\\beta\\\\:}_{1}{IWSI}_{dif}+\\\\:{{\\\\beta\\\\:}_{2}irrArea}_{up}{{+\\\\:\\\\beta\\\\:}_{3}domWW}_{up}{{+\\\\:\\\\beta\\\\:}_{4}indWW}_{up}+{{\\\\:\\\\beta\\\\:}_{5}discharge}_{up}\\\\\\\\\\\\:\\\\alpha\\\\:{\\\\prime\\\\:}\\\\:+\\\\:{\\\\beta\\\\:}_{1}{\\\\prime\\\\:}{IWSI}_{dif}+\\\\:{{\\\\beta\\\\:}_{2}{\\\\prime\\\\:}irrWW}_{up}{{+\\\\:\\\\beta\\\\:}_{3}{\\\\prime\\\\:}domWW}_{up}{{+\\\\:\\\\beta\\\\:}_{4}{\\\\prime\\\\:}indWW}_{up}+{{\\\\:\\\\beta\\\\:}_{5}{\\\\prime\\\\:}discharge}_{up}\\\\end{array}\\\\right.$$\\u003c/div\\u003e\\u003cdiv class=\\\"EquationNumber\\\"\\u003e4\\u003c/div\\u003e\\u003c/div\\u003e\\u003c/p\\u003e \\u003cp\\u003eThe subscripts \\u0026ldquo;down\\u0026rdquo;, \\u0026ldquo;dif\\u0026rdquo;, and \\u0026ldquo;up\\u0026rdquo; represent downstream, downstream-upstream differences, and upstream, respectively. \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:\\\\alpha\\\\:\\\\)\\u003c/span\\u003e\\u003c/span\\u003e is a constant term, and \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\beta\\\\:}_{1}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\beta\\\\:}_{1}{\\\\prime\\\\:}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e) to \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\beta\\\\:}_{5}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e (\\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:{\\\\beta\\\\:}_{5}{\\\\prime\\\\:}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e) are coefficients to be estimated. All water withdrawals are in km\\u003csup\\u003e\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e\\u003c/sup\\u003e\\u0026middot;yr\\u003csup\\u003e-\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u003c/sup\\u003e while irrigated area is provided in Mha. We applied Z-score standardization to eliminate the effects of magnitude differences between variables, enabling direct comparison within the regression model. The adjusted R-squared values obtained from the multiple regression analysis were 0.466 for irrigated area and 0.36 for irrigation water withdrawal. These p-values were less than 0.001, and Variance Inflation Factor (VIF) values were less than 10, indicating that the MRMs could effectively explain changes in downstream IWS.\\u003c/p\\u003e \\u003cp\\u003eAdditionally, we utilized SEM to understand each indicator's direct and indirect impacts on downstream IWS, analyzing the pathways through which upstream spillover effects are realized. Latent variables were not considered in this analysis. We constructed this SEM using AMOS software\\u003csup\\u003e\\u003cspan additionalcitationids=\\\"CR82\\\" citationid=\\\"CR81\\\" class=\\\"CitationRef\\\"\\u003e81\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR83\\\" class=\\\"CitationRef\\\"\\u003e83\\u003c/span\\u003e\\u003c/sup\\u003e. The goodness-of-fit statistics for the model were within expected ranges: the probability level of 0.001, indicating an extremely significant model fit; the Chi-square Ratio of Degrees of Freedom (CMIN/DF) of 2.203, which falls within the acceptable range of 1 to 3; the Root Mean Square Error of Approximation (RMSEA) of 0.029, meeting the requirement of being less than 0.1; and the Comparative Fit Index (CFI) of 0.999, the Normed Fit Index (NFI) of 0.998, and the Tucker-Lewis Index (TLI) of 0.995, all exceeding the threshold of 0.9. These indicators suggest an acceptable fit for the model, implying that this structural model can effectively represent the relationship between these variables and downstream IWS.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003ch2\\u003eCompeting interests\\u003c/h2\\u003e\\n\\u003cp\\u003eThe authors declare no competing interests.\\u003c/p\\u003e\\n\\u003ch2\\u003eAuthor Contributions\\u003c/h2\\u003e\\n\\u003cp\\u003eJ.D. and Q.G. conceptualized the study. X.C. collected and processed the data and constructed the figures. X.H., Y.Z., and S.S. improved experiments. D.J., Y.Q., Y.W., G.S., J. Q., J.L., and X.X. contributed to writing, reviewing, and editing the manuscript.\\u003c/p\\u003e\\n\\u003ch2\\u003eAcknowledgments\\u003c/h2\\u003e\\n\\u003cp\\u003eThis study was supported by the National Natural Science Foundation of China (42271375, 72221002), the National Key Research and Development Program of China (2022YFF0802400), and the Youth Interdisciplinary Team Project of the Chinese Academy of Science (JCTD-2021-04).\\u003c/p\\u003e\\n\\u003ch2\\u003eData availability\\u003c/h2\\u003e\\n\\u003cp\\u003eIrrigation water stress index data are available through Figshare, and source data are available from the corresponding author.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eYao F et al (2023) Satellites reveal widespread decline in global lake water storage. 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Int J Hospitality Manage 94:102824\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"nature-portfolio\",\"isNatureJournal\":true,\"hasQc\":false,\"allowDirectSubmit\":false,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"\",\"title\":\"Nature Portfolio\",\"twitterHandle\":\"\",\"acdcEnabled\":false,\"dfaEnabled\":false,\"editorialSystem\":\"ejp\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-5351205/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-5351205/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"The expansion of irrigated cropland exacerbates water scarcity, while geopolitical environment further intensifies the spatial imbalance of water resources, particularly in transboundary rivers. However, little is known about the evolution of water stress in upstream and downstream regions within transboundary river basins and their potential interrelationships. Here, we find that 396 of 431 sub-basins (91.9%) experience increasing irrigation water stress (IWS) between 1901 and 2005, with the number of sub-basins facing irrigation water scarcity doubling from 51 to 118. Disparities in IWS between upstream and downstream regions widen in 92.4% of transboundary river basins, especially in South Asia, Central Asia, and Africa. The expansion of upstream irrigated areas (6 Mha·yr-1) and associated water withdrawals (20.4 km3·yr-1) exacerbate downstream IWS by 34.3 ± 3.5% from 1901 to 2005, with this spatial spillover effect projected to intensify through 2099. Our findings emphasize the urgent need for cooperative water management in transboundary basins.\",\"manuscriptTitle\":\"Spillover effects of upstream irrigation expansion on downstream water stress in transboundary river basins\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-12-19 06:20:48\",\"doi\":\"10.21203/rs.3.rs-5351205/v1\",\"editorialEvents\":[],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"nature-communications\",\"isNatureJournal\":true,\"hasQc\":false,\"allowDirectSubmit\":false,\"externalIdentity\":\"NCOMMS\",\"sideBox\":\"Learn more about [Nature Communications](http://www.nature.com/ncomms/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://mts-ncomms.nature.com/\",\"title\":\"Nature Communications\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"ejp\",\"reportingPortfolio\":\"Nature Communications\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"1b2ae865-1fb3-4d8f-b4e7-753970c1c9b8\",\"owner\":[],\"postedDate\":\"December 19th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[{\"id\":40415417,\"name\":\"Earth and environmental sciences/Hydrology\"},{\"id\":40415418,\"name\":\"Scientific community and society/Agriculture\"}],\"tags\":[],\"updatedAt\":\"2024-12-19T06:20:48+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-12-19 06:20:48\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-5351205\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-5351205\",\"identity\":\"rs-5351205\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}