Abstract
Water resources are essential for human survival and development. Ensuring sufficient water supply to meet living, production, and ecological demands has emerged as a critical challenge for regions facing water scarcity. In response to the increasingly severe water shortage in Central Yunnan, the Chinese government has invested over 100 billion yuan in implementing the Central Yunnan Water Diversion Project (CYWDP). Accurately assessing and predicting the project’s impact on water shortage risk and its spatiotemporal imbalance in the receiving regions is crucial for fostering sustainable and high-quality regional development. In this study, we propose an integrated methodological framework that combines Copula functions with a concentration index to evaluate the effects of the CYWDP on water shortage risk and its spatiotemporal imbalance in the Yuxi water-receiving area. First, based on risk theory, we develop a water shortage risk assessment model by integrating Kernel Density Estimation (KDE) with Copula functions to evaluate water shortage risk under various scenarios. This model accounts for the heterogeneous importance of domestic, industrial, agricultural, and ecological water uses. Second, a simplified concentration index is introduced to quantify the spatiotemporal imbalance of water shortage risk. The results reveal that: (1) Water shortage risk in the Yuxi water-receiving area exhibits strong seasonality, with spring being the highest-risk period, in approximately 80% of years falling into moderate-risk zones. Interannual variability in risk is high and strongly negatively correlated with precipitation, and only in wet years can water supply security be effectively ensured. (2) Due to differences in precipitation, groundwater availability, and the number of water storage projects, sub-regions display heterogeneous distributions of water shortage risk, with significant spatial imbalance. Intra annual variations are also evident, and more pronounced in dry years compared with wet years. (3) Without considering the supplemental supply from the CYWDP, the receiving area is projected to enter a high-risk zone by 2030 and 2040. However, with the CYWDP, the overall risk is expected to decrease to a moderate level by 2030, and by 2040, increased water transfers could alleviate risks and mitigate spatial imbalances. This study provides critical insights for evaluating the socio-economic benefits of the CYWDP and offers guidance for water resource allocation strategies. Moreover, the proposed framework serves as a methodological reference for analogous studies.
Data availability
The raw data supporting the conclusions of this article will be made available by the corresponding author on request.
References
IPCC. : Summary for Policymakers. In: Climate Change 2023: Synthesis Report. Contribution of Working Groups I, II and III to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change [R]. Geneva, Switzerland: IPCC, 2023. (2023).
Min, S.-K. et al. Human contribution to more-intense precipitation extremes. Nature 470, 378–81 (2011).
Gudmundsson, L. et al. Globally observed trends in mean and extreme river flow attributed to climate change. Science 371, 1159–62 (2021).
Li, D., Zou, L. & Zhou, T. Extreme climate event changes in China in the 1.5 and 2°C warmer climates: Results from statistical and dynamical downscaling [J]. J. Geophys. Res. Atmos. 123(18), 10,215-10,30 (2018).
Jiang, D. & Wang, N. Water cycle changes: Interpretation of IPCC AR6. Climate Change Research 17(06), 699–704 (2021).
Liu, J., Chen, H. & Tian, Z. Interpretation of IPCC AR6: Climate change and water security. Climate Change Research 18(04), 405–13 (2022).
Nuannukul, W., Phumiphan, A. & Kangrang, A. Cross-drainage culvert design under global climate and land use changes. ARPN J. Eng. Appl. Sci. 16(10), 1036–44 (2021).
Suwannachai, L. et al. Integrating hydrological models for improved flash flood risk assessment and mitigation strategies in Northeastern Thailand. Water 17(3), 345 (2025).
Brauman, K. A. et al. Water depletion: An improved metric for incorporating seasonal and dry-year water scarcity into water risk assessments. Elementa: Science of the Anthropocene 4, 000083 (2016).
D. R, B. et al. Alleviating water scarcity by optimizing crop mixes. Nat. Water 1(12), 1035–47 (2023).
Mekonnen, M. M. & Hoekstra, A. Y. Four billion people facing severe water scarcity. Sci. Adv. 2(2), e1500323 (2016).
Salehi, M. Global water shortage and potable water safety; Today’s concern and tomorrow’s crisis. Environ. Int. 158, 106936 (2022).
Han, L. et al. Characteristics and origins of drought disasters in Southwest China in nearly 60 years [J]. Acta Geogr. Sin. 69 (05), 632–639 (2014).
Sun, S., Fang, C. & Lv, J. Spatial inequality of water footprint in China: A detailed decomposition of inequality from water use types and drivers. J. Hydrol. 553, 398–407 (2017).
Jiang, Y. China’s water security: Current status, emerging challenges and future prospects. Environ. Sci. Policy 54, 106–25 (2015).
Zhao, Y. The most representative National Key Water Conservancy Project under construction: Central Yunnan Water Diversion Project. Tunnel Construction 39(3), 511–22 (2019).
Di, L. et al. South-to-North water diversion stabilizing Beijing’s groundwater levels. Nat. Commun. 11(1), 3665 (2020).
Xu, Y. et al. Continuing severe water shortage in the water-receiving area of the South-To-North Water Diversion Eastern Route Project from 2002 to 2020. Water Resour. Res. https://doi.org/10.1029/2022WR034365 (2023).
Zhou, X. Evaluation of water scarcity in China considering inter-basin water transfer [D] (Dalian University of Technology, 2022).
Gu, W., Shao, D. & Jiang, Y. Risk evaluation of water shortage in source area of Middle Route Project for South-to-North Water Transfer in China. Water Resour. Manage. 26(12), 3479–93 (2012).
Liao, Q., Zhang, S. & Chen, J. Risk assessment and prediction of water shortages in Beijing. Resour. Sci. 35(1), 140–7 (2013).
Wenting, Y. & Di, L. R. S B, et al. Human intervention will stabilize groundwater storage across the north China plain [J]. Water Resour. Research, 58(2). (2022).
Liu, X. et al. Study on Water Resources Risk in Beijing after South-North Water Transfer Project [J]. J. China Hydrology. 35 (04), 55–61 (2015).
Zhang, J., Su, S. & Zuo, Q. Risk analysis of water resource shortage in Baiyangdian District before and after cross basin water transfer [J]. Journal of North China university of water resources and electric power (natural science edition) 1, 1–10 (2024).
Sun, S. et al. Unraveling the effect of inter-basin water transfer on reducing water scarcity and its inequality in China. Water Res. 194, 116931 (2021).
Ma, T. et al. Pollution exacerbates China’s water scarcity and its regional inequality. Nat. Commun. 11(1), 650 (2020).
Zhang, C. et al. The effectiveness of the South-to‐North Water Diversion Middle Route Project on water delivery and groundwater recovery in North China Plain [J]. Water Resour. Res. 56(10), e2019WR026759 (2020).
Qian, L. et al. Risk loss model of water supply and water demand based on copula function and its applications [J]. Syst. Engineering-Theory Pract. 36 (02), 517–527 (2016).
Gu, S. et al. Daily reference evapotranspiration and meteorological drought forecast using high-dimensional Copula joint distribution model [J]. Trans. Chin. Soc. Agricultural Eng. 36 (09), 143–151 (2020).
Yang, X. et al. Development of a multi-GCMs Bayesian copula method for assessing multivariate drought risk under climate change: A case study of the Aral Sea basin [J]. Catena https://doi.org/10.1016/j.catena.2022.106048 (2022).
Chen, J., Gu, S. & Zhang, T. Synchronous-asynchronous encounter probability analysis of high-low runoff for Jinsha River, China, using Copulas* [J]. MATEC Web Conf. 246, 01094 (2018).
Qian, T. et al. A water shortage risk assessment model based on kernel density estimation and copulas. Water 16(11), 1465 (2024).
Qian, L. et al. A new multiple integral model for water shortage risk assessment and its application in Beijing, China. Nat. Hazards 80(1), 43–67 (2015).
Qian, L. et al. Monthly risk assesment model of water supply and demand based on logistic regression DEA and its application [J]. J. Nat. Resour. 31 (01), 177–186 (2016).
Qian, L. et al. Model for water shortage risk econimic losses based on M-Copula and its application [J]. J. Appl. Basic. Eng. Sci. 30 (04), 907–917 (2022).
Wongarmart, P. et al. Improving Reservoir Operation Efficiency Using Electric Eel Foraging Optimization and Transit Search Algorithms with Standard Operating Policy: Nong Han-Kumphawapi Case Study [J]. ARPN J. Eng. Appl. Sci. 20 (6), 300–313 (2025).
Chen, Y. Inequality Indexes for Measuring Between-Groups Mean Difference of Size and Spatial Distributions [J]. Acta Sci. Nat. Univ. Pekin. 55(06), 1097–1102 (2019).
Wang, H. et al. Theory and assessment method of water resources risk [J]. J. Hydraul. Eng. 50 (08), 980–989 (2019).
Yang, P. et al. Risk assessment of water resource shortages in the Aksu River basin of northwest China under climate change [J]. J. Environ. Manage. 305, 114394 (2022).
Allan, C., Xia, J. & Pahl-Wostl, C. Climate change and water security: Challenges for adaptive water management [J]. Curr. Opin. Environ. Sustain. 5(6), 625–632 (2013).
Jin, Y. et al. Studies on Distribution Characteristics and Variation Trend of the Regional Drought Events over Yunnan in Recent 55 Years [J]. Meteorological Monthly. 44 (9), 1169–1178 (2018).
Chen, C. et al. Water resource vulnerability assessment methods and applications [J]. South-to-North Water Transfers Water Sci. Technol. 23 (01), 99–109 (2025).
Ruan, B. et al. Fuzzy comprehensive assessment of water shortage risk [J]. Journal Hydraulic Engineering, (08): 906–912. (2005).
Rejda, G. E. & McNamara, M. J. Principles of risk management and insurance [M] (Cengage Learning, 2021).
Ningpeng, D., Wenhai, G. & Jixue, C. A hybrid hydrologic modelling framework with data-driven and conceptual reservoir operation schemes for reservoir impact assessment and predictions. J. Hydrol. https://doi.org/10.1016/j.jhydrol.2023.129246 (2023).
Wang, B. et al. Research on the Social Stability Risk Assessment and Empirical Analysis of Hydraulic Engineering Construction [J]. China Popul. Resour. Environ. 25 (04), 149–154 (2015).
Junguo, L. et al. Water scarcity assessments in the past, present and future. Earth’s Future 5(6), 545–59 (2017).
Kang, J. et al. Analysis on the spatial distribution characteristics of Chinese traditional villages [J]. Process. Geogr. 35 (7), 839–850 (2016).
Li, M. et al. Spatial Distribution and Influencing Factors of Traditional Villages: A Case of Wuyue Culture Region [J]. Resour. Environ. Yangtze Basin. 27 (08), 1693–1702 (2018).
Silverman, B. W. Density Estimation for Statistics and Data Analysis [M] (Routledge, 1998).
Zhao, H. et al. Research on the Spatiotemporal Differentiation and Prediction of Water Resource Scarcity Risks in Yuxi City [J]. Water Sav. Irrigation, (01): 57–60. (2019).
Chen, J., Zhang, T. & Gu, S. Study on encounter of runoff abundance and scarcity between source area and receiving area of the Central Yunnan Irrigation Proiect and the hydrological drought. Water Resour. Power 42(06), 23–7 (2024).
Willmott, C. J. & Matsuura, K. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Clim. Res. 30(1), 79–82 (2005).
Lianzhou, W., Xiaoling, S. & Te, Z. Challenges of typical inter-basin water transfer projects in China: Anticipated impacts of climate change on streamflow and hydrological drought under CMIP6 [J]. Journal Hydrology, (2023). 627(PA).
Cuiping, Q., Zhongrui, N. & Yan, W. Impact of climate change on water availability in water source areas of the South-to-North Water Diversion Project in China. Front. Earth Sci. https://doi.org/10.3389/feart.2021.747429 (2021).
Zhao, J. et al. Risk assessment of regional water resources and forewarning model at different time scales. J. Hydrol. Eng. 18(9), 1114–1121 (2013).
Salmivaara, A. et al. Exploring the modifiable areal unit problem in spatial water assessments: A case of water shortage in Monsoon Asia. Water 7(3), 898–917 (2015).
Veldkamp, T. I. E. et al. Towards a global water scarcity risk assessment framework: Incorporation of probability distributions and hydro-climatic variability. Environ. Res. Lett. 11(2), 024006 (2016).
Kosasaeng, S. et al. Hybrid Modeling for Future Inflow Prediction of Huai Luang Reservoir Under Climate Change [J]. Int. J. GEOMATE. 29 (132), 98–109 (2025).
Joseph, J., Valentina, R. & Ali, A. Assessment of Future Risks of Seasonal Municipal Water Shortages Across North America [J]. Frontiers Earth Science, 9. (2021).
Xianneng, Z., Huaiwei, S. & Hao, J. Coupling Bayesian network and copula theory for water shortage assessment: A case study in source area of the South-to-North Water Division Project (SNWDP). J. Hydrol. https://doi.org/10.1016/j.jhydrol.2023.129434 (2023).
Acknowledgements
Acknowledgments: The authors would like to thank the National Meteorological Science Data Center for providing the precipitation data free of charge and the editors and reviewers for their comments and suggestions, which have helped to improve this manuscript.
Funding
The authors gratefully acknowledge the financial support from the Demonstration project of comprehensive government management and large-scale industrial application of the major special project of CHEOS (No. 89-Y50G31-9001-22/23 − 05) and the Open Funds of the Key Laboratory of Mountain Hazards and Engineering Resilience, Chinese Academy of Sciences (No. KLMHER-K25).
Author information
Authors and Affiliations
Contributions
T.Q. and W.X. wrote the main manuscript text; methodology, T.Q. and X.L.; software, T.Q. and Z.Z.; validation, J.C. and Z.G.; formal analysis, T.Q. and W.X.; resources, S.G., and X.L.; data curation, Xiankai Li; original draft preparation, T.Q. and S.G.; project administration, D.Z.; funding acquisition, S.G., and Z.Y.; All authors reviewed the manuscript.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Ethics approval
This is an original article that did not use other information, which requires ethical approval.
Consent to participate
All authors participated in this article.
Consent for publication
All authors have given consent for publication.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Qian, T., Zhou, D., Yuan, Z. et al. Spatiotemporal imbalance of regional water shortage risk based on copulas and concentration index. Sci Rep (2026). https://doi.org/10.1038/s41598-026-41016-1
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-026-41016-1