Impacts of climate change and land cover factor on runoff in the Coastal Chinese Mainland region

Abstract

The increasingly frequent storms pose significant threats to the sustainable development of coastal regions, particularly in densely populated and economically vibrant areas. Comprehending the dynamics and intricate mechanisms underlying runoff generation is crucial in the context of climate change and anthropogenic interference. Based on hydro-meteorological and land-use data from 1980 to 2018, this study investigates the runoff variation and its driving factors in the Coastal Chinese Mainland (CCM). The aims of this study are to reveal the temporal and spatial trends of runoff yield, to clarify the sensitivity of runoff in coastal cities to the integrated and individual parameters of climate change and anthropogenic interference, including precipitation (P), potential evapotranspiration (E₀), and land cover factor (n), and to support the establishment of spatially tailored adaptation strategies. The results show that: (1) runoff has generally increased over the study period, particularly in regions such as the Yangtze River Delta, Shandong, and Guangxi, while it has decreased in western Liaoning and eastern Guangdong; (2) in the northern CCM with larger aridity index, the land cover factor plays a dominant role in runoff production, while in the wetter southern CCM, precipitation is more influential, and potential evapotranspiration mainly hinders runoff generation all over CCM; (3) urban expansion tends to negatively impact n, while the loss of grasslands and shrinkage of croplands tend to undermine the value of n. To facilitate the achievement of sustainable development goals in the CCM, it is imperative to introduce a more comprehensive and theoretical framework that encompasses the natural, technical, and social dimensions of human-water systems into traditional flood regulation and water resource management. This framework should promote interdisciplinary collaboration from an integrated perspective, to bridge the administrative and watershed boundaries, to effectively address the complex challenges posed by climate change and anthropogenic activities on runoff and water resources in coastal regions, and to enhance the realization of local sustainable development goals (UN SDGs).

Keywords

Coastal Chinese Mainland / Runoff production / Elastic analysis / Climate change / Anthropogenic interference

CRediT authorship contribution statement

Song Song: Conceptualization, Project administration, Funding acquisition, Writing – original draft, Writing – review & editing. Ziqiang Ye: Data curation, Software, Validation, Visualization. Zhijie Zhou: Data curation, Software, Validation, Visualization. Xiaowei Chuai: Investigation, Methodology, Supervision, Validation, Writing – review & editing. Rui Zhou: Formal analysis, Investigation, Resources. Jinwei Zou: Data curation, Project administration. Yi Chen: Data curation, Resources.

Declaration of competing interests

We declare that we have no financial and personal relationships with other people organizations that can inappropriately influence our work. There is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled.

Acknowledgements

This research is supported by the National Natural Science Foundation of China (Grant No. 42271311); Open Project of State Key Laboratory of Estuarine and Coastal Sciences (Grant No. SKLEC-KF202204); Guangzhou city-Guangzhou university joint funding program (Grant No. 202201020215); Key Project of the National Natural Science Foundation of China-Guangdong Joint Fund (Grant No. U1901219). The authors would like to give our thanks and appreciations to the students from Guangzhou University who have help in data collection, including Ziying Shen, Mingxi Liang, Zihao Li, Yulin Zhuang, and Jingxi Liu.

References

[1] AghaKouchak, A, Mirchi, A, Madani, K, Di Baldassarre, G, Nazemi, A, Alborzi, A, Anjileli, H, Azarderakhsh, M, Chiang, F, Hassanzadeh, E, Huning, L. S., Mallakpour, I, Martinez, A, Mazdiyasni, O, Moftakhari, H, Norouzi, H, Sadegh, M, Sadeqi, D, Van Loon, A. F., Wanders, N., 2021. Anthropogenic drought: definition, challenges, and opportunities. Rev. Geophys., 59, Article e2019RG000683. doi: 10.1029/2019RG000683.

[2] Albertini, C, Mazzoleni, M, Totaro, V, Iacobellis, V, Di Baldassarre, G., 2020. Socio-hydrological modelling: the influence of reservoir management and societal responses on flood impacts. Water, 12, p. 1384. doi: 10.3390/w12051384.

[3] Amirataee, B, Zeinalzadeh, K., 2016. Trends analysis of quantitative and qualitative changes in groundwater with considering the autocorrelation coefficients in west of Lake Urmia, Iran. Environ. Earth Sci., 75, p. 371. doi: 10.1007/s12665-015-4917-2.

[4] Bai, M, Mo, X, Liu, S, Hu, S., 2019. Contributions of climate change and vegetation greening to evapotranspiration trend in a typical hilly-gully basin on the Loess Plateau, China. Sci. Total Environ., 657, pp. 325-339. doi: 10.1016/j.scitotenv.2018.11.360.

[5] Berghuijs, W. R., Larsen, J. R., van Emmerik, T. H. M., Woods, R. A., 2017. A global assessment of runoff sensitivity to changes in precipitation, potential evaporation, and other factors. Water Resour. Res., 53, pp. 8475-8486. doi: 10.1002/2017WR021593.

[6] Berghuijs, W. R., Woods, R. A., 2016. Correspondence: space-time asymmetry undermines water yield assessment. Nat. Commun., 7, p. 11603. doi: 10.1038/ncomms11603.

[7] Bharat, S, Mishra, V., 2021. Runoff sensitivity of Indian sub-continental river basins. Sci. Total Environ., 766, Article 142642. doi: 10.1016/j.scitotenv.2020.142642.

[8] Chen, C, Jiang, J, Liao, Z, Zhou, Y, Wang, H, Pei, Q., 2022. A short-term flood prediction based on spatial deep learning network: a case study for Xi County, China. J. Hydrol., 607, Article 127535. doi: 10.1016/j.jhydrol.2022.127535.

[9] Dey, P, Mishra, A., 2017. Separating the impacts of climate change and human activities on streamflow: a review of methodologies and critical assumptions. J. Hydrol., 548, pp. 278-290. doi: 10.1016/j.jhydrol.2017.03.014.

[10] Di Baldassarre, G, Martinez, F, Kalantari, Z, Viglione, A., 2017. Drought and flood in the Anthropocene: feedback mechanisms in reservoir operation. Earth Syst. Dyn., 8, pp. 225-233. doi: 10.5194/esd-8-225-2017.

[11] Di Baldassarre, G, Sivapalan, M, Rusca, M, Cudennec, C, Garcia, M, Kreibich, H, Konar, M, Mondino, E, Mrd, J, Pande, S., 2019. Sociohydrology: scientific challenges in addressing the sustainable development goals. Water Resour. Res., 55, pp. 6327-6355. doi: 10.1029/2018WR023901.

[12] Ding, Y, Peng, S., 2021. Spatiotemporal change and attribution of potential evapotranspiration over China from 1901 to 2100. Theor. Appl. Climatol., 145, pp. 79-94. doi: 10.1007/s00704-021-03625-w.

[13] Dong, W, Cui, B, Liu, Z, Zhang, K., 2014. Relative effects of human activities and climate change on the river runoff in an arid basin in northwest China. Hydrol. Process., 28, pp. 4854-4864. doi: 10.1002/hyp.9982.

[14] Donnelly, J, Abolfathi, S, Pearson, J, Chatrabgoun, O, Daneshkhah, A., 2022. Gaussian process emulation of spatio-temporal outputs of a 2D inland flood model. Water Res., 225, Article 119100. doi: 10.1016/j.watres.2022.119100.

[15] Dooge, J. C. I., Bruen, M, Parmentier, B., 1999. A simple model for estimating the sensitivity of runoff to long-term changes in precipitation without a change in vegetation. Adv. Water Resour., 23, pp. 153-163. doi: 10.1016/S0309-1708(99)00019-6.

[16] Edmonds, D. A., Caldwell, R. L., Brondizio, E. S., Siani, S. M. O., 2020. Coastal flooding will disproportionately impact people on river deltas. Nat. Commun., 11, p. 4741. doi: 10.1038/s41467-020-18531-4.

[17] Fang, J, Lincke, D, Brown, S, Nicholls, R. J., Wolff, C, Merkens, J. L., Hinkel, J, Vafeidis, A. T., Shi, P, Liu, M., 2020. Coastal flood risks in China through the 21st century–an application of DIVA. Sci. Total Environ., 704, Article 135311. doi: 10.1016/j.scitotenv.2019.135311.

[18] Fernandes, M. R., Aguiar, F. C., Martins, M. J., Rivaes, R, Ferreira, M. T., 2020. Long-term human-generated alterations of Tagus River: effects of hydrological regulation and land-use changes in distinct river zones. Catena, 188, Article 104466. doi: 10.1016/j.catena.2020.104466.

[19] Garcia, M, Koebele, E, Deslatte, A, Ernst, K, Manago, K. F., Treuer, G., 2019. Towards urban water sustainability: analyzing management transitions in Miami, Las Vegas, and Los Angeles. Glob. Environ. Change, 58, Article 101967. doi: 10.1016/j.gloenvcha.2019.101967.

[20] Gou, J, Miao, C, Samaniego, L, Xiao, M, Wu, J, Guo, X., 2021. CNRD v1.0: a high-quality natural runoff dataset for hydrological and climate studies in China. Bull. Am. Meteorol. Soc., 102, pp. E929-E947. doi: 10.1175/BAMS-D-20-0094.1.

[21] Griffiths, J, Chan, F. K. S., Shao, M, Zhu, F, Higgitt, D. L., 2020. Interpretation and application of sponge city guidelines in China. Philos. Trans. R. Soc. Math. Phys. Eng. Sci., 378, Article 20190222. doi: 10.1098/rsta.2019.0222.

[22] Guan, M, Sillanpää, N, Koivusalo, H., 2016. Storm runoff response to rainfall pattern, magnitude and urbanization in a developing urban catchment. Hydrol. Process. 30, 543–557. doi: 10.1002/hyp.10624.

[23] Guan, X, Zhang, J, Bao, Z, Liu, C, Jin, J, Wang, G., 2021. Past variations and future projection of runoff in typical basins in 10 water zones, China. Sci. Total Environ., 798, p. 149277. doi: 10.1016/j.scitotenv.2021.149277.

[24] Guo, K., Guan, M., Yu, D., 2021. Urban surface water flood modelling–a comprehensive review of current models and future challenges. Hydrol. Earth Syst. Sci. 25, 2843– 2860. doi: 10.5194/hess-25-2843-2021.

[25] Guo, Y, Quan, L, Song, L, Liang, H., 2022. Construction of rapid early warning and comprehensive analysis models for urban waterlogging based on AutoML and comparison of the other three machine learning algorithms. J. Hydrol., 605, Article 127367. doi: 10.1016/j.jhydrol.2021.127367.

[26] Hallegatte, S, Green, C, Nicholls, R. J., Corfee-Morlot, J., 2013. Future flood losses in major coastal cities. Nat. Clim. Change, 3, pp. 802-806. doi: 10.1038/nclimate1979.

[27] Hamed, K. H., Rao, A. R., 1998. A modified Mann-Kendall trend test for autocorrelated data. J. Hydrol., 204, pp. 182-196. doi: 10.1016/S0022-1694(97)00125-X.

[28] Hasan, E, Tarhule, A, Kirstetter, P. E., Clark, R, Hong, Y., 2018. Runoff sensitivity to climate change in the Nile River Basin. J. Hydrol., 561, pp. 312-321. doi: 10.1016/j.jhydrol.2018.04.004.

[29] Hohmann, C, Kirchengast, G, Sungmin, O, Rieger, W, Foelsche, U., 2020. Runoff sensitivity to spatial rainfall variability: a hydrological modeling study with dense rain gauge observations. Hydrol. Earth Syst. Sci. Discuss., pp. 1-28. doi: 10.5194/hess-2020-453.

[30] Hossain, B., 2020. Role of organizations in preparedness and emergency response to flood disaster in Bangladesh. Geoenviron. Disasters, 7, p. 33. doi: 10.1186/s40677-020-00167-7.

[31] Huq, E, Abdul-Aziz, O. I., 2021. Climate and land cover change impacts on stormwater runoff in large-scale coastal-urban environments. Sci. Total Environ., 778, Article 146017. doi: 10.1016/j.scitotenv.2021.146017.

[32] Imhoff, R. O., Brauer, C. C., van Heeringen, K. J., Uijlenhoet, R, Weerts, A. H., 2022. Large-sample evaluation of radar rainfall nowcasting for flood early warning. Water Resour. Res., 58, Article e2021WR031591. doi: 10.1029/2021WR031591.

[33] Irwin, E. G., Culligan, P. J., Fischer-Kowalski, M, Law, K. L., Murtugudde, R, Pfirman, S., 2018. Bridging barriers to advance global sustainability. Nat. Sustain., 1, pp. 324-326. doi: 10.1038/s41893-018-0085-1.

[34] Jiang, H, Xu, X, Zhang, T, Xia, H, Huang, Y, Qiao, S., 2022. The relative roles of climate variation and human activities in vegetation dynamics in coastal China from 2000 to 2019. Remote Sens., 14, p. 2485. doi: 10.3390/rs14102485.

[35] Jiang, T, Su, B, Huang, J, Zhai, J, Xia, J, Tao, H, Wang, Y, Sun, H, Luo, Y, Zhang, L, Wang, G, Zhan, C, Xiong, M, Kundzewicz, Z. W., 2020. Each 0.5°C of warming increases annual flood losses in China by more than US$60 billion. Bull. Am. Meteorol. Soc., 101, pp. E1464-E1474. doi: 10.1175/BAMS-D-19-0182.1.

[36] Jin, H, Chen, X, Wu, P, Song, C, Xia, W., 2021. Evaluation of spatial-temporal distribution of precipitation in mainland China by statistic and clustering methods. Atmos. Res., 262, p. 105772. doi: 10.1016/j.atmosres.2021.105772.

[37] Jin, Z, Guo, L, Yu, Y, Luo, D, Fan, B, Chu, G., 2020. Storm runoff generation in headwater catchments on the Chinese Loess Plateau after long-term vegetation rehabilitation. Sci. Total Environ., 748, Article 141375. doi: 10.1016/j.scitotenv.2020.141375.

[38] Kundzewicz, Z. W., Su, B, Wang, Y, Huang, J, Jiang, T., 2019. Flood risk in a range of spatial perspectives – from global to local scales. Nat. Hazards Earth Syst. Sci., 19, pp. 1319-1328. doi: 10.5194/nhess-19-1319-2019.

[39] Lai, Y, Li, J, Gu, X, Chen, Y. D., Kong, D, Gan, T. Y., Liu, M, Li, Q, Wu, G., 2020. Greater flood risks in response to slowdown of tropical cyclones over the coast of China. Proc. Natl. Acad. Sci. U.S.A., 117, pp. 14751-14755. doi: 10.1073/pnas.1918987117.

[40] Li, B., Shi, X., Lian, L., Chen, Y., Chen, Z., Sun, X., 2020a. Quantifying the effects of climate variability, direct and indirect land use change, and human activities on runoff. J. Hydrol. 584, 124684. doi: 10.1016/j.jhydrol.2020.124684.

[41] Li, C, Liu, M, Hu, Y, Zhou, R, Wu, W, Huang, N., 2021. Evaluating the runoff storage supply-demand structure of green infrastructure for urban flood management. J. Clean. Prod., 280, Article 124420. doi: 10.1016/j.jclepro.2020.124420.

[42] Li, S., Wang, Z., Lai, C., Lin, G., 2020b. Quantitative assessment of the relative impacts of climate change and human activity on flood susceptibility based on a cloud model. J. Hydrol. 588, 125051. doi: 10.1016/j.jhydrol.2020.125051.

[43] Li, X., Zhang, G., He, C., 2015. Watershed science: bridging new advances in hydrological science with good management of river basins. Sci. China Earth Sci. 58, 1–2. doi: 10.1007/s11430-014-5037-7.

[44] Li, Y, Liu, C, Yu, W, Tian, D, Bai, P., 2019. Response of streamflow to environmental changes: a Budyko-type analysis based on 144 river basins over China. Sci. Total Environ., 664, pp. 824-833. doi: 10.1016/j.scitotenv.2019.02.011.

[45] Li, Z, Quiring, S. M., 2021. Identifying the dominant drivers of hydrological change in the contiguous United States. Water Resour. Res., 57, Article e2021WR029738. doi: 10.1029/2021WR029738.

[46] Liang, L., Li, L., Liu, Q., 2010. Temporal variation of reference evapotranspiration during 1961–2005 in the Taoer River basin of Northeast China. Agric. For. Meteorol. 150, 298–306. doi: 10.1016/j.agrformet.2009.11.014.

[47] Liu, D., 2016. China's sponge cities to soak up rainwater. Nature, 537, p. 307. doi: 10.1038/537307c.

[48] Lourenço, I. B., Beleño de Oliveira, A. K., Marques, L. S., Quintanilha Barbosa, A. A., Veról, A. P., Magalhães, P. C., Miguez, M. G., 2020. A framework to support flood prevention and mitigation in the landscape and urban planning process regarding water dynamics. J. Clean. Prod., 277, Article 122983. doi: 10.1016/j.jclepro.2020.122983.

[49] Luo, Y, Yang, Y, Yang, D, Zhang, S., 2020. Quantifying the impact of vegetation changes on global terrestrial runoff using the Budyko framework. J. Hydrol., 590, Article 125389. doi: 10.1016/j.jhydrol.2020.125389.

[50] Luppichini, M, Barsanti, M, Giannecchini, R, Bini, M., 2022. Deep learning models to predict flood events in fast-flowing watersheds. Sci. Total Environ., 813, Article 151885. doi: 10.1016/j.scitotenv.2021.151885.

[51] MacKenzie, K. M., Singh, K, Binns, A. D., Whiteley, H. R., Gharabaghi, B., 2022. Effects of urbanization on stream flow, sediment, and phosphorous regime. J. Hydrol., 612, p. 128283. doi: 10.1016/j.jhydrol.2022.128283.

[52] McKinnon, S., 2019. Remembering and forgetting 1974: the 2011 Brisbane floods and memories of an earlier disaster. Geogr. Res., 57, pp. 204-214. doi: 10.1111/1745-5871.12335.

[53] Miao, C, Gou, J, Fu, B, Tang, Q, Duan, Q, Chen, Z, Lei, H, Chen, J, Guo, J, Borthwick, A. G. L., Ding, W, Duan, X, Li, Y, Kong, D, Guo, X, Wu, J., 2022. High-quality reconstruction of China’s natural streamflow. Sci. Bull., 67, pp. 547-556. doi: 10.1016/j.scib.2021.09.022.

[54] Minister of Housing and Urban-Rural Development of People’s Republic of China (MOHURD), 2017. Code for Urban Wastewater and Stormwater Engineering Planning (GB 58318–2017). MOHURD, Beijing.

[55] Ministry of Water Resources of the People’s Republic of China (MWRC), 2011. China Water Resources Bulletin 2011. China Water & Power Press, Beijing.

[56] Mori, S, Pacetti, T, Brandimarte, L, Santolini, R, Caporali, E., 2021. A methodology for assessing spatio-temporal dynamics of flood regulating services. Ecol. Indic., 129, Article 107963. doi: 10.1016/j.ecolind.2021.107963.

[57] Myhre, G, Samset, B. H, Hodnebrog, Ø, Andrews, T, Boucher, O, Faluvegi, G, Fläschner, D, Forster, P. M., Kasoar, M, Kharin, V, Kirkevåg, A, Lamarque, J. F., Olivié, D, Richardson, T. B., Shawki, D, Shindell, D, Shine, K. P., Stjern, C. W., Takemura, T, Voulgarakis, A., 2018. Sensible heat has significantly affected the global hydrological cycle over the historical period. Nat. Commun., 9, p. 1922. doi: 10.1038/s41467-018-04307-4.

[58] National Bureau of Statistics of the People’s Republic of China, 2022. China Statistics Yearbook 2022. China Statistics Press, Beijing.

[59] National Bureau of Statistics of the People’s Republic of China, 2018. China Statistical Yearbook 2018. China Statistics Press, Beijing.

[60] Paprotny, D, Sebastian, A, Morales-Nápoles, O, Jonkman, S. N., 2018. Trends in flood losses in Europe over the past 150 years. Nat. Commun., 9, p. 1985. doi: 10.1038/s41467-018-04253-1.

[61] Pollard, J. A., Spencer, T, Jude, S., 2018. Big data approaches for coastal flood risk assessment and emergency response. WIREs Clim. Change, 9, p. e543. doi: 10.1002/wcc.543.

[62] Qi, W, Ma, C, Xu, H, Chen, Z, Zhao, K, Han, H., 2021. A review on applications of urban flood models in flood mitigation strategies. Nat. Hazards, 108, pp. 31-62. doi: 10.1007/s11069-021-04715-8.

[63] Roxy, M. K., Ghosh, S, Pathak, A, Athulya, R, Mujumdar, M, Murtugudde, R, Terray, P, Rajeevan, M., 2017. A threefold rise in widespread extreme rain events over central India. Nat. Commun., 8, pp. 1-11. doi: 10.1038/s41467-017-00744-9.

[64] Sadler, J. M., Goodall, J. L., Morsy, M. M., Spencer, K., 2018. Modeling urban coastal flood severity from crowd-sourced flood reports using Poisson regression and Random Forest. J. Hydrol., 559, pp. 43-55. doi: 10.1016/j.jhydrol.2018.01.044.

[65] Salmoral, G, Rivas Casado, M, Muthusamy, M, Butler, D, Menon, P. P., Leinster, P., 2020. Guidelines for the use of unmanned aerial systems in flood emergency response. Water, 12, p. 521. doi: 10.3390/w12020521.

[66] Song, S, Xu, Y. P., Wu, Z. F., Deng, X. J., Wang, Q., 2019. The relative impact of urbanization and precipitation on long-term water level variations in the Yangtze River Delta. Sci. Total Environ., 648, pp. 460-471. doi: 10.1016/j.scitotenv.2018.07.433.

[67] Wallington, K, Cai, X., 2020. Feedback between reservoir operation and floodplain development: implications for reservoir benefits and beneficiaries. Water Resour. Res., 56, p. e24524. doi: 10.1029/2019WR026610.

[68] Wang, H, Mei, C, Liu, J. H., Shao, W. W., 2018. A new strategy for integrated urban water management in China: sponge city. Sci. China Technol. Sci., 61, pp. 317-329. doi: 10.1007/s11431-017-9170-5.

[69] Wang, P, Li, Y, Fan, J, Kong, F, Zhang, D, Hu, T., 2023. Future changes in urban drainage pressure caused by precipitation extremes in 285 cities across China based on CMIP6 models. Sustain. Cities Soc., 91, Article 104404. doi: 10.1016/j.scs.2023.104404.

[70] Wei, X, Li, Q, Zhang, M, Giles-Hansen, K, Liu, W, Fan, H, Wang, Y, Zhou, G, Piao, S, Liu, S., 2018. Vegetation cover–another dominant factor in determining global water resources in forested regions. Glob. Change Biol., 24, pp. 786-795. doi: 10.1111/gcb.13983.

[71] Xia, J, Zhang, Y, Xiong, L, He, S, Wang, L, Yu, Z., 2017. Opportunities and challenges of the sponge city construction related to urban water issues in China. Sci. China Earth Sci., 60, pp. 652-658. doi: 10.1007/s11430-016-0111-8.

[72] Xiao, M, Gao, M, Vogel, R. M., Lettenmaier, D. P., 2020. Runoff and evapotranspiration elasticities in the western United States: are they consistent with Dooge's complementary relationship?. Water Resour. Res., 56, Article e2019WR026719. doi: 10.1029/2019WR026719.

[73] Yang, H, Yang, D., 2011. Derivation of climate elasticity of runoff to assess the effects of climate change on annual runoff. Water Resour. Res., 47 . doi: 10.1029/2010WR009287.

[74] Yin, J, Gentine, P, Zhou, S, Sullivan, S. C., Wang, R, Zhang, Y, Guo, S., 2018. Large increase in global storm runoff extremes driven by climate and anthropogenic changes. Nat. Commun., 9, p. 4389. doi: 10.1038/s41467-018-06765-2.

[75] Yu, D, Yin, J, Wilby, R. L., Lane, S. N., Aerts, J. C. J. H., Lin, N, Liu, M, Yuan, H, Chen, J, Prudhomme, C, Guan, M, Baruch, A, Johnson, C. W. D., Tang, X, Yu, L, Xu, S., 2020. Disruption of emergency response to vulnerable populations during floods. Nat. Sustain., 3, pp. 728-736. doi: 10.1038/s41893-020-0516-7.

[76] Zang, Y, Meng, Y, Guan, X, Lv, H, Yan, D., 2022. Study on urban flood early warning system considering flood loss. Int. J. Disaster Risk Reduct., 77, Article 103042. doi: 10.1016/j.ijdrr.2022.103042.

[77] Zhang, H., 2020. Comprehensive evaluation of the effects of climate change and land use and land cover change variables on runoff and sediment discharge. Proceedings of the EGU General Assembly 2020

[78] Zhang, Z, Shi, M, Chen, K. Z., Yang, H, Wang, S., 2021. Water scarcity will constrain the formation of a world-class megalopolis in North China. npj Urban Sustain., 1, pp. 1-10. doi: 10.1038/s42949-020-00012-8.

[79] Zhang, J, Zhang, Y, Sun, G, Song, C, Li, J, Hao, L, Liu, N., 2022. Climate variability masked greening effects on water yield in the Yangtze River basin during 2001–2018. Water Resour. Res., 58, Article e2021WR030382. doi: 10.1029/2021WR030382.

[80] Zhang, Q, Gu, X, Singh, V. P., Shi, P, Sun, P., 2018. More frequent flooding? Changes in flood frequency in the Pearl River basin, China, since 1951 and over the past 1000 years. Hydrol. Earth Syst. Sci., 22, pp. 2637-2653. doi: 10.5194/hess-22-2637-2018.