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Spatiotemporal evolution of population exposure to multi-scenario rainstorms in the Yangtze River Delta urban agglomeration

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Abstract

Population exposure is a dominant representation of rainstorm hazard risks. However, the refined precipitation data in temporal resolution and the comparison of exposure to different rainstorm events remain relatively unexplored. Hourly precipitation data from 165 meteorological stations w used to investigate the spatiotemporal evolution of population exposure to different rainstorm scenarios in the prefecture-level cities for different periods and age groups. The Geographical Detector was adopted to quantitatively analyze the influencing factors and contribution rates to changes in population exposure during each period. The results revealed that population exposure to persistent rainstorms and abrupt rainstorms was low in the center and high in the surrounding areas, and the high exposure value decreased significantly in the 2010s. Additionally, as the duration of rainstorm events increased, the center of the high-value area of population exposure shifted southward. The distribution of population exposure was closely related to the age structure, demonstrating strong consistency with the distribution of different age groups. Except for abrupt rainstorms, the contribution rates of the average land GDP and urbanization rate to the exposure of all rainstorm scenarios increased significantly. This implies that the main factors influencing population exposure have shifted from meteorological to socioeconomic factors.

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References

  • Afuecheta E, Omar M H, 2021. Characterization of variability and trends in daily precipitation and temperature extremes in the Horn of Africa. Climate Risk Management, 32: 100295.

    Article  Google Scholar 

  • Boussad Y, Chen X, Legout A et al., 2022. Longitudinal study of exposure to radio frequencies at population scale. Environment International, 162: 107144.

    Article  Google Scholar 

  • Cao Y, Hua Z, Chen T et al., 2023. Understanding population movement and the evolution of urban spatial patterns: An empirical study on social network fusion data. Land Use Policy, 125: 106454.

    Article  Google Scholar 

  • Chen H, Sun J, 2019. Increased population exposure to precipitation extremes in China under global warming scenarios. Atmospheric and Oceanic Science Letters, 13(1): 63–70.

    Article  Google Scholar 

  • Chen J, Li Q, Wang H et al., 2020. A machine learning ensemble approach based on random forest and radial basis function neural network for risk evaluation of regional flood disaster: A case study of the Yangtze River Delta, China. International Journal of Environmental Research and Public Health, 17(1): 19.

    Google Scholar 

  • Chen W, Wang G, Zeng J, 2023. Impact of urbanization on ecosystem health in Chinese urban agglomerations. Environmental Impact Assessment Review, 98: 106964.

    Article  Google Scholar 

  • Chen Y, Wang X, Huang L et al., 2021. Spatial and temporal characteristics of abrupt heavy rainfall events over Southwest China during 1981–2017. International Journal of Climatology, 41(5): 3286–3299.

    Article  CAS  Google Scholar 

  • Christenson E, Elliott M, Banerjee O et al., 2014. Climate-related hazards: A method for global assessment of urban and rural population exposure to cyclones, droughts, and floods. International Journal of Environmental Research and Public Health, 11(2): 2169–2192.

    Article  Google Scholar 

  • Cynthia A B, Linda P, 2003. Evaluation of methods for classifying epidemiological data on choropleth maps in series. Annals of the Association of American Geographers, 92(4): 662–681.

    Google Scholar 

  • Daufresne M, Lengfellner K, Sommer U, 2009. Global warming benefits the small in aquatic ecosystems. Proceedings of the National Academy of Sciences, 106(31): 12788–12793.

    Article  CAS  Google Scholar 

  • Hauer M E, Hardy D, Kulp S A et al., 2021. Assessing population exposure to coastal flooding due to sea level rise. Nature Communications, 12: 6900.

    Article  CAS  Google Scholar 

  • He B, Zhai P, 2018. Changes in persistent and non-persistent extreme precipitation in China from 1961 to 2016. Advances in Climate Change Research, 9(3): 177–184.

    Article  Google Scholar 

  • Hitchens N M, Brooks H E, Schumacher R S, 2013. Spatial and temporal characteristics of heavy hourly rainfall in the United States. Monthly Weather Review, 141(12): 4564–4575.

    Article  Google Scholar 

  • Hofmann G, Balakrishnan N, 2006. A nonparametric test for trend based on initial ranks. Journal of Statistical Computation and Simulation, 76(9): 829–837.

    Article  Google Scholar 

  • Hu P, Chen B, Shi P, 2021. Spatiotemporal patterns and influencing factors of rainstorm-induced flood disasters in China. Acta Geographica Sinica, 76(5): 1148–1162. (in Chinese)

    Google Scholar 

  • Huang D, Zhang L, Gao G et al., 2018. Projected changes in population exposure to extreme heat in China under a RCP8.5 scenario. Journal of Geographical Sciences, 28(10): 1371–1384.

    Article  Google Scholar 

  • Huang G, Luo H, Lu X et al., 2020. Study on risk analysis and zoning method of urban flood disaster. Water Resources Protection, 36(6): 1–6, 17. (in Chinese)

    Google Scholar 

  • Jiang T, Su B, Huang J et al., 2020. Each 0.5°C of warming increases annual flood losses in China by more than US$60 billion. Bulletin of the American Meteorological Society, 101(8): E1464–E1474.

    Article  Google Scholar 

  • Jing Y, Fang J, Shi P, 2020. Analysis of population exposure to extreme precipitation in Hubei province under the climate change scenarios. Journal of Beijing Normal University (Natural Science), 56(5): 700–709. (in Chinese)

    Google Scholar 

  • Jones B, O’Neill B C, McDaniel L et al., 2015. Future population exposure to US heat extremes. Nature Climate Change, 5(7): 652–655.

    Article  Google Scholar 

  • Jongman B, Winsemius H C, Aerts J C J H et al., 2015. Declining vulnerability to river floods and the global benefits of adaptation. Proceedings of the National Academy of Sciences, 112(18): E2271–E2280.

    Article  CAS  Google Scholar 

  • Li C, Dash J, Asamoah M et al., 2022a. Increased flooded area and exposure in the White Volta river basin in Western Africa, identified from multi-source remote sensing data. Scientific Reports, 12(1): 3701.

    Article  CAS  Google Scholar 

  • Li J, Guo P, Sun Y et al., 2022b. Population exposure changes to one heat wave and the influencing factors using mobile phone data: A case study of Zhuhai city, China. Sustainability, 14(2): 997.

    Article  Google Scholar 

  • Li Y, Gong S, Zhang Z et al., 2021. Vulnerability evaluation of rainstorm disaster based on ESA conceptual framework: A case study of Liaoning province, China. Sustainable Cities and Society, 64: 102540.

    Article  Google Scholar 

  • Li Y, Zhang Z, Gong S et al., 2020. Risk assessment of rainstorm disasters under different return periods: A case study of Bohai Rim, China. Ocean & Coastal Management, 187: 105107.

    Article  Google Scholar 

  • Liang P, Xu W, Ma Y et al., 2017. Increase of elderly population in the rainstorm hazard areas of China. International Journal of Environmental Research and Public Health, 14(9): 963.

    Article  Google Scholar 

  • Liao X, Xu W, Zhang J et al., 2019. Global exposure to rainstorms and the contribution rates of climate change and population change. Science of the Total Environment, 663: 644–653.

    Article  CAS  Google Scholar 

  • Liao X, Xu W, Zhang J et al., 2022. Analysis of affected population vulnerability to rainstorms and its induced floods at county level: A case study of Zhejiang province, China. International Journal of Disaster Risk Reduction, 75: 102976.

    Article  Google Scholar 

  • Lin W, Sun Y, Nijhuis S et al., 2020. Scenario-based flood risk assessment for urbanizing deltas using future land-use simulation (FLUS): Guangzhou Metropolitan Area as a case study. Science of the Total Environment, 739: 139899.

    Article  CAS  Google Scholar 

  • Liu Y, Li L, Zhang W et al., 2019. Rapid identification of rainstorm disaster risks based on an artificial intelligence technology using the 2DPCA method. Atmospheric Research, 227: 157–164.

    Article  Google Scholar 

  • Ma Z, Sun P, Zhang Q et al., 2022. The characteristics and evaluation of future droughts across China through the CMIP6 multi-model ensemble. Remote Sensing, 14(5): 1097.

    Article  Google Scholar 

  • Man H de, Berg H H J L van den, Leenen E J T M et al., 2014. Quantitative assessment of infection risk from exposure to waterborne pathogens in urban floodwater. Water Research, 48: 90–99.

    Article  Google Scholar 

  • Merkens J, Lincke D, Hinkel J et al., 2018. Regionalisation of population growth projections in coastal exposure analysis. Climatic Change, 151: 413–426.

    Article  Google Scholar 

  • Michael K L, Seong N H, 2008. Households’ perceived personal risk and responses in a multihazard environment. Risk Analysis, 28(2): 539–556.

    Article  Google Scholar 

  • Mouri G, Minoshima D, Golosov V et al., 2013. Probability assessment of flood and sediment disasters in Japan using the Total Runoff-Integrating Pathways model. International Journal of Disaster Risk Reduction, 3: 31–43.

    Article  Google Scholar 

  • National Bureau of Statistics of China, 2020. China Statistical Yearbook 2020. China Statistics Press, Beijing, Beijing. http://www.stats.gov.cn/english.

    Google Scholar 

  • Popp A, Calvin K, Fujimori S et al., 2017. Land-use futures in the shared socio-economic pathways. Global Environmental Change, 42: 331–345.

    Article  Google Scholar 

  • Qin P, 2022. More than six billion people encountering more exposure to extremes with 1.5 degrees °C and 2.0 degrees °C warming. Atmospheric Research, 273: 106165.

    Article  Google Scholar 

  • Qin X, Wu Y, Lin T et al., 2023. Urban flood dynamic risk assessment based on typhoon rainfall process: A case study of typhoon “Lupit” (2109) in Fuzhou, China. Remote Sensing, 15: 3116.

    Article  Google Scholar 

  • Reis S, Liška T, Vieno M et al., 2018. The influence of residential and workday population mobility on exposure to air pollution in the UK. Environment International, 121: 803–813.

    Article  CAS  Google Scholar 

  • Saqib S, Ahmad M M, Panezai S et al., 2016. Factors influencing farmers’ adoption of agricultural credit as a risk management strategy: The case of Pakistan. International Journal of Disaster Risk Reduction, 17: 67–76.

    Article  Google Scholar 

  • Şen Z, 2017. Innovative trend significance test and applications. Theoretical and Applied Climatology, 127(3/4): 939–947.

    Article  Google Scholar 

  • Shen L, Wen J, Zhang Y et al., 2022. Changes in population exposure to extreme precipitation in the Yangtze River Delta, China. Climate Service, 27: 100317.

    Article  Google Scholar 

  • Shi X, Chen J, Gu L et al., 2021. Impacts and socioeconomic exposures of global extreme precipitation events in 1.5 and 2.0 °C warmer climates. Science of The Total Environment, 766: 142665.

    Article  CAS  Google Scholar 

  • Smith A, Bates P D, Wing O et al., 2019. New estimates of flood exposure in developing countries using high-resolution population data. Nature Communications, 10(1): 1814.

    Article  Google Scholar 

  • Soneja S, Jiang C, Fisher J et al., 2016. Exposure to extreme heat and precipitation events associated with increased risk of hospitalization for asthma in Maryland, U.S.A. Environmental Health, 15(1): 57.

    Article  Google Scholar 

  • Ta Z, Li K, Han H et al., 2022. Population and GDP exposure to extreme precipitation events on Loess Plateau under the 1.5 degrees C global warming level. Atmosphere, 13(9): 1423.

    Article  Google Scholar 

  • Takaya Y, Ishikawa I, Kobayashi C et al., 2020. Enhanced Meiyu-Baiu rainfall in early summer 2020: Aftermath of the 2019 Super IOD Event. Geophysical Research Letters, 47(22): e2020G–e90671G.

    Article  Google Scholar 

  • Tang R, Dai Z, Mei X et al., 2023. Joint impacts of dams and floodplain on the rainfall-induced extreme flood in the Changjiang (Yangtze) River. Journal of Hydrology, 627: 130428.

    Article  Google Scholar 

  • Wang A, Tao H, Ding G et al., 2023. Global cropland exposure to extreme compound drought heatwave events under future climate change. Weather and Climate Extremes, 40: 100559.

    Article  Google Scholar 

  • Wang J, Li X, Christakos G et al., 2010. Geographical Detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region, China. International Journal of Geographical Information Science, 24(1): 107–127.

    Article  CAS  Google Scholar 

  • Wang J, Zhang T, Fu B, 2016. A measure of spatial stratified heterogeneity. Ecological Indicators, 67: 250–256.

    Article  Google Scholar 

  • Wang X, Ding S, Cao W et al., 2020a. Research on network patterns and influencing factors of population flow and migration in the Yangtze River Delta urban agglomeration, China. Suatainability, 12(17): 6803.

    Article  Google Scholar 

  • Wang Y, Xu Y, Tabari H et al., 2020b. Innovative trend analysis of annual and seasonal rainfall in the Yangtze River Delta, eastern China. Atmospheric Research, 231: 104673.

    Article  Google Scholar 

  • Winsemius H C, Jongman B, Veldkamp T I E et al., 2018. Disaster risk, climate change, and poverty: Assessing the global exposure of poor people to floods and droughts. Environment and Development Economics, 23(3): 328–348.

    Article  Google Scholar 

  • Wu Y, Wu S, Wen J et al., 2016. Future changes in mean and extreme monsoon precipitation in the middle and lower Yangtze River Basin, China, in the CMIP5 Models. Journal of Hydrometeorology, 17(11): 2785–2797.

    Article  Google Scholar 

  • Xiao R, Cao W, Liu Y et al., 2022. The impacts of landscape patterns spatio-temporal changes on land surface temperature from a multi-scale perspective: A case study of the Yangtze River Delta. Science of The Total Environment, 821: 153381.

    Article  CAS  Google Scholar 

  • Xu Z, Yin Y, 2021. Regional development quality of Yangtze River Delta: from the perspective of urban population agglomeration and ecological efficiency coordination. Sustainability, 13(22): 12818.

    Article  Google Scholar 

  • Yao R, Zhang S, Sun P et al., 2022. Diurnal variations in different precipitation duration events over the Yangtze River Delta urban agglomeration. Remote Sensing, 14(20): 5244.

    Article  Google Scholar 

  • Yin S, Gao G, Li W et al., 2011. Long-term precipitation change by hourly data in Haihe River Basin during 1961–2004. Science China Earth Sciences, 54: 1576–1585.

    Article  Google Scholar 

  • Yu R, Yuan W, Li J, 2013. The asymmetry of rainfall process. Chinese Science Bulletin, 58(16): 1850–1856.

    Article  Google Scholar 

  • Yue S, Pilon P, Phinney B et al., 2002. The influence of autocorrelation on the ability to detect trend in hydrological series. Hydrological Processes, 16(9): 1807–1829.

    Article  Google Scholar 

  • Zhang J, Yuan J, Wang Y, 2023. Spatio-temporal evolution and influencing factors of coupling coordination between urban resilience and high-quality development in Yangtze River Delta area, China. Frontiers in Environmental Science, 11: 1174875.

    Article  Google Scholar 

  • Zhang W, Zhou T, 2020. Increasing impacts from extreme precipitation on population over China with global warming. Science Bulletin, 65(3): 243–252.

    Article  Google Scholar 

  • Zhang W, Zhou T, Zou L et al., 2018. Reduced exposure to extreme precipitation from 0.5°C less warming in global land monsoon regions. Nature Communications, 9(1): 3153.

    Article  Google Scholar 

  • Zhao Y, Zhou T, Zhang W et al., 2022. Change in precipitation over the Tibetan Plateau projected by Weighted CMIP6 Models. Advances in Atmospheric Sciences, 39: 1133–1150.

    Article  Google Scholar 

  • Zheng F, Sun C, Li J, 2012. Climate change: New dimension in disaster risk, exposure, vulnerability, and resilience. Climate Change Research, 8(2): 79–83. (in Chinese)

    Google Scholar 

  • Zhou Z, Xie S, Zhang R, 2021. Historic Yangtze flooding of 2020 tied to extreme Indian Ocean conditions. Proceedings of the National Academy of Sciences, 118(12): e2022255118.

    Article  CAS  Google Scholar 

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Correspondence to Shuliang Zhang.

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Foundation: National Natural Science Foundation of China, No.42071364, No.42271483; The Postgraduate Research & Practice Innovation Program of Jiangsu Province, No.KYCX22_1585

Author: Zhang Yaru (1998–), Master, specialized in urban flood simulation and flood risk assessment.

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Zhang, Y., Yao, R., Zhu, Z. et al. Spatiotemporal evolution of population exposure to multi-scenario rainstorms in the Yangtze River Delta urban agglomeration. J. Geogr. Sci. 34, 654–680 (2024). https://doi.org/10.1007/s11442-024-2222-2

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