Skip to main content
SpringerLink
Log in

Sensitivity analysis of coastal cities to effects of rainstorm and flood disasters

  • Research
  • Published:
Environmental Monitoring and Assessment Aims and scope Submit manuscript

Abstract

Heavy rains and floods cause human, material, and economic damage in cities worldwide. The severity of flooding has intensified due to accelerating urbanization. While much of the existing research on flood hazards emphasizes simulation and assessment, the correlation between indicators has yet to be explored. This study employs the Tree Gaussian Process sensitivity analysis method. Through rigorous sampling and correlation analysis, the model identifies critical determinants. Significantly, factors such as the water supply penetration rate (Var3), water pipeline density in built-up areas (Var4), centralized treatment rate of sewage treatment plants (Var6), agricultural land for forestry (Var13), and urban, village, and industrial and mining land (Var15) stand out as primary influencers on the flood-affected populace. These variables reflect a city’s flood management capability and its dedication to resource stewardship and ecological equilibrium, underscoring its critical role in flood risk assessment and strategic mitigation. The study further illuminates that the interplay of these variables can exacerbate flood consequences, suggesting a compounded impact when variables operate in tandem. Recognizing these synergistic effects reveals a more pronounced flood threat than previously estimated, indicating that viewing these factors in silos might underrepresent the risk involved.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Data availability

All relevant data are available upon request from the authors.

Code availability

All computer code used in the data analysis is available from the corresponding author upon reasonable request.

References

  • Abily, M., Bertrand, N., Delestre, O., Gourbesville, P., & Duluc, C.-M. (2016). Spatial Global Sensitivity Analysis of High Resolution classified topographic data use in 2D urban flood modelling. Environmental Modelling & Software, 77, 183–195. https://doi.org/10.1016/j.envsoft.2015.12.002

    Article  Google Scholar 

  • Aliewi, A., & Al-Khatib, I. A. (2015). Hazard and risk assessment of pollution on the groundwater resources and residents’ health of Salfit District, Palestine. Journal of Hydrology: Regional Studies, 4, 472–486. https://doi.org/10.1016/j.ejrh.2015.07.006

    Article  Google Scholar 

  • Alipour, A., Jafarzadegan, K., & Moradkhani, H. (2022). Global sensitivity analysis in hydrodynamic modeling and flood inundation mapping. Environmental Modelling & Software, 152, 105398. https://doi.org/10.1016/j.envsoft.2022.105398

    Article  Google Scholar 

  • Anayah, F. (2006). An assessment of the nitrate and chloride in the West Bank groundwater resources using GIS (pp. 37–50). An-Najah National University.

    Google Scholar 

  • Balica, S. F., Wright, N. G., & van der Meulen, F. (2012). A flood vulnerability index for coastal cities and its use in assessing climate change impacts. Natural Hazards, 64(1), 73–105. https://doi.org/10.1007/s11069-012-0234-1

    Article  Google Scholar 

  • Baroni, G., & Francke, T. (2020). An effective strategy for combining variance- and distribution-based global sensitivity analysis. Environmental Modelling & Software, 134, 104851. https://doi.org/10.1016/j.envsoft.2020.104851

    Article  Google Scholar 

  • Barragán, J. M., & de Andrés, M. (2015). Analysis and trends of the world’s coastal cities and agglomerations. Ocean & Coastal Management, 114, 11–20. https://doi.org/10.1016/j.ocecoaman.2015.06.004

    Article  Google Scholar 

  • Bellanova, P., Frenken, M., Reicherter, K., Jaffe, B., Szczuciński, W., & Schwarzbauer, J. (2020). Anthropogenic pollutants and biomarkers for the identification of 2011 Tohoku-oki tsunami deposits (Japan). Marine Geology, 422, 106117.

    Article  Google Scholar 

  • Burcham, D. C., & Au, S.-K. (2022). Identifying modal properties of trees with Bayesian inference. Agricultural and Forest Meteorology, 316, 108804. https://doi.org/10.1016/j.agrformet.2021.108804

    Article  Google Scholar 

  • Carpino, C., Bruno, R., Carpino, V., & Arcuri, N. (2022). Improve decision-making process and reduce risks in the energy retrofit of existing buildings through uncertainty and sensitivity analysis. Energy for Sustainable Development, 68, 289–307. https://doi.org/10.1016/j.esd.2022.04.007

    Article  Google Scholar 

  • Chang, L.-F., & Huang, S.-L. (2015). Assessing urban flooding vulnerability with an emergy approach. Landscape and Urban Planning, 143, 11–24.

    Article  Google Scholar 

  • Cole, H. D., Cole, M. J., Simpson, K. J., Simpson, N. P., Ziervogel, G., & New, M. G. (2021). Managing city-scale slow-onset disasters: Learning from Cape Town’s 2015–2018 drought disaster planning. International Journal of Disaster Risk Reduction, 63, 102459. https://doi.org/10.1016/j.ijdrr.2021.102459

    Article  Google Scholar 

  • Crawford, S. E., Brinkmann, M., Ouellet, J. D., Lehmkuhl, F., Reicherter, K., Schwarzbauer, J., Bellanova, P., Letmathe, P., Blank, L. M., Weber, R., et al. (2022). Remobilization of pollutants during extreme flood events poses severe risks to human and environmental health. Journal of Hazardous Materials, 421, 126691.

    Article  CAS  Google Scholar 

  • Delenne, C., Cappelaere, B., & Guinot, V. (2012). Uncertainty analysis of river flooding and dam failure risks using local sensitivity computations. Reliability Engineering & System Safety, 107, 171–183. https://doi.org/10.1016/j.ress.2012.04.007

    Article  Google Scholar 

  • Du, C., Li, B., Liu, H., Ji, Y., Yao, R., & Yu, W. (2019). Quantification of personal thermal comfort with localized airflow system based on sensitivity analysis and classification tree model. Energy and Buildings, 194, 1–11. https://doi.org/10.1016/j.enbuild.2019.04.010

    Article  Google Scholar 

  • EM-DAT. (2018, September 24). EM-DAT Public. EM-DAT. https://www.emdat.be/database

  • Esser, V., Buchty-Lemke, M., Schulte, P., Podzun, L. S., & Lehmkuhl, F. (2020). Signatures of recent pollution profiles in comparable central European rivers–Examples from the international River Basin District Meuse. CATENA, 193, 104646.

    Article  CAS  Google Scholar 

  • Garambois, P. A., Roux, H., Larnier, K., Castaings, W., & Dartus, D. (2013). Characterization of process-oriented hydrologic model behavior with temporal sensitivity analysis for flash floods in Mediterranean catchments. Hydrology and Earth System Sciences, 17(6), 2305–2322. https://doi.org/10.5194/hess-17-2305-2013

    Article  Google Scholar 

  • Ghadim, H. B., Hin, L. S., Bu, C. H., & Chin, R. J. (2017). Effectiveness of BIOECODS for peak flow attenuation: An appraisal using InfoWorks SD. Hydrological Sciences Journal, 62(3), 421–430. https://doi.org/10.1080/02626667.2016.1176175

    Article  Google Scholar 

  • Gill, K., Shahzad, N., Ashraf, I., Saeed, U., & Laghari, F. (2012). Impact of floods 2010 in coastal area of Pakistan–a case study of Kharo Chann. Thatta District. Rec Zool. Surv. Pakistan, 21, 29–34.

    Google Scholar 

  • Goto, K., Fujima, K., Sugawara, D., Fujino, S., Imai, K., Tsudaka, R., Abe, T., & Haraguchi, T. (2012). Field measurements and numerical modeling for the run-up heights and inundation distances of the 2011 Tohoku-oki tsunami at Sendai Plain, Japan. Earth, Planets and Space, 64(12), 1247–1257.

    Article  Google Scholar 

  • Hagemann, L., Buchty-Lemke, M., Maaß, A.-L., Schüttrumpf, H., Lehmkuhl, F., & Schwarzbauer, J. (2020). Potential hotspots of persistent organic pollutants in alluvial sediments of the meandering Wurm River. Germany. Journal of Soils and Sediments, 20(2), 1034–1045.

    Article  CAS  Google Scholar 

  • Lake, I. R., Foxall, C. D., Fernandes, A., Lewis, M., White, O., Mortimer, D., Dowding, A., & Rose, M. (2014). The effects of river flooding on dioxin and PCBs in beef. Science of the Total Environment, 491, 184–191.

    Article  Google Scholar 

  • Laner, D., Fellner, J., Brunner, P. H., Neuhold, C., & Kolesar, C. (2008). Environmental relevance of flooded MSW landfills in Austria (pp. 3–6). ISWA/WMRAS World Congress: ISWA/WMRAS.

    Google Scholar 

  • Liao, S., Wu, Y., Wong, S. W., & Shen, L. (2020). Provincial perspective analysis on the coordination between urbanization growth and resource environment carrying capacity (RECC) in China. Science of the Total Environment, 730, 138964. https://doi.org/10.1016/j.scitotenv.2020.138964

    Article  CAS  Google Scholar 

  • Liu, Y., Hou, X., Li, X., Song, B., & Wang, C. (2020). Assessing and predicting changes in ecosystem service values based on land use/cover change in the Bohai Rim coastal zone. Ecological Indicators, 111, 106004. https://doi.org/10.1016/j.ecolind.2019.106004

    Article  Google Scholar 

  • Liu, Y., Huang, D., Liu, B., Feng, Q., & Cai, B. (2021). Adaptive ranking based ensemble learning of Gaussian process regression models for quality-related variable prediction in process industries. Applied Soft Computing, 101, 107060. https://doi.org/10.1016/j.asoc.2020.107060

    Article  Google Scholar 

  • Loliyana, V. D., & Patel, P. L. (2020). A physics based distributed integrated hydrological model in prediction of water balance of a semi-arid catchment in India. Environmental Modelling & Software, 127, 104677. https://doi.org/10.1016/j.envsoft.2020.104677

    Article  Google Scholar 

  • Löwe, R., Urich, C., Domingo, N. S., Mark, O., Deletic, A., & Arnbjerg-Nielsen, K. (2017). Assessment of urban pluvial flood risk and efficiency of adaptation options through simulations–A new generation of urban planning tools. Journal of Hydrology, 550, 355–367.

    Article  Google Scholar 

  • Maier, M., Kühlers, D., Brauch, H.-J., Fleig, M., Maier, D., Jirka, G. H., Mohrlok, U., Bethge, E., Bernhart, H. H., Lehmann, B., et al. (2006). Flood retention and drinking water supply-preventing conflicts of interest. Journal of Soils and Sediments, 6(2), 113.

    Article  Google Scholar 

  • Mao, J., Yang, J. H., Afshari, A., & Norford, L. K. (2017). Global sensitivity analysis of an urban microclimate system under uncertainty: Design and case study. Building and Environment, 124, 153–170.

    Article  Google Scholar 

  • Marlow, D. R., Boulaire, F., Beale, D. J., Grundy, C., & Moglia, M. (2011). Sewer performance reporting: Factors that influence blockages. Journal of Infrastructure Systems, 17(1), 42–51.

    Article  Google Scholar 

  • Mobini, S., Pirzamanbein, B., Berndtsson, R., & Larsson, R. (2022). Urban flood damage claim analyses for improved flood damage assessment. International Journal of Disaster Risk Reduction, 77, 103099. https://doi.org/10.1016/J.IJDRR.2022.103099

    Article  Google Scholar 

  • National Bureau of Statistics. (2018a, October 8). China Meteorological Disaster Yearbook. https://data.cnki.net/yearbook/Single/N2019120044

  • National Bureau of Statistics. (2018b, October 8). China National Land and Resources Statistical Yearbook. https://data.cnki.net/yearbook/Single/N2020030130

  • National Bureau of Statistics. (2018c, October 8). China Urban Construction Statistical Yearbook. https://data.cnki.net/Yearbook/Single/N2020050213

  • Nossent, J., Elsen, P., & Bauwens, W. (2011). Sobol’ sensitivity analysis of a complex environmental model. Environmental Modelling & Software, 26(12), 1515–1525. https://doi.org/10.1016/j.envsoft.2011.08.010

    Article  Google Scholar 

  • Östberg, J., Martinsson, M., Stål, Ö., & Fransson, A.-M. (2012). Risk of root intrusion by tree and shrub species into sewer pipes in Swedish urban areas. Urban Forestry & Urban Greening, 11(1), 65–71.

    Article  Google Scholar 

  • Palla, A., Gnecco, I., & La Barbera, P. (2017). The impact of domestic rainwater harvesting systems in storm water runoff mitigation at the urban block scale. Journal of Environmental Management, 191, 297–305. https://doi.org/10.1016/j.jenvman.2017.01.025

    Article  CAS  Google Scholar 

  • Pickaver, A. H., Gilbert, C., & Breton, F. (2004). An indicator set to measure the progress in the implementation of integrated coastal zone management in Europe. Ocean & Coastal Management, 47(9–10), 449–462.

    Article  Google Scholar 

  • Sfeir, G., Rodrigues, F., & Abou-Zeid, M. (2022). Gaussian process latent class choice models. Transportation Research Part c: Emerging Technologies, 136, 103552. https://doi.org/10.1016/j.trc.2022.103552

    Article  Google Scholar 

  • Sharifan, R. A., Roshan, A., Aflatoni, M., Jahedi, A., & Zolghadr, M. (2010). Uncertainty and sensitivity analysis of SWMM model in computation of manhole water depth and subcatchment peak flood. Procedia - Social and Behavioral Sciences, 2(6), 7739–7740. https://doi.org/10.1016/j.sbspro.2010.05.205

    Article  Google Scholar 

  • Si, F., Huang, T., Li, N., Li, K., Wen, G., Li, Y., & Zhang, H. (2022). Effects of flood discharge on the water quality of a drinking water reservoir in China – Characteristics and management strategies. Journal of Environmental Management, 314, 115072. https://doi.org/10.1016/j.jenvman.2022.115072

    Article  CAS  Google Scholar 

  • Smith, D., Li, J., & Banting, D. (2005). A PCSWMM/GIS-based water balance model for the Reesor Creek watershed. Atmospheric Research, 77(1), 388–406. https://doi.org/10.1016/j.atmosres.2004.12.010

    Article  Google Scholar 

  • Szczuciński, W., Kokociński, M., RzeszewskiMichałand Chagué-Goff, C., Cachão, M., Goto, K., & Sugawara, D. (2012). Sediment sources and sedimentation processes of 2011 Tohoku-oki tsunami deposits on the Sendai Plain, Japan—Insights from diatoms, nannoliths and grain size distribution. Sedimentary Geology, 282, 40–56.

    Article  Google Scholar 

  • Talaei, S., Beitollahi, A., Moshirabadi, S., & Fallahian, M. (2018). Vibration-based structural damage detection using Twin Gaussian Process (TGP). Structures, 16, 10–19. https://doi.org/10.1016/j.istruc.2018.08.006

    Article  Google Scholar 

  • Thanvisitthpon, N. (2019). Impact of land use transformation and anti-flood infrastructure on flooding in world heritage site and peri-urban area: A case study of Thailand’s Ayutthaya province. Journal of Environmental Management, 247, 518–524.

    Article  Google Scholar 

  • Theja Bhavaraju, S. K., Beyney, C., & Nicholson, C. (2019). Quantitative analysis of social media sensitivity to natural disasters. International Journal of Disaster Risk Reduction, 39, 101251. https://doi.org/10.1016/j.ijdrr.2019.101251

    Article  Google Scholar 

  • Tian, W. (2013). A review of sensitivity analysis methods in building energy analysis. Renewable and Sustainable Energy Reviews, 20, 411–419. https://doi.org/10.1016/j.rser.2012.12.014

    Article  Google Scholar 

  • Tsubaki, R., & Kawahara, Y. (2013). The uncertainty of local flow parameters during inundation flow over complex topographies with elevation errors. Journal of Hydrology, 486, 71–87. https://doi.org/10.1016/j.jhydrol.2013.01.042

    Article  Google Scholar 

  • van der Sterren, M., Rahman, A., & Ryan, G. (2014). Modeling of a lot scale rainwater tank system in XP-SWMM: A case study in Western Sydney, Australia. Journal of Environmental Management, 141, 177–189. https://doi.org/10.1016/j.jenvman.2014.02.013

    Article  CAS  Google Scholar 

  • Weber, R., Herold, C., Hollert, H., Kamphues, J., Blepp, M., & Ballschmiter, K. (2018). Reviewing the relevance of dioxin and PCB sources for food from animal origin and the need for their inventory, control and management. Environmental Sciences Europe, 30(1), 1–42.

    Article  CAS  Google Scholar 

  • Williams, J. B., Clarkson, C., Mant, C., Drinkwater, A., & May, E. (2012). Fat, oil and grease deposits in sewers: Characterisation of deposits and formation mechanisms. Water Research, 46(19), 6319–6328.

    Article  CAS  Google Scholar 

  • Wu, M., Wu, Z., Ge, W., Wang, H., Shen, Y., & Jiang, M. (2021). Identification of sensitivity indicators of urban rainstorm flood disasters: A case study in China. Journal of Hydrology, 599, 126393.

    Article  Google Scholar 

  • Yang, S., Tian, W., Cubi, E., Meng, Q., Liu, Y., & Wei, L. (2016). Comparison of sensitivity analysis methods in building energy assessment. Procedia Engineering, 146, 174–181. https://doi.org/10.1016/j.proeng.2016.06.369

    Article  Google Scholar 

  • Yang, Y., Chen, S., Chang, T., Ma, J., & Sun, Y. (2021). Uncertainty and global sensitivity analysis on thermal performances of pipe-embedded building envelope in the heating season. Energy Conversion and Management, 244, 114509. https://doi.org/10.1016/j.enconman.2021.114509

    Article  Google Scholar 

  • Yarali, E., Rivaz, F., & Khaledi, M. J. (2022). A Bayesian nonparametric spatial model with covariate-dependent joint weights. Spatial Statistics, 100662. https://doi.org/10.1016/j.spasta.2022.100662

  • Young, A. F., & Jorge Papini, J. A. (2020). How can scenarios on flood disaster risk support urban response? A case study in Campinas Metropolitan Area (São Paulo, Brazil). Sustainable Cities and Society, 61, 102253. https://doi.org/10.1016/j.scs.2020.102253

    Article  Google Scholar 

  • Zheng, Z., Wu, Z., Chen, Y., Yang, Z., & Marinello, F. (2020). Exploration of eco-environment and urbanization changes in coastal zones: A case study in China over the past 20 years. Ecological Indicators, 119, 106847. https://doi.org/10.1016/j.ecolind.2020.106847

    Article  Google Scholar 

  • Zhu, L., Zhang, J., Gao, Y., Tian, W., Yan, Z., Ye, X., Sun, Y., & Wu, C. (2022). Uncertainty and sensitivity analysis of cooling and heating loads for building energy planning. Journal of Building Engineering, 45, 103440. https://doi.org/10.1016/j.jobe.2021.103440

    Article  Google Scholar 

Download references

Funding

This work was sponsored by China’s National Key Research and Development Program (Grant No. 2018YFC0704400) and the National Natural Science Foundation of China (Grant No. 52078329).

Author information

Author notes

  1. Li Zhu

    Present address: APEC Sustainable Energy Center, Tianjin, 300072, China

Authors and Affiliations

  1. School of Architecture, Tsinghua University, Beijing, 100084, China

    Junrong Ma

  2. APEC Sustainable Energy Center, Tianjin, 300072, China

    Steivan Defilla & Zhexing Yan

  3. Tianjin University, Tianjin, China

    Li Zhu, Chen Wang, Steivan Defilla & Zhexing Yan

Authors
  1. Li Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Junrong Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chen Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Steivan Defilla
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhexing Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

L.Z. and J.M. designed the research; J.M., Z.Y., and C.W. conducted data analysis and calculation; J.M. and S.D. wrote the paper; all the authors contributed to the interpretation of the results.

Corresponding author

Correspondence to Junrong Ma.

Ethics declarations

Ethics approval

Not applicable.

Consent to participate

Not applicable.

Consent for publication

Not applicable.

Conflict of interest

The authors declare no competing interests.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhu, L., Ma, J., Wang, C. et al. Sensitivity analysis of coastal cities to effects of rainstorm and flood disasters. Environ Monit Assess 196, 386 (2024). https://doi.org/10.1007/s10661-024-12516-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10661-024-12516-2

Keywords

Search

Navigation