Abstract
Floods are common natural disasters in Asia. Flood datasets from 48 countries in Asia were collected to investigate the spatiotemporal distribution and influencing factors, using the Mann–Kendall trend test and the Spearman’s rank correlation. These results show that flood occurrences and damages increased significantly in Asia, with the mortality rates and deaths decreasing. Southern and Eastern Asia are flood vulnerable regions, with Central Asia being the least flood-occurrence region, and China and India are also flood-prone countries with a largest population and land area in Asia. Least flood disasters occurred in Bahrain, Cyprus, Brunei Darussalam and Singapore, with a smaller population and land area. The spatial disparities of flood disasters were positively influenced by population and land area, and negatively influenced by urbanization rate and per capita GDP. The largest proportion of flood disasters were discovered in riverine floods, followed by flash floods, with coastal floods being the least. The highest and second-highest mortality rates were observed in flash floods and coastal floods, which showed decreasing trends, and the mortality rate of riverine floods was the lowest, with an increasing trend. The rain was the main triggering origin of floods, and tropical cyclone contributed to the second, followed by snowmelt, convective storms, and dam-break flows. This analysis can help to provide a useful insight into the formulation of flood risk maps, disaster mitigation measures and emergency management.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Alipour A, Ahmadalipour A, Moradkhani H (2020) Assessing flash flood hazard and damages in the southeast United States. J Flood Risk Manag 13(2): https://doi.org/10.1111/jfr3.12605
Baldassarre GD, Montanari A, Lins H, Koutsoyiannis D, Brandimarte L, Bloschl G (2010) Flood fatalities in Africa: from diagnosis to mitigation. Geophys Res Lett 37(22):L22402. https://doi.org/10.1029/2010GL045467
Brears R (2015) Integrated flood risk management, lessons from the Rhine and Danube for South Asia. New Water Policy Pract 1(2):59–67. https://doi.org/10.18278/nwpp.1.2.5
Centre for Research on the Epidemiology of Disasters (2018). Economic Losses, Poverty & Disasters (1998–2017). Brussels: CRED. https://www.cred.be/publications.
Centre for Research on the Epidemiology of Disasters (2020). Natural Disasters 2019: Now is the time to not give up. Brussels: CRED. https://www.cred.be/publications.
de Jong C, Collins D, Ranzi R (2005) (Eds) Climate and hydrology in mountain areas. Wiley, New York.
Granato GE (2006). Kendall-Theil Robust Line (KTRLine-version 1.0) - a visual basic program for calculating and graphing robust nonparametric estimates of linear regression coefficients between two continuous variables. In: Techniques and Methods of the U.S. Geological Survey, 31 pp. (Book 4, Chapter A7).
Helsel DR, Hirsch RM (2002) Statistical methods in water resources. Techniques of Water Resources Investigations, Book 4, chapter A3. U.S. Geological Survey, p 522. https://doi.org/10.3133/twri04A3.
Hu P, Zhang Q, Shi PJ, Chen B, Fang JY (2018) Flood-induced mortality across the globe: spatiotemporal pattern and influencing factors. Sci Total Environ 643:171–182. https://doi.org/10.1016/j.scitotenv.2018.06.197
IPCC (2012) Managing the risks of extreme events and disasters to advance climate change adaptation. n. A special report of working groups i and ii of the intergovernmental panel on climate change. In Field CB, V Barros, TF Stocker, D Qin, DJ Dokken, KL Ebi, MD Mastrandrea, KJ Mach, G-K Plattner, SK Allen, M Tignor, PM Midgley (eds) Cambridge University Press, Cambridge, UK, and New York, NY, USA, p 582
Jonkman SN (2005) Global Perspectives on Loss of Human Life Caused by Floods. Nat Hazards 34(2):151–175. https://doi.org/10.1007/s11069-004-8891-3
Jongman B, Ward PJ, Aerts J (2012) Global exposure to river and coastal flooding: long term trends and changes. Global Environ Chang 22(4):823–835. https://doi.org/10.1016/j.gloenvcha.2012.07.004
Kendall MG (1938) A new measure of rank correlation. Biometrika 30(1–2):81–94. https://doi.org/10.2307/2332226
Kundzewicz ZW, Pinskwar I, Brakenridge GR (2013) Large floods in Europe, 1985–2009. Hydrolog Sci J 58(1):1–7. https://doi.org/10.1080/02626667.2012.745082
Kundzewicz ZW, Kanae S, Seneviratne SI, Handmer J, Nicholls N, Peduzzi P et al (2014) Flood risk and climate change: global and regional perspectives. Hydrolog Sci J 59(1):1–28. https://doi.org/10.1080/02626667.2013.857411
Li CJ, Chai YQ, Yang LS, Li HR (2016) Spatio-temporal distribution of flood disasters and analysis of influencing factors in Africa. Nat Hazards 82(1):721–731. https://doi.org/10.1007/s11069-016-2181-8
Lim J, Skidmore M (2019) Flood fatalities in the United States: the roles of socioeconomic factors and the National Flood Insurance Program. Southern Econ J 85(4):1032–1057. https://doi.org/10.1002/soej.12330
Mann HB (1945) Nonparametric tests against trend. Econometrica 13(3):245–259. https://doi.org/10.2307/1907187
Mirza M (2002) Global warming and changes in the probability of occurrence of floods in Bangladesh and implications. Global Environ Chang 12(3):127–138. https://doi.org/10.1016/S0959-3780(02)00002-X
Mirza MMNQ (2011) Climate change, flooding in South Asia and implications. Reg Environ Change 11:95–107. https://doi.org/10.1007/s10113-010-0184-7
Nie C, Li H, Yang L, Wu S, Yi L, Liao Y (2012) Spatial and temporal changes in flooding and the affecting factors in China. Nat Hazards 61(2):425–439. https://doi.org/10.1007/s11069-011-9926-1
Nguyen HD, Fox D, Dang DK, Pham LT, Du Viet QV, Nguyen THT, Dang TN, Tran VT, Vu PL, Nguyen QH et al (2021) Predicting future urban flood risk using land change and hydraulic modeling in a river watershed in the central Province of Vietnam. Remote Sens 13(2):262. https://doi.org/10.3390/rs13020262
Osti R, Hishinuma S, Miyake K, Inomata H (2011) Lessons learned from statistical comparison of flood impact factors among southern and eastern Asian countries. J Flood Risk Manag 4(3):203–215. https://doi.org/10.1111/j.1753-318X.2011.01107.x
Osti R, Nakasu T (2016) Lessons learned from southern and eastern Asian urban floods: from a local perspective. J Flood Risk Manag 9(1):22–35. https://doi.org/10.1111/jfr3.12107
Paprotny D, Sebastian A, Morales-Nápoles O, Jonkman SN (2018) Trends in flood losses in Europe over the past 150 years. Nat Commun 9:1985. https://doi.org/10.1038/s41467-018-04253-1
Shen G, Hwang SN (2019) Spatial-Temporal snapshots of global natural disaster impacts revealed from EM-DAT for 1900–2015. Geomat Nat Haz Risk 10(1):912–934. https://doi.org/10.1080/19475705.2018.1552630
Siegel AF (1982) Robust regression using repeated medians. Biometrika 69(1):242–244. https://doi.org/10.1093/biomet/69.1.242
Spearman C (2010) The proof and measurement of association between two things. Int J Epidemiol 39(5):1137–1150. https://doi.org/10.1093/ije/dyq191
Sui J, Koehler G (2001) Rain-on-snow induced flood events in Southern Germany. J Hydrol 252(1–4):205–220. https://doi.org/10.1016/S0022-1694(01)00460-7
Takeuchi K, Chavoshian A, Simonovic SP (2018) Floods: from risk to opportunity. J Flood Risk Manag 11(4): https://doi.org/10.1111/jfr3.12046
Theil H (1992) A rank-invariant method of linear and polynomial regression analysis. In: Raj B., Koerts J. (eds). Henri Theil’s contributions to economics and econometrics Advanced studies in theoretical and applied econometrics, vol 23, pp 345–381. https://doi.org/10.1007/978-94-011-2546-8_20.
Yue S, Pilon P, Cavadias G (2002) Power of the Mann-Kendall and Spearman’s rho tests for detecting monotonic trends in hydrological series. J Hydrol 259:254–271. https://doi.org/10.1016/S0022-1694(01)00594-7
Wang XJ, Jiang RG, Xie JC, Zhao Y, Li FW, Zhu JW (2019) Multiscale variability of precipitation and their teleconnection with large-scale climate anomalies: a case study of Xi’an City. China J Coastal Res 93(sp1):417–426. https://doi.org/10.2112/SI93-055.1
Zhao Y, Deng Q, Lin Q, Cai C (2017a) Quantitative analysis of the impacts of terrestrial environmental factors on precipitation variation over the Beibu Gulf Economic Zone in Coastal Southwest China. Sci Rep 7:44412. https://doi.org/10.1038/srep44412
Zhao XK, Li ZY, Zhu QK (2017b) Change of precipitation characteristics in the water-wind erosion crisscross region on the Loess Plateau, China, from 1958 to 2015. Sci Rep 7(1):8048. https://doi.org/10.1038/s41598-017-08600-y
Zhu JX, Dai Q, Deng YH, Zhang A, Zhang YZ, Zhang SL (2018) Indirect damage of urban flooding: investigation of flood-induced traffic congestion using dynamic modeling. Water 10(5):622. https://doi.org/10.3390/w10050622
Acknowledgements
This study was funded by the National Natural Science Foundation of China (Grant Nos. 41890823; 51725902); the Royal Academy of Engineering through the Urban Flooding Research Policy Impact Programme (Grant No. UUFRIP\100031); and the Newton Advanced Fellowships from the NSFC and the UK Royal Society (Grant Nos. 52061130219; NAF\R1\201156).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Wang, X., Xia, J., Dong, B. et al. Spatiotemporal distribution of flood disasters in Asia and influencing factors in 1980–2019. Nat Hazards 108, 2721–2738 (2021). https://doi.org/10.1007/s11069-021-04798-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11069-021-04798-3