Natural and Socioeconomic Factors and Their Interactive Effects on House Collapse Caused by Typhoon Mangkhut

Abstract

Typhoons are an environmental threat that mainly affects coastal regions worldwide. The interactive effects of natural and socioeconomic factors on the losses caused by typhoon disasters need further examination. In this study, GeoDetector was used to quantify the determinant powers of natural and socioeconomic factors and their interactive effects on the rate of house collapse in Guangdong and Guangxi Provinces of southeast China caused by Typhoon Mangkhut in 2018. We further identify the dominant factors that influenced the disaster losses. The local indicators of spatial association method was then introduced to explain the spatial heterogeneity of the disaster losses under the influence of the dominant factor. The results indicate that both natural and socioeconomic factors significantly affected the house collapse rate. The maximum precipitation was the dominant factor, with a q value of 0.21, followed by slope and elevation, with q values of 0.17 and 0.13, respectively. Population density and per capita gross domestic product had q values of 0.15 and 0.13, respectively. Among all of the interactive effects of the influencing factors, the interactive effect of elevation and the ratio of brick-wood houses had the greatest influence (q = 0.63) on the house collapse rate. These results can contribute to the formulation of more specific safety and property protection policies.

Keywords

China / Coastal regions / GeoDetector / House collapse rate / Interactive effects / Typhoon Mangkhut

References

[1] Anselin L, Syabri I, Kho Y. GeoDa: An introduction to spatial data analysis. Geographical Analysis, 2006, 38(1): 5-22

[2] Atanas P, Hristo S. Risk for the population along the Bulgarian black seacoast from flooding caused by extreme rise of sea level. Information & Security: An International Journal, 2009, 24: 65-75.

[3] Boots B, Tiefelsdorf M. Global and local spatial autocorrelation in bounded regular tessellations. Journal of Geographical Systems, 2000, 2(4): 319-348

[4] Brown RC, Trapp SK, Berenz EC, Bigdeli TB, Acierno R, Trinh Luong T, Trung LT, Tam NT Pre-typhoon socioeconomic status factors predict post-typhoon psychiatric symptoms in a Vietnamese sample. Social Psychiatry and Psychiatric Epidemiology, 2013, 48(11): 1721-1727

[5] Cheung NKW. At risk: Natural hazards, people’s vulnerability and disasters. Geographical Journal, 2007, 173(2): 189-190

[6] Di Baldassarre, G., A. Montanari, H. Lins, D. Koutsoyiannis, L. Brandimarte, and G. Bloeschl. 2010. Flood fatalities in Africa: From diagnosis to mitigation. Geophysical Research Letters 37(22): Article L22402.

[7] Elsner JB, Liu KB. Examining the ENSO-typhoon hypothesis. Climate Research, 2003, 25(1): 43-54

[8] Fernandez DS, Lutz MA. Urban flood hazard zoning in Tucuman Province, Argentina, using GIS and multicriteria decision analysis. Engineering Geology, 2010, 111(1–4): 90-98

[9] Gong P, Liang S, Carlton EJ, Jiang Q, Wu J, Wang L, Remais JV. Urbanisation and health in China. Lancet, 2012, 379(9818): 843-852

[10] Gong Z, Hu L. Influence factor analysis of typhoon disaster assessment. Journal of Natural Disasters, 2015, 24(1): 203-213.

[11] Hu P, Zhang Q, Shi P, Chen B, Fang J. Flood-induced mortality across the globe: Spatiotemporal pattern and influencing factors. Science of the Total Environment, 2018, 643: 171-182

[12] Huang Y, Cheng H. The impact of climate change on coastal geological disasters in southeastern China. Natural Hazards, 2013, 65(1): 377-390

[13] Huo XN, Li H, Sun DF, Zhou LD, Li BG. Combining geostatistics with Moran’s I analysis for mapping soil heavy metals in Beijing, China. International Journal of Environmental Research and Public Health, 2012, 9(3): 995-1017

[14] Jongman B, Ward PJ, Aerts JCJH. Global exposure to river and coastal flooding: Long term trends and changes. Global Environmental Change - Human and Policy Dimensions, 2012, 22(4): 823-835

[15] Khouakhi A, Villarini G, Vecchi GA. Contribution of tropical cyclones to rainfall at the global scale. Journal of Climate, 2017, 30(1): 359-372

[16] Knutson TR, McBride JL, Chan J, Emanuel K, Holland G, Landsea C, Held I, Kossin JP Tropical cyclones and climate change. Nature Geoscience, 2010, 3(3): 157-163

[17] Li E, Huang Y, Chen H, Zhang J. Floristic diversity analysis of the Ordos Plateau, a biodiversity hotspot in arid and semi-arid areas of China. Folia Geobotanica, 2018, 53(4): 405-416

[18] Lin, G., J. Fu, D. Jiang, W. Hu, D. Dong, Y. Huang, and M. Zhao. 2014. Spatio-temporal variation of PM_2.5 concentrations and their relationship with geographic and socioeconomic factors in China. International Journal of Environmental Research and Public Health 11(1): 173–186.

[19] Lin, Y., M. Zhao, and M. Zhang. 2015. Tropical cyclone rainfall area controlled by relative sea surface temperature. Nature Communications 6: Article 6951.

[20] Liu D, Pang L, Xie B. Typhoon disaster in China: prediction, prevention, and mitigation. Natural Hazards, 2009, 49(3): 421-436

[21] Liu, X., Q. Wang, C. Liu, Y. He, S. Wang, P. Hou, X. Zhu, and Z. Wu. 2020. Wind field reconstruction and analysis of Super Typhoon Mangkhut (1822). Journal of Coastal Research 99(SI): 151–157.

[22] Mei, W., and S.P. Xie. 2016. Intensification of landfalling typhoons over the northwest Pacific since the late 1970 s. Nature Geoscience 9(10): Article 753.

[23] Mei, W., S.P. Xie, F. Primeau, J.C. McWilliams, and C. Pasquero. 2015. Northwestern Pacific typhoon intensity controlled by changes in ocean temperatures. Science Advances 1: Article e1500014.

[24] Nigusse AG, Adhanom OG. Flood hazard and flood risk vulnerability mapping using geo-spatial and MCDA around Adigrat, Tigray region, Northern Ethiopia. Momona Ethiopian Journal of Science, 2019, 11(1): 90-107

[25] Pearce JL, Beringer J, Nicholls N, Hyndman RJ, Tapper NJ. Quantifying the influence of local meteorology on air quality using generalized additive models. Atmospheric Environment, 2011, 45(6): 1328-1336

[26] Peduzzi P, Chatenoux B, Dao H, De Bono A, Herold C, Kossin J, Mouton F, Nordbeck O. Global trends in tropical cyclone risk. Nature Climate Change, 2012, 2(4): 289-294

[27] Shen J, Feng Z, Zeng G, Zhu BP. Risk factors for injury during Typhoon Saomei. Epidemiology, 2009, 20(6): 892-895

[28] Shi P, Ye T, Wang Y, Zhou T, Xu W, Du J, Wang J, Li N Disaster risk science: A geographical perspective and a research framework. International Journal of Disaster Risk Science, 2020, 11(4): 426-440

[29] Smyth CG, Royle SA. Urban landslide hazards: incidence and causative factors in Niteroi, Rio de Janeiro State. Brazil. Applied Geography, 2000, 20(2): 95-117

[30] Syvitski JPM, Kettner AJ, Overeem I, Hutton EWH, Hannon MT, Brakenridge GR, Day J, Vörösmarty C Sinking deltas due to human activities. Nature Geoscience, 2009, 2(10): 681-686

[31] Tian B, Wu W, Yang Z, Zhou Y. Drivers, trends, and potential impacts of long-term coastal reclamation in China from 1985 to 2010. Estuarine, Coastal and Shelf Science, 2016, 170: 83-90

[32] Vrijling JK, Vanhengel W, Houben RJ. A framework for risk-evaluation. Journal of Hazardous Materials, 1995, 43(3): 245-261

[33] Wang JF, Xu CD. Geodetector: Principle and prospective. Acta Geographica Sinica, 2017, 72(1): 116-134.

[34] Wang JF, Li XH, Christakos G, Liao YL, Zhang T, Gu X, Zheng XY. 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, 2010, 24(1): 107-127

[35] Wang JF, Zhang TL, Fu BJ. A measure of spatial stratified heterogeneity. Ecological Indicators, 2016, 67: 250-256

[36] Xu, L., Y. He, W. Huang, and S. Cui. 2016. A multi-dimensional integrated approach to assess flood risks on a coastal city, induced by sea-level rise and storm tides. Environmental Research Letters 11(1): Article 014001.

[37] Xu, X., S. Peng, X. Yang, H. Xu, D.Q. Tong, D. Wang, Y. Guo, J.C.L. Chan, et al. 2013. Does warmer China land attract more super typhoons? Scientific Reports 3: Article 1522.

[38] Yan Q, Wei T, Korty RL, Kossin JP, Zhang Z, Wang H. Enhanced intensity of global tropical cyclones during the mid-Pliocene warm period. Proceedings of the National Academy of Sciences of the United States of America, 2016, 113(46): 12963-12967

[39] Yang D, Wang X, Xu J, Xu C, Lu D, Ye C, Wang Z, Bai L Quantifying the influence of natural and socioeconomic factors and their interactive impact on PM_2.5 pollution in China. Environmental Pollution, 2018, 241: 475-483

[40] Yang L, Scheffran J, Qin H, You Q. Climate-related flood risks and urban responses in the Pearl River Delta. China. Regional Environmental Change, 2015, 15(2): 379-391

[41] Ying, M., B. Chen, and G. Wu. 2011. Climate trends in tropical cyclone-induced wind and precipitation over mainland China. Geophysical Research Letters 38(2): Article L01702.

[42] Yuan, S., S.M. Quiring, L. Zhu, Y. Huang, and J. Wang. 2020. Development of a typhoon power outage model in Guangdong, China. International Journal of Electrical Power & Energy Systems 117: Article 105711.

[43] Zhang H, Ma WC, Wang XR. Rapid urbanization and implications for flood risk management in hinterland of the Pearl River Delta, China: The Foshan study. Sensors, 2008, 8(4): 2223-2239

[44] Zhang Q, Gu X, Shi P, Singh VP. Impact of tropical cyclones on flood risk in southeastern China: Spatial patterns, causes and implications. Global and Planetary Change, 2017, 150: 81-93

[45] Zhang, Q., V.P. Singh, J. Li, and X. Chen. 2011a. Analysis of the periods of maximum consecutive wet days in China. Journal of Geophysical Research - Atmospheres 116(D23): Article 106.

[46] Zhang Q, Zhang W, Chen YD, Jiang T. Flood, drought and typhoon disasters during the last half-century in the Guangdong province. China. Natural Hazards, 2011, 57(2): 267-278

[47] Zhang Q, Zhang W, Lu X, Chen YD. Landfalling tropical cyclones activities in the south China: intensifying or weakening?. International Journal of Climatology, 2012, 32(12): 1815-1824