Flash Flood Risk Assessment and Driving Factors: A Case Study of the Yantanxi River Basin, Southeastern China

Abstract

In the context of climate change, the impact of extreme precipitation and its chain effects has intensified in the southeastern coastal region of China, posing a serious threat to the socioeconomic development in the region. This study took tropical cyclones–extreme precipitation–flash floods as an example to carry out a risk assessment of flash floods under climate change in the Yantanxi River Basin, southeastern China. To obtain the flash flood inundation characteristics through hydrologic–hydrodynamic modeling, the study combined representative concentration pathway (RCP) and shared socioeconomic pathway (SSP) scenarios to examine the change of flash flood risk and used the geographical detector to explore the driving factors behind the change. The results show that flash flood risk in the Yantanxi River Basin will significantly increase, and that socioeconomic factors and precipitation are the main driving forces. Under the RCP4.5-SSP2 and RCP8.5-SSP5 scenarios, the risk of flash floods is expected to increase by 88.79% and 95.57%, respectively. The main drivers in the case study area are GDP density (q = 0.85), process rainfall (q = 0.74), asset density (q = 0.68), and population density (q = 0.67). The study highlights the influence of socioeconomic factors on the change of flash flood disaster risk in small river basins. Our findings also provide a reference for regional planning and construction of flood control facilities in flash flood-prone areas, which may help to reduce the risk of flash floods.

Keywords

Asset values / China / Climate change / Extreme precipitation / Flash flood risk / Geographical detector / Tropical cyclones

References

[1] Armal, S., J.R. Porter, B. Lingle, Z. Chu, M.L. Marston, and O.E.J. Wing. 2020. Assessing property level economic impacts of climate in the US, new insights and evidence from a comprehensive flood risk assessment tool. Climate 8(10): Article 116.

[2] Asian Disaster Reduction Center. 2005. Total disaster risk management: Good practices. Kobe, Janpa: Asian Disaster Reduction Center (ADRC).

[3] Chen C, Tung Y, Liou J, Li H, Cheng C, Chen Y, Oguchi T. Assessing landslide characteristics in a changing climate in northern Taiwan. Catena, 2019, 175(3): 263-277

[4] Chen H. Projected change in extreme rainfall events in China by the end of the 21st century using CMIP5 models. Chinese Science Bulletin, 2013, 58(12): 1462-1472

[5] Chen H, Sun J, Li H. Future changes in precipitation extremes over China using the NEX-GDDP high-resolution daily downscaled data-set. Atmospheric and Oceanic Science Letters, 2017, 10(6): 403-410

[6] Chen X, Nordhaus W. A test of the new VIIRS lights data set: Population and economic output in Africa. Remote Sensing, 2015, 7(4): 4937-4947

[7] Chen, Y., C. Chen, Y. Chao, Y. Tung, J. Liou, H. Li, and C. Cheng. 2020. Future landslide characteristic assessment using ensemble climate change scenarios: A case study in Taiwan. Water 12(2): Article 564.

[8] Cui P, Zou Q. Theory and method of risk assessment and risk management of debris flows and flash floods. Progress in Geography, 2016, 35(2): 137-147 in Chinese

[9] Dahm, R.J., U.K. Singh, M. Lal, M. Marchand, F.C. Sperna Weiland, S.K. Singh, and M.P. Singh. 2016. Downscaling GCM data for climate change impact assessments on rainfall: A practical application for the Brahmani-Baitarani river basin. Hydrology and Earth System Sciences Discussions. https://doi.org/10.5194/hess-2015-499.

[10] Dai G. Records of water conservancy in Yongjia, 2006, Beijing: ZhongHua Book Company Press in Chinese

[11] Deshmukh DS, Chaube UC, Hailu AE, Gudeta DA, Kassa MT. Estimation and comparision of curve numbers based on dynamic land use land cover change, observed rainfall-runoff data and land slope. Journal of Hydrology, 2013, 492: 89-101

[12] Dutta D, Herath S, Musiake K. A mathematical model for flood loss estimation. Journal of Hydrology, 2003, 277(1–2): 24-49

[13] Fang J, Du J, Xu W, Shi P, Kong F. Advances in the study of climate change impacts on flood disaster. Advances in Earth Science, 2014, 29(9): 1085-1093 in Chinese

[14] Fang, J., D. Lincke, S. Brown, R.J. Nicholls, C. Wolff, J.-L. Merkens, J. Hinkel, A.T. Vafeidis, et al. 2020. Coastal flood risks in China through the 21st century—An application of DIVA. Science of the Total Environment 704: Article 135311.

[15] Fang W, Wang J, Shi P. Fang W. Integrated risk prevention, disaster database and risk assessment system. Integrated risk prevention: Database, risk map and web platform, 2011, Beijing: Science Publishing Press 139-144 in Chinese

[16] Goodarzi L, Banihabib ME, Roozbahani A. A decision-making model for flood warning system based on ensemble forecasts. Journal of Hydrology, 2019, 573: 207-219

[17] Griffiths JA, Zhu F, Chan FKS, Higgitt DL. Modelling the impact of sea-level rise on urban flood probability in SE China. Geoscience Frontiers, 2019, 10(2): 363-372

[18] Gusain, A., M. Mohanty, S. Ghosh, C. Chatterjee, and S. Karmakar. 2020. Capturing transformation of flood hazard over a large river basin under changing climate using a top-down approach. Science of the Total Environment 726: Article 138600.

[19] Hirabayashi Y, Mahendran R, Koirala S, Konoshima L, Yamazaki D, Watanabe S, Kim H, Kanae S. Global flood risk under climate change. Nature Climate Change, 2013, 3(9): 816-821

[20] 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

[21] Huang J, Qin D, Jiang T, Wang Y, Feng Z, Zhai J, Cao L, Chao Q Effect of fertility policy changes on the population structure and economy of China: from the perspective of the shared socioeconomic pathways. Earth’s Future, 2019, 7(3): 250-265

[22] Hurkmans R, Terink W, Uijlenhoet R, Torfs P, Jacob D, Troch PA. Changes in streamflow dynamics in the Rhine basin under three high-resolution regional climate scenarios. Journal of Climate, 2010, 23(3): 679-699

[23] Hurtt GC, Chini LP, Frolking S, Betts R, Feddema J, Fischer G, Fisk J, Hibbard K Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Climatic Change, 2011, 109(1): 117-161

[24] Jian Y, Fu J, Li B, Zhou F. Increased extreme hourly precipitation over China’s rice paddies from 1961 to 2012. Scientific Reports, 2020, 10(1): 1-12

[25] Jiang T, Jing Z, Lige C, Yanjun W, Buda S, Cheng J, Run W, Chao G. Projection of national and provincial economy under the shared socioeconomic pathways in China. Climate Change Research, 2018, 14(1): 50-58.

[26] Jiang Z, Li W, Xu J, Li L. Extreme precipitation indices over China in CMIP5 models. Part I: Model evaluation. Journal of Climate, 2015, 28(21): 8603-8619

[27] Lafon T, Dadson S, Buys G, Prudhomme C. Bias correction of daily precipitation simulated by a regional climate model: A comparison of methods. International Journal of Climatology, 2013, 33(6): 1367-1381

[28] Lavell, A., M. Oppenheimer, C. Diop, J. Hess, R. Lempert, J. Li, R. Muir-Wood, and S. Myeong. 2012. Climate change: New dimensions in disaster risk, exposure, vulnerability, and resilience. In Managing the risks of extreme events and disasters to advance climate change adaptation. Special report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC), ed. C.B. Field, V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, et al., 25–64. Cambridge and New York: Cambridge University Press.

[29] Li, L., J. Yang, and J. Wu. 2020. Future flood risk assessment under the effects of land use and climate change in the Tiaoxi Basin. Sensors 20(21): Article 6079.

[30] Li N, Zhang Z, Chen X, Feng J. Importance of economic loss evaluation in natural hazard and disaster research. Progress in Geography, 2017, 36(2): 256-263 in Chinese

[31] Liao Y. Analysis of typhoon precipitation patterns and characteristics in Yongjia County over the years. China Water Resources, 2009, 23: 60-61 in Chinese

[32] Liao Y. Characteristics of heavy rainfall and defensive measures against flash floods in small river basin, Yongjia County Zhejiang. China Flood and Drought, 2011, 21(1): 13-14 in Chinese

[33] Lin, W., Y. Sun, S. Nijhuis, and Z. Wang. 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: Article 139899.

[34] Liu, Y. 2011. Multi-scale natural disaster scenarios’ risk assessment and zoning—A case study of Wenzhou city, Zhejiang Province. Doctoral dissertation. Shanghai: East China Normal University in Chinese.

[35] Liu Y, Yang Z, Huang Y, Liu C. Spatiotemporal evolution and driving factors of China’s flash flood disasters since 1949. Science China Earth Sciences, 2018, 61(12): 1804-1817

[36] Luo Y, Wu M, Ren F, Li J, Wong W. Synoptic situations of extreme hourly precipitation over China. Journal of Climate, 2016, 29(24): 8703-8719 in Chinese

[37] Ma T, Zhou C, Pei T, Haynie S, Fan J. Responses of Suomi-NPP VIIRS-derived nighttime lights to socioeconomic activity in China’s cities. Remote Sensing Letters, 2014, 5(2): 165-174

[38] Merz B, Kreibich H, Schwarze R, Thieken A. Review article "Assessment of economic flood damage". Natural Hazards and Earth System Sciences, 2010, 10(8): 1697-1724

[39] Mishra BK, Rafiei Emam A, Masago Y, Kumar P, Regmi RK, Fukushi K. Assessment of future flood inundations under climate and land use change scenarios in the Ciliwung River Basin Jakarta. Journal of Flood Risk Management, 2018, 11(S2): S1105-S1115

[40] Moss RH, Edmonds JA, Hibbard KA, Manning MR, Rose SK, Van Vuuren DP, Carter TR, Emori S The next generation of scenarios for climate change research and assessment. Nature, 2010, 463(7282): 747-756

[41] O’Brien JS, Julien PY, Fullerton W. Two-dimensional water flood and mudflow simulation. Journal of Hydraulic Engineering, 1993, 119(2): 244-261

[42] Peng S, Lu S. FLO-2D simulation of mudflow caused by large landslide due to extremely heavy rainfall in southeastern Taiwan during Typhoon Morakot. Journal of Mountain Science, 2013, 10(2): 207-218

[43] Pistrika A, Tsakiris G, Nalbantis I. Flood depth-damage functions for built environment. Environmental Processes, 2014, 1(4): 553-572

[44] Qiu W, Ren F, Wu L, Chen L, Ding C. Characteristics of tropical cyclone extreme precipitation and its preliminary causes in Southeast China. Meteorology and Atmospheric Physics, 2019, 131(3): 613-626

[45] Rong, G., K. Li, L. Han, S. Alu, J. Zhang, and Y. Zhang. 2020. Hazard mapping of the rainfall–landslides disaster chain based on GeoDetector and Bayesian Network Models in Shuicheng County, China. Water 12(9): Article 2572.

[46] Shi P. Shi P. Harzards, disaster, risk. Disaster risk science, 2016, Beijing: Beijing Normal University Publishing House Press 35-36 in Chinese

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

[48] Sobel AH, Camargo SJ, Hall TM, Lee C, Tippett MK, Wing AA. Human influence on tropical cyclone intensity. Science, 2016, 353(6296): 242-246

[49] Su Z, Ren F, Wei J, Lin X, Shi S, Zhou X. Changes in monsoon and tropical cyclone extreme precipitation in southeast China from 1960 to 2012. Tropical Cyclone Research and Review, 2015, 4(1): 12-17.

[50] Sun Q, Miao C, Duan Q. Comparative analysis of CMIP3 and CMIP5 global climate models for simulating the daily mean, maximum, and minimum temperatures and daily precipitation over China. Journal of Geophysical Research: Atmospheres, 2015, 120(10): 4806-4824

[51] UNDRR (United Nations Office for Disaster Risk Reduction). 2013. Global assessment report on disaster risk reduction 2013. http://www.unisdr.org/we/inform/publications/33013. Accessed 20 Jun 2020.

[52] Wang J, Xu C. Geodetector: Principle and prospective. Acta Geographica Sinica, 2017, 72(1): 116-134 in Chinese

[53] Wang J, Li X, Christakos G, Liao Y, Zhang T, Gu X, Zheng X. 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

[54] Wang J, Zhang T, Fu B. A measure of spatial stratified heterogeneity. Ecological Indicators, 2016, 67: 250-256

[55] Wang, P., Y. Li, P. Yu, and Y. Zhang. 2021. The analysis of urban flood risk propagation based on the modified Susceptible Infected Recovered model. Journal of Hydrology 603: Article 127121.

[56] Wen K, Xi G, Xu W. Dictionary of Chinese meteorological disaster, Zhejiang Volume, 2006, Beijing: China Meteorology Press in Chinese

[57] Wenzhou Municipal Bureau of Statistics Wenzhou statistical yearbook, 2019, Beijing: China Statistics Press in Chinese

[58] Wu J, Li N, Shi P. Benchmark wealth capital stock estimations across China’s 344 prefectures: 1978 to 2012. China Economic Review, 2014, 31: 288-302

[59] Wu J, Li Y, Li N, Shi P. Development of an asset value map for disaster risk assessment in China by spatial disaggregation using ancillary remote sensing data. Risk Analysis, 2018, 38(1): 17-30

[60] Wu, J., C. Wang, X. He, X. Wang, and N. Li. 2017. Spatiotemporal changes in both asset value and GDP associated with seismic exposure in China in the context of rapid economic growth from 1990 to 2010. Environmental Research Letters 12(3): Article 034002.

[61] Yang, X., R. He, J. Ye, M.L. Tan, X. Ji, L. Tan, and G. Wang. 2020. Integrating an hourly weather generator with an hourly rainfall SWAT model for climate change impact assessment in the Ru River Basin, China. Atmospheric Research 244: Article 105062.

[62] Ye M, Wu J, Wang C, He X. Historical and future changes in asset value and GDP in areas exposed to tropical cyclones in China. Weather, Climate and Society, 2019, 11(2): 307-319

[63] Yi, S., Y. Xiao, and Y. Huang. 2014. Uncertainty and information fusion for integrated urban watershed flood risk assessment. Paper presented at the 22nd International Conference on Geoinformatics, 25–27 July 2014, Kaohsiung, Taiwan, China.

[64] Ying M, Zhang W, Yu H, Lu X, Feng J, Fan Y, Zhu Y, Chen D. An overview of the China Meteorological Administration tropical cyclone database. Journal of Atmospheric and Oceanic Technology, 2014, 31(2): 287-301

[65] Yongjia County Bureau of Statistics. 2020. Yongjia National Economic and Social Development Statistical Bulletin in 2019. http://www.yj.gov.cn/art/2019/3/29/art_1229248207_2301376.html. Accessed 20 Jun 2020 (in Chinese).

[66] Zhang, J., W. Xu, X. Liao, S. Zong, and B. Liu. 2021. Global mortality risk assessment from river flooding under climate change. Environmental Research Letters 16(6): Article 064036.

[67] Zhang P, Ren H, Zhang M, Hu W, Shi L, Zhang B. A preliminary study on the zoning of flash flood disaster prevention and control in China. Journal of Water Resources Research, 2006, 27(4): 15-18.

[68] Zhang, Y., Y. Wang, Y. Chen, F. Liang, and H. Liu. 2019. Assessment of future flash flood inundations in coastal regions under climate change scenarios—A case study of Hadahe River basin in northeastern China. Science of the Total Environment 693: Article 133550.

[69] Zhao, M., W. Cheng, C. Zhou, M. Li, N. Wang, and Q. Liu. 2017. GDP spatialization and economic differences in South China based on NPP-VIIRS nighttime light imagery. Remote Sensing 9(7): Article 673.