Regional Rainfall Damage Functions to Estimate Direct Economic Losses in Rainstorms: A Case Study of the 2016 Extreme Rainfall Event in Hebei Province of China

Abstract

Developing a regional damage function to quickly estimate direct economic losses (DELs) caused by heavy rain and floods is crucial for providing scientific supports in effective disaster response and risk reduction. This study investigated the factors that influence regional rainfall-induced damage and developed a calibrated regional rainfall damage function (RDF) using data from the 2016 extreme rainfall event in Hebei Province, China. The analysis revealed that total precipitation, asset value exposure, per capita GDP, and historical geological disaster density at both the township and county levels significantly affect regional rainfall-induced damage. The coefficients of the calibrated RDF indicate that doubling the values of these factors leads to varying increases or decreases in rainfall-induced damage. Furthermore, the study demonstrated a spatial scale dependency in the coefficients of the RDF, with increased elasticity values for asset value exposure and per capita GDP at the county level compared to the township level. The findings emphasize the challenges of applying RDFs across multiple scales and highlight the importance of considering socioeconomic factors in assessing rainfall-induced damage. Despite the limitations and uncertainties of the RDF developed, this study contributes to our understanding of the relationship between physical and socioeconomic factors and rainfall-induced damage. Future research should prioritize enhancing exposure estimation and calibrating RDFs for various types of rainfall-induced disasters to improve model accuracy and performance. The study also acknowledges the variation in RDF performance across different physical environments, especially concerning geological disasters and slope stability.

Keywords

Regional rainfall damage function / Loss attribution / Direct economic losses / Rainfall intensity / Asset value / Vulnerability

References

[] Adelodun B, Odey G, Lee S, Choi KS. Investigating the causal impacts relationship between economic flood damage and extreme precipitation indices based on ARDL-ECM framework: A case study of Chungcheong region in South Korea. Sustainable Cities and Society, 2023, 95: 104606,

[] Blumenthal B, Nyberg L. The impact of intense rainfall on insurance losses in two Swedish cities. Journal of Flood Risk Management, 2019, 12: e12504,

[] Boulange J, Hanasaki N, Yamazaki D, Pokhrel Y. Role of dams in reducing global flood exposure under climate change. Natural Communication, 2021, 12: 417,

[] Bryan BA, Gao L, Ye Y, Sun X, Connor JD, Crossman ND, Stafford-Smith M, Wu J, et al.. China’s response to a national land-system sustainability emergency. Nature, 2018, 559(7713): 193-204,

[] Chen X. . Evaluation of rainstorm and flood disaster in Hebei Province, 2018 Beijing China Meteorological Press (in Chinese)

[] Chen L, Yan Z, Li Q, Xu Y. Flash flood risk assessment and driving factors: A case study of the Yantanxi River Basin, southeastern China. International Journal of Disaster Risk Science, 2022, 13(2): 291-304,

[] Cortès M, Turco M, Llasat-Botija M, Llasat MC. The relationship between precipitation and insurance data for floods in a Mediterranean region (northeast Spain). Natural Hazards and Earth System Sciences, 2018, 18: 857-868,

[] Davenport FV, Burke M, Diffenbaugh NS. Contribution of historical precipitation change to US flood damages. Proceedings of the National Academy of Sciences of the United States of America, 2021, 118: e2017524118,

[] de Moel H, Aerts JCJH. Effect of uncertainty in land use, damage models and inundation depth on flood damage estimates. Natural Hazards, 2011, 58(1): 407-425,

[] Ding W, Wu J, Tang R, Chen X, Xu Y. A review of flood risk in China during 1950–2019: Urbanization, socioeconomic impact trends and flood risk management. Water, 2022, 14: 3246,

[] DITSC (Disaster Investigation Team of the State Council). 2022. Investigation report of the “7 20” extraordinary rainstorm disaster in Zhengzhou, Henan Province. https://www.mem.gov.cn/gk/sgcc/tbzdsgdcbg/202201/P020220121639049697767.pdf. Accessed 11 May 2024.

[] Doan QV, Chen F, Kusaka H, Dipankar A, Khan A, Hamdi R, Roth M, Niyogi D. Increased risk of extreme precipitation over an urban agglomeration with future global warming. Earth’s Future, 2022, 10: e2021EF002563,

[] Eberenz S, Lüthi S, Bresch DN. Regional tropical cyclone impact functions for globally consistent risk assessments. Natural Hazards and Earth System Sciences, 2021, 21(1): 393-415,

[] Elmer F, Thieken AH, Pech I, Kreibich H. Influence of flood frequency on residential building losses. Natural Hazards and Earth System Sciences, 2010, 10: 2145-2159,

[] Endendijk T, Botzen WJW, de Moel H, Aerts JCJH, Slager K, Kok M. Flood vulnerability models and household flood damage mitigation measures: An econometric analysis of survey data. Water Resource Research, 2023, 59: e2022WR034192,

[] Fuchs S, Heiser M, Schlögl M, Zischg A, Papathoma-Köhle M, Keiler M. Short communication: A model to predict flood loss in mountain areas. Environmental Modelling & Software, 2019, 117: 176-180,

[] Geiger T, Frieler K, Levermann A. High-income does not protect against hurricane losses. Environmental Research Letters, 2016, 11(8): 084012,

[] Guo X, Cheng J, Yin C, Li Q, Chen R, Fang J. The extraordinary Zhengzhou flood of 7/20, 2021: How extreme weather and human response compounding to the disaster. Cities, 2023, 134: 104168,

[] Hallegatte S. A normative exploration of the link between development, economic growth, and natural risk. Economics of Disasters and Climate Change, 2017, 1(1): 5-31,

[] Hebei Bureau of Statistics. . Hebei statistical yearbook, 2016 Beijing China Statistics Press (in Chinese)

[] Koks EE, Jongman B, Husby TG, Botzen WJ. Combining hazard, exposure and social vulnerability to provide lessons for flood risk management. Environmental Science & Policy, 2015, 47: 42-52,

[] Kreibich H, Seifert I, Merz B, Thieken AH. Development of FLEMOcs—A new model for the estimation of flood losses in the commercial sector. Hydrological Sciences Journal, 2010, 55(8): 1302-1314,

[] Li K, Wu S, Dai E, Xu Z. Flood loss analysis and quantitative risk assessment in China. Natural Hazards, 2012, 63: 737-760,

[] Lin Q, Lima P, Steger S, Glade T, Jiang T, Zhang J, Liu T, Wang Y. National scale data-driven rainfall induced landslide susceptibility mapping for China by accounting for incomplete landslide data. Geoscience Frontiers, 2021, 12(6): 101248,

[] Liu C, Guo L, Ye L, Zhang S, Zhao Y, Song T. A review of advances in China’s flash flood early-warning system. Natural Hazards, 2018, 92: 619-634,

[] Liu W, Wu J, Tang R, Ye M, Yang J. Daily precipitation threshold for rainstorm and flood disaster in the mainland of China: An economic loss perspective. Sustainability, 2020, 12: 407,

[] McGrath H, Abo El Ezz A, Nastev M. Probabilistic depth-damage curves for assessment of flood-induced building losses. Natural Hazards, 2019, 97: 1-14,

[] MEM (Ministry of Emergency Management of the People’s Republic of China). 2024. Top ten natural disasters in China for the year 2023. https://www.mem.gov.cn/xw/yjglbgzdt/202401/t20240120_475696.shtml. Accessed 11 May 2024.

[] Mendelsohn R, Emanuel K, Chonabayashi S, Bakkensen L. The impact of climate change on global tropical cyclone damage. Nature Climate Change, 2012, 2(3): 205-209,

[] Merz B, Kreibich H, Lall U. Multi-variate flood damage assessment: A tree-based data-mining approach. Natural Hazards and Earth System Sciences, 2013, 13: 53-64,

[] Nguyen NY, Kha DD, Ichikawa Y. Developing a multivariable lookup table function for estimating flood damages of rice crop in Vietnam using a secondary research approach. International Journal of Disaster Risk Reduction, 2021, 58: 102208,

[] Papathoma-Köhle M, Schlögl M, Dosser L, Roesch F, Borga M, Erlicher M, Keiler M, Fuchs S. Physical vulnerability to dynamic flooding: Vulnerability curves and vulnerability indices. Journal of Hydrology, 2022, 607: 127501,

[] Pastor-Paz J, Noy I, Sin I, Sood A, Fleming-Munoz D, Owen S. Projecting the effect of climate change on residential property damages caused by extreme weather events. Journal of Environmental Management, 2020, 276: 111012,

[] Paulik R, Zorn C, Wotherspoon L. Evaluating the spatial application of multivariable flood damage models. Journal of Flood Risk Management, 2023, 16(4): e12934,

[] Penning-Rowsell E, Johnson C, Tunstall S, Morris J, Chatterton J, Green C, Koussela K, Fernandez-Bilbao A. . The benefits of flood and coastal risk management: A handbook of assessment techniques, 2005 London Middlesex University Press

[] Pińskwar I, Choryński A, Graczyk D. Risk of flash floods in urban and rural municipalities triggered by intense precipitation in Wielkopolska of Poland. International Journal of Disaster Risk Science, 2023, 14(3): 440-457,

[] Porter JR, Marston ML, Shu E, Bauer M, Lai K, Wilson B, Pope M. Estimating pluvial depth-damage functions for areas within the United States using historical claims data. Natural Hazards Review, 2023, 24: 1-10,

[] Rashid MM, Wahl T, Villarini G, Sharma A. Fluvial flood losses in the contiguous United States under climate change. Earth’s Future, 2023, 11: e2022EF003328,

[] Shrestha BB, Kawasaki A, Zin WW. Development of flood damage assessment method for residential areas considering various house types for Bago region of Myanmar. International Journal of Disaster Risk Reduction, 2021, 66: 102602,

[] Spekkers MH, Kok M, Clemens FHLR, ten Veldhuis JAE. A statistical analysis of insurance damage claims related to rainfall extremes. Hydrology and Earth System Sciences, 2013, 17: 913-922,

[] Tang R, Wu J, Ding W, Nie J. Spatial scale dependence of tropical cyclone damage function: Evidence from the mainland of China. Earth’s Future, 2023, 11: e2023EF003762,

[] Tong S, Wu QC, Qiu YB, Xiao M, Dong L. Analysis on historical flood and countermeasures in prevention and control of flood in Daqing River Basin. Environmental Research, 2021, 196: 110895,

[] UNISDR (United Nations International Strategy for Disaster Reduction). 2015. Global assessment report on disaster risk reduction 2015. Making development sustainable: The future of disaster risk management. Geneva: United Nations.

[] Van Ootegem L, Van Herck K, Creten T, Verhofstadt E, Foresti L, Goudenhoofdt E, Reyniers M, Delobbe L, et al.. Exploring the potential of multivariate depth-damage and rainfall-damage models. Journal of Flood Risk Management, 2018, 11: S916-S929

[] Wagenaar D, Lüdtke S, Schröter K, Bouwer LM, Kreibich H. Regional and temporal transferability of multivariable flood damage models. Water Resource Research, 2018, 54: 3688-3703,

[] World Bank. 2020. Learning from experience: Insights from China’s progress in disaster risk management. Washington, DC: World Bank. http://hdl.handle.net/10986/34090. Accessed 11 May 2024.

[] Wu J, Han G, Zhou H, Li N. Economic development and declining vulnerability to climate-related disasters in China. Environmental Research Letters, 2018, 13(3): 034013,

[] 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: 17-30,

[] Wu J, He X, Li Y, Shi P, Ye T, Li N. How earthquake-induced direct economic losses change with earthquake magnitude, asset value, residential building structural type and physical environment: An elasticity perspective. Journal of Environmental Management, 2019, 231: 321-328,

[] Ye M, Wu J, Liu W, He X, Wang C. Dependence of tropical cyclone damage on maximum wind speed and socioeconomic factors. Environmental Research Letters, 2020, 15(9): 094061,

[] Zhou Q, Panduro TE, Thorsen BJ, Arnbjerg-Nielsen K. Verification of flood damage modelling using insurance data. Water Science and Technology, 2013, 68(2): 425-432,