Probabilistic Models Significantly Reduce Uncertainty in Hurricane Harvey Pluvial Flood Loss Estimates

TY - JOUR

T1 - Probabilistic Models Significantly Reduce Uncertainty in Hurricane Harvey Pluvial Flood Loss Estimates

AU - Rözer, Viktor

AU - Kreibich, Heidi

AU - Schröter, Kai

AU - Müller, Meike

AU - Sairam, Nivedita

AU - Doss-Gollin, James

AU - Lall, Upmanu

AU - Merz, Bruno

N1 - Funding Information: The data collection campaign after the flood event in Münster, Germany, in 2014 was supported by the project ‘“EVUS Real-Time Prediction of Pluvial Floods and Induced Water Contamination in Urban Areas” (BMBF, 03G0846B), the University of Potsdam, and Deutsche Rückversicherung AG. The data collection campaigns after the pluvial floods in Lohmar and Hersbruck in 2005 were undertaken within the project “URBAS - urban flash floods”; we thank the German Ministry of Education and Research (BMBF; 0330701C) for financial support. Data collection after the pluvial flood in Osnabrück in 2010 were funded by the University of Potsdam, the German Research Centre for Geosciences GFZ, and the Deutsche Rückversicherung AG. Additional financial support is gratefully acknowledged from the German-American Fulbright Commission for V. R. J. D.-G. thanks the NSF GRFP program for support(Grant DGE 16-44869). We would also like to acknowledge JBA Risk Management, who supported our work by providing the pluvial flood inundation map for Hurricane Harvey. The pluvial flood inundation map from JBA Risk Management is available via the OASIS Hub (https:// oasishub.co/dataset/ surface-water-flooding-footprint- hurricane-harvey-august-2017-jba). The data sets of the flood events in Germany from 2005 and 2010 are available via the German flood damage data base HOWAS21 (http://howas21. gfz-potsdam.de/howas21/). The data set from 2014 will be made available via the HOWAS21 database in June 2023. All other data sets used for the application in Harris County, TX, are openly available and cited in the text and SI. Detailed information on all data sets used for this study and how to access them are available in the supporting information (SI; Data section). Publisher Copyright: ©2019. The Authors.

PY - 2019/4

Y1 - 2019/4

N2 - Pluvial flood risk is mostly excluded in urban flood risk assessment. However, the risk of pluvial flooding is a growing challenge with a projected increase of extreme rainstorms compounding with an ongoing global urbanization. Considered as a flood type with minimal impacts when rainfall rates exceed the capacity of urban drainage systems, the aftermath of rainfall-triggered flooding during Hurricane Harvey and other events show the urgent need to assess the risk of pluvial flooding. Due to the local extent and small-scale variations, the quantification of pluvial flood risk requires risk assessments on high spatial resolutions. While flood hazard and exposure information is becoming increasingly accurate, the estimation of losses is still a poorly understood component of pluvial flood risk quantification. We use a new probabilistic multivariable modeling approach to estimate pluvial flood losses of individual buildings, explicitly accounting for the associated uncertainties. Except for the water depth as the common most important predictor, we identified the drivers for having loss or not and for the degree of loss to be different. Applying this approach to estimate and validate building structure losses during Hurricane Harvey using a property level data set, we find that the reliability and dispersion of predictive loss distributions vary widely depending on the model and aggregation level of property level loss estimates. Our results show that the use of multivariable zero-inflated beta models reduce the 90% prediction intervalsfor Hurricane Harvey building structure loss estimates on average by 78% (totalling U.S.$3.8 billion) compared to commonly used models.

AB - Pluvial flood risk is mostly excluded in urban flood risk assessment. However, the risk of pluvial flooding is a growing challenge with a projected increase of extreme rainstorms compounding with an ongoing global urbanization. Considered as a flood type with minimal impacts when rainfall rates exceed the capacity of urban drainage systems, the aftermath of rainfall-triggered flooding during Hurricane Harvey and other events show the urgent need to assess the risk of pluvial flooding. Due to the local extent and small-scale variations, the quantification of pluvial flood risk requires risk assessments on high spatial resolutions. While flood hazard and exposure information is becoming increasingly accurate, the estimation of losses is still a poorly understood component of pluvial flood risk quantification. We use a new probabilistic multivariable modeling approach to estimate pluvial flood losses of individual buildings, explicitly accounting for the associated uncertainties. Except for the water depth as the common most important predictor, we identified the drivers for having loss or not and for the degree of loss to be different. Applying this approach to estimate and validate building structure losses during Hurricane Harvey using a property level data set, we find that the reliability and dispersion of predictive loss distributions vary widely depending on the model and aggregation level of property level loss estimates. Our results show that the use of multivariable zero-inflated beta models reduce the 90% prediction intervalsfor Hurricane Harvey building structure loss estimates on average by 78% (totalling U.S.$3.8 billion) compared to commonly used models.

KW - climate change adaptation

KW - Hurricane Harvey

KW - loss modeling

KW - pluvial flooding

KW - probabilistic

KW - urban flooding

UR - http://www.scopus.com/inward/record.url?scp=85064056521&partnerID=8YFLogxK

U2 - 10.1029/2018EF001074

DO - 10.1029/2018EF001074

M3 - Article

AN - SCOPUS:85064056521

SN - 2328-4277

VL - 7

SP - 384

EP - 394

JO - Earth's Future

JF - Earth's Future

IS - 4

ER -