TY - GEN A1 - Sieg, Tobias A1 - Shinko, Thomas A1 - Vogel, Kristin A1 - Mechler, Reinhard A1 - Merz, Bruno A1 - Kreibich, Heidi T1 - Integrated assessment of short-term direct and indirect economic flood impacts including uncertainty quantification T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Understanding and quantifying total economic impacts of flood events is essential for flood risk management and adaptation planning. Yet, detailed estimations of joint direct and indirect flood-induced economic impacts are rare. In this study an innovative modeling procedure for the joint assessment of short-term direct and indirect economic flood impacts is introduced. The procedure is applied to 19 economic sectors in eight federal states of Germany after the flood events in 2013. The assessment of the direct economic impacts is object-based and considers uncertainties associated with the hazard, the exposed objects and their vulnerability. The direct economic impacts are then coupled to a supply-side Input-Output-Model to estimate the indirect economic impacts. The procedure provides distributions of direct and indirect economic impacts which capture the associated uncertainties. The distributions of the direct economic impacts in the federal states are plausible when compared to reported values. The ratio between indirect and direct economic impacts shows that the sectors Manufacturing, Financial and Insurance activities suffered the most from indirect economic impacts. These ratios also indicate that indirect economic impacts can be almost as high as direct economic impacts. They differ strongly between the economic sectors indicating that the application of a single factor as a proxy for the indirect impacts of all economic sectors is not appropriate. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 708 KW - June 2013 KW - Damage KW - Model KW - Inoperability KW - Disasters KW - Hazards KW - Germany KW - Losses KW - Event KW - Costs Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-429119 IS - 708 ER - TY - GEN A1 - Metin, Ayse Duha A1 - Dung, Nguyen Viet A1 - Schröter, Kai A1 - Guse, Björn A1 - Apel, Heiko A1 - Kreibich, Heidi A1 - Vorogushyn, Sergiy A1 - Merz, Bruno T1 - How do changes along the risk chain affect flood risk? T2 - Postprints der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe N2 - Flood risk is impacted by a range of physical and socio-economic processes. Hence, the quantification of flood risk ideally considers the complete flood risk chain, from atmospheric processes through catchment and river system processes to damage mechanisms in the affected areas. Although it is generally accepted that a multitude of changes along the risk chain can occur and impact flood risk, there is a lack of knowledge of how and to what extent changes in influencing factors propagate through the chain and finally affect flood risk. To fill this gap, we present a comprehensive sensitivity analysis which considers changes in all risk components, i.e. changes in climate, catchment, river system, land use, assets, and vulnerability. The application of this framework to the mesoscale Mulde catchment in Germany shows that flood risk can vary dramatically as a consequence of plausible change scenarios. It further reveals that components that have not received much attention, such as changes in dike systems or in vulnerability, may outweigh changes in often investigated components, such as climate. Although the specific results are conditional on the case study area and the selected assumptions, they emphasize the need for a broader consideration of potential drivers of change in a comprehensive way. Hence, our approach contributes to a better understanding of how the different risk components influence the overall flood risk. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1067 KW - global sensitivity analysis KW - climate change KW - river floods KW - frequency KW - Europe KW - model KW - vulnerability KW - adaptation KW - strategies KW - catchment Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-468790 SN - 1866-8372 IS - 1067 ER - TY - GEN A1 - Sieg, Tobias A1 - Vogel, Kristin A1 - Merz, Bruno A1 - Kreibich, Heidi T1 - Seamless Estimation of Hydrometeorological Risk Across Spatial Scales T2 - Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe N2 - Hydrometeorological hazards caused losses of approximately 110 billion U.S. Dollars in 2016 worldwide. Current damage estimations do not consider the uncertainties in a comprehensive way, and they are not consistent between spatial scales. Aggregated land use data are used at larger spatial scales, although detailed exposure data at the object level, such as openstreetmap.org, is becoming increasingly available across the globe.We present a probabilistic approach for object-based damage estimation which represents uncertainties and is fully scalable in space. The approach is applied and validated to company damage from the flood of 2013 in Germany. Damage estimates are more accurate compared to damage models using land use data, and the estimation works reliably at all spatial scales. Therefore, it can as well be used for pre-event analysis and risk assessments. This method takes hydrometeorological damage estimation and risk assessments to the next level, making damage estimates and their uncertainties fully scalable in space, from object to country level, and enabling the exploitation of new exposure data. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 743 KW - spatial scales KW - risk assessment KW - hydro-meterological hazards KW - object-based damage modeling KW - uncertainty KW - probabilistic approaches Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-435341 SN - 1866-8372 IS - 743 SP - 574 EP - 581 ER - TY - JOUR A1 - Schroeter, Kai A1 - Kreibich, Heidi A1 - Vogel, Kristin A1 - Riggelsen, Carsten A1 - Scherbaum, Frank A1 - Merz, Bruno T1 - How useful are complex flood damage models? JF - Water resources research N2 - We investigate the usefulness of complex flood damage models for predicting relative damage to residential buildings in a spatial and temporal transfer context. We apply eight different flood damage models to predict relative building damage for five historic flood events in two different regions of Germany. Model complexity is measured in terms of the number of explanatory variables which varies from 1 variable up to 10 variables which are singled out from 28 candidate variables. Model validation is based on empirical damage data, whereas observation uncertainty is taken into consideration. The comparison of model predictive performance shows that additional explanatory variables besides the water depth improve the predictive capability in a spatial and temporal transfer context, i.e., when the models are transferred to different regions and different flood events. Concerning the trade-off between predictive capability and reliability the model structure seem more important than the number of explanatory variables. Among the models considered, the reliability of Bayesian network-based predictions in space-time transfer is larger than for the remaining models, and the uncertainties associated with damage predictions are reflected more completely. KW - floods KW - damage KW - model validation KW - Bayesian networks KW - regression tree Y1 - 2014 U6 - https://doi.org/10.1002/2013WR014396 SN - 0043-1397 SN - 1944-7973 VL - 50 IS - 4 SP - 3378 EP - 3395 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Sieg, Tobias A1 - Vogel, Kristin A1 - Merz, Bruno A1 - Kreibich, Heidi T1 - Tree-based flood damage modeling of companies: Damage processes and model performance JF - Water resources research N2 - Reliable flood risk analyses, including the estimation of damage, are an important prerequisite for efficient risk management. However, not much is known about flood damage processes affecting companies. Thus, we conduct a flood damage assessment of companies in Germany with regard to two aspects. First, we identify relevant damage-influencing variables. Second, we assess the prediction performance of the developed damage models with respect to the gain by using an increasing amount of training data and a sector-specific evaluation of the data. Random forests are trained with data from two postevent surveys after flood events occurring in the years 2002 and 2013. For a sector-specific consideration, the data set is split into four subsets corresponding to the manufacturing, commercial, financial, and service sectors. Further, separate models are derived for three different company assets: buildings, equipment, and goods and stock. Calculated variable importance values reveal different variable sets relevant for the damage estimation, indicating significant differences in the damage process for various company sectors and assets. With an increasing number of data used to build the models, prediction errors decrease. Yet the effect is rather small and seems to saturate for a data set size of several hundred observations. In contrast, the prediction improvement achieved by a sector-specific consideration is more distinct, especially for damage to equipment and goods and stock. Consequently, sector-specific data acquisition and a consideration of sector-specific company characteristics in future flood damage assessments is expected to improve the model performance more than a mere increase in data. Y1 - 2017 U6 - https://doi.org/10.1002/2017WR020784 SN - 0043-1397 SN - 1944-7973 VL - 53 SP - 6050 EP - 6068 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Sieg, Tobias A1 - Schinko, Thomas A1 - Vogel, Kristin A1 - Mechler, Reinhard A1 - Merz, Bruno A1 - Kreibich, Heidi T1 - Integrated assessment of short-term direct and indirect economic flood impacts including uncertainty quantification JF - PLoS ONE N2 - Understanding and quantifying total economic impacts of flood events is essential for flood risk management and adaptation planning. Yet, detailed estimations of joint direct and indirect flood-induced economic impacts are rare. In this study an innovative modeling procedure for the joint assessment of short-term direct and indirect economic flood impacts is introduced. The procedure is applied to 19 economic sectors in eight federal states of Germany after the flood events in 2013. The assessment of the direct economic impacts is object-based and considers uncertainties associated with the hazard, the exposed objects and their vulnerability. The direct economic impacts are then coupled to a supply-side Input-Output-Model to estimate the indirect economic impacts. The procedure provides distributions of direct and indirect economic impacts which capture the associated uncertainties. The distributions of the direct economic impacts in the federal states are plausible when compared to reported values. The ratio between indirect and direct economic impacts shows that the sectors Manufacturing, Financial and Insurance activities suffered the most from indirect economic impacts. These ratios also indicate that indirect economic impacts can be almost as high as direct economic impacts. They differ strongly between the economic sectors indicating that the application of a single factor as a proxy for the indirect impacts of all economic sectors is not appropriate. KW - June 2013 KW - Damage KW - Model KW - Inoperability KW - Disasters KW - Hazards KW - Germany KW - Losses KW - Event KW - Costs Y1 - 2019 U6 - https://doi.org/10.1371/journal.pone.0212932 SN - 1932-6203 VL - 14 IS - 4 PB - Public Library of Science CY - San Francisco ER - TY - JOUR A1 - Sieg, Tobias A1 - Vogel, Kristin A1 - Merz, Bruno A1 - Kreibich, Heidi T1 - Seamless Estimation of Hydrometeorological Risk Across Spatial Scales JF - Earth's Future N2 - Hydrometeorological hazards caused losses of approximately 110 billion U.S. Dollars in 2016 worldwide. Current damage estimations do not consider the uncertainties in a comprehensive way, and they are not consistent between spatial scales. Aggregated land use data are used at larger spatial scales, although detailed exposure data at the object level, such as openstreetmap.org, is becoming increasingly available across the globe.We present a probabilistic approach for object-based damage estimation which represents uncertainties and is fully scalable in space. The approach is applied and validated to company damage from the flood of 2013 in Germany. Damage estimates are more accurate compared to damage models using land use data, and the estimation works reliably at all spatial scales. Therefore, it can as well be used for pre-event analysis and risk assessments. This method takes hydrometeorological damage estimation and risk assessments to the next level, making damage estimates and their uncertainties fully scalable in space, from object to country level, and enabling the exploitation of new exposure data. KW - spatial scales KW - risk assessment KW - hydro-meterological hazards KW - object-based damage modeling KW - uncertainty KW - probabilistic approaches Y1 - 2019 U6 - https://doi.org/10.1029/2018EF001122 SN - 2328-4277 VL - 7 IS - 5 SP - 574 EP - 581 PB - Wiley-Blackwell CY - Hoboken, NJ ER - TY - JOUR A1 - Rözer, Viktor A1 - Kreibich, Heidi A1 - Schröter, Kai A1 - Müller, Meike A1 - Sairam, Nivedita A1 - Doss-Gollin, James A1 - Lall, Upmanu A1 - Merz, Bruno T1 - Probabilistic Models Significantly Reduce Uncertainty in Hurricane Harvey Pluvial Flood Loss Estimates JF - Earths future 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. KW - pluvial flooding KW - loss modeling KW - urban flooding KW - probabilistic KW - Hurricane Harvey KW - climate change adaptation Y1 - 2019 U6 - https://doi.org/10.1029/2018EF001074 SN - 2328-4277 VL - 7 IS - 4 SP - 384 EP - 394 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Sairam, Nivedita A1 - Schroeter, Kai A1 - Rözer, Viktor A1 - Merz, Bruno A1 - Kreibich, Heidi T1 - Hierarchical Bayesian Approach for Modeling Spatiotemporal Variability in Flood Damage Processes JF - Water resources research N2 - Flood damage processes are complex and vary between events and regions. State-of-the-art flood loss models are often developed on the basis of empirical damage data from specific case studies and do not perform well when spatially and temporally transferred. This is due to the fact that such localized models often cover only a small set of possible damage processes from one event and a region. On the other hand, a single generalized model covering multiple events and different regions ignores the variability in damage processes across regions and events due to variables that are not explicitly accounted for individual households. We implement a hierarchical Bayesian approach to parameterize widely used depth-damage functions resulting in a hierarchical (multilevel) Bayesian model (HBM) for flood loss estimation that accounts for spatiotemporal heterogeneity in damage processes. We test and prove the hypothesis that, in transfer scenarios, HBMs are superior compared to generalized and localized regression models. In order to improve loss predictions for regions and events for which no empirical damage data are available, we use variables pertaining to specific region- and event-characteristics representing commonly available expert knowledge as group-level predictors within the HBM. KW - flood risk KW - flood loss model transfer KW - multilevel probabilistic flood loss model Y1 - 2019 U6 - https://doi.org/10.1029/2019WR025068 SN - 0043-1397 SN - 1944-7973 VL - 55 IS - 10 SP - 8223 EP - 8237 PB - American Geophysical Union CY - Washington ER - TY - JOUR A1 - Sairam, Nivedita A1 - Schröter, Kai A1 - Lüdtke, Stefan A1 - Merz, Bruno A1 - Kreibich, Heidi T1 - Quantifying Flood Vulnerability Reduction via Private Precaution JF - Earth future N2 - Private precaution is an important component in contemporary flood risk management and climate adaptation. However, quantitative knowledge about vulnerability reduction via private precautionary measures is scarce and their effects are hardly considered in loss modeling and risk assessments. However, this is a prerequisite to enable temporally dynamic flood damage and risk modeling, and thus the evaluation of risk management and adaptation strategies. To quantify the average reduction in vulnerability of residential buildings via private precaution empirical vulnerability data (n = 948) is used. Households with and without precautionary measures undertaken before the flood event are classified into treatment and nontreatment groups and matched. Postmatching regression is used to quantify the treatment effect. Additionally, we test state-of-the-art flood loss models regarding their capability to capture this difference in vulnerability. The estimated average treatment effect of implementing private precaution is between 11 and 15 thousand EUR per household, confirming the significant effectiveness of private precautionary measures in reducing flood vulnerability. From all tested flood loss models, the expert Bayesian network-based model BN-FLEMOps and the rule-based loss model FLEMOps perform best in capturing the difference in vulnerability due to private precaution. Thus, the use of such loss models is suggested for flood risk assessments to effectively support evaluations and decision making for adaptable flood risk management. KW - flood loss KW - average treatment effect KW - matching methods KW - loss models KW - risk analysis KW - adaptation Y1 - 2019 U6 - https://doi.org/10.1029/2018EF000994 SN - 2328-4277 VL - 7 IS - 3 SP - 235 EP - 249 PB - American Geophysical Union CY - Washington ER -