@article{SchroeterKreibichVogeletal.2014, author = {Schroeter, Kai and Kreibich, Heidi and Vogel, Kristin and Riggelsen, Carsten and Scherbaum, Frank and Merz, Bruno}, title = {How useful are complex flood damage models?}, series = {Water resources research}, volume = {50}, journal = {Water resources research}, number = {4}, publisher = {American Geophysical Union}, address = {Washington}, issn = {0043-1397}, doi = {10.1002/2013WR014396}, pages = {3378 -- 3395}, year = {2014}, abstract = {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.}, language = {en} } @article{KreibichBottoMerzetal.2016, author = {Kreibich, Heidi and Botto, Anna and Merz, Bruno and Schr{\"o}ter, Kai}, title = {Probabilistic, Multivariable Flood Loss Modeling on the Mesoscale with BT-FLEMO}, series = {Risk analysis}, volume = {37}, journal = {Risk analysis}, number = {4}, publisher = {Wiley}, address = {Hoboken}, issn = {0272-4332}, doi = {10.1111/risa.12650}, pages = {774 -- 787}, year = {2016}, abstract = {Flood loss modeling is an important component for risk analyses and decision support in flood risk management. Commonly, flood loss models describe complex damaging processes by simple, deterministic approaches like depth-damage functions and are associated with large uncertainty. To improve flood loss estimation and to provide quantitative information about the uncertainty associated with loss modeling, a probabilistic, multivariable Bagging decision Tree Flood Loss Estimation MOdel (BT-FLEMO) for residential buildings was developed. The application of BT-FLEMO provides a probability distribution of estimated losses to residential buildings per municipality. BT-FLEMO was applied and validated at the mesoscale in 19 municipalities that were affected during the 2002 flood by the River Mulde in Saxony, Germany. Validation was undertaken on the one hand via a comparison with six deterministic loss models, including both depth-damage functions and multivariable models. On the other hand, the results were compared with official loss data. BT-FLEMO outperforms deterministic, univariable, and multivariable models with regard to model accuracy, although the prediction uncertainty remains high. An important advantage of BT-FLEMO is the quantification of prediction uncertainty. The probability distribution of loss estimates by BT-FLEMO well represents the variation range of loss estimates of the other models in the case study.}, language = {en} } @article{SiegVogelMerzetal.2017, author = {Sieg, Tobias and Vogel, Kristin and Merz, Bruno and Kreibich, Heidi}, title = {Tree-based flood damage modeling of companies: Damage processes and model performance}, series = {Water resources research}, volume = {53}, journal = {Water resources research}, publisher = {American Geophysical Union}, address = {Washington}, issn = {0043-1397}, doi = {10.1002/2017WR020784}, pages = {6050 -- 6068}, year = {2017}, abstract = {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.}, language = {en} } @article{KreibichDiBaldassarreVorogushynetal.2017, author = {Kreibich, Heidi and Di Baldassarre, Giuliano and Vorogushyn, Sergiy and Aerts, Jeroen C. J. H. and Apel, Heiko and Aronica, Giuseppe T. and Arnbjerg-Nielsen, Karsten and Bouwer, Laurens M. and Bubeck, Philip and Caloiero, Tommaso and Chinh, Do T. and Cortes, Maria and Gain, Animesh K. and Giampa, Vincenzo and Kuhlicke, Christian and Kundzewicz, Zbigniew W. and Llasat, Maria Carmen and Mard, Johanna and Matczak, Piotr and Mazzoleni, Maurizio and Molinari, Daniela and Dung, Nguyen V. and Petrucci, Olga and Schr{\"o}ter, Kai and Slager, Kymo and Thieken, Annegret and Ward, Philip J. and Merz, Bruno}, title = {Adaptation to flood risk}, series = {Earth's Future}, volume = {5}, journal = {Earth's Future}, publisher = {Wiley}, address = {Hoboken}, issn = {2328-4277}, doi = {10.1002/2017EF000606}, pages = {953 -- 965}, year = {2017}, abstract = {As flood impacts are increasing in large parts of the world, understanding the primary drivers of changes in risk is essential for effective adaptation. To gain more knowledge on the basis of empirical case studies, we analyze eight paired floods, that is, consecutive flood events that occurred in the same region, with the second flood causing significantly lower damage. These success stories of risk reduction were selected across different socioeconomic and hydro-climatic contexts. The potential of societies to adapt is uncovered by describing triggered societal changes, as well as formal measures and spontaneous processes that reduced flood risk. This novel approach has the potential to build the basis for an international data collection and analysis effort to better understand and attribute changes in risk due to hydrological extremes in the framework of the IAHSs Panta Rhei initiative. Across all case studies, we find that lower damage caused by the second event was mainly due to significant reductions in vulnerability, for example, via raised risk awareness, preparedness, and improvements of organizational emergency management. Thus, vulnerability reduction plays an essential role for successful adaptation. Our work shows that there is a high potential to adapt, but there remains the challenge to stimulate measures that reduce vulnerability and risk in periods in which extreme events do not occur.}, language = {en} } @article{MerzApelDungNguyenetal.2018, author = {Merz, Bruno and Apel, Heiko and Dung Nguyen, Viet-Dung and Falter, Daniela and Guse, Bj{\"o}rn and Hundecha, Yeshewatesfa and Kreibich, Heidi and Schr{\"o}ter, Kai and Vorogushyn, Sergiy}, title = {From precipitation to damage}, series = {Global flood hazard : applications in modeling, mapping and forecasting}, volume = {233}, journal = {Global flood hazard : applications in modeling, mapping and forecasting}, publisher = {American Geophysical Union}, address = {Washington}, isbn = {978-1-119-21788-6}, issn = {0065-8448}, doi = {10.1002/9781119217886.ch10}, pages = {169 -- 183}, year = {2018}, abstract = {Flood risk assessments for large river basins often involve piecing together smaller-scale assessments leading to erroneous risk statements. We describe a coupled model chain for quantifying flood risk at the scale of 100,000 km(2). It consists of a catchment model, a 1D-2D river network model, and a loss model. We introduce the model chain and present two applications. The first application for the Elbe River basin with an area of 66,000 km(2) demonstrates that it is feasible to simulate the complete risk chain for large river basins in a continuous simulation mode with high temporal and spatial resolution. In the second application, RFM is coupled to a multisite weather generator and applied to the Mulde catchment with an area of 6,000 km(2). This approach is able to provide a very long time series of spatially heterogeneous patterns of precipitation, discharge, inundation, and damage. These patterns respect the spatial correlation of the different processes and are suitable to derive large-scale risk estimates. We discuss how the RFM approach can be transferred to the continental scale.}, language = {en} } @article{SairamSchroeterRoezeretal.2019, author = {Sairam, Nivedita and Schroeter, Kai and R{\"o}zer, Viktor and Merz, Bruno and Kreibich, Heidi}, title = {Hierarchical Bayesian Approach for Modeling Spatiotemporal Variability in Flood Damage Processes}, series = {Water resources research}, volume = {55}, journal = {Water resources research}, number = {10}, publisher = {American Geophysical Union}, address = {Washington}, issn = {0043-1397}, doi = {10.1029/2019WR025068}, pages = {8223 -- 8237}, year = {2019}, abstract = {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.}, language = {en} } @article{SiegVogelMerzetal.2019, author = {Sieg, Tobias and Vogel, Kristin and Merz, Bruno and Kreibich, Heidi}, title = {Seamless Estimation of Hydrometeorological Risk Across Spatial Scales}, series = {Earth's Future}, volume = {7}, journal = {Earth's Future}, number = {5}, publisher = {Wiley-Blackwell}, address = {Hoboken, NJ}, issn = {2328-4277}, doi = {10.1029/2018EF001122}, pages = {574 -- 581}, year = {2019}, abstract = {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.}, language = {en} } @article{MerzKuhlickeKunzetal.2020, author = {Merz, Bruno and Kuhlicke, Christian and Kunz, Michael and Pittore, Massimiliano and Babeyko, Andrey and Bresch, David N. and Domeisen, Daniela I. and Feser, Frauke and Koszalka, Inga and Kreibich, Heidi and Pantillon, Florian and Parolai, Stefano and Pinto, Joaquim G. and Punge, Heinz J{\"u}rgen and Rivalta, Eleonora and Schr{\"o}ter, Kai and Strehlow, Karen and Weisse, Ralf and Wurpts, Andreas}, title = {Impact forecasting to support emergency management of natural hazards}, series = {Reviews of geophysics}, volume = {58}, journal = {Reviews of geophysics}, number = {4}, publisher = {American Geophysical Union}, address = {Washington}, issn = {8755-1209}, doi = {10.1029/2020RG000704}, pages = {52}, year = {2020}, abstract = {Forecasting and early warning systems are important investments to protect lives, properties, and livelihood. While early warning systems are frequently used to predict the magnitude, location, and timing of potentially damaging events, these systems rarely provide impact estimates, such as the expected amount and distribution of physical damage, human consequences, disruption of services, or financial loss. Complementing early warning systems with impact forecasts has a twofold advantage: It would provide decision makers with richer information to take informed decisions about emergency measures and focus the attention of different disciplines on a common target. This would allow capitalizing on synergies between different disciplines and boosting the development of multihazard early warning systems. This review discusses the state of the art in impact forecasting for a wide range of natural hazards. We outline the added value of impact-based warnings compared to hazard forecasting for the emergency phase, indicate challenges and pitfalls, and synthesize the review results across hazard types most relevant for Europe.}, language = {en} } @article{WietzkeMerzGerlitzetal.2020, author = {Wietzke, Luzie M. and Merz, Bruno and Gerlitz, Lars and Kreibich, Heidi and Guse, Bj{\"o}rn and Castellarin, Attilio and Vorogushyn, Sergiy}, title = {Comparative analysis of scalar upper tail indicators}, series = {Hydrological sciences journal = Journal des sciences hydrologiques}, volume = {65}, journal = {Hydrological sciences journal = Journal des sciences hydrologiques}, number = {10}, publisher = {Routledge, Taylor \& Francis Group}, address = {Abingdon}, issn = {0262-6667}, doi = {10.1080/02626667.2020.1769104}, pages = {1625 -- 1639}, year = {2020}, abstract = {Different upper tail indicators exist to characterize heavy tail phenomena, but no comparative study has been carried out so far. We evaluate the shape parameter (GEV), obesity index, Gini index and upper tail ratio (UTR) against a novel benchmark of tail heaviness - the surprise factor. Sensitivity analyses to sample size and changes in scale-to-location ratio are carried out in bootstrap experiments. The UTR replicates the surprise factor best but is most uncertain and only comparable between records of similar length. For samples with symmetric Lorenz curves, shape parameter, obesity and Gini indices provide consistent indications. For asymmetric Lorenz curves, however, the first two tend to overestimate, whereas Gini index tends to underestimate tail heaviness. We suggest the use of a combination of shape parameter, obesity and Gini index to characterize tail heaviness. These indicators should be supported with calculation of the Lorenz asymmetry coefficients and interpreted with caution.}, language = {en} } @article{PaprotnyKreibichMoralesNapolesetal.2020, author = {Paprotny, Dominik and Kreibich, Heidi and Morales-Napoles, Oswaldo and Wagenaar, Dennis and Castellarin, Attilio and Carisi, Francesca and Bertin, Xavier and Merz, Bruno and Schr{\"o}ter, Kai}, title = {A probabilistic approach to estimating residential losses from different flood types}, series = {Natural hazards : journal of the International Society for the Prevention and Mitigation of Natural Hazards}, volume = {105}, journal = {Natural hazards : journal of the International Society for the Prevention and Mitigation of Natural Hazards}, number = {3}, publisher = {Springer}, address = {New York}, issn = {0921-030X}, doi = {10.1007/s11069-020-04413-x}, pages = {2569 -- 2601}, year = {2020}, abstract = {Residential assets, comprising buildings and household contents, are a major source of direct flood losses. Existing damage models are mostly deterministic and limited to particular countries or flood types. Here, we compile building-level losses from Germany, Italy and the Netherlands covering a wide range of fluvial and pluvial flood events. Utilizing a Bayesian network (BN) for continuous variables, we find that relative losses (i.e. loss relative to exposure) to building structure and its contents could be estimated with five variables: water depth, flow velocity, event return period, building usable floor space area and regional disposable income per capita. The model's ability to predict flood losses is validated for the 11 flood events contained in the sample. Predictions for the German and Italian fluvial floods were better than for pluvial floods or the 1993 Meuse river flood. Further, a case study of a 2010 coastal flood in France is used to test the BN model's performance for a type of flood not included in the survey dataset. Overall, the BN model achieved better results than any of 10 alternative damage models for reproducing average losses for the 2010 flood. An additional case study of a 2013 fluvial flood has also shown good performance of the model. The study shows that data from many flood events can be combined to derive most important factors driving flood losses across regions and time, and that resulting damage models could be applied in an open data framework.}, language = {en} } @article{KreibichHudsonMerz2021, author = {Kreibich, Heidi and Hudson, Paul and Merz, Bruno}, title = {Knowing what to do substantially improves the effectiveness of flood early warning}, series = {Bulletin of the American Meteorological Society}, volume = {102}, journal = {Bulletin of the American Meteorological Society}, number = {7}, publisher = {American Meteorological Soc.}, address = {Boston}, issn = {0003-0007}, doi = {10.1175/BAMS-D-20-0262.1}, pages = {E1450 -- E1463}, year = {2021}, abstract = {Flood warning systems are longstanding success stories with respect to protecting human life, but monetary losses continue to grow. Knowledge on the effectiveness of flood early warning in reducing monetary losses is scarce, especially at the individual level. To gain more knowledge in this area, we analyze a dataset that is unique with respect to detailed information on warning reception and monetary losses at the property level and with respect to amount of data available. The dataset contains 4,468 loss cases from six flood events in Germany. These floods occurred between 2002 and 2013. The data from each event were collected by computer-aided telephone interviews in four surveys following a repeated cross-sectional design. We quantitatively reveal that flood early warning is only effective in reducing monetary losses when people know what to do when they receive the warning. We also show that particularly long-term preparedness is associated with people knowing what to do when they receive a warning. Thus, risk communication, training, and (financial) support for private preparedness are effective in mitigating flood losses in two ways: precautionary measures and more effective emergency responses.}, language = {en} } @article{FarragBrillNguyenetal.2022, author = {Farrag, Mostafa and Brill, Fabio Alexander and Nguyen, Viet Dung and Sairam, Nivedita and Schr{\"o}ter, Kai and Kreibich, Heidi and Merz, Bruno and de Bruijn, Karin M. and Vorogushyn, Sergiy}, title = {On the role of floodplain storage and hydrodynamic interactions in flood risk estimation}, series = {Hydrological sciences journal = Journal des sciences hydrologiques}, volume = {67}, journal = {Hydrological sciences journal = Journal des sciences hydrologiques}, number = {4}, publisher = {Routledge, Taylor \& Francis Group}, address = {Abingdon}, issn = {0262-6667}, doi = {10.1080/02626667.2022.2030058}, pages = {508 -- 534}, year = {2022}, abstract = {Hydrodynamic interactions, i.e. the floodplain storage effects caused by inundations upstream on flood wave propagation, inundation areas, and flood damage downstream, are important but often ignored in large-scale flood risk assessments. Although new methods considering these effects sometimes emerge, they are often limited to a small or meso scale. In this study, we investigate the role of hydrodynamic interactions and floodplain storage on flood hazard and risk in the German part of the Rhine basin. To do so, we compare a new continuous 1D routing scheme within a flood risk model chain to the piece-wise routing scheme, which largely neglects floodplain storage. The results show that floodplain storage is significant, lowers water levels and discharges, and reduces risks by over 50\%. Therefore, for accurate risk assessments, a system approach must be adopted, and floodplain storage and hydrodynamic interactions must carefully be considered.}, language = {en} }