@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} } @article{SairamBrillSiegetal.2021, author = {Sairam, Nivedita and Brill, Fabio Alexander and Sieg, Tobias and Farrag, Mostafa and Kellermann, Patric and Viet Dung Nguyen, and L{\"u}dtke, Stefan and Merz, Bruno and Schr{\"o}ter, Kai and Vorogushyn, Sergiy and Kreibich, Heidi}, title = {Process-based flood risk assessment for Germany}, series = {Earth's future / American Geophysical Union}, volume = {9}, journal = {Earth's future / American Geophysical Union}, number = {10}, publisher = {Wiley-Blackwell}, address = {Hoboken, NJ}, issn = {2328-4277}, doi = {10.1029/2021EF002259}, pages = {12}, year = {2021}, abstract = {Large-scale flood risk assessments are crucial for decision making, especially with respect to new flood defense schemes, adaptation planning and estimating insurance premiums. We apply the process-based Regional Flood Model (RFM) to simulate a 5000-year flood event catalog for all major catchments in Germany and derive risk curves based on the losses per economic sector. The RFM uses a continuous process simulation including a multisite, multivariate weather generator, a hydrological model considering heterogeneous catchment processes, a coupled 1D-2D hydrodynamic model considering dike overtopping and hinterland storage, spatially explicit sector-wise exposure data and empirical multi-variable loss models calibrated for Germany. For all components, uncertainties in the data and models are estimated. We estimate the median Expected Annual Damage (EAD) and Value at Risk at 99.5\% confidence for Germany to be euro0.529 bn and euro8.865 bn, respectively. The commercial sector dominates by making about 60\% of the total risk, followed by the residential sector. The agriculture sector gets affected by small return period floods and only contributes to less than 3\% to the total risk. The overall EAD is comparable to other large-scale estimates. However, the estimation of losses for specific return periods is substantially improved. The spatial consistency of the risk estimates avoids the large overestimation of losses for rare events that is common in other large-scale assessments with homogeneous return periods. Thus, the process-based, spatially consistent flood risk assessment by RFM is an important step forward and will serve as a benchmark for future German-wide flood risk assessments.}, language = {en} } @article{KellermannSchroeterThiekenetal.2020, author = {Kellermann, Patric and Schr{\"o}ter, Kai and Thieken, Annegret and Haubrock, S{\"o}ren-Nils and Kreibich, Heidi}, title = {The object-specific flood damage database HOWAS 21}, series = {Natural hazards and earth system sciences}, volume = {20}, journal = {Natural hazards and earth system sciences}, number = {9}, publisher = {Copernicus}, address = {G{\"o}ttingen}, issn = {1561-8633}, doi = {10.5194/nhess-20-2503-2020}, pages = {2503 -- 2519}, year = {2020}, abstract = {The Flood Damage Database HOWAS 21 contains object-specific flood damage data resulting from fluvial, pluvial and groundwater flooding. The datasets incorporate various variables of flood hazard, exposure, vulnerability and direct tangible damage at properties from several economic sectors. The main purpose of development of HOWAS 21 was to support forensic flood analysis and the derivation of flood damage models. HOWAS 21 was first developed for Germany and currently almost exclusively contains datasets from Germany. However, its scope has recently been enlarged with the aim to serve as an international flood damage database; e.g. its web application is now available in German and English. This paper presents the recent advancements of HOWAS 21 and highlights exemplary analyses to demonstrate the use of HOWAS 21 flood damage data. The data applications indicate a large potential of the database for fostering a better understanding and estimation of the consequences of flooding.}, language = {en} } @article{MetinDungSchroeteretal.2020, author = {Metin, Ayse Duha and Dung, Nguyen Viet and Schr{\"o}ter, Kai and Vorogushyn, Sergiy and Guse, Bj{\"o}rn and Kreibich, Heidi and Merz, Bruno}, title = {The role of spatial dependence for large-scale flood risk estimation}, series = {Natural hazards and earth system sciences}, volume = {20}, journal = {Natural hazards and earth system sciences}, number = {4}, publisher = {European Geosciences Union (EGU) ; Copernicus}, address = {G{\"o}ttingen}, issn = {1561-8633}, doi = {10.5194/nhess-20-967-2020}, pages = {967 -- 979}, year = {2020}, abstract = {Flood risk assessments are typically based on scenarios which assume homogeneous return periods of flood peaks throughout the catchment. This assumption is unrealistic for real flood events and may bias risk estimates for specific return periods. We investigate how three assumptions about the spatial dependence affect risk estimates: (i) spatially homogeneous scenarios (complete dependence), (ii) spatially heterogeneous scenarios (modelled dependence) and (iii) spatially heterogeneous but uncorrelated scenarios (complete independence). To this end, the model chain RFM (regional flood model) is applied to the Elbe catchment in Germany, accounting for the spatio-temporal dynamics of all flood generation processes, from the rainfall through catchment and river system processes to damage mechanisms. Different assumptions about the spatial dependence do not influence the expected annual damage (EAD); however, they bias the risk curve, i.e. the cumulative distribution function of damage. The widespread assumption of complete dependence strongly overestimates flood damage of the order of 100\% for return periods larger than approximately 200 years. On the other hand, for small and medium floods with return periods smaller than approximately 50 years, damage is underestimated. The overestimation aggravates when risk is estimated for larger areas. This study demonstrates the importance of representing the spatial dependence of flood peaks and damage for risk assessments.}, 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{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{NiedSchroeterLuedtkeetal.2017, author = {Nied, Manuela and Schr{\"o}ter, Kai and L{\"u}dtke, Stefan and Nguyen, Viet Dung and Merz, Bruno}, title = {What are the hydro-meteorological controls on flood characteristics?}, series = {Journal of hydrology}, volume = {545}, journal = {Journal of hydrology}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0022-1694}, doi = {10.1016/j.jhydrol.2016.12.003}, pages = {310 -- 326}, year = {2017}, abstract = {Flood events can be expressed by a variety of characteristics such as flood magnitude and extent, event duration or incurred loss. Flood estimation and management may benefit from understanding how the different flood characteristics relate to the hydrological catchment conditions preceding the event and to the meteorological conditions throughout the event. In this study, we therefore propose a methodology to investigate the hydro-meteorological controls on different flood characteristics, based on the simulation of the complete flood risk chain from the flood triggering precipitation event, through runoff generation in the catchment, flood routing and possible inundation in the river system and floodplains to flood loss. Conditional cumulative distribution functions and regression tree analysis delineate the seasonal varying flood processes and indicate that the effect of the hydrological pre-conditions, i.e. soil moisture patterns, and of the meteorological conditions, i.e. weather patterns, depends on the considered flood characteristic. The methodology is exemplified for the Elbe catchment. In this catchment, the length of the build-up period, the event duration and the number of gauges undergoing at least a 10-year flood are governed by weather patterns. The affected length and the number of gauges undergoing at least a 2-year flood are however governed by soil moisture patterns. In case of flood severity and loss, the controlling factor is less pronounced. Severity is slightly governed by soil moisture patterns whereas loss is slightly governed by weather patterns. The study highlights that flood magnitude and extent arise from different flood generation processes and concludes that soil moisture patterns as well as weather patterns are not only beneficial to inform on possible flood occurrence but also on the involved flood processes and resulting flood characteristics.}, 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{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{MerzNguyenApeletal.2018, author = {Merz, Bruno and Nguyen, Viet Dung and Apel, Heiko and Gerlitz, Lars and Schr{\"o}ter, Kai and Steirou, Eva Styliani and Vorogushyn, Sergiy}, title = {Spatial coherence of flood-rich and flood-poor periods across Germany}, series = {Journal of hydrology}, volume = {559}, journal = {Journal of hydrology}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0022-1694}, doi = {10.1016/j.jhydrol.2018.02.082}, pages = {813 -- 826}, year = {2018}, abstract = {Despite its societal relevance, the question whether fluctuations in flood occurrence or magnitude are coherent in space has hardly been addressed in quantitative terms. We investigate this question for Germany by analysing fluctuations in annual maximum series (AMS) values at 68 discharge gauges for the common time period 1932-2005. We find remarkable spatial coherence across Germany given its different flood regimes. For example, there is a tendency that flood-rich/-poor years in sub-catchments of the Rhine basin, which are dominated by winter floods, coincide with flood-rich/-poor years in the southern sub-catchments of the Danube basin, which have their dominant flood season in summer. Our findings indicate that coherence is caused rather by persistence in catchment wetness than by persistent periods of higher/lower event precipitation. Further, we propose to differentiate between event-type and non-event-type coherence. There are quite a number of hydrological years with considerable nonevent-type coherence, i.e. AMS values of the 68 gauges are spread out through the year but in the same magnitude range. Years with extreme flooding tend to be of event-type and non-coherent, i.e. there is at least one precipitation event that affects many catchments to various degree. Although spatial coherence is a remarkable phenomenon, and large-scale flooding across Germany can lead to severe situations, extreme magnitudes across the whole country within one event or within one year were not observed in the investigated period. (C) 2018 Elsevier B.V. All rights reserved.}, language = {en} }