TY  - JOUR
A1  - Sairam, Nivedita
A1  - Brill, Fabio Alexander
A1  - Sieg, Tobias
A1  - Farrag, Mostafa
A1  - Kellermann, Patric
A1  - Viet Dung Nguyen, 
A1  - Lüdtke, Stefan
A1  - Merz, Bruno
A1  - Schröter, Kai
A1  - Vorogushyn, Sergiy
A1  - Kreibich, Heidi
T1  - Process-based flood risk assessment for Germany
JF  - Earth's future / American Geophysical Union
N2  - 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.
KW  - risk model chain
KW  - continuous simulation
KW  - expected annual damage
KW  - risk
KW  - curves
KW  - multi-sector risk
Y1  - 2021
U6  - https://doi.org/10.1029/2021EF002259
SN  - 2328-4277
VL  - 9
IS  - 10
PB  - Wiley-Blackwell
CY  - Hoboken, NJ
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  - JOUR
A1  - Merz, Bruno
A1  - Apel, Heiko
A1  - Dung Nguyen, Viet-Dung
A1  - Falter, Daniela
A1  - Guse, Björn
A1  - Hundecha, Yeshewatesfa
A1  - Kreibich, Heidi
A1  - Schröter, Kai
A1  - Vorogushyn, Sergiy
T1  - From precipitation to damage
BT  - a coupled model chain for spatially coherent, large-scale flood risk assessment
JF  - Global flood hazard : applications in modeling, mapping and forecasting
N2  - 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.
Y1  - 2018
SN  - 978-1-119-21788-6
SN  - 978-1-119-21786-2
U6  - https://doi.org/10.1002/9781119217886.ch10
SN  - 0065-8448
VL  - 233
SP  - 169
EP  - 183
PB  - American Geophysical Union
CY  - Washington
ER  - 
TY  - JOUR
A1  - Kreibich, Heidi
A1  - Botto, Anna
A1  - Merz, Bruno
A1  - Schröter, Kai
T1  - Probabilistic, Multivariable Flood Loss Modeling on the Mesoscale with BT-FLEMO
JF  - Risk analysis
N2  - 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.
KW  - Damage modeling
KW  - multiparameter
KW  - probabilistic
KW  - uncertainty
KW  - validation
Y1  - 2016
U6  - https://doi.org/10.1111/risa.12650
SN  - 0272-4332
SN  - 1539-6924
VL  - 37
IS  - 4
SP  - 774
EP  - 787
PB  - Wiley
CY  - Hoboken
ER  - 
TY  - JOUR
A1  - Metin, Ayse Duha
A1  - Nguyen Viet Dung, 
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?
JF  - Natural hazards and earth system sciences
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.
Y1  - 2018
U6  - https://doi.org/10.5194/nhess-18-3089-2018
SN  - 1561-8633
SN  - 1684-9981
VL  - 18
IS  - 11
SP  - 3089
EP  - 3108
PB  - Copernicus
CY  - Göttingen
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  - 
TY  - JOUR
A1  - Paprotny, Dominik
A1  - Kreibich, Heidi
A1  - Morales-Napoles, Oswaldo
A1  - Wagenaar, Dennis
A1  - Castellarin, Attilio
A1  - Carisi, Francesca
A1  - Bertin, Xavier
A1  - Merz, Bruno
A1  - Schröter, Kai
T1  - A probabilistic approach to estimating residential losses from different flood types
JF  - Natural hazards : journal of the International Society for the Prevention and Mitigation of Natural Hazards
N2  - 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.
KW  - fluvial floods
KW  - coastal floods
KW  - pluvial floods
KW  - Bayesian networks
KW  - flood
KW  - damage surveys
Y1  - 2020
U6  - https://doi.org/10.1007/s11069-020-04413-x
SN  - 0921-030X
SN  - 1573-0840
VL  - 105
IS  - 3
SP  - 2569
EP  - 2601
PB  - Springer
CY  - New York
ER  - 
TY  - JOUR
A1  - Merz, Bruno
A1  - Kuhlicke, Christian
A1  - Kunz, Michael
A1  - Pittore, Massimiliano
A1  - Babeyko, Andrey
A1  - Bresch, David N.
A1  - Domeisen, Daniela I.
A1  - Feser, Frauke
A1  - Koszalka, Inga
A1  - Kreibich, Heidi
A1  - Pantillon, Florian
A1  - Parolai, Stefano
A1  - Pinto, Joaquim G.
A1  - Punge, Heinz Jürgen
A1  - Rivalta, Eleonora
A1  - Schröter, Kai
A1  - Strehlow, Karen
A1  - Weisse, Ralf
A1  - Wurpts, Andreas
T1  - Impact forecasting to support emergency management of natural hazards
JF  - Reviews of geophysics
N2  - 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.
KW  - impact forecasting
KW  - natural hazards
KW  - early warning
Y1  - 2020
U6  - https://doi.org/10.1029/2020RG000704
SN  - 8755-1209
SN  - 1944-9208
VL  - 58
IS  - 4
PB  - American Geophysical Union
CY  - Washington
ER  - 
TY  - JOUR
A1  - Metin, Ayse Duha
A1  - Dung, Nguyen Viet
A1  - Schröter, Kai
A1  - Vorogushyn, Sergiy
A1  - Guse, Björn
A1  - Kreibich, Heidi
A1  - Merz, Bruno
T1  - The role of spatial dependence for large-scale flood risk estimation
JF  - Natural hazards and earth system sciences
N2  - 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.
Y1  - 2020
U6  - https://doi.org/10.5194/nhess-20-967-2020
SN  - 1561-8633
SN  - 1684-9981
VL  - 20
IS  - 4
SP  - 967
EP  - 979
PB  - European Geosciences Union (EGU) ; Copernicus
CY  - Göttingen
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  -