TY  - JOUR
A1  - Kellermann, Patric
A1  - Schröter, Kai
A1  - Thieken, Annegret
A1  - Haubrock, Sören-Nils
A1  - Kreibich, Heidi
T1  - The object-specific flood damage database HOWAS 21
JF  - Natural hazards and earth system sciences
N2  - 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.
Y1  - 2020
U6  - https://doi.org/10.5194/nhess-20-2503-2020
SN  - 1561-8633
SN  - 1684-9981
VL  - 20
IS  - 9
SP  - 2503
EP  - 2519
PB  - Copernicus
CY  - Göttingen
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  - 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  - 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  - 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  - 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  -