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  - 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  - 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  - 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  - Farrag, Mostafa
A1  - Brill, Fabio Alexander
A1  - Nguyen, Viet Dung
A1  - Sairam, Nivedita
A1  - Schröter, Kai
A1  - Kreibich, Heidi
A1  - Merz, Bruno
A1  - de Bruijn, Karin M.
A1  - Vorogushyn, Sergiy
T1  - On the role of floodplain storage and hydrodynamic interactions in flood risk estimation
JF  - Hydrological sciences journal = Journal des sciences hydrologiques
N2  - 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.
KW  - hydrodynamic interactions
KW  - derived flood risk analysis
KW  - flood modelling;
KW  - Rhine basin
Y1  - 2022
U6  - https://doi.org/10.1080/02626667.2022.2030058
SN  - 0262-6667
SN  - 2150-3435
VL  - 67
IS  - 4
SP  - 508
EP  - 534
PB  - Routledge, Taylor & Francis Group
CY  - Abingdon
ER  -