TY - JOUR A1 - Ullrich, Sophie Louise A1 - Hegnauer, Mark A1 - Nguyen, Dung Viet A1 - Merz, Bruno A1 - Kwadijk, Jaap A1 - Vorogushyn, Sergiy T1 - Comparative evaluation of two types of stochastic weather generators for synthetic precipitation in the Rhine basin JF - Journal of hydrology N2 - Stochastic modeling of precipitation for estimation of hydrological extremes is an important element of flood risk assessment and management. The spatially consistent estimation of rainfall fields and their temporal variability remains challenging and is addressed by various stochastic weather generators. In this study, two types of weather generators are evaluated against observed data and benchmarked regarding their ability to simulate spatio-temporal precipitation fields in the Rhine catchment. A multi-site station-based weather generator uses an auto-regressive model and estimates the spatial correlation structure between stations. Another weather generator is raster-based and uses the nearest-neighbor resampling technique for reshuffling daily patterns while preserving the correlation structure between the observations. Both weather generators perform well and are comparable at the point (station) scale with regards to daily mean and 99.9th percentile precipitation as well as concerning wet/dry frequencies and transition probabilities. The areal extreme precipitation at the sub-basin scale is however overestimated in the station-based weather generator due to an overestimation of the correlation structure between individual stations. The auto-regressive model tends to generate larger rainfall fields in space for extreme precipitation than observed, particularly in summer. The weather generator based on nearest-neighbor resampling reproduces the observed daily and multiday (5, 10 and 20) extreme events in a similar magnitude. Improvements in performance regarding wet frequencies and transition probabilities are recommended for both models. KW - Rainfall generation KW - Rainfall occurrence KW - Multi-site stochastic weather KW - generator KW - Resampling weather generator KW - Time series analysis Y1 - 2021 U6 - https://doi.org/10.1016/j.jhydrol.2021.126544 SN - 0022-1694 SN - 1879-2707 VL - 601 PB - Elsevier CY - Amsterdam [u.a.] ER - TY - JOUR A1 - Speckhann, Gustavo Andrei A1 - Kreibich, Heidi A1 - Merz, Bruno T1 - Inventory of dams in Germany JF - Earth system science data : the data publishing journal N2 - Dams are an important element of water resources management. Data about dams are crucial for practitioners, scientists, and policymakers for various purposes, such as seasonal forecasting of water availability or flood mitigation. However, detailed information on dams on the national level for Germany is so far not freely available. We present the most comprehensive open-access dam inventory for Germany (DIG) to date. We have collected and combined information on dams using books, state agency reports, engineering reports, and internet pages. We have applied a priority rule that ensures the highest level of reliability for the dam information. Our dam inventory comprises 530 dams in Germany with information on name, location, river, start year of construction and operation, crest length, dam height, lake area, lake volume, purpose, dam structure, and building characteristics. We have used a global, satellite-based water surface raster to evaluate the location of the dams. A significant proportion (63 %) of dams were built between 1950-2013. Our inventory shows that dams in Germany are mostly single-purpose (52 %), 53% can be used for flood control, and 25% are involved in energy production. The inventory is freely available through GFZ (GeoForschungsZentrum) Data Services (https://doi.org/10.5880/GFZ.4.4.2020.005) Y1 - 2021 U6 - https://doi.org/10.5194/essd-13-731-2021 SN - 1866-3508 SN - 1866-3516 VL - 13 IS - 2 SP - 731 EP - 740 PB - Copernicus CY - Göttingen 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 - 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 - Barendrecht, Marlies H. A1 - Viglione, Alberto A1 - Kreibich, Heidi A1 - Merz, Bruno A1 - Vorogushyn, Sergiy A1 - Blöschl, G. T1 - The Value of Empirical Data for Estimating the Parameters of a Sociohydrological Flood Risk Model JF - Water resources research N2 - In this paper, empirical data are used to estimate the parameters of a sociohydrological flood risk model. The proposed model, which describes the interactions between floods, settlement density, awareness, preparedness, and flood loss, is based on the literature. Data for the case study of Dresden, Germany, over a period of 200years, are used to estimate the model parameters through Bayesian inference. The credibility bounds of their estimates are small, even though the data are rather uncertain. A sensitivity analysis is performed to examine the value of the different data sources in estimating the model parameters. In general, the estimated parameters are less biased when using data at the end of the modeled period. Data about flood awareness are the most important to correctly estimate the parameters of this model and to correctly model the system dynamics. Using more data for other variables cannot compensate for the absence of awareness data. More generally, the absence of data mostly affects the estimation of the parameters that are directly related to the variable for which data are missing. This paper demonstrates that combining sociohydrological modeling and empirical data gives additional insights into the sociohydrological system, such as quantifying the forgetfulness of the society, which would otherwise not be easily achieved by sociohydrological models without data or by standard statistical analysis of empirical data. Y1 - 2019 U6 - https://doi.org/10.1029/2018WR024128 SN - 0043-1397 SN - 1944-7973 VL - 55 IS - 2 SP - 1312 EP - 1336 PB - American Geophysical Union CY - Washington 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 - Steirou, Eva A1 - Gerlitz, Lars A1 - Apel, Heiko A1 - Sun, Xun A1 - Merz, Bruno T1 - Climate influences on flood probabilities across Europe JF - Hydrology and earth system sciences : HESS N2 - The link between streamflow extremes and climatology has been widely studied in recent decades. However, a study investigating the effect of large-scale circulation variations on the distribution of seasonal discharge extremes at the European level is missing. Here we fit a climate-informed generalized extreme value (GEV) distribution to about 600 streamflow records in Europe for each of the standard seasons, i.e., to winter, spring, summer and autumn maxima, and compare it with the classical GEV distribution with parameters invariant in time. The study adopts a Bayesian framework and covers the period 1950 to 2016. Five indices with proven influence on the European climate are examined independently as covariates, namely the North Atlantic Oscillation (NAO), the east Atlantic pattern (EA), the east Atlantic-western Russian pattern (EA/WR), the Scandinavia pattern (SCA) and the polar-Eurasian pattern (POL). It is found that for a high percentage of stations the climate-informed model is preferred to the classical model. Particularly for NAO during winter, a strong influence on streamflow extremes is detected for large parts of Europe (preferred to the classical GEV distribution for 46% of the stations). Climate-informed fits are characterized by spatial coherence and form patterns that resemble relations between the climate indices and seasonal precipitation, suggesting a prominent role of the considered circulation modes for flood generation. For certain regions, such as northwestern Scandinavia and the British Isles, yearly variations of the mean seasonal climate indices result in considerably different extreme value distributions and thus in highly different flood estimates for individual years that can also persist for longer time periods. Y1 - 2019 U6 - https://doi.org/10.5194/hess-23-1305-2019 SN - 1027-5606 SN - 1607-7938 VL - 23 IS - 3 SP - 1305 EP - 1322 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 - Wendi, Dadiyorto A1 - Merz, Bruno A1 - Marwan, Norbert T1 - Assessing hydrograph similarity and rare runoff dynamics by cross recurrence plots JF - Water resources research N2 - This paper introduces a novel measure to assess similarity between event hydrographs. It is based on cross recurrence plots (CRP) and recurrence quantification analysis (RQA), which have recently gained attention in a range of disciplines when dealing with complex systems. The method attempts to quantify the event runoff dynamics and is based on the time delay embedded phase space representation of discharge hydrographs. A phase space trajectory is reconstructed from the event hydrograph, and pairs of hydrographs are compared to each other based on the distance of their phase space trajectories. Time delay embedding allows considering the multidimensional relationships between different points in time within the event. Hence, the temporal succession of discharge values is taken into account, such as the impact of the initial conditions on the runoff event. We provide an introduction to cross recurrence plots and discuss their parameterization. An application example based on flood time series demonstrates how the method can be used to measure the similarity or dissimilarity of events, and how it can be used to detect events with rare runoff dynamics. It is argued that this methods provides a more comprehensive approach to quantify hydrograph similarity compared to conventional hydrological signatures. KW - runoff dynamics KW - cross recurrence plot in hydrology KW - rare flood dynamics KW - hydrograph similarity KW - time delay embedding for runoff series Y1 - 2019 U6 - https://doi.org/10.1029/2018WR024111 SN - 0043-1397 SN - 1944-7973 VL - 55 IS - 6 SP - 4704 EP - 4726 PB - American Geophysical Union CY - Washington ER -