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Flood damage estimation is a core task in flood risk assessments and requires reliable flood loss models. Identifying the driving factors of flood loss at residential buildings and gaining insight into their relations is important to improve our understanding of flood damage processes. For that purpose, we learn probabilistic graphical models, which capture and illustrate (in-)dependencies between the considered variables. The models are learned based on postevent surveys with flood-affected residents after six flood events, which occurred in Germany between 2002 and 2013. Besides the sustained building damage, the survey data contain information about flooding parameters, early warning and emergency measures, property-level mitigation measures and preparedness, socioeconomic characteristics of the household, and building characteristics. The analysis considers the entire data set with a total of 4,468 cases as well as subsets of the data set partitioned into single flood events and flood types: river floods, levee breaches, surface water flooding, and groundwater floods, to reveal differences in the damaging processes. The learned networks suggest that the flood loss ratio of residential buildings is directly influenced by hydrological and hydraulic aspects as well as by building characteristics and property-level mitigation measures. The study demonstrates also that for different flood events and process types the building damage is influenced by varying factors. This suggests that flood damage models need to be capable of reproducing these differences for spatial and temporal model transfers.
Widespread flooding in June 2013 caused damage costs of €6 to 8 billion in Germany, and awoke many memories of the floods in August 2002, which resulted in total damage of €11.6 billion and hence was the most expensive natural hazard event in Germany up to now. The event of 2002 does, however, also mark a reorientation toward an integrated flood risk management system in Germany. Therefore, the flood of 2013 offered the opportunity to review how the measures that politics, administration, and civil society have implemented since 2002 helped to cope with the flood and what still needs to be done to achieve effective and more integrated flood risk management. The review highlights considerable improvements on many levels, in particular (1) an increased consideration of flood hazards in spatial planning and urban development, (2) comprehensive property-level mitigation and preparedness measures, (3) more effective flood warnings and improved coordination of disaster response, and (4) a more targeted maintenance of flood defense systems. In 2013, this led to more effective flood management and to a reduction of damage. Nevertheless, important aspects remain unclear and need to be clarified. This particularly holds for balanced and coordinated strategies for reducing and overcoming the impacts of flooding in large catchments, cross-border and interdisciplinary cooperation, the role of the general public in the different phases of flood risk management, as well as a transparent risk transfer system. Recurring flood events reveal that flood risk management is a continuous task. Hence, risk drivers, such as climate change, land-use changes, economic developments, or demographic change and the resultant risks must be investigated at regular intervals, and risk reduction strategies and processes must be reassessed as well as adapted and implemented in a dialogue with all stakeholders.
Widespread flooding in June 2013 caused damage costs of €6 to 8 billion in Germany, and awoke many memories of the floods in August 2002, which resulted in total damage of €11.6 billion and hence was the most expensive natural hazard event in Germany up to now. The event of 2002 does, however, also mark a reorientation toward an integrated flood risk management system in Germany. Therefore, the flood of 2013 offered the opportunity to review how the measures that politics, administration, and civil society have implemented since 2002 helped to cope with the flood and what still needs to be done to achieve effective and more integrated flood risk management. The review highlights considerable improvements on many levels, in particular (1) an increased consideration of flood hazards in spatial planning and urban development, (2) comprehensive property-level mitigation and preparedness measures, (3) more effective flood warnings and improved coordination of disaster response, and (4) a more targeted maintenance of flood defense systems. In 2013, this led to more effective flood management and to a reduction of damage. Nevertheless, important aspects remain unclear and need to be clarified. This particularly holds for balanced and coordinated strategies for reducing and overcoming the impacts of flooding in large catchments, cross-border and interdisciplinary cooperation, the role of the general public in the different phases of flood risk management, as well as a transparent risk transfer system. Recurring flood events reveal that flood risk management is a continuous task. Hence, risk drivers, such as climate change, land-use changes, economic developments, or demographic change and the resultant risks must be investigated at regular intervals, and risk reduction strategies and processes must be reassessed as well as adapted and implemented in a dialogue with all stakeholders.
In June 2013, widespread flooding and consequent damage and losses occurred in Central Europe, especially in Germany. This paper explores what data are available to investigate the adverse impacts of the event, what kind of information can be retrieved from these data and how well data and information fulfil requirements that were recently proposed for disaster reporting on the European and international levels. In accordance with the European Floods Directive (2007/60/EC), impacts on human health, economic activities (and assets), cultural heritage and the environment are described on the national and sub-national scale. Information from governmental reports is complemented by communications on traffic disruptions and surveys of flood-affected residents and companies.
Overall, the impacts of the flood event in 2013 were manifold. The study reveals that flood-affected residents suffered from a large range of impacts, among which mental health and supply problems were perceived more seriously than financial losses. The most frequent damage type among affected companies was business interruption. This demonstrates that the current scientific focus on direct (financial) damage is insufficient to describe the overall impacts and severity of flood events.
The case further demonstrates that procedures and standards for impact data collection in Germany are widely missing. Present impact data in Germany are fragmentary, heterogeneous, incomplete and difficult to access. In order to fulfil, for example, the monitoring and reporting requirements of the Sendai Framework for Disaster Risk Reduction 2015–2030 that was adopted in March 2015 in Sendai, Japan, more efforts on impact data collection are needed.
In June 2013, widespread flooding and consequent damage and losses occurred in Central Europe, especially in Germany. This paper explores what data are available to investigate the adverse impacts of the event, what kind of information can be retrieved from these data and how well data and information fulfil requirements that were recently proposed for disaster reporting on the European and international levels. In accordance with the European Floods Directive (2007/60/EC), impacts on human health, economic activities (and assets), cultural heritage and the environment are described on the national and sub-national scale. Information from governmental reports is complemented by communications on traffic disruptions and surveys of flood-affected residents and companies.
Overall, the impacts of the flood event in 2013 were manifold. The study reveals that flood-affected residents suffered from a large range of impacts, among which mental health and supply problems were perceived more seriously than financial losses. The most frequent damage type among affected companies was business interruption. This demonstrates that the current scientific focus on direct (financial) damage is insufficient to describe the overall impacts and severity of flood events.
The case further demonstrates that procedures and standards for impact data collection in Germany are widely missing. Present impact data in Germany are fragmentary, heterogeneous, incomplete and difficult to access. In order to fulfil, for example, the monitoring and reporting requirements of the Sendai Framework for Disaster Risk Reduction 2015–2030 that was adopted in March 2015 in Sendai, Japan, more efforts on impact data collection are needed.
Das Hochwasser im Juni 2013
(2015)
Rivers have always flooded their floodplains. Over 2.5 billion people worldwide have been affected by flooding in recent decades. The economic damage is also considerable, averaging 100 billion US dollars per year. There is no doubt that damage and other negative effects of floods can be avoided. However, this has a price: financially and politically. Costs and benefits can be estimated through risk assessments. Questions about the location and frequency of floods, about the objects that could be affected and their vulnerability are of importance for flood risk managers, insurance companies and politicians. Thus, both variables and factors from the fields of hydrology and sociol-economics play a role with multi-layered connections. One example are dikes along a river, which on the one hand contain floods, but on the other hand, by narrowing the natural floodplains, accelerate the flood discharge and increase the danger of flooding for the residents downstream. Such larger connections must be included in the assessment of flood risk. However, in current procedures this is accompanied by simplifying assumptions. Risk assessments are therefore fuzzy and associated with uncertainties.
This thesis investigates the benefits and possibilities of new data sources for improving flood risk assessment. New methods and models are developed, which take the mentioned interrelations better into account and also quantify the existing uncertainties of the model results, and thus enable statements about the reliability of risk estimates. For this purpose, data on flood events from various sources are collected and evaluated. This includes precipitation and flow records at measuring stations as well as for instance images from social media, which can help to delineate the flooded areas and estimate flood damage with location information. Machine learning methods have been successfully used to recognize and understand correlations between floods and impacts from a wide range of data and to develop improved models.
Risk models help to develop and evaluate strategies to reduce flood risk. These tools also provide advanced insights into the interplay of various factors and on the expected consequences of flooding. This work shows progress in terms of an improved assessment of flood risks by using diverse data from different sources with innovative methods as well as by the further development of models. Flood risk is variable due to economic and climatic changes, and other drivers of risk. In order to keep the knowledge about flood risks up-to-date, robust, efficient and adaptable methods as proposed in this thesis are of increasing importance.
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.
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.
Pluvial floods have caused severe damage to urban areas in recent years. With a projected increase in extreme precipitation as well as an ongoing urbanization, pluvial flood damage is expected to increase in the future. Therefore, further insights, especially on the adverse consequences of pluvial floods and their mitigation, are needed. To gain more knowledge, empirical damage data from three different pluvial flood events in Germany were collected through computer-aided telephone interviews. Pluvial flood awareness as well as flood experience were found to be low before the respective flood events. The level of private precaution increased considerably after all events, but is mainly focused on measures that are easy to implement. Lower inundation depths, smaller potential losses as compared with fluvial floods, as well as the fact that pluvial flooding may occur everywhere, are expected to cause a shift in damage mitigation from precaution to emergency response. However, an effective implementation of emergency measures was constrained by a low dissemination of early warnings in the study areas. Further improvements of early warning systems including dissemination as well as a rise in pluvial flood preparedness are important to reduce future pluvial flood damage.
Pluvial floods have caused severe damage to urban areas in recent years. With a projected increase in extreme precipitation as well as an ongoing urbanization, pluvial flood damage is expected to increase in the future. Therefore, further insights, especially on the adverse consequences of pluvial floods and their mitigation, are needed. To gain more knowledge, empirical damage data from three different pluvial flood events in Germany were collected through computer-aided telephone interviews. Pluvial flood awareness as well as flood experience were found to be low before the respective flood events. The level of private precaution increased considerably after all events, but is mainly focused on measures that are easy to implement. Lower inundation depths, smaller potential losses as compared with fluvial floods, as well as the fact that pluvial flooding may occur everywhere, are expected to cause a shift in damage mitigation from precaution to emergency response. However, an effective implementation of emergency measures was constrained by a low dissemination of early warnings in the study areas. Further improvements of early warning systems including dissemination as well as a rise in pluvial flood preparedness are important to reduce future pluvial flood damage.
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.
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.
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.
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.
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.
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.
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.
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.