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Compound natural hazards likeEl Ninoevents cause high damage to society, which to manage requires reliable risk assessments. Damage modelling is a prerequisite for quantitative risk estimations, yet many procedures still rely on expert knowledge, and empirical studies investigating damage from compound natural hazards hardly exist. A nationwide building survey in Peru after theEl Ninoevent 2017 - which caused intense rainfall, ponding water, flash floods and landslides - enables us to apply data-mining methods for statistical groundwork, using explanatory features generated from remote sensing products and open data. We separate regions of different dominant characteristics through unsupervised clustering, and investigate feature importance rankings for classifying damage via supervised machine learning. Besides the expected effect of precipitation, the classification algorithms select the topographic wetness index as most important feature, especially in low elevation areas. The slope length and steepness factor ranks high for mountains and canyons. Partial dependence plots further hint at amplified vulnerability in rural areas. An example of an empirical damage probability map, developed with a random forest model, is provided to demonstrate the technical feasibility.
Flood risk management in Europe and worldwide is not static but constantly in a state of flux. There has been a trend towards more integrated flood risk management in many countries. However, the initial situation and the pace and direction of change is very different in the various countries. In this paper, we will present a conceptual framework that seeks to explain why countries opt for different flood risk management portfolios. The developed framework utilises insights from a range of policy science concepts in an integrated way and considers, among others, factors such as geographical characteristics, the experience with flood disasters, as well as human behavioural aspects.
One common approach to cope with floods is the implementation of structural flood protection measures, such as levees or flood-control reservoirs, which substantially reduce the probability of flooding at the time of implementation. Numerous scholars have problematized this approach. They have shown that increasing the levels of flood protection can attract more settlements and high-value assets in the areas protected by the new measures. Other studies have explored how structural measures can generate a sense of complacency, which can act to reduce preparedness. These paradoxical risk changes have been described as "levee effect", "safe development paradox" or "safety dilemma". In this commentary, we briefly review this phenomenon by critically analysing the intended benefits and unintended effects of structural flood protection, and then we propose an interdisciplinary research agenda to uncover these paradoxical dynamics of risk.
Floods frequently cause substantial economic and human losses, particularly in developing countries. For the development of sound flood risk management schemes that reduce flood consequences, detailed insights into the different components of the flood risk management cycle, such as preparedness, response, flood impact analyses and recovery, are needed. However, such detailed insights are often lacking: commonly, only (aggregated) data on direct flood damage are available. Other damage categories such as losses owing to the disruption of production processes are usually not considered, resulting in incomplete risk assessments and possibly inappropriate recommendations for risk management. In this paper, data from 858 face-to-face interviews among flood-prone households and small businesses in Can Tho city in the Vietnamese Mekong Delta are presented to gain better insights into the damage caused by the 2011 flood event and its management by households and businesses.
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.
After the flood in 2002, the level of private precautions taken increased considerably. One contributing factor is the fact that, in general, a larger proportion of people knew that they were at risk of flooding. The best level of precaution was found before the flood events in 2006 and 2011. The main reason for this might be that residents had more experience with flooding than residents affected in 2005 or 2010. Yet, overall, flood experience and knowledge did not necessarily result in building retrofitting or flood-proofing measures, which are considered as mitigating damages most effectively. Hence, investments still need to be stimulated in order to reduce future damage more efficiently.
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.
Damage due to floods has increased during the last few decades, and further increases are expected in several regions due to climate change and growing vulnerability. To address the projected increase in flood risk, a combination of structural and non-structural flood risk mitigation measures is considered as a promising adaptation strategy. Such a combination takes into account that flood defence systems may fail, and prepares for unexpected crisis situations via land-use planning and private damage reduction, e.g. via building precautionary measures, and disaster response. However, knowledge about damage-reducing measures is scarce and often fragmented since based on case studies. For instance, it is believed that private precautionary measures, like shielding with water shutters or building fortification, are especially effective in areas with frequent flood events and low flood water levels. However, some of these measures showed a significant damage-reducing effect also during the extreme flood event in 2002 in Germany. This review analyses potentials of land-use planning and private flood precautionary measures as components of adaptation strategies for global change. Focus is on their implementation, their damage-reducing effects and their potential contribution to address projected changes in flood risk, particularly in developed countries.
Adaptation to flood risk
(2017)
As flood impacts are increasing in large parts of the world, understanding the primary drivers of changes in risk is essential for effective adaptation. To gain more knowledge on the basis of empirical case studies, we analyze eight paired floods, that is, consecutive flood events that occurred in the same region, with the second flood causing significantly lower damage. These success stories of risk reduction were selected across different socioeconomic and hydro-climatic contexts. The potential of societies to adapt is uncovered by describing triggered societal changes, as well as formal measures and spontaneous processes that reduced flood risk. This novel approach has the potential to build the basis for an international data collection and analysis effort to better understand and attribute changes in risk due to hydrological extremes in the framework of the IAHSs Panta Rhei initiative. Across all case studies, we find that lower damage caused by the second event was mainly due to significant reductions in vulnerability, for example, via raised risk awareness, preparedness, and improvements of organizational emergency management. Thus, vulnerability reduction plays an essential role for successful adaptation. Our work shows that there is a high potential to adapt, but there remains the challenge to stimulate measures that reduce vulnerability and risk in periods in which extreme events do not occur.
Flood warning systems are longstanding success stories with respect to protecting human life, but monetary losses continue to grow. Knowledge on the effectiveness of flood early warning in reducing monetary losses is scarce, especially at the individual level. To gain more knowledge in this area, we analyze a dataset that is unique with respect to detailed information on warning reception and monetary losses at the property level and with respect to amount of data available. The dataset contains 4,468 loss cases from six flood events in Germany. These floods occurred between 2002 and 2013. The data from each event were collected by computer-aided telephone interviews in four surveys following a repeated cross-sectional design. We quantitatively reveal that flood early warning is only effective in reducing monetary losses when people know what to do when they receive the warning. We also show that particularly long-term preparedness is associated with people knowing what to do when they receive a warning. Thus, risk communication, training, and (financial) support for private preparedness are effective in mitigating flood losses in two ways: precautionary measures and more effective emergency responses.
Flood damage can be mitigated if the parties at risk are reached by flood warnings and if they know how to react appropriately. To gain more knowledge about warning reception and emergency response of private households and companies, surveys were undertaken after the August 2002 and the June 2013 floods in Germany. Despite pronounced regional differences, the results show a clear overall picture: in 2002, early warnings did not work well; e.g. many households (27 %) and companies (45 %) stated that they had not received any flood warnings. Additionally, the preparedness of private households and companies was low in 2002, mainly due to a lack of flood experience. After the 2002 flood, many initiatives were launched and investments undertaken to improve flood risk management, including early warnings and an emergency response in Germany. In 2013, only a small share of the affected households (5 %) and companies (3 %) were not reached by any warnings. Additionally, private households and companies were better prepared. For instance, the share of companies which have an emergency plan in place has increased from 10% in 2002 to 34% in 2013. However, there is still room for improvement, which needs to be triggered mainly by effective risk and emergency communication. The challenge is to continuously maintain and advance an integrated early warning and emergency response system even without the occurrence of extreme floods.
Costing natural hazards
(2014)
From precipitation to damage
(2018)
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.
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.
Auswirkungen und Schäden
(2015)
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.
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.
Hierarchical Bayesian Approach for Modeling Spatiotemporal Variability in Flood Damage Processes
(2019)
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.
Flood loss modeling is a central component of flood risk analysis. Conventionally, this involves univariable and deterministic stage-damage functions. Recent advancements in the field promote the use of multivariable and probabilistic loss models, which consider variables beyond inundation depth and account for prediction uncertainty. Although companies contribute significantly to total loss figures, novel modeling approaches for companies are lacking. Scarce data and the heterogeneity among companies impede the development of company flood loss models. We present three multivariable flood loss models for companies from the manufacturing, commercial, financial, and service sector that intrinsically quantify prediction uncertainty. Based on object-level loss data (n = 1,306), we comparatively evaluate the predictive capacity of Bayesian networks, Bayesian regression, and random forest in relation to deterministic and probabilistic stage-damage functions, serving as benchmarks. The company loss data stem from four postevent surveys in Germany between 2002 and 2013 and include information on flood intensity, company characteristics, emergency response, private precaution, and resulting loss to building, equipment, and goods and stock. We find that the multivariable probabilistic models successfully identify and reproduce essential relationships of flood damage processes in the data. The assessment of model skill focuses on the precision of the probabilistic predictions and reveals that the candidate models outperform the stage-damage functions, while differences among the proposed models are negligible. Although the combination of multivariable and probabilistic loss estimation improves predictive accuracy over the entire data set, wide predictive distributions stress the necessity for the quantification of uncertainty.
We investigate the usefulness of complex flood damage models for predicting relative damage to residential buildings in a spatial and temporal transfer context. We apply eight different flood damage models to predict relative building damage for five historic flood events in two different regions of Germany. Model complexity is measured in terms of the number of explanatory variables which varies from 1 variable up to 10 variables which are singled out from 28 candidate variables. Model validation is based on empirical damage data, whereas observation uncertainty is taken into consideration. The comparison of model predictive performance shows that additional explanatory variables besides the water depth improve the predictive capability in a spatial and temporal transfer context, i.e., when the models are transferred to different regions and different flood events. Concerning the trade-off between predictive capability and reliability the model structure seem more important than the number of explanatory variables. Among the models considered, the reliability of Bayesian network-based predictions in space-time transfer is larger than for the remaining models, and the uncertainties associated with damage predictions are reflected more completely.
Hydrometeorological hazards caused losses of approximately 110 billion U.S. Dollars in 2016 worldwide. Current damage estimations do not consider the uncertainties in a comprehensive way, and they are not consistent between spatial scales. Aggregated land use data are used at larger spatial scales, although detailed exposure data at the object level, such as openstreetmap.org, is becoming increasingly available across the globe.We present a probabilistic approach for object-based damage estimation which represents uncertainties and is fully scalable in space. The approach is applied and validated to company damage from the flood of 2013 in Germany. Damage estimates are more accurate compared to damage models using land use data, and the estimation works reliably at all spatial scales. Therefore, it can as well be used for pre-event analysis and risk assessments. This method takes hydrometeorological damage estimation and risk assessments to the next level, making damage estimates and their uncertainties fully scalable in space, from object to country level, and enabling the exploitation of new exposure data.
Hydrometeorological hazards caused losses of approximately 110 billion U.S. Dollars in 2016 worldwide. Current damage estimations do not consider the uncertainties in a comprehensive way, and they are not consistent between spatial scales. Aggregated land use data are used at larger spatial scales, although detailed exposure data at the object level, such as openstreetmap.org, is becoming increasingly available across the globe.We present a probabilistic approach for object-based damage estimation which represents uncertainties and is fully scalable in space. The approach is applied and validated to company damage from the flood of 2013 in Germany. Damage estimates are more accurate compared to damage models using land use data, and the estimation works reliably at all spatial scales. Therefore, it can as well be used for pre-event analysis and risk assessments. This method takes hydrometeorological damage estimation and risk assessments to the next level, making damage estimates and their uncertainties fully scalable in space, from object to country level, and enabling the exploitation of new exposure data.
Reliable flood risk analyses, including the estimation of damage, are an important prerequisite for efficient risk management. However, not much is known about flood damage processes affecting companies. Thus, we conduct a flood damage assessment of companies in Germany with regard to two aspects. First, we identify relevant damage-influencing variables. Second, we assess the prediction performance of the developed damage models with respect to the gain by using an increasing amount of training data and a sector-specific evaluation of the data. Random forests are trained with data from two postevent surveys after flood events occurring in the years 2002 and 2013. For a sector-specific consideration, the data set is split into four subsets corresponding to the manufacturing, commercial, financial, and service sectors. Further, separate models are derived for three different company assets: buildings, equipment, and goods and stock. Calculated variable importance values reveal different variable sets relevant for the damage estimation, indicating significant differences in the damage process for various company sectors and assets. With an increasing number of data used to build the models, prediction errors decrease. Yet the effect is rather small and seems to saturate for a data set size of several hundred observations. In contrast, the prediction improvement achieved by a sector-specific consideration is more distinct, especially for damage to equipment and goods and stock. Consequently, sector-specific data acquisition and a consideration of sector-specific company characteristics in future flood damage assessments is expected to improve the model performance more than a mere increase in data.
A wide variety of processes controls the time of occurrence, duration, extent, and severity of river floods. Classifying flood events by their causative processes may assist in enhancing the accuracy of local and regional flood frequency estimates and support the detection and interpretation of any changes in flood occurrence and magnitudes. This paper provides a critical review of existing causative classifications of instrumental and preinstrumental series of flood events, discusses their validity and applications, and identifies opportunities for moving toward more comprehensive approaches. So far no unified definition of causative mechanisms of flood events exists. Existing frameworks for classification of instrumental and preinstrumental series of flood events adopt different perspectives: hydroclimatic (large-scale circulation patterns and atmospheric state at the time of the event), hydrological (catchment scale precipitation patterns and antecedent catchment state), and hydrograph-based (indirectly considering generating mechanisms through their effects on hydrograph characteristics). All of these approaches intend to capture the flood generating mechanisms and are useful for characterizing the flood processes at various spatial and temporal scales. However, uncertainty analyses with respect to indicators, classification methods, and data to assess the robustness of the classification are rarely performed which limits the transferability across different geographic regions. It is argued that more rigorous testing is needed. There are opportunities for extending classification methods to include indicators of space-time dynamics of rainfall, antecedent wetness, and routing effects, which will make the classification schemes even more useful for understanding and estimating floods. This article is categorized under: Science of Water > Water Extremes Science of Water > Hydrological Processes Science of Water > Methods
Das Hochwasser im Juni 2013
(2015)
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.
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.
Widespread flooding in June 2013 caused damage costs of (sic)6 to 8 billion in Germany, and awoke many memories of the floods in August 2002, which resulted in total damage of (sic)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.
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.
Different upper tail indicators exist to characterize heavy tail phenomena, but no comparative study has been carried out so far. We evaluate the shape parameter (GEV), obesity index, Gini index and upper tail ratio (UTR) against a novel benchmark of tail heaviness - the surprise factor. Sensitivity analyses to sample size and changes in scale-to-location ratio are carried out in bootstrap experiments. The UTR replicates the surprise factor best but is most uncertain and only comparable between records of similar length. For samples with symmetric Lorenz curves, shape parameter, obesity and Gini indices provide consistent indications. For asymmetric Lorenz curves, however, the first two tend to overestimate, whereas Gini index tends to underestimate tail heaviness. We suggest the use of a combination of shape parameter, obesity and Gini index to characterize tail heaviness. These indicators should be supported with calculation of the Lorenz asymmetry coefficients and interpreted with caution.