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Megafloods that far exceed previously observed records often take citizens and experts by surprise, resulting in extremely severe damage and loss of life. Existing methods based on local and regional information rarely go beyond national borders and cannot predict these floods well because of limited data on megafloods, and because flood generation processes of extremes differ from those of smaller, more frequently observed events. Here we analyse river discharge observations from over 8,000 gauging stations across Europe and show that recent megafloods could have been anticipated from those previously observed in other places in Europe. Almost all observed megafloods (95.5%) fall within the envelope values estimated from previous floods in other similar places on the continent, implying that local surprises are not surprising at the continental scale. This holds also for older events, indicating that megafloods have not changed much in time relative to their spatial variability. The underlying concept of the study is that catchments with similar flood generation processes produce similar outliers. It is thus essential to transcend national boundaries and learn from other places across the continent to avoid surprises and save lives.

We analyze trends in compound flooding resulting from high coastal water levels (HCWLs) and peak river discharge over northwestern Europe during 1901–2014. Compound peak discharge associated with 37 stream gauges with at least 70 years of record availability near the North and Baltic Sea coasts is used. Compound flooding is assessed using a newly developed index, compound hazard ratio, that compares the severity of river flooding associated with HCWL with the at‐site, T‐year (a flood with 1/Tchance of being exceeded in any given year) fluvial peak discharge. Our findings suggest a spatially coherent pattern in the dependence between HCWL and river peaks and in compound flood magnitudes and frequency. For higher return levels, we find upward trends in compound hazard ratio frequency at midlatitudes (gauges from 47°N to 60°N) and downward trends along the high latitude (>60°N) regions of northwestern Europe. Compound floods in delta areas, that is, the co‐occurrence of high coastal water levels (HCWLs) and high river discharge, are a particular challenge for disaster management. Such events are caused by two distinct mechanisms: (1) HCWLs may affect river flows and water levels by backwater effects or by reversing the seaward flow of rivers, particularly in regions with elevation less than 10 m in northwestern Europe. (2) The correlation between HCWL and river flow peaks may also stem from a common meteorological driver. Severe storm periods may be associated with high winds leading to storm surges, and at the same time with high precipitation followed by inland flooding. Understanding the historical trends in compound flooding, owing to changes in relative sea levels, in river flooding and in the dependence between these two drivers, is essential for projecting future changes and disaster management. The risk assessment frameworks are often limited to assessing flood risk from a single driver only. We present a new approach to assess compound flood severity resulting from extreme coastal water level and peak river discharge. We find upward trends in compound flooding for midlatitude regions and downward trends for high latitudes in northwestern Europe. We analyze space‐time trends in compound flooding in northwestern Europe during 1901–2014 using compound hazard ratio indexWe show a spatially coherent pattern in compound flood magnitude and frequency in middle and high latitudes in northwestern EuropeOur analysis reveals upward trends in compound flood frequency for larger return levels at stream gauges across middle latitudes

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. Hierarchical Bayesian model (HBM) captures spatiotemporal variability in flood damage processes better than established depth‐damage functionsRegion‐ and event‐specific characteristics that explain the variability in damage processes improve spatiotemporal transfer of the HBMThe HBM is tested for transferability using empirical damage data from six events in the Elbe, Danube, Rhine and Oder catchments in Germany

Climate change has led to concerns about increasing river floods resulting from the greater water-holding capacity of a warmer atmosphere1. These concerns are reinforced by evidence of increasing economic losses associated with flooding in many parts of the world, including Europe2. Any changes in river floods would have lasting implications for the design of flood protection measures and flood risk zoning. However, existing studies have been unable to identify a consistent continental-scale climatic-change signal in flood discharge observations in Europe3, because of the limited spatial coverage and number of hydrometric stations. Here we demonstrate clear regional patterns of both increases and decreases in observed river flood discharges in the past five decades in Europe, which are manifestations of a changing climate. Our results—arising from the most complete database of European flooding so far—suggest that: increasing autumn and winter rainfall has resulted in increasing floods in northwestern Europe; decreasing precipitation and increasing evaporation have led to decreasing floods in medium and large catchments in southern Europe; and decreasing snow cover and snowmelt, resulting from warmer temperatures, have led to decreasing floods in eastern Europe. Regional flood discharge trends in Europe range from an increase of about 11 per cent per decade to a decrease of 23 per cent. Notwithstanding the spatial and temporal heterogeneity of the observational record, the flood changes identified here are broadly consistent with climate model projections for the next century4,5, suggesting that climate-driven changes are already happening and supporting calls for the consideration of climate change in flood risk management. Analysis of a comprehensive European flood dataset reveals regional changes in river flood discharges in the past five decades that are consistent with models suggesting that climate-driven changes are already happening.

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. This manuscript presents the first application of recurrence plots in catchment hydrologyCRP is used as a novel method for quantifying comprehensive hydrograph similarity based on runoff dynamicsTime delay embedded phase space trajectory allows to consider relationships between magnitudes of different point in time

Reducing flood risk through disaster planning and risk management requires accurate estimates of exposure, damage, casualties, and environmental impacts. Models can provide such information; however, computational or data constraints often lead to the construction of such models by aggregating high‐resolution flood hazard grids to a coarser resolution, the effect of which is poorly understood. Through the application of a novel spatial classification framework, we derive closed‐form solutions for the location (e.g., flood margins) and direction of bias from flood grid aggregation independent of any study region. These solutions show bias of some key metric will always be present in regions with marginal inundation; for example, inundation area will be positively biased when water depth grids are aggregated and volume will be negatively biased when water surface elevation grids are aggregated through averaging. In a separate computational analysis, we employ the same framework to a 2018 flood and successfully reproduce the findings of our study‐region‐independent derivation. Extending the investigation to the exposure of buildings, we find regions with marginal inundation are an order of magnitude more sensitive to aggregation errors, highlighting the importance of understanding such artifacts for flood risk modelers. Of the two aggregation routines considered, averaging water surface elevation grids better preserved flood depths at buildings than averaging of water depth grids. This work provides insight into, and recommendations for, aggregating grids used by flood risk models. Through a novel framework, we show analytically that hazard grid aggregation leads to bias of key metrics independent of any study regionThis aggregation is shown to always positively bias inundation area when water depth grids are aggregatedFor example, aggregating from 1 to 512 m resolution resulted in a doubling of the inundated area for a 2018 flood in Canada Through a novel framework, we show analytically that hazard grid aggregation leads to bias of key metrics independent of any study region This aggregation is shown to always positively bias inundation area when water depth grids are aggregated For example, aggregating from 1 to 512 m resolution resulted in a doubling of the inundated area for a 2018 flood in Canada

•Compound pluvial and fluvial flooding (CPFF) is little studied.•CPFF is more hazardous compared to individual flood types occurring in isolation.•Occurrence probabilities of compound flooding and its temporal changes are largely unknown.•Adding damage models would allow to go from CPFF hazard to risk statements.

This study refines the method for calibrating a glacio‐hydrological model based on Hydrograph Partitioning Curves (HPCs), and evaluates its value in comparison to multidata set optimization approaches which use glacier mass balance, satellite snow cover images, and discharge. The HPCs are extracted from the observed flow hydrograph using catchment precipitation and temperature gradients. They indicate the periods when the various runoff processes, such as glacier melt or snow melt, dominate the basin hydrograph. The annual cumulative curve of the difference between average daily temperature and melt threshold temperature over the basin, as well as the annual cumulative curve of average daily snowfall on the glacierized areas are used to identify the starting and end dates of snow and glacier ablation periods. Model parameters characterizing different runoff processes are calibrated on different HPCs in a stepwise and iterative way. Results show that the HPC‐based method (1) delivers model‐internal consistency comparably to the tri‐data set calibration method; (2) improves the stability of calibrated parameter values across various calibration periods; and (3) estimates the contributions of runoff components similarly to the tri‐data set calibration method. Our findings indicate the potential of the HPC‐based approach as an alternative for hydrological model calibration in glacierized basins where other calibration data sets than discharge are often not available or very costly to obtain. The HPC‐based method (1) delivers model‐internal consistency comparably to the tri‐data set calibration methodThe HPC‐based method improves the stability of calibrated parameter values across various calibration periodsThe HPC‐based method estimates the contributions of runoff components similarly to the tri‐data set calibration method

For attributing hydrological changes to anthropogenic climate change, catchment models are driven by climate model output. A widespread approach to bridge the spatial gap between global climate and hydrological catchment models is to use a weather generator conditioned on weather patterns (WPs). This approach assumes that changes in local climate are characterized by between‐type changes of patterns. In this study we test this assumption by analyzing a previously developed WP classification for the Rhine basin, which is based on dynamic and thermodynamic variables. We quantify changes in pattern characteristics and associated climatic properties. The amount of between‐ and within‐type changes is investigated by comparing observed trends to trends resulting solely from WP occurrence. To overcome uncertainties in trend detection resulting from the selected time period, all possible periods in 1901–2010 with a minimum length of 31 years are analyzed. Increasing frequency is found for some patterns associated with high precipitation, although the trend sign highly depends on the considered period. Trends and interannual variations of WP frequencies are related to the long‐term variability of large‐scale circulation modes. Long‐term WP internal warming is evident for summer patterns and enhanced warming for spring/autumn patterns since the 1970s. Observed trends in temperature and partly in precipitation are mainly associated with frequency changes of specific WPs, but some amount of within‐type changes remains. The classification can be used for downscaling of past changes considering this limitation, but the inclusion of thermodynamic variables into the classification impedes the downscaling of future climate projections. Analysed large (490 stations) and long(111 years) data set for Rhine catchmentStationarity of weather pattern characteristics using multiple periods has been tested for the purpose of downscalingChanges in temperature and precipitation are mainly related to changes in the occurrence of patterns

The June 2013 flood was the most severe large-scale flood in Germany, at least for the last 6 decades for which a hydrological flood severity has been calculated. Many gauges along the Elbe and Danube rivers showed record water levels. The flood severity index, a measure which combines magnitude and spatial extension, is almost twice as large as the index of the August 2002 flood which has been the most expensive natural disaster for Germany to date. The enormous hydrological severity was caused by widespread and intense rainfall in combination with wet catchments due to exceptionally high rainfall in the month preceding the event. Preliminary damage estimates are in the order of 8.7 to 12 billion €. Hence, the losses seem to be lower compared to the 2002 flood (11.8 billion € for Germany). Although detailed analyses have not been performed to date, it can be assumed that the investments and improvements in flood risk management since 2002 have reduced the flood risk and prevented higher damage.

Most of previous assessments of hydrologic model performance are fragmented, based on small number of catchments, different methods or time periods and do not link the results to landscape or climate characteristics. This study uses large‐sample hydrology to identify major catchment controls on daily runoff simulations. It is based on a conceptual lumped hydrological model (GR6J), a collection of 29 catchment characteristics, a multinational set of 1103 catchments located in Austria, France, and Germany and four runoff model efficiency criteria. Two analyses are conducted to assess how features and criteria are linked: (i) a one‐dimensional analysis based on the Kruskal‐Wallis test and (ii) a multidimensional analysis based on regression trees and investigating the interplay between features. The catchment features most affecting model performance are the flashiness of precipitation and streamflow (computed as the ratio of absolute day‐to‐day fluctuations by the total amount in a year), the seasonality of evaporation, the catchment area, and the catchment aridity. Nonflashy, nonseasonal, large, and nonarid catchments show the best performance for all the tested criteria. We argue that this higher performance is due to fewer nonlinear responses (higher correlation between precipitation and streamflow) and lower input and output variability for such catchments. Finally, we show that, compared to national sets, multinational sets increase results transferability because they explore a wider range of hydroclimatic conditions. The article uses large sample hydrology to identify catchment controls on daily runoff simulations from the GR6J lumped modelNonflashy, nonseasonal, large, and nonarid catchments show the best performance for four uncorrelated efficiency criteriaThe study underline the value of multinational dataset to increase results robustness

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. Different damage‐influencing variables are identified for the various company sectors and assetsPrediction accuracies for random forests improve slightly with an increasing amount of training dataA sector‐specific consideration of flood damage is more effective than an increase in training data

Trends in flood magnitudes vary across the conterminous USA (CONUS). There have been attempts to identify what controls these regionally varying trends, but these attempts were limited to certain—for example, climatic—variables or to smaller regions, using different methods and datasets each time. Here we attribute the trends in annual maximum streamflow for 4,390 gauging stations across the CONUS in the period 1960–2010, while using a novel combination of methods and an unprecedented variety of potential controlling variables to allow large‐scale comparisons and minimize biases. Using process‐based flood classification and complex networks, we find 10 distinct clusters of catchments with similar flood behavior. We compile a set of 31 hydro‐climatological and land use variables as predictors for 10 separate Random Forest models, allowing us to find the main controls the flood magnitude trends for each cluster. By using Accumulated Local Effect plots, we can understand how these controls influence the trends in the flood magnitude. We show that hydro‐climatologic changes and land use are of similar importance for flood magnitude trends across the CONUS. Static land use variables are more important than their trends, suggesting that land use is able to attenuate (forested areas) or amplify (urbanized areas) the effects of climatic changes on flood magnitudes. For some variables, we find opposing effects in different regions, showing that flood trend controls are highly dependent on regional characteristics and that our novel approach is necessary to attribute flood magnitude trends reliably at the continental scale while maintaining sensitivity to regional controls. A wide variety of controls are necessary to explain flood magnitude trends across the United States between 1960 and 2010Climatic changes and land cover conditions are of similar importance for flood magnitude trends at the regional scaleControls on flood trends can have highly nonlinear effects and can have opposing effects in different hydro‐climatological subregions A wide variety of controls are necessary to explain flood magnitude trends across the United States between 1960 and 2010 Climatic changes and land cover conditions are of similar importance for flood magnitude trends at the regional scale Controls on flood trends can have highly nonlinear effects and can have opposing effects in different hydro‐climatological subregions

Changes in the river flood regime may be due to atmospheric processes (e.g., increasing precipitation), catchment processes (e.g., soil compaction associated with land use change), and river system processes (e.g., loss of retention volume in the floodplains). This paper proposes a new framework for attributing flood changes to these drivers based on a regional analysis. We exploit the scaling characteristics (i.e., fingerprints) with catchment area of the effects of the drivers on flood changes. The estimation of their relative contributions is framed in Bayesian terms. Analysis of a synthetic, controlled case suggests that the accuracy of the regional attribution increases with increasing number of sites and record lengths, decreases with increasing regional heterogeneity, increases with increasing difference of the scaling fingerprints, and decreases with an increase of their prior uncertainty. The applicability of the framework is illustrated for a case study set in Austria, where positive flood trends have been observed at many sites in the past decades. The individual scaling fingerprints related to the atmospheric, catchment, and river system processes are estimated from rainfall data and simple hydrological modeling. Although the distributions of the contributions are rather wide, the attribution identifies precipitation change as the main driver of flood change in the study region. Overall, it is suggested that the extension from local attribution to a regional framework, including multiple drivers and explicit estimation of uncertainty, could constitute a similar shift in flood change attribution as the extension from local to regional flood frequency analysis. A new framework for consistently attributing flood changes in a regionSpatial scaling of flood changes allows attribution of multiple driversBayesian framing allows estimation of the attribution uncertainties

Statistical distributions of flood peak discharge often show heavy tail behavior, that is, extreme floods are more likely to occur than would be predicted by commonly used distributions that have exponential asymptotic behavior. This heavy tail behavior may surprise flood managers and citizens, as human intuition tends to expect light tail behavior, and the heaviness of the tails is very difficult to predict, which may lead to unnecessarily high flood damage. Despite its high importance, the literature on the heavy tail behavior of flood distributions is rather fragmented. In this review, we provide a coherent overview of the processes causing heavy flood tails and the implications for science and practice. Specifically, we propose nine hypotheses on the mechanisms causing heavy tails in flood peak distributions related to processes in the atmosphere, the catchment, and the river system. We then discuss to which extent the current knowledge supports or contradicts these hypotheses. We also discuss the statistical conditions for the emergence of heavy tail behavior based on derived distribution theory and relate them to the hypotheses and flood generation mechanisms. We review the degree to which the heaviness of the tails can be predicted from process knowledge and data. Finally, we recommend further research toward testing the hypotheses and improving the prediction of heavy tails. Statistical distributions are used to estimate the probability of flood peaks, which in turn is needed for risk management and the design of flood protection. Flood peak distributions often show heavy tail behavior, that is, extreme floods are more likely to occur than would be predicted by commonly used distributions that have exponential asymptotic (light tailed behavior). This heavy tail behavior may surprise flood managers and citizens, as human intuition tends to expect light tail behavior. In this review, we summarize the knowledge about the causes of heavy flood tails. To this end, we discuss the flood generation processes in the atmosphere, catchment, and river system, that tend to generate heavy‐tailed flood peak distributions. Heavy tail behavior of flood peak distributions may lead to surprise and high flood damageWe propose nine hypotheses on the mechanisms causing heavy tails in flood peak distributionsWe review to which extent the current knowledge supports or contradicts these hypotheses Heavy tail behavior of flood peak distributions may lead to surprise and high flood damage We propose nine hypotheses on the mechanisms causing heavy tails in flood peak distributions We review to which extent the current knowledge supports or contradicts these hypotheses

In some catchments, the distribution of annual maximum streamflow shows heavy tail behavior, meaning the occurrence probability of extreme events is higher than if the upper tail decayed exponentially. Neglecting heavy tail behavior can lead to an underestimation of the likelihood of extreme floods and the associated risk. Partly contradictory results regarding the controls of heavy tail behavior exist in the literature and the knowledge is still very dispersed and limited. To better understand the drivers, we analyze the upper tail behavior and its controls for 480 catchments in Germany and Austria over a period of more than 50 years. The catchments span from quickly reacting mountain catchments to large lowland catchments, allowing for general conclusions. We compile a wide range of event and catchment characteristics and investigate their association with an indicator of the tail heaviness of flood distributions, namely the shape parameter of the GEV distribution. Following univariate analyses of these characteristics, along with an evaluation of different aggregations of event characteristics, multiple linear regression models, as well as random forests, are constructed. A novel slope indicator, which represents the relation between the return period of flood peaks and event characteristics, captures the controls of heavy tails best. Variables describing the catchment response are found to dominate the heavy tail behavior, followed by event precipitation, flood seasonality, and catchment size. The pre‐event moisture state in a catchment has no relevant impact on the tail heaviness even though it does influence flood magnitudes. For each river catchment, we can estimate how likely it is that floods of certain magnitudes occur in that river. This is called a probability distribution. In some rivers, the occurrence of extreme floods is more likely than in others – their probability distribution decays slower and has a so‐called heavy tail. Here, we examine which factors lead to higher probabilities of extreme floods in some rivers compared to others. To this aim, we look at the annual maximum river flows of 480 rivers in Germany and Austria over a period of more than 50 years. As potential factors influencing the likelihood of extreme floods, we analyze characteristics describing the river catchments in general and characteristics describing specific flood events. Using modeling approaches, we find that how a catchment responds to heavy rainfall has the strongest effect on the probability of extremes. A catchment is more likely to experience extreme flooding, if the largest observed floods are characterized by (a) high runoff coefficients, meaning a large share of rainfall becomes direct streamflow and (b) short event time scales, meaning the floods are short but intense. The rainfall itself and the season in which floods usually occur also have an influence. Differences between large and small floods are described by a novel slope indicator and are decisive for the emergence of heavy tailsHeavy tail behavior is mainly controlled by the catchment response and event precipitation, and by flood seasonality and catchment areaPre‐event moisture state within a catchment has no noticeable impact on tail heaviness Differences between large and small floods are described by a novel slope indicator and are decisive for the emergence of heavy tails Heavy tail behavior is mainly controlled by the catchment response and event precipitation, and by flood seasonality and catchment area Pre‐event moisture state within a catchment has no noticeable impact on tail heaviness

The June 2013 flood was the most severe large-scale flood in Germany, at least for the last 6 decades for which a hydrological flood severity has been calculated. Many gauges along the Elbe and Danube rivers showed record water levels. The flood severity index, a measure which combines magnitude and spatial extension, is almost twice as large as the index of the August 2002 flood which has been the most expensive natural disaster for Germany to date. The enormous hydrological severity was caused by widespread and intense rainfall in combination with wet catchments due to exceptionally high rainfall in the month preceding the event. Preliminary damage estimates are in the order of 8.7 to 12 billion €. Hence, the losses seem to be lower compared to the 2002 flood (11.8 billion € for Germany). Although detailed analyses have not been performed to date, it can be assumed that the investments and improvements in flood risk management since 2002 have reduced the flood risk and prevented higher damage.

Abstract: Although flood risk assessments are frequently associated with significant uncertainty, formal uncertainty analyses are the exception rather than the rule. We propose to separate two fundamentally different types of uncertainty in flood risk analyses: aleatory and epistemic uncertainty. Aleatory uncertainty refers to quantities that are inherently variable in time, space or populations of individuals or objects. Epistemic uncertainty results from incomplete knowledge and is related to our inability to understand, measure and describe the system under investigation. The separation between aleatory and epistemic uncertainty is exemplified for the flood risk analysis of the city of Cologne, Germany. This flood risk assessment consists of three modules: (1) flood frequency analysis, (2) inundation estimation and (3) damage estimation. By the concept of parallel models, the epistemic uncertainty of each module is quantified. The epistemic uncertainty associated with the risk estimate is reduced by introducing additional information into the risk analysis. Finally, the contribution of different modules to the total uncertainty is quantified. The flood risk analysis results in a flood risk curve, representing aleatory uncertainty, and in associated uncertainty bounds, representing epistemic uncertainty. In this way, the separation reveals the uncertainty (epistemic) that can be reduced by more knowledge and the uncertainty (aleatory) that is not reducible.

By a common definition, flood risk assessments are comprised of two parts: a hazard and vulnerability assessment. The hazard assessment investigates the extent and magnitude of usually large flood events, which are associated to a certain exceedance probability, whereas the vulnerability part assesses the impact of the flooding on specified targets, e.g., building, people or infrastructure. Being inherently speculative flood risk assessments should always be accompanied by an uncertainty assessment in order to assist consequent decision properly. In this paper a dynamic-probabilistic method is proposed, which enables a cumulated flood risk assessment of a complete river reach considering dike failures at all dike locations. The model uses simple but computational efficient modules to simulate the complete process chain of flooding. These modules are embedded into a Monte Carlo framework thus enabling a risk assessment which is physically based thus mapping the real flooding process, and which is also probabilistic and not based on scenarios. The model also provides uncertainty estimates by quantifying various epistemic uncertainty sources of the hazard as well as the vulnerability part in a second layer of Monte Carlo simulations. These uncertainty estimates are associated to defined return intervals of the model outputs, i.e., the derived flood frequencies at the end of the reach and the risk curves for the complete reach, thus providing valuable information for the interpretation of the results. By separating single uncertainty sources a comparison of the contribution of different uncertainty sources to the overall predictive uncertainty in terms of derived flood frequencies and monetary risks could be performed. This revealed that the major uncertainties are extreme value statistics, resp. the length of the data series used and the discharge-stage relation used for the transformation of discharge into water levels in the river.

In August 2002, a severe flood event occurred in Central Europe. In the following year, a poll was performed in Germany in which 1697 private households were randomly selected from three regions: (a) the River Elbe area, (b) the Elbe tributaries in Saxony and Saxony-Anhalt, and (c) the Bavarian Danube catchment. Residents were interviewed about flood characteristics, early warning, damage, recovery, preparedness and previously experienced floods. Preparedness, response, financial losses and recovery differed in the three regions under study. This could be attributed mainly to differences in flood experience and flood impact. Knowledge about self-protection, residents' home-ownership and household size influenced the extent and type of private precautions taken, as well as the residents' ability to perform mitigation measures. To further improve preparedness and response during future flood events, flood warnings should include more information about possible protection measures. In addition, different information leaflets with flood mitigation options for specific groups of people, e.g. tenants, homeowners, elderly people or young families, should be developed.

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. Forecasting and early warning systems are important investments to protect lives, properties and livelihood. While such systems are frequently used to predict the magnitude, location, and timing of potentially damaging events, they rarely provide impact estimates, such as the expected physical damage, human consequences, disruption of services, or financial loss. Extending hazard forecast systems to include impact estimates promises many benefits for the emergency phase, for instance, for organizing evacuations. We review and compare the state of the art of impact forecasting across a wide range of natural hazards and outline opportunities and key challenges for research and development of impact forecasting. Probabilistic forecasts and rapid estimates of event impacts offer new possibilities for coping with damaging events in the emergency phaseDeveloping impact forecasting that includes exposure and vulnerability estimates will tap into synergies across disciplinesExtending single‐hazard to multihazard impact forecasts considering interactions between hazards and vulnerabilities is the next challenge

The magnitudes of river floods in Europe have been observed to change, but their alignment with changes in the spatial coverage or extent of individual floods has not been clear. We analyze flood magnitudes and extents for 3,872 hydrometric stations across Europe over the past five decades and classify each flood based on antecedent weather conditions. We find positive correlations between flood magnitudes and extents for 95% of the stations. In central Europe and the British Isles, the association of increasing trends in magnitudes and extents is due to a magnitude‐extent correlation of precipitation and soil moisture along with a shift in the flood generating processes. The alignment of trends in flood magnitudes and extents highlights the increasing importance of transnational flood risk management. If multiple rivers flood at the same time because of large‐scale rainfall, the resulting damage can exceed the capacities of disaster recovery and insurance companies. We find that events with a large spatial coverage or extent tend to be associated with above average magnitudes of the flooding level. During 1960–2010 flood extents increased in central Europe and the British Isles but decreased in Eastern Europe. These trends are caused by changes in flood generation processes due to a changing climate. If these trends persist into the future, the combination of stronger floods and larger extents is likely to increase the flood risk substantially. Flood magnitudes and extents are correlated across EuropeBoth have increased in central Europe and the British Isles but decreased in Eastern EuropeThese trends are driven by regional changes in flood generation

River floods cause extensive losses to economy, ecology, and society throughout the world. They are driven by the space‐time structure of catchment rainfall, which is determined by large‐scale, or even global‐scale, atmospheric processes. The identification of coherent, large‐scale atmospheric circulation structures that determine the moisture transport and convergence associated with rainfall‐induced flooding can help improve its predictability and phenomenology. In this paper, we extend a methodology, used for the analysis of extreme rainfall events, to high streamflow events (HSEs). The approach combines multiple machine learning methods to link HSEs to atmospheric circulation patterns. An application to the German streamflow network using reanalysis data for the period 1960 to 2012 is presented. Daily streamflow from 166 gauges, homogeneously distributed across Germany, are used. Geopotential height fields and integrated vapor transport (IVT) are derived from reanalysis data. An unsupervised neural network, Self Organizing Maps, is applied to geopotential height to identify a finite number of circulation patterns (CPs). Event synchronization between CPs and HSEs is used to establish if they are linked or not. If they are linked, the Event Synchronization method computes the delay between the occurrence of a CP and a HSE. Finally, local logistic regression is used to estimate the probability of occurrence of a HSE, as function of CP and IVT. We demonstrate that our approach is very effective to evaluate HSE probability occurrence across Germany. Atmospheric circulation patterns that generate high streamflow events in Germany are identifiedEvent synchronization is used to determine synchronization and delay between atmospheric circulation patterns and high streamflow eventsThe occurrence probability of high streamflow events is conditioned on atmospheric circulation patterns and integrated water vapor transport

In the aftermath of a severe flood event in August 2002 in Germany, 1697 computer‐aided telephone interviews were undertaken in flood‐affected private households. Besides the damage to buildings and contents a variety of factors that might influence flood damage were queried. It is analyzed here how variables describing flood impact, precaution, and preparedness as well as characteristics of the affected buildings and households vary between the lower and upper damage quartiles of all affected households. The analysis is supplemented by principal component analyses. The investigation reveals that flood impact variables, particularly water level, flood duration, and contamination are the most influential factors for building and for content damage. This group of variables is followed by items quantifying the size and the value of the affected building/flat. In comparison to these factors, temporal and permanent resistance influences damage only to a small fraction, although in individual cases, precaution can significantly reduce flood damage.