European Flood Alert System Research Articles

Technical note: Surface fields for global environmental modelling

Abstract. Climate change has resulted in more frequent occurrences of extreme events, such as flooding and heavy snowfall, which can have a significant impact on densely populated or industrialised areas. Numerical models are used to simulate and predict these extreme events, enabling informed decision-making and planning to minimise human casualties and to protect costly infrastructure. LISFLOOD is an integrated hydrological model underpinning the European Flood Awareness System and Global Flood Awareness System (EFAS and GloFAS, respectively), developed by the Copernicus Emergency Management Service (CEMS). The CEMS_SurfaceFields_2022 dataset is a new set of high-resolution surface fields at 1 and 3 arcmin resolution (approximately 2 and 6 km at the Equator, respectively) based on a wide variety of high-resolution and up-to-date data sources. The 1 arcmin fields cover Europe, while the surface fields at 3 arcmin cover the global land surface (excluding Antarctica). The dataset encompasses (i) catchment morphology and river networks, (ii) land use, (iii) vegetation cover type and properties, (iv) soil properties, (v) lake information, and (vi) water demand. This paper details the complete workflow used to generate the CEMS_SurfaceFields_2022 fields, including the data sources and methodology. Whilst created together with upgrades to the open source LISFLOOD code, the CEMS_SurfaceFields_2022 fields can be used independently for a wide range of applications, including as input to hydrological, Earth system, or environmental models or for carrying out general analyses across spatial scales, ranging from global and regional levels to local levels (especially useful for regions outside Europe), expected to improve the accuracy, detail and realism of applications.

The freshwater discharge into the Adriatic Sea revisited

The present study reconstructs the river discharge climatology and its respective historical series for all rivers of the Adriatic Sea with averaged climatological daily river discharge above 1 m3s−1, to reach a better representation of the Adriatic rivers in hydrodynamic models and, consequently, to develop a more realistic freshwater balance in the different regions of the hydrographic basin. Based on the European Flood Awareness System (EFAS) data set, a careful method of identification and selection of the Adriatic rivers, followed by a rigorous assessment against observational data, was developed to evaluate the current state of the Adriatic river discharges and their respective trends throughout several climate indicators from 1991 to 2022. Observational data are limited to 85% of the identified rivers, totaling 98% of the overall freshwater input into the Adriatic Sea. The results confirm that the Shallow Northern Adriatic receives the largest freshwater inputs with a daily average exceeding 2,400 m3s−1, which amounts to 61% of the overall Adriatic discharges. Consequently, this region guides the freshwater seasonal cycle of the Adriatic Sea, which presents a well-defined pattern of two flood peaks in late autumn and late spring, separated by a minimum discharge period at mid-summer. From the Central to the Southern Adriatic subregions, the absence of snow-melting effects prevents the secondary flood peak during the spring, shaping the seasonal cycle of river discharges from a single flood peak in late autumn to a drought period in August. The 32 years of continuous river discharge data reveal a negligible trend in the overall Adriatic Sea but a negative trend for the last decade (2013–2022). This decadal decrease is driven by the extreme drought that drastically pounded the northern Adriatic in 2022.

Seasonal Streamflow Forecasting for Fresh Water Reservoir Management in the Netherlands: An Assessment of Multiple Prediction Systems

Abstract For efficient management of the Dutch surface water reservoir Lake IJssel, (sub)seasonal forecasts of the water volumes going in and out of the reservoir are potentially of great interest. Here, streamflow forecasts were analyzed for the river Rhine at Lobith, which is partly routed through the river IJssel, the main influx into the reservoir. We analyzed seasonal forecast datasets derived from the European Flood Awareness System (EFAS), the Swedish Meteorological and Hydrological Institute (SMHI) European Hydrological Predictions for the Environment (E-HYPE), and Hydrology Tiled ECMWF Scheme of Surface Exchanges over Land (HTESSEL), which differ in their underlying hydrological formulation, but are all forced by meteorological forecasts from ECMWF’s fifth generation seasonal forecast system (SEAS5). We postprocessed the streamflow forecasts using quantile mapping (QM) and analyzed several forecast quality metrics. Forecast performance was assessed based on the available reforecast period, as well as on individual summer seasons. QM increased forecast skill for nearly all metrics evaluated. Averaged over the reforecast period, forecasts were skillful for up to 4 months in spring and early summer. Later in summer the skillful period deteriorated to 1–2 months. When investigating specific years with either low- or high-flow conditions, forecast skill increased with the extremity of the event. Although raw forecasts for both E-HYPE and EFAS were more skillful than HTESSEL, bias correction based on QM can significantly reduce the difference. In operational mode, the three forecast systems show comparable skill. In general, dry conditions can be forecasted with high success rates up to 3 months ahead, which is very promising for successful use of Rhine streamflow forecasts in downstream reservoir management. Significance Statement Lake IJssel is the Netherlands’ largest freshwater reservoir, with its main water source coming from a branch of the river Rhine. We investigate whether seasonal forecasts of river discharge can help in managing the lake level to create extra buffer capacity for dry periods. We compare three seasonal forecast systems and assess their quality. We find that statistical corrections are needed for all systems to be used. In spring discharge can be predicted up to 4 months ahead due to snow processes. In summer this time is shorter, but it increases with event extremity: severe low-flow events can be predicted longer ahead. This offers potential for water managers to base their lake management on other similar reservoirs.

Implementation of hydrometeorological thresholds for regional landslide warning in Catalonia (NE Spain)

Soil moisture plays a vital role in slope stability. As water infiltrates into the soil, shear strength decreases eventually leading to failure. However, most of the existing regional-scale landslide early warning systems (LEWS) rely solely on rainfall information and use rainfall thresholds to determine if the landslide triggering conditions are met. The original version of the Catalonia region LEWS combines real-time rainfall observations and susceptibility to compute warnings. The LEWS applies a set of rainfall intensity-duration thresholds to determine if the rainfall conditions have the potential to trigger a landslide. This work explores the potential of using modelled soil moisture data in the Catalonia region LEWS. Volumetric water content (VWC) from the LISFLOOD hydrological simulations of the European Flood Awareness System and rainfall estimates have been analysed at the location of recent landslide events. Based on this data, a set of empirical hydrometeorological thresholds combining rainfall and soil moisture information has been obtained for their application into the Catalonia region LEWS. The LEWS has been run for nine months (April–December 2020) using two approaches: (i) combining susceptibility and rainfall intensity-duration (I-D) thresholds and (ii) combining susceptibility and the new hydrometeorological thresholds including soil moisture information. Generally, both LEWS approaches issued moderate or high warnings in the areas where significant rainfall accumulations were recorded. The outputs have been compared at specific locations where landslides were reported during the analysed period. Results show that at the analysed locations false positives are generally reduced when employing the hydrometeorological thresholds in the LEWS. Therefore, this approach is promising and could help improve regional scale LEWS in Catalonia.

An Evaluation of the Dynamics of Some Meteorological and Hydrological Processes along the Lower Danube

The objective of the present work was to perform a 30-year analysis of some significant meteorological and hydrological processes along the Lower Danube. This was motivated by the fact that, due to the effects of climate change, the global configuration of the environmental matrix has suffered visible transformations in many places. Another important factor considered is related to the constant development noticed in the last few decades of European inland navigation, in general, and in the Lower Danube sector, in particular. From this perspective, the processes analysed were the wind speed at a 10 m height, the air temperature at a 2 m height, precipitation, and river discharge. The 30-year period of 1991–2020 was considered for analysis. The ERA5 reanalysis data were processed and analysed in the case of the first three processes, while for the river discharge, the data provided by the European Flood Awareness System were used. The emphasis was placed on the evolution of the extreme values and on the identification of the geographical locations with a higher probability of occurrence. The average values and the seasonal variations of the four processes were also considered. The results indicated that the maximum wind speed and air temperature values along the Lower Danube did not suffer significant changes in the last few decades. However, the values of the minimum air temperatures increased with an average value of about 0.8 °C per decade, and the same tendency was noticed also for the average temperatures. Regarding the precipitation, the trend indicated a tendency to decrease by about 0.5 mm per decade, while for the river discharge, a clear increase of more than 1200 m3/s corresponded to each ten-year period. Finally, it can be concluded that the present analysis provided a global and more comprehensive perspective of the recent environmental dynamics along the Lower Danube, delivering useful information for inland navigation, as well as for other human activities.

The suitability of a seasonal ensemble hybrid framework including data-driven approaches for hydrological forecasting

Abstract. Hydrological forecasts are important for operational water management and near-future planning, even more so in light of the increased occurrences of extreme events such as floods and droughts. Having a forecasting framework, which is flexible in terms of input forcings and forecasting locations (local, regional, or national) that can deliver this information in fast and computational efficient manner, is critical. In this study, the suitability of a hybrid forecasting framework, combining data-driven approaches and seasonal (re)forecasting information from dynamical models, to predict hydrological variables was explored. Target variables include discharge and surface water levels for various stations at a national scale, with the Netherlands as the focus. Five different machine learning (ML) models, ranging from simple to more complex and trained on historical observations of discharge, precipitation, evaporation, and seawater levels, were run with seasonal (re)forecast data, including the European Flood Awareness System (EFAS) and ECMWF seasonal forecast system (SEAS5), of these driver variables in a hindcast setting. The results were evaluated using the evaluation metrics, i.e. anomaly correlation coefficient (ACC), continuous ranked probability (skill) score (CRPS and CRPSS), and Brier skill score (BSS), in comparison to a climatological reference hindcast. Aggregating the results of all stations and ML models revealed that the hindcasting framework outperformed the climatological reference forecasts by roughly 60 % for discharge predictions (80 % for surface water level predictions). Skilful prediction for the first lead month, independently of the initialization month, can be made for discharge. The skill extends up to 2–3 months for spring months due to snowmelt dynamic captured in the training phase of the model. Surface water level hindcasts showed similar skill and skilful lead times. While the different ML models showed differences in performance during a testing and training phase using historical observations, running the ML framework in a hindcast setting showed only minor differences between the models, which is attributed to the uncertainty in seasonal forecasts. However, despite being trained on historical observations, the hybrid framework used in this study shows similar skilful predictions to previous large-scale forecasting systems. With our study, we show that a hybrid framework is able to bring location-specific skilful seasonal forecast information with global seasonal forecast inputs. At the same time, our hybrid approach is flexible and fast, and as such, a hybrid framework could be adapted to make it even more interesting to water managers and their needs, for instance, as part of a fast model-predictive control framework.

Evaluating the impact of post-processing medium-range ensemble streamflow forecasts from the European Flood Awareness System

Abstract. Streamflow forecasts provide vital information to aid emergency response preparedness and disaster risk reduction. Medium-range forecasts are created by forcing a hydrological model with output from numerical weather prediction systems. Uncertainties are unavoidably introduced throughout the system and can reduce the skill of the streamflow forecasts. Post-processing is a method used to quantify and reduce the overall uncertainties in order to improve the usefulness of the forecasts. The post-processing method that is used within the operational European Flood Awareness System is based on the model conditional processor and the ensemble model output statistics method. Using 2 years of reforecasts with daily timesteps, this method is evaluated for 522 stations across Europe. Post-processing was found to increase the skill of the forecasts at the majority of stations in terms of both the accuracy of the forecast median and the reliability of the forecast probability distribution. This improvement is seen at all lead times (up to 15 d) but is largest at short lead times. The greatest improvement was seen in low-lying, large catchments with long response times, whereas for catchments at high elevation and with very short response times the forecasts often failed to capture the magnitude of peak flows. Additionally, the quality and length of the observational time series used in the offline calibration of the method were found to be important. This evaluation of the post-processing method, and specifically the new information provided on characteristics that affect the performance of the method, will aid end users in making more informed decisions. It also highlights the potential issues that may be encountered when developing new post-processing methods.

Compound flood impact forecasting: integrating fluvial and flash flood impact assessments into a unified system

Abstract. Floods can arise from a variety of physical processes. Although numerous risk assessment approaches stress the importance of taking into account the possible combinations of flood types (i.e. compound floods), this awareness has so far not been reflected in the development of early warning systems: existing methods for forecasting flood hazards or the corresponding socio-economic impacts are generally designed for only one type of flooding. During compound flood events, these flood type-specific approaches are unable to identify overall hazards or impacts. Moreover, from the perspective of end-users (e.g. civil protection authorities), the monitoring of separate flood forecasts – with potentially contradictory outputs – can be confusing and time-consuming, and ultimately impede an effective emergency response. To enhance decision support, this paper proposes the integration of different flood type-specific approaches into one compound flood impact forecast. This possibility has been explored through the development of a unified system combining the simulations of two impact forecasting methods: the Rapid Risk Assessment of the European Flood Awareness System (EFAS RRA; representing fluvial floods) and the radar-based ReAFFIRM method (representing flash floods). The unified system has been tested for a recent catastrophic episode of compound flooding: the DANA event of September 2019 in south-east Spain (Depresión Aislada en Niveles Altos, meaning cut-off low). The combination of the two methods identified well the overall compound flood extents and impacts reported by various information sources. For instance, the simulated economic losses amounted to about EUR 670 million against EUR 425 million of reported insured losses. Although the compound impact estimates were less accurate at municipal level, they corresponded much better to the observed impacts than those generated by the two methods applied separately. This demonstrates the potential of such integrated approaches for improving decision support services.

Extreme floods in Europe: going beyond observations using reforecast ensemble pooling

Abstract. Assessing the rarity and magnitude of very extreme flood events occurring less than twice a century is challenging due to the lack of observations of such rare events. Here we develop a new approach, pooling reforecast ensemble members from the European Flood Awareness System (EFAS), to increase the sample size available to estimate the frequency of extreme local and regional flood events. We assess the added value of such pooling, determine where in Central Europe one might expect the most extreme events, and evaluate how event severity is related to physiographic and meteorological catchment characteristics. We work with a set of 234 catchments from the Global Runoff Data Centre matched to EFAS catchments and for which the performance of simulated floods is good when compared to observed streamflow. We pool EFAS-simulated flood events for 10 perturbed ensemble members and lead times ranging from 22 to 46 d, where flood events are only weakly dependent (<0.25 average correlation across lead times). The resulting large ensemble (130 time series instead of 1) enables the analyses of very extreme events which occur less than twice a century. We demonstrate that such ensemble pooling produces more robust estimates with considerably reduced uncertainty bounds (by ∼80 % on average) than observation-based estimates but may equally introduce biases arising from the simulated meteorology and hydrological model. Our results show that, for a given return period, specific floods are highest in steep, cold, and wet regions and are comparably low in regions with strong flow regulation through dams. Furthermore, our pooled flood estimates indicate that the probability of regional flooding is higher in Central Europe and Great Britain than in Scandinavia. We conclude that reforecast ensemble pooling is an efficient approach to increase sample size and to derive robust local and regional flood estimates in regions with good hydrological model performance.

On the implementation of post-processing of runoff forecast ensembles

AbstractDifferent post-processing techniques are frequently employed to improve the outcome of ensemble forecasting models. The main reason is to compensate for biases caused by errors in model structure or initial conditions, and as a correction for under- or overdispersed ensembles. Here we use the Ensemble Model Output Statistics method to post-process the ensemble output from a continental scale hydrological model, LISFLOOD, as used in the European Flood Awareness System (EFAS). We develop a method for local calibration and interpolation of the post-processing parameters and compare it with a more traditional global calibration approach for 678 stations in Europe based on long term observations of runoff and meteorological variables. For the global calibration we also test a reduced model with only a variance inflation factor. Whereas the post-processing improved the results for the first 1-2 days lead time, the improvement was less for increasing lead times of the verification period. This was the case both for the local and global calibration methods. As the post-processing is based on assumptions about the distribution of forecast errors, we also present an analysis of the ensemble output that provides some indications of what to expect from the post-processing.

Increasing Timeliness of Satellite-Based Flood Mapping Using Early Warning Systems in the Copernicus Emergency Management Service

Remotely sensed images have become an important source of information for actors involved in disaster management and satellite-based emergency mapping (SEM) is increasingly used to support the response phase in the first hours and days after a disaster occurs. The delivery timeliness of the crisis information is key to the success of SEM. In the Copernicus Emergency Management Service (CEMS), a procedure was tested during the past 5 years which links the European Flood Awareness System (EFAS) with the on-demand Rapid Mapping module in order to anticipate satellite tasking in view of an upcoming activation of the service for riverine floods. This study aims at assessing if the procedure has helped to improve the delivery timeliness of the first flood impact information. For the assessment, we used the Rapid Mapping performance statistics recorded from June 2016 to December 2020. Standard Rapid Mapping activations for floods were compared with those preceded by an EFAS based pre-tasking request. The focus was on essential time stamps such as activation start, provision of imagery and the availability of derived information products. For the pre-tasking-related activations, we further compared the EFAS predictions with the Rapid Mapping user request and compared flood predictions with actual observations. Our results show that the EFAS based pre-tasking improves the timeliness of the first product delivery due to the fact that satellite images could be acquired earlier compared to activations without pre-tasking.

RMetS National Meeting ‐ Progress in Weather Forecasting ‐ the user's perspective

RMetS National Meeting ‐ Progress in Weather Forecasting ‐ the user's perspective

The benefit of seamless forecasts for hydrological predictions over Europe

Abstract. Two different systems provide long-range forecasts at ECMWF. On the sub-seasonal timescale, ECMWF issues an extended-range ensemble prediction system (ENS-ER) which runs a 46-day forecast integration issued twice weekly. On longer timescales, the current seasonal forecasting system (SYS4) produces a 7-month outlook starting from the first of each month. SYS4 uses an older model version and has lower spatial and temporal resolution than ENS-ER, which is issued with the current operational ensemble forecasting system. Given the substantial differences between the ENS-ER and the SYS4 configurations and the difficulties of creating a seamless integration, applications that rely on weather forcing as input such as the European Flood Awareness System (EFAS) often follow the route of the creation of two separate systems for different forecast horizons. This study evaluates the benefit of a seamless integration of the two systems for hydrological applications and shows that the seamless system outperforms SYS4 in terms of skill for the first 4 weeks, but both forecasts are biased. The benefit of the new seamless system when compared to the seasonal forecast can be attributed to (1) the use of a more recent model version in the sub-seasonal range (first 46 days) and (2) the much more frequent updates of the meteorological forecast.

Skilful seasonal forecasts of streamflow over Europe?

Abstract. This paper considers whether there is any added value in using seasonal climate forecasts instead of historical meteorological observations for forecasting streamflow on seasonal timescales over Europe. A Europe-wide analysis of the skill of the newly operational EFAS (European Flood Awareness System) seasonal streamflow forecasts (produced by forcing the Lisflood model with the ECMWF System 4 seasonal climate forecasts), benchmarked against the ensemble streamflow prediction (ESP) forecasting approach (produced by forcing the Lisflood model with historical meteorological observations), is undertaken. The results suggest that, on average, the System 4 seasonal climate forecasts improve the streamflow predictability over historical meteorological observations for the first month of lead time only (in terms of hindcast accuracy, sharpness and overall performance). However, the predictability varies in space and time and is greater in winter and autumn. Parts of Europe additionally exhibit a longer predictability, up to 7 months of lead time, for certain months within a season. In terms of hindcast reliability, the EFAS seasonal streamflow hindcasts are on average less skilful than the ESP for all lead times. The results also highlight the potential usefulness of the EFAS seasonal streamflow forecasts for decision-making (measured in terms of the hindcast discrimination for the lower and upper terciles of the simulated streamflow). Although the ESP is the most potentially useful forecasting approach in Europe, the EFAS seasonal streamflow forecasts appear more potentially useful than the ESP in some regions and for certain seasons, especially in winter for almost 40 % of Europe. Patterns in the EFAS seasonal streamflow hindcast skill are however not mirrored in the System 4 seasonal climate hindcasts, hinting at the need for a better understanding of the link between hydrological and meteorological variables on seasonal timescales, with the aim of improving climate-model-based seasonal streamflow forecasting.

Application of European numerical weather prediction models for hydrological purposes

Nowadays, hydrological forecasts are based on wide range of meteorological inputs, including observations and forecasts. In this paper four main areas are covered. First of all, milestones covering last four decades from usage of a simple statistical method to regional limited area modeling are summarized. Then an overview of the main activities of the European Flood Awareness System (EFAS) is given. Usage of ensemble forecasts for providing uncertainty is getting larger and larger attention for hydrological applications too. Benefits of a locally developed new tool, the ensemble calibration method based on reforecast model climate is given in the third part. Finally, local developments on regional hydrostatic and non-hydrostatic models are shown. It is shown that a high resolution limited area non-hydrostatic model can predict summer heavy precipitation more accurately.

An operational procedure for rapid flood risk assessment in Europe

Abstract. The development of methods for rapid flood mapping and risk assessment is a key step to increase the usefulness of flood early warning systems and is crucial for effective emergency response and flood impact mitigation. Currently, flood early warning systems rarely include real-time components to assess potential impacts generated by forecasted flood events. To overcome this limitation, this study describes the benchmarking of an operational procedure for rapid flood risk assessment based on predictions issued by the European Flood Awareness System (EFAS). Daily streamflow forecasts produced for major European river networks are translated into event-based flood hazard maps using a large map catalogue derived from high-resolution hydrodynamic simulations. Flood hazard maps are then combined with exposure and vulnerability information, and the impacts of the forecasted flood events are evaluated in terms of flood-prone areas, economic damage and affected population, infrastructures and cities.An extensive testing of the operational procedure has been carried out by analysing the catastrophic floods of May 2014 in Bosnia–Herzegovina, Croatia and Serbia. The reliability of the flood mapping methodology is tested against satellite-based and report-based flood extent data, while modelled estimates of economic damage and affected population are compared against ground-based estimations. Finally, we evaluate the skill of risk estimates derived from EFAS flood forecasts with different lead times and combinations of probabilistic forecasts. Results highlight the potential of the real-time operational procedure in helping emergency response and management.

How do I know if I’ve improved my continental scale flood early warning system?

Flood early warning systems mitigate damages and loss of life and are an economically efficient way of enhancing disaster resilience. The use of continental scale flood early warning systems is rapidly growing. The European Flood Awareness System (EFAS) is a pan-European flood early warning system forced by a multi-model ensemble of numerical weather predictions. Responses to scientific and technical changes can be complex in these computationally expensive continental scale systems, and improvements need to be tested by evaluating runs of the whole system. It is demonstrated here that forecast skill is not correlated with the value of warnings. In order to tell if the system has been improved an evaluation strategy is required that considers both forecast skill and warning value.The combination of a multi-forcing ensemble of EFAS flood forecasts is evaluated with a new skill-value strategy. The full multi-forcing ensemble is recommended for operational forecasting, but, there are spatial variations in the optimal forecast combination. Results indicate that optimizing forecasts based on value rather than skill alters the optimal forcing combination and the forecast performance. Also indicated is that model diversity and ensemble size are both important in achieving best overall performance. The use of several evaluation measures that consider both skill and value is strongly recommended when considering improvements to early warning systems.

Regionalization of post-processed ensemble runoff forecasts

Abstract. For many years, meteorological models have been run with perturbated initial conditions or parameters to produce ensemble forecasts that are used as a proxy of the uncertainty of the forecasts. However, the ensembles are usually both biased (the mean is systematically too high or too low, compared with the observed weather), and has dispersion errors (the ensemble variance indicates a too low or too high confidence in the forecast, compared with the observed weather). The ensembles are therefore commonly post-processed to correct for these shortcomings. Here we look at one of these techniques, referred to as Ensemble Model Output Statistics (EMOS) (Gneiting et al., 2005). Originally, the post-processing parameters were identified as a fixed set of parameters for a region. The application of our work is the European Flood Awareness System (http://www.efas.eu), where a distributed model is run with meteorological ensembles as input. We are therefore dealing with a considerably larger data set than previous analyses. We also want to regionalize the parameters themselves for other locations than the calibration gauges. The post-processing parameters are therefore estimated for each calibration station, but with a spatial penalty for deviations from neighbouring stations, depending on the expected semivariance between the calibration catchment and these stations. The estimated post-processed parameters can then be used for regionalization of the postprocessing parameters also for uncalibrated locations using top-kriging in the rtop-package (Skøien et al., 2006, 2014). We will show results from cross-validation of the methodology and although our interest is mainly in identifying exceedance probabilities for certain return levels, we will also show how the rtop package can be used for creating a set of post-processed ensembles through simulations.

Understanding the roles of modernity, science, and risk in shaping flood management

WIREs WaterVolume 2, Issue 5 p. 593-593 ErratumFree Access Understanding the roles of modernity, science, and risk in shaping flood management This article corrects the following: Understanding the roles of modernity, science, and risk in shaping flood management Sébastien Nobert, Kristian Krieger, Florian Pappenberger, Volume 2Issue 3Wiley Interdisciplinary Reviews: Water pages: 245-258 First Published online: March 3, 2015 First published: 08 August 2015 https://doi.org/10.1002/wat2.1095AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL In Nobert et al.1, the following error was published in Figure 1 legend. Deterministic and probabilistic forecasts from the European Flood Awareness System. The blue solid line is a flood forecast using the deterministic model of the German weather service (Deutscher Wetterdienst). The blue solid line is the high-resolution forecast of ECMWF and the box-plots show the ensemble forecast of ECMWF. The text was incorrect and should have read: Deterministic and probabilistic forecasts from the European Flood Awareness System. The blue solid line is a flood forecast using the deterministic model of the German weather service (Deutscher Wetterdienst). The red solid line is the high-resolution forecast of ECMWF and the box-plots show the ensemble forecast of ECMWF. We apologize for this error. REFERENCE 1Nobert S, Krieger K, Pappenberger F. Understanding the roles of modernity, science, and risk in shaping flood management. WIREs Water 2015, 2: 245– 58. Volume2, Issue5September/October 2015Pages 593-593 ReferencesRelatedInformation

The monetary benefit of early flood warnings in Europe

Effective disaster risk management relies on science-based solutions to close the gap between prevention and preparedness measures. The consultation on the United Nations post-2015 framework for disaster risk reduction highlights the need for cross-border early warning systems to strengthen the preparedness phases of disaster risk management, in order to save lives and property and reduce the overall impact of severe events. Continental and global scale flood forecasting systems provide vital early flood warning information to national and international civil protection authorities, who can use this information to make decisions on how to prepare for upcoming floods. Here the potential monetary benefits of early flood warnings are estimated based on the forecasts of the continental-scale European Flood Awareness System (EFAS) using existing flood damage cost information and calculations of potential avoided flood damages. The benefits are of the order of 400 Euro for every 1 Euro invested. A sensitivity analysis is performed in order to test the uncertainty in the method and develop an envelope of potential monetary benefits of EFAS warnings. The results provide clear evidence that there is likely a substantial monetary benefit in this cross-border continental-scale flood early warning system. This supports the wider drive to implement early warning systems at the continental or global scale to improve our resilience to natural hazards.