In urban environments, urban flooding can lead to significant economic losses due to high population density and valuable economic properties. The complexity of urban flood disasters and the diverse entities affected present substantial challenges for accurate flood risk assessment. In response to this critical need, we have developed a comprehensive urban flood risk assessment method that evaluates the flood risk for primary affected entities, including residents' lives, ground buildings, and underground spaces. This proposed assessment method is based on both scenario analysis and the index system method. Initially, it predicts the disaster-causing hydraulic characteristics, such as water depth, flow velocity, and building inundation, using a high-performance 1D/2D coupled urban flood hydrodynamic model. Subsequently, it assesses the flood risk of disaster-affected objects, such as people, ground buildings, and underground spaces, based on hazard, vulnerability, and exposure indices. We successfully applied this comprehensive urban flood risk assessment method to a highly developed urban area in Wuhan City, China. To address the challenge of data acquisition, we utilized web crawling to gather information on industrial distribution, property prices, and shop rents to support flood risk analysis. The flooding process and corresponding risk levels of primarily affected objects under different rainfall return period scenarios were comprehensively evaluated. The established model can serve as a reference for disaster prevention and reduction technologies for other cities threatened by urban flood disasters.

How to cite: Dong, B., Huang, B., Tan, C., Gao, S., and Xia, J.: Comprehensive flood risk assessment of urban flooding based on the 1D/2D coupled hydrodynamic model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-407, https://doi.org/10.5194/egusphere-egu24-407, 2024.

Increasing threats of flash flood call for effective and operative ways to offer accurate forecasting and warning. In this study, a Time-Space varying Distributed Unit Hydrograph (TS-DUH) based on publicly-available-only data is proposed for efficient flash flood forecasting. As in the traditional spatially distributed unit hydrograph (SDUH) method, TS-DUH initially estimates the runoff travel time (and flow velocity) from each location within a catchment to the outlet based on topographic and hydroclimate characteristics. However, the delineation of the runoff-drainage process is further adjusted by considering the heterogeneous and dynamic runoff contribution caused by rainfall and soil moisture variations. The excess rainfall is estimated by the widely used Global Flood Monitoring System (GFMS) which provides long-term (2000-present) well-archived and real-time operative global runoff datasets from a state-of-the-art DRIVE model (DRIVE-Runoff). An alternative excess rainfall input is taken from the Soil Conservation Service's curve number method (CN-Runoff). The performance of the TS-GUH method is evaluated using 6,324 flash flood events of 281 small-to-medium-sized catchments in the CONUS, with 1,686 events used for calibration. The validation results show that using DRIVE-Runoff is better than CN-Runoff, 99% and 71% of events have KGE values greater than 0 and 0.5, respectively, with a median KGE value of 0.6 and the probability of detection (POD) of flood events 0.9. More importantly, using near real-time satellite rainfall-driven DRIVE-Runoff, long-term flow simulation (2003-2020) without calibration at 803 gauges shows better performance of TS-DUH than the original GFMS, with a median KGE improvement of 0.15. This combined UH and numerical hydrological model approach showed great potential for flash food monitoring and forecasting at regional or global scales.

How to cite: Hu, Y., Wu, H., Chen, W., Li, C., Wu, W., Huang, Z., Jiang, L., and Huang, Z.: A Time-Space varying Distributed Unit Hydrograph (TS-DUH) for lare-scale operative flash flood forecast, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5139, https://doi.org/10.5194/egusphere-egu24-5139, 2024.

The escalating frequency and severity of flash floods have heightened concerns, presenting a unique challenge compared to traditional fluvial flooding. Unlike large-scale fluvial events, managing flash flood risks through infrastructure approaches is less effective. Early warning systems emerge as crucial components in responding to these rapid and intense floods. For a response to be effective, an early warning system must provide stakeholders with sufficient actionable information about the imminent flood and lead time, underscoring the need for computational flood forecasting models.

Hydrodynamic models, which shine for their accuracy in predicting floods in complex topographies and urban environments affected by flash floods, face a drawback—they are computationally intensive, potentially limiting their application in early warning systems.

This contribution delves into the utilisation of two 2D flood forecasting models: the local-inertia solver RIM2D and the full shallow water equation solver SERGHEI. To minimise runtime, both solvers are implemented to run on GPUs, with a focus on maximising forecast lead time. RIM2D, less computationally intensive than SERGHEI, is expected to be well-suited for this purpose. On the other hand, to offset the higher computational cost, SERGHEI allows for multi-GPU use, specifically tailored for large-scale High-Performance Computing (HPC) systems.

The study assesses the applicability and trade-offs associated with these solvers, concentrating on the flood event in the Eifel in 2021, with a specific focus on the lower Ahr valley—from Altenahr to the Rhine. Simulations with identical conditions are conducted using both solvers, spanning resolutions from 1m to 10m. Evaluation criteria include accuracy in terms of maximum flood levels and computational performance in terms of required resources and runtime, and we explore the nature of the differences of the results produced by both solvers and their potential implications for flood forecasting and early warning.

Results indicate that at coarser resolutions, both solvers yield similar accuracy. Discrepancies emerge at higher resolutions due to the distinct mathematical formulations. Computational costs escalate rapidly with resolution for both solvers. Notably, for resolutions equal to or coarser than 5m, flood forecasts are at least 75 times faster than real-time. This efficiency makes them suitable for augmenting existing operational flood forecast systems but retaining excellent lead times, thus, enabling detailed flood impact forecasting and immediate responses. 

However, at higher resolutions, the computational demands exceed the capacity of a single scientific-grade GPU, necessitating multi-GPU implementations and some HPC capabilities for operational use. While such high(er) resolution models may seem excessive for managing specific flood events, they underscore the growing need for state-of-the-art scientific software and HPC technology in addressing larger flood domains.

How to cite: Caviedes-Voullième, D., Khosh Bin Ghomash, S., and Apel, H.: Towards enhancing flood preparedness and early warning: a comparative study of 2D high-resolution simulations of the 2021 Ahr floods, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7639, https://doi.org/10.5194/egusphere-egu24-7639, 2024.

Heavy rainfall and flooding are extreme events with high hazard and risk potential for people and the environment. While there has been extensive research on the individual events of heavy rainfall and flooding, there is still a significant need for research into their combination. Analysing them separately may lead to an underestimation of the hazards and risks involved. Especially in small catchment areas or headwater catchments, a heavy rainfall event can cause flash flooding and a flood event in a river simultaneously. The relationship between the investigated area and its associated hydrological catchment area is crucial. In such areas, classic flood protection measures may not always be the most sensible option. Targeted measures and precautions must be taken, and knowledge of these events is of great importance.

Analyses are carried out to understand the relationship between combined heavy rainfall and flood events and the characteristics of the study area. For this purpose, we use precipitation radar data from the German Weather Service (RADKLIM, 5 min & 1x1 km) and discharge data from water gauges provided by the NRW State Office for Nature, Environment and Consumer Protection, as well as various water associations. The data is used to generate area averages of precipitation of various durations for the catchment areas under investigation. The compound events are analysed using value pairs, which are defined based on AMAX or threshold values. Statistical extreme value analyses were conducted on individual events using different extreme value distributions, including the metastatic and general extreme value distributions. These analyses were necessary to determine the probabilities of compound events such as heavy rainfall and flooding, with the aid of copula functions. The joint occurrence probabilities and correlation between precipitation and runoff events along a watercourse are also considered.

The results of these analyses provide insights into the integral consideration of floods and heavy rainfall, particularly in small catchment areas and during short heavy rainfall events. In addition, these methods can be used to define the joint probabilities of occurrence of these events. Furthermore, the investigations can not only contribute to a more precise assessment of the hazards and risks of compound events, but can also serve as a starting point for integral heavy rain and flood hazard maps. The maps derived from this can make a valuable contribution to the development of more precise and comprehensive risk management strategies.

How to cite: Simon, F. and Mudersbach, C.: Compound heavy rainfall and river flood events in small catchments, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15119, https://doi.org/10.5194/egusphere-egu24-15119, 2024.

Hydrological modelling is an important tool for understanding and predicting runoff behaviour in catchments. It is essential for flood risk management and flood forecasting. This study conducts a comparative analysis of two modelling approaches: a Long Short-Term Memory (LSTM) neural network model and a conventional rainfall-runoff model. Both models are used to simulate runoff dynamics in a catchment in Bavaria, Germany.

LSTM models are known for their ability to capture temporal dependencies and nonlinear relationships in sequential data. This research aims to comprehensively evaluate and compare the performance, accuracy, and predictive capabilities of the physical rainfall-runoff model widely used in hydrology against the LSTM model. The objective is to replicate the intricate processes governing rainfall-induced runoff. This study analyses the ability of the LSTM model to predict runoff patterns by leveraging historical hydrological data and meteorological inputs. The model learns from temporal sequences of precipitation and other relevant factors. The traditional rainfall-runoff model, which operates on established hydrological principles and parameterizations, is also assessed for its accuracy in simulating runoff within the same catchment. The comparison includes assessments of prediction accuracy, model robustness under varying conditions, computational efficiency, and the ability to capture the complex non-linear relationships inherent in hydrological processes.

The results of this study have important implications for the further development of hydrological modelling techniques. Understanding the comparative strengths and limitations of the LSTM model against the conventional rainfall-runoff model provides valuable insights for improving the accuracy and reliability of runoff predictions. Such information can improve decision making in flood risk management, assist in more accurate flood forecasting and help reduce the loss of human life. By identifying the comparative effectiveness of these modelling approaches in reproducing the complex dynamics of runoff, this research aims to advance the field of hydrological modelling and pave the way for more robust and accurate prediction tools.

How to cite: Hotzel, A. and Mudersbach, C.: Comparative Analysis of a LSTM and a Rainfall-Runoff Model for Catchment Runoff Simulation: Advancing Hydrological Modelling and Forecasting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1734, https://doi.org/10.5194/egusphere-egu24-1734, 2024.

Floods are one of the most dangerous and disastrous natural hazards that cause economic damages and human loss. In particular, flash floods related to heavy precipitation represent a major hazard on the French Eastern Mediterranean coast where growing population and tourism increase the exposure and vulnerability of coastal cities to extreme events. A few well recorded severe events have demonstrated the vulnerability of this area to river floods during the last 15 years. Consequently, various modelling approaches has been recently proposed to support flood prevention and mitigation in urban region. However, mapping floods events with limited computational costs remains challenging. In this study, four open-source numerical flood mapping tools are compared regarding their ability at simulating past flood events over five catchments in the southeastern region of France. The flood mapping models range from the simple Height Above Nearest Drainage approach (MHYST) to more complex methods that solve the full shallow water equations (LISFLOOD-FP DG2). Models of intermediate complexity, such as a 1D shallow water solver (HEC-RAS) and a 2D cellular automata (CAflood), are also included. Model evaluations are performed based on water depth accuracy estimation against high water marks data. The flood mapping tools are also compared in terms of flood extent using critical success indices. This study outlines how the more complex models provide the more accurate and realistic flood simulations, however with high computationally demanding which requires the deployment of substantial computer resources before their use in operational flood systems.

How to cite: Royer-Gaspard, P., Troin, M., and Fox, D.: Balancing simplicity with efficiency: a comparison of river flood mapping models on past events on the French Mediterranean coast, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10666, https://doi.org/10.5194/egusphere-egu24-10666, 2024.

Long-term hazard and risk assessments are produced by combining many hazard-model simulations, each based on a slightly different set of inputs to cover the uncertainty space. While most input parameters for these models are relatively well-constrained, atmospheric parameters remain problematic unless working on very short-time scales (hours to days). Precipitation is a key trigger for many natural hazards including floods, landslides, and lahars. This work presents a stochastic weather model that takes openly available ERA5-land data, and produces long-term (e.g., decadal), hourly, spatially varying precipitation data that mimics the statistical dimensions of real-data. Thus, allowing precipitation to be robustly included in hazard-model simulations.

The stochastic weather model (SWM) comprises three steps: Data conversion, block construction, and stochastic weather generation. Due to the relative simplicity of the model and exploiting some coding efficiencies in the R package dplyr, 10 years of hourly data can be generated across a 10 by 10 cell grid (~110 km by 110 km) on a standard desktop computer in < 5 seconds.

How to cite: Whitehead, M. and Bebbington, M.: SWM: a Stochastic Weather Model for precipitation-related hazard assessments using ERA5-land data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3652, https://doi.org/10.5194/egusphere-egu24-3652, 2024.

In recent decades, the frequency and intensity of extreme precipitation events has increased worldwide as a result of climate change. It is necessary to set up early warning systems to enable effective disaster prevention in flood-prone areas. Despite efforts to develop modern forecasting systems for extreme precipitation, there are still problems such as low hit rates, high false alarms and spatio-temporal distortions. In particular, the crucial aspect of forecasting rainfall risk at the national level for India has not been addressed in the existing literature. In this study, an attempt is made to predict the flood hazards caused by extreme rainfall by estimating the probability of occurrence of an extreme rainfall event based on the predicted rainfall values with a certain lead time. The hazard model is based on the conditional probability of historical observed and predicted rainfall data. In applying the method in India, reliable data sources are used, including observed gridded precipitation data from the India Meteorological Department (IMD) and forecast precipitation from the Global Ensemble Forecast System (GEFS) Reforecast Version 2 for the period from 1985 to 2018 (34 years). Extreme precipitation days are identified as those that exceed the 95^th percentile value for a given grid. Hazard assessments are carried out at grid level for lead times of 1, 3, 5, 10 and 15 days. The resulting hazard maps are consistent with the observed rainfall patterns confirmed by the recent rainfall-induced floods in India. This model gives stakeholders the ability to identify regions that are at risk in the near future (weeks). This facilitates proactive evacuation and mitigation planning prior to the occurrence of extreme rainfall events.

How to cite: Chakraborty, A., Ghosh, S., and Karmakar, S.: India-wide Extreme Rainfall Driven Flood Hazard Forecasting, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-2485, https://doi.org/10.5194/egusphere-egu24-2485, 2024.

Rain cell tracking methods are essential to the object-based rainfall nowcasting of convective storms. These methods identify rain cells from radar images and the tracks associate cells between any two successive time steps. Based upon the identified cells and tracks, the positions of the cells in the next few time steps can be forecasted. Many existing nowcasting methods assume Lagrangian persistence. That is, they generally lack the mechanisms to predict the temporal evolution of cell properties and their types. This deficiency may have a great impact to the accuracy of the convective storm nowcasting. To improve cell tracking methods, a two-stage analogue model is proposed to address the limits of existing cell tracking methods.

• Predicting cell type: three machine learning classifiers –KNN, logistic regression and random forest—are employed to predict the cell types based on rain cell properties.
• Predict temporal evolution of cell properties: an ensemble forecast (0-1h lead times) of cell mean intensity, maximum intensity, size and major axis length is obtained using an analogue method. This method assumes that rainfall cells with similar conditions will evolve similarly. Analogues are chosen based on the predicted cell type from the previous step.

In this study, a dataset of rainfall cells from a total of 165 convective storms between 2005 and 2017 is used. These rainfall cells are identified using enhanced TITAN. The study area is centred at Birmingham city, with an area of 512 × 512 km². Results show that the random forest classifier has the best performance in predicting track types. As the temporal profile of the selected cell properties is incorporated into the prediction process, the prediction accuracy of the random forest classifier can be higher than 80%. Results also show that predicting cell type prior to the selection of analogues improves the forecasting of temporal evolution of cell properties at a lead time of 5 minutes. Overall, the analogue method enhances the prediction of temporal evolution of cell properties compared with assuming Lagrangian persistence. At the moment, cell types are predicted for a 5-minute lead time.

How to cite: Shu, Z., Onof, C., and Wang, L.: Improving rain cell tracking for convective rainfall nowcasting: a two-stage analogue model approach, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6111, https://doi.org/10.5194/egusphere-egu24-6111, 2024.

Flash floods account for a large proportion of flood-based fatalities, and they are becoming more frequent due to climate change. A global flash flood warning system therefore has the potential to be life saving.
Standard approaches to flash flood forecasting – such as the Flash Flood Guidance (FFG) system in the US National Weather Service (NWS), or the European Runoff Index based on Climatology (ERIC) in the European Flood Awareness System (EFAS) – are utilizing recent weather and soil conditions, physiographic characteristics of basins, and weather forecasts, in order to produce a forecast for possible flash floods. These forecasts are not disseminated directly to the public. Instead, they are firstly refined by hydrologists that have intimate knowledge of the relevant basins and of previous flood events. This introduces a difficulty to scaling these methods worldwide, as the training of professional hydrologists in every region is costly and time consuming.
Recent applications of Machine Learning (ML) to hydrology show that a learning system has the potential to train on data-rich basins and generalize to data-poor basins, with a skill that is comparable to state of the art hydrological models. 
In this work we attempt to build a ML model to produce daily flash flood forecasts, based on globally available weather reanalysis and physiographic characteristics from HydroATLAS. We discuss the model architecture, and evaluate it against NWS Flash Flood Warnings (FFW). While such models may not surpass the skill of a professional hydrologist, they have the potential to provide reasonable warnings in regions that do not currently have any such system in place.

How to cite: Zlydenko, O., Cohen, D., Gauch, M., Gerzi Rosenthal, A., Kratzert, F., Nearing, G., Shalev, G., and Gilon, O.: Reproducing flash flood warnings with Machine Learning, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8697, https://doi.org/10.5194/egusphere-egu24-8697, 2024.

Short-term flood and inundation forecasts, especially for spatially limited, convective heavy rainfall events, are challenging due to the very short lead time of heavy rainfall predictions. They are subject to different uncertainties. Due to the short forecasting period, data-driven machine learning methods have been developed to predict the inundated areas in almost real time. The time-consuming numerical, hydrodynamic simulation of flooding depths and flow velocities is replaced by a surrogate model.

We use a neural network as surrogate model, which is trained with a large ensemble of hydrodynamically simulated water levels and flow velocities for a specific catchment using an ensemble of spatial and temporal surface runoff. The surface runoff is calculated by the hydrological model RoGeR at a 5 m resolution and spatial and temporal varying heavy rainfall events as input. The neural networks are able to predict spatially resolved maximum water depths, maximum discharges and maximum flow velocities for an event in the specific catchment area of 25km² in significantly less than one second.

The fast prediction time allows to consider uncertainties in the forecast. Uncertainties in flood forecasts typically result mainly from uncertain precipitation input (or forecast), initial conditions (e.g. soil moisture), lack of data of relevant parameters or their limited transferability in space. Although the model error of the hydrological and hydraulic model itself, such as assumptions about geometry and parameters or underlying flow equations, is an important source of uncertainty, we address in this presentation only the uncertainties of input and initial conditions.

To generate ensemble calculations that represent the probability distribution of predicted flood height and velocity, we create a large ensemble of input variables by statistically varying the initial soil moisture as well as the location and intensity of the precipitation fields. Since the trained neural network topology is another source of uncertainty, differently trained networks with different network topologies are also considered in the ensembles.

Using the ensembles, we can specify prediction intervals for spatially resolved maximum water levels, maximum discharge and flow velocities, which result from the uncertainty of the model input, model parameters and the inaccuracy of the surrogate model. Using the ensemble approach, we discuss the impact of the different sources of uncertainty on the predictions.

As an example, it is found that the statistical variation of the meteorological input data has a greater influence on the prediction interval width than the statistical variation of the initial soil moisture. In general, it can be concluded that sufficient variability in the training data needs to be covered to make reasonable uncertainty predictions.

How to cite: Reinecke, A., Neuweiler, I., Steinbrich, A., Leistert, H., Hänsler, A., Weiler, M., Brendt, T., and Huth, B.: Flash Flood Prediction with Neural Networks using Ensemble Methods to address Input and Model Uncertainties, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10524, https://doi.org/10.5194/egusphere-egu24-10524, 2024.

PREDICTOR (PREDICTing flooding impacts from cOnvective Rainfall) has been developed to improve the approach to forecasting the impacts of surface water flooding. PREDICTOR is a next generation decision-support tool that utilises the latest Met Office convective precipitation ensemble forecasting capabilities and Scotland’s National Flood Risk Assessment (NFRA) flood maps.

The Impact-based Forecasting (IbF) approach of PREDICTOR combines the likelihood ("the chance") of flood-producing rainfall (from the Met Office ensemble forecasts) and the potential impact (from NFRA) to produce "Flood Risk" forecasts. The precipitation forecast product used is the Best Short Range (BSR) ensemble from the Met Office (MOGREPS-UK). 15-minute precipitation accumulations are available, extending out to ~32 hours and issued 4 times a day with 24 ensemble members. The NFRA  surface water flooding maps have been generated using design rainfall inputs from the Flood Estimation Handbook (FEH) plus outputs from a number of different flood modelling studies, and used to consider property and road impacts. 

Neighbourhood or ‘in-vicinity’ post processing of precipitation forecasts is performed to calculate exceedance probability (or ensemble confidence) of the forecast rainfall that would lead to surface water flooding impacts. This is calculated on a 10km grid basis across Scotland to provide individual gridded risk assessments of the likelihood and impact of flooding. The web-based system has been successfully used by SEPA forecasters during 2023 and in partnership with Transport Scotland to assess the value of predicting the risk on the trunk road network.

How to cite: Cranston, M., Rae, J., Cole, S. J., Anderson, S., Nash, G., Black, K., Moore, R. J., and Roberts, N.: Impact-based forecasting for convective rainfall: a new approach combining rainfall ensembles and hazard impacts, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12249, https://doi.org/10.5194/egusphere-egu24-12249, 2024.

There is an urgent need for reliable now- and forecasting of (extreme) precipitation on the African continent. Early warning for extreme rainfall contributes to disaster preparedness and can decrease the associated risks. Moreover, reliable, and seamless precipitation data are of high value for (hydrological) flood models. Flash floods are often caused by intense and localized rainfall over a short period of time. Timely anticipation on the highly dynamic causes of flashfloods requires precipitation data with a high temporal resolution as well as short lead times. The lack of ground-based radar stations on the African continent hinders the availability of such precipitation data and leaves many regions prone to high risks associated with extreme precipitation.

Numerical Weather Predication (NWP) models provide valuable information concerning precipitation forecasting. However, due to large computational demands NWP models are commonly not applicable for short lead times. Nowcasting methods which extrapolate observations show skillful lead times of 0-4 hours. Nevertheless, a significant decrease in skill is observed for longer lead times. Efforts by Imhoff et al., (2023), Radhakrishnan & Chandrasekar, (2020) and Nerini et al., (2019) show promising results using a blending approach which incorporates extrapolation based nowcast data derived from ground-radar and NWP-data.

The high spatio-temporal resolution of Meteosat data (15 minutes and 3 km) in combination with its relative short latency offers potential to partly overcome the shortage of ground-based radar data on the African continent. This research evaluates the applicability and accuracy of precipitation nowcasts based on Meteosat data. For these analyses the open-source Python nowcasting environment Pysteps is utilized. As rainfall retrieval algorithm, the Cloud Physical Properties (MSG-CPP) model, as developed by the Royal Dutch Meteorological institute (KNMI), is applied.

Additionally, this research explores the possibility for blended ensemble precipitation now- and forecasting, combining NWP forecasts with satellite-based observation extrapolations. Meteosat data and the open-source Global Forecast System (GFS) are used as input for this blended precipitation model. Ground measurement data collected by the Trans-African Hydro-Meteorological Observatory (TAHMO) organization is utilized to evaluate the performance of the nowcasting products.

For this study, Ghana is selected as case study area. Ghana has a tropical climate which is strongly influenced by West African monsoon winds. On a yearly basis, (flash) floods cause fatalities and large social-economic damages. This stresses the urgent need for disaster risk management actions wherein access to seamless precipitation now- and forecast models is of high value. The data sources used in this research are all openly available for the complete African continent. By solely utilizing open data sources with short latencies, this research aims to contribute to operational and open access of seamless precipitation now- and forecasts. These efforts are in line with the Early Warning for All initiative as called for by the United National Secretary-General in 2022.

How to cite: Glas, V., Lugt, D., Hurkmans, R., Booij, M., Rientjes, T., Imhoff, R., and Annor, F.: The development and evaluation of a seamless rainfall forecasting system for Ghana using Meteosat data and the GFS model., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12956, https://doi.org/10.5194/egusphere-egu24-12956, 2024.

Heavy rain and convective storms trigger a number of hazards (floods, landslides, debris flows…) that have impacts on people’s life and goods. In these situations, Civil Protection Agencies (CPAs) face multiple challenges in their decision-making processes such as the absence of multi-hazard forecasts or difficulty in translating hazards forecasts in impact-based decisions, or the coordination between CPAs during extreme and/or large-scale events affecting multiple regions and neighbouring countries.

The EDERA project, funded by the EU Civil Protection Mechanism, focuses on the integration of real-time pan-European forecasts of storm and heavy rainfall impacts in the Early Warning Systems of CPAs. The main objective of the project is assessing the added value of these products during an 18-months demonstration in collaboration with end users.

The study focuses on the evaluation of the quality of the hazard and impact forecasts during recent events at two different scales: (i) at the European scale (analysing the skill of the products to identify/anticipate the occurrence of the most significant events), and (ii) in two pilot sites (one focused in Finland and the other one covering Spain and Portugal), analysing their usefulness to support emergency management with the participation of relevant authorities.

How to cite: Berenguer, M., Park, S., Baugh, C., O'Regan, K., Prudhomme, C., Pulkkinen, S., Myllykoski, H., Santiago, A., Durán, A. M., Torres, R. M., Vara, M., Gomes, A., Pereira Colonese, J., and Tasev, D.: Early warning Demonstration of pan-European rainfall-induced impact forecasts – the EDERA project, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17162, https://doi.org/10.5194/egusphere-egu24-17162, 2024.