Operational (early) warning systems are the result of progress and innovations in the science of forecasting. New opportunities have risen in physically based modelling, AI/machine learning, coupling meteorological and hydrological forecasts, ensemble forecasting, impact-based forecasting, and real-time control. Often, the sharing of knowledge and experience about developments are limited to the particular field (e.g. flood forecasting or landslide warnings) for which the operational system is used. Increasingly, humanitarian, disaster risk management and climate adaptation practitioners are using forecasts and warning information to enable anticipatory early action that saves lives and livelihoods. It is important to understand their needs, their decision-making process and facilitate their involvement in forecasting and warning design and implementation (co-generation).
The focus of this session will be on bringing the expertise from different fields together as well as exploring differences, similarities, problems and solutions between forecasting systems for varying hazards including climate emergency. Real-world case studies of system implementations - configured at local, regional, national, continental and global scales - will be presented. An operational warning system can include, for example, monitoring of data, analysing data, making and visualizing forecasts, impact-based solutions, giving warning signals and suggesting early action and response measures.
Contributions are welcome from both scientists and practitioners who are involved in developing and using operational forecasting and/or management systems for climate and water-related hazards, such as flood, drought, tsunami, landslide, hurricane, hydropower etc. We also welcome contributions from early career practitioners and scientists, and those working in multi-disciplinary projects (e.g. EU Horizon Disaster Resilience Societies).
Given the increasing vulnerability due to more frequent and severe hydrological hazards under a changing climate, it is imperative to develop accurate and reliable global and local hydrological prediction systems at subseasonal-to-seasonal (S2S) timescales. However, operational forecasts tailored to specific local regions remain limited due to lack of both local observations and regional hydrological models, while global hydrological models often lack calibration for local conditions, making it challenging for them to capture local-scale dynamics. Additionally, users and decision-makers face difficulties in effectively interpreting ensemble forecasts from multiple hydrological models in operational settings especially when compared to the simplicity and clarity of a single model approach. To bridge this gap, it is essential to integrate existing hydrological forecasting systems across global, regional, and local scales, with the goal of delivering skilful, standardized, and comprehensible predictions. As part of the World Meteorological Organization's (WMO) Global Hydrological Status and Outlook System (HydroSOS) initiative, we are exploring various approaches to: (i) validate and enhance the skill of current hydrological probabilistic forecasts, and (ii) blend multi-model ensemble simulations to develop integrated and reliable operational forecasts. Here, we aim to develop a framework for bias-correcting and blending global multi-model ensemble forecasts, based on the skill of each modelling system for each catchment, to deliver unique probabilistic forecasts. Our research, using global hindcasts from various modelling systems, has demonstrated that applying this framework to post-process raw model simulations can deliver reliable S2S hydrological forecasts across diverse global catchments operationally. This approach has the potential for improved water resource management and hydrological hazard mitigation, particularly in data-sparse regions.
How to cite: Facer-Childs, K., Bulut, B., Chevuturi, A., Hannaford, J., Tanguy, M., Andersson, J., Du, Y., and Pechlivanidis, I.: Blending Subseasonal-to-Seasonal Hydrological Predictions from Multiple Forecasting Systems, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1463, https://doi.org/10.5194/egusphere-egu25-1463, 2025.
Climate change has led to a high demand for an appropriate system for monitoring and forecasting climate extremes, which could support disaster risk reduction and climate change mitigation. This has also led to global initiatives like WMO’s Early Warnings for All Initiative, which aims to provide early warning systems to support decision-making processes by the end of 2027. In this context, there is an urgent need to accelerate the transition from research, primarily conducted in academia, to a sustainable application for developing long-term operational environmental services. Here, we argue that this transition can be enabled and accelerated through Open-Source software tools and libraries, containerization, and the professionalization of research software engineering. They play a crucial role at all stages of technology development, from early research and prototyping to system deployment and scaling. The Technology Readiness Level (TRL) is an effective and standardized measure to assess the maturity of such developments. However, it is still unclear how the TRL can be applied in research-based tools and services and what preparatory steps need to be taken to ensure a certain pre-defined TRL.
In this talk, we will discuss best practices in developing a climate service system, using the example of the ongoing OUTLAST project (operational, multi-sectoral global drought hazard forecasting system), in which an operational drought forecasting system will be developed. OUTLAST is one of the first attempts to build a ready-to-be-transferred system using a cloud-ready concept to seamlessly transfer research-based developments into an operational system among governmental institutions. The present work will show how the currently developed software tools can support researchers in overcoming the current obstacles in technology development. We use OUTLAST to demonstrate how the automated pipeline is executed, from downloading the newly released climate data (ERA5 and SEAS5) provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) to triggering models and generating drought hazard indicators to be pushed to a webpage. In this approach, each processing step and its dependencies in the model chain are encapsulated in a "container" at the research institution before being transferred to run in an infrastructure at an external government institution. The containers are then orchestrated to allow upscaling of the system based on computational requirements and availability of hardware resources. We will then discuss the obstacles in building such a system and how the flexibility and portability can be improved.
Our work highlights the benefits using cutting-edge research software engineering practices for facilitating a seamless transition from research to operational systems and propose best practices, including the necessary preparatory steps. We further present our work as a blueprint for similar initiatives to ultimately support the development and deployment of advanced environmental service systems, which can provide the urgently needed information for decision-makers, stakeholders, and other potential end-users.
How to cite: Vu, T., Reinecke, R., Lorenz, C., Dietrich, S., and Zink, M.: Best practice for transforming an inter-institutional research on climate services into an operational system referring Technology Readiness Level (TRL), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15309, https://doi.org/10.5194/egusphere-egu25-15309, 2025.
Droughts are a recurring global climate hazard that incur human, economic and environmental costs. In Eastern Africa, pastoralist communities whose livelihoods depend on the availability of pasturelands are particularly vulnerable to the impacts of drought. In response to this vulnerability, the University of Sussex developed vegetation condition forecasts for pastoralist communities using remote sensing data and machine learning techniques. These forecasts are designed to be used by the Kenyan National Drought Management Authority in monthly drought early warning bulletins.
Building on stakeholder feedback and given the impacts of drought vary within a county/sub-county we identify a need for higher-resolution forecasts of the onset of drought. Here we present the initial findings from a comparative study exploring a range of machine learning techniques to generate higher resolution vegetation condition forecasts for transboundary pastoralist regions in eastern Africa. We aim to evaluate how the forecast skill varies depending on: machine learning technique, resolution of input data and satellite indicators included.
This work is part of PASSAGE, a CLARE (https://clareprogramme.org/) funded project working towards strengthening pastoralist livelihoods through effective anticipatory action.
How to cite: Hopling, C., Rowhani, P., Muthoka, J., Todd, M., Kniveton, D., and Mwangi, E.: Improving vegetation condition forecasting for drought early warning in East Africa, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17325, https://doi.org/10.5194/egusphere-egu25-17325, 2025.
As a result of climate change, the frequency and severity of droughts in Switzerland is set to increase, with potentially devastating impacts on the environment, economy, and human health. To help mitigate these risks, the MaLeFiX project is developing interdisciplinary extension to the established www.drought.ch platform that will provide comprehensive four-week multi-hazards forecasts of drought-related extremes (https://www.drought.ch/de/impakt-vorhersagen-malefix/).
Droughts are complex phenomena that have significant implications for many aspects of the environment and human life. Understanding droughts and predicting their impacts is crucial for effective preparation and mitigation. The MaLeFiX project is therefore extending the portfolio of drought predictions to a set of relevant impacts across disciplines. The newely developed tools provide comprehensive four-week drought forecasts for the whole of Switzerland, integrating advanced models across hydrology, forest fires, glacier balance, aquatic biodiversity, groundwater, and bark beetle dynamics. Utilizing hybrid AI and meteorological data, the platform will deliver accurate and user-friendly information to help policymakers, stakeholders, scientists, and the public make informed decisions.
The reliability of single forecasts decreases significantly the further they look into the future, making accurate predictions beyond one to two weeks challenging. To overcome this, the MaLeFiX platform uses ensemble forecasts. Its advanced models are fed with meteorological data from MeteoSwiss, which provides monthly forecasts with daily temporal resolution twice weekly. Each forecast is repeated 51 times with slight variations in initial conditions, allowing the MaLeFiX platform to estimate the probability of extreme events up to three to four weeks in advance.
Key recent developments:
- AI-Based Models: Two new AI models have been created to assess forest fire risks and calculate water temperature to evaluate the danger of stress to aquatic life forms, enhancing the accuracy of these critical forecasts.
- Model Harmonization: Existing models for hydrology, glacier balance, and bark beetle dynamics have been refined to work seamlessly with the same input data, enabling clear analysis and interpretation of the overall situation and potential exacerbating factors.
- Multi-model ensemble: the traditional distributed hydrological model PREVAH used at WSL model has been complemented with a multi-model system consisting of 11 different lumped models being operated for 87 headwater catchments.
After the harmonization of models the team is currently working on provide users with a comprehensive overview of the overall drought situation by displaying the possible combined impacts of various drought-related processes (e.g., low runoff and high water temperature).
How to cite: Zappa, M., Padrón Flasher, R. S., Bernhard, L., Buchecker, M., Chang, Y.-Y. A., Cremona, A., Farinotti, D., Gossner, M., Maidl, E., McElderry, R., Pellissier, L., Pezzati, G. B., Schirmer, M., and Bogner, K.: Operational machine learning aided sub-seasonal forecasting of drought related extremes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3452, https://doi.org/10.5194/egusphere-egu25-3452, 2025.
Accurate and timely prediction of streamflow is critical for managing the increasing risks associated with floods, particularly in developing countries where traditional in-situ monitoring systems are often sparse or non-existent. This study introduces a novel probabilistic multi-step ahead prediction model that leverages Graph Neural Networks (GNNs), self-attention mechanisms via the Informer network, and a distributional output layer to enhance the predictive accuracy and uncertainty quantification of streamflow time series. By integrating satellite-derived data, this approach addresses the acute data scarcity prevalent in regions most vulnerable to the impacts of climate change and hydrological extremes. The proposed model captures complex, non-linear spatiotemporal dependencies within multi-sensors data, offering significant improvements over conventional geo-spatiotemporal analysis. This approach is validated across multiple case studies, demonstrating superior performance in both accuracy and reliability enhanced accuracy and reliability over conventional neural network architectures such as Vanilla LSTM, CNN-LSTM, traditional Transformers, and Informers. The incorporation of probabilistic outputs alongside sophisticated self-attention mechanisms significantly improves the model's capability to forecast streamflow over extended sequences, addressing critical gaps in flood forecasting. The findings underscore its potential as a practical tool for enhancing disaster preparedness and optimizing water resource management strategies in data-scarce regions, thereby contributing significantly to the resilience of vulnerable communities against climate-induced threats.
How to cite: Ghobadi, F., Tayerani Charmchi, A. S., Lee, J., Kim, M. I., and jung, K.: Enhancing Streamflow Prediction in Vulnerable Regions through Probabilistic Deep Learning and Satellite-Derived Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17902, https://doi.org/10.5194/egusphere-egu25-17902, 2025.
We present the latest developments on our integrated information physical quantum technological system dynamic framework for multiscale multidomain spatiotemporal multi-hazard intelligence. Advancing system dynamic sensing, awareness, understanding and prediction of multiscale spatiotemporal compound, cascading, coevolutionary and synergistic multi-hazards.
Our next-generation platform leverages the methodological, technological and operational capabilities of Neuro-Quantum Cyber-Physical Intelligence (NQCPI), introduced in Perdigão (2024). NQCPI entails a novel framework for nonlinear natural-based neural post-quantum information physics, along with further advances in far-from-equilibrium thermodynamics and evolutionary cognition in post-quantum neurobiochemistry, for next-generation information physical systems intelligence and security. Rooted in the inherent information physical properties of nature, NQCPI seamlessly operates across classical, quantum and post-quantum environments.
Fundamentally, NQCPI harnesses and operates with emerging nonlinear quantum properties elusive to traditional classical and quantum technological and systems intelligence structures, including new classes of high-order coevolution, emergence and entanglement. It further harnesses new neuro-quantum physical properties, with higher-order post-quantum-proof improvements in security, storage and relaying of information, crucial to fast, robust and secure operation in sensitive prediction and emergency systems.
In the scope of the Earth System Sciences and Natural Hazards, our technology is implemented as a coherent coevolutionary information physical solution spanning across the operational value chain ranging from sensing, analytics, prediction and decision support. For this purpose, it synergistically articules with our maturing technologies including QITES (Perdigão 2020), AIPSI (Perdigão and Hall 2023) and SynQ-WIN (Perdigão and Hall 2024).
The implementation is devised and operated in a cross-platform manner, encompassing seamless articulation and backward compatibility with state-of-art systems across diverse sectors. These include, but are not limited, to hydro-meteorological, naval and aerospace, civil protection and emergency management, among others.
Practical use cases are also addressed, ranging from event-scale early-warning systems to long-term decision support, where our technology has been tested and implemented. Benchmarking tests are also conducted, validating our simulations relative to observational records and assessing the added value of our solution relative to state-of-art approaches, ranging from purely physically and purely data-based to hybrid physically informed machine learning, deep learning and systems intelligence.
A window of opportunity is thus provided for further collaborations and co-creative tailored developments with further end-users, ranging from research laboratories to operational centers, given the cross-platform capabilities for workflow articulation among novel and existing infrastructures.
Acknowledgements: This contribution is developed in the scope of the Meteoceanics Flagship on Quantum Information Technologies in the Earth Sciences (QITES), and of the C2IMPRESS project supported by the Εuropean Union under the Horizon Europe grant 101074004.
References:
- Perdigão, R.A.P.; Hall, J. (2023): Augmented Information Physical Systems Intelligence (AIPSI). https://doi.org/10.46337/230414
- Perdigão, R.A.P.; Hall, J. (2024): Synergistic Nonlinear Quantum Wave Intelligence Networks (SyNQ-WIN). https://doi.org/10.46337/240118
- Perdigão, R.A.P. (2020): QITES – Quantum Information Technologies in the Earth Sciences. https://doi.org/10.46337/qites.200628
- Perdigão, R.A.P. (2024): Neuro-Quantum Cyber-Physical Intelligence (NQCPI). https://doi.org/10.46337/241024
How to cite: Perdigão, R. A. P. and Hall, J.: Neuro-Quantum Cyber-Geophysical Platform for Operational Multi-Hazard System Dynamic Intelligence, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6726, https://doi.org/10.5194/egusphere-egu25-6726, 2025.
Effective flood risk management crucially depends on precise floodplain inundation mapping and proactive early warning systems. This study introduces an innovative framework that automates the Hydrologic Engineering Center's River Analysis System (HEC-RAS) for 2D unsteady flow simulations, integrated with a state-of-the-art probabilistic deep learning model for enhanced streamflow prediction. This framework innovatively forecasts both lower and upper inundation bounds, substantially improving the accuracy and reliability of flood risk assessments. It employs a probabilistic deep learning model using a Transformer-based neural network with a distribution head, allowing dynamic adaptation to diverse hydrological conditions. This adaptation supports the generation of precise flood scenarios and enables effective, timely interventions. Validation across a series of South Korean case studies, selected for their hydrological diversity, confirms the framework's enhanced predictive capabilities in mapping flood extents over conventional methods. Additionally, integrating automated parameter optimization, Monte Carlo simulations, and adaptive learning algorithms within HEC-RAS enhances the scalability and adaptability of flood modeling efforts. The automated framework streamlines complex simulation processes while effectively addressing inherent model uncertainties and integration challenges in practical applications. By providing a robust, scalable, and adaptable tool, this framework contributes to hydrological modeling and transforming flood risk management in flood-prone areas worldwide.
How to cite: tayerani charmchi, A. S., ghobadi, F., Kim, M. I., Lee, J., and Jung, K.: Advanced Automation of HEC-RAS for Predictive Floodplain Mapping and Early Warning through Probabilistic Deep Learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17140, https://doi.org/10.5194/egusphere-egu25-17140, 2025.
Coastal regions, particularly deltaic systems, are highly susceptible to flood risks arising from the complex interactions of storm surges, riverine flooding, upstream reservoir discharges, and heavy inland rainfall. Traditional flood forecasting models often struggle to integrate these diverse factors effectively, leading to significant uncertainties in predicting flood extents. To address this critical gap, this study presents a novel and comprehensive coastal flood inundation forecasting framework designed for regions frequently impacted by tropical cyclones and extreme hydrological events. The framework integrates multiple components, including real-time reservoir inflow forecasting by SWAT-Pothole+WBiLSTM model, HEC-ResSim-based reservoir outflow predictions governed by operational rule curves, storm-surge and tide predictions utilizing ADCIRC+SWAN hydrodynamic and WBiLSTM deep learning approaches, and flood inundation modeling by HEC-RAS two-dimensional hydrodynamic simulation. The methodology was applied to the twin Brahmani-Baitarani river systems in eastern India, a region prone to recurrent cyclonic storms and severe flooding. Validation of simulated flood extents was conducted using Sentinel-1 satellite imagery from several tropical cyclone events, demonstrating the model's robust predictive capabilities. The results showed that the framework achieved accuracy levels ranging from 86.72% to 38.12% for lead times between one and eight days. Additionally, the model underscores the importance of incorporating all contributing factors, including the dynamic interaction of coastal and inland flooding processes, to achieve realistic flood forecasts. This research not only advances the understanding of coastal flooding but also offers a practical and scalable tool for enhancing early warning systems through informed flood risk management strategies in vulnerable coastal regions worldwide.
How to cite: Mohanty, A., Sahoo, B., and Kale, R. V.: An Integrated Framework for Coastal Flood Inundation Forecasting: Advancing Early Warning Systems in Vulnerable Deltaic Regions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15042, https://doi.org/10.5194/egusphere-egu25-15042, 2025.
This study compares operational flood forecasting and early warning systems (FFEWSs) in transboundary river basins in Northwestern Europe, covering parts of Luxembourg, Germany, the Netherlands and Belgium. This region was hit by an extreme flood event in 2021 with over 200 fatalities. Expert interviews from the region revealed strong improvements of the FFEWSs after this flood event in all countries. All regions have invested in probabilistic flood forecasting systems to improve warnings, and all countries now use mobile phone-based alerts. The interviews also revealed that, while ensemble forecasting systems are well-developed, the translation of those meteorological and hydrological forecasts to impacts, warnings and actionable advices remains difficult. A main challenge is the operational implementation of impact-based forecasts and warnings. For example, interviewees highlighted the need for operational flood inundation predictions. However, Flanders is the only region where such forecasts are provided. Hydrological forecasts for smaller upstream tributaries are generally unavailable or subject to large uncertainties. Strong differences exist in flood warning levels and color codes across and within the countries. These differences can hamper information exchange between regions and institutions and may confuse crisis managers and the public. In response to the extreme flood event in 2021, Luxembourg and some regions of Germany have recently introduced an additional violet warning code for the most extreme weather and hydrological events. However, it is still under debate whether additional warning levels support more effective communication to the public and responders. It is strongly recommended to enhance forecasts with impact-based information, including maps delineating potential inundation areas and people and objects at risk. Such information can enable crisis managers and first responders to take more timely and appropriate actions.
How to cite: Busker, T., Rodriguez Castro, D., Kwadijk, J., Loureiro, R., J. Murdock, H., Pfister, L., Dewals, B., Slager, K., Thieken, A., Verkade, J., Vorogushyn, S., Willems, P., Zoccatelli, D., and C.J.H. Aerts, J.: Comparing Flood Forecasting and Early Warning Systems in Transboundary River Basins, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11485, https://doi.org/10.5194/egusphere-egu25-11485, 2025.
Impact-based forecasting (IBF) represents a significant advancement in natural disaster risk management by focusing on the vulnerabilities of people, livelihoods, and assets. Here we introduce the methodology of PANDORA (Impact based forecasting to cope with riverine floods in the Huallaga river basin), a system designed to provide impact-based forecasts for a basin in the Andean-Amazon region of Peru. PANDORA integrates a large-scale hydrological model with precipitation forecasts resampled from historical meteorological data to produce 5-day probabilistic streamflow forecasts. These are compared against flood thresholds for 2, 5, and 10-year return periods, corresponding to moderate, heavy, and extreme severity levels. Subsequently, they are linked with key flood-exposed elements: (i) population, (ii) educational institutions, (iii) health centers, (iv) road networks, and (v) agricultural areas. Potential impacts can be assessed at various administrative levels, including districts, provinces, and departments. The system’s performance was evaluated during December 2023, when significant river floods occurred in the basin. Results show that flood events were primarily forecasted between December 27 and 30, while the IBFs indicated extreme severity (red level) for the exposed elements mainly on December 27, 30 and 31. These findings align with reports from the Information System for Response and Rehabilitation of Peru. Despite existing limitations, PANDORA is currently operational and demonstrates great potential to support local authorities in decision-making processes for flood risk management.
How to cite: Saavedra, D., Alencar Siqueira, V., Schmitt Quedi, E., de Araújo Gama, C. H., Collischonn, W., and Lavado, W.: Impact based forecasting to cope with riverine floods in Peruvian Andes—Amazon basin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14611, https://doi.org/10.5194/egusphere-egu25-14611, 2025.
Denmark, as a low-lying country, is subject to acute flood risks including urban flash floods (cloudbursts) from intense convective storms, fluvial flooding from heavy rainfall and extreme sea levels and storm surge along more than 7,300 km of coastline. Motivated by the extensive flooding in Denmark in 2020 and the devastating 2021 floods in Central Europe, the Danish Meteorological Institute (DMI) became the national authority for flood forecasting and warning, tasked with developing an operational system to forecast storm surge, pluvial and fluvial, flood risks.
To support anticipatory early actions, the key goals were to provide an operational 24/7 capability to issue timely, accurate and reliable flood forecasts, early warnings and associated flood impacts. With significant pressure to develop and deploy operational tools to support Denmark's emergency authorities within the first 18 months, we adopted pragmatic and simplified modeling approaches, balancing resolution, complexity, data availability and computational efficiency.
We present the rapid development of operational capabilities to support the local and national emergency services, for storm surges, pluvial and fluvial flood events in Denmark, guided by initial consultations with the emergency services. Within the first year we developed, together with Scalgo, a real time flood mapping service for elevated sea levels and storm surges. This covers the entire Danish coastline, based on hourly water level forecasts, 5 days ahead. This service became operational in October 2022, immediately prior to the 2022 storm surge season. The timely launch allowed us to evaluate the performance of this service against the 100+-year storm that hit the coasts of Denmark in October 2023. A new cloudburst flood mapping service was developed, also together with Scalgo, including a new topography-based flood mapping approach to account, in a computationally efficient way, for effects of infiltration and urban drainage systems. This service became operational for all of Denmark in May 2023, at the beginning of the 2023 cloudburst season. Feedback from meetings with the emergency services during 2024 confirmed the value of this mapping service. Finally, for fluvial flooding, a rule-based warning system was initially developed. This approach uses statistical analysis of river levels and precipitation thresholds and exploits a newly developed national inventory of historical floods. Manual warnings, to the emergency services, based on this approach began in July 2023 focussed on high-risk areas and stations with good quality water level data. Our own evaluations of these new capabilities during the first year of operations were shared, in a series of workshops, with the local and national emergency services. The workshop objectives were to obtain their feedback and to understand their needs for the next development phase. We discuss how this rapid process for operational implementation of a national system was achieved. This includes our initial evaluations, operational challenges and solutions, as well as future end-user involvement and development plans. We are currently developing both machine learning approaches and conceptual hydrological modelling to extend our forecasting capability towards multi-model fluvial forecasting.
How to cite: Henry Madsen, M., Payet-Burin, R., Butts, M., Dhaubanjar, S., Wied Pedersen, J., Martinsen, G., Aarestrup, P., Agata Plum, C., Thrysøe, C., Lerer, S., and Dybro Thomassen, E.: Rapid development of impact-based national flood early warning system, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10588, https://doi.org/10.5194/egusphere-egu25-10588, 2025.
The national flood warning system for Norway is undergoing a renewal process, moving towards risk-based flood forecasting. The FlomRisk project, launched in 2022, involves user participation from five municipalities as pilot study areas. Different flood forecasting setups have been evaluated over three years, including hydrological and hydraulic model selection and methods for aggregating local impact. The project aims to i) improve regional warnings from the national flood warning service by better reflecting local impacts and ii) identifying the municipalities' information needs during critical flooding stages.
A service design approach was used to focus national warning services on creating relevant and useful products. The involvement and codesign with municipalities began in 2022. In 2023, over 100 user meetings and interviews were conducted, covering more than five municipalities, national flood experts, and consultants. Information was gathered on the stages of decision-making during flooding events: before (preparation phase), during (coordination and handling during the crisis), and after (event evaluation and future learning points). Four key needs were identified by the municipalities:
- Early information to get an overview of possible situations. What kind of challenges might the emergency response units anticipate.
- Useful and locally relevant information about the situation and possible consequences.
- More effective communication, both internally and externally, towards media and inhabitants.
- Easier documentation of consequences and adaptation measures during ongoing situations.
In 2024, using insights from the previous year, a prototype for Voss Municipality was developed. Voss faces complex flood and natural hazard challenges. The prototype was codeveloped with knowledge from local flood contacts, emergency response leaders, modeling teams, existing products, and efforts across institutions and sectors. The prototype consists of two modes, one is for an ongoing situation, whereas the second is to evaluate the impact of different flood scenarios.
This initial prototype will be presented to a panel of different municipalities and users, essential for suggestions and making alterations. Different users provide useful feedback and insight based on their varying experiences with flood and natural hazard challenges, knowledge, and organizational structures of emergency responses. This approach helps formulate suggestions on how municipalities can build or integrate their decision support systems to improve local flood responses to regional warnings.
How to cite: Hegdahl, T. J., Vidringstad, T., and Timbadia, A.: From Regional Flood Warnings to Local Decision Support: Applying a Service Design Approach for Voss Municipality, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4853, https://doi.org/10.5194/egusphere-egu25-4853, 2025.
Flash floods are a significant societal problem, that rank as the World Meteorological Organisation’s top priority hazard. Pinpointing where and when they will hit is however extremely challenging beyond lead times of an hour or two, even when using state of the art convection-resolving ensembles, due mainly to significant ensemble size limitations. There has been more success in highlighting areas at risk from flash floods by post-processing numerical model output, either from these limited area ensembles, or from global ensembles with parametrised convection, or by blending the two.
A benefit of using global ensembles is that they are much less constrained spatially and in terms of lead times. One successful post-processing approach applied here has been the ECMWF “ecPoint” system. This can deliver finite probabilities for very large, localised totals that ordinarily the raw ensemble system cannot, and should not, predict itself. These have verified very well but could be considered less actionable by users because the probabilities delivered, for a point in a given gridbox, in advance of extreme events, are often very small (e.g. 1-5%). This presentation will outline three developments related to the ecPoint approach that make it more amenable to users by 1) providing an estimate of likely maxima within a gridbox, that 2) tailor better to flash flood risk than purely to rainfall totals by cross referencing a new global point-rainfall climatology, and that 3) demonstrate clear ‘financial’ utility even if probabilities are small, via computations of potential economic value. Case studies will be used for illustration.
How to cite: Pillosu, F., Hewson, T., and Zsoter, E.: Making Low Probability forecasts of High Impact Hydrological Events more useful for Society, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18796, https://doi.org/10.5194/egusphere-egu25-18796, 2025.
From 7. August 2023 to 9. August 2023, Norway was hit by the extreme weather event “Hans”. Especially in the southern and western parts of the country, municipalities were warned about precipitation of up to 100 mm within 24 hours (Norwegian Meteorological Institute, 2023) causing extensive flooding and landslides. Rain, flood and landslides warnings were issued early and for large areas. Not all municipalities reacted with the necessary urgency to the situation for various reasons. In a survey of the affected municipalities after “Hans”, many municipalities described that they were overwhelmed with the circumstances or had no prior experience with the size of the predicted floodings. On the other hand, there were municipalities that had experienced major floodings before, but underestimated the severity of “Hans”. The general opinion of Norwegian municipalities is that there are too many warnings, leading to warning fatigue. Hence, this study aims to: i) analyse the total number of issued warnings, as well as the warning level assigned, and ii) analyse the warning response of selected municipalities before and after “Hans”.
Through a statistical review of all municipalities’ warnings, clusters of municipalities prone to warning fatigue, or under-preparedness are found. By comparing the response to different extreme weather events, the goal is to identify patterns resulting in over-warning or warning fatigue. It is expected that the number of issued warnings, will have increased over the last ten years, however cross-referencing with the preparedness to future events by installing mitigation measures, citizen education on natural hazards and such, differences between municipalities will become apparent.
How to cite: Bauer, C., Jahr Hegdahl, T., and Berthling, I.: Identification of Warning Fatigue and Its Impact on Municipal Preparedness in Norway, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10096, https://doi.org/10.5194/egusphere-egu25-10096, 2025.
Rapid Flood Guidance for flash floods – an operational service for England and Wales in summer 2025
Charlie Pilling, Adrian Wynn, Neil Armstrong, Russell Turner, Julia Perez, Chris Lattimore, Catherine Birch
Flash floods, or rapid response catchment flooding, can be defined at flooding impacts between 0-6 hours of impactful rainfall occurring. Nowcasting can be defined at the 0-2 or 0-6 hour time scale. Further tragic rapid-onset events across Europe and globally during 2024 re-enforced the need for improved prediction and communication of rapid flooding to save lives. To save lives, warnings at these very short lead times, whether they are for urban areas or ravines, need to be issued rapidly to a receptive customer base.
During summer 2024, the UK Met Office (UKMO) Expert Weather Hub and the Flood Forecasting Centre (FFC) for England and Wales ran a pilot from May to September to nowcast and warn for intense rainfall and rapid onset flooding. The Expert Weather Hub operated a surge capacity drawing on rapidly updating diagnostics to identify areas of intense convection and flood producing rainfall, as well as other hazards. At the same time, FFC piloted a Rapid Flood Guidance Service where days 1 and/or day 2 of the daily Flood Guidance Statement are highlighted as susceptible to rapid flooding. This highlighted potentially affected areas of England and Wales to emergency responders. Then as storms broke out and the risk of rapid flooding increased, the detailed output from the Expert Weather Hub was used by the FFC to issue Rapid Flood Guidance to emergency responders at short lead times, less than 6 hours, and possibly less than 2 hours’ notice.
This presentation will explain the components of the Rapid Flood Guidance trial and present key findings from research to operations, as well as a summary of the evaluation from the hundreds of emergency responders. It will also highlight key findings from the evaluation of the surface water impact models, with a focus on less than 24 hours lead time. We will highlight development areas to the science and operational development and suggest how such short notice warnings can best be communicated to potential users to incite the appropriate actions. This will also highlight finding and recommendations from the Met Office Summer Forecasting Testbed 2024 which compared two rapid surface water flooding hazard impact models. The Surface Water Flooding Hazard Impact Model, SWFHIM, was developed through the Natural Hazards Partnership and is currently used operationally in the FFC. The second, FOREWARNS, has been developed by the University of Leeds and the Met Office.
The design of the 2025 operational Rapid Flood Guidance service will be described on the ‘eve’ of its launch May 2025.
How to cite: Pilling, C.: Rapid Flood Guidance for flash floods – an operational service for England and Wales in summer 2025 , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14408, https://doi.org/10.5194/egusphere-egu25-14408, 2025.
