HS4.5 | Reducing the impacts of natural hazards through forecast-based action: new insights from people-centered, impact-based warning systems
EDI
Reducing the impacts of natural hazards through forecast-based action: new insights from people-centered, impact-based warning systems
Co-organized by NH10
Convener: Marc van den Homberg | Co-conveners: Stefan Schneiderbauer, Andrea FicchìECSECS, Faith Mitheu, Tim BuskerECSECS, Marta Giambelli, Patricia Trambauer
Orals
| Thu, 18 Apr, 08:30–10:15 (CEST)
 
Room 2.31
Posters on site
| Attendance Thu, 18 Apr, 16:15–18:00 (CEST) | Display Thu, 18 Apr, 14:00–18:00
 
Hall A
Posters virtual
| Attendance Thu, 18 Apr, 14:00–15:45 (CEST) | Display Thu, 18 Apr, 08:30–18:00
 
vHall A
Orals |
Thu, 08:30
Thu, 16:15
Thu, 14:00
The Early Warning for All initiative in alignment with the Sendai Framework for Disaster Risk Reduction (SFDRR) recognizes that increased efforts are required to develop life-saving risk-informed and impact-based multi-hazard early warning systems. Despite remarkable advances in disaster forecasting and warning technology, it remains challenging to produce useful forecasts and warnings that are understood and used to trigger early actions. Overcoming these challenges requires progress that goes beyond the improved skill of natural hazard forecasts. It is crucial to ensure that forecasts reflect on-the-ground impacts, provide actionable information and to understand which implementation barriers exist to do so. This, in turn, requires commitment to the creation and dissemination of risk and impact data as well as the collaborative production of impact-based forecasting services. To deal with these challenges, novel science-based frameworks have recently emerged. For example, Forecast-based Financing and Impact-based Multi-Hazard Early Warning Systems are currently being implemented operationally by both governmental and non-governmental organisations in several countries. This achievement is the result of a concerted international effort by academic, governmental/intergovernmental and humanitarian organizations to reduce disaster losses and ensure reaching the objectives of SFDRR. This session aims to offer valuable insights and share best practices on impact-based multi-hazards early warning systems from the perspective of both the knowledge producers and users. Topics of interest include, but are not limited to:
● Practical applications and use-cases of impact-based forecasts
● Development of cost-efficient early action portfolios
● Methods for translating climate-related and geohazard forecasts into actionable impact-based information
● Action-oriented forecast verification and post-processing techniques to tailor forecasts for early action
● Triangulation of indigenous and scientific knowledge for leveraging forecasts, multi-hazard risk information and climate services to last-mile communities
● Bridging the gaps in risk and impact data to support impact-based forecasting, collecting and expanding data on interventions to build an evidence base for early actions
● Innovative solutions to address challenges in implementing forecast-based actions effectively, including the application of Artificial Intelligence, harnessing big data and earth observations.

Session assets

Orals: Thu, 18 Apr | Room 2.31

Chairperson: Marc van den Homberg
08:30–08:35
08:35–08:45
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EGU24-5309
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Highlight
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On-site presentation
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Andrea Libertino, Lorenzo Alfieri, Laura Poletti, Nicola Testa, Alessandro Masoero, Simone Gabellani, Marco Massabò, Jully Ouma, Ahmed Amdihun, Godefroid Nshimirimana, John Mathias KiriwaiJ, Lusajo Ambukeje, Luca Rossi, Katarina Mouakkid Soltesova, and Huw Beynon

The Africa Multi-Hazard Early Warning and Action System for Disaster Risk Reduction (AMHEWAS for DRR) is a joint effort, led by the African Union Commission (AUC) in coordination with Regional Economic Communities and Member States and with the technical and scientific support of UNDRR and CIMA Foundation, aimed at strengthening Africa's resilience to natural hazards. This comprehensive system encompasses a multi-scale approach, spanning from continental to regional and national levels, to enhance early warning capabilities and promote effective disaster risk management strategies. 

On the continental scale, AMHEWAS operates through a network of Situation Rooms. These interconnected hubs facilitate real-time information exchange, coordination of response efforts, and dissemination of advisories on potential threads and related impacts to national institutions. To ensure standardized operational procedures across the continent, AMHEWAS has established unified standard operating procedures, ensuring consistent application protocols and methodologies. 

Central to AMHEWAS' approach is the Continental Watch (CW), an impact-based forecast bulletin for rain, wind and flood hazards, that synthesizes insights from automated impact-based forecast systems. The CW provides timely and actionable information to decision-makers across the continent, enabling proactive measures to mitigate potential disaster impacts. Ongoing disasters can trigger Disaster Situation Reports (DSRs), co-produced by the AUC with the affected Regional Economic Communities (RECs) and the national AMHEWAS stakeholders, for informing disaster risk reduction (DRR) efforts and ensuring timely and appropriate responses to emergencies.  

AMHEWAS integrates risk data and forecasting products from global and regional authoritative sources to produce advisories as a combination of hazards, exposure, vulnerability and national copying capacity. Based on the possible expected impacts in the next 5 days, advisories are issued with a threshold-based mechanism with 4 levels of activation of the system. High level is related with the potential of the estimated impacts to overcome the capacity of the countries, while for lower advisories the effects are expected to be managed by national or subnational authorities. The potential impacts are estimated with an innovative automatic approach, that involves the overlap of the forecasted hazards, with layers of exposed elements, taking into consideration the lack of copying capacity derived from the INFORM database. 

In order to maximize the robustness of the forecasts AMHEWAS adopts a multimodel approach. As regards wind and rain, the forecast is carried out considering the combination of different meteorological global models. As regards flood, the reference model is GLOFAS, combined for the Great Horn of Africa region with the results of the impact-based flood forecast system FloodPROOFS East Africa (FPEA). FPEA is an operational system based on open-source technologies that employs an impact-based approach, integrating weather forecasting, hydrology and hydraulic modeling, as well as risk assessment to provide accurate and actionable flood forecasts up to five days in advance. Given its cross-border nature, the system allows for a comprehensive approach to large-scale hydrological assessment, easily scalable in an operational framework on a national scale. 

AMHEWAS is working on further integration of regional forecasting products from WMO specialized centers and national level, in order to improve the risk knowledge and information products generated.

How to cite: Libertino, A., Alfieri, L., Poletti, L., Testa, N., Masoero, A., Gabellani, S., Massabò, M., Ouma, J., Amdihun, A., Nshimirimana, G., KiriwaiJ, J. M., Ambukeje, L., Rossi, L., Mouakkid Soltesova, K., and Beynon, H.: Africa Multi-Hazard Early Warning and Early Action System for Strengthening Resilience to Natural Hazards , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5309, https://doi.org/10.5194/egusphere-egu24-5309, 2024.

08:45–08:55
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EGU24-13096
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ECS
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Highlight
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On-site presentation
Sumiran Rastogi, Micha Werner, and Marc van den Homberg

Climate services are increasingly being co-produced through a negotiation process between climate service providers, purveyors, and end users. Their different knowledge systems (scientific and local) determine to a large extent this process. Local knowledge, covers a range of different knowledges, and includes how individuals perceive their surroundings, validate new information such as coming from science-based climate services, and solve problems. As such, local knowledge holders can range from indigenous, rural, or urban communities to professionals working at various levels of governance and various positions across the climate services value chain (e.g., service providers and purveyors).

Given the diversity of knowledges and knowledge holders, the actual integration of local knowledge in a climate service is challenging. In this research, we present an approach to collect, understand, and integrate local knowledge for climate services through the utilization of decision timelines. Decision timelines are effective tools for elucidating and understanding the decision-making process, allowing stakeholders to visualise changes and patterns over time (e.g., months, seasons, multiple years, etc). Through visual representation, decision timelines offer an effective way to understand links between different knowledges, stimulate discussions, co-design, and co-evaluate climate services with users. Traditionally such timelines have been limited to agricultural users to introduce the topic of climate information and how it relates to the key decisions that farmers need to make. However, in this research, we expand the scope of these timelines to different sectors (e.g., tourism, urban environment) and also to other actors in the climate services value chain (so not only the end user of a climate service). The timelines are instrumental to understand the decision-making over time and to elicit environmental and socio-economic cues (from local or scientific knowledge). Making timelines for those actors more upstream in the climate services value chain also allows to understand the co-production and knowledge management underpinning the governance process and climate service provision itself. We present examples from the different living labs that have been established in the I-CISK project (an EU research initiative), where these decision timelines have been used as a tool to elicit and understand local knowledge.

How to cite: Rastogi, S., Werner, M., and van den Homberg, M.: Harnessing decision timelines to improve understanding and integration of local and scientific knowledges across the Climate Services value chain, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13096, https://doi.org/10.5194/egusphere-egu24-13096, 2024.

08:55–09:05
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EGU24-12075
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On-site presentation
Michele Calvello, Guido Rianna, and Brian Golding

The contribution addresses, from a conceptual point of view, the complex issue of evaluating the performance of warning systems that are operating over large areas to cope with the risk posed by extreme weather events. In the protocol, the performance of the systems is evaluated, at each step in the warning production process, considering the “warning value chain” schematization developed in the HIWeather project of the World Meteorological Organization (http://hiweather.net/Lists/130.html). In a perfect warning chain, the warning received by the end user would contain precise and accurate information that perfectly met their need, contributed by each of the many players in the chain; in real warning chains, information, and hence value, are always lost as well as gained at each link in the chain (Golding 2022, https://link.springer.com/book/10.1007/978-3-030-98989-7).

The protocol is structured as a three-part evaluation process: 1) description of the system; 2) assessment of criticalities during high impact events; 3) routine assessment of daily operations. For each part, the protocol prescribes a set of must-do. The description of the warning system must be based on the schematic subdivision of the warning value chain, i.e., six main capabilities and outputs and five information exchanges elements. An important focus on the evaluation of an operational warning system must be devoted to high impact events. For such cases, the evaluation must include: essential information on the event; information on how each element of the warning value chain has been working during the event; synthetic assessment on the performance of the warning system. Finally, the routine assessment must include: identification of the system’s operational elements; identification of the areas covered by the system; identification of period for which to conduct the assessment and sources of data to be used; identification of appropriate and computable (considering the available data) performance indicators for the different elements of the warning value chain; analysis of relevant data for the chosen time period in the identified areas; evaluation of the performance of the different elements of the waring value chain; final judgment on the overall performance of the system.

This study is being carried out within the Horizon Europe project “The HuT: The Human-Tech Nexus - Building a Safe Haven to cope with Climate Extremes” (https://thehut-nexus.eu/). The protocol has been developed considering two cases studies, and will be further put to test during the remaining part of the project. Through this action, detailed information from many different warning systems will be collected and used for a comparative study between warning systems operating, in different areas of the world, for different weather and climate related risks.

How to cite: Calvello, M., Rianna, G., and Golding, B.: Protocol for an end-to-end evaluation of operational warning systems, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12075, https://doi.org/10.5194/egusphere-egu24-12075, 2024.

09:05–09:15
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EGU24-15644
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On-site presentation
Sabrina Meninno, Marta Giambelli, Miranda Deda, Rocco Masi, Antonio Gioia, Enrico Ponte, Marco Massabò, Marina Morando, Romanella Vio, Chiara Paniccia, and Stefania Renzulli

The development and implementation of an effective Early Warnings to Early Actions system (EW-EAS) represent a complex system that integrates scientific insights with practical preventive measures on the ground. This complexity is enhanced by the involvement of diverse actors from various sectors and territorial levels, making the system vulnerable to potential breakdowns arising from factors such as unclear messages, unmet user needs, and implementation gaps.

 Recognizing this complexity and the necessity of merging scientific knowledge with operational field expertise, a set of general criteria for establishing “effective links between EW and EA” as related to floods was formulated in in the framework of the IPA Floods and Fires program for the Western Balkans and Türkiye. They resulted from a collaborative capacity development process conducted by experts from the CIMA Research Foundation and the Italian Civil Protection Department in collaboration with Disaster Risk Management Authorities and hydrometeorological services of the IPA countries.

Specifically designed for technicians and operators of the National Hydro-Meteorological and civil protection agencies, the general criteria serve as valuable resource of knowledge, experience and guidance for practitioners of national and local institutions which have the mandate to protect people, assets and the environment, by reducing the impacts of a flood and preventing the occurrence of emergency situations.

The General Criteria address several areas of the EWS with the ultimate purpose of enhancing a timely response to warnings before a flood occurs, in a progressive way and through early actions that are coordinated among all actors and integrated into plans and procedures. More specifically, the general criteria explore four key areas:

  • Early Warning. As an example, providing clear, consistent, and informative early warning messages (stating who produces the warning, to whom it is addressed, what the expected hazard scenario is, where it is likely to occur, when it is expected, and why it is significant) permits a correct and informed activation of the system.
  • Early Actions and the integration of an EW-EA link within emergency response plans. For instance, defining activation phases of the civil protection system linked to specific alerts enables a systematic and incremental mobilization of resources as flood severity escalates. This key area also offers guidance for constructing a set of early actions, ensuring early actions align with forecasted alert levels and risk information codified within the early warning system.
  • Communication flows for the dissemination of EWs and exchange of information among operational centres and institutions before, during and after the emergency and consequently an effective response. Central to this is the coordination and collaboration across actors in EW-EA, optimizing scarce resources for effective delivery.
  • Simulation exercises. Testing through simulation exercises enables continuous improvements and corrections of gaps to further refine the system.

The general criteria offer a framework for practitioners and institutions for improving the link from EW to EA, transforming risk information into actions on the field that can reduce the impacts of floods to communities.

How to cite: Meninno, S., Giambelli, M., Deda, M., Masi, R., Gioia, A., Ponte, E., Massabò, M., Morando, M., Vio, R., Paniccia, C., and Renzulli, S.: Establishing effective links between early warnings and early action: general criteria for floods , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15644, https://doi.org/10.5194/egusphere-egu24-15644, 2024.

09:15–09:25
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EGU24-14892
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On-site presentation
Bill Fry, Christopher Mueller, Chris Moore, Emily Lane, Jen Andrews, Chris Zweck, Aditya Gusman, Sophia Tsang, Emeline Wavelet, Anna Kaiser, Ciaran King, Xiaoming Wang, and Biljana Lukovic

Since the 1960s, tsunami early warning has, for the most part, been predicated on using earthquake characterisation as proxy information for tsunami generation. Shortcomings with this approach include large epistemic uncertainties in wave forecasts that typically preclude actionable impact-based forecasts. Fortunately, the tsunami early warning paradigm is shifting. Here we present a prototype next-generation tsunami early warning system implemented by the New Zealand RCET (Rapid Characterisation of Earthquakes and Tsunamis) programme that is currently operational on a best-endeavours basis in New Zealand. This system is based on 1) observational advances including the densification of deep-ocean tsunami meters, 2) scientific advances provided by direct tsunami inversion 3) ensemble and time-dependent forecasting and 4) co-creation with end users of impact-based forecasts products. We call this system TiDeTEW (Time Dependent Tsunami Early Warning)

Following the recent deployment of the 12-station NZ DART tsunamimeter array (Fry et al., 2020), New Zealand’s Tsunami Expert Panel (TEP) can now use direct observations of tsunamis to underpin time-dependent tsunami early warning forecasts. By using DART inversions and ensemble modelling, we reduce uncertainties in forecasts enough to generate actionable early warning products that provide information about the evolution of the threat prior to land arrival, analogous to weather forecasting of storm evolution. Our forecasting products are being improved through co-development with at risk coastal communities that are dominantly indigenous Māori. In past natural disasters, the social structure of Māori communities has proven to be a major advantage in response and incorporation of Māori values into decisions around risk tolerance of the early warning products guides our levels of forecast conservatism. Understanding the response structure in these communities and its strong reliance on marae (Māori communal meeting houses) is also guiding our product development.

In an aligned effort within the UNESCO Intergovernmental Oceanographic Commission (UNESCO-IOC), we have developed a risk-based approach to assess the efficacy of this tsunami early warning method. We quantify the relative number of tsunami sources for which data support at least 20 minutes of pre-impact warning time to vulnerable coastal populations. We further map the warning gaps to population density of exposed coastlines. We apply this scheme using the NZ DART network to better quantify domestic and Southwest Pacific risk and resilience gains delivered by NZ DART and further highlight existing gaps and opportunities, largely around local source tsunamis.

How to cite: Fry, B., Mueller, C., Moore, C., Lane, E., Andrews, J., Zweck, C., Gusman, A., Tsang, S., Wavelet, E., Kaiser, A., King, C., Wang, X., and Lukovic, B.: Enduser Driven and Impact-based Time Dependent Tsunami Early Warning (TiDeTEW) in Aotearoa New Zealand, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-14892, https://doi.org/10.5194/egusphere-egu24-14892, 2024.

09:25–09:35
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EGU24-15879
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On-site presentation
Annabelle Moatty, Mangeney Anne, Le Friant Anne, Poulain Pablo, Marboeuf Alexis, Silver Maxwell, Lemoine Anne, and Pedreros Rodrigo

Mayotte island is divided in two main islands, Grande Terre (363 km²) and Petite Terre (11 km²), and is located in the Comoros archipelago in the Indian Ocean between Madagascar and Mozambique. From a social point of view, this French department is characterised by a young and highly vulnerable population (over 70% live below the poverty line). Furthermore, many households are exposed to hazards such as floods and landslides, cyclones, earthquakes and tsunamis. Concerning these last two, the 2018 seismo-volcanic crisis linked to Fani Maoré (the submarine volcano located 50 km east of Mayotte) has generated a demand from the local and national authorities for decision support elements to implement a risk prevention strategy and anticipate crisis situations.

The objective of this study is to question the interdisciplinary contributions of landslide-generated tsunami numerical modelling and geographical analysis in order to characterise Mayotte’s vulnerability regarding tsunami hazard. By combining the results of numerical simulations performed with the HySea model (Poulain et al, 2022) with available data on the assets (location, level of vulnerability to tsunami risk, etc. (Sahal, 2011)), we carried out a spatial analysis to identify the critical areas in the event of a tsunami, and the consequences of their potential damage.

Our results provide a characterisation of land use in hazard prone areas for four levels of hazard, from low to very high, resulting from the correlation of water depths and velocity. They also support an analysis of the vulnerability of part of the built environment of Petite Terre (which is most at-risk) by mapping these hazard data with vulnerability data at building level. Although the proportion of buildings and roads potentially affected remains relatively low (around 3%), the modelled scenario highlights major organisational vulnerability. Indeed, early warning strategies and systems are challenged on the one hand by the arrival times of the first simulated wave (between 4 min at the airport in the south of Petite Terre, and 13,5 min in Mamoudzou, the capital located to the east of Grande Terre (Poulain et al., 2022)), and on the other by the complexity of detecting a submarine landslide in advance if it is not generated by an earthquake.

References:

Poulain, P., le Friant, A., Pedreros, R., Mangeney, A., Filippini, A. G., Grandjean, G., Lemoine, A., Fernández-Nieto, E. D., Castro Díaz, M. J., and Peruzzetto, M. (2022) Numerical simulation of submarine landslides and generated tsunamis: application to the on-going Mayotte seismo-volcanic crisis. Comptes Rendus - Geoscience 354(S2): 1–30.

Sahal A. (2011), Le risque tsunami en France : contributions méthodologiques pour une évaluation intégrée par scénarios de risque, Thèse de doctorat de géographie, dir. Pr. F. Lavigne et F. Leone, Université Paris 1 Panthéon-Sorbonne.

How to cite: Moatty, A., Anne, M., Anne, L. F., Pablo, P., Alexis, M., Maxwell, S., Anne, L., and Rodrigo, P.: From Tsunami Hazard Modelling to Vulnerability Assessment in Mayotte’s east coast: an Interdisciplinary Risk Analysis, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15879, https://doi.org/10.5194/egusphere-egu24-15879, 2024.

09:35–09:45
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EGU24-16805
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On-site presentation
Shinju Park, Berenguer Marc, and Daniel Sempere-Torres

Catalonia is located in north-eastern Spain and in a predominantly subtropical Mediterranean climatic zone. Due to the diverse geographical and orographic features, the climate within the region exhibits variations due to local continental, oceanic, and alpine influences.
Within the Horizon Europe RESIST project (2023-2027), Catalonia is one of the leading regions for the demonstration of climate change adaptation strategies toward climate change resilience through innovation, science, and technology. The strategies being analyzed in Catalonia focus on the sector of civil protection to achieve improved preparedness and tools for disaster risk and emergency management for weather-related hazards (e.g., flash floods, wildfires, heat waves, etc.).
The presentation will address the key enabling tools and activities toward better adaptation; e.g., improving and expanding the existing Multi-Hazard Early Warning System (EWS) over Catalonia, improving the assessment of vulnerabilities and including vulnerable communities, raising awareness. These aspects will be evaluated during a long-term demonstration in several local municipalities of the region. The first results obtained during 2023 will be presented; particularly for the major flood event in June 2023 in Terrassa city.

How to cite: Park, S., Marc, B., and Sempere-Torres, D.: Toward large-scale demonstration of local multi-hazard early warning tools, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16805, https://doi.org/10.5194/egusphere-egu24-16805, 2024.

09:45–09:55
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EGU24-11496
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ECS
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On-site presentation
Daniela I.V. Domeisen, Maria Pyrina, Dominik Büeler, Ana M. Vicedo-Cabrera, Sidharth Sivaraj, Adel Imamovic, Christoph Spirig, and Lionel Moret

Heatwaves have various impacts on human health, including an increase in premature mortality. The summers of 2018 and 2022 are two prominent examples with record-breaking temperatures leading to thousands of excess deaths in Europe. Nevertheless, there is a limited assessment of the potential for heat-health warning systems on timescales up to several weeks ahead at a regional level. This study combines methods of climate epidemiology and sub-seasonal forecasting to predict the expected heat-related mortality for two regions in Switzerland during the summers of 2018 and 2022. The sub-seasonal forecasts were first downscaled to a 2km-by-2km grid using a quantile mapping approach. The statistical heat-mortality relationship for the Swiss cantons of Zurich and Geneva between 1990 and 2017 was estimated in a two-stage time-series analysis using observed daily temperature and mortality. Then, heat-related mortality in the summers of 2018 and 2022 was calculated using the estimated heat-mortality relationship and the observed total mortality and temperature. The resulting estimated heat-related mortality was subsequently compared with the predicted heat-related mortality based on sub-seasonal temperature forecasts. Preliminary results show that we can successfully predict short-term heat-related mortality peaks for lead times up to 2 weeks, while longer periods of heat-related mortality can be anticipated by lead week 3 and even lead week 4 forecasts. Our findings demonstrate that sub-seasonal forecasts can be a valuable tool for estimating and potentially issuing warnings for the excess health burden observed during central European summers.

How to cite: Domeisen, D. I. V., Pyrina, M., Büeler, D., Vicedo-Cabrera, A. M., Sivaraj, S., Imamovic, A., Spirig, C., and Moret, L.: Sub-seasonal prediction of heat-related mortality in Switzerland, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11496, https://doi.org/10.5194/egusphere-egu24-11496, 2024.

09:55–10:05
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EGU24-1316
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On-site presentation
Enhanced Machine Learning Classification with Metaheuristics for Early Geotechnical Hazard Warnings
(withdrawn)
Jui-Sheng Chou and Julian Pratama Putra Thedja
10:05–10:15
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EGU24-11868
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ECS
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On-site presentation
Dariana Isamel Avila-Velasquez, Hector Macian-Sorribes, and Manuel Pulido-Velazquez

Raw meteorological forecasts from global meteorological models are always biased and require post-processing to tailor them to the regional and local climatic features before they can be used for other applications.  However, this might be challenging depending on the features and the meteorological variable considered. This contribution applies and evaluates the use of an artificial intelligence (AI) technique, fuzzy logic (FL), to post-processing meteorological seasonal forecasts, comparing its performance in terms of improved forecasting skills with other post-processing techniques for different forecasting systems and variables. The analysis is applied to the Jucar basins River Basin (Eastern Spain), which are characterized by extreme meteorological events (heavy rains, droughts, heatwaves).

For this area, six daily-scale seasonal forecasting systems from the Copernicus Climate Change Service (C3S) and six variables (precipitation; minimum, mean and maximum temperature; solar radiation and wind speed) are considered. ERA5 is used as reference dataset for post-processing, and daily data for the period 1995-2014 is employed to perform the comparison. The evaluation of the performance of AI is done by comparing the skill of AI-based post-processed forecasts with two common post-processing algorithms: linear scaling (LS) and quantile mapping (QM). The algorithms for all three post-processing methods are coded in a Python script. For each system, variable and post-processing alternative, the forecasting skill is measured using the Continuous Range Probability Skill Score (CRPSS).

Results show that, with the exception of precipitation, the relative performance of thes methods does not depend on the forecasting system but on the variable considered. FL dominates in maximum and minimum temperature and linear scaling in average temperature, wind speed, and solar radiation. However, LS shows the worst performance in maximum and minimum temperatures, while FL never yields the lowest skill. For precipitation, the ranking between methods depends on the forecasting system. According to the results, FL logic provides robust, skillful post-processing across variables, providing adequate performance for all variables and forecasting systems, while the rest of the methods show a wider spread of performance, from poor to the best.

Acknowledgments: This research has been supported by the University Teacher Training (FPU) grant from the Ministry of Universities of Spain (FPU20/0749); the project “INtegrated FORecasting System for Water and the Environment (WATER4CAST)”, funded by the Valencian Government through the Program for the promotion of scientific research, technological development and innovation in the Valencian Community for research groups of excellence, PROMETEO 2021 (ref: PROMETEO/2021/074); and "THE HUT project” (The Human-Tech Nexus– Building a Safe Haven to cope with Climate Extremes), under the European Union’s horizon research and innovation programme (GA No. 101073957) 

How to cite: Avila-Velasquez, D. I., Macian-Sorribes, H., and Pulido-Velazquez, M.: Post-processing seasonal meteorological forecasts with artificial intelligence, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11868, https://doi.org/10.5194/egusphere-egu24-11868, 2024.

Posters on site: Thu, 18 Apr, 16:15–18:00 | Hall A

Display time: Thu, 18 Apr 14:00–Thu, 18 Apr 18:00
Chairperson: Marc van den Homberg
A.42
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EGU24-1297
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Highlight
Claudia Di Napoli and Fredrik Wetterhall

As the impacts of climate change on human health become increasingly evident, so does the need for a systemic and interdisciplinary understanding on the climate-health connection. Achieving such an understanding is key to the development of effective and rational adaptation plans, including those involving the creation of weather forecasts-driven systems that can increase the preparedness and response to health hazards.

To address this shortcoming, the Horizon Europe project TRIGGER (SoluTions foR mItiGatinG climate-induced hEalth thReats) aims to generate and disseminate information about upcoming conditions detrimental to human health, such as heatwaves and cold spells, via an innovative prototype that integrates state-of-the-art climate and weather indicators with personal exposure monitoring data.

We here present the TRIGGER prototype with a focus on the hydrometeorological prediction system that is tasked to forecasts health-impacting climate variables and indicators on temporal scales ranging from the short-range (hours) to sub-seasonal lead-time. Using a co-design approach involving medical doctors and epidemiologists, we describe how the system utilizes the ECMWF forecasts, provides probabilistic predictions for the near future, and enables the assessment of the associated uncertainty.

How to cite: Di Napoli, C. and Wetterhall, F.: Better weather forecasts = better human health? Yes, with TRIGGER(s), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-1297, https://doi.org/10.5194/egusphere-egu24-1297, 2024.

A.43
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EGU24-3437
A Network of Excellence (NoE) on impact-based Multi-hazard Early Warning Systems and Anticipatory Action across Africa 
(withdrawn)
Joerg Szarzynski, James Wanjohi Nyaga, and Marco Massabo
A.44
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EGU24-3809
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ECS
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Highlight
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Johannes Fleisch and Giora Gershtein

MeteoAlarm serves as a central and comprehensive one-stop shop for hydrometeorological warnings across 38 European countries. Designed to provide critical awareness information for preparing and responding to hazardous weather events, MeteoAlarm consolidates warnings from National Meteorological and Hydrological Services (NMHSs) on a unified platform, aggregating and making them readily accessible through the MeteoAlarm Visualisation and Feeds.

The platform's primary objective is to present the current awareness situation coherently, ensuring a consistent interpretation throughout Europe in an easily comprehensible manner. This is achieved by using a simple three-colour code (yellow, orange, and red) and by providing impact scenarios and advisories to the general public. This approach enables individuals to stay informed about the latest warnings, take necessary precautions, and minimise risks associated with hazardous weather conditions, supporting decision-makers on the European level, such as the Emergency Response Coordination Centre (ERCC) of the European Commission. Essential to MeteoAlarm's success are its redistributors, such as AccuWeather, Apple, Google, or IBM/The Weather Company, fundamental in disseminating warnings to hundreds of millions of end-users.

MeteoAlarm actively engages in the RODEO project, a collaborative effort involving eleven European NMHSs, ECMWF, and EUMETNET. This initiative spans from 2023 to 2025 and aims to develop a Federated European Meteo-hydrological Data Infrastructure (FEMDI). The realisation of FEMDI includes the creation of a user interface and Application Programming Interfaces (APIs) designed for accessing meteorological datasets designated as High-Value Datasets under the EU Open Data Directive. Within this project, MeteoAlarm focuses on enhancing the accessibility and usability of its warnings. The goal is to ensure warnings remain reliable, of high quality, and standardised across diverse regions and countries. The development of APIs not only facilitates machine-readable data but also enables near-real-time access through bulk downloads and cross-border querying, seamlessly integrated with the existing MeteoAlarm Service. In parallel, efforts concentrate on improving the quality and harmonisation of warnings, achieved through collaborations with data providers, redistributors, and international frameworks related to the Common Alerting Protocol (CAP).

Looking ahead, MeteoAlarm prioritises key initiatives to maintain its prominent role in weather warning services. A central focus is the shift towards an impact-based multi-hazard approach, aligned with the WMO-led initiative, Early Warnings for All (EW4All). This goes hand in hand with the aim to advance the MeteoAlarm CAP Profile, emphasising adaptability for diverse weather events. The establishment of an Impact-based Warning (IbW) Working Group and the extension of early warnings beyond the current two-day limit are short-term objectives, supporting MeteoAlarm's overarching vision. Strengthening collaboration with redistributors and enhancing knowledge sharing and communication between MeteoAlarm Members collectively reinforces resilience, adaptability, and engagement within the meteorological community.

The anticipated impact of MeteoAlarm’s efforts will enhance the ability of individuals and organisations to engage in more efficient disaster preparedness and response at both national and international levels, ensuring a safer and more resilient future for all.

How to cite: Fleisch, J. and Gershtein, G.: MeteoAlarm – Towards Tomorrow’s Warnings, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-3809, https://doi.org/10.5194/egusphere-egu24-3809, 2024.

A.45
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EGU24-9073
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ECS
|
Anup Shrestha, Anise McCrone, Josias Láng-Ritter, Maija Taka, and Olli Varis

Safeguarding lives and properties during major disasters, such as floods, relies on timely and comprehensive communication and dissemination of early warning information. According to UNDRR, an effective Early Warning System (EWS) consists of four pillars: risk knowledge, monitoring and warning services, dissemination and communication and response capability. It is crucial to assess the operational status of EWS, particularly in vulnerable rural areas of developing countries, where technical EWS capacity as well as residents' awareness, understanding of messages, and taking appropriate actions may be hindered by multifaceted factors such as communication of complex forecast information and their pathways, lack of sufficient monitoring stations, low literacy, geographical challenges, and other socio-economic factors.  

The present study focuses on advancing knowledge on the challenges in implementing the four pillars of flood EWS from the perspective of vulnerable communities for planning necessary interventions to enhance flood resilience. We conducted community surveys, key informant interviews, and reviewed publicly available information in the flood prone West Rapti Basin of Nepal. Further, we applied statistical tests to analyze the community surveys and examined the key informant interviews through thematic analysis based on the four EWS pillars. Finally, we assessed the potential economic impacts across various flooding scenarios to integrate early actions in EWS for saving lives and properties. 

Our study reveals that most of the local population face difficulties interpreting associated risks when they are communicated with risk maps. However, the understanding of early warning and reception of SMS alerts varies strongly among rural municipalities due to language, literacy status, and mobile network problems. The community’s interest to participate in warning process and to help in warning others suggests the importance of a community-centric approach and feedback mechanism to the existing top-down approach of EWS. The study also highlights the potential of impact-based risk maps integrated with the findings of community surveys and key informant interviews to plan early actions for informed decision making. 

The potential improvements of EWS include upgradation of warning information dissemination, participatory early warning process, development of protocols for early actions and response mechanism, warning production based on impact-based forecast, improving technical capabilities for monitoring hazards, and creating community-level database to record the post flood impacts and community feedback to validate warning and impact-based forecasts. Our study contributes to strengthening EWS through impact-based quantitative risk analysis which is implementable worldwide. Future research is called for on how to develop the impact-based forecasting chain for different future scenarios and incorporate citizen science to improve this process.

How to cite: Shrestha, A., McCrone, A., Láng-Ritter, J., Taka, M., and Varis, O.: Bridging gaps, saving lives: Integrating communities’ voices in advancing flood early warning system in developing countries , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9073, https://doi.org/10.5194/egusphere-egu24-9073, 2024.

A.46
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EGU24-10635
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ECS
Nibesh Shrestha, Alexander Buddrick, Benjamin Mewes, and Henning Oppel

Heavy rainfall, a prominent consequence of climate change, induces substantial pluvial flooding as the urban drainage systems fail to deal with the water surge. The risks intensify with the cloud-burst rain on a catchment area without any gauge. Especially in topographically complex watersheds, the limitations associated with conventional precipitation monitoring tend to exacerbate. These heavy-rain events, if undetected, pose severe threats, causing extensive damage to the settlements and industries without timely warning.

With a motive to bridge this gap, we present the exemplary development of a cutting-edge AI-supported early warning system and cell detection (now-casting) of heavy rainfall events. Utilizing an IoT-based optical method, we record qualitative rainfall intensity data with a high-density swarm network of rainfall sensors spread across the target region. These data can be immediately used to forecast the path of the rain with the physical optical-flow method. Furthermore, these data are used to train the AI, generating heavy rain forecasts up to 60 minutes before the rain reaches points of interest. This lead time is crucial for citizens and rescue forces to reduce the chaos phase and prepare themselves on time even before the heavy rain cells reach their location and create havoc.

The innovative optical rainfall sensors have been installed and tested in Liederbach am Taunus since the summer of 2022, demonstrating their efficacy and accuracy during the August 2023 heavy rainfall storm event. The system adeptly captured heavy rainfall data, showcasing great potential for early warnings when implemented at a full scale alongside AI applications.

How to cite: Shrestha, N., Buddrick, A., Mewes, B., and Oppel, H.: Heavy-rain Forecasting with the Application of High-density Swarm Network of Optical Rain Sensors and Artificial Intelligence., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10635, https://doi.org/10.5194/egusphere-egu24-10635, 2024.

A.47
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EGU24-11042
Pui Man Kam, Fabio Ciccone, Chahan M. Kropf, Lukas Riedel, Christopher Fairless, and David N. Bresch

Tropical cyclones (TCs) displace the second-largest number of people each year among all natural hazards, following floods.  While TCs impose hardships and threaten lives, the negative impacts can be mitigated through anticipatory action such as evacuation, emergency protection, and humanitarian aid coordination. An impact-based forecast can support anticipatory action planning by providing detailed information about the numbers and locations of people at risk of displacement.

Here we introduce the first implementation of a globally consistent and regionally calibrated TC-related displacement forecast that combines the (1) TC weather forecast with (2) the spatially explicit representation of population distribution and (3) their vulnerability. Furthermore, we emphasise the importance of incorporating uncertainties from all three components in a global uncertainty analysis to reveal the full range of possible outcomes. Additionally, sensitivity analysis can help us helps us understand how the forecast outcomes depend on uncertain inputs.

We demonstrate the TC displacement forecast through a case study of storm Yasa in the Fidji in 2020. Additionally, we conduct a global uncertainty and sensitivity analysis for all recorded TC displacement events from 2017 to 2020. Our findings suggest that for longer forecast lead times, decision-making should focus more on meteorological uncertainty, while greater emphasis should be placed on the vulnerability of the local community shortly before TC landfall. The open-source code and implementations are also readily transferable to other hazards and impact types.

How to cite: Kam, P. M., Ciccone, F., Kropf, C. M., Riedel, L., Fairless, C., and Bresch, D. N.: Impact-based forecasting for human displacement by tropical cyclones to support anticipatory humanitarian action, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11042, https://doi.org/10.5194/egusphere-egu24-11042, 2024.

A.48
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EGU24-12509
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ECS
Emma Dybro Thomassen, Michael Butts, Sanita Dhaubanjar, Jonas Wied Pedersen, Sara Lerer, Mathias Rav, Morten Andreas Dahl Larsen, Kristine Skovgaard Madsen, Phillip Aarestrup, and Grith Martinsen

Estimating the geographical flood extent is a key element in impact-based flood forecasting and crucial for countries with long coastlines, and places where storm surges pose a significant risk, such as Denmark. For local flood mitigation measures and climate adaptation strategies, inundation mapping is often performed using physical models. However, in the context of flood forecasting and early warning, these are computationally demanding and therefore may not be able to provide timely forecasts and effective warnings.

The Danish Meteorological Institute (DMI) has developed a real-time flood forecasting system for storm surge events in Denmark together with the company SCALGO. This system couples the HBM regional oceanic storm surge forecasting model, developed by DMI, with a rapid inundation mapping, developed by SCALGO, using a 0.4 m resolution Digital Elevation Model (DEM). All inland pixels in the DEM are connected to a coastline pixel through pre-computed hydrological flow paths. The predicted water level from the storm surge model at each coastline pixel is then instantaneously projected inland through the pre-mapped flow paths. This study evaluates the performance of the flood forecasting system on the Oct. 20-21 (2023) storm surge event, with an estimated return period of over 100 years and affecting large parts of southern Denmark (and northern Germany).

This flood forecasting system creates a simple inundation mapping based on forecasted sea levels based on a high-resolution DEM modified to account for hydrological flow processes. This real-time flood mapping allows for a visualization of full five-day ocean model forecasts updated continuously at 6h intervals and has been operational for flood warning since October 2022, to supplement DMIs operational ocean forecasting system [1]. 

The evaluation is performed by comparing the inundation map from the flood forecasting system with media reports, photographs, and other data sources to get an overview of spatial and temporal accuracy and accuracy of the severity of the event. We see a large overlap between areas with forecasted flood risks and actual flooded areas. In some cases, the extent of the flooding differs from the area at risk due to errors in the DEM or local emergency services mitigation strategies.

We conclude that the flood forecasting system is useful for identifying coastal areas at risk. While it does not account for detailed physics of flow on land, it is able to reflect the effects of, even very local, geographical variations in sea level that determine the distributions of local-scale flood risk. The current inundation mapping does not currently include the impact of waves, which resulted in larger differences between predictions and actual flooded areas, for easterly-facing locations exposed to large waves. Proposed activities to include the effect of waves will therefore improve the flood forecasting system. 

[1] Andrée, E., Su, J., Larsen, M. A. D., Madsen, K. S., & Drews, M. (2021). Simulating major storm surge events in a complex coastal region. Ocean Modelling, 162, 101802.

How to cite: Thomassen, E. D., Butts, M., Dhaubanjar, S., Pedersen, J. W., Lerer, S., Rav, M., Larsen, M. A. D., Madsen, K. S., Aarestrup, P., and Martinsen, G.: Evaluating a +100-year storm surge using a real-time distributed flood forecasting system , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12509, https://doi.org/10.5194/egusphere-egu24-12509, 2024.

A.49
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EGU24-16961
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ECS
Forecasting and Quantifying Risks of Crop and Water Supply Failures Using Machine Learning and Remote Sensing
(withdrawn)
Alicja Grudnowska, Marthe Wens, Amelia Fernández Rodriguez, Sergio Contreras, and Gabriela Nobre
A.50
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EGU24-17969
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ECS
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Anastasiya Shyrokaya, Sameer Uttarwar, Giuliano Di Baldassarre, Bruno Majone, and Gabriele Messori

The reliable prediction of drought impacts on crop yield in India poses a significant challenge due to the complex interactions of climatic variables, systems vulnerabilities and impacts propagation. Advanced approaches, such as impact-based forecasting, become necessary to address the intricate nature of this challenge. In this study, we leveraged remote sensing-based vegetation indicators as proxies for crop yield, along with multiple drought indices across various accumulation periods, to establish a robust indicator-impact relationship. We further performed a comparative analysis of various machine-learning algorithms to assess their efficacy in predicting crop yield impacts on a subseasonal-to-seasonal scale. We finally evaluated the accuracy of predicting the crop yield impacts based on drought indices computed from ECMWF’s seasonal forecast system SEAS5.

Our analysis not only unveils seasonal trends and spatio-temporal patterns in indicator-impact links but also marks a pioneering effort in comparing diverse machine-learning algorithms for establishing an impact-based forecasting model. As such, these findings offer valuable insights into the dynamics of drought impacts on crop yield, providing a monitoring tool and a foundational basis for implementing targeted drought mitigation actions within the agricultural sector. This research contributes to advancing the understanding of impact-based forecasting models and their practical application in addressing the challenges associated with drought impacts on crop yield in India.

How to cite: Shyrokaya, A., Uttarwar, S., Di Baldassarre, G., Majone, B., and Messori, G.: Drought impact-based forecasting of crop yield in India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17969, https://doi.org/10.5194/egusphere-egu24-17969, 2024.

Posters virtual: Thu, 18 Apr, 14:00–15:45 | vHall A

Display time: Thu, 18 Apr 08:30–Thu, 18 Apr 18:00
Chairperson: Stefan Schneiderbauer
vA.12
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EGU24-6158
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ECS
Ayat-Allah Bouramdane

Effective management and communication of earthquake risks is crucial for enhancing societal preparedness and resilience. This study investigates earthquake management strategies using Multi-Criteria Decision-Making (MCDM), specifically the Analytic Hierarchy Process (AHP). The focal earthquake event driving this investigation occurred on September 8, 2023, at 11:11 PM local time. With a magnitude of 6.8, the seismic incident had its epicenter approximately 72 km southwest of Marrakech within the Al Haouz province.
A comprehensive assessment is conducted on ten distinct earthquake management strategies in Morocco. These encompass building codes and construction standards (S1), early warning systems (S2), public education and awareness (S3), land use planning (S4), emergency response plans (S5), international cooperation (S6), research and monitoring (S7), infrastructure resilience (S8), community preparedness (S9), and insurance and financial preparedness (S10). The evaluation involves a thorough examination against a set of criteria encompassing aspects such as effectiveness in risk reduction (C1), cost-effectiveness (C2), inclusivity and social equity (C3), adaptability and flexibility (C4), environmental impact (C5), compliance with standards and insurance uptake (C6), interagency collaboration (C7), and data utilization (C8).
The resulting criteria weights underscore their relative importance, with C1 deemed highly significant (30%), C2 and C3 moderately important (20% and 15%, respectively), and C4, C5, C6, C7, and C8 holding lesser significance (ranging from 10% to 5%).
Performance scores are assigned to rank the earthquake management strategies, revealing that A2 attains the highest score (0.45), followed by A4 (0.43), A10 (0.42), A9 (0.41), A3 (0.4), A8 (0.39), A7 (0.38), A6 (0.37), and A5 (0.35). A1 achieves a moderate score (0.32), providing valuable insights for decision-making in earthquake risk reduction.
This research underscores the pivotal role of early warning systems in earthquake management, emphasizing the significance of timely alerts, community engagement, and financial preparedness within Morocco's comprehensive risk reduction strategy. The study advocates for data-driven decision-making to enhance preparedness, response capabilities, and mitigation measures. Moreover, this research holds implications for recent seismic events, such as the magnitude 7.6 earthquake in Japan on January 1, 2024.

How to cite: Bouramdane, A.-A.: Morocco’s Earthquake Risk Management: A Multi-Criteria Decision-Making Approach and Implications for the Recent Japan Earthquake, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6158, https://doi.org/10.5194/egusphere-egu24-6158, 2024.

vA.13
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EGU24-19580
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ECS
Resilience against the extreme weather: Scaling Anticipatory Action in Southeast Asia Region
(withdrawn)
Ferosa Arsadita
vA.14
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EGU24-16207
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ECS
Sandeep Sharma, Saurabh Basu, Suvam Suvabrata Behera, Sumit Kumar Jha, Akshay Dawar, Niraj Kant Kushwaha, Sabyasachi Majumdar, Smriti Sachdev, Anugandula Naveen Kumar, Manish Bhaskar, Arun Yadav, and Pankaj Kumar Dalela

Disaster risk reduction is a pressing global challenge owing to climate change and other anthropogenic factors. Communicating timely, trusted, and actionable life-saving information to the public in emergency or disaster situations can make a significant difference by reducing the potential impacts and improving preparedness and mitigation efforts. In the direction of building a disaster-resilient India, inline with the global initiatives like Early Warnings for All, an end-to-end AI-driven Multi-Hazard Early Warning System has been established, standardizing and streamlining the flow of disaster warning dissemination in the country. The system utilizes International Telecommunication Union (ITU’s) Common Alerting Protocol (CAP) for disaster warning information exchange between the entities. Existing non-CAP compliant legacy infrastructure have also been integrated with the system by implementation of cost-efficient Interworking Systems (IWS). More ways for enhanced communication, making use of different ICTs and networks, including telecom (SMS and Cell Broadcast), broadcasting (Radio and Television), satellite, internet (Mobile Application, Web Dashboards, Browser-based Notifications), public addressing systems (Coastal Sirens, Railway Passenger announcement systems) etc. have been integrated for ensuring last mile reachability. The implementation of an indigenously developed cell broadcast system allows warnings to be disseminated within a few seconds to a large area population. Satellite based messaging services have been integrated for areas with no network coverage, such as alerting fishermen in high sea and targeting the tough terrain. The platform has been rigorously utilized in recent disaster situations, including Cyclone Michaung, Biparjoy, Mandous, Sitrang, etc. and more than 14 billion SMS have been disseminated till date across different geographical regions. It is operational across PAN India in all 36 State/ UTs, integrating 100+ stakeholders on the converged platform, supporting dissemination in over 22 regional languages, and addressing massive climatic, digital, linguistic, and geographic diversity in the country. The collective efforts have resulted in key advancements in the direction of disaster risk reduction.

How to cite: Sharma, S., Basu, S., Behera, S. S., Jha, S. K., Dawar, A., Kushwaha, N. K., Majumdar, S., Sachdev, S., Kumar, A. N., Bhaskar, M., Yadav, A., and Dalela, P. K.: Communicating Life-Saving Information in Emergencies: Implementation of Multi-Hazard Early Warning System in India, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16207, https://doi.org/10.5194/egusphere-egu24-16207, 2024.

vA.15
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EGU24-15463
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ECS
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Md Rayhan, Md. Hasanur Rahman, Rashel Dewyan, Shampa Shampa, Sonia Binte Murshed, and Shammi Haque

Forecast-Based Early Action (FbA) is a promising disaster risk reduction technique that allows communities to take proactive steps with the help of accurate forecasting before a disaster strikes. The current global evidence indicates that timely FbA can save more lives and minimize the impact on communities in the emergency and recovery stages. However, the FbA funded by humanitarians or governments needs some specific forecast window (e.g., 7 to 9 days for riverine floods in Bangladesh) from impact identification to intervention deployment. But in the case of rapid on-set disasters (such as flash floods (FF)), such forecast windows might be difficult to identify as these disasters might happen within 5 to 6 hours. In such cases, our research focuses on how the last mile community takes anticipatory action (AA). As a case study site, we selected the north-eastern (NE) region of Bangladesh, which experienced extreme FF during June 2022.

The first goal of this study was to look into how flash floods change the impact dynamics of last-mile communities over time. The second goal was to investigate how forecasting can be improved in terms of effectiveness and inclusiveness. The third goal was to investigate community-led AA during normal and extreme FF events. To understand local experiences and observations related to climate and environmental cues, 12 Key Informant Interviews (KIIs) and 14 Focus Group Discussions (FGDs) were conducted during Nov-Dec 2023. The Key Informant Interviews (KII) were conducted with representatives from NGOs, CBOs, trade organizations, and government officials. FGDs were held with a variety of groups, including women, the elderly, the disabled, ethnicity, religion, and occupation.

Our research found that rather than official forecasting, communities rely on indigenous knowledge such as cloud patterns, wind flow, atmospheric changes in hilly areas, sudden water temperature drops, color changes, and so on. These indicators serve as early warning signs of impending flash floods, allowing residents to plan ahead of time. Based on these predictive indicators, they take proactive measures such as elevating house plinths and safeguarding essential assets related to their livelihoods around 2.5 months before the FF period. Because the global lead time for FF is short, any AA must rely on community action. Because the NE region of Bangladesh has a long history of FF, their solution would be beneficial for other parts of the world to learn about, especially as the world experiences more FF because of climate change.

How to cite: Rayhan, M., Rahman, Md. H., Dewyan, R., Shampa, S., Murshed, S. B., and Haque, S.: Community-led AA for flash floods: Lessons learned from the last-mile community during 2022 Extreme Event in North-Eastern Bangladesh , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15463, https://doi.org/10.5194/egusphere-egu24-15463, 2024.