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.

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.

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.

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.

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.

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.

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.

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.