Droughts in Switzerland have become more frequent and severe in recent years, and this trend is expected to continue. At the same time, increasing water demand and competition between different actors are putting more pressure on existing water resources. Having recognized drought as a significant risk for various economic sectors in Switzerland, a comprehensive national monitoring and forecasting system, to be launched in 2025, is being established through the joint efforts of three different government agencies (Federal Office for the Environment, Federal Office for Meteorology and Climatology, and Federal Office of Topography).

We will present the Swiss national drought project with a particular focus on in-situ, modelled and satellite-based monitoring, the integration of sub-seasonal forecasts, and the drought early-warning and information system. Specifically, this means creation of a national in-situ soil moisture monitoring network with approximately 30 stations, the development of meteorological and ecological drought products and indices derived from satellite and in-situ data, as well as the establishment of near real-time, downscaled, monthly forecasts. The integration of these diverse data streams into seamless products ranging from historical observations to sub-seasonal forecasts, all within a consistent climatological baseline and as open government data, is expected to be a significant step forward of great benefit to downstream user applications. The improved meteorological basis will directly feed into impact-relevant drought indices and hydrological models, with the aim of refining the early warning system to meet the needs of a very diverse user community, such as hydropower production, fluvial navigation, agriculture, forestry, artificial snow production, or ecology.

Overall, the project provides important information on the current and future drought situation on a regional to local scale. Daily updated maps and infographics are accessible through a user-friendly web platform designed to facilitate informed discussion and decision-making. Ultimately, the project aims to increase preparedness by facilitating emergency planning, reducing impacts and enhancing drought resilience across the affected sectors in Switzerland.

The system was designed by actively integrating user needs. The results of a user survey showed that although drought is multidimensional and affects stakeholders in different ways, one of their main needs is still a holistic "combined" drought index that can serve as a common basis for discussion and decision-making. Simple, locally focused designs were found to be the most efficient and useful, while designs, that present nation-wide maps or scientific quantities (SPI, etc.) were judged to be the least meaningful to educated but non-specialist users.

Drought is a water deficit and a persistent and recurring natural hazard that affects ecological and socio-economic systems. This results in a socio-economic drought (agriculture, drinking water, forestry, hydropower, tourism, etc.) where decision-makers need to quickly get an overview of the drought situation in their region. Use cases are e.g. that a community representative has to decide where the use of water should be regulated (e.g. watering gardens), or that a farmer analyses the drought situation of the past years to potentially evaluate a change of crop variety.

Currently, many specialized platforms on e.g. precipitation or soil moisture exist across administrative levels with graphs of varying spatio-temporal resolution. The challenge for the decision-makers is to collect the relevant data from all these platforms to get an overview of the drought situation in their region. This is time-consuming and prone to misinterpretation.

How do we design a public platform on drought (as part of the Swiss national drought project) that covers the requirements on analysis for decision-makers?

Our approach is «User-Centered Design».

1. Research with people from the target group to determine the requirements from a user’s perspective. Their biggest problem is to get an overview of data at different platforms to see the whole picture of drought in their region, and to currently make decisions based on a gut feeling.
2. Conception of the interface into a prototype to get an understanding of the platform among stakeholders.
3. Usability-Test of the prototype to evaluate the concept with the users. Results showed that this platform enables users to efficiently make data-based and not a gut feeling decisions because data streams from different government agencies (e.g. Federal Office for the Environment, MeteoSwiss) are collectively displayed.
4. Visual Design of the concept to ensure an interface based on user-centered GUI standards, incl. accessibility.
5. Specification of the concept to ensure users’ needs during technical development (currently in progress). Along the process, we included data providers to ensure feasibility of data structures.

With User-Centered Design, we designed a platform - for and with the users - that supports decision-making regarding drought in Switzerland.

The Standardized Precipitation Evapotranspiration Index (SPEI) is the WMO recommended drought index. It is computed using precipitation and evapotranspiration data and indicates deviation from a chosen historical mean, i.e. the reference period. MeteoSwiss provides the SPEI index at several measurement stations around Switzerland for 1-,3-,6-, and 12-month accumulation periods. The reference period used to compute these indices is 1961-present (11.08.2024 at time of writing). I hypothesize that this reference period does not account for climate change that occurred in the 20th century and risks under-estimating current SPEI values. Given a non-stationary climate, the first half of the reference period is different to the second half, and especially the decades in the 21st century where all climate records are being broken. It is unclear whether the water balance is stationary, a desired quality for the SPEI estimation methods. While this does not invalidate the model, the practical impact is critical; using a reference period that includes recent climate will reduce SPEI values, under-estimating the recent drought risk! This could impact agriculture, insurance, water management etc.

I compute SPEI for all of Switzerland using different reference periods and verify atmospheric water balance stationarity. The data used is the reanalysis ERA-5 Land (0.1*x0.1*), for monthly precipitation and evapotranspiration. SPEI is computed using the R package "SPEI", with log-logistic distribution fit and 3-month accumulation. The atmospheric water balance(wb) is tested for trends using the MannKendal trend test and the wb data for pre- and post-1991 is compared using t-tests. SPEI values are computed with reference periods starting in 1961 and ending in 2021, with iterative reductions of the period end. The SPEI results are evaluated at the 2012-2022 period SPEI monthly means.

Results of the trend test indicate a significant increasing trend (p<0.05) for the Spring/Autumn period in areas of the Rhone and Rhein valleys, Ticino, and Bernese Alps. These results are corroborated with the t-tests. There is no indication of significant wb trend in the rest of the country. I compared the 2011-2021 SPEI monthly means for 6 reference periods, with control reference period (1961-2021), and 5 periods each ending a decade earlier down to 1961-1971. Normalizing them with the control period, I observe seasonally and spatially variable results. For winter and summer (Figure 1), there is a monotonic increase in SPEI values with reference period reduction. However, spring/autumn results require further inquiry to explain observed trends; it is not monotonic and there is a spatial discontinuity (Figure 2.). Differences for the 1961-2011 period are minor, while the 1961-1981 and 1961-1971 reference periods results are spatially incoherent, indicating bad SPEI distribution fits.

The SPEI reference period must balance data non-stationarity, and model estimation errors. Maximizing these two requirements, a reference period from 1961 to 1991 or 2001 has demonstrated spatially coherent results, with sufficient deviation from the control period. This will incorporate recent climate change and result in higher SPEI intensity for our preceding decade which will reflect in the computed return periods of recent historical drought events, i.e. the drought risk.

Identifying drought indices that effectively predict future drought impacts remains a critical challenge in seasonal forecasting, as these indices provide the necessary actionable information that enables stakeholders to anticipate better and respond to drought-related challenges. This study evaluates how drought indices balance forecast skill and relevance for estimating European impacts. Using ECMWF SEAS5 seasonal predictions and ERA5 reanalysis as benchmarks, we assessed the predictability of drought indices over various accumulation periods and their relevance in estimating drought impacts across Europe to enhance impact-based forecasting (IbF). Our findings reveal higher predictability in Northern and Southern Europe, particularly during winter and summer, with some regions showing extended predictability for up to six months, depending on the season. Focusing on case studies in the UK and Germany, our results highlight regions and seasons where accurate impact predictions are possible. In both countries, high impact predictability was found up to six months ahead, with sectors such as Agriculture, Water Supply, and Tourism in the UK and Agriculture and Water Transportation in Germany, depending on the region and season. This analysis represents a significant step in identifying the most suitable drought indices for predicting European impacts. Our approach introduces a new method for evaluating the relationship between drought indices and effects and addresses the challenge of selecting indices for estimating impacts. This framework advances the development of operational impact-based drought forecasting systems for Europe.

Europe has been identified as a heatwave hotspot, with numerous temperature records having been broken in recent summers and roughly 60,000 and 50,000 heat-related deaths occurring in the summers of 2022 and 2023, respectively. With recent summers, like that of 2022, projected to become the new norm, there is a pressing need to further develop heat-health warning systems to help society adapt to a warming climate. Here, we evaluate the skill of daily temperature related mortality forecasts, which can inform heat-warning systems, for the summers of 2022 and 2023. For most parts of Europe, enhanced temperature related mortality forecasts were associated with milder temperatures, close to the minimum mortality temperature. However, some of the hottest regions in Europe showed increased predictability associated with higher temperatures, suggesting that mortality forecasts can provide valuable information in regions also associated with high levels of temperature related mortality.

Diarrheal disease (DD) is a significant global public health issue and represents the leading cause of malnutrition and the second leading cause of death worldwide, resulting in the deaths of an estimated 829,000 people annually (Keddy, 2018). DD is a particularly urgent public health threat accounting for nearly 10% of global deaths of children under the age of five (Colombara, 2016).

Climate change and the natural environment are associated with DD risk. The World Health Organization (WHO) estimates that climate change will cause an additional 48,000 deaths per year from diarrhea between 2030 and 2050 (WHO, 2018). Much of the literature supports an association between high rainfall and increased risk of diarrhea (Mertens, 2019, Levy, 2016. How local variability in the natural environment impacts DD risk is understudied and requires remote, big data approaches to enhance community preparedness to reduce DD incidence among vulnerable populations.

This study applied Random Forest (RF) machine learning models to climatic, environmental, health, socio-economic, and geospatial data. The data used for this study originated from the Demographic and Health Surveys Program (DHS). Data was accumulated at the household (HH) level, with each HH belonging to a village that represents a collective of geographically proximal HHs.

Two RF classification algorithms were used to investigate feature importance at the HH and village scales. Model A was used for binary coding of DD occurrence at the HH level. Model B applied a prevalence threshold and coded households within a village according to DD prevalence.

The results of this study suggest that the contextual features most important for predicting DD are different at the household and village scales. At the household scale (Model A, Figure 1), the features identified as most important are related to the age and stunting status of the child, followed by geographic and weather-related variables. At the village scale (Model B, Figure 2), the features identified as most important were generally geographic and weather-related, including distance from the ocean and precipitation.

These results demonstrate the importance of scale when considering preparedness and emergency planning and demonstrate the utility of machine learning in preparedness, warning, and response efforts. Our findings suggest nuance is required in the analysis of DD across regions of the world where local variability in climate and natural hazard vulnerability may be an important consideration in devising appropriate preparedness and response strategies for reducing diarrheal disease burden.

Climate change has exposed communities to multiple hazards, making them vulnerable to more than one hazard. Scientific predictions indicate that global climate changes are likely to further increase exposure to multiple risks, affecting the magnitude, frequency, and spatial distribution of disastrous events. However, risk assessments often consider hazards as independent crises, ignoring their combined impacts. Consequently, there is a critical need to adopt a multi-hazard approach for risk assessment, enabling institutions and policymakers to devise more effective mitigation strategies. Against this backdrop firstly, the study aims to identify the hotspots in the Indian state of Rajasthan affected by the combined impacts of heat waves and droughts using a multi-hazard approach. The study seeks to understand the relationship between two extreme events in the region. Secondly, the study tries to examine the implementation of multi-risk governance approach at the institutional level, highlighting associated challenges and gaps. To better understand these gaps, the study includes a stakeholder analysis approach and grassroots community investigation through primary surveys in both rural and urban areas. A household survey of 150 farmers and three focus group discussions (FGDs) were conducted within the agricultural community in one of the identified rural hotspot region. To understand the urban scenarios, four FGDs were conducted in four slum regions of the capital city of Jaipur.

The study maps both extreme events as hazards in the region using the Standardized Precipitation Index and the India Meteorological Department classification, utilizing data from 165 meteorological stations. The study establishes a positive feedback mechanism between both events in the region. The finding of the study identifies the northeastern part of the state as the hotspot region, which is at greater risk from the combined impacts of heat waves and droughts. In the identified hotspot regions, the study establishes Stakeholder interviews at the institutional level, along with household surveys and focus group discussions (FGDs), revealed several governance challenges. These challenges span institutional, operational, social, economic, behavioral, communication, information and awareness, and political domains. To enhance community resilience to these events, it is essential to address these challenges through a bottom-up approach, ensuring the last-mile integration of policies and plans. A shift from recovery to preparedness is necessary, along with long-term planning, efficient communication, and collaboration among different stakeholders. Implementing Community-Based Disaster Management (CBDM) plans and conducting awareness drives are also crucial steps in fostering resilience. Thus, the finding of study opines that the drought and heat wave mitigation measures deployed in the state are inadequate, ineffective and mostly reactive in nature. As a result, the integration and bridging the gap between scientific risk assessments and the practices implemented at both institutional and community levels is crucial for enhancing disaster risk reduction (DRR) strategies and ensuring more effective and practical approaches to managing risks. The study further leaves the scope for a holistic framework for proactive multi hazard management which will focus on building a resilient and drought and heat wave proof society in the country by emphasizing on interdisciplinary research and collaboration for targeted interventions.

Heavy rainfall regularly leads to local flooding in Germany, urban areas with a lot of sealed surfaces are particularly at risk. In Berlin alone, heavy rainfall has caused damage of €174 million between 2002 and 2021 according to the German Insurance Association. To enhance preparedness and reduce losses, advanced methods for nowcasting are vital for disaster management and emergency response. Typical factors such as water depth and flow velocity give a first indication on the magnitude of an event. But how can this information be translated into impacts such as the expected number of affected households or potential damage to buildings?

For the rapid impact assessment in a real-time framework, we developed the “Flood Damage Estimation Tool” (FlooDEsT), a machine learning algorithm trained on survey data from past heavy rainfall events. Using recursive partitioning, the algorithm captures non-linear relations between variables addressing the hazard and potentially affected objects. In its application, the trained model only requires the forecasted maximum water depth and velocity. Further, building characteristics are needed for a specific damage estimation. If missing, these variables are sampled randomly from the survey data distributions, while providing an uncertainty range. With a short running time, the model is applicable in real-time. Regardless of the property’s size or market value, relative qualitative damage classes are provided on the building level. Validation with external insurance data showed a more precise damage assessment for heavy rainfall events with the new model than with typical river flood damage models. FlooDEsT offers a fast identification of potentially affected objects and associated impacts to complement real-time hazard information and predictions for the near future. Based on the information output, impact-based warnings can be issued for a better understanding of the situation-related consequences within the population.

Additionally, regional available data can be added to the results for further information. For example, evaluating the affectedness of critical infrastructures and points of interest (e.g., fire brigades, hospitals), which are important for emergency response teams. By masking population density with the affected areas, the number of potentially affected people can be estimated, too.

Apart from the real-time application the model can be used for scenario-based risk analysis. Data aggregation and intersection with additional data can show regional hot spots of vulnerability. The model thus allows a prospect of impacts for regional planning in hot spot regions and the potential for increased resilience towards heavy rain events. These outcomes can therefore support risk communication and raise awareness.

Neural networks have demonstrated the capability to surpass process-based hydrological models in runoff prediction, achieving remarkable computational efficiency. However, these models typically lack representation of sub-catchment hydrodynamics and lateral water transport processes, such as streamflow routing. Conversely, routed semi-distributed hydrological models enhance spatial representation by partitioning the catchment into finer hydrological response units (HRUs), enabling more accurate simulation of spatial variability in land use, soil characteristics, topography, and river routing.
Recent case studies have indicated the potential of neural networks for semi-distributed river routing. Nevertheless, these methodologies are currently not scalable to larger regions due to their computational inefficiency, or require advanced engineering and substantial computational resources. Our goal is to develop a relatively simple yet accurate semi-distributed streamflow model for hydrological Switzerland with explicit routing capabilities, leveraging deep learning techniques. This end-to-end system aims to improve local runoff predictions and routed streamflow simulations, facilitating real-time, spatially explicit forecasts that can enhance water resource management and risk mitigation strategies.

Reliable flood hazard modeling is crucial for enhancing disaster preparedness, supporting decision-making, and protecting lives and properties. The rising frequency and intensity of flood events, exacerbated by climate change, demands high-resolution predictions that incorporate building-level details, quantify uncertainties, and can be rapidly updated in response to dynamic environmental changes. Current flood hazard models often struggle with slow computation times and limited resolution, hindering their effectiveness in real-time applications. To overcome these limitations, we present a novel AI-driven flood hazard modeling methodology that dramatically accelerates computation speeds, offering highly accurate results thousands of times faster compared to conventional numerical methods.

The AI model is trained with data generated by validated high-fidelity, physics-constrained numerical algorithms. Generating our own training datasets ensures continuous feeding of statistically relevant cases for optimal fine-tuning of the model. The achieved speed and accuracy remove the bottleneck of the computational cost, enabling several key enhancements in the flood prediction methodology. The most significant advancement facilitated by the AI model is the ability to perform real-time nowcasting with high-resolution spatiotemporal data. Additionally, the model effectively addresses the inherent uncertainty in flood relevant input data, a challenge that conventional numerical models have difficulty coping with due to extreme computational requirements. Given that typical flood forcings like weather forecasts and event sets are probabilistic, treating them as distributions rather than fixed points provides a more reliable representation of reality. Our approach tackles this by running multiple hazard simulations accounting for the full range of possible input scenarios, mapping the probability of specific weather events to their potential impacts on the ground.

Our AI-based flood hazard model is designed for seamless integration into existing workflows across multiple disciplines, including meteorology, disaster management, and cartography. This integration fosters operational synergies throughout all phases of disaster management—monitoring, forecasting, early action, preparedness, response, and recovery. By significantly reducing computation time and enriching information provided, our approach allows stakeholders to make quicker, more informed decisions, enhancing the overall effectiveness of disaster mitigation efforts.

By integrating AI with traditional hydrological and meteorological models, we provide a scientifically robust and practically applicable tool. This approach transforms complex data streams and impact forecasts into actionable insights, supporting cross-organizational information management. Our AI-based hazard modeling approach represents a substantial advancement in disaster risk management, offering a powerful tool that can be readily integrated into existing systems to improve preparedness, response, and recovery efforts.

Mangrove forests are vital ecosystems in Thailand, providing essential ecological services such as coastal protection, carbon sequestration, and biodiversity preservation. However, these ecosystems are under threat due to urbanization, industrialization, and tourism, particularly in Chonburi Province, where mangrove degradation has led to significant environmental challenges. Accurate species classification is fundamental to the restoration and sustainable management of these forests. This study integrates high-resolution multispectral satellite imagery from Pleiades Neo with machine learning (ML) models, specifically Random Forest (RF) and Decision Tree (DT), to classify five mangrove species:
Rhizophora mucronata (Rm), Rhizophora apiculata (Ra), Avicennia marina (Am), Avicennia alba (Aa), and miscellaneous species (Ms).
The research methodology involved the acquisition of six-band Pleiades Neo imagery, field data collection, and the application of ML algorithms. Advanced processing tools like ArcGIS Pro, ENVI, and Python libraries were employed to preprocess data and train the ML models. RF and DT were compared based on their classification accuracy, precision, and recall, with RF demonstrating superior performance. The RF model achieved an overall accuracy of 85.23%, outperforming DT's 82.37%. These results underscore RF's robustness and precision in handling highdimensional and heterogeneous datasets typical of mangrove ecosystems.
A detailed analysis of the classified mangrove areas revealed that RF provided more reliable results, especially for species such as Avicennia alba and miscellaneous species, where DT struggled. Table 1 below summarizes the specieswise classification accuracy of both models.

The integration of ML with satellite imagery offers transformative potential for ecological research and resource management. By providing accurate, scalable, and cost-effective methods for monitoring mangrove forests, the findings of this study can significantly contribute to the development of targeted conservation strategies. Furthermore, the successful application of RF highlights its suitability for ecological applications, particularly in diverse landscapes requiring precise species differentiation.

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 developing a new interdisciplinary information system to provide comprehensive four-week drought forecasts for the whole of Switzerland, integrating advanced models across hydrology, forest fires, glacier balance, aquatic biodiversity, and bark beetle dynamics. Utilizing 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.

Machine Learning models have been applied (1) to post-process the ensembles of the impact models to increase the forecast reliability and (2) 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.

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.

The ultimate goal is to 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).

One of the significant challenges the MaLeFiX project must address is the integration of diverse information into a single, user-friendly platform. To achieve this, social scientists have conducted extensive surveys and interviews with representatives from the public sector, industry, and business. Their insights are being utilized to ensure the platform meets a wide range of user needs and expectations.

Upon completion, the MaLeFiX platform will offer several important benefits:

• For the Public: Easy access to understandable and actionable drought information.
• For Scientists: A unified tool that enhances research and collaboration across various disciplines.
• For Policymakers and Stakeholders: Reliable data to inform policies and strategies for drought mitigation.
• For the Media: Accurate and timely information to report on drought conditions and impacts.

In the face of the risks and uncertainties associated with a changing climate, the MaLeFiX platform will offer its users the opportunity to anticipate and mitigate the impacts of drought-related extremes – helping Switzerland to achieve a more resilient, and less uncertain, future.

The MaLeFix project is part of WSL’s Extremes Research Program, which aims to equip Swiss stakeholders with the strategies and resources needed to meet the challenges of future extreme events.

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Background. Over 110,000 Europeans died as a result of the record-breaking temperatures of 2022 and 2023, emphasising the urgent need to strengthen existing emergency and resilience plans. Inherent to the adaptation strategy against climate change, governments need to develop a new generation of heat-cold-health early warning systems, using epidemiological models to transform the physical information of weather forecasts into health-related forecasts, and specifically targeting vulnerable groups. The forecast skill horizon of these impact-based systems however needs to be first demonstrated in order to generate trust among public health authorities and end-users.

Methods. Here we tested the forecast skill horizon of temperature related mortality forecasts in Europe, as a necessary step towards the release of the first operational continental heat-cold-health early warning system. We used a spatiotemporally-homogeneous daily mortality database, including almost 60 million counts of death in 147 contiguous European regions, representing their entire urban and rural population of 420 million people. We used state-of-the-art temperature-lag-mortality epidemiological models to transform bias-corrected ensemble weather forecasts into daily predictions of temperature related mortality. We compared the predictive skill of temperature forecasts and temperature related mortality predictions by using predictability assessment techniques widely used in operational weather forecasting.

Findings. We found that temperature forecasts can be used to issue skilful forecasts of temperature related mortality at lead times beyond 15 days in winter and beyond 11 days in summer, accounting for the real impacts of temperature on human health at very long lead times. Nonetheless, when compared with the original temperature forecasts, the forecast skill horizon of the forecasting system was differently reduced by season and location due to the epidemiological models. Overall, our study showed that the forecast skill is to a very large extent influenced by the forecast skill of the original weather forecasts, and to a much lesser extent by the epidemiological models. This means that further advancements in weather forecasting would automatically turn into an increase in the forecast skill horizon of heat-related forecasts.

Interpretation. The forecast skill assessment of operational weather forecast schemes, which are routinely done by meteorologist, cannot be used to directly characterise the forecast skill horizon of any derived impact-based health early warning system. Overall, our results indicate that a rigorous assessment of the forecast skill of health early warning systems is an unavoidable requisite to generate trust among public health authorities, and in this way, increase resilience and strengthen our early adaptation response to climate change.

Forecaster-Dot-Health (https://forecaster.health/), funded by the ERC Proof-of-Concept Grants HHS-EWS and FORECAST-AIR, is the first continental, impact-based early warning system issuing health warnings of heat- and cold-related mortality risks by sex and age (Ballester et al. 2024).

Every day, the system automatically downloads and processes the latest available (i) temperature observations and (ii) 51 ensemble member forecasts for the next 15 days. Then, it post-processes the ensemble of temperature forecasts to bias-correct them against the temperature observations used in the epidemiological models (see below). We chose a bias-correction method considering the most recent N = 30 pairs of observations and forecasts with respect to each forecast start date (BC-30).

To transform the bias-corrected weather forecasts into predictions of heat- and cold-related mortality risks, we used a time-series quasi-Poisson regression model to derive estimates of region-specific temperature-lag-mortality risks (Ballester et al. 2023, Gallo et al. 2024). For that purpose, we used the daily temperature and mortality dataset of the ERC Consolidator project EARLY-ADAPT (https://early-adapt.eu/), which includes spatiotemporally homogeneous data for the period 2000-2019 in 654 contiguous regions from 32 European countries. We used these epidemiological associations to transform the temperature forecast for a given location, forecast date and forecast lead time into 5 warning categories: a baseline warning state (“none”), when the risk of death is minimum, and 4 categories of heat and cold warnings (“low”, “moderate”, “high” and “extreme”), corresponding to increasing levels of risk of death.

A key aspect of the system is that, in each location, we estimated separate epidemiological associations for each sex and age group. These sex-specific and age-specific epidemiological associations were exclusively estimated with mortality records of the respective sex or age group, and therefore, they quantify the actual risk of death of the population subgroup at any given temperature and location based on real data. This means that we issue independent health warnings for each population subgroup based on the temperature forecasts and its corresponding sex-specific or age-specific epidemiological association.

In this presentation, I will give an overview of the methodology, including (i) the processing of the weather forecasts, (ii) the fitting of the epidemiological associations, (iii) the interdisciplinary integration of data and models in both areas, and (iv) the automatization of the system to deliver updated health warnings every day. I will also show (a) initial results for the health warnings by sex, age and geographic area, and (b) the gained experience and feedback from a range of European public health agencies after running the system for almost 8 months. Finally, I will sketch the new features that the system will include during the year 2025.

The projected increase in heatwave intensity and frequency will have far-reaching consequences for the human and natural environment of Switzerland. Two particularly important consequences are heat-related excess mortality in the low-lying areas and heat-related acceleration of climate-change-induced alpine permafrost thawing in high-elevation areas. The latter will potentially have far-reaching impacts on alpine hazards, ecosystems, infrastructure, and tourism. In this interdisciplinary project, we assess the potential of using subseasonal heatwave predictions as a basis for early warning systems for the above-mentioned sectors in Switzerland. For the health sector, we show that the (observation-based) statistical relationship between temperature and mortality in combination with downscaled subseasonal temperature forecasts can be used to predict mortality attributable to heat. We demonstrate that for two densely populated areas of Switzerland (Cantons of Zurich and Geneva) and two past hot summers (2018 and 2022) this system is able to predict individual heat-related mortality peaks up to two weeks ahead and anticipate longer-lasting periods of heat-related excess mortality up to four weeks in advance. For the alpine sector, we show that individual summer heatwaves can play an important role in accelerating permafrost thawing, even though the process is driven by long-term climate change. We demonstrate this with idealized sensitivity experiments with the SNOWPACK model (a physical model that predicts the evolution of the snowpack and the ground temperature below). They indicate that both the duration of heatwaves as well as their timing within an individual summer are important for the intensity of the ground warming in permafrost regions. In summary, this project demonstrates a large potential for using subseasonal heatwave predictions for early warning systems for the health sector. For the alpine sector, it highlights the potential importance of individual heatwaves for permafrost thawing and raises the question if subseasonal heatwave predictions could support monitoring and early warning systems in high-elevation areas in some way.