Since the launch of the ERS mission early in the 1990’s, Synthetic Aperture Radars (SAR) mounted on satellites provide information on spatial and temporal surface changes associated with natural and anthropic activities, day and night, and independently of weather conditions. Despite the progress achieved in terms of data quality/resolution and processing methods, this technology has been constrained in a limited number of applications until the venue of the ESA Copernicus Sentinel-1 mission. The latter opened a new era in the radar remote sensing scenario, mainly because of systematic, global, open data availability. In addition, new missions from several space agencies, as well as commercial operators worldwide, constantly increase the potential of SAR not only for back analyses and surveys at local and regional scales, but also in monitoring applications and early warning scenarios.

In this contribution, we present key results obtained with satellite SAR data in the framework of alpine mass movement detection and monitoring. The focus is not only on scientific achievements, but also on their practical applications. The study areas range from the Swiss Alps to the Himalayas, and the examples of application include: (i) identification of snow wetness conditions and mapping of snow avalanches based on change detection methods exploiting SAR backscatter; (ii) decadal analyses based on radar interferometry aimed at the interpretation of spatial and temporal slope displacements patterns; (iii) monitoring accelerated slope deformation and characterization of catastrophic slope failure events; (iv) combined use of SAR and optical/multispectral sensors to enhance the interpretation of complex alpine phenomena. Moreover, we discuss the current challenges, and the perspectives offered by new sensors with very high spatial resolution (<1m) and frequent revisit time (sub-daily).

Flash floods frequently occurred in Switzerland during summer 2024. It damaged the infrastructures, human habitats and landscapes. This research observed that the flash floods often followed by the high precipitation of several days before the day of the flash flood occurred in Switzerland and mountainous regions such as Hindu Kush Himalaya. The objective of this research is an attempt to detect the potential flash floods based on the satellite based daily precipitation signals in the human habitat zone of the mountainous regions and adjacent areas in Switzerland. The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) is a unified satellite precipitation product produced by National Aeronautics and Space Administration (NASA) to estimate surface precipitation over most of the globe. The daily observed precipitation data is downloaded and calculate the daily mean and daily maximum precipitation of each district over Switzerland. Number of days that received above the threshold daily mean and maximum precipitations were categorized in the attribute tables of each district, as the preliminary flash flood risk maps. Furthermore, the time series observed daily precipitation signals or graphs were plotted for selected district which have received high mean precipitation and maximum precipitation. Statistically significant clusters and outliers of districts were identified using the space and time data mining of daily satellite observed precipitation data cube. The time series signals, and preliminary risk maps, clusters and outliers of districts jointly indicated that the flash floods do not occur suddenly. It requires certain days to set the stage for happening flood event soon. Therefore, it could provide enough time to inform the forewarning to the people and infrastructure operators to endure that the flash floods occurred with the minimal damage, to save lives and infrastructures. Python programming language and ArcGIS Pro software are applied in this research. This research attempts to contribute saving lives and infrastructures from the flash floods using remotely sensed estimated daily precipitation data from the IMERG satellites.

Natural hazards in mountainous regions present significant risks to communities, infrastructure, and ecosystems, demanding advanced systems for forecasting, preparedness, warning, and response. This oral presentation presents *GUARDAVAL*, an innovative hazard monitoring platform implemented in the Canton of Valais, Switzerland. Developed in 2003, and continually upgraded, GUARDAVAL integrates real-time data from over 200 monitoring sites, including extensometers, GNSS stations, hydrometric sensors, and satellite-based InSAR measurements, to track and forecast geophysical hazards such as landslides, rockfalls, and floods.

The system employs a modular web-GIS portal that consolidates data from both local and national networks (e.g., SwissMetNet, SLF) into a centralized, real-time monitoring platform. Additionally, GUARDAVAL supports multi-hazard forecasting by incorporating meteorological and hydrological models, such as MINERVE, enabling accurate flood predictions. Through automated alert systems and comprehensive risk visualization tools, the platform enhances decision-making for public authorities and private agencies responsible for hazard mitigation.

This presentation will demonstrate GUARDAVAL's technological innovations, operational framework, and its role in improving disaster preparedness and response in challenging alpine environments. The discussion will also explore lessons learned from nearly two decades of deployment, highlighting the potential of such systems in improving resilience against climate-induced hazards globally.

IntroductionThe integration of machine learning (ML) in Geographic Information Systems (GIS) has opened new avenues for spatial analysis, enabling more efficient and accurate geospatial data processing. The performance of ML models is often considered highly dependent on the granularity of input data, but the effects of different granularity levels have not been fully clear. In emergency applications, granularity may significantly affect the resulting actions. For example, a better understanding of the watercourse network is crucial in many applications and even lifesaving, e.g. flood modelling and preparedness (Petty, 2016). The National Land Survey of Finland (NLS) is currently renewing its watercourse mapping from high-resolution point clouds. In this study, we present a comparative analysis of six distinct datasets, varying in point cloud density and spatial resolution, to evaluate their effectiveness as input data for ML.

Methods

This study was conducted across two 36 km² study areas in central Finland, one near Ristijärvi and the other near Heinävesi. We trained six ML models with different input datasets, varying in point cloud density and spatial resolution. The datasets consisted of elevation models generated from lidar point clouds with densities of 20 points per square meter (20p) and 5 points per square meter (5p), at three spatial resolutions: 0.25 meters, 0.50 meters, and 1.00 meter. The NLS produced the datasets.

The primary objective was to assess the performance of these datasets as input for ML models in the research of watercourse networks. We implemented various ML training runs to achieve this, systematically altering several key parameters. Specifically, we varied the tile side length, cropping strategies, and the number of training epochs to observe how these factors influenced model performance.

We recorded multiple metrics to evaluate the models during the ML runs, including F1-score, precision, recall, and runtime. These metrics provided a comprehensive view of the effectiveness and efficiency of each dataset when used in ML tasks. We also calculated [KC1] relaxed recall, precision, and F1-score, with a spatial toleration of 1 meter, to better assess the F1-scores in relation to feature detection. The results from each run were carefully documented and analyzed.

After completing the ML runs, the outcomes were further analyzed and discussed within our group of four researchers to interpret the results and identify the datasets that yielded the best performance with visual analysis. Hillshading, relative topographic position, NLS orthophotos and the NLS topographic map were used in the visual analysis. This discussion also focused on understanding trade-offs between data granularity, computational efficiency, and the accuracy of the models.

Tropical cyclones (TCs) displace millions annually, ranking second only to floods in terms of people displaced by natural hazards. While TCs impose significant hardships and pose threats to lives, their negative impacts can be mitigated through anticipatory actions, such as evacuation, emergency protection, and coordinated humanitarian aid. Impact-based forecasting offers a valuable tool for planning these actions by providing detailed information on the numbers and locations of people at risk of displacement.

We present a fully open-source, globally consistent, and regionally calibrated TC-related displacement forecasting system. This system integrates meteorological forecasts with spatial data on population exposure and vulnerability, offering a cost-effective solution for predicting displacement risks. Through a case study of TC Yasa, which struck Fiji in December 2020, we illustrate the practical application of this forecast system. We highlight the importance of accounting for uncertainties in hazard, exposure, and vulnerability through a comprehensive global uncertainty analysis, which reveals a wide range of potential outcomes.

Additionally, we conducted a sensitivity analysis on all recorded TC displacement events from 2017 to 2020 to better understand the influence of uncertain inputs on forecast accuracy. Our findings suggest that for longer lead times, decision-making should prioritize meteorological uncertainties, whereas closer to landfall, the focus should shift towards the vulnerability of local communities. Our open-source code and methodologies are easily adaptable to other hazards and impact scenarios, making them valuable tools for broader applications in anticipatory humanitarian action.

Heavy rainfall events have become more frequent in recent years and in many cases the large amount of precipitation within a very short period of time leads to severe flooding and damage. Recent studies on climate change support this observation and show increasing trends in the frequency of extreme weather events, which include these high-intensity precipitation events.

In Germany, the high intensity as well as the extent and damaging impacts of flooding gain increasing attention among the society and the federal and state governments. Attention focus shifts towards heavy rainfall risk management and prevention of catastrophic harm and damage to people and property.

Contributing to an optimal preparation for the consequences of heavy rainfall events the Federal Agency for Cartography and Geodesy (BKG) introduces a nationwide scenario-based high-resolution pluvial flood map showing simulated information. The map evolves in close partnership with federal and state authorities and is currently being successively made available to politicians, authorities and the general public as OpenData for damage prevention and civil protection (Wimmer et al. 2023).

The foundation of the hydronumerical two-dimensional modelling are high-resolution geoinformation provided by the federal and state governments. A one-meter resolution Digital Terrain Model (DTM) is modified with culverts, 3D buildings, land cover data and further geoinformation to achieve a hydrologically effective and realistic discharge.

The resulting map provides two flood scenarios in line with the heavy rainfall index by Schmitt et al. (2018) which classifies the hazardous character of heavy rainfall events based on the return period. It is increasingly used in risk communication towards the general public in Germany. Based on a dataset of regionalized precipitation heights as a function of precipitation duration and annularity (KOSTRA) by the German Meteorological Service (DWD), a 100-year event is simulated. Additionally, the result of a heavy rainfall event with a precipitation of 100 mm/h is published. Both scenarios are visualized with flood depths, flow velocities and flow directions (geoportal.de/map.html?map=tk_04-starkregengefahrenhinweise-nrw).

The successively published data are used as initial assessment of risk potential. Combining the map with local expertise and government action guidelines simplifies the planning of measures. The map serves as an important and nationwide tool for identifying areas at risk of pluvial flooding. Local authorities, planners and emergency services are enabled to derive appropriate measures, both preventively and immediately before the event.

Building on the initial map, the BKG is currently developing a dynamic tool being part of the Digital Twin Germany project. It aims to provide event-related information on flooding caused by heavy rainfall. By simulating the effects of changes in infrastructure on the extent of pluvial flooding, users will be able to virtually test possible measures in advance in order to successfully minimize the impact.

Stream water temperatures influence water quality with effects on aquatic biodiversity, drinking water provision, electricity production, agriculture, and recreation. More frequent challenges from changes in water temperature are expected as we undergo the effects of the human-induced climate crisis. The current evidence on observed and projected long-term stream water temperature regional warming trends is often insufficient for stakeholders that stand to benefit from an operational forecasting service providing timely information from individual streams at high temporal resolution. Here we do so with deep learning models that efficiently generate multi-horizon probabilistic forecasts across multiple stations at once.

We train and evaluate several state-of-the-art models using 10 years of data from 54 stations across Switzerland. Static catchment features, time of the year, meteorological observations from the past 64 days, and their ensemble forecasts for the following 32 days are included as predictors in the models to estimate daily maximum water temperature over the next 32 days. The considered meteorological variables are daily average near surface air temperature, precipitation, and daily fraction of sunshine duration. Gridded observations of these variables are provided by MeteoSwiss at a spatial resolution of 2 km, and their forecasts correspond to a downscaled version of the Extended-range forecasts from the European Centre for Medium-Range Weather Forecasts.

Results show that the Temporal Fusion Transformer (TFT) model performs best with a Continuous Rank Probability Score (CRPS) of 0.70 ºC averaged over all lead times, stations, and 90 forecasts distributed over 1 year. The TFT is followed by the Recurrent Neural Network Encoder – Decoder with a CRPS of 0.74 ºC, and the Neural Hierarchical Interpolation for Time Series with a CRPS of 0.75 ºC. These deep learning models outperform other simpler models trained at each station: Random Forest (CRPS = 0.80 ºC), Multi-layer Perceptron neural network (CRPS = 0.81 ºC), and Autoregressive linear model (CRPS = 0.96 ºC). The average CRPS of the TFT degrades from 0.38 ºC at lead time of 1 day to 0.90 ºC at lead time of 32 days, largely driven by the uncertainty of the meteorological ensemble forecasts.

We find that the TFT also outperforms the other models when predicting water temperature at stations on which the models were not trained and at ungauged locations. The error at new stations increases 0.11 ºC reaching an average CRPS of 0.83 ºC, whereas it increases to 1.04 ºC when local water temperature is unavailable to the model. Furthermore, the TFT outputs feature importance and attention weights that provide valuable interpretability. In our case we find a dominant role of observed water temperature and future air temperature, while including precipitation and time of the year further improve the predictive skill.

Operational probabilistic forecasts of daily maximum water temperature for the next 32 days at 54 stations across Switzerland are generated twice per week with our TFT model and made publicly available at https://www.drought.ch/de/impakt-vorhersagen-malefix/wassertemperatur-prognosen/. Overall, this study provides insights on the extended range predictability of stream water temperature, and on the applicability of deep learning models in hydrology.

Collaborative GIS is spatial groupwork software and spatial decision support systems that bring people together in a digital map-based workspace to work on a shared geography-related topic. They have been useful in several disciplines, such as maritime spatial planning (Kettunen, 2020) and urban air mobility planning (Rönneberg, 2024). Collaborative GIS has a high potential for practical impact in emergency planning and action, as human actors can better communicate and become aware of the current spatial situation and its development scenarios for the near or more distant future.

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.

We propose a double session in which the first session would consist of presentation of scientific and applied projects, and second session would have an interactive panel (forum with experts but with audience participation).

In the conference session we will invite (or include papers that are submitted to the conference already) topics that are focusing on current technologies both from a scientific perspective demonstrating innovation and discoveries, and applied projects demonstrating case studies. In both scientific and applied contributions, we expect a reflection that outlines the relevance, strengths and limitations of the visualization or XR solution that are tested, used or proposed.

In the panel + forum session, we will facilitate two levels of dialogue: 1) Among experts to frame and provide an overview of the current understanding and future directions of the covered topics, 2) Among audience and experts based on the current bottlenecks, pain points, challenges and exciting developments that might offer solutions.

Both sessions will cover visualization and extended (i.e., virtual, augmented or mixed) reality related research and applications in relation to conference themes (i.e., these presentations and discussions will be directed at work that intersects the common phases of crisis management), and specifically touch upon user-centered technologies (user experience, empirical studies) as well as latest technology and science breakthroughs in automation of visualization and 3D modeling and other related processes through e.g., generative AI and other solutions.

With increasing disasters, effective emergency response information systems (ERISs) are vital for mitigating impacts on communities. Traditional methods often prove inadequate in providing timely, detailed information needed for decision-making. To address this, a voxel-based urban digital twin for ERISs is introduced. The proposed framework is designed to integrate real-time data from various sensors into a georeferenced voxel grid. This approach intends to provide continuous updates to ensure an accurate 3D representation and interaction with the urban environment during emergency operations. Therefore, an exemplary scenario of firefighters' operation is developed for the research.

The produced voxel-based urban digital twin is rendered through a web application. Constructed from a high-resolution classified point cloud, the 3D voxel model incorporates crucial elements for fire emergency response, such as hydrants, smoke sensors, and their associated attributive information. The system's key functionalities include multiple exploration modes, dynamic rendering, a focus+context visualization technique, and the use of transparency as a visual variable to highlight critical information and ensure clear, efficient communication of high-priority data to the user. The web application provides specialized tools for navigation and interaction, aimed at enhancing situational awareness and efficiency of firefighting operation.

Feedback from firefighters highlights significant improvements in the web application over traditional methods, while also identifying areas for usability enhancement. The research demonstrates the potential of a voxel-based digital twin as a more interactive and immersive tool for emergency management than conventional 2D and basic 3D visualizations.

Management of natural hazards and associated risks requires access to multivariate information. Access to rich spatiotemporal data that contains information on all aspects of the hazardous event— e.g., factors that led to the event, what was affected by the event, impact of any previous (or planned) interventions—should support proper understanding as well as informed decision making for current and future actions (Gołębiowska et al., 2023). However, studying multiple variables and the interactions between them is cognitively demanding, and when it is not done right, it can impair human comprehension and decision making rather than improving it (Keskin et al., 2023; Cheng et al., 2024). In this context, we examine geodashboards that contain multiple linked visualizations, which offer opportunities for exploration and communication of spatiotemporal data from many perspectives through, e.g. maps, plots, graphs, spreadsheets, networks etc. (Golebiowska et al., 2017, 2020), though their complexity could lead to high levels of cognitive load (Nadj et al., 2020).

We conducted several user experiments where participants are given natural hazard related sense making and decision making tasks with such complex dashboards as described above, and measure their performance as well as eye movements, from which we can surmise their cognitive load to some degree (Ke et al., 2023). Specifically, we investigated user experience and layout design related challenges; i.e., inexperienced participants’ process while learning the complex interface, their process of information retrieval from multiple-view tools, and the effect of different layouts of geodashboards.

Combining usability performance metrics (efficiency, effectiveness and satisfaction), and eye tracking data (Çöltekin et al., 2009), we get insights into the users’ reasoning and cognitive processes. The tested geovisualizations present data on preparedness, i.e., vulnerability and exposure to natural hazards (floods, landslides, storms), as well as consequences of natural hazards in a form of insurance compensations due to natural hazards (storms, floods, landslides, storm surge, water intrusion). Participants were asked to carry out various task types using the presented geovisualization tools. Our results broadly suggest that despite the visual complexity of the tools, even the inexperienced participants find them convenient and helpful in exploring large sets of spatio-temporal data. We thus posit that properly designed geodashboards can be effective tools supporting users, enabling them access to complex data.

GIS tools ubiquitously employ maps to aid visualization of the geographically referenced information (geo-data) across diverse disciplines, including civil engineering, forestry, geology, ecology, and archaeology. In this applied science project, we collaborated with OpenGIS.ch, a company that developed QField (https://qfield.org), an award-winning mobile tool to collect, manage, and edit geo-data in situ tailored to the needs of the GIS fieldwork. Beyond traditional uses in civil engineering, such as construction, urban planning, and infrastructure work, QField has also been employed to facilitate disaster management and recovery tasks. For example, it has been used in mapping flooding damage to houses, infrastructure and vehicles in Canton Ticino, Switzerland [4], assessing flooding damage of the croplands in Fiji [7], and monitoring the (agricultural) recovery of the damaged lands and infrastructure in Tonga due to a volcano eruption [6, 13].

However, despite its interoperability (QField is based on a popular QGIS open-source project https://qgis.org), several challenges remain in rendering and interacting with geospatial data in situ. Specifically, interactions using the current mobile/tablet app are constrained to the manipulation of 2D data points on the map interface, which can often cause issues such as overplotting and occlusion [1] or are prone to difficulties in spatial interpretation and decision-making processes [12].

To address the above-mentioned challenges of user perception and interaction and take advantage of the strengths of both 2D and 3D visualizations, we propose an Augmented Reality (AR) viewer for Qfield. By implementing AR, we enable the placement and rendering of 3D geo-data in situ. Previous research shows mixed evidence regarding the usefulness and usability of the advantage of 3D visualizations in AR for understanding statistical data, local topography, and reading maps [2, 3, 5 9, 11]. We believe, in this case, the AR viewer will facilitate the efficiency of decision-making in the field by visualizing relevant geo-data in the immediate real-world environment, supporting various field tasks from planning underground utilities beneath the surface [8, 10] to virtual demarcation of the forecasted flood territories. We design and develop an AR viewer for QField for both handheld and wearable AR experiences to support a broad range of tasks and enhance interactivity with geo-data and real-world immersion, thus improving spatial understanding and decision-making in situ. A specific strength of the AR in this case is to display the relevant information in its spatial context, which we hypothesize should facilitate quicker comprehension of the situation, as it offers an experience-based approach rather than strictly an analytical one.

The contribution of our work-in-progress is threefold: (1) we elicit the AR needs of field workers when it comes to in situ interactivity with geospatial data; based on these needs (2) we design and develop an interactive prototype of the AR viewer for QField; and through continuous user evaluations (3) we examine how data points in AR can be represented across different form factors, such as handheld and wearable AR, by referencing scholarly discussions on the visualization of 2D vs. 3D data for both experiential and analytical tasks [2, 5, 9, 11].

In this brief position paper, we outline some key arguments about why extended (i.e., virtual, augmented and mixed) reality, i.e., XR, might work well for skills training, specifically in the context of emergency preparedness. XR offers a wide variety of benefits in skills-training and experience based learning (Çöltekin et al., 2020a, 2020b). Not only can we fully control and simulate all imaginable scenarios in XR, but we can do this safely, enabling learning from past experiences and preparing for future events. XR might not fully replace traditional training in emergency preparedness, but it can greatly improve them, most pronouncedly for high-severity, low-frequency events. While the implied benefits are numerous, in our view, XR technologies for training for emergency preparedness are becoming accessible considering cost- and user-centric perspectives only recently.

XR has been proposed for a long time in the emergency related fields and used since the 2010s, where training dominates much of the discourse (Zhu et al., 2021, Khanal et al., 2022). Of the different types of XR, VR excels in fully controlled experiments and the simulation of rare events, where full immersion leads to memorable experiences (e.g., Lokka & Çöltekin 2026, Lokka et al., 2018), which is key to learning, In contrast, AR and MR excel at augmenting existing training methods with relevant information embedded “in-situ”, offering most value in the field exercises and during interventions. Furthermore, AR/MR enables information push as needed or on-demand, which can potentially lower the responders’ task (and thus cognitive) load (Elmasllari, 2018). Previous work has shown that XR can effectively transfer knowledge into skills (e.g. del Amo et al., 2018). Skills training is experience based, i.e., arguably, does not require much conscious thought (e.g. del Amo et al., 2018, Rasmussen 1983). As XR enables unlimited repetitions and variations for even the rarest scenarios, such experience based learning, i.e., effectively converting knowledge into skill is possible. Furthermore, by recording scenarios from the participant’s point of view, XR allows for a much better post-training briefing than would be possible from the participant’s recollection alone (Forondo et al., 2016). Also importantly, XR can enable team-based simulation and training to address collaboration and coordination needs during emergency response (Reed et al., 2017). With current technologies, shared XR exercises can be organized by single units or even single responders wanting to learn together. Small teams could participate (virtually) in shared large-scale XR exercises, for which travel time in the real world would have been a limiting factor (Kanal et al., 2022).

In conclusion, based on the above arguments, we surmise that as XR can respond to needs of safety, cost-effectiveness, logistics, and collaboration, it can meet a large part of the requirements of a training environment, and thus are strong candidates in most emergency related scenarios, but especially in low-frequency, high-impact cases. With the current developments in artificial intelligence (AI), we anticipate AI-generated sound, video, behaviors etc. will strengthen XR even more, and foster new opportunities for experiential learning.

Integrated risk management in the Alps and the Arctic - Fostering knowledge exchange and capacity building
Alpine and Arctic regions are affected by a number of similar natural hazards (e.g., avalanches, landslides, etc.), expected to occur with increased severity and frequency due to thawing permafrost and extreme weather events. Consequently, much can be gained from strengthening collaboration and knowledge exchange at all levels of integrated disaster risk management, including effective monitoring, modeling, and implementing risk mitigation. During short presentations and an expert panel discussion, scientists and practitioners working in Switzerland and the Arctic regions will present and reflect on best practices in integrated disaster risk management in these regions and the transfer of knowledge and implementation within different national technical guidelines and strategic frameworks.

In recent years, Greenland has experienced a number of mass movements, including a large and devastating rock avalanche that triggered a tsunami in Uummannaq Fjord System in June 2017, causing the death of four people.

We have since then launched monitoring initiatives and mitigation measures to reduce the impact of such risks to the Greenlandic population. Our country’s first two ever landslide monitoring systems are now installed and operated, led by the Department of Geology.

Greenland has some unique preconditions with respect to monitoring, such as remoteness, harsh climatic conditions, polar night and seasonal sea ice coverage. This means that monitoring methods must be adapted and sometimes rethought in unconventional ways before they can fully meet our needs.

The Greenland Government is working together with several national and international authorities, research institutes and industries to overcome these challenges. Our collaboration with these experts is focused on implementing an effective early warning and alarm system tailored to remote Arctic conditions. The presentation will shed light on the ongoing monitoring efforts and highlight some of the achievements on natural hazard mitigation in Greenland so far.

Flooding is a common disaster worldwide, having enormous detrimental impacts on society. The frequency and intensity of floods are rising due to climate change and the consequential damage is dramatically increasing as a result of elevated exposure. With the increasing frequency and intensity of flooding, it has become imperative to mitigate flood risks effectively. A great amount of research is focused on risk assessment and mitigation strategies. The effects can be significantly reduced through the implementation of risk mitigation strategies including providing decision-makers with relevant, accurate risk assessment information that can aid in establishing effective emergency management protocols and communicating with the public to save lives and property. Flood risk assessment contributes significantly to managing floods effectively. These assessments enable us to identify possible threats at both the global and local levels, providing vital information for mapping high-risk areas. Machine Learning (ML) models can simulate flood risk by identifying critical non-linear relationships between flood damage locations and flood risk factors (FRFs). Tampa Bay, Florida, is selected as a test site. The study's goal is to simulate flood risk and identify dominant FRFs using five ML models: Decision Tree (DT), Support Vector Machine (SVM), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). Historical flood damage data is the target variable, with 17 FRFs as predictors. RF and XGBoost outperform other models in accuracy tests. RF classifies 2.23% of Tampa Bay as very high risk and 2.55% as high risk, while XGBoost classifies 3.77% as very high risk and 1.09% as high risk. Key FRFs include low elevation, distance from water bodies, extreme precipitation, and population density. This study demonstrates an effective approach to assessing flood risk by considering a wide spectrum of FRFs using advanced supervised ML algorithms. Through this approach, we were able to identify the zones under very high and high flood risks in Tampa Bay, along with the identification of responsible FRFs, in addition to potential exposed populations. Such risk identification procedures will enable us to take precautions and to better prepare for floods at both household and institutional levels. The risk identification is believed to ultimately advance flood resilience. All of the findings contribute to flood risk management both scientifically and practically. The findings can be meaningful in the vast scientific field for flood management studies around the world and the approach will directly assist the policymakers during their decision-making regarding flood risk management.

The increasing complexities of climate change and natural hazard risks pose significant challenges for communities globally, necessitating a deeper understanding of how these risks are perceived at both societal and community levels. The negative impacts of natural hazards and climate change are often exacerbated when these risks are not adequately integrated into land use planning. Currently, New Zealand lacks a comprehensive national framework to establish tolerable risk thresholds related to life, property, and the economy. While several local councils have attempted to develop their own risk-based frameworks, these efforts are constrained by several factors: insufficient community awareness about acceptable risk levels, limited understanding and motivation among decision-makers to adopt risk-based approaches, and inherent uncertainties associated with natural hazards and climate change.

This research explores the complex relationship between individuals' perceptions of climate change and natural hazard risks, examining how these perceptions are shaped by diverse contexts and backgrounds in the societal, geographical and cultural environments. It specifically investigates the factors that influence community perceptions of these risks, focusing on the level of awareness within communities about the significance of these risks and how this awareness affects their actions in managing and mitigating them.

The literature on risk perception in New Zealand, situated within a global context, suggests that risk perception is influenced by various factors, including environmental and cultural characteristics, with significant regional variations (Fan et al., 2022). Research indicates that risk perception is shaped by social representations of the associated risks and that personal values significantly impact attitudes towards climate change (Han et al., 2022). In New Zealand, social vulnerability indicators have been developed to identify populations at risk of flooding—a common natural hazard in the region. These indicators assess community resilience by considering factors such as exposure, health and disability status, social connectedness, and housing (Mason et al., 2021). Moreover, public perceptions of risk often diverge from expert assessments, which are based on empirical data and specialised knowledge (Xiao et al., 2023). Factors influencing risk perception include gender, age, race, education, region of residence, cultural background, social status, income, knowledge of risks, disaster experience, and communication strategies (Xiao et al., 2023).

The Henderson-Massey Local Board area in the Auckland region in New Zealand was selected as a case study, with data collected through both direct household surveys and online questionnaires to assess community ‘understanding’ and ‘awareness’ of their exposure to natural hazards and climate change risks. Preliminary findings highlight a complex interaction of cognitive and social factors that shape these perceptions, subsequently influencing community decision-making and behaviour concerning risk mitigation.

This study advances the field of risk management by integrating community perspectives and providing new insights and developments relevant to both local and international contexts in mainstream climate change adaptation. The findings offer valuable implications for future policy and planning initiatives aimed at enhancing urban resilience and adaptive capacity to natural hazards and climate change exacerbated impacts.

In the complexity of international poly-crisis phenomena, the emergency response to earthquakes involves a multifaceted process, from immediate relief to long-term reconstruction. Geoinformation, cartographic, and aerial data are essential in Disaster Risk Reduction, Disaster Management, and Building Damage Assessment. However, documenting early recovery architectures remains challenging during the in-between period and the reconstruction transition, despite advancements in knowledge platforms, data management, and rapid multiscalar information sharing encouraged by international scientific programs such as the Integrated Research on Disaster Risk and the Sendai Framework for Disaster Risk Reduction 2015-2030.
Geomatic and photogrammetric techniques are central to documenting the emergency process and projects across territorial, urban, and building scales, defining a common semantic and geometric codification for mapping temporary phenomena.
This research focuses on the long-term spatial impacts of all temporary solutions, which have significantly and permanently altered the post-seismic Italian landscapes, particularly in the regions of Central Italy that have been affected by a succession of catastrophic events in the last 25 years.
The research has revealed the lack of overall cartographic documentation of these interventions, which is responsible for new urban configurations, despite the collective planning, economic and regulatory effort made, and the digital technological potential available today. Existing documentation is incomplete, fragmented, and outdated, necessitating a systematic, evidence-based approach to bridge this gap.
The methodology combines diverse data sources – satellite imagery, UAV-based photogrammetry, and temporal and economic processual data – into a unified multiscale geospatial database organized within the GIS platform. This database serves as the conceptual model for the Operative Atlas, facilitating the visualization and analysis of emergency responses and urban infrastructure.
One key aspect of the research is the implementation of attribute specification in existing official cartographic data, developing a standardized terminology and classification system for mapping temporary interventions, and making them recognizable within the national and international cartography during and after emergencies. By developing a conceptual and logical model, the research defines geometric entities in the GIS data structure to document and digitalize these provisional contexts. UAV point clouds are integrated with non-metric data to ensure detailed descriptions through a multiscalar approach. The methodology was validated in the study of Visso (Macerata), a small historical mountain village in Central Italy, where specific temporary interventions were effectively mapped and
documented. In conclusion, the research adopts an interdisciplinary and integrated approach to overcome the existing gaps in the documentation of post-disaster interventions. Combining architectural studies with geomatic tools, the Operative Atlas is valuable for managing heterogeneous geospatial emergency data. Digital visualization techniques can support different emergency phases and various stakeholders involved in post-disaster development strategies, such as local and national emergency management authorities, urban planners, and practitioners, in facilitating the transition from emergency response to permanent settlement. It offers digital visualization techniques to support, in different emergency phases, stakeholders involved in post-disaster development strategies, including local and national emergency management authorities, urban planners, and practitioners in facilitating the transition from emergency response to permanent settlement.

This work presents our current methodological efforts to explore, extract, visualize, and analyze potentially relevant information captured by the citizens' weather reports.

We started this project at the beginning of 2022 when MeteoSwiss shared with us their collection of citizens' observations on weather conditions reported by users of the MeteoSwiss mobile application. We followed an iterative and participatory design process that can be divided into three main phases. In each phase, we developed prototypes, gathered experts' requirements, designed and ran user experts' studies, and collected domain experts' feedback to improve our solution's features.

In Phase 1, we focused on the research of novel visual representations for the spatio-temporal analysis of only the citizens' reports. In Phase 2, we focused on facilitating the visual comparison of radar data and citizens' reports, exploring reports' images and extra metadata, and re-engineering the tool to make the software more accessible, modular, and robust.

Phase 3 is an ongoing effort, and its main goal is to provide meteorologists with information extracted from the images to estimate the impact and damage on the ground as reported by citizens.

A prototype of our visual tools is available at https://weatherva.ifi.uzh.ch/.

Multidimensional datasets are often represented by layering them within a single map, which can lead to an exaggeration of information. As an alternative representation, this study investigates the use of a small multiple layout for web applications, in which several maps with a common spatial area, each representing a different topic, are arranged next to each other and can be viewed simultaneously.

The area of spatial planning was used as a use case, as many different topics of a common area, which are largely obtained from the ÖREB-cadastre, have to be identified and analysed in the planning process. Spatial planning experts took part in the qualitative usability study, carrying out a fictitious task that is typical for this specialist area. The aim of the study was to analyse how the experts rated the small multiple layout in terms of usability and efficiency. The usability test was analysed using a qualitative content analysis.

The experts were able to quickly gain an overview of the topics and make an efficient cross-comparison, particularly when weighing up interests. They were able to complete their tasks efficiently and were predominantly positive about the layout during and after completing the tasks. Limitations arise if the area under consideration extends beyond the boundaries of the small multiple map window. The study concludes that small multiples layouts are a useful and efficient tool for the simultaneous visual analysis of multiple spatially coincident themes, with potential applications extending beyond spatial planning to other fields that use multidimensional spatial data.

Climate change is an ongoing environmental threat, and its mitigation poses significant societal challenges. Policymakers are tasked to make time-critical decisions rapidly, including hazard forecasting, preparedness, warning, and response to mitigate potentially harmful consequences of climate change. This often involves map-based decision-making with visualized climate data, which are confronted with various sources of uncertainty. Uncertainty is an inherently tricky concept; thus, it is challenging to communicate clearly with decision-makers and the public. Especially in the emotionally charged context of climate change, with segments of the population already showing scepticism towards climate change, the communication of uncertain information in map displays still needs deeper investigation.
Applying a mixed factorial (3x2x2) map-based, online experiment, we aimed to study the visualization of (un)certainty in static climate change forecast maps and how this might interact with map readers' emotions and attitudes. Inspired by the CH2018 Scenarios for the year 2060 (NCCS, 2018), we designed change prediction map stimuli with different climate variables in three versions: without any uncertainty information, with uncertainty visualized as black dots (Figure 1) or lines, using empirically validated depiction guidelines (Retchless & Brewer, 2016). Based on prior research, we chose the term 'certainty' instead of 'uncertainty' in our stimuli (Johannsen et al., 2018). Each map was accompanied by context information on its right-hand side. It either included a graphic character with a fitting narrative (between factor: emotion), intended to elicit an emotional response (Fig. 1a), or it just contained information equivalent to facts (Fig. 1b).

Leveraging the crowd-sourcing platform Prolific.com, we recruited 109 people (f: 53, m = 54, nb = 2; average age = 34 yrs.) to participate in this online study. We balanced the number of climate change sceptical and believing participants (between factors: attitude) using screeners in Prolific. All participants were exposed to 18 maps in random order, without any time pressure. They were asked to select the locations predicted to be most/least affected by climate change shown on the map (out of six given options, A-F in Figure 1), to assess the depicted change certainty and severity, and to indicate their amount of trust in the depicted information, on a scale from least (1) to most (7).
Preliminary results (ART ANOVA) indicate that participants reporting a sceptical climate change attitude rate the severity of the depicted change significantly lower compared to believing participants (F = 5.446, p < .05), irrespective of the emotional context. Regardless of their attitudes, participants, on average, trust maps without any certainty information significantly less (F = 167.872, p < .001) compared to when certainty is visually communicated, irrespective of the certainty visualization type.

Our initial findings not only underline the importance of deriving suitable visualization guidelines to communicate uncertainty climate change forecast maps effectively but also suggest adapting the visual communication method to population segments with varying attitudes towards climate change. Future research should deepen our understanding of emotion's role in the visual communication of climate change forecast maps and how different types of climate change scepticism might interact with uncertainty visualization.

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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HEIs must frequently adapt broad, varied emergency management policies to deal with the scope of emergencies and disasters that can occur in on-campus settings. Fires, earthquakes, floods, and some of the most common natural disasters possess the capacity for losses of life and property, with the potential to effectively disrupt and damage a university community. Man-made crises, such as cybersecurity threats, CBRN hazards, protestors and campus shootings among others also pose a serious threat to life and property; to preemptively reduce or prevent the severity of emergencies, universities must coordinate and implement policies to effectively eliminate unnecessary risks' and decrease potential losses.

Incidents vary among continents and it is worthy to examine the threats perceived by European, American, Japanese and other Universities and consider the steps these institutions may take to protect their communities from harm.

HEIS need to have a well-designed plan of procedures to respond to emergencies. These plans of response provide the entire campus with specific guidelines to properly prepare, respond, and recover from emergencies. The University as an organization, including facility members, students, staff, and suppliers should all be familiar with the plan's procedures, and use it as a quick reference for effective action.

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.

As climate-related disasters and humanitarian crises become more frequent, the need for faster and more effective responses is crucial. Anticipatory Action (AA) shifts from reacting to disasters after they happen to taking action before they strike, based on scientific forecasts.

This keynote will explore how scientific data, like weather and climate forecasts, can trigger early humanitarian interventions. We'll look at real-world examples where UN OCHA has used these triggers in Africa and Asia, discussing how reliable science can help decide when and where to act. We'll also address challenges in scaling this approach, such as the need for strong partnerships and accurate data.

The goal is to show how science can improve humanitarian efforts, making them faster and more effective, ultimately saving lives and reducing suffering. This talk will encourage collaboration between scientists and humanitarian workers to refine these methods, ensuring that future actions are not just reactive but also anticipatory.