Advancements in web development enable the transfer of large amounts of data via the web, making it feasible to integrate insights gained from flood modelling into early-warning systems and even near real-time applications. The proposed web tool leverages hydrological forecasts, such as those issued by the Federal Office for the Environment in Switzerland (FOEN 2024), to map predicted floods. It offers an interactive map that visualizes potential flooding areas based on different members of a probabilistic forecast, enabling users to explore a range of flood scenarios.

The key features displayed include flood depths, temporal information, hazard classes indicating flood severity for human life and infrastructure, and estimations of potential damage, including the number of potentially exposed buildings, population, and workplaces. This comprehensive visualization enhances awareness and understanding of anticipated flood events for target users, such as emergency responders, governmental authorities, and insurance companies.

The tool facilitates proactive decision-making by providing near-real-time information on probable flood threats, thereby supporting early warning and strategic planning in flood risk management. It utilizes GeoServer as an interface to transfer requested flood information from a PostgreSQL database, where results from precomputed flood scenarios are stored, directly to the client side.

Developed using insights from Mosimann et al. (2023) and Mosimann et al. (2024), this tool also addresses the challenges of providing probabilistic flood hazard and impact information. It serves as a proof of concept for implementing the surrogate flood model approach in near-real-time flood warning systems and illustrates the potential for forecasting systems to meet the diverse needs of stakeholders. For example, insurance companies might focus on potential damages to plan resource allocation, while emergency responders prioritize information on population and the areas likely to experience severe flood intensities.

Publication bibliography

FOEN (2024): Forecasts and flood alerts. Stations with forecasts. Federal Office for the Environment FOEN. Available online at https://www.hydrodaten.admin.ch/en/messstationen-vorhersage, checked on 8/28/2024.

Mosimann, Markus; Kauzlaric, Martina; Schick, Simon; Martius, Olivia; Zischg, Andreas Paul (2023): Evaluation of surrogate flood models for the use in impact-based flood warning systems at national scale. In Environmental Modelling & Software, p. 105936. DOI: 10.1016/j.envsoft.2023.105936.

Mosimann, Markus; Martius, Olivia; Zischg, Andreas Paul (2024): Two Sides of the Same Coin? Hydrometeorological Uncertainties in Impact-Based Flood Warning Systems and Climate Change Sensitivity of Floodplains. DOI: 10.2139/ssrn.4893831.

This presentation will explore the development and implementation of Germany's Naturgefahrenportal (NGP), a centralized platform providing the general public with authoritative information on natural hazards. The NGP aims to consolidate various sources of hazard-related data into a single accessible platform, enhancing public understanding and disaster response.

Designed for clarity and ease of use, the NGP offers real-time warnings and guidance on preventive measures and emergency actions. It serves as an introductory resource, linking to regional or single-hazard portals for deeper insights, and does not compete with existing warning apps, as it does not provide active alert features, such as push notifications.

Key Aspects of the Presentation:

1. Challenges in Standardizing Data: A significant challenge in developing the NGP is the standardization of data sourced from various stakeholders. The presentation will delve into the strategies employed to harmonize these diverse data sets. This standardization is vital for presenting a unified and coherent picture of risks, which facilitates quicker and more effective public response.
2. Harmonization of Maps: The presentation will also address the efforts to harmonize map data, a critical component of the NGP. Different institutions often use varying map scales, symbols, and legends, leading to potential confusion among users. By standardizing these elements, the NGP ensures that risk information is presented clearly and consistently. This harmonization is essential for providing users with an intuitive and accurate understanding of geographic risks, thus enabling more informed decision-making.
3. Incorporation of Socio-Economic Research: A key innovation of the NGP is the integration of the latest socio-economic research to enhance public comprehension of natural hazards. The presentation will discuss how insights from socio-economic studies are being applied to tailor the portal’s communication strategies to the diverse needs of the population. By understanding how different demographic groups perceive risk and respond to warnings, the NGP can present information in a manner that maximizes its effectiveness, ultimately leading to better public preparedness and response.

Added Value and Impact:

The integration of all relevant information into a single, unified platform significantly enhances the value of the data provided, offering a holistic view rather than isolated fragments of information.

The NGP represents a major step forward in the communication of natural hazards in Germany. By addressing the challenges of data standardization, map harmonization, and the incorporation of socio-economic research, the NGP sets a new benchmark for how risk and threat information is conveyed to the public. With its barrier-free design, it is a valuable resource for users with various impairments or disabilities, while recognizing that individual needs may vary. This presentation will offer useful insights into the processes and strategies behind the development of this key public resource, illustrating how it can serve as a model for effective public communication in disaster preparedness and response.

In conclusion, the NGP is more than just a portal—it is a critical instrument for enhancing public safety and resilience.

Traditionally, most natural hazard warnings are verified based on whether they exceeded the predefined physical threshold, or resulted in the expected impact, in a specific region during the respective time period. If these boxes can be ticked, the warning is generally considered a “hit” - the job is done. In recent years, however, initiatives such as the ‘Early Warnings for All (EW4A)’ and the ‘HIWeather Project’ by the World Meteorological Organization (WMO) have shifted the focus towards the “last mile of the warning value chain”, calling to involve the recipients in the warnings production process. Thus, a natural hazard warning is only a real “hit” if it helps recipients make informed decisions to protect themselves and minimize the damage caused by the hazard. Furthermore, only if the hazard warning levels and thresholds match the perception of the recipients will the warnings have the desired effect and prevent reduced risk perception and negligence of the necessary measures. To this end, the recipients must be given a voice in
the warning verification, completing the traditional verification with a “social verification”.

For this purpose, MeteoSwiss has launched an on-going pilot project in 2022 to survey the affected population in the warning area directly after a natural hazard using so-called flash-polls, following the example of other European weather services (e.g., UK Met Office and KNMI). Specifically, people in the affected area are asked whether they received, understood and acted upon the hazard warning, and whether they found the warning useful and helpful in assessing the risk and deciding what action to take. Finally, they are asked whether they consider the warning level to be appropriate given the impact and intensity of the natural hazard event, which serves as a social verification. Taken together, this information provides valuable insights into people's risk perception and behavior and can indicate opportunities for improvement in the current warning process.

The presentation will introduce the preliminary findings of the ongoing pilot project and the flash-polls conducted to date, including insights on the perception of natural hazards by the population and the social verification of the warnings, as well as indications of possible further improvements of the warning system at MeteoSwiss. Finally, the main challenges and limitations of the pilot project will be discussed and an outlook will be given on how the social verification will be continued after the pilot project is completed.

From an end-to-end perspective, distribution is integral to achieving a high reach in disseminating natural hazards. Which channels suit this, and what does the trend look like? What needs to be considered to make an app attractive?

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.

Introduction

The Gebäudeversicherung Bern (GVB) experiences annual losses from natural hazard of approximately 60 million CHF, primarily from windstorms, hail, and flooding. A comprehensive risk analysis for 2023 and 2024 was conducted to address this, focusing on the risk: hazard x damage potential x vulnerability. While GVB has a solid understanding of hazards and damage potential, a knowledge gap remains in assessing vulnerability. This project aims to reduce this gap by developing a systematic approach to evaluate and mitigate vulnerability.

Methodology

To address the knowledge gap, hazard overviews and vulnerability indicators were developed for hail, windstorms, and flooding. This was achieved through literature reviews, expert interviews, and existing vulnerability indicators. A physical vulnerability index was created based on these sources to quantify the vulnerability of buildings.

Additionally, a catalog of behavioral guidelines was created for property owners based on their vulnerability index results. Risk was calculated using spatial data from hazard maps for hail, wind, and flooding.

A clickable prototype was developed and tested through 20 customer interviews to evaluate its effectiveness. Feedback was integrated into an updated prototype, which was further tested for usability.

Results

Sixteen key indicators were identified for assessing building vulnerability. These included:

1. Number of above-ground floors
2. Number of underground floors
3. Residential use of the basement
4. Presence of raised ground floor
5. Entry doors at ground level
6. Windows and doors in the basement
7. Value accumulation in the basement
8. Roof material
9. Solar installations
10. Roof overhangs
11. External shading systems
12. Number of motorized sunshades
13. Year of construction
14. Pool covers
15. Skylights and roof domes
16. Presence of backflow valve

Responses were categorized into vulnerability scores from 1 (low vulnerability) to 5 (high vulnerability). The unweighted sum of these indicators produced a score from 6 to 50, which classified properties into three risk categories. Behavioral recommendations were then provided based on the score.

For example, if a building had plastic skylights, the recommendation was to install protective grids and replace damaged parts. These actions provide both hail protection and fall-through prevention, and GVB offers financial support for these measures.

The prototype received an average rating of 8/10 from users, though feedback indicated that some found it difficult to distinguish between hazard and risk, prompting a greater focus on risk in future iterations. Based on the positive feedback, the tool will be implemented on the GVB customer platform by the end of 2024.

Conclusion

The vulnerability indicators developed in this project provide a solid foundation for increasing homeowner awareness of natural hazards. The customer interviews were invaluable in improving the prototype’s usability and gaining insights into user behavior. Future plans include extending this methodology to additional natural hazards such as landslides, debris flows, avalanches, and rockfalls to further enhance risk mitigation.

The Swiss Government stresses with its decision to implement cell broadcasting the importance of early-warnings in Switzerland, which is in line with global developments to better prepare for natural hazards. Studies show that effective early warnings are impact-based and personalized. To tailor warnings, personal data is needed which is not openly available, i.e. on caring duties or medical issues, but would have to be shared with the provider of the warnings With a representative survey, we explored i) the interest of the Swiss public for tailored (heat) warnings, ii) the impact of tailored warnings to take protective action and iii) the willingness to share the necessary data. Our results show that the Swiss public would appreciate tailored warnings but is not willing to share the necessary data, which is an example of the privacy paradox. To make early warnings more effective, developers would have to find ways to overcome this paradox. At the RIMMA 2025, we would like to discuss these results and share potential policy impacts of our findings.

As the federal agency responsible, the Federal Office for the Environment (FOEN) coordinates, informs and warns the population about the danger of forest fires. Since 2022, a new system has been in place in collaboration with the cantons. IGNIS (from the Latin ‘fire’) is an information system developed by the FOEN in collaboration with Natural Resources Canada (NRCan). IGNIS is based on the Canadian Fire Weather Index (FWI), developed in Canada in the 1970s, which has been adapted and optimized for the Swiss Alpine region. It consists of six components that account for the effects of fuel moisture and weather conditions on fire behavior. Every day, MeteoSwiss provides the FOEN with measurements from its stations and weather forecasts. These data are subsequently processed and made available to federal and cantonal experts. These two authorities are responsible for the daily assessment of forest fire danger and the publication of danger levels for the different alert regions. In addition to its prevention purpose, IGNIS enables authorities to take preventive measures quickly and speed up decision-making processes for the allocation of fire-fighting resources. Furthermore, the FWI system is widely used in the EU and worldwide, which facilitates the exchange of knowledge with international experts. IGNIS is an integral part of the système d'alerte modulaire (SAM). SAM is a data management system, which ensures the flow and processing of data, the execution of tasks, the smooth running of operational flows and manages the distribution of products to external parties. As IGNIS is part of SAM, it is possible to inform and alert the population about forest fires quickly and safely.

Climate services (CS) provide climate information that supports decision-making, adaptation, and resilience in a changing climate. These services are tailored to meet the specific needs of end-users, including the information itself, frequency, and dissemination channels. CS offer benefits across various sectors such as agriculture, water resources, energy, health, and disaster risk management.

For these services to be effective, they must be co-produced between the relevant actors, National Meteorological and Hydrological Services (NMHS) and end-users throughout the entire process, from the initial acquisition of information to its delivery. This facilitates the establishment of trust by actively engaging end-users. This collaborative approach reduces the use of a top-down approach, where institutions create and deliver services without consideration of the end-user, in addition to promoting ownership and engagement among stakeholders. Despite its increasing prevalence, the concept of co-production undefined in the field. In contrast to conventional, top-down methodologies, knowledge is collectively constructed by stakeholders, thereby ensuring that CS are tailored to the specific requirements and context.

Studies that assess the socioeconomic benefits of CS are of great importance, as they determine their value by quantifying tangible and intangible benefits. Such studies help justify investments in NMHS and deepen the comprehension of how to enhance CS to serve user needs.

The ENANDES “Enhancing Adaptive Capacity of Andean Communities through CS” and ENANDES+ “Building Regional Adaptive Capacity and Resilience to Climate Variability and Change in Vulnerable Sectors in the Andes” projects have the objective to enhance resilience through the provision of CS and entails a collaborative effort between institutions. The participating countries are Argentina, with a NMHS and a Regional Formation Center (CRF), NMHS Bolivia, NMHS Chile, NMHS and Regional Climate Center (CRC) Colombia, NMHS and CRC Ecuador, NMHS and CRF Peru. In addition, there is a Regional Expertise Hub (NUREX), a virtual platform for sharing information created for the project and managed by Peru. Moreover, other institutions are involved, including NMHS Switzerland, Bern University of Applied Sciences (BFH), International Research Centre on El Niño (CIIFEN), and World Meteorological Organization (WMO).

Co-production enables integration of perspectives and expertise, a crucial aspect for addressing the heterogeneous climate challenges. This facilitates the formation of a collective ownership of the project, enhancing its effectiveness and sustainability beyond its duration. However, organizing co-production across different institutions, interfaces, and levels presents several challenges. It requires establishing transparent communication, and shared objectives. For this, a standardized approach for co-production must be defined.

Co-production should be seen as a continuous process, crucial for building trust among actors and ensuring that the information remains relevant and used by end-users. The ENANDES and ENANDES+ projects serve as a fertile environment to define a framework for co-production in CS. As climate challenges intensify, adopting a co-production approach becomes fundamental to deliver CS benefits of supporting adaptation, resilience, and decision-making, in addition to strengthening regional cooperation.

The Rhineland-Palatinate Flood Warning Service has been issuing flood warnings in Rhineland-Palatinate since 1986. Initially, the warnings were issued based on measured values at the gauging stations of the major rivers. Over time, the Flood Forecasting Centre's products have been expanded to include forecasts at water gauges, including ensemble forecasts and region-specific flood warnings in small catchments. In addition, flood reports are published on a modernised website and via warning apps (Johst & Demuth 2022). The floods on the River Ahr in 2021, in particular, once again demonstrated the importance of flood forecasting and the timely issuing and disseminating of warnings. This contribution aims to present the current status and future developments in flood forecasting and the issuing and disseminating flood warnings for Rhineland-Palatinate.

The Flood Forecasting Centre uses the deterministic-conceptual water balance model LARSIM (LEG 2024) for operational flood forecasting. However, the forecasts produced are always subject to a certain degree of uncertainty. To visualize this uncertainty, ensemble forecasts of the weather forecasts (ICON-D2-EPS of the DWD) are used as model input for a forecast period of 48 hours. A discharge forecast is thus produced for each of the 20 ensemble members. On the Flood Forecasting Service website, the result of the ensemble calculation is displayed as a bandwidth around the median in a hydrograph. The darker the colouring within the band, the more likely it is that the expected values will lie within this range (Johst & Demuth, 2022). In addition, the thresholds of certain flood return periods (2-, 10-, 20-, 50- and 100-year return periods) are also shown in the graph for the respective water level for information purposes. The warning map on the Flood Forecasting Centre's website visually shows the flood risk for the small rivers in individual warning regions in relation to the next 24 hours.

The warning regions are coloured according to the flood return period from low to extreme (Johst & Demuth 2022). The flood reports produced in the event of flooding are sent by e-mail to subordinate authorities that coordinate disaster control on-site, as well as to the radio and press. In addition, the flood reports and flood warnings from the region-specific warnings are sent to the Länderübergreifendes Hochwasserportal (LHP) and the warning apps Meine Pegel, NINA and KATWARN (Johst & Demuth 2022). This also ensures that the public is informed at an early stage.

As part of a ‘co-design’ project realised together with the German Weather Service, the user-oriented communication of weather and flood information is to be improved. The project aims to provide users with a better understanding of the uncertainties associated with the forecasts and the probability information provided, thereby creating an improved basis for decision-making for future flood events.

Extremes of river flow such as fluvial floods and hydrological droughts are expected to be influenced by human-induced climate change. To design effective adaptation measures, knowledge on projected changes in streamflow along river networks is crucial. Global climate models (GCMs) like those contributing to the CMIP6 archive offer future projections, however, they typically only provide runoff at the grid cell level rather than routed river discharge. One way to overcome this limitation is to combine GCMs with global hydrology models (GHMs) which are designed to close the terrestrial water balance. However, this approach can only consider a limited number of GCM projections. As a result, estimating the robustness of projected changes in river flow is challenging as both uncertainties associated with GCMs as well as effects of internal climate variability may be underestimated.

To bridge this gap, we route 250 years of daily runoff from 21 CMIP6 models along the global river network at a horizontal resolution of 0.1 degrees. Specifically, runoff from the historical CMIP6 experiment as well as future Shared Socioeconomic Pathways (SSP) is used to construct a new dataset of global daily routed streamflow. For the routing step, the multiscale Routing Model (mRM) is used which can flexibly be adapted to a wide range of spatial scales. In mRM, gridded daily runoff data is routed through an upscaled river network derived from high-resolution morphological data based on a kinematic wave equation. The fidelity of the proposed modelling chain is carefully evaluated with special focus on the underlying assumptions and the scale mismatch between spatial resolution of the GCMs and routing model. To this end, simulated river discharge climatologies are compared with observations. Leveraging the new global streamflow projections, we study how anthropogenic climate change has affected mean and extreme river flows. In addition, we harness the comprehensiveness of the newly created streamflow projections and compare routed runoff across all available CMIP6 models to espread ad agreement between models is evaluatexplore the range of future streamflow projections and their robustness.

Geological Hazards have always caused and still creates a threat to the important part of the population, also causes damage/destroy of existing infrastructure facilities. In the last decades, protection of the population from debrisflow hazards and safe operation of infrastructure objects became significant social-economic and geoecological problem for the most countries in the word. These problems are more in countries with the complicated geology, relief, climate, seismicity, human activities (Gaprindashvili M. et. al 2021; Fourth National Communication of Georgia 2021).

Georgia belongs to the most complicated region among the world’s mountainous countries with development scale of debrisflow hazard, recurrence of these processes, and with negative impacts to the population, infrastructural objects, agricultural lands and environment, and the most tragically, it causes human casualties. Hundreds of settlements, infrastructure objects are periodically affected by debrisflow disaster (Tsereteli E. et. al 2022). Annually the cases of debrisflow processes increase significantly. During the period of 2011-2023 the activity of debrisflows has been recorded in 2973 river gorges/ravines throughout the country.

Activation of debrisflow hazards and their risk depends on the geological environment, such as lithological composition of the rocks of the territory, their physical and mechanical properties and energy potential of terrain, as it is known that development of hazards at high hypsometric levels is faster and more intense. Accordingly, the magnitudes of the slope of the relief surface and the erosive intrusion depths of the rivers/gorges, the coefficients of horizontal divisions and, most importantly, the tensions of the gravity fields increase, together with process-driven factors such as meteorological events, seismicity and anthropogenic pressure (Gaprindashvili M. 2022).

Different methods are used for debrisflow hazard asssessment, which is based on the data availability: Qualitative, Quantitave, Rapid Mass Movement Simulation/Modelling, Spatial Multi Criteria decision-making (SMCE) et al. In Georgia different researches were conducted for the purpose of debrsiflow Hazard Assessment (Gaprindashvili G., et al. 2018, Gaprindashvili M. 2022).

References

Fourth National Communication of Georgia Under the United Nations Framework Convention on Climate Change. Chapter 4.9 Geological Hazards in Georgia, Tbilisi, 2021, pp. 278-286

Gaprindashvili G., Tsereteli E., Gaprindashvili M. Landslide hazard assessment methodology in Georgia. // Special Issue: XVI DECGE Proceedings of the 16th Danube ‐ European Conference on Geotechnical Engineering, 2018, vol. 2, N 2-3, pp. 217-222.

Gaprindashvili M., Kurtsikidze O., Gaprindashvili G., Rikadze Z., et. al, Informational Bulletin - The results of the development of natural geological processes in Georgia in 2023 and the forecast for 2024, Department of Geology, National Environmental Agency, Tbilisi, Georgia, 2024, 488 pages

Gaprindashvili M., Assessment of geo-ecological hazards caused by geodynamic processes on the territory of Tbilisi and their prevention. PhD dissertation, 2022, 168 pages.

Gaprindashvili, M., Tsereteli, E., Gaprindashvili, G., Kurtsikidze, O. (2021). Landslide and Mudflow Hazard Assessment in Georgia. In: Bonali, F.L., Pasquaré Mariotto, F., Tsereteli, N. (eds) Building Knowledge for Geohazard Assessment and Management in the Caucasus and other Orogenic Regions. NATO Science for Peace and Security Series C: Environmental Security. Springer, Dordrecht. https://doi.org/10.1007/978-94-024-2046-3_14

A sophisticated geoportal has been developed for EG.D, a company focused on the distribution of electricity and gas in the southern region of the Czech Republic. This portal provides hourly meteorological information and various warnings, covering not only the present but also the past (several days back) and the future (a few days ahead). Users can access map layers with a resolution of 0.5 km. The system also automatically sends warnings via SMS and email based on user-defined criteria.

The forecasts include essential meteorological elements crucial for the energy sector, such as air temperature and humidity, wind speed and gusts, global radiation, and snow water equivalent. Additionally, the system provides warnings for weather events that could disrupt the distribution and transmission network, such as strong winds, thunderstorms, frost, or heavy snow. These warnings are categorized by severity in relation to the needs of energy distribution.

The geoportal also offers centralized access to information from radars, lightning detection systems (for storm identification), and satellite images. For frost prediction, data from EG.D’s measuring stations are integrated into the system. Artificial intelligence is used daily for calibration and optimization.

Data is updated hourly based on the latest measurements from the Czech Hydrometeorological Institute (ČHMÚ). The forecasts are generated by combining several models suitable for the area, such as ICON, IFS, ARPEGE, and WRF, tuned to our specific conditions. The transition between station measurements and model outputs is managed hourly through nowcasting, refining the forecasts for the upcoming hours. All calculations are performed for every pixel of the background maps, ensuring precise spatial nowcasting.

Based on user requirements, a data warehouse has also been created on the MySQL platform. This warehouse enables data analysis according to specific criteria, such as individual points, area aggregation, or weighted averages considering population density.

The use of process-based numerical models for the simulation of mass movements is an important component of modern integral natural hazard management. When informed by high quality data and expert interpretation, numerical simulations aid process understanding, scenarios building as well as the design of mitigation measures. Despite a relatively long history and tradition in model development and application, problems still exist both in the expert’s community and at the interface between policy makers and the public. Understanding the domain in which the model and its results can be validated, the sources and magnitude of errors and uncertainty or the physical and numerical approximations needed are only some examples of problematic topics.

A spectrum where people swing between systematic scepticism versus over-trust in the models and in their potential exist both amongst experts and the public. This paradigm has been exacerbated in the past few decades, with the fast development of computing power and numerical methods, which has led to an imbalance in what is technically possible and what the community can handle. A large gap appears in front of us with regards to the application of three-dimensional multi-physics models to engineering and geological problems. In this abstract, the authors propose to discuss one aspect of this impending challenge: results visualization and communication with stakeholders.

In particular, we use the case of the Material Point Method (MPM), which enables the simulation of elasto-plastic constitutive relations integrated within a hybrid Eulerian/Lagrangian numerical scheme. The method, initially developed for civil engineering problems involving large deformations, later gained widespread recognition in the movie industry, in particular after its use in Disney movie Frozen. However, its application to real-world physical problems soon revealed its significant research and engineering potential. In the years that followed, numerous highlighted its effectiveness in modelling snow, ice, rock avalanches and multi-phase process cascades at various scales (Cicoira et al., 2022).

The application of MPM to alpine mass movements is emblematic in many ways, highlighting the complexities at both ends of the spectrum. The scepticism and the overconfidence in the model are both possibly driven by the animated movies and complex renderings that can be created from the results. On the one hand, the association with the movie industry misleadingly leads some to view the results as mere illustrations, lacking in scientific validity. On the other hand, the realistic representation of the results enhances the understanding of the process and builds trust in the model. Interestingly, in both scenarios, the visual graphics overshadow the model itself and its results.

With this abstract, we want to discuss some strategies and address open challenges in the communication of complex numerical simulations by means of cartography, three dimensional renderings and animations. With the anticipated rapid growth of such visual tools in the coming years, it is crucial to anchor these advancements in strong fundamental principles of research, engineering, and science communication.

Warning communication is effective if it reaches people with the information that they need, at the right time and in a format that they find useful and usable. This task appears to be particularly difficult when decisions by stakeholders and citizens have to be made within contexts where uncertainty is high, multiple sources of information are available for the receiver, and decisions are urgent. This poses several challenges for the development of two-way and people-centred communication for risk information management. In this presentation, we discuss some of these challenges. By focusing on natural hazards, we look at the evidence of how information sources, social and environmental cues, channel access/preferences, and receiver’s characteristics influence behavioural responses to warnings. Moreover, we present research findings of how people respond to different types of warnings (standard vs. impact based) and to inconsistent warning information provided by public and private weather offices in Switzerland. We focus also on evaluating the results of warning communication efforts and on the role of new technologies that increasingly allow to evaluate communication effectiveness, sometimes even in real time through smartphone applications. We conclude with some reflections about the key aspects of the warning that need to be considered to improve the relationship between warner and receiver, when designing or upgrading communication strategies for risk information management.

The climate crisis enhances early warning systems' relevance and impact on preparedness. By tailoring their messages to the receiver, they become more effective. However, this requires personal data that is not always openly accessible. Employing a representative study, we explored whether the Swiss population

• i) likes to receive tailored warnings in the event of a severe heatwave
• ii) whether these would help them to better prepare for the situation
• iii) And whether they would be willing to share the necessary data.

The results are consistent with previous findings: Tailored warnings are attractive and improve preparedness, but there are concerns about data protection. As a result, warnings can currently only be tailored to a limited extent. We would like to discuss our findings and share further implications in our presentation.

Risk communication is critical to build resilient communities, raise awareness and increase preparedness. Moreover it contributes to advance knowledge and to infleunce the adoption of protective behaviours before, during and after a disaster. Over the last two decades, seismic risk communication has evolved significantly, as shown also by trends in scientific publications. This evolution has led to a reorientation from a predominantly “one-way”, top-down communication model to promoting new models that focus on people, their needs and their participation in disaster risk management. The recommendations of the Sendai Framework 2015-2030, recent disaster experiences and research have made it clear that new models of risk communication can improve its effectiveness. In this contribution, we critically address this transition by conducting a scoping review (n=109 publications) on seismic risk communication in Europe. We analyze the approaches, messages, tools and channels used for seismic risk communication and how they have changed over time. The results show that the stated objectives of seismic risk communication are, in decreasing order, to share information, raise awareness, change behaviors/beliefs and increase preparedness. Pupils, students and citizens are the main addressees of communication activities. Over the years, one trend has emerged quite clearly: communication has been progressively aimed more at encouraging proactive behaviors than simply informing the public. To do so, face-to-face conversations, hands-on activities, serious games and videos have been increasingly used as risk communication tools. The results also show the growing importance of social media for reaching different target groups. A striking result is that only a fifth of the analysed publications explicitly build on or test risk communication theories. Future research could focus on comparing practices in different countries and for different risks (e.g. earthquakes and floods) and on innovating communication theories and methods, particularly by incorporating the role of information technologies and social media.

Informing the public about natural hazards enables informed decision-making and enhances resilience. Achieving this requires translating scientific information into accessible and understandable formats. This presentation demonstrates how incorporating evaluation into the design process can improve these efforts. Our findings reveal that neither professionals nor the public consistently make optimal decisions.

Wildfires pose an urgent social problem in Europe due to demographic and climatic changes. As the threat increases, community preparedness has a critical role to play in mitigating the risk and easing the burden on civil protection personnel. This presentation focuses on wildfire awareness and preparedness among at-risk communities in Switzerland. Building on an online survey and interviews with residents in the Cantons of Bern, Wallis, Ticino and Graubünden, we examine the cultural, socioeconomic, political and environmental factors that influence risk perceptions, awareness raising and coping strategies. The results provide valuable insights into dominant narratives, local needs, motivations and vulnerabilities among different communities. These insights can assist official and community efforts to build wildfire resilience in Switzerland before the predicted threat becomes acute on the southern and northern sides of the Alps. They also contribute to a multi-year comparative study of different European countries as part of the SNSF-funded FiRES project.

The increasing risk and prevalence of wildfires are strongly associated with human-induced climate change. An example is Australia, where the most destructive wildfires in decades occurred in 2019-2020. However, there is still a challenge in developing effective models to understand wildfire susceptibility and pre-fire vegetation conditions. The recent launch of NASA’s ECOSTRESS presents an opportunity to monitor fire dynamics with a high resolution of 70m by measuring ecosystem stress and drought conditions preceding the wildfires. We incorporated ECOSTRESS data, vegetation indices, rainfall, and topographic data as independent variables and fire events as dependent variables into machine learning algorithms. We predicted over 90% of all wildfire occurrences one week ahead of these wildfire events. Our models identified vegetation conditions with a three-week time lag before wildfire events in the 4th week and predicted the probability of wildfire occurrences in the subsequent week (5th week). ECOSTRESS water use efficiency (WUE) consistently emerged as the leading factor in all models predicting wildfires., Results suggest that the pre-fire vegetation was affected by wildfires in areas with WUE above 2 g C kg ⁻¹ H ₂O at 95% probability level. Additionally, the ECOSTRESS evaporative stress index (ESI) and slope data were identified as significant contributors in predicting wildfire susceptibility. These results indicate a significant potential for ECOSTRESS data to predict and analyze wildfires and emphasize the crucial role of drought conditions in wildfire events, as evident from ECOSTRESS data. Our approaches developed in this study and outcome can help policymakers, fire managers, and city planners assess, manage, prepare, and mitigate wildfires in the future.

Extreme temperatures and drought in the summer 2018 resulted in widespread early leaf discoloration in Switzerland’s European Beech (Fagus sylvatica L.) forests. In subsequent years, early discolored trees exhibited higher rates of crown dieback and tree mortality. With more frequent and severe droughts expected under a changing climate, the future resilience of European Beech on the Swiss plateau remains unclear. Climate-smart forest management which accounts for species resistance to drought under an uncertain future is required to maintain important ecosystem services like biodiversity, timber production, and protection from gravitational hazards, but requires a more robust understanding of European Beech vulnerability to drought. To that end, we use a novel seasonal standardization of Sentinel-2 derived vegetation indices and in-situ field observations in a support vector classifier to model empirical European Beech discoloration with 90% accuracy (as compared to a subset of withheld field observations). This model is applied to predict monthly European Beech discoloration across Switzerland from 2017 to 2023 at a 10 m spatial resolution and is validated using independent manual mapping of discoloration in PLANET data. This unprecedented multi-temporal record reveals spatio-temporal hot spots of European Beech discoloration across multiple years suggesting that, independent of meteorological forcings, site specific factors significantly predispose some stands to discoloration over others (and thereby increase the likelihood of tree mortality). Using this newly developed empirical dataset and a combination of high-resolution soil maps, meteorological data, topographic derivatives, and information on Swiss forest structure, we are training additional ensemble machine learning models to examine which site-specific factors predispose European Beech to early discoloration. Forward applying this environmental model will 1) allow us to identify European Beech stands vulnerable to drought under a changing climate, 2) evaluate the influence of management strategies on European Beech vulnerability, and 3) provide a series of high-resolution risk maps for European Beech under various climate and management scenarios.

Extreme meteorological events, such as heat and drought, can induce significant damage to vegetation and ecosystems. The frequency and intensity of extreme events are subject to change due to anthropogenic global warming. It is therefore crucial to quantify the impact of such events for better preparedness.
Here, we focus on forest damage in Europe, defined as negative anomalies of the normalized difference vegetation index (NDVI, a measure of vegetation greenness). Compound drought and heat wave events are known to trigger low NDVI events in summer. A dry summer combined with moist conditions during the previous autumn can also have a negative impact. Hence, our study aims to find, among all the hydro-meteorological variables available as output from the sub-seasonal to seasonal forecasts in the ECMWF model, the most relevant ones to predict forest damage. For this purpose, we apply a Random Forest procedure to identify the compound hydro-meteorological conditions leading to low NDVI events at the S2S timescale. We train the model using ERA5 and ERA5-Land reanalysis datasets for the explicative variables. These variables include temperature, precipitation, dew point temperature, surface latent heat flux, soil moisture, and soil temperature. We provided an automated procedure with strong predictive performance for identifying low-greenness events during summer based on prior hydro-meteorological conditions. The most essential preceding periods and variables are location and forest-type dependent.

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.

The landslide of Cadegliano Viconago in the province of Varese (IT) has been for over twenty years a danger to the population and infrastructures located along the course of the Tresa river, at the border between Italy and Switzerland. The landslide leads to the accumulation of debris which has caused several events, involving the road passing at the foot of the landslide. The slope even shows traces of deep damage which manifests with the exposure of scarps and traction fractures, with even centimeter displacements during intense and continuous rainfall events. A catastrophic collapse would lead to the partial or total interruption of the road system, a barrage of the Tresa river with the possible formation of a basin and the destruction of wells field for drinking water use in Switzerland.

Even though in recent years of monitoring it was observed a trend towards smaller displacements, intense and prolonged rains could drastically influence the dynamic behavior of the landslide and cause bigger displacements that could even lead to a catastrophic failure.

For this reason, thanks to the financing of an Interreg project with the involvement of the Interregional Agency for the Po River and the Canton Ticino Land Department, two DMS (Differential Monitoring of Stability) columns (Lovisolo,et al. 2003) were installed in March 2021 to investigate the landslide activity and to define alert thresholds. DMS multiparametrical columns provide continuous data with detailed displacement, accelerometric, and piezometric measurements. The deep monitoring data joined by spatially distributed geodetic measurements since 2006, provide information on the landslide’s dynamics. The monitoring, the cores from the drilling, and the in-situ surveys provide a basis to set a numerical stability model to understand the slope kinematics and the response to the water table changes.

The displacement data ranged between 1 cm/year in the first three years, up to 1 cm/week in an intense event in late March 2024 connected to a remarkable lifting of the water table. Similarly, intense rain events recorded in autumn 2002 and 2014, led to small-scale debris flows, partial destruction of the road and development of decimeters-width tensile fractures, making a catastrophic collapse plausible.

The definition of the humidity status identified by the Standard Precipitation Index (SPI), showed a very high value in correspondence of the landslide maximum activity, which makes it a possible factor for setting alert status.

The observation of recorded displacement by DMS and the stratigraphic data, suggests a slip surface at a depth of about thirty meters, which can plausibly lead to the detachment of a volume mass ranging from 70’000 to over 2’000’000 m³.

The numerical stability model was followed by expansion models of the unstable mass based on the observation of previous collapses. The modelled scenarios remarked the hazard for the infrastructures in both Italian and Swiss territories.

The World Meteorological Organization strongly encourages the development and use of impact-based early warning systems (IBEWS), along with recent advancements in research, e.g., Merz et al. (2020). As Potter et al. (2021) note, IBEWS improve public understanding of warnings, enhance interagency communication, and reduce "false alarms". This
the study aims to quantify the added value of an IBEWS specifically designed for flash flood anticipation by focusing on detecting impacted sectors.
In France, local flood forecasting services assess flood risk on major rivers, coordinated by the Central Service of Hydrometeorology and Flood Forecasting (SCHAPI), which provides a national risk map with four levels (green, yellow, orange, red) that incorporate both hazard intensity and potential impacts. However, small ungauged rivers are not covered by this service. An automated warning system based solely on the flood return period exceedance has been implemented for municipalities along these rivers.

We extended this hazard-based system to an impact-based approach for ≃20,000 km of river network in the French Mediterranean area, which is particularly vulnerable to flash floods (Gaume et al., 2016). The model structure aligns with existing European methods (Dottori et al., 2017), using hydrological and hydraulics approaches developed in France
(Piotte et al., 2020, Davy et al., 2017). The real added value of this work lies in the quality and diversity of observed impact data sources for evaluating the benefits of impact-based versus hazard-based warnings and the validation methods employed. [...]

In the impact model literature, predictive models often focus on a country or a region. This is the case of the 510 model (Teklesadik et al., 2023) or the grid-based model by Kooshki Forooshani et al. (2024), both statistical models for predicting impact on buildings in the Philippines or the Hybrid model (Hou et al., 2020) which also predicts impact on buildings in China. However, resources exist at the global level, including the Advanced Disaster Analysis and Mapping (ADAM) system which provides estimates of exposed population to different windspeed and accumulated rainfall ranges (World Food Programme, 2016) at the administrative 2 level or the DisasterAWARE platform (Sharma et al., 2020) by the Pacific Disaster Center which estimates not only population exposed but also infrastructure (Hospitals, schools, etc) and capital exposed at a grid level (30m×30m tiles). In this work, we propose a statistical model (Extreme Gradient Boosting or XGBoost) that predicts the affected population in cases of Tropical Cyclones (TC) at a grid level (1 km2 tile) in 60 countries. This model not only considers weather-related variables like wind speed and accumulated rainfall but also considers country-specific features such as topographical measures and poverty indexes. Furthermore, by employing XGBoost, we can handle non-linear relationships between variables.

Addressing data scarcity in the Global South is increasingly critical as climate change is expected to result in more frequent and severe flood events in highly vulnerable regions. A novel risk assessment framework tailored to these challenges is demonstrated on a use case based in Angwan Iku, a flood prone community in Gwagwalada, Nigeria. The community experienced a high magnitude flood event in 2020, which sustained adverse consequences for built structures, local residents, and their livelihoods. Key inputs include local context provided through community engagement, high resolution data acquired conducting household-level field surveys, and freely available medium resolution (30 m) topography data.

The study first identifies key local flood risk drivers, including flood mechanisms and building vulnerability classes. A multivariate analysis is performed on field data to identify building features that contribute most to flood damage. Flood risk zones are further delineated by performing contour analysis on identified flood threshold levels on local river banks. These zones are then validated against a 2020 flood extent map derived from field surveys, which served as a point of reference or ground truth. Finally, adaptation strategies are proposed for at-risk buildings, informed by the risk zones and building vulnerability classes.

The approaches within this framework can generate custom, baseline flood risk profiles for data-scarce communities, which can also be further refined and validated over time. Integration of community insights ensures that local risk factors are accurately captured and that proposed adaptation measures directly address community defined priorities and needs and are sustainable. Moreover, the methods make use of data that can be acquired for any data-scarce region, and are therefore transferrable to comparable communities and scalable with available resources. Most notably, we demonstrate how the framework extends beyond working with existing data gaps, towards the translation of evidence-based insights into actionable solutions specific to the needs of each community, so that flood risk is effectively minimised on the ground.

Groundwater is among the most important sources of freshwater for drinking water production and irrigation purposes across Switzerland. Recent drought events have shown that these groundwater resources, traditionally considered a safe source of water supply, are affected by prolonged periods with limited or no rainfall and high evaporation. Early warning systems may help inform groundwater resource managers about the possible future state of groundwater levels in different aquifers across Switzerland. Such a system could provide crucial support by enabling timely responses to mitigate the potential consequences of droughts on groundwater resources.

This study aims to investigate the feasibility of forecasting groundwater levels in Switzerland up to 32 days into the future using sub-seasonal meteorological forecasts provided by MeteoSwiss. This study employs lumped-parameter models from the Pastas software (Collenteur et al., 2019) to simulate groundwater levels at seven strategically selected monitoring wells across Switzerland. These models are driven by ensembles of daily meteorological forecasts to generate ensemble daily groundwater level predictions. A noise model is applied to enhance forecast accuracy, a step that was found critical to improving forecast quality. The forecast quality is verified using weekly re-forecasts from 2012-2023 and compared against two naive benchmarks: persistence and climatology-based forecasts. The results indicate that skilful forecasts of groundwater levels can be made for the entire forecast period. Whether the ensemble groundwater predictions outperform, the naive forecasts are related to the response time of the groundwater system.

At MeteoSwiss, most hazard warnings are currently verified manually by a team of forecasters. While this approach has proven to be very successful and makes it possible to incorporate the extensive knowledge of the forecasters into the warning verification, it also has its limitations. Manual verification is labor-intensive and somewhat subjective, which can lead to inconsistencies in the assessment of warnings. Furthermore, manual verification only allows for limited granularity in the verification process and long-term statistics, making it difficult to identify systematic biases or potential areas of improvement in the warning system.

To address these challenges, we are developing an objective and automated verification system to improve the efficiency, consistency, and detail of the warning evaluation. Such an approach will lead to a more standardized verification of warnings, reduce human bias, capture a richer set of observation data, and be able to generate a more extensive set of key figures and scores. Automated verification also enables a more straightforward evaluation of past warnings and, therefore, the possibility to identify trends, patterns, and potential biases that might have been overlooked with case-by-case manual verification. In this regard, an automatic verification system will also be beneficial for developing and testing automated warning proposals.

This presentation will share preliminary results from our ongoing work, including verification results from case studies and a comparison to the traditional manual approach. Moreover, we will present a statistical analysis based on the objective verification of past warnings and some first-identified shortcomings of the warning system at MeteoSwiss. We will also highlight some of the key challenges we have encountered so far.

Our goal is to demonstrate the potential benefits of an objective warning verification system, particularly in terms of its ability to gain deeper insights into the quality of issued hazard warnings and to drive the continuous improvement of the warning system at MeteoSwiss.

The Snow Avalanche Forecast Editor SAFE was introduced in the Swiss avalanche warning system in December 2023. This is based on European standards, but for the first time also permits the integration of machine-learning models by including the model data as an additional virtual forecaster in the decision-making process. It is available as open source to all interested parties.

Severe weather poses significant threats to society, necessitating the development of effective forecasting and warning systems to mitigate their impacts. In the framework of the renewal of the warning system at MeteoSwiss, we use the possibility to redesign the software as well as the scientific approach of generating warnings. The production chain is designed such that the data is guided and refined along the pathway. Here, we present the operational production process currently in development for the next generation warning system. The production chain can be divided into four steps. The first step is the combination of all available data into one data stream (seamless weather). The second step identifies extreme weather events in this data stream and prepares warning proposals for the forecasters. The third step consists of the forecasters evaluating the proposals, changing them if necessary, and issuing the official warnings. In this step, the only human interaction with the otherwise automatic warning system takes place. In the fourth step, the warning products are customized and distributed to our customers. In the fifth and last step the warnings are verified automatically to monitor the quality of the system.

MeteoSwiss furthermore aims to integrate impact-based forecasts into warnings and the warning chain introduced above. The premise of reducing people’s harm by introducing possible impacts in extreme weather warnings is a paradigm change for many national weather services (Geiger et al. 2024). Being at the intersection of probabilistic forecasts, uncertainty analysis and state-of-the art risk modelling, this new paradigm also entails significant challenges. Through collaboration with stakeholders including first responders and local authorities, MeteoSwiss draws on pilot regions to find specific use cases from professionals impacted by severe weather to learn about the specific needs and enhancement potentials for early warning. Co-developing these use cases, conducting impact studies, and eventually providing stable products serves as a learning for further development and scaling up of impact-based warnings.

This requires an operational impact model for Switzerland, which flows back into the warning production chain, posing various scientific and technical challenges on the way; we will discuss a solution developed at MeteoSwiss, and highlight the need for seamless conceptual and technical integration within the current warning production processes, with the ultimate aim to better cater to the diverse needs of society in the face of severe weather events while maintaining a reliable warning chain.

Traffic on the Czech rail network is often interrupted by falling trees as 30 % of the rail network is closer than 50 m to forests. Tree falls on railway tracks and overhead power lines cause considerable damage. In order to help the national rail infrastructure administrator (Správa železnic, SZ) deal with these incidents, a web-map application called Stromynazeleznici (i.e., trees on railway tracks) has been developed. It provides a forecast of tree-fall hazard on a 3-hour basis for the following two days. The model incorporates data from weather forecasts (Aladin model) and a tree-fall susceptibility layer which delimits the locations where falling trees are capable of crossing railway tracks.

The tree-fall susceptibility layer is prepared from the raster of a normalized digital surface model. One-meter cells contain information about the absolute height of the surface above the relief model. All non-vegetated areas (all types of buildings, tall objects, bridges, masts, etc.) and areas with low vegetation that do not pose a hazard are filtered out. Impact zone buffers are defined for the remaining vegetation areas according to the actual height of the vegetation. The final output is a proportion of the length of railway lines per unit section which are threatened by falling trees.

Stromynazeleznici contains tree fall evidence for recording, presenting, and exporting incidents. Data can be entered either via a web form or through a mobile application for the Android system. The forecast is based on a regression model programmed in R (server solution Project R). A multivariate logistic regression was chosen as the most suitable approach to construct the model according to cross-validation results and practical requirements. The following meteorological elements and characteristics of the rail infrastructure surroundings were selected as explanatory variables in the logistic regression: maximum daily wind gust, soil saturation index, snow index, the occurrence of thunderstorms, the season, the range of altitudes in the vicinity of the rail track, the median height of trees along the railway tracks, and the length of the rail track section with trees along the rail track.

Meteorological data are sent four times a day via an SFTP server by the Czech Hydrometeorological Institute. The hazard level of tree falls is calculated for the "hectolines" (i.e., 100-meter segments) of the railway track. These are then aggregated into three levels of administrative units defined by SZ. The hazard level is calculated for three-hour intervals, covering a 45-hour forecast period – resulting in 15 time slots for each hectoline. The forecast is updated four times a day as new meteorological data become available.

The data is stored in a database and presented in the form of graphs, tables, and an interactive map. The tree-fall hazard level is represented by a five-level colour scale for individual administrative units. When zooming in, the risk is shown in relation to the hectolines. A timeline is located at the bottom of the screen, allowing users to switch between different time slots or aggregated time windows.

Transportation network is a vital part of moder-day society. It allows for the mobility of people and goods across large distances. When natural disasters hit transportation networks the results are often a number of closed parts. As a results, certain roads or rail tracks may be even destroyed, but the majority of them are usually only closed for traffic and can be reopened after a relatively short period of time. Functioning transportation network is among the primary environments securing economic growth. Therefore, its robustness and resilience have to be maintained. Data about these incidents which can affect the transportation network performance is important for designing relevant security measures.
In Czechia, data on all problems in road transport (including traffic collisions, planned maintenance) is being gathered by numerous organizations and provided in an online system of traffic information (JSDI). The main aim of this system is to offer an overview on actual situation on roads. Among other features, it offers an automatic data interface. Records are sent in real-time using the HTTP POST protocol in XML format. The JSDI database has not been planned as a source of this kind of information. Therefore, all information regarding natural hazards and their impacts had to be data-mined from text descriptions which is among the attributes. We developed a full-text filter that determines whether a disruption has occurred and, if so, what type. In the application, we distinguish disruptions caused by flooding, landslides, rock falls, falling trees, and snow.
Similarly, also data for railways are available, albeit from a different data source. The state-owned company, Správa železnic (SZ), which is responsible for the majority of rail tracks in Czechia, collects information on all problems that affected rail network. In addition, there is a database of the fire brigade unit, which deals with the consequences of these incidents.

CDV stores this data from all these sources for further analyses in order to study and evaluate the impacts of natural processes on transportation infrastructure. For this purpose, we created a spatial database which includes data for roads as of 1997 and railways (as of 2002). The spatial database, called RUPOK, is automatically updated.

For road network, the majority of complete road blockages were caused by fallen trees (64%), followed by snowing (31%). Flooding and landsliding (including rockfalls) caused 4% incident (1% respectively), but with considerable higher impacts on infrastructure.
For railways, the situation is similar as for roads. The majority of railway track blockages were caused by fallen trees (90%), followed by snowing (6%) and flooding (4%). The least common were landslides and rockfall incidents with less than 2% share. It is important to mention, however, that incidents may overlap in part, as snowing can also cause tree fall.
Data on incidents and certain elementary statistics is presented via a webmap application. The core is a MariaDB database with the Spatial extension, which allows for the management of spatial data. The application is programmed using PHP, jQuery, and the Google Maps API.

Floods are one of the most relevant natural hazards Worldwide and in Switzerland, causing significant socio-economic damage every year. Despite the recent progress in assessing flood hazards and risks, predicting rivers' responses to flooding and anticipating their consequences remains challenging. This is particularly true in forested mountain rivers, where floods are much more than extreme discharges, as they trigger geomorphological changes, such as bank erosion and channel widening, leading to significant sediment erosion and transport while recruiting and mobilizing trees and large pieces of wood. However, flood hazard and risk analysis rarely quantify or fully consider these cascade processes.
During large flood events, entrained and transported instream wood (i.e., large wood, which includes trunks, logs, branches and root wads) may accumulate at particularly vulnerable locations such as bridges, culverts, and other hydraulic structures, enhancing flooding impacts. However, unlike flow and sediment monitoring, the monitoring of wood in rivers is scarce, with a generalized lack of data, monitoring stations or standard metrics to quantify the instream wood regime. Therefore, monitoring large wood transport during flood events is critical for improving flood hazard assessment and infrastructure management.
The work presented here summarizes several research projects aiming at designing a monitoring framework for wood transport in rivers and identifying critical bridges in terms of wood trapping.
Our research combines fieldwork, remote sensing, drone surveys, and in-situ sensor networks to track wood movement during flood conditions, ranging from large floods to more frequent, seasonal floods, and to assess the factors influencing wood mobilization and deposition. We propose a monitoring framework that combines stationary or drone-mounted cameras with a novel machine-learning algorithm to automatically detect wood transport.
The research also focuses on identifying variables related to river morphology, surrounding forest, bridge geometry and characteristics that control wood trapping. These variables are then used to train a machine-learning decision tree and random forest that classify wood-prone bridges.
The results revealed that wood transport during floods is highly episodic, occurring predominantly during the rising limb and peak discharge, and is influenced not just by the river characteristics and flood magnitude but by other factors, such as the wood availability, flood hydrograph shape, sequence of floods, and the presence of obstacles and human structures.

The presence and number of bridge piers, their shape and the channel energy (in terms of stream power) were particularly important for identifying bridges prone to trapping large wood.
This study provides a more comprehensive understanding of wood transport during floods. More importantly, the monitoring framework using cameras and the model to identify critical infrastructures can be easily replicated at other geographical locations with varying features and characteristics. Integrating these methods into flood hazard assessment will improve the analysis of potential risk and guide the design of more resilient infrastructure to mitigate the effects of large wood accumulations during extreme events.

The effect of global warming is triggering natural disasters to occur more often and of greater severity, prompting the need for enhanced disaster management measures. Particularly dangerous to people's lives, property, and economy are floods, as recent devastating events in Germany and other countries have shown. Static flood forecast maps are a primary tool used in conventional flood management methods. However helpful, these maps frequently lack real-time data integration and are dependent on assumptions. This study addresses these shortcomings by introducing a cloud-based platform that uses dynamic Geographic Information Systems (GIS) and enhanced flooding modeling to improve catastrophe preparedness and response.

Near-real-time flood simulations are produced by the suggested method, which combines high-performance computers, real-time sensor data, and weather forecasts. This makes it possible for decision-makers to evaluate risks, visualize changing flood situations, and plan efficient actions. The platform offers an integrated and interactive environment for stakeholders to assist all aspects of disaster management, including mitigation, readiness, response, and recovery.

A communication platform that promotes data sharing and cooperation between first responders and other stakeholders, is one of the system's essential elements. To provide a thorough and current operating picture, it integrates information from social media, sensors, and geographic data. With the help of the flood simulation component, which is based on cutting-edge hydrological models and real-time data, users may plan evacuation routes, assess potential effects, and forecast the course of flooding. The platform offers a new degree of operational efficiency during emergencies thanks to its near-real-time functioning, which is a major improvement over conventional technique.

The implementation of this technology at various stages of flooding occurrences is also examined in this article. Through 2D and 3D simulations, the technology helps with data collecting and analysis prior to flooding, providing reliable flood forecasts. The platform's high-performance processing capabilities enable quick simulation updates during flooding, giving decision-makers vital information. The technology helps with recovery planning and damage assessment after floods by helping to coordinate reconstruction operations and visualizing the effects of the storm.

All things considered, a holistic approach to catastrophe management is provided by the combination of dynamic GIS, real-time flooding simulation, and sophisticated communication technologies. By improving situational awareness and decision-making, this technology eventually helps mitigate the negative effects of disasters on communities. The significance of mentioned aspects helps in building more resilient communities capable of successfully handling the challenges posed by disasters resulting from climate change is emphasized in the end of this paper.

Incoherence in coordination between institutions that address climate
change adaptation (CCA), disaster risk reduction (DRR) and food security (FS)
policy areas exacerbates FS challenges in most developing countries, including
South Africa. To address such incoherencies, this article seeks to develop a
coordination mechanism that could help in aligning FS functions undertaken by
CCA, DRR and FS institutions. The coordination mechanism was developed
using aspects from the functional resonance analysis method (input, time,
preconditions, resources, control and output) and data gathered through policy
analysis, key informant interviews, and written responses. The coordination
mechanism allows for simultaneous participation of relevant stakeholders
through a systematic characterisation of FS functions. It allows policymakers
and practitioners to identify variabilities, challenges, and opportunities that
should be considered before any FS activity is executed.

The passability of a terrain is understood as the possibility of traversing it cross-country, outside the regular road network, taking into account weather and soil conditions. The analysis of passability is mainly applicable in the planning of military operations, which very often take place in roadless areas. The issue of passability is also very relevant in the emergency management process, especially in less developed areas, where there is a need for emergency vehicles to reach facilities located away from the regular road network via roadless roads.

The presentation will outline the factors to be taken into account in the process of passability modelling and the system being developed at the Military University of Technology for the automated generation of terrain passability maps, which can also be used in the crisis management process. In presented project, the problem of terrain classification to the respective category of passability was solved (among others) by applying artificial neural networks to generate (calculate) the Index of Passability (IOP). The main methodological assumption of the conducted research was to refer the index of passability of the terrain to the primary fields of various shapes and sizes.

The basis for calculating IOP are elements of land cover, weather and soil type that exist in the given primary field. The results show a comprehensive analysis of the reliability of the neural network parameters, considering the number of neurons, learning algorithm, activation functions and input data configuration. The studies and tests carried out have shown that a well-trained neural network can automate the process of terrain classification in terms of passability conditions. The Authors assumed that the values of indices of passability obtained with use of the algorithms may differ, even if the same methods and source data are used, depending on the type of the primary field used, i.e., its shape and size. Considering the above, the Authors analyzed the influence of the shape and size of the primary field on the results of automated terrain classification for the purposes of developing passability maps. The Authors determined indices of passability for square primary fields of various side lengths ranging from 1 m to 10 km. The Authors has demonstrated that terrain classification for passability purposes may also be performed with use both: military and civilian data sources.

What is important developed system makes enable of using terrain passability maps for generating graphs that enable the determination of the optimum route between two points. The proposed methodology enables the determination of different variants of routes: a longer route that passes all terrain obstacles or a route that is shorter but more difficult to pass.

The results obtained allow the conclusion to be drawn that the modelling of terrain passability allows the rescue operation to be planned more efficiently, which in the case of emergency management can be crucial to the success of the overall operation.

This session deals with the analysis of the economic and insured losses as well as an assessment of the effectiveness of prevention and early warning in relation to the weather extreme "Bernd" of 2021.

In a comparative study between the three countries Germany, Austria and Switzerland, it has been analysed whether and how the different levels of damage caused by the weather extreme (a combined heavy rain- run off, floods and hail storm event) can be attributed to meteorological, geomorphological, technical and organisational characteristics of the catastrophe management. The preventive effect of protective and early warning measures were studied in the Ahr Valley, the Eastern Alps (Upper Bavaria and Salzburg) and the Swiss canton of Lucerne in detailed field studies. In addition to a comprehensive literature review, interviews with experts and local simulations were carried out (and could be reproduced “on demand” in the session for stronger interaction with the audience).

We would like to discuss among the panellists and with the audience on:

(1) The opportunities regional heavy rain-strategies, the international state and the barriers of implementation;

(2) How precipitation scenarios of the future could account for climate change and also include nature-based solutions (land protection; run-off infiltration strategies);

(3) Ways to strengthen local risk awareness and private precautions;

(4) The concept of integrated catastrophe management (i.e. prevention, crisis intervention and insurance) and its benefits in the areas of early warning and financial management of natural disasters.

The subjects will be discussed beyond the findings of the comparative study and include European and international perspectives.

The target group are national and international interested parties from practitioners, experts and politicians.

The increasing risks due to hydro-meteorological events necessitate innovative and flexible forecasting tools for effective management of flood risks. FloodWaive addresses this challenge with DeepWaive, a groundbreaking generalized Deep Learning (DL) model for probabilistic 2D flood forecasting. Our approach overcomes the limitations of traditional hydrodynamic models, offering rapid, accurate, and scalable impact-based flood forecasting across diverse geographical domains.

DeepWaive integrates in-house developed DL architectures with hydrodynamic 2D models, enabling ad-hoc simulations of pluvial and fluvial events of varying intensities and durations over extensive areas. Unlike conventional AI models that require retraining for each new domain, DeepWaive generalizes across different topographies and regional characteristics, eliminating the need for domain-specific retraining and enhancing scalability.

With a speed-up factor of up to 10^6, DeepWaive can translate precipitation or discharge values into spatial hydraulic flooding processes within seconds. This capability facilitates the processing of several ensemble rainfall forecasts into impact and probability-based forecasts and warnings. The model's applications extend to dynamic risk analyses, real-time evaluation of flood protection measures, and dam break simulations.

While still in development, DeepWaive represents a significant leap in flood forecasting technology. Our goal is to offer a universally deployable and comprehensible, real-time flood prediction tool, empowering crisis and flood risk management to make informed decisions quickly, potentially saving lives and reducing economic losses.

In the real world, people must decide how to respond to hazardous weather or climate events. Forecasts are crucial for timely decision-making, yet relevant services may be lacking or not tailored to user needs and often fail to prompt action. The I-CISK project (Innovating Climate Services through Integrating Scientific and Local Knowledge) aims to develop a new generation of climate services that directly address users' requirements.

Evaluating the performance of these services is key to their success and guides their development. Understanding the strengths and weaknesses of a service helps users decide whether to incorporate it into their operational activities. However, standard statistical verification is often complex and not easily applicable in real-world, action-driven scenarios. A 'user-centred' evaluation approach considers the specific context in which forecast data is used, making the results more relevant and understandable.

We present a flexible process for user-centred evaluation and demonstrate its application to cold wave forecasting for disaster risk reduction in Lesotho, one of the I-CISK' Living Laboratories'. Lesotho's mountainous terrain makes its population vulnerable to cold waves, which have caused fatalities, livestock losses, and severe transport disruptions. We evaluate ECMWF temperature and snow forecasts within the Lesotho Red Cross Society's draft Early Action Protocol framework for cold wave disaster risk reduction.

The importance of early warning for flood disaster preparedness cannot be less stressed on. Techniques of visualization and communication have evolved from desktop application of dash boards to mobile friendly application alerts. The dovetailing of real time data and 72-hour early warning forecasted data from the Meteorological Department has helped save lives and conduct safe evacuation of livestock and persons with disabilities, from hospitals and jails, amongst others.R2R framework has also stressed on the importance of early warning systems for preparedness and response to disasters.

This paper will include case studies from India demonstrating the preparedness and responsiveness to flood disasters. Their early warning system architecture and alert protocols will be presented.

The use of real time data and integration of numerically forecasted data towards creating an early warning system for South East Asia, and various parts of India will be showcased in this study including the use of hydrological models. The endeavour of the author is to imbibe the sharing of best practices, as well as providing room for discussion of methodological problems in risk modeling and visualization, including resilience analytics and improvements towards disaster management.

The coastline of the Oeiras municipality, Portugal is quite popular all year round among residents and tourists, especially due to the beaches. Previous research conducted by the authors (Santos at al., 2022) shows that a tsunami similar to the 1755 event would inundate the beaches. Moreover, the same study shows the first tsunami wave arrived at Paço de Arcos beach 31 minutes after the earthquake, inundating the beach up to 4.4 m high. For this reason, many people could die if they do not evacuate the tsunami inundation zone immediately after the earthquake and before the arrival of the first tsunami wave. In addition, this beach is very interesting to study because it has 5 beach accesses and high ground nearby. On the other hand, the pandemic situation in 2020-2021 allowed a unique opportunity to conduct a detailed analysis of the present population in the Paço de Arcos beach, with the use and collection of access turnstiles control data (CMO, 2021). Thus, the objective of this study is to conduct a tsunami evacuation plan for Paço de Arcos beach.

The research was developed on a GIS (Geographic Information System) environment, on which the cartography of the inundated area was considered, as well as the beach access locations. In addition, the Safe Area was identified; this is an area that must be located on high ground and outside the tsunami inundation zone. Moreover, the number of beach users was evenly distributed over the beach area, and the low-cost paths were calculated by using the Network Analyst tolls for the roads’ network. Finally, the calculation of the beach evacuation time and the total evacuation time was carried out. The population data at the beach consisted on a 24 h records during the summer months of June to September 2021 (Fernandes, 2023). The data is important for this research because there is no available data of the number of present population at the beach, before and after 2021.

The main results show the human carrying capacity of the Paço de Arcos beach was 1000 users, but the data of access turnstiles control data show the maximum occupation was recorded on August 15, which is a national holiday, at 5 pm, with 611 people (Fernandes, 2023). On the other hand, the Safe Area near the beach has a capacity of 1236 people. Therefore, with or without social distance the safe areas are large enough to accommodate the beach users of Paço de Arcos. The results also show the beach can be evacuated very quickly, in less than 3 minutes. The Safe Area can be reached between 8 and 12 minutes, given a total evacuation time of about 10 to 17 minutes, which is less than the tsunami travel time of 31 minutes. However, if people do not evacuate immediately after the earthquake, the total evacuation time can range between 16 to 43 minutes. Therefore, delays in the evacuation may lead to a chaotic evacuation causing unnecessary fatalities.

Against the background of modern climate warming, the intensity of atmospheric precipitations, melting of glaciers, the arrival of landslides and rock avalanches, as well as floods and mudflow, droughts and related forest fires have intensified in the world. This is the main issue for the world's climate warming management policy.

Georgia is also distinguished by the frequency of such natural events, especially its mountainous part. It should be noted that the melting of glaciers, waterfalls and floods have become more active against the background of climate warming. The dammed lakes is also connected with these processes. Their breakthrough is accompanied by catastrophic floods. There have been examples of them in Georgia in the past and it is actively taking place now, a classic example is August 2023 in Racha, Shovi resort. (S. Gorgijanidze2023).

This was preceded by the melting of glaciers, which is taking place in all those areas where there is an intensity of global climatic warming. Topographic maps were prepared, and with their help, the action of the glacier in the entire Buba River valley was investigated during that period. (T.gorgodze 2023). Military units of the Ministry of Defense helped in the rescue process. Soldiers searched for people based on studying the topographical map and using modern techniques.

It is important to learn to manage them. It is important to mention the relations between the National Environmental Agency of Georgia and the international consulting Swiss company "GEOTEST AG". As a result, an early warning system has been installed on the Devodrak glacier.

It should be noted that currently monitoring and observation are not carried out everywhere. In 2017, in June, on the 56th kilometer of the Pshaveli-Abano-Omalo highway, at the place of Nashliani, about landslides of mass fell, which in fact completely blocked the Alazani River of Gometsri, and created dam lake Khiso. The danger is high, because every time it rains, the lake level rises, covering the highway.

The Tsaneri lake on the Tsaneri glacier is of such a new origin. However, this lake is a geographical object formed in the moraines and depressions there during the retreat of the glacier over time. Currently, the lake is not fully studied, although periodic observations are being made. As Levan Tielidze (2021) notes, the lake can burst and flood at any time, (GLOF).It is important to study all the maps of this region, and the hydrographic situation. It is important to install early warning systems in all critical areas. Channels and drains should be made taking into account the mechanism of natural occurrence.

References (optional)

Tengiz Gordeziani*, Zurab Laoshvili, Gocha Gudzuadze,*, Tedo Gorgodze, Manana Sharashenidze, Gocha Jincharadze, Mariam Gagoshashvili . Heoretical cartography structure, connections, functions. Abstracts of the ICA. Olomoutsi. 2023

Gorgijanidze, S. M., Jincharadze, G. A., Silagadze, M. M., & Tchintcharauli, I. R. (2023). The Geography of Risks of Breakthrough of Glacial Lakes and Valleys. Journals of Georgian Geophysical Society, 26(2). https://doi.org/10.60131/ggs.2.2023.7442

Glaciers of the Greater Caucasus- levan tielidze 2021

Crisis management encompasses activities focused on preventing, preparing for, responding to, and recovering from crisis situations, with particular attention to their spatial dimensions. Access to detailed spatial information is crucial for determining optimal access routes to danger zones, especially in remote areas without established transportation infrastructure. This research addresses the challenge of planning emergency routes in such off-road areas and presents practical solutions to this problem. Specifically, the study demonstrates the potential use of a previously developed methodology for identifying access routes to hard-to-reach locations outside the regular transport network. By integrating the existing road network with terrain passability maps, high-resolution digital terrain models, and vehicle traction parameters, the approach enables a detailed analysis of microrelief, ensuring that inaccessible areas are effectively excluded from potential routes. This comprehensive method enhances the ability to navigate challenging terrains during crisis situations, thereby improving the overall effectiveness of crisis management efforts.

A key factor in crisis situations is the speed of reaching the destination. Challenges arise when the destination lies outside the established road network, requiring rescue vehicles to traverse difficult terrain. In this study, the process of generating access routes is divided into two stages. First, the route is determined using the existing paved road network. The second stage involves determining the route from the nearest paved road to the destination, using passability maps developed with an automated passability map generation system.

The methodology operates as follows: first, a passability map is created based on land cover data. Next, the starting and ending points for the route are identified. If the destination is situated away from the road network, an additional point is selected on a paved road, positioned as close as possible to the destination. With all essential points determined, the algorithm calculates the most efficient route between the starting point and the additional point on the paved road. The final stage involves mapping out a cross-country route using a graph with traversal costs derived from the terrain passability map, while excluding impassable areas based on detailed microrelief analysis.

The methodology was applied in a case study conducted in Warsaw-West County, Poland. It focused on two single-family homes situated far from paved roads, complicating access for rescue operations. It modelled a fire emergency scenario requiring intervention by a fire truck from the local County Fire Station (MAN TGM 15.290 BL vehicle), highlighting the challenges of reaching these remote locations. The results were further validated through terrain verification.

In conclusion, effective crisis management depends on accurate spatial information to ensure swift access to emergency sites, especially in remote areas. This study showcases a methodology for determining access routes to hard-to-reach locations by combining passability maps, terrain models, and vehicle traction parameters. The approach, validated through a case study in Warsaw-West County, involves first mapping routes via paved roads and then optimizing cross-country access. This methodology improves navigation through challenging terrains, enhancing overall emergency response effectiveness.

The increasing frequency and intensity of natural disasters lead to justified expectations of new concepts for solving them and significant improvement in disaster risk reduction [DRR] supported by new methodologies and technologies. The abstract's author (further author) reflects on selected questions against the background of key global initiatives and
clarifies some approaches, especially those socially oriented, associated with improving the whole process of DRR. The Sendai Framework [SF], Agenda 2030 (Sustainable Development Goals-SDGs), or COPERNICUS are key enablers respecting the era of Big Data and its Geospatial one, and other available sources of digital data (GEO, GEOSS, GGIM, DBAR, and INSPIRE). World scientific and professional organizations such as ICA (especially the Commission on Cartography for Early Warning and Disaster Risk Management) are introducing new approaches to deal with Early Warning [EW], disaster risk management [DRM], and DRR, respecting the main tasks of SF global objectives and formulating and fulfilling its global indicators. Several are related to the umbrella approach of the International Society of Digital Earth [ISDE]. In connection with the Digital Earth concept. Annoni et al. (2023) and Annoni (2022) described the aspects of the „Digital transformation of global society“, which can be characterized by the „continuous development of Digital Channels, Digital Analytics, and Digital Business model(s)“. There is a huge demand for disaster risk management using digitalization as a key enabler for effective and efficient disaster risk management systems. Digital and intelligence technologies can help solve key aspects of the disaster management cycle. An urgent requirement is the incorporation of the latest approaches from cutting-edge scientific studies into practice in the form of comprehensible, well-thought-out applications (Ariyachandra & Wedawatta, 2023; Ford &
Wolf, 2020; Kremers, 2022; Lienert et al., 2022; Lienert et al., 2021). The effectiveness of realising contemporary challenges depends on investigating relations between Geographical Space and newly established Cyberspace (Chen et al., 2023). The geographical space humankind is living, using, and still investigating and recognizing everyday challenges, but it has a new, strong, and, in many aspects, different partner - Cyberspace.
Artificial Intelligence [AI], Digital Analytics and Visualization, and Digital Twins play an important role in its development. AI is developing through Capabilities-based AI (narrow, general, and super ones) and Functionality AI (reactive machines, limited memory, theory of mind, and self-awareness). Cyberspace is generally considered a global domain within the information environment consisting of the interdependent network of information technology
infrastructures, including the internet, telecommunication networks, computer systems, and embedded processors and controllers. (Kirwan & Zhiyong, 2020)
This transformation brings opportunities and challenges to obtain valuable insights into how these two spaces (geographical space and cyberspace) can be mapped and interact with each other. The review of current challenges and future directions offered by close interactions between the two spaces, as illustrated in Fig. 1.
[...].