The speech will mainly concentrate on three parts: the introduction of the situation of disaster emergency mapping, the current research hotspots and the development trends of cartography for emergency and disaster management.

The overall situation part: Firstly, starting from the impact of disasters and their management needs, based on the current research ”the body of knowledge for cartography”, it will introduce the classification of disaster and emergency management mapping based on disaster types, disaster management processes, spatial dimensions and spatial temporal scales.

The current research hotspots will mainly involve the following aspects: big data-driven disaster emergency mapping, dynamic simulation of disaster processes, user-centred map design and implementation and disaster scenarios and digital twins.

Facing the development trends, it will explore new methods and ideas driven by new technologies such as artificial intelligence, as well as the design and implementation of disaster narrative maps based on the coupling of nature, humanity, and memory for long-term disaster prevention and disaster education.

Issuing warnings, be it hazard-based or impact-oriented, requires a data processing pipeline to generate a reliable warning product which can be distributed to the end-user. Each step along the chain, including both manual and automatic ones, offers its own challenges to refine the data.

Traditionally, hazard-based warnings are derived from initially complex, gridded weather forecast data and have to be simplified for easy understanding by the public. Yet, hazard warnings do not provide specific information regarding their consequences as, for example, physical damage to infrastructure, disruption of societal activities, or economic losses. Under the umbrella initiative Early Warnings for All (EW4A) the World Meteorological Organization advocates for the advancement of early warning systems, increasingly tailoring them to the needs of specific users, with a focus on impacts, informing actions to mitigate damage. Developing accurate and useful impact-based forecasts is challenged by limited data and information, lack of standardized technical protocols, issues sharing impact data and little knowledge on the needs of various user groups.

To ensure the quality of any warning system, verification is crucial. A dense network of measurements. Yet even if this is given, as simplifications are made to issue a pleasing product to the user, verifying warnings poses a number of challenges. Impact warnings are even more challenging to verify and guidelines are needed to do so.

This session aims to unite scientists, natural catastrophe modelers, weather forecasters, tool developers, stakeholders, and policy professionals, and discuss advancements and challenges related to the entire warning chain. We welcome inputs on the identification of extreme weather and impacts, the generation of hazard or impact warnings and forecasts, their verification, visualization, uncertainty, and user needs. The session features expert presentations and a panel discussion to allow the community to collaborate on developing storylines, marking a significant step forward in weather and impact modeling.

We present the development and application of the GVZ (cantonal building insurance Zurich) windstorm risk model to assess building damages in the Canton of Zurich, Switzerland. This model offers two key applications that support both rapid damage estimation immediately after a windstorm event and probabilistic risk modelling.

The first application uses high-resolution meteorological data from MeteoSwiss's ICON model to provide impact forecasting and post-event analysis. The model estimates the number of buildings affected and the potential damages at the municipal level, visualized through an interactive dashboard. This tool supports resource allocation and informed decision-making during and immediately after windstorm events.

The second application involves a probabilistic risk assessment based on a winterstorm hazard event set generated using the CLIMADA platform (ETH Zurich, Aznar-Siguan and Bresch, 2019) and the method described by Schwierz et al. (2010) and Welker et al. (2021). We estimate return periods for extreme events like Winterstorm Lothar. Our findings indicate that a storm of Lothar's magnitude today would result in approximately CHF 80 million in damages, corresponding to a 130-year return period.

Hail is a significant contributor to weather-related damages to buildings, cars, and agriculture in Switzerland, demanding actionable information on hail risks and forecasts across sectors. The research project scClim (https://scclim.ethz.ch/) addresses this demand by establishing a seamless model chain from observing and modeling hail events to the quantification of hail impacts, including simulations to compare hail occurrence in current and future climate. Within the project, we have developed a hail impact forecast prototype that has been co-designed with relevant stakeholders from public and private institutions and is running in a pre-operational fashion during the 4-year research project. Combining ensemble weather forecasts with exposure and vulnerability information, we use the open-source risk assessment platform CLIMADA to provide impact-based forecasts for buildings and different crop types in Switzerland. Furthermore, the platform provides post-event assessments of hail impacts based on operational radar data and/or crowdsourced hail reports. Finally, by means of high-resolution convection-resolving climate simulations using a pseudo global warming approach for a 3 K global warming level that have been conducted within the project, we discuss hail impact distributions in current climate and hail impact projections to future climate.

Increasingly intense rainfall events are causing serious damages to infrastructures and endangering human lives. To better protect them, early warning systems can be set up to evacuate people, move and protect cars or protect infrastructure by installing flood barriers. However, warnings must be issued with sufficient lead times (tens of minutes) before the flood event occurs in order to be useful.

Deterministic forecasts based on the advection of precipitation radar measurements can anticipate flash flood precipitation. However, these systems are subject to considerable uncertainties, especially for extreme convective events that tend to cause flash floods. These uncertainties include the growth and decay of the storm cell, as well as the estimation of the cell's displacement. The use of these deterministic forecasts leads to low detection probabilities for localized intense precipitation events. The generation of ensemble precipitation forecasts improves on deterministic forecasts by proposing several precipitation scenarios, some of which may lead to higher discharge forecasts.

As part of the Radar4Infra project, a flash flood forecasting and alert system based on NowPrecip1.0 radar forecast (Sideris et al., 2020) is being developed for several small catchments. This system builds on recent improvements of the weather radar network and data processing (Germann et al., 2022), sophisticated nowcasting algorithms (Sideris et al., 2020) and a state-of-the-art rainfall-funoff model adapted for Alpine catchments (Jordan, 2007). More precisely, it consists of a radar precipitation forecast, with a spatial resolution of 1 km², a temporal resolution of 10 min, a forecast horizon of 6 hours and a forecast update rate of 10 min. This forecast is then introduced into the Routing System rainfall-runoff simulation model (Schäfli et al., 2005; Jordan, 2007), also with a 10 min temporal resolution. The rainfall-runoff simulation model is calibrated on flow measurements, with input data from rain gauges or precipitation fields (CPCH from MeteoSwiss, Sideris et al., 2014).

The methodology followed consists of evaluating the quality of deterministic flow forecasts using the "probability of detection" and "false alarm ratio" indicators, calculated at several flow thresholds (Cosson, 2023). These benchmark forecasts are then compared with ensemble forecasts. The latter are derived from preliminary tests (NowPrecip2.0) carried out by MeteoSwiss for a few selected flood events.

Two examples of hindcasts showed promising results : the Anniviers event (catchment area 88 km² in the Swiss Alps), and the Cressier event (catchment area 4.5 km² in the Jura region). In the Anniviers example, the system predicted a flood peak almost two hours ahead of time, while the watershed response time was only one hour in this situation. In the Cressier example, the "NowPrecip2.0 RZC-based" ensemble forecast was not able to predict the peak discharge, but the system predicted a smaller flood one hour ahead, although the response time of the catchment area is only 20min. Moreover, in both cases, the deterministic NowPrecip1.0 RZC-based forecast was not able to predict any flood discharge at all.

Ensemble radar-based hydro-meteorological cascade allowed to predict flash floods better than deterministic radar advectionpredictions.

Accurate and precise weather forecasting is crucial for issuing timely weather hazard warnings. However, current numerical weather prediction (NWP) models often struggle to accurately represent extreme weather events due to limitations in spatial and temporal resolutions. Additionally, with only a few model runs typically initialized per day, the effectiveness of the data assimilation process in capturing rapidly changing environments and improving initial conditions is limited. These constraints prevent NWP models from capturing small-scale weather features, such as severe convective thunderstorms. Furthermore, standard fixed thresholds used in issuing weather warnings may not adequately account for the varying levels of risk associated with different locations and use cases. This uniform approach can lead to either underestimation or overestimation of the actual risk.

To address these challenges, Meteomatics has developed the operational high-resolution NWP model EURO1k. Characterized by a 1 km horizontal grid spacing, a 72-hour forecast horizon, and an hourly refresh rate across the pan-European domain, the EURO1k model strongly enhances forecast accuracy. This high resolution allows EURO1k to accurately represent small-scale weather patterns, resulting in precise forecasts of extreme weather events. Additionally, thanks to its hourly refresh rate and data assimilation capabilities, the EURO1k model can be utilized for nowcasting. In addition to assimilating standard data sources like radar, satellite data, weather stations, and radiosondes, the EURO1k model also integrates data from a network of Meteodrones—small unmanned aircraft systems (UAS) developed by Meteomatics that collect vertical atmospheric profiles up to 6000m in altitude.

Moreover, Meteomatics has developed a highly customizable weather warning system, where multiple weather variables can be combined, and specific thresholds selected to enable targeted warnings for specific locations. The integration of the high-resolution EURO1k model with this customizable alert system allows for more accurate and use-case-specific warnings, optimally addressing relevant local risks. By employing this advanced approach, Meteomatics substantially enhances the reliability and precision of weather hazard warnings, ultimately improving preparedness and response measures.

National, Cross-Border, International and Global actions in all phases of Disater Management are in due need of technical, organizational and legal standards that allow for the required cross-organizational as well as cross-cultural Harmonization.

While first international regulations on transnational information flow and processes already have been implemented successfully (transnationally, european, globally) in other domains since years (Environment, Geoinformation etc.) and further more overarching regulations already have been decided upon (European Data Act, Interoperable Europe Act etc.), implementation in the RISK domains still lack the concepts of how to approach the associated complexity, encourage and promote the elaboration of Lighthous realization, discuss and make available suitable Testbeds, and mobilize adequate workforce in order to achieve Facet Components that support data, information and workflow in predefined achievable timeframes.

This side-event is open for international experts' experiences and opinions exchange RIMMA2025 Participants less experienced but interested in the Digital Future of RISK Information Management are welcome to join.

A software developed at the WSL for collecting data on past and current natural hazards

Abstract

Since its invention, photography has been a simple and direct means of documenting landscapes. Historical or current terrestrial oblique photographs capturing natural hazards often provide detailed information and can nowadays be easily taken by anyone using various devices.

The image2world software, originally developed at WSL (wsl.ch/monoplotting) and now taken over by the company image2world GmbH (i2w.ch), allows the use of individual terrestrial or aerial oblique images to map natural hazards (and other landscape features) in a cost-effective and efficient manner. Conventional photographs taken by smartphone, drone, or helicopter are transformed into 3D maps available to professionals, researchers, or just the curious.

This presentation will show examples of the software's use in the context of natural hazards, including the mapping of floods, landslides, and rockfalls, the analysis of the effectiveness of snow bridges, as well as in the field of immediate geolocation of natural events (Rapid Mapping), which can contribute to the organization of search and rescue operations.

Due to their complexity, large-scale wildfire and flood events put immense pressure on authorities to quickly gain a clear overview of the disaster situation for an adequate operational planning. Satellite-based emergency mapping (SEM) services such as the Copernicus Emergency Management Service (CEMS) rapid mapping service provide geospatial crisis information on demand and fast in support of authorities and responders before, during or immediately following a disaster. Although the standard SEM workflow has evolved in recent years, particularly in the field of satellite image analysis, the gap between the initial warning and the SEM activation still delays product availability.

For understanding where the delays stem from, we analysed the steps taken by the actors involved in the SEM process. Service providers perform the rapid mapping upon SEM activation and publish the produced crisis information, e.g. via the CEMS. In order to produce timely and accurate map products such as burnt or flooded area maps service providers need the SEM process to be triggered as early as possible with clearly defined areas of interest (AOIs). The SEM is typically activated by end users such as civil protection authorities and emergency services. Though a number of early warning tools are available, some crucial steps until SEM activation remain user-driven (Mühlbauer et al., 2024). First, end users need to manually identify the AOI, often from multi-source data such as warnings, weather forecasts, observations, etc. In addition, they need to put effort in getting aware of the availability of satellite data to capture the AOIs once they get affected by the event. Service providers usually use acquisition planning tools where they intersect the AOI with planned satellite overpasses. Furthermore, it is unclear to end users when the generated products eventually become available.

Accordingly, our research question here revolves around technical ways of improving users’ situational awareness and hence reducing the time needed from the initial warning to satellite data acquisition to the availability of analysis results. For addressing the latter, we examined and developed a tool that automatically processes and fuses multi-source web data (e.g., public alerts, sensor observations, weather forecasts), identifies AOIs and intersects them with relevant satellite acquisitions. Our approach improves the end user's situational awareness by automatically generating decision proposals regarding EO data and product availability. Situational awareness is further improved by an interactive spatiotemporal visualization of AOIs and satellite acquisitions. The user is supported by transparency on the underlying data sources, the expected (and actual) time of satellite data acquisition, attributes of relevance and overlap of satellites for events.

The talk will focus on how Rapid Mapping works, the challenges involved and our experiences with Rapid Mapping.

Rapid Mapping is a 24/7 on-call service of the Swiss Federal Government for the timely collection and/or provision of geodata (e.g. aerial or satellite imagery) in the event of natural disasters for the purpose of event documentation and, in certain cases, event management. In cooperation with the National Emergency Operations Centre (NEOC), the Federal Office for the Environment (FOEN) coordinates the needs of federal and cantonal agencies and, if necessary, other stakeholders in the event of large-scale or significant events with a high degree of urgency for data collection. After a positive assessment, the FOEN instructs the Federal Office of Topography, swisstopo, to obtain the data.

swisstopo is Switzerland’s geoinformation center and is responsible for the provision of analysis-ready geodata before and after natural hazard events and supports various stakeholders (federal government, cantons, municipalities) in documenting natural hazard events. swisstopo makes data freely available within the framework of Open Government Data by offering newly collected data (post-disaster) and existing swisstopo geodata (pre-disaster) for comparison purposes.

Rapid Mapping offers a range of so-called base products. These include digital imagery from a variety of imaging platforms (satellites, aircraft, helicopters) and sensors, selected according to the task at hand and availability. As with all airborne or spaceborne imagery, there are limiting factors that make it impossible to guarantee rapid mapping products within a given timeframe. These include weather conditions (e.g. cloud cover), but also the general availability of specific resources (manpower, equipment, etc.) at the time of the event.

Of particular importance is the fact that swisstopo's flight service has been upgraded to the highest priority level for rapid mapping missions within the framework of national airspace usage priorities, thus recognising the benefits of this service and facilitating its use in crisis management. While swisstopo operates its own flight service in collaboration with the Swiss Air Force to produce aerial image data, satellite data is acquired and managed through the National Point of Contact for Satellite Images (NPOC, 2024), which is managed by swisstopo. Through this contact point, various additional satellite data - both free and commercial - can be obtained, such as products from the Copernicus Sentinel portfolio or very high resolution imagery.

Access to analysis-ready rapid mapping products is provided through the Federal Geodata Infrastructure. The products are freely available in read-only formats at map.geo.admin.ch. Both pre- and post-disaster data are presented via this geodata infrastructure, together with a functionality that facilitates image comparison (Fig. 1).

The summer of 2024 was particularly challenging for the Rapid Mapping Service. Due to recurrent heavy rainfall, the service was activated four times in less than two weeks in different regions of Switzerland. Thanks to the various collection platforms, useful data was made available to the relevant authorities within the required timeframe in all four cases, making an important contribution to the management and documentation of the events and even to the situation.

Disasters such as floods cause severe damage and affect millions of people every year. To respond quickly and effectively, emergency services need up-to-date, comprehensive and accurate information on the extent of the hazard, exposed assets and damage. In recent years, the rapidly growing number of satellites in orbit and the data they provide have also made it possible to prepare for specific events or to monitor vulnerable regions of the world on an ongoing basis.

The recent floods in Germany, for example, demonstrated the importance of continuous monitoring and close cooperation with humanitarian actors. The heavy rainfall events and subsequent widespread flooding in southern Germany in June 2024 were preceded by official warnings from the German Weather Service, and emergency services were able to take preparatory measures before the onset of the flooding. In addition to the activities by the Copernicus Emergency Management Service (CEMS) and national entities, the Center for Satellite-based Crisis Information (ZKI) of the German Aerospace Center (DLR) provided first aid responders with updated web-based crisis information on the evolving flood situation. To improve the situational awareness of the event, the web application included not only aerial images and the analyzed flood extent from different satellite sensors but also datasets on population, buildings, land use and critical infrastructure (ZKI, 2024).

As part of the Indicator Monitoring for Early Acquisition of Innovative Satellite Sensors in Natural Disasters (IFAS) project, the DLR is working to improve satellite-based emergency mapping by initiating the process chain in anticipatory action. A study by CEMS has shown the timely benefit of early tasking satellite imagery based on hydrological forecasts (Wania et al., 2021). In regards to this finding and to improve the response time for flood disasters, the DLR is collaborating closely with European Space Imaging (EUSI), an industrial partner providing very high-resolution satellite imagery, as well as with first aid responders. By automating components of the rapid mapping process, this project aims to minimize the time delay in the availability of satellite data and the provision of crisis information to anticipate the development of a crisis at an early stage.

The project therefore touches on various aspects of different phases in disaster management: In the monitoring and preparedness phase, heterogeneous data sources including official alerts and forecasting information are collected and matched with possible satellite overpasses to initiate a timely acquisition of (very high-resolution) satellite data for a potential crisis event. The aggregation of data sources can further facilitate the pre-definition of areas of interest for satellite data acquisition, which is then automated using EUSI's Tasking Archive API. Once satellite imagery has been delivered, the data is automatically downloaded, rapidly analyzed, and integrated into a web-based crisis information product and shared with dedicated civil protection actors to support response activities. Their feedback is then used to continuously improve the visualization of crisis information products.

Detecting buildings from remote sensing data typically requires high-resolution satellite images, which are costly and limited with respect to accessibility. Although open-access satellite data offer global coverage with frequent revisits, low resolution poses a significant challenge for accurate building detection. By constructing multiple detection models tailored to specific building location characteristics, such as building density and size, we develop an approach that enhances building detection accuracy. First, we fine-tune a super-resolution model to increase the resolution of Sentinel-2 images from 10 m to 2.5 m, thereby improving the visibility of relatively small buildings. We then classify the study area using a logistic regression model based on population, Normalized Difference Vegetation Index, nighttime light intensity, and distance to the coastline. This classification facilitates the grouping of areas into distinct categories based on the building location characteristics. Subsequently, we develop separate building detection models for each classified area and evaluate their performance against a general detection model trained on all data. Our results demonstrate that models optimized for specific building location characteristics significantly outperform the general model, particularly for areas with large buildings. This study highlights the importance of considering building location characteristics when constructing detection models and provides a framework for improving the accuracy of building detection using low-resolution, open-access satellite data.