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.

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.

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.

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

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

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

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

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

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

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

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

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

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

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