Andreas Stoffel

Automated weather stations (AWS) measuring snow-related parameters are essential for avalanche warning in many regions. Key data include new snow accumulation, wind direction, and wind velocity, especially in remote, high-elevation terrain. This information is critical for decisions such as when to close and reopen roads. Given the high spatial variability of snow depth distribution in mountain areas, the positioning of AWS is crucial. High-resolution, spatially coherent snow depth measurements acquired by drones, airplanes, or satellites reveal significant variability within short distances of just a few meters. Therefore, it is important to place weather stations at relatively flat locations with representative snow depth values. Areas where the snowpack is strongly influenced by wind or avalanches, either removing or depositing large amounts of snow, are unsuitable.

We developed an automated approach combining remotely sensed snow depth maps with terrain characteristics (e.g., slope or homogeneity) and simulated avalanche scenarios to identify optimal positions for AWS. We demonstrate how this approach can enhance safety-relevant information in the Dischma Valley near Davos, Switzerland. This approach could be applied globally wherever high-quality digital elevation models are available and spatially coherent snow depth maps can be acquired. Currently, the positioning of AWS relies heavily on expert judgment. Our tool could help make these decisions more comprehensible and serve as a second opinion, ensuring the optimal placement of AWS.

We demonstrate how high spatial and temporal resolution spaceborne synthetic aperture radar (SAR) imagery can improve slope deformation monitoring. We process ICEYE data over the Brienz/Brinzauls slope instability in the Swiss Alps, where a catastrophic failure occurred on 15 June 2023. Image correlation applied to retrieve surface velocities shows results comparable to in situ measurements. Pre- and post-failure acquisitions used to map areas invaded by debris and to compute associated volumetric changes are in good agreement with photogrammetric data. This study sets the baseline for new-generation satellite SAR data aimed at providing timely information in landslide early warning scenarios.

The snow-covered winter surface differs significantly from its snow-free equivalent due to the smoothing caused by progressive snow accumulation and erosion. This smoothing effect becomes more pronounced as the absolute snow depth increases. However, variations in accumulation, settling and redistribution occur in complex mountain terrain. The spatial variability of snow depth plays a crucial role in the formation and dynamics of snow avalanches. Surface shape and roughness serve as established metrics to assess the effect of snow on terrain. In this study, we investigate snow surface smoothing within the release areas of typical skier-triggered avalanches observed in the vicinity of Davos, Switzerland. We analyze therelease areas of nearly 2000 avalanches of class size 2 to 3, using high spatial resolution (0.5 m) annual snow depth maps recorded during the peak of winter over 6 years and covering an area of 275 km2. These avalanche release areas are grouped according to their main terrain shape. Within each group, the influence of snow depth on surface shape and roughness is assessed. Finally, we investigate the effect of different DEM resolutions on the terrain smoothing factor and analyze the intra-annual consistency of the terrain smoothing. For the first time, we assess the role of snow depth on surface shape and surface roughness in avalanche release areas over a large sample size and a large area. We find strong agreement in the intra-annual terrain smoothing behavior despite significant differences in mean snow depths observed over the study period, ranging from 0.73 m in 2023 to 2.2 m in 2019.

Die räumliche Variabilität der Schneedecke im Gebirge wird durch eine komplexe Kombination topographischer und meteorologischer Faktoren beeinflusst. Innerhalb weniger Meter Distanz finden sich schneefreie Stellen, aber auch Mulden mit Schneehöhen von mehreren Metern. Dies hat grosse Auswirkungen auf die Lawinengefahr, die Wasserspeicherkapazität der Schneedecke sowie Flora und Fauna im Gebirge. Meist wird die Schneehöhe im Gebirge mit Punktmessungen an Wetterstationen oder von Hand erfasst. Allerdings können solche Messungen nur ein unvollständiges Bild der tatsächlichen Schneeverteilung zeichnen. Mit neuen Fernerkundungsplattformen wie Drohnen, Flugzeugen oder optischen Satelliten können photogrammetrisch sehr präzise Oberflächenmodelle erstellt werden. Subtrahiert man ein Oberflächenmodell ohne Schneebedeckung von einem Oberflächenmodell mit Schneebedeckung, erhält man eine räumlich zusammenhängende Kartierung der Schneehöhenverteilung. Am SLF werden seit 2010 solche präzisen Schneehöhenkarten erstellt. Diese erweisen sich als wichtige Grundlage für zahlreiche Anwendungen.

The spatial variability of the mountain snowpack is influenced by a complex combination of topographical and meteorological factors. Within a few meters there are snow-free spots, but also hollows with snow depths of several meters. This has a major impact on the risk of avalanches, the water storage capacity of the snowpack and the flora and fauna in the mountains. The snow depth in the mountains is usually recorded with point measurements at weather stations or by hand. However, such measurements can only paint an incomplete picture of the actual snow distribution. With new remote sensing platforms such as drones, airplanes or optical satellites, very precise surface models can be created photogrammetrically. Subtracting a surface model without snow cover from a surface model with snow cover results in a spatially coherent mapping of the snow depth distribution. Such precise snow depth maps have been created at the SLF since 2010. These prove to be an important basis for numerous applications.

Information on snow depth and its spatial distribution is important for numerous applications, including natural hazard management, snow water equivalent estimation for hydropower, the study of the distribution and evolution of flora and fauna, and the validation of snow hydrological models. Due to its heterogeneity and complexity, specific remote sensing tools are required to accurately map the snow depth distribution in Alpine terrain. To cover large areas (>100 km²), airborne laser scanning (ALS) or aerial photogrammetry with large-format cameras is needed. While both systems require piloted aircraft for data acquisition, ALS is typically more expensive than photogrammetry but yields better results in forested terrain. While photogrammetry is slightly cheaper, it is limited due to its dependency on favourable acquisition conditions (weather, light conditions). In this study, we present photogrammetrically processed high-spatial-resolution (0.5 m) annual snow depth maps, recorded during the peak of winter over a 5-year period under different acquisition conditions over a study area around Davos, Switzerland. Compared to previously carried out studies, using the Vexcel UltraCam Eagle Mark 3 (M3) sensor improves the average ground sampling distance to 0.1 m at similar flight altitudes above ground. This allows for very detailed snow depth maps in open areas, calculated by subtracting a snow-off digital terrain model (DTM, acquired with ALS) from the snow-on digital surface models (DSMs) processed from the airborne imagery. Despite challenging acquisition conditions during the recording of the UltraCam images (clouds, shaded areas and fresh snow), 99 % of unforested areas were successfully photogrammetrically reconstructed. We applied masks (high vegetation, settlements, water, glaciers) to increase the reliability of the snow depth calculations. An extensive accuracy assessment was carried out using check points, the comparison to DSMs derived from unpiloted aerial systems and the comparison of snow-free DSM pixels to the ALS DTM. The results show a root mean square error of approximately 0.25 m for the UltraCam X and 0.15 m for the successor, the UltraCam Eagle M3. We developed a consistent and reliable photogrammetric workflow for accurate snow depth distribution mapping over large regions, capable of analysing snow distribution in complex terrain. This enables more detailed investigations on seasonal snow dynamics and can be used for numerous applications related to snow depth distribution, as well as serving as a ground reference for new modelling approaches and satellite-based snow depth mapping.

Mountain forests have a substantial protective function in preventing natural hazards, in part due to the presence of dead wood on the forest floor. Rates of deadwood accumulation have increased within the Alps and are predicted to rise further, due to natural disturbances. In particular, higher windthrow event frequencies are expected, primarily due to large-scale even-aged forest stands in many alpine regions combined with climate change. We quantified the rockfall protection effect of mountain forests with and without deadwood, in unprecedented detail, in experiments using two rock shapes with important hazard potential and masses of 200-3200 kg. Based on a multi-camera setup, pre- and post-experimentally retrieved high-resolution lidar data, and rock data measured in situ, we precisely reconstructed 63 trajectories. The principal parameters of interest describing the rockfall kinematics were retrieved for each trajectory. A total of 164 tree impacts and 55 deadwood impacts were observed, and the currently applied energy absorption curves - partially only derived theoretically - could consequently be corroborated or even expanded to a greater absorption performance of certain species than hitherto assumed. Standing trees, in general, and deadwood, in particular, were found to strongly impede the notorious lateral spreading of platy rocks. Platy rocks featured a shorter mean runout distance than their compact counterparts of similar weight, even in the absence of deadwood. These results indicate that the higher hazard potential of platy rocks compared with more compact rocks, previously postulated for open-field terrain, applies less to forested areas. Last, reproducing the experimental setting showcases how complex forest states can be treated within rockfall simulations. Overall, the results of this study highlight the importance of incorporating horizontal forest structures accurately in simulations in order to obtain realistic deposition patterns.

Debris flows are a severe hazard in mountainous regions. However, cost-effective long-term studies of debris flows are seldom, which leads to substantial uncertainties in hazard mitigation methods. This paper investigates whether cost-effective remote sensing techniques can be applied to assess the hazards of mountain torrents and to gather accurate long-term information on the development of the watershed. Torrents that are prone to debris flows are often devoid of vegetation and can thus be well surveyed using photogrammetric methods based on uncrewed aerial vehicle (UAV) surveys. The possibility of extracting automatically torrent parameters from high-resolution terrain models, such as cross-sectional area or slope, is explored. The presented methodology yields continuous and automatically derived parameters along the torrent, which is a major advantage over pointwise field surveys. Cross-validation with field measurements reveals a strong agreement. These parameters are very accurate along highly incised sections, while they are severely limited along sections with steep adjacent hillslopes and/or dense vegetation. We show that these kinds of assessments greatly gain from UAV data followed by automatic parameter extraction. The extracted parameters provide insights so that key sections and weak points can be identified and accurately assessed in the field. We find that UAV data can contribute to a comprehensive, reproducible and objective assessment of torrent processes and predispositions. However, ground-based fieldwork is still essential and further research on remote sensing-based hazard assessment of torrents prone to debris flows is crucial.

Debris flows are a hazard in mountainous regions. Cost-effective, long-term studies of debris-flow torrents, however, are rare, leading to uncertainties in hazard assessment and hazard prevention. Here, we address the question of whether cost-effective remote sensing techniques can be applied for assessment of mountain torrents and possibly further gather accurate, long-term information on the evolution of the catchment. Torrents prone to debris flows are often devoid of vegetation in the near channel area and hence can be well captured with photogrammetrically derived methods using uncrewed aerial vehicle (UAV) surveys. The possibility of automatically extracting specific torrent parameters from high-resolution terrain models, such as cross-section area or gradient, is investigated. The presented methodology yields continuous and automatically derived geometrical parameters such as torrent bed width, inclination and cross-section area, which is a major advantage compared with point-based, often dangerous field surveys. Their cross-validation with field measurements shows strong agreement. Those parameters are accurate along sharply incised sections with strong limitations along sections with steep adjacent slopes and/or dense vegetation. The information along the torrent allows fast identification of key sections and weak spots which can be precisely evaluated in the field. The study highlights that proper classification of real ground points poses the key challenge. We show that photogrammetric routines to derive a high-resolution digital terrain model (DTM) are limited in the case of dense vegetation coverage. In such cases, LiDAR surveys have clear advantages even though they are also limited by very dense vegetation. We find that UAV data can be used for an objective method of estimating debris-flow torrent geometric properties. And the introduced approaches therefore build a stepping stone towards a more comprehensive, reproducible and objective assessment of torrent processes and predispositions. However, ground-referencing fieldwork remains essential, and further research on remote sensing supported hazard assessment of debris-flow-prone torrents is indispensable.

Snow avalanche hazard mapping has a long tradition in the European Alps. Hazard maps delineate areas of potential avalanche danger and are only available for selected areas where people and significant infrastructure are endangered. They have been created over generations, at specific sites, mainly based on avalanche activity in the past. For a large part of the area (90 % in the case of the canton of Grisons) only strongly generalized hazard indication maps are available (SilvaProtect), not showing impact information such as pressure. This is a problem when new territory with no or an incomplete historical record is to be developed. It is an even larger problem when trying to predict the effects of climate change at the state scale, where the historical record may no longer be valid. To close this gap, we develop an automated approach to generate spatially coherent hazard indication mapping based on a digital elevation model for the canton of Grisons (7105 km²) in the Swiss Alps. We calculate eight different scenarios with return periods ranging from frequent to very rare as well as with and without taking the protective effects of the forest into account, resulting in a total of approximately 2 million individual avalanche simulations. This approach combines the automated delineation of potential release areas, the calculation of release depths and the numerical simulation of the avalanche dynamics. We find that between 47 % (most frequent scenario) and 67 % (most extreme scenario) of the cantonal area can be affected by avalanches. Without forest, approximately 20 % more area would be endangered. This procedure can be applied worldwide, where high-spatial-resolution digital elevation models, detailed information on the forest and data on the snow climate are available, enabling reproducible hazard indication mapping also in regions where no avalanche hazard maps yet exist. This is invaluable for climate change studies. The simulation results are validated with official hazard maps, by assessments of avalanche experts, and by existing avalanche cadastres derived from manual mapping and mapping based on satellite datasets. The results for the canton of Grisons are now operationally applied in the daily hazard assessment work of the authorities. Based on these experiences, the proposed approach can be applied for further mountain regions.

Gefahrenkarten bilden eine wichtige Grundlage für den Umgang mit der Lawinengefahr im Alpenraum. Allerdings existieren diese Karten nur im Siedlungsbereich, für einen Grossteil der Fläche sind keine oder nur sehr begrenzte Informationen zur Lawinengefährdung verfügbar. Aus diesem Grund hat das WSL Institut für Schnee- und Lawinenforschung SLF zusammen mit dem Amt für Wald und Naturgefahren (AWN) des Kantons Graubünden eine Methode entwickelt, um flächendeckende Gefahrenhinweiskarten automatisiert zu erstellen. Die Kombination von Anrissgebiets-Ausscheidung mit der numerischen Simulation der Auslaufstrecken mit der Software RAMMS macht dies möglich. Dabei halten wir uns so weit wie möglich an die Vorgehensweise, wie sie in der Schweiz zur Erstellung der Gefahrenkarten verwendet wird.

Hazard maps are a crucial base for tackling avalanche hazard in the Alps. But such maps are only elaborated within specific, mostly settled areas, for the large part none or only very limited information on avalanche danger is available. Therefore, the WSL Institute for Snow and Avalanche Research SLF and the office for forest and natural hazards (AWN) of the canton Grisons developed a methodology to calculate spatial continuous hazard indication maps for large Regions. The combination of release area delineation with the numerical simulation of avalanche runout with the software RAMMS makes this possible. We stick as much as possible with the procedure applied for the hazard mapping generation in Switzerland.

Forests are rockfall-protective ecological infrastructures as a significant amount of kinetic energy is absorbed during consecutive rock-tree impacts. Although many recent works have considered rock impacts with standing trees, the effect of lying deadwood in forests has not yet been considered thoroughly, either experimentally or numerically. Here, we present a complete examination of induced rockfall experiments with sensor-equipped, 45kg, artificial rocks on a forested area in three different management stages. The trilogy is conducted in a spruce forest stand (i) in its original state of forest; (ii) after a logging operation with fresh, lying deadwood; and (iii) after the removal of the deadwood. The tests allow us to directly quantify the effect of fresh deadwood on overall rockfall risk for the same forest (slope, species) under three different conditions. The study yields quantitative results on the barrier efficiency of the deadwood logs as only 3.6 of the rocks surpass the deadwood section. The mean run-out distance is reduced by 42. Conversely, the run-out distance increases by 17 when the cleared stand is compared to the original forest. These results quantitatively confirm the benefits of nature-based mitigation measures integrated into forestry practice, whose detailed effect has to be scrutinized for higher rockfall energies. Based on the experimental results, we extended a modern rockfall code by three-dimensional deadwood logs to incorporate such complex but realistic forest boundary conditions.

Studying how deadwood mitigates the rockfall hazard in mountain forests is key to understanding the influence of climate-induced disturbances on the protective capacity of mountain forests. Both experimental quantification and numerical process modeling are needed to address this question. Modeling provides detailed insights into the rock-deadwood interaction and can therefore be used to develop effective forest management strategies. Here, we introduce an automatic deadwood generator (ADG) for assessing the impact of fresh woody storm debris on the protective capacity of a forest stand against rockfall. The creation of various deadwood scenarios allows us to directly quantify the mitigation potential of deadwood. To demonstrate the functionality of the proposed ADG method, we compare deadwood log patterns, deadwood effective height, and mesoscale surface ruggedness observed in field surveys in a natural windthrow area with their simulated counterparts. Specifically, we consider two sites near Lake Klöntal, Switzerland, where a major windthrow event occurred in 2019. We perform rockfall simulations for the time (a) before, (b) directly after, and (c) 10 years after the windthrow event. We further compare the results with (d) a simulation with complete clearing of the thrown wood: in other words, a scenario with no standing forest remaining. We showcase an integration of deadwood into rockfall simulations with realistic deadwood configurations alongside a diameter at breast height (DBH)- and rot-fungi-dependent maximum deadwood breaking energy. Our results confirm the mitigation effect of deadwood, which significantly reduces the jump heights and velocities of 400 kg rocks. Our modeling results suggest that, even a decade after the windthrow event, deadwood has a stronger protective effect against rockfall than that provided by standing trees. We conclude that an ADG can contribute to the decision-making involved in forest and deadwood management after disturbances.

The mitigation of rapid mass movements involves a subtle interplay between field surveys, numerical modelling, and experience. Hazard engineers rely on a combination of best practices and, if available, historical facts as a vital prerequisite in establishing reproducible and accurate hazard zoning. Full-scale field tests have been performed to reinforce the physical understanding of debris flows and snow avalanches. Rockfall dynamics are - especially the quantification of energy dissipation during the complex rock-ground interaction - largely unknown. The awareness of rock shape dependence is growing, but presently, there exists little experimental basis on how rockfall hazard scales with rock mass, size, and shape. Here, we present a unique data set of induced single-block rockfall events comprising data from equant and wheel-shaped blocks with masses up to 2670 kg, quantifying the influence of rock shape and mass on lateral spreading and longitudinal runout and hence challenging common practices in rockfall hazard assessment.

Snow depth has traditionally been estimated based on point measurements collected either manually or at automated weather stations. Point measurements, though, do not represent the high spatial variability in snow depths present in alpine terrain. Photogrammetric mapping techniques have progressed in recent years and are capable of accurately mapping snow depth in a spatially continuous manner, over larger areas and at various spatial resolutions. However, the strengths and weaknesses associated with specific platforms and photogrammetric techniques as well as the accuracy of the photogrammetric performance on snow surfaces have not yet been sufficiently investigated. Therefore, industry-standard photogrammetric platforms, including high-resolution satellite (Pléiades), airplane (Ultracam Eagle M3), unmanned aerial system (eBee+ RTK with SenseFly S.O.D.A. camera) and terrestrial (single lens reflex camera, Canon EOS 750D) platforms, were tested for snow depth mapping in the alpine Dischma valley (Switzerland) in spring 2018. Imagery was acquired with airborne and space-borne platforms over the entire valley, while unmanned aerial system (UAS) and terrestrial photogrammetric imagery was acquired over a subset of the valley. For independent validation of the photogrammetric products, snow depth was measured by probing as well as by using remote observations of fixed snow poles.
When comparing snow depth maps with manual and snow pole measurements, the root mean square error (RMSE) values and the normalized median absolute deviation (NMAD) values were 0.52 and 0.47 m, respectively, for the satellite snow depth map, 0.17 and 0.17 m for the airplane snow depth map, and 0.16 and 0.11 m for the UAS snow depth map. The area covered by the terrestrial snow depth map only intersected with four manual measurements and did not generate statistically relevant measurements. When using the UAS snow depth map as a reference surface, the RMSE and NMAD values were 0.44 and 0.38 m for the satellite snow depth map, 0.12 and 0.11 m for the airplane snow depth map, and 0.21 and 0.19 m for the terrestrial snow depth map. When compared to the airplane dataset over a large part of the Dischma valley (40 km²), the snow depth map from the satellite yielded an RMSE value of 0.92 m and an NMAD value of 0.65 m. This study provides comparative measurements between photogrammetric platforms to evaluate their specific advantages and disadvantages for operational, spatially continuous snow depth mapping in alpine terrain over both small and large geographic areas.

In many countries with seasonally snow-covered mountain ranges warnings are issued to alert the public about imminent avalanche danger, mostly employing an ordinal, five-level danger scale. However, as avalanche danger cannot be measured, the characterization of avalanche danger remains qualitative. The probability of avalanche occurrence in combination with the expected avalanche type and size decide on the degree of danger in a given forecast region (≳100 km²). To describe avalanche occurrence probability, the snowpack stability and its spatial distribution need to be assessed. To quantify the relation between avalanche occurrence and avalanche danger level, we analyzed a large data set of visually observed avalanches (13 918 in total) from the region of Davos (eastern Swiss Alps, ∼ 300 km²), all with mapped outlines, and we compared the avalanche activity to the forecast danger level on the day of occurrence (3533 danger ratings). The number of avalanches per day strongly increased with increasing danger level, confirming that not only the release probability but also the frequency of locations with a weakness in the snowpack where avalanches may initiate from increase within a region. Avalanche size did not generally increase with increasing avalanche danger level, suggesting that avalanche size may be of secondary importance compared to snowpack stability and its distribution when assessing the danger level. Moreover, the frequency of wet-snow avalanches was found to be higher than the frequency of dry-snow avalanches for a given day and danger level; also, wet-snow avalanches tended to be larger. This finding may indicate that the danger scale is not used consistently with regard to avalanche type. Even though observed avalanche occurrence and avalanche danger level are subject to uncertainties, our findings on the characteristics of avalanche activity suggest reworking the definitions of the European avalanche danger scale. The description of the danger levels can be improved, in particular by quantifying some of the many proportional quantifiers. For instance, based on our analyses, "many avalanches", expected at danger level 4-High, means on the order of at least 10 avalanches per 100 km². Whereas our data set is one of the most comprehensive, visually observed avalanche records are known to be inherently incomplete so that our results often refer to a lower limit and should be confirmed using other similarly comprehensive data sets.

This study tested the potential of a short time series of very high spatial resolution (cm to dm) remote sensing datasets obtained from unmanned aerial system (UAS)-based photogrammetry and terrestrial laser scanning (TLS) to monitor snow cover ablation in the upper Dischma valley (Davos, Switzerland). Five flight missions (for UAS) and five scans (for TLS) were carried out simultaneously: Four during the snow-covered period (9, 10, 11, and 27 May 2016) and one during the snow-free period (24 June 2016 for UAS and 31 May 2016 for TLS). The changes in both the areal extent of the snow cover and the snow depth (HS) were assessed together in the same case study. The areal extent of the snow cover was estimated from both UAS- and TLS-based orthophotos by classifying pixels as snow-covered and snow-free based on a threshold value applied to the blue band information of the orthophotos. Also, the usage possibility of TLS-based orthophotos for mapping snow cover was investigated in this study. The UAS-based orthophotos provided higher overall classification accuracy (97%) than the TLS-based orthophotos (86%) and allowed for mapping snow cover in larger areas than the ones from TLS scans by preventing the occurrence of gaps in the orthophotos. The UAS-based HS were evaluated and compared to TLS-based HS. Initially, the CANUPO (CAractérisation de NUages de POints) binary classification method, a proposed approach for improving the quality of models to obtain more accurate HS values, was applied to the TLS 3D raw point clouds. In this study, the use of additional artificial ground control points (GCPs) was also proposed to improve the quality of UAS-based digital elevation models (DEMs). The UAS-based HS values were mapped with an error of around 0.1 m during the time series. Most pixels representing change in the HS derived from the UAS data were consistent with the TLS data. The time series used in this study allowed for testing of the significance of the data acquisition interval in the monitoring of snow ablation. Accordingly, this study concluded that both the UAS- and TLS-based high-resolution DSMs were biased in detecting change in HS, particularly for short time spans, such as a few days, where only a few centimeters in HS change occur. On the other hand, UAS proved to be a valuable tool for monitoring snow ablation if longer time intervals are chosen.

Präzise Schneemessungen sind auch im Zeitalter der Satellitentechnik und des Einsatzes von Fernerkundungsmethoden eine grosse Herausforderung. Für die Abschätzung der Schneedeckenverteilung stellen dabei moderne Hilfsmittel, wie beispielsweise der Einsatz von Drohnen, die mit digitalen Kameras bestückt sind, eine wertvolle Hilfe dar. Die photogrammetrisch ermittelten Schneehöhen weisen räumlich eine viel höhere Genauigkeit auf als die Interpolation von manuell erfassten Bodenmessungen. Die Befliegung ganzer Einzugsgebiete ist jedoch aufwendig und kostspielig, sodass ein Einsatz dieser Messtechniken genau geplant werden muss. Für die Abschätzung des Schneewasseräquivalents eines Gebietes benötigt man zudem auch die räumliche Verteilung der Schneedichte, eine Messgrösse, die aus Drohnenmessungen nicht eruiert werden kann. Diesbezüglich sind konventionelle Messmethoden, wie sie seit 1943 im Wägital (SZ) vorgenommen werden, von grossem Wert. Solche kontinuierlich erfassten Messdatenreihen leisten einen wichtigen Beitrag zum Verständnis des hydrologischen Geschehens in Einzugsgebieten. Mit Hilfe dieser Messreihen, die auch vom Klimabeobachtungssystem GCOS (siehe Kasten) als besonders wertvoll eingestuft wurden, kö nnen die Entwicklungen der vergangenen Jahrzehnte nachvollzogen werden. Diese Entwicklungen sind für verschiedenste Bereiche der Wirtschaft von grosser Bedeutung und leisten zudem ihren Beitrag zum besseren Verständnis der aktuellen Debatte im Bereich «Climate Change».

Snow avalanche hazard is threatening people and infrastructure in all alpine regions with seasonal or permanent snow cover around the globe. Coping with this hazard is a big challenge and during the past centuries, different strategies were developed. Today, in Switzerland, experienced avalanche engineers produce hazard maps with a very high reliability based on avalanche database information, terrain analysis, climatological data sets and numerical modeling of the flow dynamics for selected avalanche tracks that might affect settlements. However, for regions outside the considered settlement areas such area-wide hazard maps are not available mainly because of the too high cost, in Switzerland and in most mountain regions around the world. Therefore, hazard indication maps, even though they are less reliable and less detailed, are often the only spatial planning tool available. To produce meaningful and cost-effective avalanche hazard indication maps over large regions (regional to national scale), automated release area delineation has to be combined with volume estimations and state-of-the-art numerical avalanche simulations.
In this paper we validate existing potential release area (PRA) delineation algorithms, published in peer-reviewed journals, that are based on digital terrain models and their derivatives such as slope angle, aspect, roughness and curvature. For validation, we apply avalanche data from three different ski resorts in the vicinity of Davos, Switzerland, where experienced ski-patrol staff have mapped most avalanches in detail for many years. After calculating the best fit input parameters for every tested algorithm, we compare their performance based on the reference data sets. Because all tested algorithms do not provide meaningful delineation between individual PRAs, we propose a new algorithm based on object-based image analysis (OBIA). In combination with an automatic procedure to estimate the average release depth (d0), defining the avalanche release volume, this algorithm enables the numerical simulation of thousands of avalanches over large regions applying the well-established avalanche dynamics model RAMMS. We demonstrate this for the region of Davos for two hazard scenarios, frequent (10–30-year return period) and extreme (100–300-year return period). This approach opens the door for large-scale avalanche hazard indication mapping in all regions where high-quality and high-resolution digital terrain models and snow data are available.

Mit Drohnen kann die Schneehöhenverteilung in Lawinenanrissgebieten genau kartiert werden. Wir zeigen anhand von drei Fallbeispielen auf, wo die Möglichkeiten und Grenzen dieser neuen Technologie liegen, um in Zukunft die Planung und Überprüfung von Lawinenverbauungen zu verbessern.