Suchergebnisse

Abstract. Potential avalanche release area (PRA) modeling is critical for
generating automated avalanche terrain maps which provide low-cost, large-scale spatial representations of snow avalanche hazard for both
infrastructure planning and recreational applications. Current methods are
not applicable in mountainous terrain where high-resolution (≤5 m)
elevation models are unavailable and do not include an efficient method to
account for avalanche release in forested terrain. This research focuses on
expanding an existing PRA model to better incorporate forested terrain using
satellite imagery and presents a novel approach for validating the model
using local expertise, thereby broadening its application to numerous
mountain ranges worldwide. The study area of this research is a remote
portion of the Columbia Mountains in southeastern British Columbia, Canada,
which has no pre-existing high-resolution spatial datasets. Our research
documents an open-source workflow to generate high-resolution digital elevation models (DEMs) and forest
land cover datasets using optical satellite data processing. We validate
the PRA model by collecting a polygon dataset of observed potential release
areas from local guides, using a method which accounts for the uncertainty
in human recollection and variability in avalanche release. The validation
dataset allows us to perform a quantitative analysis of the PRA model
accuracy and optimize the PRA model input parameters to the snowpack and
terrain characteristics of our study area. Compared to the original PRA
model our implementation of forested terrain and local optimization improved the percentage of validation polygons accurately modeled by 11.7 percentage points and reduced the number of validation polygons that were
underestimated by 14.8 percentage points. Our methods demonstrate
substantial improvement in the performance of the PRA model in forested
terrain and provide means to generate the requisite input datasets and
validation data to apply and evaluate the PRA model in vastly more
mountainous regions worldwide than was previously possible.

Abstract. Snow avalanches are destructive mass movements in mountain regions that continue to claim lives and cause infrastructural damage and traffic detours. Given that avalanches often occur in remote and poorly accessible steep terrain, their detection and mapping is extensive and time consuming. Nonetheless, systematic avalanche detection over large areas could help to generate more complete and up-to-date inventories (cadastres) necessary for validating avalanche forecasting and hazard mapping. In this study, we focused on automatically detecting avalanches and classifying them into release zones, tracks, and run-out zones based on 0.25 m near-infrared (NIR) ADS80-SH92 aerial imagery using an object-based image analysis (OBIA) approach. Our algorithm takes into account the brightness, the normalised difference vegetation index (NDVI), the normalised difference water index (NDWI), and its standard deviation (SDNDWI) to distinguish avalanches from other land-surface elements. Using normalised parameters allows applying this method across large areas. We trained the method by analysing the properties of snow avalanches at three 4 km−2 areas near Davos, Switzerland. We compared the results with manually mapped avalanche polygons and obtained a user's accuracy of > 0.9 and a Cohen's kappa of 0.79–0.85. Testing the method for a larger area of 226.3 km−2, we estimated producer's and user's accuracies of 0.61 and 0.78, respectively, with a Cohen's kappa of 0.67. Detected avalanches that overlapped with reference data by > 80 % occurred randomly throughout the testing area, showing that our method avoids overfitting. Our method has potential for large-scale avalanche mapping, although further investigations into other regions are desirable to verify the robustness of our selected thresholds and the transferability of the method.

Abstract. Surface roughness influences the release of avalanches and the
dynamics of rockfall, avalanches and debris flow, but it is often not
objectively implemented in natural hazard modelling. For two study areas, a
treeline ecotone and a windthrow-disturbed forest landscape of the European
Alps, we tested seven roughness algorithms using a photogrammetric digital
surface model (DSM) with different resolutions (0.1, 0.5 and 1 m) and
different moving-window areas (9, 25 and 49 m2). The
vector ruggedness measure roughness algorithm performed best overall in distinguishing between
roughness categories relevant for natural hazard modelling (including shrub
forest, high forest, windthrow, snow and rocky land cover). The results with
1 m resolution were found to be suitable to distinguish between the
roughness categories of interest, and the performance did not increase with
higher resolution. In order to improve the roughness calculation along the
hazard flow direction, we tested a directional roughness approach that
improved the reliability of the surface roughness computation in channelised
paths. We simulated avalanches on different elevation models (lidar-based)
to observe a potential influence of a DSM and a digital terrain model (DTM)
using the simulation tool Rapid Mass Movement Simulation (RAMMS). In this way,
we accounted for the surface roughness based on a DSM instead of a DTM,
which resulted in shorter simulated avalanche runouts by 16 %–27 % in the
two study areas. Surface roughness above a treeline, which in comparison to
the forest is not represented within the RAMMS, is therefore underestimated.
We conclude that using DSM-based surface roughness in combination with DTM-based surface roughness
and considering the directional roughness is promising for achieving better
assessment of terrain in an alpine landscape, which might improve the natural
hazard modelling.

Abstract. Snow avalanches can endanger people and infrastructure, especially in densely populated mountainous regions. In Switzerland, the public is informed by an avalanche bulletin issued twice a day during winter which is based on weather information and snow and avalanche reports from a network of observers. During bad weather, however, information about avalanches that have occurred can be scarce or even be missing completely. To assess the potential of weather-independent radar satellites, we compared manual and automatic change detection avalanche mapping results from high-resolution TerraSAR-X (TSX) stripmap images and medium-resolution Sentinel-1 (S1) interferometric wide-swath images for a study site in central Switzerland. The TSX results were also compared to available mapping results from high-resolution SPOT-6 optical satellite images. We found that avalanche outlines from TSX and S1 agree well with each other. Cutoff thresholds of mapped avalanche areas were found with 500 m2 for TSX and 2000 m2 for S1. S1 provides a much higher spatial and temporal coverage and allows for mapping of the entire Alps at least every 6 d with freely available acquisitions. With costly SPOT-6 images the Alps can even be covered in a single day at meter resolution, at least for clear-sky conditions. For the SPOT-6 and TSX mapping results, we found a fair agreement, but the temporal information from radar change detection allows for a better separation of overlapping avalanches. Still, the total mapped avalanche area differed by at least a factor of 3 because with radar mainly the avalanche deposition zone was detected, whereas the release zone was very visible already in SPOT-6 data. With automatic avalanche mapping we detected around 70 % of manually mapped new avalanches, at least when the number of old avalanches is low. To further improve the radar mapping capabilities, we combined S1 images from multiple orbits and polarizations and obtained a notable enhancement of resolution and speckle reduction such that the obtained mapping results are almost comparable to the single-orbit TSX change detection results. In a multiorbital S1 mosaic covering all of Switzerland, we manually counted 7361 new avalanches which occurred during an extreme avalanche period around 4 January 2018.

Abstract. 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.

Abstract. 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.

Abstract. Mining activities in cold regions are vulnerable to snow avalanches. Unlike operational facilities, which can be constructed in secure locations outside the reach of avalanches, access roads are often susceptible to being cut, leading to mine closures and significant financial losses. In this paper we discuss the application of avalanche runout modelling to predict the operational risk to mining roads, a long-standing problem for mines in high-altitude, snowy regions. We study the 35 km long road located in the "Cajón del rio Blanco" valley in the central Andes, which is operated by the Codelco Andina copper mine. In winter and early spring, this road is threatened by over 100 avalanche paths. If the release and snow cover conditions can be accurately specified, we find that avalanche dynamics modelling is able to represent runout, and safe traffic zones can be identified. We apply a detailed, physics-based snow cover model to calculate snow temperature, density and moisture content in three-dimensional terrain. This information is used to determine the initial and boundary conditions of the avalanche dynamics model. Of particular importance is the assessment of the current snow conditions along the avalanche tracks, which define the mass and thermal energy entrainment rates and therefore the possibility of avalanche growth and long runout distances.

Abstract. Snow avalanches are recurring natural hazards that affect the population and infrastructure in mountainous regions, such as in the recent avalanche winters of 2018 and 2019, when considerable damage was caused by avalanches throughout the Alps. Hazard decision makers need detailed information on the spatial distribution of avalanche hazards and risks to prioritize and apply appropriate adaptation strategies and mitigation measures and thus minimize impacts. Here, we present a novel risk assessment approach for assessing the spatial distribution of avalanche risk by combining large-scale hazard mapping with a state-of-the-art risk assessment tool, where risk is understood as the product of hazard, exposure and vulnerability. Hazard disposition is modeled using the large-scale hazard indication mapping method RAMMS::LSHIM (Rapid Mass Movement Simulation::Large-Scale Hazard Indication Mapping), and risks are assessed using the probabilistic Python-based risk assessment platform CLIMADA, developed at ETH Zürich. Avalanche hazard mapping for scenarios with a 30-, 100- and 300-year return period is based on a high-resolution terrain model, 3 d snow depth increase, automatically determined potential release areas and protection forest data. Avalanche hazard for 40 000 individual snow avalanches is expressed as avalanche intensity, measured as pressure. Exposure is represented by a detailed building layer indicating the spatial distribution of monetary assets. The vulnerability of buildings is defined by damage functions based on the software EconoMe, which is in operational use in Switzerland. The outputs of the hazard, exposure and vulnerability analyses are combined to quantify the risk in spatially explicit risk maps. The risk considers the probability and intensity of snow avalanche occurrence, as well as the concentration of vulnerable, exposed buildings. Uncertainty and sensitivity analyses were performed to capture inherent variability in the input parameters. This new risk assessment approach allows us to quantify avalanche risk over large areas and results in maps displaying the spatial distribution of risk at specific locations. Large-scale risk maps can assist decision makers in identifying areas where avalanche hazard mitigation and/or adaption is needed.

Abstract. Consistent estimates of avalanche size are crucial for communicating not only among avalanche practitioners but also between avalanche forecasters and the public, for instance in public avalanche forecasts. Moreover, applications such as risk management and numerical avalanche simulations rely on accurately mapped outlines of past avalanche events. Since there is not a widely applicable and objective way to measure avalanche size or to determine the outlines of an avalanche, we need to rely on human estimations. Therefore, knowing about the reliability of avalanche size estimates and avalanche outlines is essential as errors will impact applications relying on this kind of data. In the first of three user studies, we investigate the reliability in avalanche size estimates by comparing estimates for 10 avalanches made by 170 avalanche professionals working in Europe or North America. In the other two studies, both completed as pilot studies, we explore reliability in the mappings of six avalanches from oblique photographs from 10 participants and the mappings of avalanches visible on 2.9 km2 of remotely sensed imagery in four different spatial resolutions from 5 participants.
We observed an average agreement of 66 % in the most frequently given avalanche size, while agreement with the avalanche size considered "correct" was 74 %. Moreover, European avalanche practitioners rated avalanches significantly larger for 8 out of 10 avalanches, compared to North Americans. Assuming that participants are equally competent in the estimation of avalanche size, we calculated a score describing the factor required to obtain the observed agreement rate between any two size estimates. This factor was 0.72 in our dataset. It can be regarded as the certainty related to a size estimate by an individual and thus provides an indication of the reliability of a label. For the outlines mapped from oblique photographs, we noted a mean overlapping proportion of 52 % for any two avalanche mappings and 60 % compared to a reference mapping. The outlines mapped from remotely sensed imagery had a mean overlapping proportion of 46 % (image resolution of 2 m) to 68 % (25 cm) between any two mappings and 64 % (2 m) to 80 % (25 cm) when compared to the reference.
The presented findings demonstrate that the reliability of size estimates and of mapped avalanche outlines is limited. As these data are often used as reference data or even ground truth to validate further applications, the identified limitations and uncertainties may influence results and should be considered.

Abstract. Natural hazard models need accurate digital elevation models (DEMs) to simulate mass movements on real-world terrain. A variety of platforms (terrestrial, drones, aerial, satellite) and sensor technologies (photogrammetry, lidar, interferometric synthetic aperture radar) are used to generate DEMs at a range of spatial resolutions with varying accuracy. As the availability of high-resolution DEMs continues to increase and the cost to produce DEMs continues to fall, hazard modelers must often choose which DEM to use for their modeling. We use satellite photogrammetry and topographic lidar to generate high-resolution DEMs and test the sensitivity of the Rapid Mass Movement Simulation (RAMMS) software to the DEM source and spatial resolution when simulating a large and complex snow avalanche along Milford Road in Aotearoa/New Zealand. Holding the RAMMS parameters constant while adjusting the source and spatial resolution of the DEM reveals how differences in terrain representation between the satellite photogrammetry and topographic lidar DEMs (2 m spatial resolution) affect the reliability of the simulation estimates (e.g., maximum core velocity, powder pressure, runout length, final debris pattern). At the same time, coarser representations of the terrain (5 and 15 m spatial resolution) simulate avalanches that run too far and produce a powder cloud that is too large, though with lower maximum impact pressures, compared to the actual event. The complex nature of the alpine terrain in the avalanche path (steep, rough, rock faces, treeless) makes it a suitable location to specifically test the model sensitivity to digital surface models (DSMs) where both ground and above-ground features on the topography are included in the elevation model. Considering the nature of the snowpack in the path (warm, deep with a steep elevation gradient) lying on a bedrock surface and plunging over a cliff, RAMMS performed well in the challenging conditions when using the high-resolution 2 m lidar DSM, with 99 % of the simulated debris volume located in the documented debris area.

Abstract. 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.

Abstract. To mark the 20th anniversary of Natural Hazards and Earth System Sciences (NHESS), an interdisciplinary and international journal dedicated
to the public discussion and open-access publication of high-quality studies
and original research on natural hazards and their consequences, we
highlight 11 key publications covering major subject areas of NHESS that
stood out within the past 20 years. The papers cover all the topics contemplated in the European Geosciences Union (EGU) Division on Natural Hazards including dissemination, education, outreach and teaching. The selected articles thus represent excellent scientific contributions in the major areas of natural hazards and risks and helped NHESS to become an exceptionally strong journal representing interdisciplinary areas of natural hazards and risks. At its 20th anniversary, we are proud that NHESS is not only used by scientists to disseminate research results and novel ideas but also by practitioners and decision-makers to present effective solutions and strategies for sustainable disaster risk reduction.

Abstract. 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, 45 kg, 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.