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 (
Snow avalanches are a severe threat in alpine regions around the world, endangering people, buildings and traffic infrastructure. In Switzerland an average of 25 people die per year in avalanches, the vast majority during winter sport activities (Techel et al., 2015), and avalanches often cause infrastructure damage. In winter 1999 the total damage was more than EUR 500 million (SLF, 2000). Switzerland has long-term experience coping with avalanche hazards. These range from spatial planning measures, such as avoiding building where there is an avalanche hazard, usually achieved by trial and error over centuries up to constructional measures such as the splitting wedge at the church of Davos Frauenkirch, built in 1603 after the previous church was destroyed by a large avalanche. One of the most important measures is the generation of hazard maps. Such maps, the first one was already released 1953 in the Bernese Alps, are based today on avalanche database information, climatic information on extreme snowfall events, terrain analysis and numerical simulations of the avalanche dynamics. All this information is combined by experienced experts into scenarios. In Switzerland, hazard maps are based on 30-,100- and 300-year scenarios (Fig. 11a). Hazard maps show the hazard degree based on the frequency and intensity of avalanches. The elaboration of hazard maps is very demanding with respect to time and expertise. Furthermore it can only be applied to single avalanche tracks that are, in particular, endangering critical infrastructure (Gruber and Margreth, 2001). But these maps are the backbone of the Swiss avalanche mitigation strategy and are legally binding for where new infrastructure can be built and where not.
Hazard indication maps, however, are less detailed and accurate than hazard maps but can give a spatially continuous overview on avalanche hazards based on numerical simulations over large regions. Hazard indication maps are based on an extreme scenario and do not show different hazard degrees. This is particularly useful for regions with sparse avalanche database information, which is the case for most alpine regions around the world. High-spatial-resolution digital terrain models (DTMs) generated from remote-sensing instruments are becoming more and more available for mountain regions (Bühler et al., 2012; Fonstad et al., 2013; ASPRS, 2001) and open the door for meaningful numerical avalanche simulations (Bühler et al., 2011) over large areas, e.g., entire valleys, states or even countries.
To perform dynamic avalanche simulations with state-of-the-art software such as RAMMS (Christen et al., 2010) or SAMOS (Sampl and Zwinger, 2004), an accurate identification of the release areas and the release volumes is mandatory. Prerequisites for triggering a snow slab avalanche can be summarized in three categories: (1) terrain, (2) snow cover specific factors and (3) meteorological factors (Schweizer et al., 2003). In the past, different algorithms have been developed to identify potential snow avalanche release areas (PRAs) mainly based on terrain-specific parameters. So far, these algorithms to identify PRAs have never been compared and tested against a large reference data set of observed and carefully mapped avalanche release areas. Therefore, the first aim of this study is to conduct a comparison of the existing algorithms based on avalanche database information from Davos, Switzerland. Based on the findings gained from this comparison, we develop a new algorithm for automated PRA delineation and validate it at three selected test sites around Davos, Switzerland, where we have an excellent avalanche database. We constrain the reference data set to slab avalanches and exclude loose snow avalanches, which can also start at point locations in very steep terrain but usually do not mobilize masses that could be dangerous for people and infrastructure in the Alps. However, such avalanches can become more dangerous in other regions such as the Chilean Andes (Vera Valero et al., 2016).
In the past, DTM-based identification of PRAs for different types of mass movements have been developed, in particular for shallow and deep-seated landslides (Carrara, 1983; Carrara and Guzetti, 1995; van Westen et al., 2003; Singh et al., 2005; van Westen et al., 2008; Gruber et al., 2009; Pradhan and Buchroithner, 2012; Budimir et al., 2015), as well as for debris flows and rockfalls (Singh et al., 2005; Michoud et al., 2012). The most important DTM-derived parameter for landslides and rockfalls is the slope angle, which strongly determines the distribution of unstable areas (Carrara and Guzetti, 1995). In addition, aspect and curvature are also considered (Singh et al., 2005; Pradhan and Buchroithner, 2012).
Similar attempts have been made to automatically delineate snow avalanche release areas. Voellmy (1955) already stated that the terrain parameter slope angle plays a decisive role in identifying PRAs. The first automated approaches to identifying PRAs, considering different terrain parameters, began with the availability of DTMs with quite coarse resolution in the range of 25 to 30 m (Maggioni et al., 2002; Maggioni, 2005; Maggioni and Gruber, 2003). DTMs with higher spatial resolution (1 to 10 m) enable the calculation of DTM derivatives such as ruggedness or curvature, which are of major importance for avalanche release (van Herwijnen and Heierli, 2009; McClung, 2001; Schweizer et al., 2003; Vontobel, 2011). Table 1 gives an overview of PRA delineation algorithms, published in peer-reviewed journals found on Web of Science, and the terrain derivatives they apply.
Overview of the published avalanche release area delineation algorithms including the applied parameters. Parameters in parentheses are partially included in the algorithm.
All listed algorithms apply the parameter slope; most apply plan curvature but only two of them also include the parameter roughness. For this study, the following three existing algorithms are compared and validated: one simple algorithm considering just the parameter slope after Voellmy (1955) as a benchmark and the two recent algorithms taking the terrain roughness into account (Bühler et al., 2013; Veitinger et al., 2016). The algorithm of Maggioni et al. (2002) was also tested but only produces meaningful results with DEM resolutions on the order of 25 m. Furthermore, this algorithm was written in ARC Macro Language and is difficult to run with the current software. The other algorithms by Ghinoi and Chung (2005), Barbolini et al. (2011), Andres and Chueca Cia (2012), Pistocchi and Notarnicola (2013), and Chueca Cia et al. (2014) have been developed by other research groups and were not available for comparison.
For a meaningful comparison and validation of the three selected algorithms, a good reference data set is mandatory. Explicit and accurate reference data on avalanche release areas are very scarce because the release areas are remote, mostly in poorly accessible terrain. Even though some approaches exist to automatically map snow avalanches from high-spatial-resolution remote-sensing data (Bühler et al., 2009; Lato et al., 2012; Eckerstorfer et al., 2016; Korzeniowska et al., 2017), such data sets are only available for isolated time periods. Recent advances using freely available Sentinel-1 radar data succeed in mapping a part of the avalanche deposits but do not produce reliable information on release areas (Eckerstorfer et al., 2017). Therefore, the best available information on avalanche release areas still comes from manual mapping in the field.
For the region of Davos, Switzerland, the SLF maintains an event database whereby the avalanche contours and the release areas are mapped by experienced staff. This event database is considered to be one of the best reference data sets available today. The reference data set contains 5785 mapped release areas from the year 1970 until 2016. The avalanches of the extreme winter 1999 are included in the data set as well. Out of this reference data set, three test sites were deliberately chosen: Parsenn, Jakobshorn and Rinerhorn. These test sites are located in the three largest ski resorts of Davos. Consequently, these areas are surveyed more or less constantly during winter operations, and observed avalanche events, naturally and artificially triggered, are mapped by the ski patrol and included in the SLF database. We limit the test regions to the areas that are well observed from the ski resorts and exclude terrain sections where observations of avalanche are difficult. Thus, it can be assumed that, as far as possible, all potential release areas have already been mapped at least once. However, a check of the reference data set with the local ski patrol heads showed that certain release areas known to them are still not included. We also included these observations in the reference data set at the three test sites to achieve a result as complete as possible.
The test site Parsenn (Fig. 1) is the largest
with an area of 7.3 km
Reference data sets including the manually mapped avalanche release areas (red polygons) in the three test regions Parsenn, Jakobshorn and Rinerhorn. The inset indicates the location of the test region Davos within Switzerland (pixmaps© 2018 swisstopo (5704 000 000), reproduced with the permission of swisstopo (JA100118)). Data are from Bühler and von Rickenbach (2018).
The three selected algorithms subdivide the area within a certain perimeter
into the two classes: PRA and no potential release area (NoPRA). The model output of these
algorithms can be seen as thematic maps with the two discrete classes, PRA
and NoPRA. Hence, for validation, an error matrix
(Fig. 2) is calculated as it is known for the
accuracy assessment of thematic mapping in remote sensing (Congalton
and Green, 1999). To produce an error matrix, a reference data set is needed.
Each value in the error matrix represents the total intersectional area of a
specific reference class and algorithm class. For most of the
state-of-the-art avalanche dynamic simulation software, PRAs are required as discrete vector objects to be able to perform dynamic
avalanche simulations. Therefore, the algorithms also output the release
areas as vector objects. Consequently, the area values calculated for the
error matrix are square meters and not number of pixels, which would be
rather common for the error matrix (Stehman and Wickham, 2011).
Based on this error matrix, different accuracy measures can be calculated
(Allouche et al., 2006).
Schema of the applied validation measures after Congalton and Green (1999).
The PSS is applied as an accuracy measure to find the
optimal values for the input parameters. The PSS is also known as true skill
statistics (TSS) or Hanssen–Kuipers discriminant named after
Hanssen and Kuipers (1965). In this paper, the PSS is scaled by
a factor of 100 to enhance the interpretability and thus ranges from
The algorithm created by Veitinger et al. (2016) requires parametrization for snow depth, wind direction and wind direction variability. We specify no dominant wind direction as this cannot be clearly identified as the region faces different wind regimes ranging from northwest to south (Schüepp and Urfer, 1962). Furthermore, in most regions a broad hazard indication mapping scenario cannot be reliably connected to a specific wind regime. The roughness is calculated depending on the mean snow depth. Afterwards, the algorithm applies an individual fuzzy membership function to roughness and slope. Based on this multi-scale fuzzy logic approach, the algorithms' output indicates the avalanche release probability in a continuous range from 0 (not probable) to 1 (highly probable). The other algorithms apply Boolean classifiers and thus these outputs exhibit the two discrete classes 0 (NoPRA) and 1 (PRA). Additionally, they have the option to eliminate PRA with an area smaller than a certain threshold. Therefore, the algorithm of Veitinger et al. (2016) was extended by the option to set a probability threshold for discrete classification and the option to define a minimum release area. The algorithm by Bühler et al. (2013) requires a value for the following input parameters: minimum slope angle, maximum slope angle, cell size for the moving window to calculate the roughness, maximum roughness, maximum curvature, minimum release area. To run the algorithms, we apply the swissALTI3D digital elevation model from swisstopo with a spatial resolution of 5 m (swisstopo, 2018).
Each of the selected existing algorithms requires certain values as input
parameters. Depending on the value set for the input parameters, the model
output varies considerably. In order to be able to compare the algorithms
with each other, the following approach is chosen: the goal is to achieve
the best possible performance of each algorithm and to compare it with the
other algorithms. Therefore, the aim is to find the optimal values for each
input parameter. To do so, for an algorithm the values of a parameter are
changed systematically at a time (e.g., 20, 21 and
22 up to 40
Identification of the optimal input parameter settings for the
selected algorithms:
Some parameters such as the maximum slope angle or minimum release area are
not sensitive and only slightly change with parameter variation. Other
parameters such as the minimum slope angle or the minimum susceptibility are
much more sensitive and change the output considerably. The minimum slope
angle in Fig. 3a reaches percentage values of
more than 100 % (right axis) because we test minimum slope angles smaller
than 28
We compare the performance of the selected algorithms with their specific
optimal parameter settings (Table 2). To quantify
the optimization effect of the algorithms, we also list the percentage of PRA delimited by the respective algorithm to the PRA identified in the
slope-only approach (Voellmy, 1955), which corresponds to all area with a
slope angle
between 28 and 60
Performance of the tested algorithms compared to the manually mapped avalanche release areas at all three test sites, Parsenn, Jakobshorn and Rinerhorn (Fig. 1).
The major problem of these three tested algorithms is the final delineation of the individual PRA, which is the base for the coupling with dynamic avalanche models. The slope-only and Veitinger et al. (2016) algorithms do not delineate individual PRA. The Bühler et al. (2013) algorithm applies a basic delineation based on flow direction but the results are unsatisfactory because artificial straight lines of delineation occur (Fig. 4). As dynamic avalanche models are very sensitive to PRA location and volume, only a sophisticated delineation of the PRA can be applied for meaningful scenario-based modeling.
Results of the best parameter setting for the algorithms
from
To overcome the abovementioned issues concerning PRA delineation, we develop a new algorithm based on object based image analysis (OBIA), originally developed to analyze remote-sensing data (Blaschke, 2010), applying the Trimble eCognition Developer 9.3 software. The OBIA algorithm is based on the Bühler et al. (2013) algorithm and optimizes the delineation process.
To run the OBIA analysis we use the swissALTI3D DEM provided by swisstopo, with a spatial resolution resampled from the original 2 m to 5 m. In several tests including manual evaluation with avalanche experts, we concluded that a 5 m resolution is sufficient to picture the terrain features relevant for avalanche release. We apply the elevation and its derivatives: slope angle, terrain ruggedness, aspect and fold (Fig. 5). Additionally, we apply a forest layer, which is binary (forest or no forest). The most important parameters and their settings are described in the following.
Input data to delineate the PRA derived from the digital elevation
model, at the Weissfluhjoch region in the ski resort Parsenn as an example:
To produce the PRA for the frequent scenario, we first identify steep
slopes between 30 and 60
Segmentation and classification of the terrain based on slope angle
and ruggedness into PRA (red) and non-PRA (light green)
In a second step, the susceptible area is further segmented with a finer-scale parameter. We apply a multiresolution segmentation that considers variations in aspect sectors, slope and fold. We weigh variations in aspect sectors 3 times more than variations in slope and fold as changes in aspect sector are the most important delineation parameters between individual PRAs (Fig. 6b). Finally, we classify PRAs that are covered by forest (Fig. 6c).
To produce the PRA for the
Classification of the frequent PRAs based on their most frequent
aspect sectors
To visualize the results of the OBIA-based PRA algorithm, we look at the
greater region of Davos, with an extent of 20 by 25 km, which equals
500 km
Results for the OBIA-based PRA algorithms for the frequent
scenario
Results of the OBIA algorithm for the frequent scenario
The validation described in Sect. 2.2 is now also applied to the OBIA algorithm (Table 3). The OBIA algorithm for the frequent scenario is slightly better in POD and POFD than the Bühler et al. (2013) algorithm and exhibits an improved delineation of the individual PRAs. The comparison of the algorithms of Veitinger et al. (2016) and Voellmy (1955) to the OBIA algorithm reveals a better performance, which is quantified by the higher PSS and HSS scores and the lower amount of total delineated area.
Validation results of all tested algorithms.
To perform an avalanche dynamics simulation not only the release area and its location are needed but also an average release depth, measured perpendicular to the slope. Combining these two pieces of information, the avalanche volume can be calculated. In state-of-the-art avalanche dynamic models such as RAMMS, the applied friction values depend on the release volume (Christen et al., 2010).
We implement the release depth calculation approach developed by
Salm et al. (1990), which is applied for hazard mapping in
Switzerland and is therefore well established. The estimation of release
depth is based on the maximum snow depth increase within 3 days
Now an elevation correction factor is applied to account for increasing snow
depth with increasing elevation. In Switzerland
The calculated release areas and the release depth define the avalanche release volume, which is necessary as input for the numerical avalanche dynamic simulations. We adapted the RAMMS::AVALANCHE software (Christen et al., 2010), applied for the generation of hazard maps, to automatically process a large number of release areas. This new module, RAMMS::LSHM, applies the well-established friction parameter sets defined by Gruber and Margreth (2001) to all PRA polygons generated with the OBIA algorithm described in Sect. 3. We split the PRA polygons into four volume classes as defined in Christen et al. (2010) and apply their specific friction parameters given in Table 4
RAMMS friction parameters applied for the simulation of the PRA for the frequent and extreme scenarios.
The resulting maximum avalanche pressure values shown in (Fig. 10) are later classified to a large-scale hazard indication map.
RAMMS-simulated maximum impact pressure values based on the PRA
depicted in Fig. 8 for the frequent scenario
The validation of PRA is a very difficult task as avalanche release areas often occur in poorly accessible terrain and may not be observed in time due to new snowfall or snow drift. Furthermore, accurate mapping of observed release areas is very demanding in complex and steep terrain. In the region of Davos there are a lot of avalanche mapping activities performed by SLF and the local ski patrol staff but in most cases only the avalanche outline is mapped manually and not the release area specifically. The uncertainty concerning avalanches that have occurred but were not mapped is very high. Accurate avalanche mapping based on optical or radar aerial imagery or satellite data with sufficient spatial resolution can only be applied occasionally due to high data acquisition costs (Bühler et al., 2009; Lato et al., 2012; Eckerstorfer et al., 2016; Korzeniowska et al., 2017). Therefore, no complete reference data sets over longer time periods exist to our knowledge. To overcome this limitation and to enable a meaningful quantitative validation of the algorithms, we produce a manually completed reference data set for three test sites (Sect. 2.1). Since the automatic PRA delineation was carried out with regard to hazard indication mapping, high values for POD are requested to not miss PRAs that could produce destructive avalanches. For other purposes such as scenarios with very short return periods (1–5 years) usually applied for traffic line safety assessment, a lower POFD is maybe more important. To enable a validation that is as objective as possible, we calculated the best parameter setting for every investigated algorithm based on the reference data set (Sect. 2.2).
The validation shows that the POD is best with a
value of 98.69 % for the simple slope angle approach even though not
100 % are reached because of mapping errors in the reference data set and
isolated PRA section above 60
The algorithm based on Veitinger et al. (2016) reduces the POFD to 20.50 %
due to consideration of terrain roughness (dependent on snow depth) but
keeps a high POD of 96.74 %. These values indicate the suitability of
this algorithm for large-scale hazard indication mapping, resulting in higher
overall accuracy measures (PSS
The algorithm based on Bühler et al. (2013) achieves a
slightly lower POD of 95.06 % but an improved POFD of 16.06 %. In
addition to
the roughness, this algorithm also excludes areas with high curvature
values. This leads to the highest overall accuracy measures of the three
tested algorithms (PSS
The largest performance differences among the tested algorithms are not in the POD, which is very good for all algorithms, but in the POFD for which we find considerable differences. Low POFD values mean that much less area is delineated as PRA, saving many time-consuming numerical simulations. For example, the Bühler et al. (2013) approach results in 24 % less PRA than the slope-only approach.
To overcome the limited possibilities, present in the tested algorithms for
the delineation of the final PRA polygons, we develop a new PRA algorithm
(Sect. 3). With this algorithm, different
scenarios with varying PRA sizes can be generated, which is a big advantage
for large-scale hazard indication mapping. The OBIA algorithm for the
frequent scenario (5–30-year return period) achieves a POD of 95.39 % and the
lowest POFD value of 15.88 %, eliminating most areas where avalanches do
not occur. Therefore, this algorithm achieves the highest overall accuracy
measures of all tested algorithms (PSS
Additionally, the OBIA algorithm was extended for an extreme avalanche scenario
(100–300-year return period). The individual PRAs grow into areas with
minimum slope inclination of 28
Comparison of the official hazard map
The OBIA algorithm is a novel and useful approach to generate two different PRA scenarios for large-scale hazard indication mapping and enables regional- to national-scale applications. In particular in regions where no or only limited avalanche cadasters exist and no experienced avalanche engineers have produced hazard maps, such an automated approach can be very helpful for a preliminary hazard assessment. The delineation of the individual PRA is very difficult to validate. Compared to the algorithm of Bühler et al. (2013), only the OBIA algorithm performs a specific delineation of the individual PRA and shows obvious improvements in particular within homogenous slopes (Figs. 4 and 9). This is achieved by the improved implementation of the aspect, curvature, fold and slope terrain characteristics into region growth algorithms within the eCognition software. However, further investigations are needed to validate, refine and extend the delineation of the individual PRA, the definitions of the different scenarios and the adaption to specific local conditions.
Already in 2004 the project SilvaProtect performed automated avalanche
dynamic simulations over the entire area of Switzerland to identify
protection forests (Gruber and Baltensweiler, 2004). At this time the digital
elevation model available only had a resolution of 25 m (DHM25) and only a
single scenario was calculated with a precursor version of RAMMS (AVAL-2D).
The delineation of two PRA scenarios generated with the OBIA approach
enables,
for the first time, the calculation of dynamic numerical avalanche simulations
over large areas with detailed terrain resolution. In combination with
extrapolated extreme snow depth values describing potential release volumes,
meaningful hazard intensity maps are generated that can be easily translated
into hazard indication maps. The procedure follows the simulation part that
is applied for operational hazard mapping in Switzerland but can now also be
applied to areas of up to several thousands of square kilometers in regions
where no hazard maps exist. A preliminary validation of the results with
existing hazard maps in the canton Grisons, Switzerland
(
The comparison of the automated hazard indication maps to the hazard maps and the database information generally show a very good agreement (Fig. 11). However, for some avalanche tracks such as the one in the middle of the map coming from the south, no hazard maps exist. This can be due to existing mitigation measures such as avalanche dams or snow supporting structures, which are not taken into account for the automated simulations or due to missing observations and therefore no detailed hazard assessment was performed. But the major differences between the automated approach and the hazard maps can be explained by the applied forest layer. The protection forest is highly dynamic and the forest in high elevations in Switzerland shows a tendency to grow more dense (Bebi et al., 2009). Since the avalanche database contains events recorded more than 60 years ago, changes in forest occurred at several locations. Most obvious is this for the two avalanche runouts from the database reaching the valley floor in the southwestern part of the map: the database's runout distances are poorly simulated in the scenario with forest but well modeled in the scenario without forest. These events were observed in 1986 when the forest structure was less dense and contained more larch trees than today. This example illustrates the crucial role of forest information for large-scale hazard indication mapping in regions with protection forests. In the future, with better up-to-date forest information derived from remote sensing (Waser et al., 2015), this source of error might be less important.
In this research we present the processing chain for dry snow flowing avalanches. By incorporating information on snow temperature, snow erosion and free water content, this approach could be extended with the scientific version of RAMMS (Bartelt et al., 2016; Bartelt and Buser, 2016) to simulate powder snow avalanches, wet snow avalanches, small-skier-triggered avalanches or glide snow avalanches. However, the validation of such simulations is very demanding in terms of valuable reference data but is planned for the future.
The development of automated potential release area (PRA) delineation algorithms based on digital elevation models (DEMs) started in the early 2000s. As high-quality DEM data becomes more and more available even for mountain areas in remote regions (Bühler et al., 2012), such approaches now have the potential to be combined with numerical avalanche simulations to produce automated hazard indication maps. The validation of three different published approaches based on a nearly complete avalanche reference data set from the region of Davos, Switzerland, reveals that the current detection performance of these algorithms is quite good (PSS 69.80 %–79.01 %). The algorithms considering more than just slope angle improve the accuracy of the PRA delineation. Considering just slope angle works well for smooth terrain. For rough terrain, however, curvature and roughness provide essential additional information, which should be considered in a successful algorithm. Important for the coupled numerical simulations is the total area delineated as PRA as every simulation consumes computational power and time. The tested algorithms reduce the total PRA by up to 24.09 %, resulting in much fewer individual simulations and producing a more realistic output. However, the delineation of the individual PRA is insufficient and no connection to hazard scenarios is possible.
Therefore, we develop and validate a new PRA delineation algorithm, based on object-based image analysis (OBIA), which performs even better (PSS 79.51 %), limits the total PRA area by 24.30 % and produces PRA with meaningful delineation. A meaningful validation of the PRA delineation would be of great value. However, such reference data do not to our knowledge yet exist. With the OBIA approach it is possible to produce different hazard scenarios linked to return periods as the individual size of the PRA is variable. This is the prerequisite to produce meaningful hazard indication maps to automatically evaluate avalanche hazard over large areas. The comparison to existing hazard maps shows a good agreement and illustrates the potential value of such maps in particular for regions where not much information and experience with avalanche hazard exist. In any case, up-to-date and accurate DEM data and information on the protection forest are crucial.
Our reference data set is the most complete we know of considering PRA; however, it is only from a very limited region around Davos and does mostly contain a small PRA. Therefore, we do not know how representative this data set is for other regions. However, even though snow conditions may vary a lot among different locations, the basic terrain parameters leading to an avalanche release are estimated to be quite constant all over the world. Because we do not take information on the snow cover into account, we assume that our findings can be applied globally. But more research is necessary to prove this assumption. Most important would be a validation for the extreme scenario with a data set consisting of PRA from large avalanches. Unfortunately, such a data set is not available today with sufficient quality.
In the long term, the current work could enable the coupling of terrain information, meteorological data, snowpack simulations and numerical avalanche simulations to achieve near-real-time hazard assessment over large areas as proposed by Vera Valero et al. (2016) and Veitinger and Sovilla (2016). However, the required input information in sufficient quality and resolution necessary for such a coupled system is very hard to get. In addition, the sensitivity of the individual information components has to be evaluated carefully. To obtain a reliable hazard assessment is therefore very difficult and we do not expect results that are applicable in practice in the near future. However, to achieve this goal in the long term, we encourage all research concerning this topic.
The produced potential release areas (PRAs) and the RAMMS
simulations described in this paper are publicly available on ENVIDAT
(
YB and DvR designed the study and performed the calculations. MC and AS programmed parts of the necessary algorithms. SM and LS reviewed the results and proposed improvements. All authors contributed to the writing and validation of the paper.
The authors declare that they have no conflict of interest.
The authors thank Roderick Kühne and Christian Willhelm from the Amt für Wald und Naturgefahren, Kanton Graubünden, for their technical and financial support. We thank the ski patrol heads of the Davos Klosters Mountains resorts for their support to improve the PRA reference data sets: Romano Pajarola (Parsenn), Vali Meier (Jakobshorn) and Nigg Conrad (Rinerhorn). We thank Betty Sovilla from SLF for the discussions on the parameter settings for the Veitinger et al. (2016) algorithm. Edited by: Andreas Günther Reviewed by: two anonymous referees