Mapping of avalanche start zones susceptibility: Arazas basin, Ordesa and Monte Perdido National Park (Spanish Pyrenees)

Andrés, Asunción Julián; ́a, Javier Chueca Cı

doi:10.1080/17445647.2012.668414

Cited by 9 publications

(7 citation statements)

References 9 publications

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“…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.…”

Section: Existing Algorithmsmentioning

confidence: 99%

Automated snow avalanche release area delineation – validation of existing algorithms and proposition of a new object-based approach for large-scale hazard indication mapping

Bühler¹,

Rickenbach²,

Stoffel³

et al. 2018

Nat. Hazards Earth Syst. Sci.

100

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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.

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Section: Existing Algorithmsmentioning

confidence: 99%

Automated snow avalanche release area delineation – validation of existing algorithms and proposition of a new object-based approach for large-scale hazard indication mapping

Bühler¹,

Rickenbach²,

Stoffel³

et al. 2018

Nat. Hazards Earth Syst. Sci.

100

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“…Early versions of GIS based avalanche terrain models (Ghinoi and Chung, 2005;Gruber and Haefner, 1995;Maggioni and Gruber, 2003) struggled to outperform simple slope based avalanche release area estimates (Voellmy, 1955) due to the inability of low resolution DEM (20-30 m) to detect small scale terrain features. Current PRA modelling methods evolved over the course of a decade and benefit from developments in high-resolution DEM production and remote sensing (Andres and Chueca Cía, 2012;Barbolini et al, 2011;Bühler et al, 2013Chueca Cía et al, 2014;Pistocchi and Notarnicola, 2013;Veitinger et al, 2016;Kumar et al, 2016Kumar et al, , 2019. Bühler et al (2013) found that 5 m DEM resolution is the optimal tradeoff between processing efficiency and small-scale feature identification for PRA modelling.…”

Section: Potential Avalanche Release Area Modellingmentioning

confidence: 99%

Automated snow avalanche release area delineation in data sparse, remote, and forested regions

Sykes

Haegeli

Bühler³

2021

Preprint

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Abstract. Potential avalanche release area (PRA) modelling 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 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 data sets. Our research documents an open source workflow to generate high-resolution DEM and forest land cover data sets 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 of human recollection and variability of 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 modelled 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.

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“…Terrain parameters DTM resolution Classification approach Andres and Cía (2012) slope, plan curvature 5 m (from 1 : 25 000 m map) binary Barbolini et al (2011) slope, plan curvature 10 m binary Bühler et al (2012) slope, plan curvature, roughness 5 m binary Cia et al (2014) slope, plan curvature 5 m (from 1 : 25 000 m map) binary Ghinoi and Chung (2005) slope, aspect, elevation, curvature, 20 m continuous distance to ridge Gruber and Sardemann (2003) slope, plan curvature, distance to ridge 25, 50 m for curvature binary Maggioni and Gruber (2003) slope, aspect, plan curvature, distance to ridge 25, 50 m for curvature binary Pistocchi and Notarnicola (2013) slope, aspect, elevation, profile curvature, 10 m continuous distance to ridge, topographic wetness index or the elevation of isotherms (Cia et al, 2014), or they introduce a qualitative roughness measure derived from aerial photographs (Ghinoi and Chung, 2005). An overview of the existing approaches and their corresponding terrain parameters is shown in Table 1.…”

Section: Authorsmentioning

confidence: 99%

Potential slab avalanche release area identification from estimated winter terrain: a multi-scale, fuzzy logic approach

Veitinger¹,

Purves²,

Sovilla³

2015

Preprint

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Avalanche hazard assessment requires a very precise estimation of the release area, which still depends, to a large extent, on expert judgement of avalanche specialists. Therefore, a new algorithm for automated identification of potential avalanche release areas was developed. It overcomes some of the limitations of previous tools, which are currently not often applied in hazard mitigation practice. By introducing a multi-scale roughness parameter, fine-scale topography and its attenuation under snow influence is captured. This allows the assessment of snow influence on terrain morphology and, consequently, potential release area size and location. The integration of a wind shelter index enables the user to define release area scenarios as a function of the prevailing wind direction or single storm events. A case study illustrates the practical usefulness of this approach for the definition of release area scenarios under varying snow cover and wind conditions. A validation with historical data demonstrated an improved estimation of avalanche release areas. Our method outperforms a slope-based approach, in particular for more frequent avalanches; however, the application of the algorithm as a forecasting tool remains limited, as snowpack stability is not integrated. Future research activity should therefore focus on the coupling of the algorithm with snowpack conditions.Published by Copernicus Publications on behalf of the European Geosciences Union.

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Mapping of avalanche start zones susceptibility: Arazas basin, Ordesa and Monte Perdido National Park (Spanish Pyrenees)

Cited by 9 publications

References 9 publications

Automated snow avalanche release area delineation – validation of existing algorithms and proposition of a new object-based approach for large-scale hazard indication mapping

Automated snow avalanche release area delineation – validation of existing algorithms and proposition of a new object-based approach for large-scale hazard indication mapping

Automated snow avalanche release area delineation in data sparse, remote, and forested regions

Potential slab avalanche release area identification from estimated winter terrain: a multi-scale, fuzzy logic approach

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