Snow avalanche hazard of the Krkonoše National Park, Czech Republic

Blahůt, Jan; Klimeš, Jan; Bálek, Jan; Hájek, Petr; Červená, Lucie; Lysák, Jakub

doi:10.1080/17445647.2016.1262794

Cited by 16 publications

(9 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Therefore, we selected them by reviewing the scholarly literature [1,12,13,[50][51][52][53][54] and considering data availability. Kumar [49] stated that though meteorological conditions vary from year to year, the terrain and its characteristics are constant.…”

Section: Factors Influencing Snow Avalanchesmentioning

confidence: 99%

“…Lithology dictates many surface features. In general, rocky outcrops increase surface roughness and decrease the downslope movement of the snowpack [1]. Lithology maps of the watersheds at a scale of 1:100,000 were extracted from a digitized geology map of Kurdistan Province (Figure 3m).…”

Section: (13) Lithologymentioning

confidence: 99%

“…One of the most dangerous hazards in mountainous regions is the snow avalanche. These events can greatly impact individual users of mountain environments, residential areas in avalanche zones, ecosystems, and public infrastructure [1][2][3]. From a geomorphological viewpoint, snow avalanches can produce a range of different erosional and depositional landforms (also termed as snow avalanche impact landforms) which have been well-documented [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

et al. 2019

View full text Add to dashboard Cite

Although snow avalanches are among the most destructive natural disasters, and result in losses of life and economic damages in mountainous regions, far too little attention has been paid to the prediction of the snow avalanche hazard using advanced machine learning (ML) models. In this study, the applicability and efficiency of four ML models: support vector machine (SVM), random forest (RF), naïve Bayes (NB) and generalized additive model (GAM), for snow avalanche hazard mapping, were evaluated. Fourteen geomorphometric, topographic and hydrologic factors were selected as predictor variables in the modeling. This study was conducted in the Darvan and Zarrinehroud watersheds of Iran. The goodness-of-fit and predictive performance of the models was evaluated using two statistical measures: the area under the receiver operating characteristic curve (AUROC) and the true skill statistic (TSS). Finally, an ensemble model was developed based upon the results of the individual models. Results show that, among individual models, RF was best, performing well in both the Darvan (AUROC = 0.964, TSS = 0.862) and Zarrinehroud (AUROC = 0.956, TSS = 0.881) watersheds. The accuracy of the ensemble model was slightly better than all individual models for generating the snow avalanche hazard map, as validation analyses showed an AUROC = 0.966 and a TSS = 0.865 in the Darvan watershed, and an AUROC value of 0.958 and a TSS value of 0.877 for the Zarrinehroud watershed. The results indicate that slope length, lithology and relative slope position (RSP) are the most important factors controlling snow avalanche distribution. The methodology developed in this study can improve risk-based decision making, increases the credibility and reliability of snow avalanche hazard predictions and can provide critical information for hazard managers.

show abstract

Section: Factors Influencing Snow Avalanchesmentioning

confidence: 99%

Section: (13) Lithologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Numerical simulation models have been used to reconstruct snow avalanches in various mountain regions including the Andes (Valero et al, 2016), central Europe (Blahut et al, 2017), Japan (Takeuchi et al, 2011) and Scandinavia (Gauer and Kristensen, 2016). The software RAMMS was used to conduct snow avalanche simulations at Valle Las Trancas.…”

Section: Snow Avalanche Simulationsmentioning

confidence: 99%

Assessing the interaction between mountain forests and snow avalanches at Nevados de Chillán, Chile and its implications for ecosystem-based disaster risk reduction

Casteller

Häfelfinger²,

Donoso³

et al. 2018

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. Gravitational natural hazards such as snow avalanches, rockfalls, shallow landslides and volcanic activity represent a risk to mountain communities around the world. In particular, where documentary records about these processes are rare, decisions on risk management and land-use planning have to be based on a variety of other sources including vegetation, tree-ring data and natural hazard process models. We used a combination of these methods in order to evaluate dynamics of natural hazards with a focus on snow avalanches at Valle Las Trancas, in the Biobío region in Chile. Along this valley, natural hazards threaten not only the local human population, but also the numerous tourists attracted by outdoor recreational activities. Given the regional scarcity of documentary records, tree-ring methods were applied in order to reconstruct the local history of snow avalanches and debris flow events, which are the most important weather-related processes at respective tracks. A recent version of the model Rapid Mass MovementS (RAMMS), which includes influences of forest structure, was used to calculate different avalanche parameters such as runout distances and maximum pressures, taking into consideration the presence or absence of forest along the tracks as well as different modeled return periods. Our results show that local Nothofagus broadleaf forests contribute to a reduction of avalanche runout distances as well as impact pressure on present infrastructure, thus constituting a valuable ecosystem disaster risk reduction measure that can substitute or complement other traditional measures such as snow sheds.

show abstract

“…Numerical simulation models have been used to reconstruct snow avalanches in various mountain regions including the Andes (Valero et al, 2016), Central Europe (Blahut et al, 2017), Japan (Takeuchi et al, 2011) and Scandinavia (Gauer and Christiansen, 2016). The software RAMMS was used to conduct snow avalanche simulations at Valle de las Trancas.…”

Section: Snow Avalanche Simulationsmentioning

confidence: 99%

Assessing the interaction between mountain forests and natural hazards at Nevados de Chillán, Chile, and its implications for Ecosystem-based Disaster Risk Reduction

Casteller¹,

Häfelfinger²,

Donoso³

et al. 2017

Preprint

View full text Add to dashboard Cite

Abstract.Gravitational natural hazards such as snow avalanches, rockfalls, shallow landslides and volcanic activity represent a risk factor for mountain communities around the world. In particular where documentary 15 records about these processes are rare, decisions on risk management and land-use planning have to be based on a variety of other sources including vegetation and tree-ring data and natural hazard process models. We used a combination of these methods in order to evaluate dynamics of snow avalanches and other natural hazards at Valle de las Trancas, in the Biobío Region in Chile. Along this valley, natural hazards threaten not only the local human population, but also the numerous tourists attracted by outdoor recreational activities. Given the regional 20 scarcity of documentary records, tree-ring methods were applied in order to reconstruct the local history of snow avalanches and debris flow events, which are the more important weather-related processes at respective tracks.A recent version of the model Rapid Mass MovementS (RAMMS), that includes influences of forest structure, was used to calculate different avalanche parameters such as runout distances and maximum pressures, taking into consideration the presence/absence of forest along the tracks as well as different modelled return periods. 25Our results show that local Nothofagus broadleaved forests contribute to a reduction of avalanche runout distances as well as impact pressures on present infrastructure, thus constituting a valuable ecosystem disaster risk reduction measure that can substitute or complement other traditional measures such as sheds.

show abstract

Snow avalanche hazard of the Krkonoše National Park, Czech Republic

Cited by 16 publications

References 15 publications

Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions

Assessing the interaction between mountain forests and snow avalanches at Nevados de Chillán, Chile and its implications for ecosystem-based disaster risk reduction

Assessing the interaction between mountain forests and natural hazards at Nevados de Chillán, Chile, and its implications for Ecosystem-based Disaster Risk Reduction

Contact Info

Product

Resources

About