Snow avalanche activity in Żleb Żandarmerii in a time of climate change (Tatra Mts., Poland)

Gądek, Bogdan; Kaczka, Ryszard J.; Rączkowska, Zofia; Rojan, Elżbieta; Casteller, Alejandro; Bebi, Peter

doi:10.1016/j.catena.2017.07.005

Cited by 41 publications

(19 citation statements)

References 46 publications

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“…Machine learning techniques have contributed significantly to studies of numerous natural hazards, such as landslides [26][27][28][29][30], floods [31][32][33][34][35][36], gully erosion [37][38][39][40], land subsidence [41][42][43][44][45] and debris flows [46][47][48].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…These simulations, with a few exceptions (Casteller et al, 2008), are practically nonexistent in the Andes. This type of multi-methodological approach has proven to be most effective (Gądek et al, 2017), in particular in regions where documentary data on past events is scarce. In addition to the methods and data used in this study, the availability of detailed data, e.g., on the size and precise location of avalanche release zones (including snow depth), types of snow and snow entrainment along the track would allow even more robust event modeling.…”

Section: Discussionmentioning

confidence: 99%

“…In mountainous areas, settlements and infrastructure are commonly endangered by natural hazards such as snow avalanches, rockfalls, shallow landslides or volcanic activities. The documentation of past events of these natural hazards, including a detailed description of their properties (e.g., magnitude, affected area, type of damage) as well as their effects on and interactions with ecosystems, are important for assessing risk (Stoffel and Huggel, 2012;Gądek et al, 2017). In mountain ranges with a long history of settlement such as the European Alps, documentary records of gravitational natural hazards have been compiled for more than five centuries due to the frequent contact between mountain inhabitants and these phenomena (Laely, 1984).…”

Section: Introductionmentioning

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.

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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.

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“…the entry of humans to the Tatras was parallel to the climate deterioration during the Little Ice Age (LIA). The latter resulted in the increased activity of slope processes, especially debris flows, avalanches and floods (Kotarba, 2004;Gądek et al, 2016;Gądek et al, 2017). It is assumed that the LIA lasted until the beginning of the 20 th century (Kotarba, 2004).…”

Section: Climate Changes In the Period Of Human Presence In The Tatrasmentioning

confidence: 99%

Human impact in the Tatra Mountains

Rączkowska

2019

CIG

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Human activity is one of the three main drivers of environmental changes in the mountains. Its influence on mountain natural environment is characterized by great spatiotemporal diversity. In the Tatras human impact started in the 12th-13th centuries and was related to mining and animal grazing, followed by metallurgy in the 18th century. These activities developed in the 15th-16th and with the climax in the 18th-19th, except for the animal grazing which continued till the 1960s-1970s. Since the second half of the 19th century tourism developed intensively. More than 3 million people per year visit the Polish part of the Tatras nowadays.Human activities, prior to tourism, were accompanied by a strong forest and dwarf pine shrubs extraction. They affected mainly relief and vegetation cover. Erosion in the slopes was triggered or intensified after deterioration of vegetation cover by grazing. Anthropogenic landforms like pits, mine roads, mine channels are still recognizable in landscape. Lowering of the upper forest and dwarf pine limits and changing of a natural structure of the forest into a monoculture are among the main influences on vegetation. Indirect effects of the changed forest structure are problems with bark beetles and windthrows. The later have been strongly affecting slope morphodynamics. Tourism-related impact manifest in gradual anthropogenic erosion along hiking trails. It is the major type of human impact n the Tatras now. The historical human impact until the 1960s-1970s was indeed spatial in nature. Currently, it is of a linear character or point-focused, and is relatively constant regarding particular locations.

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Snow avalanche activity in Żleb Żandarmerii in a time of climate change (Tatra Mts., Poland)

Cited by 41 publications

References 46 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

Human impact in the Tatra Mountains

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