Matches in SemOpenAlex for { <https://semopenalex.org/work/W4307260826> ?p ?o ?g. }
- W4307260826 endingPage "3525" @default.
- W4307260826 startingPage "3501" @default.
- W4307260826 abstract "Abstract. Climate change impact on avalanches is ambiguous. Fewer, wetter, and smaller avalanches are expected in areas where snow cover is declining, while in higher-altitude areas where snowfall prevails, snow avalanches are frequently and spontaneously triggered. In the present paper, we (1) analyse trends in frequency, magnitude, and orientation of wet- and slab-avalanche activity during 59 winter seasons (1962–2021) and (2) detect the main meteorological and snow drivers of wet and slab avalanches for winter seasons from 1979 to 2020 using machine learning techniques – decision trees and random forest – with a tool that can balance the avalanche-day and non-avalanche-day dataset. In terms of avalanches, low to medium–high mountain ranges are neglected in the literature. Therefore we focused on the low-altitude Czech Krkonoše mountain range (Central Europe). The analysis is based on an avalanche dataset of 60 avalanche paths. The number and size of wet avalanches in February and March have increased, which is consistent with the current literature, while the number of slab avalanches has decreased in the last 3 decades. More wet-avalanche releases might be connected to winter season air temperature as it has risen by 1.8 ∘C since 1979. The random forest (RF) results indicate that wet avalanches are influenced by 3 d maximum and minimum air temperature, snow depth, wind speed, wind direction, and rainfall. Slab-avalanche activity is influenced by snow depth, rainfall, new snow, and wind speed. Based on the balanced RF method, air-temperature-related variables for slab avalanches were less important than rain- and snow-related variables. Surprisingly, the RF analysis revealed a less significant than expected relationship between the new-snow sum and slab-avalanche activity. Our analysis allows the use of the identified wet- and slab-avalanche driving variables to be included in the avalanche danger level alerts. Although it cannot replace operational forecasting, machine learning can allow for additional insights for the decision-making process to mitigate avalanche hazard." @default.
- W4307260826 created "2022-10-31" @default.
- W4307260826 creator A5024928637 @default.
- W4307260826 creator A5041758292 @default.
- W4307260826 creator A5050200917 @default.
- W4307260826 creator A5057385935 @default.
- W4307260826 creator A5063702835 @default.
- W4307260826 creator A5067677255 @default.
- W4307260826 date "2022-10-24" @default.
- W4307260826 modified "2023-10-14" @default.
- W4307260826 title "What weather variables are important for wet and slab avalanches under a changing climate in a low-altitude mountain range in Czechia?" @default.
- W4307260826 cites W1184582322 @default.
- W4307260826 cites W1507521596 @default.
- W4307260826 cites W1649553478 @default.
- W4307260826 cites W1966664067 @default.
- W4307260826 cites W1984018015 @default.
- W4307260826 cites W1986323923 @default.
- W4307260826 cites W1992538115 @default.
- W4307260826 cites W1993206956 @default.
- W4307260826 cites W2011881077 @default.
- W4307260826 cites W2018318168 @default.
- W4307260826 cites W2027461913 @default.
- W4307260826 cites W2029102976 @default.
- W4307260826 cites W2049017883 @default.
- W4307260826 cites W2049106622 @default.
- W4307260826 cites W2055002961 @default.
- W4307260826 cites W2055303666 @default.
- W4307260826 cites W2062027093 @default.
- W4307260826 cites W2078146632 @default.
- W4307260826 cites W2117653950 @default.
- W4307260826 cites W2146224990 @default.
- W4307260826 cites W2148143831 @default.
- W4307260826 cites W2154153816 @default.
- W4307260826 cites W2169128416 @default.
- W4307260826 cites W2169281690 @default.
- W4307260826 cites W2355158081 @default.
- W4307260826 cites W2469521313 @default.
- W4307260826 cites W2471829962 @default.
- W4307260826 cites W2564740244 @default.
- W4307260826 cites W2570814331 @default.
- W4307260826 cites W2572260713 @default.
- W4307260826 cites W2589805776 @default.
- W4307260826 cites W2606280577 @default.
- W4307260826 cites W2611867101 @default.
- W4307260826 cites W2625362830 @default.
- W4307260826 cites W2734371001 @default.
- W4307260826 cites W2760320476 @default.
- W4307260826 cites W2789483231 @default.
- W4307260826 cites W2793228343 @default.
- W4307260826 cites W2793695946 @default.
- W4307260826 cites W2891777545 @default.
- W4307260826 cites W2898429973 @default.
- W4307260826 cites W2899405126 @default.
- W4307260826 cites W2995336635 @default.
- W4307260826 cites W3009231054 @default.
- W4307260826 cites W3009285895 @default.
- W4307260826 cites W3014270046 @default.
- W4307260826 cites W3017869289 @default.
- W4307260826 cites W3035834643 @default.
- W4307260826 cites W3038374003 @default.
- W4307260826 cites W3083962070 @default.
- W4307260826 cites W3090688994 @default.
- W4307260826 cites W3099802519 @default.
- W4307260826 cites W3119809428 @default.
- W4307260826 cites W3134432461 @default.
- W4307260826 cites W3144698964 @default.
- W4307260826 cites W3156606266 @default.
- W4307260826 cites W3163041554 @default.
- W4307260826 cites W3202885410 @default.
- W4307260826 cites W3205023144 @default.
- W4307260826 cites W3208017124 @default.
- W4307260826 cites W4205122403 @default.
- W4307260826 cites W4212949931 @default.
- W4307260826 cites W4213329169 @default.
- W4307260826 cites W4237168912 @default.
- W4307260826 cites W4281799447 @default.
- W4307260826 cites W634333752 @default.
- W4307260826 cites W88178686 @default.
- W4307260826 doi "https://doi.org/10.5194/nhess-22-3501-2022" @default.
- W4307260826 hasPublicationYear "2022" @default.
- W4307260826 type Work @default.
- W4307260826 citedByCount "1" @default.
- W4307260826 countsByYear W43072608262023 @default.
- W4307260826 crossrefType "journal-article" @default.
- W4307260826 hasAuthorship W4307260826A5024928637 @default.
- W4307260826 hasAuthorship W4307260826A5041758292 @default.
- W4307260826 hasAuthorship W4307260826A5050200917 @default.
- W4307260826 hasAuthorship W4307260826A5057385935 @default.
- W4307260826 hasAuthorship W4307260826A5063702835 @default.
- W4307260826 hasAuthorship W4307260826A5067677255 @default.
- W4307260826 hasBestOaLocation W43072608261 @default.
- W4307260826 hasConcept C100970517 @default.
- W4307260826 hasConcept C107775477 @default.
- W4307260826 hasConcept C111368507 @default.
- W4307260826 hasConcept C113740112 @default.
- W4307260826 hasConcept C127313418 @default.
- W4307260826 hasConcept C132651083 @default.
- W4307260826 hasConcept C153294291 @default.