Mountain permafrost probability mapping using the BTS method in two climatically dissimilar locations, northwest Canada

Abstract

The Basal Temperature of Snow (BTS) method was used to predict permafrost distribution in two climatologically dissimilar mountain environments in northwest Canada. Permafrost probability maps with 30 m  30 m grid cells were generated for part of the Ruby Range, Yukon Territory (425 km 2 ), and for the Haines Summit area, northern British Columbia (536 km 2 ), using winter BTS measurements in conjunction with late-summer ground truthing by probing and digging pits to physically verify the presence of permafrost. BTS values, and hence permafrost distribution, were modeled using elevation and potential incoming solar radiation (PISR) for the Ruby Range. PISR was not significant at Haines Summit, probably because persistent cloudiness associated with its more maritime climatic regime reduced aspect-induced variability in insolation. Probability maps indicate that *66% of the Ruby Range area and *43% of the Haines Summit area are underlain by permafrost. Therefore, the Ruby Range should be classified as extensive discontinuous permafrost, while Haines Summit is part of the sporadic discontinuous permafrost zone and not the isolated patches zone as portrayed on recent maps. Extensive ground truthing proved to be an essential part of the procedure because traditional BTS ''rulesof-thumb'' did not remain valid across the differing mountain climate zones.Résumé : La méthode de la température de base de la neige « BTS » a été utilisée pour prédire la distribution du pergéli-sol dans deux différents environnements climatiques de montagne dans le nord-ouest du Canada. Des cartes de probabilité de pergélisol, avec des mailles de 30 m  30 m, ont été produites pour une partie du Ruby Range, dans le Territoire du Yukon (425 km 2 ), et pour le secteur du sommet Haines au nord de la Colombie-Britannique (536 km 2 ). Ces cartes ont été produites en se servant de mesures hivernales de la BTS, jumelées à une confirmation sur le terrain à la fin de l'été par des sondages d'exploration et le creusage de petites fosses pour vérifier physiquement la présence de pergélisol. Les valeurs de la BST, et ainsi la distribution du pergélisol, ont été modélisées en se servant de l'élévation et du rayonnement solaire incident potentiel pour le secteur de Ruby Range. Le potentiel de rayonnement solaire incident n'était pas significatif au sommet Haines, probablement en raison de la présence constante de nuages, associée à un régime climatique plus marin, qui a réduit la variabilité induite par l'aspect de l'insolation. Les cartes de probabilité indiquent qu'environ 66 % de la région de Ruby Range et 43 % de la région du sommet Haines reposent sur le pergélisol. Ainsi, le Ruby Range devrait être classé comme pergélisol extensif discontinu, alors que le sommet Haines fait partie d'une zone de pergélisol sporadique discontinue et non d'une zone de parcelles de terrain isolées tel que montré sur des cartes récentes. Une confirmation extensive sur le terrain s'est avérée être une partie essentielle de la procédure, car la règle empirique traditionnel...

Spatial variability and landscape controls of near‐surface permafrost within the Alaskan Yukon River Basin

“…For instance, if differences in topographical positions (uplands versus lowlands) are accounted for, significant linear trends between near-surface permafrost probabilities and mean temperature and precipitation (i.e., annual and winter) emerge. The negative trend (p = 0.01) between lowland near-surface permafrost probability and mean winter precipitation is consistent with thermal modeling of snow effects [Jafarov et al, 2012], basal temperature of snow modeling [Bonnaventure and Lewkowicz, 2008], and snow-enhancement experiments [Johansson et al, 2013] that show decreasing permafrost probability with increasing snow. Insignificant trends between lowland mean annual temperature and near-surface permafrost probabilities suggest that temperature (as derived from coarse-scale climatic data sets) is not a primary factor influencing lowland near-surface permafrost distributions and that other environmental conditions (e.g., summer and winter precipitation, land cover, surficial geology, and hydrology) are the primary indicators or drivers of near-surface permafrost in lowlands.…”

“…Recent advances in geophysical techniques have shown great promise for the mapping of permafrost characteristics over large areas, where borehole or field observations help guide interpretations of airborne or ground-based electrical resistivity surveys [Hubbard et al, 2013;Minsley et al, 2012]. Statistical-empirical models have been used to relate permafrost and soil characteristics to topoclimatic and land cover factors [Morrisey, 1986;Panda et al, 2012;Mishra and Riley, 2012] and have been especially useful for mountainous terrain [Gruber and Hoelzle, 2001;Hoelzle et al, 2001;Bonnaventure and Lewkowicz, 2008;Harris et al, 2009]. This mapping approach has been expanded to use statistical methods to integrate large field data sets and a wide range of biophysical variables obtained from remote sensing [Pastick et al, 2013].…”

Permafrost Favorability Index: Spatial Modeling in the French Alps Using a Rock Glacier Inventory

“…More recently, as an international effort, the APIM (Alpine Permafrost Index Map) was conceived to estimate permafrost extent over the entire Alpine range (Boeckli et al, 2012a,b). The APIM was however calibrated using only a limited number of rock glaciers in the French Alps (only the Combeynot inventory, Cremonese et al, 2011) and, considering that many authors pointed out that permafrost distribution models tend to be site-specific and weak when transferred to others sites (Frauenfelder et al, 1998;Lambiel and Reynard, 2001;Baroni et al, 2004;Bonnaventure and Lewkowicz, 2008), its significance in this region still remains unknown. Therefore, a permafrost modeling effort focused on this region is necessary.…”

Wind-driven snow conditions control the occurrence of contemporary marginal mountain permafrost in the Chic-Choc Mountains, south-eastern Canada: a case study from Mont Jacques-Cartier

“…Several studies have been undertaken on this topic in European mountains (Haeberli, 1973;Grüber and Hoelzle, 2001;Luetschg et al, 2008;Farbrot et al, 2011Farbrot et al, , 2013Hasler et al, 2011;Pogliotti, 2011;Gisnås et al, 2014;Ardelean et al, 2015;Magnin et al, 2016;Beniston et al, 2017), in Japan (Ishikawa, 2003;Ishikawa and Hirakawa, 2000), in the Canadian Rocky Mountains (Harris, 1981;Lewkowicz and Ednie, Lewkowicz et al, 2012;Bonnaventure et al, 2012;Hasler et al, 2015), and most recently in the Andes (Apaloo et al 2012). The snow cover acts as a buffer layer controlling heat loss at the ground interface.…”

“…The measurement of the bottom temperature of snow (BTS) is the most widespread technique used to predict permafrost in mountain areas. It is well adapted to snowy environments, such as the Alps where it was developed, because a late-winter snowpack more than 80 to 100 cm thick is required to consider that the BTS values reflect the ground thermal condition and are decoupled from the atmosphere temperature (Haeberli, 1973;Hoelzle, 1992;Grüber and Hoelzle, 2001;Bonnaventure and Lewkowicz, 2008). In environments with thin snowpack, the BTS technique is not applicable, which led to the development of new techniques to predict permafrost occurrence such as the continuous monitoring of GST using temperature loggers (Hoelzle et al, 1999;Ishikawa and Hirakawa, 2000;Ishikawa, 2003;Gray et al, 2016).…”