Effect of summer snow cover on the active layer thermal regime and thickness on CALM-S JGM site, James Ross Island, eastern Antarctic Peninsula

Hrbáček, Filip; Engel, Zbyněk; Kňažková, Michaela; Smolíková, Jana

doi:10.1016/j.catena.2021.105608

Cited by 9 publications

(6 citation statements)

References 46 publications

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“…The thermal insulation effect of snow cover directly influences the ground surface temperature. Various studies have suggested that snow cover variations affect the ground thermal regime in permafrost areas [60,61]. Snow cover in the summer may lead to decreased ground surface temperature compared to the increased air temperature and may help in shortening the thawing season, which in turn reduces active layer thawing propagation.…”

Section: Discussionmentioning

confidence: 99%

Modeling Permafrost Distribution Using Geoinformatics in the Alaknanda Valley, Uttarakhand, India

et al. 2022

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The Indian Himalayan region is experiencing frequent hazards and disasters related to permafrost. However, research on permafrost in this region has received very little or no attention. Therefore, it is important to have knowledge about the spatial distribution and state of permafrost in the Indian Himalayas. Modern remote sensing techniques, with the help of a geographic information system (GIS), can assess permafrost at high altitudes, largely over inaccessible mountainous terrains in the Himalayas. To assess the spatial distribution of permafrost in the Alaknanda Valley of the Chamoli district of Uttarakhand state, 198 rock glaciers were mapped (183 active and 15 relict) using high-resolution satellite data available in the Google Earth database. A logistic regression model (LRM) was used to identify a relationship between the presence of permafrost at the rock glacier sites and the predictor variables, i.e., the mean annual air temperature (MAAT), the potential incoming solar radiation (PISR) during the snow-free months, and the aspect near the margins of rock glaciers. Two other LRMs were also developed using moderate-resolution imaging spectroradiometer (MODIS)-derived land surface temperature (LST) and snow cover products. The MAAT-based model produced the best results, with a classification accuracy of 92.4%, followed by the snow-cover-based model (91.9%), with the LST-based model being the least accurate (82.4%). All three models were developed to compare their accuracy in predicting permafrost distribution. The results from the MAAT-based model were validated with the global permafrost zonation index (PZI) map, which showed no significant differences. However, the predicted model exhibited an underestimation of the area underlain by permafrost in the region compared to the PZI. Identifying the spatial distribution of permafrost will help us to better understand the impact of climate change on permafrost and its related hazards and provide necessary information to decision makers to mitigate permafrost-related disasters in the high mountain regions.

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Section: Discussionmentioning

confidence: 99%

Modeling Permafrost Distribution Using Geoinformatics in the Alaknanda Valley, Uttarakhand, India

et al. 2022

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“…The synthesis of previous activities with many additional details is presented by Tan et al (2020) [121], describing the design, development and field trials of their own UAV-mounted snow radar for measuring snow depth over sea-ice. The extent of snow patches was delineated after image mosaics built after UAV surveys by Hrbáček et al (2021) [122] to assess the role of ephemeral snow cover on ground thermal regime and active layer thickness in ground temperature measurement profiles on a CALM site in James Ross Island, Eastern Antarctic Peninsula. The evaluation of spatial-temporal variability in snow cover was performed by Tarca et al (2022) [123], which use a local time lapse camera together with a very high resolution orthomosaic and a DEM derived from UAV surveys for identifying how snow cover changes with vegetated areas and the microtopography.…”

Section: Snowmentioning

confidence: 99%

UAVs for Science in Antarctica

Pina

Vieira

2022

Remote Sensing

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Remote sensing is a very powerful tool that has been used to identify, map and monitor Antarctic features and processes for nearly one century. Satellite remote sensing plays the main role for about the last five decades, as it is the only way to provide multitemporal views at continental scale. But the emergence of small consumer-grade unoccupied aerial vehicles (UAVs) over the past two decades has paved the way for data in unprecedented detail. This has been also verified by an increasing noticeable interest in Antarctica by the incorporation of UAVs in the field activities in diversified research topics. This paper presents a comprehensive review about the use of UAVs in scientific activities in Antarctica. It is based on the analysis of 190 scientific publications published in peer-reviewed journals and proceedings of conferences which are organised into six main application topics: Terrestrial, Ice and Snow, Fauna, Technology, Atmosphere and Others. The analysis encompasses a detailed overview of the activities, identifying advantages and difficulties, also evaluating future possibilities and challenges for expanding the use of UAV in the field activities. The relevance of using UAVs to support numerous and diverse scientific activities in Antarctica becomes very clear after analysing this set of scientific publications, as it is revolutionising the remote acquisition of new data with much higher detail, from inaccessible or difficult to access regions, in faster and cheaper ways. Many of the advances can be seen in the terrestrial areas (detailed 3D mapping; vegetation mapping, discrimination and health assessment; periglacial forms characterisation), ice and snow (more detailed topography, depth and features of ice-sheets, glaciers and sea-ice), fauna (counting penguins, seals and flying birds and detailed morphometrics) and in atmosphere studies (more detailed meteorological measurements and air-surface couplings). This review has also shown that despite the low environmental impact of UAV-based surveys, the increasing number of applications and use, may lead to impacts in the most sensitive Antarctic ecosystems. Hence, we call for an internationally coordinated effort to for planning and sharing UAV data in Antarctica, which would reduce environmental impacts, while extending research outcomes.

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“…per year (van Wessem and others, 2016; Palerme and others, 2017). Snowfall occurs irregularly within a year and snow cover depth usually does not exceed 0.3 m in lowland areas (Hrbáček and others, 2021). Spatial variation in snow depth is determined mainly by the prevailing southerly to south-westerly winds (Kňažková and others, 2020).…”

Section: Study Areamentioning

confidence: 99%

Persistent mass loss of Triangular Glacier, James Ross Island, north-eastern Antarctic Peninsula

et al. 2022

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The retreat rates of Triangular Glacier since 1979 and its mass changes during the period 2014/15–2019/20 indicate the sensitive response of small ice masses on the eastern side of the Antarctic Peninsula to air temperature evolution. This cirque glacier in the northern part of James Ross Island receded rapidly during the period of regional warming in the late 20th century, losing 30.8% of its surface area between 1979 and 2006 (−1.7% a−1). The retreat rate then dropped to −0.3% a−1 following the regional cooling trend, but started to accelerate again (−0.8 to −2.3% a−1) with increasing air temperature since the summer 2014/15. Since the glaciological year 2015/16, Triangular Glacier has experienced enhanced snow melt, wind scour and permanent mass loss with annual mass balance ranging from −0.08 ± 0.35 to −0.56 ± 0.25 m w.e. The largest mass loss was observed in the glaciological year 2019/20, which included the warmest summer of the observation period. The cumulative mass balance of −1.66 ± 0.83 m w.e. over the years 2014/15–2019/20 is consistent with the termination of the positive mass-balance period that occurred in the north-eastern Antarctic Peninsula from 2009/10 to 2014/15.

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Effect of summer snow cover on the active layer thermal regime and thickness on CALM-S JGM site, James Ross Island, eastern Antarctic Peninsula

Cited by 9 publications

References 46 publications

Modeling Permafrost Distribution Using Geoinformatics in the Alaknanda Valley, Uttarakhand, India

Modeling Permafrost Distribution Using Geoinformatics in the Alaknanda Valley, Uttarakhand, India

UAVs for Science in Antarctica

Persistent mass loss of Triangular Glacier, James Ross Island, north-eastern Antarctic Peninsula

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