Articles | Volume 17, issue 5
https://doi.org/10.5194/tc-17-1803-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-1803-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Permafrost degradation at two monitored palsa mires in north-west Finland
Department of Geographical and Historical Studies, University of Eastern Finland, Joensuu, 80101, Finland
Alexander Störmer
Institute of Physical Geography and Landscape Ecology, Leibniz University Hannover, Hanover, 30167, Germany
Kilpisjärvi Biological Station, University of Helsinki, Kilpisjärvi, 99490, Finland
Eliisa Lotsari
Department of Geographical and Historical Studies, University of Eastern Finland, Joensuu, 80101, Finland
Water and Environmental Engineering, Department of Built Environment, Aalto University, Aalto, 00076, Finland
Pasi Korpelainen
Department of Geographical and Historical Studies, University of Eastern Finland, Joensuu, 80101, Finland
Kilpisjärvi Biological Station, University of Helsinki, Kilpisjärvi, 99490, Finland
Benjamin Burkhard
Institute of Physical Geography and Landscape Ecology, Leibniz University Hannover, Hanover, 30167, Germany
Kilpisjärvi Biological Station, University of Helsinki, Kilpisjärvi, 99490, Finland
Alfred Colpaert
Department of Geographical and Historical Studies, University of Eastern Finland, Joensuu, 80101, Finland
Timo Kumpula
Department of Geographical and Historical Studies, University of Eastern Finland, Joensuu, 80101, Finland
Kilpisjärvi Biological Station, University of Helsinki, Kilpisjärvi, 99490, Finland
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We collected snow depth maps four times during the winter from two different sites and used them as input for a model to predict daily snow depth and snow water equivalent (SWE). Our results show similar snow depth patterns in different sites, where snow depths are the highest in forests and forest gaps and the lowest in open areas. The results can extend operational snow course measurements and their temporal and spatial coverage, helping hydrological forecasting and water resource management.
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Snow has a major impact on palsa development, yet understanding its distribution at small scale remains limited. We used LiDAR UAS and ground truth data in combination with machine learning to model snow distribution at three palsa sites. We identified extremes in snow depth corresponding to palsa topography, providing insights into the influence of snow distribution on their formation. The results demonstrate the applicability of machine learning for modeling snow distribution at a small scale.
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Lakes are common features in Arctic permafrost areas. Land cover change following their drainage needs to be monitored since it has implications for ecology and the carbon cycle. Satellite data are key in this context. We compared a common vegetation index approach with a novel land-cover-monitoring scheme. Land cover information provides specific information on wetland features. We also showed that the bioclimatic gradients play a significant role after drainage within the first 10 years.
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River ice cover has a significant effect on flow and related processes in a river and the effect can last for months yearly. This impact is dependent on the properties of the ice, particularly its underside. Our study introduces a new approach to studying the underside of river ice, which is typically challenging. The approach allows gaining more information and new insights on river ice and flow during winters which is especially important under environmental change.
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Snow conditions in the Northern Hemisphere are rapidly changing, and information on snow depth is important for decision-making. We present snow depth measurements using different drones throughout the winter at a subarctic site. Generally, all drones produced good estimates of snow depth in open areas. However, differences were observed in the accuracies produced by the different drones, and a reduction in accuracy was observed when moving from an open mire area to forest-covered areas.
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Information on seasonal snow cover is essential in understanding snow processes and operational forecasting. We study the spatiotemporal variability in snow depth and snow processes in a subarctic, boreal landscape using drones. We identified multiple theoretically known snow processes and interactions between snow and vegetation. The results highlight the applicability of the drones to be used for a detailed study of snow depth in multiple land cover types and snow–vegetation interactions.
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Short summary
The study revealed a stable and even decreasing thickness of thaw depth in peat mounds with perennially frozen cores, despite overall rapid permafrost degradation within 14 years. This means that measuring the thickness of the thawed layer – a commonly used method – is alone insufficient to assess the permafrost conditions in subarctic peatlands. The study showed that climate change is the main driver of these permafrost features’ decay, but its effect depends on the peatland’s local conditions.
The study revealed a stable and even decreasing thickness of thaw depth in peat mounds with...