Spatial patterns of snow distribution in the sub-Arctic

Bennett, Katrina E.; Miller, Greta; Busey, R.; Chen, Min; Lathrop, E.; Dann, J.; Nutt, Mara; Crumley, Ryan; Dillard, Shannon L.; Dafflon, Baptiste; Kumar, Jitendra; Bolton, W. R.; Wilson, Cathy J.; Iversen, Colleen M.; Wullschleger, Stan D.

doi:10.5194/tc-16-3269-2022

Cited by 13 publications

(14 citation statements)

References 109 publications

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“…In regions of discontinuous permafrost, near‐surface permafrost has been shown to be absent beneath shrubs ( Salix ) and horsetail ( Equisteum ) (Gill, 1973; Jorgenson et al., 2001; Nguyen et al., 2009). Snow becomes trapped beneath shrubs (Bennett et al., 2022; Pomeroy et al., 2006), which acts as an insulator, keeping the soil warmer than the cold winter atmospheric temperatures (Gisnås et al., 2014; Grünberg et al., 2020; Y. Zhang et al., 2018). Horsetail is a resilient plant, which grows readily in disturbed soils (Emers et al., 1995), such as those that are vertically or horizontally displaced as permafrost thaws (Nguyen et al., 2009).…”

Section: Discussionmentioning

confidence: 99%

“…In regions of discontinuous permafrost, near-surface permafrost has been shown to be absent beneath shrubs (Salix) and horsetail (Equisteum) (Gill, 1973;Jorgenson et al, 2001;Nguyen et al, 2009). Snow becomes trapped beneath shrubs (Bennett et al, 2022;Pomeroy et al, 2006), which acts as an insulator, keeping the soil warmer than the cold winter atmospheric temperatures (Gisnås et al, 2014;Grünberg et al, 2020;Y. Zhang et al, 2018).…”

Section: Feature Importancementioning

confidence: 99%

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High‐Resolution Maps of Near‐Surface Permafrost for Three Watersheds on the Seward Peninsula, Alaska Derived From Machine Learning

Thaler,

Uhleman,

Rowland

et al. 2023

Earth and Space Science

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Permafrost soils are a critical component of the global carbon cycle and are locally important because they regulate the hydrologic flux from uplands to rivers. Furthermore, degradation of permafrost soils causes land surface subsidence, damaging infrastructure that is crucial for local communities. Regional and hemispherical maps of permafrost are too coarse to resolve distributions at a scale relevant to assessments of infrastructure stability or to illuminate geomorphic impacts of permafrost thaw. Here we train machine learning models to generate meter‐scale maps of near‐surface permafrost for three watersheds in the discontinuous permafrost region. The models were trained using ground truth determinations of near‐surface permafrost presence from measurements of soil temperature and electrical resistivity. We trained three classifiers: extremely randomized trees (ERTr), support vector machines (SVM), and an artificial neural network (ANN). Model uncertainty was determined using k‐fold cross validation, and the modeled extents of near‐surface permafrost were compared to the observed extents at each site. At‐a‐site near‐surface permafrost distributions predicted by the ERTr produced the highest accuracy (70%–90%). However, the transferability of the ERTr to the sites outside of the training data set was poor, with accuracies ranging from 50% to 77%. The SVM and ANN models had lower accuracies for at‐a‐site prediction (70%–83%), yet they had greater accuracy when transferred to the non‐training site (62%–78%). These models demonstrate the potential for integrating high‐resolution spatial data and machine learning models to develop maps of near‐surface permafrost extent at resolutions fine enough to assess infrastructure vulnerability and landscape morphology influenced by permafrost thaw.

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Section: Discussionmentioning

confidence: 99%

Section: Feature Importancementioning

confidence: 99%

High‐Resolution Maps of Near‐Surface Permafrost for Three Watersheds on the Seward Peninsula, Alaska Derived From Machine Learning

Thaler,

Uhleman,

Rowland

et al. 2023

Earth and Space Science

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“…The climate of the Seward Peninsula is characterized by cool continental conditions, typified by long, cold winters and short, cool summers (Peel et al., 2007). The mean annual air temperature at Nome Airport (64.51 N, l65.43 W) was −2.3°C from 1980 to 2018, with a mean January temperature of −14.4°C and a mean July temperature of 11.2°C (Bennett et al., 2022). The mean snow depth measured at Teller and Kougarok in 2018 was about 109.0 and 75.3 cm respectively (Bennett et al., 2022).…”

Section: Methodsmentioning

confidence: 99%

“…The mean annual air temperature at Nome Airport (64.51 N, l65.43 W) was −2.3°C from 1980 to 2018, with a mean January temperature of −14.4°C and a mean July temperature of 11.2°C (Bennett et al., 2022). The mean snow depth measured at Teller and Kougarok in 2018 was about 109.0 and 75.3 cm respectively (Bennett et al., 2022).…”

Section: Methodsmentioning

confidence: 99%

PiCAM: A Raspberry Pi‐based open‐source, low‐power camera system for monitoring plant phenology in Arctic environments

Yang,

McMahon,

Hantson

et al. 2023

Methods Ecol Evol

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Time‐lapse cameras have been widely used as a tool to monitor the timing of seasonal vegetation growth. These simple, relatively inexpensive systems can provide high‐frequency observations of leaf development and demography which are critical data sets needed to characterize plant phenology from species to landscapes. This is important for understanding how plants are responding to global changes, as well as for validating satellite‐derived phenology products. However, in remote regions including the high‐latitude Arctic, deploying time‐lapse cameras could be challenging. The remoteness and lack of widespread power and telecommunications infrastructure limit options for the installation, maintenance and retrieval of data and equipment, and make it difficult for cameras to survive in extreme weather (e.g. long cold winters). To improve our understanding of Arctic phenology, new technologies are required to address these challenges. Here, we present a novel, low‐power, compact, lightweight time‐lapse camera system, called power‐interval camera automation module (PiCAM). The PiCAM was designed with explicit consideration to simplify deployment (i.e. without a need for external power supplies) of camera systems and to address the challenges of camera survival in harsh Arctic environments. In this paper, we describe the design, setup and technical details of the PiCAM and provide a roadmap for how to build and operate these systems. As proof of concept, we deployed 26 PiCAMs at three low‐Arctic tundra sites on the Seward Peninsula, Alaska in early August 2021 for characterizing Arctic plant phenology. Of the 26 PiCAMs, 70% remained active at the point of our revisit in late July 2022 despite the extreme winter temperatures they experienced (< −30°C, heavy snow cover). We extracted key plant phenology metrics from the PiCAMs and captured strong differences across key Arctic plant species. We showed that the PiCAM has the potential to be widely used for monitoring plant phenology across the broader Arctic region, addressing the need for ground‐based understanding of Arctic phenological diversity to develop knowledge of plant response to climate change and to validate remote sensing products.

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“…TPI20), on the other hand, had a stronger influence on snow cover duration. TPI has been shown to correlate well with snow accumulation patterns in mountain and tundra landscapes although the relevant radius may vary between landscapes (López-Moreno et al, 2017;Bennett et al, 2022). In addition to TPI, incoming solar radiation influenced the distribution of snow cover in late winter.…”

Section: Drivers Of Variabilitymentioning

confidence: 99%

Variability and drivers of winter near-surface temperatures over boreal and tundra landscapes

Tyystjärvi

Niittynen

Kemppinen

et al. 2023

Preprint

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Abstract. Winter near-surface temperatures have important implications for ecosystem functioning such as vegetation dynamicsand carbon cycling. In cold environments, seasonal snow cover can exert a strong control on the surface temperatures.However, the lack of in situ measurements of both snow cover and surface temperatures over high latitudes has made it difficultto estimate the spatio-temporal variability of this relationship. Here, we quantified the fine-scale variability of winternear-surface temperatures (+2 cm) and snow cover duration using a total of 441 microclimate loggers in seven study areasacross boreal and tundra landscapes during 2019–2021. We further examined the drivers behind this variation and the extentto which surface temperatures are buffered from air temperatures during winter. Our results show that while average winternear-surface temperatures stay close to 0 °C across the study domain, there are large differences in their fine-scale variabilityamong the study areas. Areas with large topographical variation, as well as areas with shallow snowpacks, showed the greatestvariation in near-surface temperatures and in the insulating effect of snow cover. In the tundra, for example, differences inminimum near-surface temperatures were close to 30 °C. In contrast, flat topography and deep snow cover lead to little spatialvariation and decoupling of the near-surface and air temperatures. Quantifying and understanding the landscape-wide variationin winter microclimates improves our ability to predict the local effects of climate change in the rapidly warming boreal andtundra regions.

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Spatial patterns of snow distribution in the sub-Arctic

Cited by 13 publications

References 109 publications

High‐Resolution Maps of Near‐Surface Permafrost for Three Watersheds on the Seward Peninsula, Alaska Derived From Machine Learning

High‐Resolution Maps of Near‐Surface Permafrost for Three Watersheds on the Seward Peninsula, Alaska Derived From Machine Learning

PiCAM: A Raspberry Pi‐based open‐source, low‐power camera system for monitoring plant phenology in Arctic environments

Variability and drivers of winter near-surface temperatures over boreal and tundra landscapes

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