5.4 Ecogrid: Integrated abiotic and biotic monitoring at metric scale to understand the interactions between snow cover, vegetation and active layer thickness in a High Arctic site. (Manuscript submitted)

(1)

100

5.4 Ecogrid: Integrated abiotic and biotic monitoring at metric scale to understand the interactions between snow cover, vegetation and active layer thickness in a High Arctic site. (Manuscript submitted)

Guglielmin M.^1*, Ponti S.¹, Mazzola, M.², Cannone N.³.

1

Department of Theoretical and Applied Sciences, Insubria University, Varese, Italy

2

Institute of Atmospheric Science and Climate, CNR, Bologna, Italy

3

Department of Science and High Technology, Insubria University, Como, Italy

* Corresponding Author: Department of Theoretical and Applied Sciences, Insubria University, Via Dunant, 3, 21100, Varese, Italy. [email protected]

5.4.1 ABSTRACT

The dynamics of terrestrial polar ecosystems are extremely sensitive to the current climate change and making a research is the priority for the understanding of the interactions both in time and space among climate (e.g., air temperature and snow cover), active layer thickness and vegetation. Here we present the results of the first 2 years of the monitoring of air temperature, snow cover, ground surface temperature, active layer thickness integrated with the mapping of vegetation and ground surface characteristics of the northernmost CALM grid in the Arctic (78.92° N, 11.86° E), close to Ny- Ålesund, West Spitsbergen. Our results confirm the high spatial variability of snow cover, ground surface temperature and active layer thickness at metric-decimetric scale. The statistical analyses indicate microtopography as the main driver of the spatial variability of snow cover, which is in turn is the main driver of the GST spatial variability. Our data remark the role of the cooling effect of thin snow cover during the winter in shaping the GST variability. Differently from other arctic areas our analyses demonstrate that snow cover thickness is also the main driver of active layer thickness followed by the soil organic layer, while vegetation at this site influences more directly the summer GST but not the active layer thickness.

Keywords: Arctic, Snow, Vegetation, Active Layer thickness, Circumpolar Active Layer Monitoring

Network (CALM), climatic change.

(2)

101 5.4.2 INTRODUCTION

Understanding the terrestrial ecosystems dynamics is challenging, especially when considering the interactions between abiotic (such as air and ground temperature, soil moisture, precipitation, snow cover) and biotic (such as vegetation) factors, likely due to their high variability both in time and space, playing a key role in determining the ecosystem/environmental responses (e.g., Guglielmin et al., 2012; Shiklomanov and Nelson, 2002). Moreover, these factors are extremely sensitive to the current climate change scenario (e.g., IPCC, 2013 Guglielmin et al., 2012; Shiklomanov and Nelson, 2002), especially in polar areas (Dafflon et al., 2017).

Snow cover has been studied at different spatial scales (e.g., Clark et al., 2011; Liston, 1999; Sankey et al., 2015) as it exerts important effects on ecosystems. At regional scale, models relating to snow cover variability improved (e.g., Aas et al., 2017; Balk et al., 2000; Bruland et al., 2004; Lehning et al., 2006) and new interpolation methods have been proposed (e.g., Broxton et al., 2016; Dozier et al., 2016), while at local scale, long term experiments have been conducted (Pedersen et al., 2016) and relationships between snowfall and accumulation have been identified (Scipión et al., 2013).

Vegetation has recognized to have a significant role in snow persistence and distribution both at mid (Trujillo et al., 2007) and high latitudes (Rasmus et al., 2011), and particularly in tundra environments (Sturm et al., 2001; Walker et al., 2001). Likewise, snow accumulation has been demonstrated to impact on vegetation communities, with the distribution of the different vegetation series following environmental gradients mainly related to the thickness and persistence of snow cover (Elvebakk, 1994; Johansson et al., 2013; Sjögerstern et al., 2006). Further, snow and vegetation have been considered very important in shaping the ground surface temperature (GST) (i.e. Guglielmin et al., 2014) and, consequently, altering also the active layer thickness (ALT) (e.g., Cannone et al., 2006;

Guglielmin et al., 2008), although sometimes the latter did not follow the pattern of GST because the ground thermal properties (e.g., Nicolsky et al., 2009) and its moisture content (e.g., Seybold et al., 2010) can deeply affect the ALT spatial distribution.

The program “circumpolar active layer monitoring” (CALM) network standardized methods used to monitor the active layer thickness (ALT) with time, (Brown et al.,2000) further improved over the years (Fagan and Nelson, 2017; Iijima et al., 2017). Despite the wide geographical distribution of the CALM network, few studies focused on the spatio-temporal distribution of ALT, especially in relation with other factors, such as microtopography (Gao et al., 2016), snow thickness (de Pablo et al., 2013; de Pablo et al., 2014; Ramos et al., 2017), ground lithology (Hrbáček et al., 2017; Iijima et al., 2017), or soil moisture (Kotzé and Meiklejohn, 2017; Schuh et al., 2017). Snow-ALT interactions were tested in the Arctic by several authors (e.g., Åkerman and Johansson, 2008; Mazhitova et al., 2004; Widhalm et al., 2017) but without a specific focus on the effect of snow spatial variability at the CALM grid scale.

Although vegetation has been considered crucial for the active layer spatial variability (e.g., Mazhitova et al 2004; Guglielmin et al., 2014; Smith et al 2009), few studies analyzed vegetation-ALT interactions at a detailed scale (e.g., Almeida et al., 2014; Cannone et al., 2006; Cannone and Guglielmin, 2009;

Guglielmin et al., 2008; Guglielmin et al., 2012, 2014; Widhalm et al., 2017).

The understanding of the above mentioned interrelationships at a small scale could allow to achieve

a better knowledge of the driving factors impacting the ALT and, therefore, upscale them to larger

regions in order to implement already existing ALT spatio-temporal distribution models (Luo et al.,

2014; Mishra and Riley, 2014; Petrone et al., 2016 ).

(3)

102 Here we aim to: a) analyse the spatial variability of snow cover, GST and ALT at metric scale in a coastal arctic tundra ecosystem like the Ny-Ålesund CALM grid; b) understand and quantify the relationships between the snow spatial variability, the vegetation and the active layer thickness.

5.4.3 STUDY AREA

The study site was set in the Brogger Peninsula (78.92° N, 11.86° E), close to Ny- Ålesund, West Spitsbergen, in the Svalbard archipelago (Norway) (Fig. 5.4.1) and the long-term monitoring grid was located at upon a flat zone called Kolhaugen, between the coast and the Austre Broggerbreen forefield. The mean annual air temperature at Ny-Ålesund is -4.2 °C, with mean monthly air temperatures around -13 °C in January and 5 °C in July, and annual precipitation of about 433 mm (water equivalent) (Cannone et al., 2016).

Geologically, this area is generally covered by quaternary deposits, mainly marine beach deposits and

weathered material or colluvium (André, 1993), where several inactive stone circles occur. Continuous

permafrost underlies the area and ranges from 100 m of depth in coastal zones to > 500 m in

montainous areas (Boike et al., 2008). Depending on the deposit and the topography, active layer

thickness ranged between 0.3 and 1.8 m of thickness (André, 1993). The study area lies in a polar semi-

desert (Bliss and Svoboda, 1984) where three main vegetation types occur (Elvebakk, 1994, Ronning,

1996, Sjögerstern et al., 2006): 1) ridge vegetation with mesic conditions dominated by Dryas

octopetala L. and Cassiope tetragona (L.) D. Don., 2) snow-bed vegetation with snow cover persistence

dominated by Salix polaris Wahlenb., 3) heath vegetation where snow melt occurs early dominated

by Carex rupestris All. and Saxifraga oppositifolia L.

(4)

103

Figure 5.4.1: Study area: a) Aerial view of Ny-Ålesund surroundings and airport, b) Location of Svalbard Islands and Ny-Ålesund, c) shot of the grid on 10-06-2015, d) shot of the grid on 22-07-2015, e) DEM of the grid (legend is in m a.s.l.). The two maps are available online from the Norwegian Polar Institute (www.npolar.no).

5.4.4 METHODS

5.4.4.1 Field measurements and laboratory analyses

In July 2014, close to the Climatic Change Tower (CCT) (a 30-meter-high tower, designed to monitor

energy balance and exchange between the air and the surface by Consiglio Nazionale delle Ricerche

(CNR)) a 50 x 50 m grid was installed and equipped according to the circumpolar active layer

monitoring (CALM) protocol (Brown et al., 2000). The grid accounted 36 nodes with a span of 10 m

(Fig. 5.4.1c) and, at each node, a plastic snow stake was anchored into the soil with its heigth fixed at

1.2 m from the surface (Fig. 5.4.1d). For the snow cover monitoring ,a reflex camera was installed on

the CCT (at a height of 9 m) and equipped with a time-lapse system (Harbotronics), able to take photos

of the whole grid every hour and allowing to calculate the snow thickness at any time (except for the

completely dark conditions during the boreal nigth) by measuring the length of the stakes remaining

outside of the snow. These values were also calibrated with several manual measurements probing

the snow cover, which were carried out in different times during the experiment. The same stakes

were used also to assess the days of snow presence/absence for each node. The total frost heave (FH)

at each stake was monitored by measuring the difference between the original heigth of 1.2 m from

the surface and the observed heigth from the surface of each stake at the beginning of each summer.

(5)

104 At each node of the grid was installed also a plot (1 x 1 m) for the long-term monitoring of vegetation.

Every summer the vegetation was monitored at each plot (1 x 1 m) at each node performing a phytosociological relevés according to the Braun-Blanquet method (Braun-Blanquet, 1951; Cannone and Pignatti, 2014) allowing information on the community structure, coverage, species richness and composition.

Furthermore, 12 of 36 nodes were equipped with 4 thermistors (Hobo U23, 0.2 °C of accuracy) inserted in the ground at different depths (2, 30, 60 cm and the maximum available depth, ranging between 79-101 cm) and recording the ground temperature every 30 minutes since July 2014.

Therefore the data of the ground temperature were referred to the two hydrological years 2014/15 and 2015/2016, while the snow data were referred to the years 2015 and 2016 because the images of 2014 were not available.

In addition, at the first installation of the grid, in order to describe better the surface and soil characteristics, the following additional parameters were recorded for all the 36 plots: a) slope and aspect (measured through a compass), b) surficial grain size through visual estimation, c) litter height and A0 horizon thickness through a caliper. Moreover samples of the topsoil (0-5 cm of depth below the A0 horizon) for each stake were sampled to determine grain size through sieving (ASTM, 2003), soil pH with a pH-o-meter at 1 M solution, the gravimetric water content (oven dried for 24 hours at 105 °C and reweighted), organic matter content through the loss on ignition (LOI) method at 550 °C (Heiri et al., 2001). Air temperature recorded at 30 minutes at the CCT was measured with an accuracy of 0.1 °C.

5.4.4.2 Data Analyses

Thawing degree days (TDD), freezing degree days (FDD) and growing degree days (GDD) were computed for air and ground temperature (Molau and Mølgaard, 1996). The N-factor according to Klene et al., (2001) was computed for understanding the effects of snow and of vegetation on the ground surface temperature.

Active layer thickness (ALT) was calculated according to Guglielmin (2006) by interpolating the two lowermost daily maximum ground temperatures available at each shallow monitored borehole, therefore representing the yearly maximum depth of the 0 °C isotherm. Indeed, the coarse nature of the sediments in this site did not allow to apply the classical probing method to define the maximum thawing depth.

To calculate the spatial and temporal variability of the ALT, the normalized index of active layer variability (INV) was calculated according to Hinkel and Nelson (2003). Finally, to better visualize the spatial distribution of all the available parameters, maps were obtained by interpolating the grid nodes in ArcGIS 10.3 with the natural neighbor algorithm (Sibson, 1981) and successively 3D maps were generated in ArcScene.

General regression models (GRM) were used to analyze the relationship between the snow cover (as

dependent variable) and the main abiotic factors within the grid (elevation, slope, aspect and, as

categorical factor, microtopography, expressed as concave, convex, flat, slope). In addition, a second

GRM was performed to assess whether the influence of vegetation (total coverage, and coverage of

shrubs, herbs, forbs and mosses, respectively) was prevailing or not on that of topographical

(6)

105 paramenters on snow cover. A last GRM was performed selecting the ALT as dependent variable (referred to the 12 selected nodes) testing the effect of both abiotic (slope as most important topographic parameter, snow cover thickness, ground water content, A0 thickness, LOI, grain size and litter thickness) and biotic (moss coverage as dominant layer of the vegetation coverage) factors. All analyses were computed using the software STATISTICA

^©

.

5.4.5 RESULTS

5.4.5.1 Ground surface characteristics

The grid was located at an elevation between 51.5 and 55.6 m a.s.l. and was almost flat (Fig. 5.4.1e), except for the southwestern corner (F1) that was gently sloping (11-15°) to West. The surface was characterised by the occurrence of large (1-3 m in diameter) and probably inactive stone circles especially abundant in the northern part of the grid. The grain size of the topsoil exhibited a high spatial variability, although the coarser material (gravel and pebbles) was generally more abundant (Fig. 5.4.2a,b,c), with gravel ranging between 50.4% (at B3) and 95.4% (at F3), while sand showed lower values (between 3.8% at F3 and 32.2% at F6) and fine material was even more limited (varying between a minimum of 0.3% at B5 and a maximum of 31.3% at B3).

The organic layer (A0, Fig. 5.4.2d) was continous and ranged between 0.4 cm to more than 7 cm (at B3, E3 and E5, in plots characterized by high coverage of S. polaris) but in most cases it was very thin (less than 2 cm).

Figure 5.4.2: Spatial distribution of topsoil (0-10 cm) grain sizes and organic layer thickness. a) fine material (<0.062 mm), b) sand (0.062-2 mm), c) gravel (2-64 mm) are expressed in percentages, while d) organic layer (A0) thickness is expressed in centimeters.

(7)

106 The pH of the topsoil was quite variable ranging between 4.95 (A3) and 6.88 (B6), with a mean of 6.0 (data not shown) and the water content (at the sampling date) was even more variable, ranging between 3 to 52%. The LOI of the topsoil was generally not very high (< 10%) although there were 6 important exceptions, (B1, B3, D1, E3, E5, F4) and in particular E3 and E5 that reached 34.7 and 40.3%

respectively. The six plots with the highest LOI coincided with the highest coverage of S. polaris and of

D. octopetala, with the only exception of B1.

The litter was also highly variable, from 0 (C3, C4) to 3.5 cm (F6, Fig. 5.4.3d). The distribution of vegetation coverage was not homogeneous within the grid (Fig. 3a,b,c), with a total coverage ranging between 40 (A6) to 98% (B1, C2), and was characterized by a higher coverage of cryptogams (in particular of mosses and Cyanobacteria) (ranging from 27.5% in A1 to 95% in B4) than shrubs or herbs and forbs, with the former ranging between 0 (B6) and 45% (E5), and the latter between 1 (F2) and 25% (E5). Generally, mosses were abundant and ranged between 27.5 (A1) and 95% (B4), reaching the highest coverage in the central part and in the southwestern corner of the grid, where herbs and shrubs exhibited their lowest coverages. The dominant vascular plants species were S. polaris (occurring in 35 nodes) and S. oppositifolia (occurring in 32 nodes). As for the thickness of A0 and for LOI, also the litter thickness highest values were mainly observed where the shrubs were more abundant (B3, C5, E5) as well as where herbs and forbs occurred.

Figure 5.4.3: Vegetation and litter distribution within the grid. The coverage of shrubs (a), herbs and forbs (b) and mosses (c) are in percentages, while the d) litter thickness is expressed in cm.

5.4.5.2 Snow Distribution

Since the onset of the monitoring, the mean snow cover height did not vary (41.2 cm in 2015 and 41.5

cm in 2016) and was characterized by a similar general spatial distribution pattern within the grid,

with the highest snow thickness on the southwestern and the northeastern corners, and the lowest

values on the southeastern corner and the central part of the grid (Fig. 5.4.4a,b). However, analyzing

(8)

107 the snow cover thickness at smaller scale, a higher variability was recorded at some single nodes, with three nodes (A1, A3, E1) showing the largest inter-annual variability (11-20 cm), seven nodes exhibiting a difference of snow cover thickness between 5 and 10 cm, while most nodes had a very limited variability (< 5 cm). During the maximum accumulation period (end of April for both the years) the snow thickness varied between 16.1 (A3) and 72.4 cm (B6) in 2015, and between 19 (A1) and 107.5 cm (F1) in 2016 (Fig. 5.4.4a,b). During the melting period (beginning of June for both years), the snow thickness varied between 0 (A1, A3, C2) and 41.2 cm (B6) in 2015, between 0 (most nodes) and 44 cm (F1) in 2016. Despite in 2016 the beginning of snow melting was delayed of 5 days (17/06/2016) than in 2015 (12/06/15), it was faster (Fig. 5.4.4c,d) as at the beginning of June in 2016 the amount of persisting snow cover was strongly reduced respect the previous year (10/06/2015 versus 8/06/2016).

Figure 5.4.4: Snow thickness distribution (cm) during the maximum winter accumulation (a and b, end of April 2015 and 2016, respectively) and at the beginning of the melting period in 2015 and 2016 (c and d, beginning of June 2015 and 2016, respectively).

The statistical analyses GRM (p < 0.01, F = 9.6, R = 0.82) showed that during the maximum winter accumulation, among the abiotic factors, the most important exerting a statistically significant influence on snow cover distribution were slope (F = 15.4, p = 0.0004) and microtopography (F = 7.1, p = 0.001), while both elevation and aspect did not have any effect. Slope exerted a positive effect on snow cover thickness (β = +0.57), while the effect of microtopography was mainly associated to the occurrence of concavity (promoting snow cover thickness, β = +0.5) or convexity (decreasing snow cover thickness, β = -0.49) of the profile. These data were confirmed also analyzing the amount of snow cover thickness in relation to microtopography (Fig. 5.4.5a), showing that the thickest snow cover was associated to concavity, followed by slope, while the thinnest snow cover occurred in correspondence of convexity (but only with small differences also on flat surfaces).

A second GRM (p < 0.01, F = 14.2, R = 0.8), performed including also the biotic factor provided by the

vegetation cover, further confirmed the importance of slope (F = 14.87, p < 0.001, β = +0.55) and

microtopography (F = 10.6, p < 0.0001; β = +0.58 for concavity, β = -0.51 for convexity) in shaping the

(9)

108 patterns of snow cover distribution, while vegetation did not exert any significant effect. Also in this case, by analyzing the partitioning of vegetation coverage with microtopography (Fig. 5.4.5b,c), it was possible to observe that the coverage both of mosses as well as of total coverage followed the same patterns already observed for snow cover thickness.

Figure 5.4.5: Relationships between the snow cover (a), moss coverage (b) total vegetation coverage (c) and microtopography within the CALM grid.

a)

Mean Mean±SE Mean±1,96*SE Concave Convex Slope Flat

Microtopography 20

30 40 50 60 70 80

Snow Cover Thickness (cm)

b)

Mean Mean±SE Mean±1.96*SE Concave Convex Slope Flat

60 65 70 75 80 85 90 95

Moss Coverage (%)

c)

Mean Mean±SE Mean±1,96*SE Concave Convex Slope Flat

65 70 75 80 85 90 95 100

Total Vegetation Coverage (%)

(10)

109 5.4.5.3 Ground thermal regime and active layer thickness

Generally, the GST showed a large spatial variability, with the mean annual ground surface temperature (MAGST) ranging between -1.7 °C (C6; 2015-2016) and -3.8 °C (C3; 2014-2015) with a mean of -2 °C (Table 5.4.1). The large spatial variability of GST was mainly due to the values observed during the snow season, ranging between -6.7 °C in C2 (2014/15) and -3.5 °C in C6 (2015/16), while during the snow free season the GST was very homogenous in the grid (ranging between 3.9 and 4.6

°C during 2015 and between 1.1 and 1.9 in 2016; Table 5.4.1). As expected, during the snow period the lowest GST were measured at the nodes showing the thinnest snow cover, while the highest GST occurred in the SE corner of the grid, one of the two areas with highest snow cover thickness during the maximum winter accumulation.

As expected, during the snow free season the GST was always warmer that the air temperature while, differently from what expected, during the snow season often the GST was lower than the air temperature (Table 5.4.1).

At 60 cm of depth (the deeper depth common to all the monitored shallow boreholes) the mean annual ground temperature (MAGT) ranged between -1.7 °C (C6, 2015-16) and -4.2 °C (E2, 2014-15), with a lower spatial variability during the snow season respect the surface (between -3 °C in C6 and - 5 °C in F5) and higher during the snow free season (between 3 °C in C6 and 4 °C in C2).

TDD at 2 cm were always lower than air TDD and exhibited a relative low spatial variability (maximum difference was around 20%), ranging between 476 and 564 °CDay in 2014/15 and between 562 and 621°CDay in 2015/16 (Table 5.4.1). The patterns of GDD were highly variable: indeed, in 2014/15 the values of GDD at 2 cm were always greater than air GDD, whereas in 2015/16 the opposite pattern was detected (Table 5.4.1). Interestingly, the summer N-factor was always negative in both years, with the lowest value always recorded at node B3 and the highest at C2 (in particular, in C2 during 2014/15 the value was equal to 1, see Table 5.4.1).

Considering the bottom of the shallow boreholes, it is remarkable that the zero-curtain effect was well expressed in early autumn when the active layer started to freeze (25-09-14 to 10-11-14 and 08-10- 15 to 22-11-15 at B3; 30-09-14 to 31-10-14 and 08-10-15 to 15-11-15 at C2; 24-09-14 to 07-11-14 and 10-10-15 to 20-11-15 at E4; 23-09-14 to 02-11-14 and 06-10-15 to 15-11-15 at F5) rather than during late spring at the beginning of snow melting (between 15 days at C2 and 21 days in B3).

Table 5.4.1: Main thermal characteristics of the ground surface at the monitored nodes of the CALM grid for the two analysed years (2014-15 and 2015-16). Legend: GST = ground surface temperature (measured at 2 cm of depth); SCP = snow cover period; SF = Snow-free period; MAGST= mean annual ground surface temperature;

TDD = Thawing Degree Days (°CDay); FDD = Freezing Degree Days (°CDay); GDD= Growing Degree Days (°CDay);

GT60= Ground temperature at 60 cm of depth.

B3 C1 C2 C4 C6 E2 E4 F5 AirT

2014-15

GST min

^-12.6 ^-21.2 ^-21.1 ^-15.8 ^-11.8 ^-15.0 ^-14.3 ^-13.6 ^-21.6

GST max

^12.7 ^12.3 ^14.8 ^13.4 ^13.7 ^13.3 ^13.5 ^13.2 ^11.4

MAGST

^-2.5 ^-3.7 ^-3.8 ^-2.9 ^-1.9 ^-2.9 ^-2.6 ^-2.0 ^-3.0

SCP Mean GST

^-4.6 ^-6.3 ^-6.7 ^-5.1 ^-3.9 ^-5.1 ^-4.7 ^-4.5 ^-5.7

(11)

110 SF 2015 Mean

GST

4.3 3.9 4.5 4.5 4.6 4.6 4.5 4.4 4.8

TDD

^476.4 ^523.2 ^563.8 ^501.4 ^509.0 ^484.1 ^511.5 ^506.3 ^564.7

FDD

^-1367.8 ^-1802.8 ^-1908.6 ^-1501.0 ^-1182.0 ^-1497.0 ^-1411.1 ^-1118.5 ^-1599.5

GDD

^398.5 ^444.2 ^486.5 ^420.1 ^444.0 ^411.7 ^451.1 ^428.8 ^340.1

N-factor Summer

0.84 0.93 1.00 0.89 0.90 0.86 0.91 0.90 1.00

N-factor Winter

0.86 1.13 1.19 0.94 0.74 0.94 0.88 0.70

GT60 min

^-10.1 ^-16.2 ^-15.9 ^- ^- ^-11.9 ^-9.9 ^-14.9

GT60 max

^7.3 ^8.4 ^8.9 ^- ^- ^8.1 ^5.8 ^8.0

GT60 mean

^-2.3 ^-3.2 ^-2.9 ^- ^- ^-4.2 ^-2.5 ^-3.2

2015-16

GST min

^-15.0 ^-15.7 ^-18.3 ^-16.0 ^-14.5 ^-19.1 ^-14.2 ^-15.6 ^-18.1

GST max

^12.8 ^12.0 ^13.9 ^13.2 ^13.6 ^13.4 ^13.8 ^12.6 ^11.3

MAGST

^-2.2 ^-2.6 ^-2.7 ^-2.4 ^-1.7 ^-2.6 ^-2.1 ^-2.6 ^-1.3

SCP Mean GST

^-4.1 ^-4.8 ^-5.1 ^-4.5 ^-3.5 ^-4.7 ^-4.0 ^-4.8 ^-3.9

SF 2016 Mean

GST

1.1 1.4 1.9 1.6 1.3 1.2 1.3 1.2 5.7

TDD

^561.9 ^578.5 ^621.2 ^596.5 ^580.4 ^583.7 ^594.4 ^579.3 ^688.6

FDD

^-1326.0 ^-1512.4 ^-1584.5 ^-1425.7 ^-1192.8 ^-1508.1 ^-1332.9 ^-1497.7 ^-1154.2

GDD

^473.0 ^467.3 ^524.5 ^494.2 ^497.1 ^498.5 ^505.0 ^486.8 ^559.0

N-factor Summer

0.82 0.84 0.90 0.87 0.84 0.85 0.86 0.84

N-factor Winter

1.15 1.31 1.37 1.24 1.03 1.31 1.15 1.30

GT60 min

^-10.4 ^-12.8 ^-13.0 ^- ^-10.7 ^- ^-10.4 ^-12.5

GT60 max

^6.9 ^7.8 ^7.7 ^- ^7.2 ^- ^6.5 ^7.4

GT60 mean

^-2.1 ^-2.5 ^-3.1 ^- ^-1.7 ^- ^-2.2 ^-2.7

Among the 12 monitored nodes, we reported in Figure 5.4.6 the example of the ground temperatures

recorded at all the four different depths in 4 selected nodes (F5, E4, C2, B3) not only because they

have the data series without gaps at all the depths but, above all, because they represent almost all

the different thermal regimes occurring within the grid.

(12)

111

Figure 5.4.6: Patterns of daily mean ground temperatures (°C) at 4 selected nodes (B3, C2, E4 and F5). Blue line

= GST (2 cm) depth, red line = 30 cm depth, green line = 60 cm depth, yellow line = bottom of the boreholes in which the depth is variable (B3 = 101 cm, C2 = 100 cm, E4 = 80 cm, F5 = 98 cm).

The active layer thickness was highly variable both in time and in space (Fig. 5.4.7). In 2014, the minimum ALT was 126 cm (B3) while the maximum reached 195 cm (E2) with a mean of 160 cm. The following year (2015), the minimum was almost the same (127 cm) but located in another node (E2), while the maximum was 200 cm (F3) with a mean of 162.5 cm. Finally, in 2016 the minimum ALT was still similar to the previous years (128 cm at B3), while the maximum was much greater (244 cm at F3) with a strongly increase reaching a mean of 167.1 cm.

The statistical analyses showed that the most important factors affecting ALT (as dependent variable)

in a statistically significant way were the snow cover thickness (F = 22.5, p = 0.04, β = -0.8) and the

thickness of the ground organic layer (F = 11.36, p = 0.01, β = -1.8), while moss coverage, ground water

content, amount of fine material (silt and clay) and slope did not exert any influence on ALT, as tested

by GRM (F = 10.63, R = 0.8, p < 0.01).

(13)

112

Figure 5.4.7: ALT thickness distribution (cm) at the end of the summer season (a) 2014, b) 2015, c) 2016) and d) INV (%) calculated on the 3 year-period. Please note that the spatialization did not characterize all the grid because was associated to the nodes where shallow boreholes are monitored.

5.4.5.4 Frost Heave

The frost heave (FH) for the whole grid was very similar between the two analyzed years, ranging between 0 (B2) and 6.5 cm (B6) in 2015, and between 1 (B2, B5, F2) and 7 cm (C5) in 2016. On the other hand, FH showed a large spatial variability in the different years considering the single nodes, as shown in Fig. 5.4.8. Indeed, despite the occurrence of nodes where the heave was absent in both years (i.e. A2, B2, F4), the higher values of FH shifted from the central part of the grid to the northern margin.

Figure 5.4.8: Frost heave (FH) distribution measured on a) 2015 and b) 2016.

(14)

113 5.4.6 DISCUSSION

Our data clearly indicate the importance of the interaction between abiotic and biotic (or linked to biotic) factors in determining the spatial and temporal patterns of snow cover, GST and ALT.

5.4.6.1 Snow spatial distribution

Our data suggest variation both in space and time of the patterns of snow accumulation (Fig. 5.4.4), despite the zones with greater accumulation within the grid were well noticeable and did not vary their distribution over time. In the Arctic few studies focused on the small scale spatial distribution of snow (e.g., Hirashima et al., 2004; Pedersen et al., 2016) and provided an overall accordance about the large spatial variability of snow cover, which has been explained by vegetation (shrubs) trapping (Domine et al., 2016; Liston et al., 2002; Widhalm et al., 2017) or snowdrifts produced by local variations in topography (Christiansen, 2004; Gisnås et al., 2014; Mazhitova et al., 2004; Schirmer et al., 2011; Sturm et al., 2010 ), such as slope (Pedersen et al., 2016) coupled with the wind direction and intensity. In our case, the grid slope and microtopography at metric-decimetric scale were the main factors of the snow distribution driven by the wind drifts because in this study only few dwarf shrubs (Dryas octopetala, Salix polaris, Cassiope tetragona) with not extensive coverage and with small height occurred. Thus, the central flat part of the grid accounted for the minimum snow cover accumulation (Christiansen, 2004), while the NW and SE inclined sides for the maximum snow accumulation. In particular, the thickest snow cover was observed at microsites characterized by higher slope (10-15°) coupled with concave profile. The same temporal pattern is apparent also in the snowmelt timing: indeed, despite the general inter-annual variation of the snowmelt onset in Svalbard (Rotschky et al., 2011), we experienced the overall same snowmelt timing over the years, even though at a plot scale snowmelt could have lasted longer or shorter depending on the local thickness of the accumulated snow (Pedersen et al., 2016). Our data also show that dwarf shrubs do not influence snow cover but partially follow its spatial distribution patterns because both of them are mainly driven microtopography, as shown in Fig. 5.4.5.

5.4.6.2 Ground surface temperature and active layer spatial distribution

Our findings about the high spatial GST variability at small scales are in accordance to other studies in

Alaska (Hinkel and Nelson, 2003), Siberia (Mazhitova et al., 2004), Greenland (Christiansen, 2004),

Svalbard (Westermann et al., 2011). In particular, several factors have been identified as drivers of

GST variability, including microtopography (Gisnås et al., 2014; Hinkel and Nelson, 2003), soil organic

layer thickness (Mazhitova et al., 2004), soil moisture (Westermann et al., 2011), free water bodies

(Langer et al., 2010), eco-hydro-geomorphology (Gangodagamage et al., 2014; Shiklomanov et al.,

2010), snow pack thickness (Christiansen, 2004). Among these factors, microtopography is thought to

be the most important driver of GST both for its direct influence on local conditions concerning aspect

(15)

114 and radiation (Westermann et al., 2011), as well as for its influence on the accumulation of snow (Zhang et al., 2005) and its melting (Cannone et al., 2006), as confirmed also by our data concerning the snow distribution patterns. Indeed, in the Arctic as well as in all high latitude or high altitude sites the GST mainly depends by air temperature, radiation and, above all, snow cover (i.e. Cook et al., 2008; Guglielmin et al., 2014; Streletskiy et al., 2008; Zhang et al., 2005). However, the net effect of snow cover on ground temperatures may change in relation with the snow duration and thickness with different impacts during the different seasons. A sufficiently thick snow cover pack can insulate the ground surface from cooler air temperatures during winter or fall (e.g., Guglielmin, 2006;

Johansson et al., 2013; Wilhelm and Bockheim, 2016; Zhang, 2005). Conversely, a thin snow cover (<

5 cm) during summer or spring can produce a cooling of the GST due to i) the insulating effect of the snow cover, ii) the latent heat fluxes due to snow melt and, iii) the higher albedo respect the snow free surfaces (Cook et al., 2008; Guglielmin et al., 2014). These trends are confirmed by our data, with the minimum winter peaks in ground temperatures and the highest fluctuations observed where snow cover was thinnest (e.g., at C2 especially in 2014/15) and, conversely, the smallest winter temperature fluctuations where the thicker snow cover was recorded (e.g., at B3 in 2014/15). The relationship between the GST and the snow cover thickness (SCT) is even clearer considering the linear regression between FDD and the maximum snow cover thickness in 2015/16 (R

²

= 0.466; p = 0.04; β = -7.82), showing a clear inverse relation between FDD and SCT. A further demonstration of the importance of snow cover in affecting GST is provided during the snow free periods, when the fluctuations in ground temperatures observed at each node of the grid are larger due to the absence of the insulating snow.

The relative homogeneity of the vegetation coverage (especially because the albedo is more similar than in bare ground) can explain why during the snow free season GST is less variable than the snow season. ALT resulted in an inhomogeneous distribution among the nodes but with the overall same average depth in 2014 and 2015 and deepened in 2016. Several nodes showed an almost constant ALT values in the time (B3, C6, E4, F5) while a few showed variations of almost 50% (E2, F3). Since little variations of the ALT occurred all over the grid, INV was averagely 13.3%, except for the eastern corner where it was higher (up to 46%). In comparison with other Arctic areas, slightly higher INV values were recorded by Mazhitova et al. (2004) and Hinkel and Nelson (2003) and slightly lower by Christiansen et al. (2004). However, the relatively low INV average detected in this study could be related to the short monitoring period (3 years), while its consistent localized maxima could be related to snow distribution variability (Guglielmin et al., 2014), rather than highly dynamic soil water content (Mazhitova et al., 2004).

FH is strictly dependent on the soil grain size and moisture (French, 2007). By assuming that it is

unlikely a grain size variation within the grid in one year, both the FH increase and its changed

distribution in 2016 may be related to the faster snow melting rate occurred in spring 2016, providing

a higher ground water content, and thus more heave events, as supported by the longer zero-curtain

effect observed in 2016 compared to 2015. As further confirmation, the zero-curtain effect during the

freezing periods was visible only at the bottom depth because of the high content of infiltrated water

after the snowmelt in the AL, and the general coarse nature of the deposits in this site (de Pablo et al.,

2014; Guglielmin et al., 2008).

(16)

115 5.4.6.3 Relationships between snow, vegetation and GST and ALT

Although vegetation is thought to be significant in AL thickening (Wilhelm and Bockheim, 2016) or acting as an insulating blanket (Cannone et al., 2006; Guglielmin et al., 2008; Guglielmin et al., 2014;

Mazhitova et al., 2004), our data shown that vegetation coverage (or the distribution of shrubs, herbs or mosses) was not the most important factor affecting ALT. The lack of vegetation influence on ALT at our site can be explained by the fact that the vegetation occurring within the grid was relatively homogeneous both in terms of mean coverage, height (with prevalence of small herbaceous species, dwarf shrubs and mosses) and with no or limited peat accumulation (hence not providing a significant insulating blanket). Rather, according to the results of the statistical analyses, after snow cover the thickness of the ground organic layer was the second most important driver of ALT, in agreement with Cannone et al. (2006), Shur and Jorgenson (2007) and Zhou et al. (2013). The smaller importance of the organic layer respect to snow cover can be explained also by the fact that, with the exception of node B3 (where it was 70 mm thick), the other nodes were characterized by a limited variability of the organic thickness, ranging between 5 and 20 mm, and likely being too limited to exert significant impacts on ALT. In addition, the relation underlined between the organic layer thickness and ALT can be interpreted as an indirect effect of vegetation coverage because, according to our data, the organic layer thickness was related to the shrub distribution, as the thickest organic layer was detected at the nodes exhibiting the highest shrub coverage, in particular of S. polaris.

Despite the apparent lack of relation with ALT, our data show a clearly visible effect of vegetation on GST during the summer season: indeed in the four nodes of the grid where the dominant vegetation coverages were rather identical (more than 80% of mosses, Table 5.4.2) the similar mean of the summer N-factor (ranging from 0.82 in B3 to 0.94 in C2) suggest that the insulating effect of the vegetation cover is very similar.

Table 5.4.2: Summer and Winter N-factor for different arctic and Antarctic sites and their vegetation conditions.

Site Vegetation

Coverage

Summer N-factor

Winter N-factor

Reference

Ny-Ål. Grid B3 Mosses with

Salix sp.

0.82 1.0 This paper

Ny-Ål. Grid C2 Mosses 0.94 1.28 This paper

Ny-Ål. Grid E4 Mosses 0.87 1.01 This paper

Ny-Ål. Grid F5 Mosses with

Salix sp.

0.85 0.99 This paper

Alaska (69-71° N) Tundra 0.76-0.91 0.35-0.57 Kade et al., 2006 Can. Arctic (78° N) Grass Forb

tundra

0.8 0.75 Walker et al., 2011 Can. Arctic (78° N) Mosses with

Forbs

1.5 0.75 Walker et al., 2011 Can. Arctic

(74°54’ N )

Barren ground with different Snow Cover

0.47-1.29 0.4 -0.99 Bonnaventure et al., 2017

(17)

116 Can. Mountains (60-

65° N)

Continental Tundra

1.05±0.03 0.68±0.06 Lewkowicz et al., 2012 Cont. Antarctica

(74°19-40’ S)

Mosses 2.8-4.0 0 Cannone and Guglielmin,

2009 J. Ross Isl. (Antarctica,

63°48’ S)

Barren ground 3.57-6.22 0.85-0.95 Hrbáček et al., 2017

These values were comparable with those reported in previous literature for similar geographical (latitude, altitude) and vegetation as in Walker et al. (2011) (Table 5.4.2). These values are higher than what found at lower latitude (although at higher elevation as in the case of the Yukon and British Columbia mountains in Lewkowicz et al., 2012), but are much lower of what found in Continental Antarctica (Cannone and Guglielmin, 2009) although in both the cases with similar vegetation (Table 5.4.2). During the snow-free season, the N-factor is related to a combination of different factors that drive the TDDs, including the incoming radiation, the albedo of the surface, the water content of the matter above the thermistors and the air temperature. In our case, despite our latitude is higher than that of the Antarctic sites, the TDDs are surely much less than what recorded in Antarctica, where the anticyclonic area is dominant and the cloudiness is lower than at Ny-Ålesund, therefore being mainly related to higher incoming radiation in Antarctica than in at our high arctic site. Reasonably, the lower values of some sites at lower latitude may be related more to the higher water content of the mosses in Yukon and British Columbia, able to induce a lowering of the N-factor due to the larger energy consumption by the latent heat released during the evaporation. On the other hand, winter N-factors refer to the cooling or insulating effect of the snowpack during the winter and depend mainly by the snow thickness, duration and density. In the winter 2014/15 the N-factor ranged between 0.70 (F5) and 0.94 (E2) in the major part of the nodes, with values comparable with what recorded in other Arctic and Antarctic areas (Table 5.4.2). An important exception are the positive values of N-factor recorded at C1 and C2 and above all the positive values of the season 2015/16 that are clearly higher of all the data at our best knowledge (Table 5.4.2). This fact may indicate an important cooling effect of the snow cover in winter: indeed, the nodes with thinner snow cover (C1 and C2) always exhibited positive N-factor indicating a much colder (higher FDD) ground surface temperature than the air. In permafrost areas, the cooling effect of the snow depends from the air temperature but also from snow cover thickness and snow density. The Figure 5.4.9 illustrates the main processes during the snow season 2014/15 in the node C2. As marked by the light blue shapes between the end of October 2014 and the February 2015 there are several periods in which the GST showed an opposite pattern respect to the air temperature, with the first decreasing when air temperature increases (phase A). Between the end of February and the end of April the GST follows the air temperature pattern, although is always colder and fluctuations are slightly smoothed (light green shapes, phase B). Afterwards both air temperature and GST have a clear warming pattern corresponding the snow melting phase (light yellow shape, phase C). The phase A periods correspond to the periods in which the air temperature increases suddenly and, probably due to the extremely thin snow cover, drives a strong latent heat towards the atmosphere (with the net cooling of the GST) related to the sublimation that in some periods (i.e 3-5 February 2015) correspond to the highest wind speed events (data not shown).

During the phases B and C, the snow pack (of at least 5-10 cm of thickness) exerts a buffering effect

respect to the air temperature, smoothing the temperature fluctuations although during the phase C

there are sudden heat transfers, as the case of 26-27/05/2015 (see mark a1 in Fig. 5.4.9) in which the

(18)

117 GST increased of several degrees in one day and that are probably due to infiltration of liquid water from the surface (that marked the beginning of the snow melting phase).

Fig. 5.4.9. Relationships between GST and Air temperature at C2 node (one of the nodes with less snow cover).

The period between 15 November 2014 and 12 July 2015 was covered by snow (with the exception of a few days at the beginning of December 2014). The light blue shapes (phase A) indicate periods of thin snow cover (<10 cm) in which GST pattern is opposite to the air temperature, while the light green shapes (phase B) indicate periods in which snow cover was thicker and GST followed air temperature pattern although remaining colder and with smoothed fluctuations. During the period underlined by the light yellow shape (phase C) the air temperature suffered a progressive warming followed by GST with two types of exceptions: a) abrupt GST warming (without correspondent air temperature warming) may be due to heat transfer through the snow pack due to the start of the snow melting (a1 on 26-27 May 2015) and important infiltration of water through the residual snow pack (a2 on 24-25 June 2015) and b) zero curtain period (8-14 July 2015) indicating the saturation of the upper 2 cm of soil (where the thermistor is allocated) and that marked the end of the snow melting.

5.4.7 CONCLUSIONS

Our data contributed to understand the terrestrial polar ecosystems dynamics (Dafflon et al., 2017),

especially in order to quantify their high variability both in time and space for what concerns abiotic

factors including snow cover, ground surface, soil characteristics, active layer thickness and biotic

factors (vegetation coverage) that are extremely sensitive to the current climate change scenario (e.g.,

Guglielmin et al., 2012; Shiklomanov and Nelson, 2002). Our results confirm the need of an accurate

understanding and knowledge of the interactions among these key factors at metric-decimetric scale,

allowing further upscaling and modeling, as well as to emphasize that widely recognized patterns and

(19)

118 relationship may change when changing the spatial and/or temporal scale of analysis (as observed for the influence of microtopography both on snow cover and vegetation when the latter is enough homogeneous and of small size), needing to be addressed for a better forecast of potential ecosystem responses to future climate and environmental changes.

Our data confirmed the high GST spatial variability that is mainly related to snow cover and probably due to the strong cooling effect exerted by a thin snow cover during the winter. In addition, snow cover followed by the thickness of the soil organic layer are the main driving factors of the ALT, in agreement with Cannone et al., (2006), Shur and Jorgenson (2007) and Zhou et al. (2013).

5.4.8 ACKNOWLEDGEMENTS

We want to thanks to CNR-ISAC that supports logistically and partially funded the research and we acknowledge also Angelo Viola, Fabio Giardi, Sebastian Westermann, Laura Caiazzo, Luigi Mazari for their help in the field work and Vito Vitale and Emanuele Liberatori for the organization.

5.4.9 REFERENCES

· Åkerman, H.J., Johansson, M., 2008. Thawing permafrost and thicker active layers in sub-arctic Sweden. Permafrost Periglac. 19, 279-292.

· Aas, K. S., Gisnås, K., Westermann, S., Berntsen, T. K., 2017. A Tiling Approach to Represent Subgrid Snow Variability in Coupled Land Surface-Atmosphere Models. J. Hydrometeorol. 18, 49-63.

· Almeida, I.C., Schaefer, C.E.G., Fernandes, R.B., Pereira, T.T., Nieuwendam, A., Pereira, A.B., 2014. Active layer thermal regime at different vegetation covers at Lions Rump, King George Island, Maritime Antarctica. Geomorphology, 225, 36-46.

· André, M., 1993. Les versants du Spitsberg. Presses Universitaires de Nancy, Nancy.

· ASTM Standard C33, 2003. "Specification for Concrete Aggregates”," ASTM International, West Conshohocken, PA., 2003, DOI: 10.1520/C0033-03, www.astm.org.

· Balk, B., Elder, K., 2000. Combining binary decision tree and geostatistical methods to estimate snow distribution in a mountain watershed. Water Resour. Res. 36, 13-26.

· Bliss, L.C. and Svoboda, J., 1984. Plant communities and plant production in the western Queen Elizabeth Islands. Holarct. Ecol. 7, 325-344.

· Boike, J., Ippisch, O., Overduin, P.P., Hagedorn, B., Roth, K., 2008. Water, heat and solute dynamics of a mud boil, Spitsbergen. Geomorphology 95, 61-73.

· Bonnaventure, P.P., Lamoureux, S.F., Favaro, E.A., 2017 Over-Winter Channel Bed Temperature Regimes Generated by Contrasting Snow Accumulation in a High Arctic River.

Permafrost Periglac. 28, 339–346.

· Brown, J., Nelson, F.E., Hinkel, K.M., 2000. The circumpolar active layer monitoring (CALM)

program research designs and initial results. Polar Geogr. 3, 165-258.

(20)

119 · Broxton, P.D., Dawson, N., Zeng, X., 2016. Linking snowfall and snow accumulation to generate spatial maps of SWE and snow depth. Earth Space Sci. 3, 246-256.

· Bruland, O., Liston, G.E., Vonk, J., Sand, K., Killingtveit, Å., 2004. Modelling the snow distribution at two high arctic sites at Svalbard, Norway, and at an alpine site in central Norway. Hydrol. Res. 35, 191-208.

· Cannone, N., Augusti, A., Malfasi, F., Pallozzi, E., Calfapietra, C., Brugnoli, E., 2016. The interaction of biotic and abiotic factors at multiple spatial scales affects the variability of CO

2

fluxes in polar environments. Polar Biol. 9, 1581-1596.

· Cannone, N., Pignatti, S., 2014. Ecological responses of plant species and communities to climate warming: upward shift or range filling processes? Climatic Change 123, 201-214.

· Cannone, N., Guglielmin, M., 2009. Influence of vegetation on the ground thermal regime in continental Antarctica. Geoderma 151, 215-223.

· Cannone, N., Evans, J. E., Strachan, R., Guglielmin, M., 2006. Interactions between climate, vegetation and the active layer in soils at two Maritime Antarctic sites. Antarct. Sci. 18, 323- 333.

· Christiansen, H.H., 2004. Meteorological control on interannual spatial and temporal variations in snow cover and ground thawing in two northeast Greenlandic Circumpolar- Active-Layer-Monitoring (CALM) sites. Permafrost Periglac. 15, 155-169.

· Clark, M.P., Hendrikx, J., Slater, A.G., Kavetski, D., Anderson, B., Cullen, N. J. et al., 2011.

Representing spatial variability of snow water equivalent in hydrologic and land-surface models: A review. Water Resour. Res. 47.

· Cook, B.I., Bonan, G.B., Levis, S., Epstein, H.E., 2008. The thermoinsulation effect of snow cover within a climate model. Clim. Dynam. 31, 107-124.

· Dafflon, B., Oktem, R., Peterson, J., Ulrich, C., Phuong Tran, A., Romanovsky, V., and Hubbard, S.S., 2017. Coincident above- and below-ground autonomous monitoring to quantify co- variability in permafrost, soil and vegetation properties in Arctic Tundra. J. Geophys. Res- Biogeo.

· de Pablo, M.A., Blanco, J.J., Molina, A., Ramos, M., Quesada, A., Vieira, G., 2013. Interannual active layer variability at the Limnopolar Lake CALM site on Byers Peninsula, Livingston Island, Antarctica. Antarct. Sci., 25, 167-180.

· de Pablo, M.A., Ramos, M., Molina, A., 2014. Thermal characterization of the active layer at the Limnopolar Lake CALM-S site on Byers Peninsula (Livingston Island), Antarctica. Solid Earth 5, 721.

· Domine, F., Barrere, M., Morin, S., 2016. The growth of shrubs on high Arctic tundra at Bylot Island: impact on snow physical properties and permafrost thermal regime. Biogeosciences 13, 6471.

· Dozier, J., Bair, E.H., Davis, R.E., 2016. Estimating the spatial distribution of snow water equivalent in the world's mountains. Wires Water 3, 461-474.

· Elvebakk, A., 1994. A survey of plant associations and alliances from Svalbard. J. Veg. Sci. 5, 791-802.

· Fagan, J.D., Nelson, F.E., 2017. Spatial Sampling Design in the Circumpolar Active Layer Monitoring Programme. Permafrost Periglac. 28, 42-51.

· French, H.M., 2007. The periglacial environment. John Wiley & Sons.

(21)

120 · Gangodagamage, C., Rowland, J.C., Hubbard, S.S., Brumby, S.P., Liljedahl, A.K., Wainwright, H.

et al, 2014. Extrapolating active layer thickness measurements across Arctic polygonal terrain using LiDAR and NDVI data sets. Water Resour. Res. 50, 6339-6357.

· Gao, T., Zhang, T., Wan, X., Kang, S., Sillanpää, M., Zheng, Y., Cao, L., 2016. Influence of microtopography on active layer thaw depths in Qilian Mountain, northeastern Tibetan Plateau. Environ. Earth Sci. 75, 1-12.

· Gisnås, K., Westermann, S., Schuler, T.V., Litherland, T., Isaksen, K., Boike, J., Etzelmüller, B., 2014. A statistical approach to represent small-scale variability of permafrost temperatures due to snow cover. Cryosphere 8, 2063–2074.

· Guglielmin, M., 2006. Ground Surface Temperature (GST), Active Layer and Permafrost Monitoring in Continental Antarctica. Permafrost Periglac 17, 133–143.

· Guglielmin, M., Dalle Fratte, M., Cannone, N., 2014. Permafrost warming and vegetation changes in continental Antarctica. Environ. Res. Lett. 9, 045001.

· Guglielmin, M., Worland, M.R., Cannone, N., 2012. Spatial and temporal variability of ground surface temperature and active layer thickness at the margin of maritime Antarctica, Signy Island. Geomorphology 155, 20-33.

· Guglielmin, M., Evans, C.J.E., Cannone, N., 2008. Active layer thermal regime under different vegetation conditions in permafrost areas. A case study at Signy Island (Maritime Antarctica).

Geoderma 144, 73-85.

· Heiri, O., Lotter, A.F., Lemcke, G., 2001. Loss on ignition as a method for estimating organic and carbonate content in sediments: reproducibility and comparability of results. J.

Paleolimnol. 25, 101-110.

· Hinkel, K.M., and Nelson, F.E., 2003. Spatial and temporal patterns of active layer thickness at circumpolar active layer monitoring (CALM) sites in northern Alaska 1995-2000. J. Geophys.

Res. 108, 81-68.

· Hirashima, H., Kodama, Y., Sato, N., Ohata, T., Yabuki, H., Georgiadi, A., 2004. Nonuniform distribution of tundra snow cover in Eastern Siberia. J. Hydrometeorol. 5, 373-389.

· Hrbáček, F., Nývlt, D., Láska, K., 2017. Active layer thermal dynamics at two lithologically different sites on James Ross Island, Eastern Antarctic Peninsula. Catena 149, 592-602.

· Iijima, Y., Park, H., Konstantinov, P.Y., Pudov, G.G., Fedorov, A.N., 2017. Active-Layer Thickness Measurements Using a Handheld Penetrometer at Boreal and Tundra Sites in Eastern Siberia.

Permafrost Periglac. 28, 306-313.

· IPCC. 2013. Summary for policymakers. In Climate Change 2013: The Physical Science Basis.

Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, Stocker, T.F., Qin, D., Plattner, G.K., Tignor, M.S.K., Allen, J.,Boschung, A., Nauels, Y., Xia, Y., Bex, V., Midgley, P.M. (eds). Cambridge University Press:

Cambridge, UK and New York, NY.

· Johansson, M., Callaghan, T.V., Bosiö, J., Åkerman, H.J., Jackowicz-Korczynski, M., Christensen, T.R., 2013. Rapid responses of permafrost and vegetation to experimentally increased snow cover in sub-arctic Sweden. Environ. Res. Lett. 8, 035025.

· Kade, A., Romanovsky, V.E.,Walker, D.A., 2006. The N-factor of nonsorted circles along a

climate gradient in Arctic Alaska. Permafrost Periglac 17, 279–289.

(22)

121 · Klene, A.E., Nelson, F.E., Shiklomanov, S.I. and Hinkel, K.M., 2001. The N-factor in Natural Landscapes: Variability of Air and Soil-Surface Temperatures, Kuparuk River Basin, Alaska, U.S.A. Arct. Antarct. Alp. Res. 33, 140-148.

· Kotzé, C., Meiklejohn, I., 2017. Temporal variability of ground thermal regimes on the northern buttress of the Vesleskarvet nunatak, western Dronning Maud Land, Antarctica.

Antarct. Sci. 29, 73-81.

· Langer, M., Westermann, S., Boike, J., 2010. Spatial and temporal variations of summer surface temperatures of wet polygonal tundra in Siberia-implications for MODIS LST based permafrost monitoring. Remote Sens. Environ. 114, 2059-2069.

· Lehning, M., Völksch, I., Gustafsson, D., Nguyen, T.A., Stähli, M., Zappa, M., 2006. ALPINE3D:

a detailed model of mountain surface processes and its application to snow hydrology. Hydrol.

Process. 20, 2111-2128.

· Lewkowicz, A.G., Bonnaventure, P.P., Smith, S. L., Kuntz, Z., 2012. Spatial and thermal characteristics of mountain permafrost, northwest Canada. Geogr. Ann. A. 94, 195-213.

· Liston, G.E., 1999. Interrelationships among snow distribution, snowmelt, and snow cover depletion: Implications for atmospheric, hydrologic, and ecologic modeling. J. Appl. Meteorol.

38, 1474-1487.

· Liston, G.E., Mcfadden, J.P., Sturm, M., Pielke, R.A., 2002. Modelled changes in arctic tundra snow, energy and moisture fluxes due to increased shrubs. Global Change Biol. 8, 17-32.

· Lund, M., Stiegler, C., Abermann, J., Citterio, M., Hansen, B.U., van As, D., 2017.

Spatiotemporal variability in surface energy balance across tundra, snow and ice in Greenland.

Ambio 46, 81-93.

· Luo, D., HuiJun, J., Marchenko, S., Romanovsky, V., 2014. Distribution and changes of active layer thickness (ALT) and soil temperature (TTOP) in the source area of the Yellow River using the GIPL model. Sci. China Earth 57, 1834-1845.

· Mazhitova, G., Chestnykh, O., Zamolodchikov, D., 2004. Active-layer spatial and temporal variability at European Russian Circumpolar-Active-Layer-Monitoring (CALM) sites.

Permafrost Periglac. 15, 123-139.

· Mishra, U., Riley, W. J., 2014. Active-layer thickness across Alaska: Comparing observation- based estimates with CMIP5 earth system model predictions. J. Soil. Sci. Soc. Am. 78, 894-902.

· Molau, U., Mølgaard, P., 1996. ITEX Manual, International Tundra Experiment. Danish Polar Center 1996. ISBN: 87-90369-04-1.

· Nicolsky, D.J., Romanovsky, V.E., Panteleev, G.G., 2009. Estimation of soil thermal properties using in-situ temperature measurements in the active layer and permafrost. Cold Reg. Sci.

Technol. 55, 120-129.

· Pedersen, S.H., Tamstorf, M.P., Abermann, J., Westergaard-Nielsen, A., Lund, M., Skov, K. et al., 2016. Spatiotemporal characteristics of seasonal snow cover in Northeast Greenland from in situ observations. Arct. Antarct. Alp. Res. 48, 653-671.

· Petrone, J., Sohlenius, G., Johansson, E., Lindborg, T., Näslund, J. O., Strömgren, M., Brydsten,

L., 2016. Using ground-penetrating radar, topography and classification of vegetation to

model the sediment and active layer thickness in a periglacial lake catchment, western

Greenland. Earth Syst. Sci. Data 8, 663.

(23)

122 · Ramos, M., Vieira, G., de Pablo, M.A., Molina, A., Abramov, A., Goyanes, G., 2017. Recent shallowing of the thaw depth at Crater Lake, Deception Island, Antarctica (2006-2014). Catena 149, 519-528.

· Rasmus, S., Lundell, R., Saarinen, T., 2011. Interactions between snow, canopy, and vegetation in a boreal coniferous forest. Plant Ecol. Divers. 4, 55-65.

· Ronning, O.I., 1996. The flora of Svalbard. Norsk Polar Institutt, Oslo.

· Rotschky, G., Schuler, T.V., Kohler, J., Isaksson, E., 2011. Spatio-temporal variability of snowmelt across Svalbard during the period 2000-08 derived from QuikSCAT/SeaWinds scatterometry. Polar Res. 30.

· Sankey, T., Donald, J., McVay, J., Ashley, M., O'Donnell, F., Lopez, S. M., Springer, A., 2015.

Multi-scale analysis of snow dynamics at the southern margin of the North American continental snow distribution. Remote Sens. Environ. 169, 307-319.

· Schirmer, M., Wirz, V., Clifton, A., Lehning, M., 2011. Persistence in intra-annual snow depth distribution: 1. Measurements and topographic control. Water Resour. Res. 47.

· Schuh, C., Frampton, A., Christiansen, H.H., 2017. Soil moisture redistribution and its effect on inter-annual active layer temperature and thickness variations in a dry loess terrace in Adventdalen, Svalbard. Cryosphere 11, 635.

· Scipión, D.E., Mott, R., Lehning, M., Schneebeli, M., Berne, A., 2013. Seasonal small-scale spatial variability in alpine snowfall and snow accumulation. Water Resour. Res. 49, 1446- 1457.

· Seybold, C.A., Balks, M.R., Harms, D.S., 2010. Characterization of active layer water contents in the McMurdo Sound region, Antarctica. Antarct. Sci. 22, 633-645.

· Shiklomanov, N.I., Nelson, F.E. 2002. Active-layer mapping at regional scales: A 13-year spatial time series for the Kuparuk region, north-central Alaska. Permafrost Periglac. 13, 219–230.

· Shiklomanov, N.I., Streletskiy, D.A., Nelson, F.E., Hollister, R.D., Romanovsky, V.E., Tweedie, C.E. et al., 2010. Decadal variations of active-layer thickness in moisture-controlled landscapes, Barrow, Alaska. J. Geophys. Res-Biogeo. 115(G4).

· Shur, Y.L., Jorgenson, M.T., 2007. Patterns of permafrost formation and degradation in relation to climate and ecosystems. Permafrost Periglac. 18, 7-19.

· Sibson, R., 1981. A Brief Description of Natural Neighbor Interpolation. Chapter 2, in: John Wiley & Sons (Eds.), Interpolating Multivariate Data. New York: pp. 21-36.

· Smith, S.L., Wolfe, S.A., Riseborough, D.W., Nixon, F.M., 2009. Active-layer characteristics and summer climatic indices, Mackenzie Valley, Northwest Territories, Canada. Permafrost Periglac. 20, 201-220.

· Streletskiy, D.A., Shiklomanov, N.I., Nelson, F.E., Klene, A.E., 2008. Thirteen years of observations at Alaskan CALM sites: long-term active layer and ground surface temperature trends, in: Proceedings of the Ninth International Conference on Permafrost, June 29-July 3, 2008, Fairbanks, Alaska (Vol. 2, pp. 1727-1732). Institute of Northern Engineering, University of Alaska Fairbanks.

· Sturm, M., Wagner, A.M., 2010. Using repeated patterns in snow distribution modeling: An Arctic example. Water Resour. Res. 46(12).

· Sturm, M., Holmgren, J., McFadden, J.P., Liston, G.E., Chapin III, F.S., Racine, C.H., 2001. Snow-

shrub interactions in Arctic tundra: a hypothesis with climatic implications. J. Climate, 14, 336-

344.

(24)

123 · Trujillo, E., Ramírez, J.A., Elder, K.J., 2007. Topographic, meteorologic, and canopy controls on the scaling characteristics of the spatial distribution of snow depth fields. Water Resour. Res.

43(7).

· Walker, D.A., Billings, W.D., De Molenaar, J.G., 2001. Snow-vegetation interactions in tundra environments. Snow Ecol. 266-324.

· Walker, D.A. Kuss, P., Epstein, H. E. Kade, A. N. Vonlanthen, C. M., Raynolds, M. K., Daniëls F.

J.A., 2011. Vegetation of zonal patterned-ground ecosystems along the North America Arctic bioclimate gradient. App. Veg. Science 440–463.

· Westermann, S., Langer, M., Boike, J., 2011. Spatial and temporal variations of summer surface temperatures of high-arctic tundra on Svalbard-implications for MODIS LST based permafrost monitoring. Remote Sens. Environ. 115, 908-922.

· Widhalm, B., Bartsch, A., Leibman, M., Khomutov, A., 2017. Active-layer thickness estimation from X-band SAR backscatter intensity. Cryosphere 11, 483.

· Wilhelm, K., Bockheim, J., 2016. Influence of soil properties on active layer thermal propagation along the western Antarctic Peninsula. Earth Surf. Proc. Land. 41, 1550-1563.