Shakhmatov et al.
Progress in Earth and Planetary Science(2022) 9:3
https://doi.org/10.1186/s40645-021-00460-5

Progress in Earth andPlanetary Science

Open Access

RESEARCH ARTICLE

Effects of snow manipulation on larch trees
in the taiga forest ecosystem in northeastern
Siberia
Ruslan Shakhmatov1, Shuhei Hashiguchi1, Trofim C. Maximov2 and Atsuko Sugimoto3,4*

Abstract
Changes in winter precipitation (snow) may greatly affect vegetation by altering hydrological and biochemical
processes. To understand the effects of changing snow cover depth and melt timing on the taiga forest ecosystem, asnow manipulation experiment was conducted in December 2015 at the Spasskaya Pad experimental larch forest in
Eastern Siberia, which is characterized by a continental dry climate with extreme cold winters and hot summers. Vari‑ables including soil temperature and moisture, oxygen and hydrogen isotope ratios of soil moisture and stem water,
foliar nitrogen and carbon contents and their isotopes, phenology, and soil inorganic nitrogen were observed at snow
removal (SNOW−), snow addition (SNOW+), and CONTROL plots. After snow manipulation, the soil temperature at
the SNOW− plot decreased significantly compared to the CONTROL and SNOW+ plots. At SNOW− plot, snowmelt
was earlier and soil temperature was higher than at other plots during spring because of low soil moisture caused byless snowmelt water. Despite the earlier snowmelt and higher soil temperature in the SNOW− plot in the early grow‑ing season, needle elongation was delayed. Leaf chemistry also differed between the CONTROL and SNOW− plots.
The needle nitrogen content in the SNOW− plot was lower in the middle of July, whereas no difference was observed
among the three plots in August. The soil inorganic nitrogen content of each plot corresponded to these results. The
amount of soil ammonium was lower in the SNOW− plot than in the other plots at the end of July, however, once
production started in August, the amount of soil ammonium in the three plots was comparable. Extremely low soil
temperatures in winter and freeze–thaw cycles in spring and dry soil condition in spring and early summer at the
SNOW− plot may have influenced the phenology and production of soil inorganic nitrogen.
Keywords: Boreal forest, Taiga, Larch, Snow manipulation, Permafrost, Stable isotopes, Phenology, Nutrient, Soil
moisture
1 Introduction
Snow plays an important role in the ecosystems of theNorthern Hemisphere (Chapin et al. 2000; Vavrus 2007).
Snow cover acts as an insulator in winter and regulates
the water and nutrient balance, thereby influencing
material cycling in summer (Aerts et al. 2004; Kreyling
et al. 2012; Freppaz et al. 2018). As stated in the IPCC
*Correspondence: atsukos@ees.hokudai.ac.jp
3Arctic Research Center, Hokkaido University, Kita 21 Nishi 11, Kita‑ku,
Sapporo 001‑0021, Japan
Full list of author information is available at the end of the article

Fifth and Sixth Assessment Reports, the snow covered
area is declining as the southern boundary turns intoa snow free area (Vaughan et al. 2013; Douville et al.
2021). Because of global warming, snow-covered areas
are expected to decrease and precipitation events are
expected to become less frequent and intense (Huntington 2006; Deser et al. 2010; Callaghan et al. 2011).
Although a general decrease in the depth of snow coveris expected because of the decrease of snowfall, in arctic
and subarctic regions snow depth may increase. According to simulation models by Park et al. (2014), snow
depth is expected to increase in Siberia by 0.99 mm per© The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://​creat​iveco​mmons.​org/​licen​ses/​by/4.​0/.

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year. This modeled trend is in accordance with observations from the central and western Siberia regions of
Russia (Bulygina et al. 2009, 2011). Snow cover depth
and duration have a major effect on ecosystems throughtheir influence on the soil water balance and thermal
regimes, nutrient availability, and duration of the growing season (Schimel et al. 2004; Grippa et al. 2005; Loranty et al. 2018). Additionally, changes in snow depth
and density have a significant influence on the soil temperature (Zhang 2005; Callaghan et al. 2011). In continuous permafrost regions, deeper snow cover can lead toa significant increase in mean annual ground temperatures, a reduction in the winter freezing depth, and anincrease in soil moisture in spring and summer (Morseet al. 2012; Johansson et al. 2013; Park et al. 2015; Karjalainen et al. 2019). Such phenomena were observed in
Eastern Siberia in 2004–2007 with heavy summer rainfall
and winter snowfall (Iijima et al. 2010). Interannual variations in snow cover conditions (timing, duration, density,and thickness) also have a strong influence on long-term
ground temperatures in cold regions (Aerts et al. 2004).
During winter and early spring, arctic soil temperatures
mainly depend on the properties of snow cover andregional climate, and even small changes in snow cover
can have a strong impact on soil frost frequency andintensity (Walker et al. 1999). In addition to the soil temperature, an increase in snow cover may cause soil subsidence and waterlogging in permafrost regions witha high ice content (Nauta et al. 2015). In high-latitude
ecosystems, snowmelt timing controls not only hydrometeorological processes, but also many biological processes such as plant phenology and productivity. There
are many case studies of snow manipulation experiments
exploring the effects of changing depths of snow cover(e.g., Wipf and Rixen 2010).
Snow cover affects soil moisture in the early growing
season because approximately half of the snow waterequivalent (SWE) infiltrates into the soil after snow melt
(Sugimoto et al. 2003). Increased water availability, especially during the early growing season, can positively
affect boreal tree growth (Zhang et al. 2019), whereas
large amounts of soil moisture may lead to overwetting
conditions (Iwasaki et al. 2010; Tei et al. 2013), resulting
in reduced gross primary production (Kotani et al. 2019).
In subarctic bogs, phenology observed by earlier flowering dates was reported in an experiment with an open top
chamber (Aerts et al. 2004). On the other hand, earlier
snow melt, followed by cold air temperatures in spring,
reduced productivity in Arctic Alaskan tundra (Stowet al. 2004) and boreal forests in northeastern Siberia(Kirdyanov et al. 2003). Models with coupled hydrological and biogeochemical processes (such as CHANGE)
have also shown that deeper snow cover causes larger

net ecosystem exchange because of higher soil moisture,especially during dry years (Park et al. 2011).
Figure 1 is a schematic diagram of the effects of changes
in snow depth on boreal forest or tundra ecosystems.
Snow influences length of growing season, soil moisture,
and N availability (Fig. 1). Because an increase in snow
cover depth causes lower thermal conductivity, leading inhigher soil temperatures. This can stimulate the decomposition of soil organic matter, resulting in increased
nitrogen availability, as generally described (Schimel andClein 1996; Hardy et al. 2001; Robinson 2002; Aerts 2006;
Wipf and Rixen 2010). However, such effects are diverseand depend on ecosystem types, which is associatednot only with climatic conditions but also with vegetation, litter quality and quantity, microbial composition,and plant N demand (Li et al. 2016). Although nitrogenproduction and uptake by plants during winter are relatively well studied in temperate forests (Andresen andMichelsen 2005; Ueda and Tokuchi 2013), the numberof studies in boreal and tundra ecosystems is still limited(Cooper 2014; Koyama and Kielland 2019). Kielland et al.
(2006) described how earlier and deeper snow packs inforest ecosystems allow microbial activity to continue
during winter in Alaska, which was previously presumed
to be biologically inactive due to a negative soil temperature. They also observed that winter (non-growing
season) nitrogen mineralization accounted for approximately 40% of the annual flux, which was significantly
higher than previously reported. Moreover, deeper snow
cover causes higher soil nitrogen availability, which leads
to increased plant N uptake, foliar nitrogen content,
photosynthesis rates (Leffler and Welker 2013), leaf areaindex (Pattison and Welker 2014), and plant production(Wahren et al. 2005).
Frost damage is another factor associated with adecrease in snow cover depth. Thin snow cover may
cause freeze–thaw cycles in winter and have a negative
effect on plant production. The experimental removal ofsnow cover has produced direct frost damage, followed
by a 50% reduction in understory vegetation coverage(Kreyling et al. 2012). Indirectly, soil frost and frequent
freeze–thaw cycles in the absence of snow cover during
winter and spring can cause a decrease in nitrogen availability (Feng et al. 2007; Frechette et al. 2011), an increasein nitrogen and phosphorus leaching (Fitzhugh et al.
2001), and root damage, which disrupts nutrient uptake(Cleavitt et al. 2008). Forests with deeper organic layers
have a lower possibility of soil frost damage during winter, even if the snow cover is less deep (Hardy et al. 2001).
Although there have been many reports on snow
manipulation experiments on tundra ecosystems, few
reports have been published on forest ecosystems.
This is because snow cover manipulation in forests is

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complicated by snow shoveling, unlike in tundra, where
snow fences can be easily used to manipulate snow cover.
In temperate and boreal forests, most snow manipulation experiments have been conducted in regions withexpected reductions in snow, for example, North America and Scandinavia, and were mainly focused on thesnow removal treatment (e.g., Groffman et al. 2001).
Snow removal experiments in forested areas have showna strong effect of snow cover on ecosystem processes,
such as heat and moisture fluxes, nutrient dynamics, andchanges in phenology and diversity (Groffman et al. 2001;
Frechette et al. 2011; Kreyling et al. 2012; Comerford
et al. 2013; Drescher and Thomas 2013; Martz et al. 2016;
Watanabe et al. 2019). Because of direct frost damage tothe root system, there have been many reports on the
reduced ability of plants to uptake water and nutrients(Pilon et al. 1994; Cleavitt et al. 2008; Blume-Werry et al.
2016). A decrease in nutrient availability from shallower
snow cover due to leaching or impaired microbial activity
has also been reported (Fitzhugh et al. 2001). Moreover,Frechette et al. (2011) observed decreases in foliar N andphotosynthetic activity through spring snow removal ina Canadian boreal forest ecosystem. In a northern hardwood forest in Japan, soil ammonium increases following the enhancement of soil freeze–thaw cycles in winterbecause DON and DOC availability for soil microbes
increase (Watanabe et al. 2019). Other effects have also
been observed, such as reductions in the terminal shootlength of sugar maple trees in the U.S. (Comerford et al.
2013), a decrease in the coverage of understory plants
in Swedish boreal forests (Kreyling et al. 2012), andreductions in sapling survival in forests in Canada andnorthern Finland (Drescher and Thomas 2013; Martzet al. 2016). These effects have negative implications forbiomass production. As described above, there are many
publications on Alaska and European boreal and temperate forest ecosystems; however, no snow manipulation
experiments have been conducted for taiga (deciduous
conifer larch forests) in northeastern Siberia, which is a
globally typical forest ecosystem because of its extremely
large area.
Eastern Siberia is a vast territory covered by taiga, characterized by deciduous conifer larch trees (Larix gmelinii
and L. cajanderi) which grow on permafrost (Archibold
1995). This region experiences extreme cold winters,
short and hot summers, and an extremely dry climate(Tei et al. 2017; Tei and Sugimoto 2018). Because of thevast coverage area of larch forests worldwide (approximately 40% of boreal forests), the response of larch forests is extremely important. Changes in snow cover depth
and snow water equivalent (SWE) in this region affectsoil moisture considerably. For larch trees, soil moisture
derived from snowmelt water is extremely important in
the early summer season when photosynthesis activity ishighest (Sugimoto et al. 2002, 2003). It is also expectedthat larch trees will be affected by changing snow coverand the related processes described above. There havebeen no reports of snow manipulation experiments forlarch forests in northeastern Siberia, conditions withextremely low winter temperature and extremely dry
climate. The objective of this research is to observe theresponses of larch trees to changes in snow cover using
snow manipulation experiments.
We conducted a snow manipulation experiment in
December 2015 by manually removing snow from thesnow removal plot (SNOW−) using shovels and adding
the removed snow to the snow addition plot (SNOW+).
The snow was transported with heavy duty bags. The
aim of this study is to examine (1) whether snow covermanipulation affects soil temperature under extreme
cold winter and short spring, and (2) whether it alters thestart of the growing season. Deeper snow cover causes
longer period of snowmelt (Fig. 1); however, soil thaw
rate in spring depends on the soil temperature, which isstrongly affected by soil moisture (or ice) content. We
also investigate (3) when the nitrogen production starts,and (4) whether soil moisture affects the nitrogen availability. Notably, soil moisture is extremely important
for larch forest in northern Siberia, because of the small
amount of annual precipitation (238 mm). Therefore, notonly water availability but also N availability for plants
are also expected to be affected by soil moisture. Such
dynamic effects will cause a change in tree production.
As no direct measurements of changes in production are
conducted in this study, we instead employ the following

Fig. 1 Schematic diagram on the effect of changing snow cover
depth. Start of growing season, soil moisture and nitrogen availability
may change following increase or decrease in snow cover depth, andaffect plant productivity

Shakhmatov et al. Progress in Earth and Planetary Science(2022) 9:3

approaches: (1) needle elongation measurements as aproxy for the growing season length, which can be used
as a proxy for production change; (2) soil nutrient availability and foliar nitrogen content, which can also be
used as a proxy for the photosynthesis rate, and effects on
production. We also use (3) water isotopes to investigate
the origin of soil moisture and carbon isotope to know
photosynthetic physiology and carbon allocation trends.

2 Methods
2.1 Site description

The experiment was conducted at Spasskaya Pad Scientific Forest Station (62° 25′ N, 129° 62′ E, elevation 220 m
above sea level) of the Institute for Biological Problemsof Cryolithozone, Siberian Branch of the Russian Academy of Sciences (Fig. 2). The station is located 20 km
northwest of Yakutsk, Sakha Republic, Russian Federation (Fig. 2). Deciduous conifers (Larix cajanderi and L.
gmelinii) are the dominant species in the study area. The
forest floor is covered by various plant species, including
deciduous shrub Arctous alpina and evergreen shrubs
Vaccinium vitis-idaea and Arctostaphylos uva-ursi, andmany grass species. This region is characterized by anintense continental climate with severe cold wintersand hot dry summers with an extreme range of monthly
mean temperatures from + 19 °C in July to − 40 °C in January. The average annual mean precipitation is 238 mm(from 1971 to 2000) and the average maximum snow
cover depth is 34 ± 7 cm (from 1959 to 2015), according
to observations at Yakutsk station of the Hydrometeorological Center of Russia.
2.2 Experimental design

During the summer of 2015, three 20 m × 20 m (400 ­m2)
experimental plots for snow addition (SNOW+), snow
removal (SNOW−), and no treatment (CONTROL) were
established (Fig. 2c). Snow cover at the SNOW− plot was
manually removed using shovels and transported to the
SNOW+ plot using heavy duty bags on December 17–21,
2015. At the time of manipulation, the snow depth was
24 cm. After transporting the snow, the SNOW− plot
had approximately 1 cm of snow cover remaining, andthe snow cover depth of the SNOW+ plot did not change
because of compaction of existing snow cover by theadded snow. Soil temperature and soil moisture observations were conducted from 2015 to 2016 summer.
In spring, the snow depth and snowmelt timing wererecorded using automatic time-lapse cameras. During
the growing season, needle elongation and thaw layer
thickness were measured manually.

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2.3 Hydrometeorological observations

Air temperature and precipitation were observed using
an automatic weather observation system (WXT520,
Vaisala, Vantaa, Finland) installed at a height of 15 m(the top of the canopy). Soil moisture was observedwith dielectric permittivity probes with data loggers (HS10 with EM50 data logger, Decagon Devices,
Pullman, Washington, USA) installed at five depths
of 0.01, 0.1, 0.2, 0.3 and 0.5 m at the SNOW− andSNOW+ plots. Measurements began on June 4, 2015.
Soil temperature measurements began on July 15,
2015, using thermistors (TMC-HD with U-12-008
data loggers (HOBO, Onset Computer Corporation,
Bourne, Massachusetts, USA) at six depths of 0.1, 0.2,
0.4, 0.6, 0.8, and 1 m for SNOW− and SNOW+ plots.
The date of snowmelt was determined at all treatment
plots using images from a time-lapse camera according to the date at which 50% of the ground surface wasvisible.
2.4 Needle elongation and samplings

In order to reach the sunlight branches, we used young
larch trees. Three typical young larch trees (2–4 mheight) were selected from each treatment plot for needle elongation measurement and sampling for foliar
chemistry analysis (C and N contents, and their isotopic compositions). For each tree, three branches weremarked, and the elongation of 3–9 randomly selected
needles was measured at each branch using digital calipers (totally 38–55 replications at each plot). Each tree
was located more than 4.8 m from the edge of the plot
and each plot was 8 m apart. Needle length measurements were conducted seven times from May 8 to July8, 2016. Data was presented as percentage of the final
length of needles observed on the last day of the observation (July 8). Needles were also sampled on June 17,
July 16, and August 20, 2016, and current year shoots
were sampled on August 20, 2016 for C and N contents
and isotope analyses. Dried needles and shoots wereground and analyzed with an elemental analyzer—massspectrometer online system (Flash EA 1112 and Delta
V, Thermo Fisher Scientific, Massachusetts, USA). The
analytical accuracy (reproducibility) was 0.3% and 0.2%
for C and N content, respectively, and less than 0.2‰
for δ13C and δ15N.
2.5 Isotopic analyses of stem water and precipitation

To analyze the isotopic composition of stem water, weselected branches from mature trees. Tree branchesfrom two mature larch trees (reaching stand height) at
each treatment plot were sampled from 4 to 6 m heightusing an extendable tree pruner. Bark and cambium

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Fig. 2 Maps of Eastern Eurasia with the locations of Yakutsk, Spasskaya Pad Forest Station, and experimental sites. Maps of a Yakutsk (62° 03′ N, 129°71′ E), b Spasskaya Pad Scientific Forest Station (62° 14′ N, 129° 37′ E) with green and white areas indicating forest and grassland respectively, andred circle marking experimental sites, and c schematic maps of snow manipulation experimental sites. Purple stars show the mature sample larchtrees used for stem sampling and green diamonds are young larch trees used for needle elongation observations and needle chemistry sampling

were removed and the remaining xylem was cut into
1 cm pieces, stored in glass vials, and stored at − 18 °C
prior to extraction. Branch samples were collected on
May 14, May 27, June 7, June 15, July 16, and August 12,
2015, and on May 29, June 17, July 15, and August 14,
2016. Water was extracted from the stem cryogenicallyon a vacuum distillation line and stored in glass vials
at 5 °C prior to analysis. Rain samples were collectedduring rain events in situ using a simple rain collector. Snow cover samples were collected on December
16–19, 2015, using a metal cylinder (12 cm diameter).

The water samples were analyzed using an online system (GasBench equipped with MAT253, Thermo Fisher
Scientific, Massachusetts, USA). Analytical accuracy(reproducibility) was 0.1 and 0.2‰ for δ18O and δD,
respectively. Delta values are defined by
δ 18 O (or δD) = Rsample /RSMOW − 1 × 1000 (‰),
where Rsample and RSMOW are isotope ratios ­(H218O/H216O
or HDO/H2O) of sample and standard (Vienna Standard
Mean Ocean Water).

Shakhmatov et al. Progress in Earth and Planetary Science(2022) 9:3

2.6 Soil inorganic nitrogen

For the soil inorganic N analysis, four soil core samples
were collected from all treatment plots on July 16 to 18,
July 22 to 25, and August 13 to 15, 2016, at four depths(organic layer, 0–10, 10–20, and 20–50 cm). Roots were
removed from the soil samples, and soil N was immediately extracted from 4 g of soil sample by 40 ml of 2 M
KCl solution and mixed on a shaker (40 min). The solution and soil particles were then separated using a centrifuge and filtrated with a 0.45 μm syringe filter. The
extracts were stored at − 18 °C prior to analysis, andammonium and nitrate concentrations in the extracted
samples were analyzed using a continuous flow spectrophotometer autoanalyzer (Bran & Luebbe, Norderstedt,
Germany).
2.7 Statistical analyses

Statistical significance was analyzed using the Student’s
t test for needle length difference among treatments andbetween consecutive dates. One-way Analysis of Variance with post-hoc Tukey test was used for needle chemistry analyses. Data were compared among treatments
and consecutive dates based on a significance level ofp < 0.05.

3 Results
3.1 Hydrometeorological conditions
during the experiment

Table 1 shows the monthly averages of air temperaturesand precipitation during warm periods (April to September) as a 7-year average (2010–2016) and for 2015
and 2016. The air temperature of the years before andafter snow manipulation (2015 and 2016, respectively)
was close to the 7-year average, except for April 2015(− 6.3 °C) and September 2016 (7.9 °C), which werecolder and warmer than the 7-year average (− 0.6 and
4.7 °C), respectively. Total precipitation during the warm
period in 2015 and 2016 was mostly similar to the 7-year
average (119, 109, and 109 mm, respectively); however,June 2015 showed a larger amount of rainfall, and August
2015 and September 2016 received smaller amounts ofrain than the 7-year average. Before snow manipulation
on December 17–21, 2015, the snow density was 106 kg/
m3 with snow water equivalent (SWE) of 25 mm.
Before snow manipulation, soil temperatures of all
three plots exhibited no significant difference, except
for lower values at 0.1 m and 0.2 m depth at CONTROL
from October to December (Fig. 3). After manipulation, the soil temperature in the SNOW− plot rapidly
decreased and became significantly lower than those in
the SNOW+ and CONTROL plots. The maximum difference between SNOW− and CONTROL was 5.8 °C
at 0.1 m on December 30, 2015, and the difference

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decreased with an increase in depth. The difference
between SNOW+ and CONTROL was 3.7 °C at 0.1 m
on the same date. At the end of March, as the daytime
air temperature increased, the soil temperature started
to increase simultaneously for all treatments. No significant difference in soil temperature was observed among
the three plots during the period from March 24 to April
18, 2016. However, after snowmelt started on April 19 at
the SNOW− plot (Fig. 4a, b), the soil temperature of all
monitored soil depths (from 0.1 to 1 m) became higher
than the soil temperature at the CONTROL plot at corresponding depths. This may be caused by a differencein soil moisture, as described below. The observed difference in soil temperature continued until the end of
the observation period. The soil temperature difference
between SNOW+ and CONTROL was much smaller
than that between SNOW− and CONTROL after snowmelt. The soil temperature at 0.1 m depth in the SNOW−
plot exceeded 0 °C on April 27 (8 days after snowmelt),
and exhibited diurnal fluctuations for 12 days (until May10), decreasing to almost 0 °C during nighttime (Fig. 4a),which indicates diurnal freeze–thaw cycles. However,soil temperature at 0.1 m depth in the SNOW+ plot only
exceeded 0 °C on May 16 (20 days after snowmelt), andshowed diurnal variations with minimum soil temperatures at or below 0 °C for only seven consecutive days
(until May 23).
Before snow manipulation, there was no significant difference in the average soil thaw depth between SNOW+(58 ± 13 cm) and SNOW− (61 ± 13 cm) plots (n = 441)
on July 7, 2015 (data not shown), indicating similar soil
properties between the two plots. However, the spatial variability of soil thaw depth showed a dependency
on understory vegetation. Under patches of deciduous
shrubs with broader leaves (Arctous alpina), the thaw
depth was shallower than that under patches of evergreen shrubs (Vaccinium vitis-idaea, Arctostaphylos uvaursi) and grasses due to shading by the higher leaf area.
After snow manipulation, in the summer of 2016,
thaw depth was measured only for designated subplots(9 ­m2) with a dominant type of understory plant (deciduous shrub, evergreen shrub, grasses). On July 26, 2016,
SNOW− subplots had the deepest average thaw depth,
whereas SNOW+ subplots were the shallowest, andCONTROL subplots exhibited intermediate thaw depths.
The average thaw layer depths of the three subplots were
84 ± 9 cm for SNOW−, 72 ± 6.7 cm for SNOW+, and
77 ± 5.3 cm for CONTROL (data not shown). As the soil
thawed in the summer of 2016, soil moisture was higher
at SNOW+ than SNOW− in the surface soil layers(0.1 m and 0.3 m) (Fig. 4c) due to the infiltration of snowmelt water.

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Table 1 Monthly air temperature and precipitationMonth

Air temperature (°C)

Precipitation (mm)7-year ave

2015

2016

− 0.6

− 6.3

0.3

1.7

6.2

7.0

10.8

Jun

14.8

13.8

15.9

24.1

Jul

19.9

18.5

17.6

Aug

14.5

15.5

Sep

4.7

4.3

Apr
May

8.1

7-year ave

Precipitation δ180
(‰)
2015

2016

2015

2016

0.1

0.1

–

–

12.6

10.7

–

45.2

27.7

–

− 18.1

32.7

28.8

41.8

13.2

25.3

13.6

27.4

− 10.0

7.9

14.1

18.4

1.1

− 13.4–18− 15.1− 14.5––Monthly data in 2015 and 2016 and the 7-year average from 2010 to 2016, and δ O (‰) values of precipitation in 2015 and 2016 observed at Spasskaya Pad during
the warm period (April to September) were shown

Fig. 3 Depth distribution of isotherms and seasonal variation of soil temperature. Depth distribution of isotherms (daily averages of soil
temperature) at the CONTROL (a), SNOW− (b), and SNOW+ (c) plots, seasonal variations in soil temperature at 0.1, 0.4, and 0.8 m, and airtemperature in the CONTROL, SNOW+ and SNOW− plots (d), from August 2015 to August 2016

Shakhmatov et al. Progress in Earth and Planetary Science(2022) 9:3

3.2 Snowmelt timing and needle elongation

Figure 6 shows the growth of needles of young larchtrees, which is a percentage of the needle length on July8, 2016 in each plot. Needle lengths on June 16 were 103,
99.2, and 101% in the SNOW−, SNOW+ and CONTROL plots, respectively, indicating that needles werealready mature on June 16. The snowmelt date was theearliest in the SNOW− plot (April 19), followed by the
CONTROL plot (April 23), then SNOW+ plot (April25), as shown in Fig. 5. Leaf opening in young larch trees
in all three plots had already started on May 8, 2016, atthe beginning of the observation period (Fig. 6). Despite
the earlier snowmelt, SNOW− plot needles exhibiteddelayed elongation, with shorter length (7.4%) than thosein the CONTROL (14.1%) and SNOW+ (12.7%) plots on
May 8 (Fig. 6). This situation continued until May 30.
In the period between May 30 and June 16, there wasa different situation. Needle length in the SNOW− plotincreased significantly from May 30 (64.9%) to June 16(103%), while only small differences were observed fromMay 30 (83% for both SNOW+ and CONTROL) to
June 16 (99.2 and 101% for SNOW+ and CONTROL),
although the differences were not statistically significant.
In the early growing season, elongation of needle length
in the SNOW− plot was delayed, and continued until thelater stages.
3.3 Stem water isotopic composition

Before snow manipulation, in the summer of 2015, theδ18O of larch stem water showed clear seasonal variations
and no difference was observed among the three plots(Fig. 7a). The δ18O values were highest in May (− 13.6‰),
with a sharp decrease in June (− 21.3‰), followed by anincrease in July (− 16.5‰) and August (− 18.6‰). Aftersnow manipulation, in the summer of 2016, the δ18O values of larch stem water showed clear differences (Fig. 7b).
The δ18O of the SNOW− plot exhibited the highest values throughout summer (− 18.5‰), with only a slightincrease of 1.3‰ from June to August. CONTROL and
SNOW+ plots exhibited similar values that were lower
than those of SNOW− in early summer, but almost equal
to those of SNOW− (− 18.1‰) in August. As shown in
Table 1 and Fig. 8, summer rainfall has higher δ18O values(− 18 to − 10‰) than snow cover (approximately − 30‰);
thus, the decreased δ18O in all plots in June 2015 and in
SNOW+ and CONTROL plots in 2016 resulted from theuptake of infiltrated snowmelt water with extremely low
δ18O values, as previously described by Sugimoto et al.
(2002).
3.4 Carbon and nitrogen contents and isotopic ratios

From June to August 2016, the needle C content ofyoung trees and their δ13C values exhibited decreasing

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trends among all three plots, although no statisticallysignificant differences were observed (Fig. 9a, b). In addition, current year shoots and needles on them of young
larch trees at SNOW+ plots showed lower δ13C values(− 30.8 ± 0.7‰ and − 30.6 ± 0.8‰) than the SNOW−
plot (− 29.9 ± 0.5‰ and − 29.9 ± 0.3‰), although this difference was not statistically significant (Fig. 10). However,
we suppose that the soil moisture difference between
SNOW− and SNOW+ plots may have caused the higher
δ13C at SNOW−, as we will discuss later.
Needle N content at SNOW− decreased drasticallyfrom June 17 (1.4 ± 0.2%) to July 16 (1.2 ± 0.1%), whereas
the average values at SNOW+ and CONTROL decreasedslightly. Then, on August 20, the N content in all plots
increased, especially in the SNOW− plot (Fig. 9c).
The observed needle δ15N values ranged from − 6.2
to − 2.1‰, revealing large variability among individual
trees. Needle δ15N values of most trees exhibited anincreasing trend from June 17 to August 20, excluding
the two cases out of the three trees in the SNOW− plot.
In the SNOW+ and CONTROL plots, the increase inδ15N values from June to August was 0.8‰ (Fig. 9d).
3.5 Soil inorganic nitrogen

Soil inorganic N exhibited temporal and spatial variations at the study site (Fig. 11). Inorganic N mostly comprised ammonium at the site. A low concentration ofammonium was found in the SNOW+ plot in the middle of July (Fig. 11a). At the end of July (Fig. 11b), theconcentration of ammonium increased in the SNOW+
and CONTROL plots. These results agree to our previous study. Soil inorganic N was small in the beginning ofsummer, and increased quickly after cumulative degree
day of the soil temperature at 20 cm reached 300 °C·days(Popova et al. 2013). On the other hand, at the end of July,a low concentration of ammonium was observed at the
SNOW− plot (Fig. 11b), and at the end of August concentration of ammonium in the SNOW− plot finally
increased (Fig. 11c).

4 Discussion
4.1 Soil temperature

In our experiment, after snow manipulation, winter soil
temperature in the SNOW− plot quickly decreased whencompared with that in the SNOW+ and CONTROL plots(Fig. 3). The absence of a snowpack at the SNOW− plot
during the coldest period in winter reduced the effect ofsnowpack insulation and we expect a higher thermal conductivity of the ground surface, including the snowpack,enabling winter cold waves to cool the deep soil layer.
Therefore, because of the lower snowpack depth andhigher thermal conductivity, the SNOW− plot had lower

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Fig. 4 Soil and air temperature, and soil moisture in spring–summer 2016. a Hourly soil temperature at 0.1 m and 0.2 m depth from April 18 to May
30, 2016; b daily average air and soil temperature at 0.1, 0.2, and 0.4 m depth, and c soil moisture at 0.01 m and 0.3 m depth from April to August in2016 for SNOW− and SNOW+ plots. SNOW− and SNOW+ plots data are represented by thin and thick lines, respectively

soil temperatures until March, when air temperature rose
substantially.

The SNOW+ plot showed higher soil temperatures
than the CONTROL plot until mid-January, however,during the period from mid-January to March, the soil

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Fig. 5 Snowmelt timing observed by time-lapse cameras at CONTROL, SNOW−, and SNOW+ plots. Date of snowmelt was determined by a criteriaof 50% ground surface appearance. Snowmelt dates were April 23, April 19, and April 25 for CONTROL, SNOW−, and SNOW+ plots, respectively

Fig. 6 Growth of needle length in young larch trees in spring–summer 2016. Growth is shown as percentage of needle length at
final observation date (July 8) in each plot. Average lengths at final
observation date were 18.4, 21.1 and 25.1 mm for the CONTROL,
SNOW− and SNOW+ plots respectively. Markers are averages andbars are standard errors in the CONTROL (green circle), SNOW− (bluetriangle), and SNOW+ (red square) plots observed on May 8, 10, 14,26, and 30, June 16, and July 8, 2016.

temperature was similar in SNOW+ and CONTROL(Fig. 3). The similarity may have been caused by compaction of the snowpack. After manipulation, the observedsnow depth at SNOW+ was 24 cm, which was similar tothat before manipulation, because the snow density was
doubled by adding snow from the SNOW− plot. Generally, cold regions such as Yakutsk have lower snow
density (108 kg/m3 in winter and 161 kg/m3 in spring,as measured in 2015 at the experimental site), which

Fig. 7 The δ18O values of stem water of larch trees. The δ18O values
observed in a 2015 and b 2016 in the CONTROL (green circle),
SNOW− (blue triangle), and SNOW+ (red square) plots are shown.
“①” or “②” after plot name indicates duplicate samplingsuggests a lower thermal conductivity of the groundsurface, including the snowpack. Conversely, we expect
larger snow density observed at the SNOW+ plot correspond to a larger thermal conductivity. Thus, compactionof the snowpack may impair the insulation effect of thesnowpack (Zhang 2005).

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Fig. 8 δD–δ18O plots of precipitation and snow. Summer rainfall
in 2015 (green triangle) and 2016 (red square), snow cover on
December 16 to 19 in 2015 (blue circle), fresh snow on January 7(blue cross) and May 6 (blue diamond) in 2016, and Global Meteoric
Waterline are also shown. Snow cover sampled on December 16 to
19 is a v sample, snow sampled on January 7 and May 6 is freshly
fallen snow on the corresponding date

4.2 Length of growing season

The SNOW− plot experienced earlier snowmelt (April19) than the SNOW+ and CONTROL plots (April 25 and23), as shown in Fig. 5. However, contrary to the schematic diagram shown in Fig. 1, the SNOW− plot did notexhibit earlier needle opening, with delayed needle elongation (Fig. 6). Needle opening started before the first dayof our observation (May 8, 2015). During the period fromMay 8 to May 30, needle length in the SNOW− plot wassignificantly shorter than that in the SNOW+ and CONTROL plots, where needle elongation continued until
May 30. On the other hand, needles at the SNOW− plot
continued to elongate until June 16. Clearly, needle elongation took longer at the SNOW− plot. One of the possible reasons for this is the dry conditions (Fig. 1). The
limited water supply at the SNOW− plot during earlysummer may also be a factor influencing slower needle
elongation (Fig. 4c). In the following subsection, we discuss the influence of soil moisture.
In contrast to these findings, previous studies havefound that earlier snowmelt leads to a longer growing
season. According to satellite and ground observation
data, Barichivich et al. (2013) showed that photosynthesis activities in northern ecosystems are enhanced by

Fig. 9 Carbon content, δ13C, nitrogen content, and δ15N of larch
needles observed in 2016. a Carbon content, b δ13C, c nitrogencontent, and d δ15N of larch needles (n = 3) in the CONTROL (green
circle), SNOW− (blue triangle), and SNOW+ (red square) plots in 2016

the longer growing season, especially by earlier snowmelt and an earlier spring. Moreover, Sutinen et al.
(2015) conducted a snow manipulation experiment andshowed that soil frost in spring caused a delay in needle and shoot growth in Norway spruce. These results,
which have generally been attributed to earlier snowmelt resulting in a longer growing season, seem inconsistent with ours. In our study, SNOW− treatment
had a higher soil temperature in spring, but the drysoil condition in the SNOW− plot may cause this. The
small amount of precipitation (approximately 238 mm
per year) at our study site (Yakutsk) may explain the
difference between our study and previous research.
Another possibility not explicitly shown in Fig. 1 is thatfrost damage of fine roots caused by freeze–thaw cycles

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Fig. 10 The δ13C values of current year shoots and shoot needles
for young larch trees. Current year shoots (n = 3) and shoot needles(n = 3) of each tree sample at the CONTROL (green crossing andtriangle), SNOW− (blue crossing and triangle), and SNOW+ (redcrossing and square) plots sampled on August 20, 2016. Average andstandard deviations are shownin spring (Fig. 4a) might cause a delay in needle elongation (Fig. 6) through a decline in water and nutrient
uptake.
4.3 Soil moisture

In 2015 and 2016 at Spasskaya Pad, the precipitation values agreed with the 7-year average (Table 1). Therefore,
our experiment was conducted in typical dry conditions,with no “overwetting” conditions in the forest after thesnowmelt in 2016 spring. The origin of water in the soil
can be determined using water isotope values (Sugimoto et al. 2002). Infiltration of larger amounts of snowmelt water in SNOW+ and CONTROL plots than in the
SNOW− plot was recorded by the δ18O values of stem
water (Fig. 7b). This means that trees in the SNOW− plot
were unable to use snowmelt water, which has lower δ18O
values (Fig. 8). During summer, rainwater with high δ18O
values recharged the soil, and in August, all plots showedsimilarly high δ18O values (Fig. 7). As seen in Fig. 4c, soil
moisture in the SNOW− plot increased gradually fromearly summer. In our study area, which is in a continental
dry climate, larch trees show high photosynthetic activity
in the early growing season, because snowmelt water canbe used by plants (Sugimoto et al. 2002). Snow removal
treatment thus shows negative effect on tree growth.
Another proxy for soil moisture is the δ13C valuesof current year shoots because C used for the production of current year shoots is mainly photosynthesized
during the growing season (Kagawa et al. 2006; Li and
Sugimoto 2018). In the SNOW− plot, the δ13C valuesof current year shoots were relatively higher than thosein the SNOW+ and CONTROL plots, which suggests

Fig. 11 Soil inorganic nitrogen at each plot (n = 4). Ammonium andnitrate observed in the organic layer and at 0–10 cm, 10–20 cm, and
20–50 cm in a mid-July, b end of July, and c mid-August at CONTROL(green), SNOW- (blue), and SNOW+ (red) plots. Ammonium andnitrate are shown by dark and lighter colors, respectively. No datameans that the concentration was below the detection limit

dry conditions in the SNOW− plot, however, these values were not statistically significant because of the small
number of samples (Fig. 10).
Delayed elongation of needles in SNOW− plot may becaused by the dry soil condition in the SNOW− plot.
4.4 Nitrogen availability

As shown in Fig. 9c, on June 17 and July 16, needle N
content in the SNOW+ and CONTROL plots showedsimilar values, whereas that at SNOW− showed a large
deviation on June 17, and the value on July 16 was significantly lower than those at the SNOW+ and CONTROL plots. The results show that N uptake by trees at
the SNOW− plot was insufficient in the first half of summer. However, on August 20, needle N content in theShakhmatov et al. Progress in Earth and Planetary Science(2022) 9:3

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SNOW− plot increased to a value comparable to those of
the SNOW+ and CONTROL plots. Similar trends werealso observed in the soil inorganic N data, as shown in
Fig. 11. As most roots of larch trees are distributed in thesoil layer within 50 cm depth (Kajimoto et al. 2007; Sugimoto personal communication), foliar N contents arecompared with the observed soil inorganic N data.
Low concentration of soil inorganic N were observed at
the SNOW+ plot on July 16 to 18 (Fig. 11a) and increased
rapidly at the end of July (Fig. 11b), whereas that of the
SNOW− plot only increased in August (Fig. 11c). Therefore, these results indicate that N availability in the
SNOW− plot was low until mid-July, which may have
led to lower needle N content in the SNOW− plot during
this period. Subsequently, trees in the SNOW− plot usedsoil inorganic N at the end of July and in August, and theneedle nitrogen content in the SNOW− plot in August
increased to roughly the same as that in the SNOW+ and
CONTROL plots (Fig. 9c). It should be noted that the soil
temperature at the SNOW− plot was higher than thoseat the SNOW+ and CONTROL plots during the growing season (Fig. 4). Foliar δ15N at the SNOW+ and CONTROL plots (Fig. 9d) showed a gradual increase from
June to August. Because soil organic matter in the deeper
layer generally has higher δ15N values than plant tissue
or atmospheric nitrogen depositions (Craine et al. 2015),
observed increases in foliar δ15N (Fig. 9d) correspond to
the use of soil N.
There are several possible reasons for the delayed
increase in soil inorganic N availability at the
SNOW− plot. One may be the freeze–thaw cycles in
the SNOW− plot during the period from April 28 (theday when the soil temperature exceeded 0 °C) to May 8(the day when the soil temperature stopped decreasing to 0.1 °C or lower during the night) (Fig. 4a), which
may damage the microbial biomass in the soil and plantroots. Another reason may be the dry soil conditions in
the first half of the summer (Figs. 4c, 7b). Under dry soil
conditions, inactive microbial decomposition may lead
to less available N at the SNOW− plot, and the inactive uptake of N by plants causes low N contents in theneedles. As described already, climate of study site (nearYakutsk) shows extremely low winter temperature, therefore extremely low soil temperature at the SNOW− plot
might affect the soil microbes and plant roots.
At the SNOW+ plot, increase in snow depth did not
significantly alter winter soil temperature. This may be
because of compaction of added snow, which leads tosnow density double, compared to the CONTROL plot,
resulting in the decreased thermal conductivity. Besides,
additional snow provided sufficient soil moisture in theearly growing season, and the start of production of soil

inorganic N in the SNOW+ plot was earlier than in the
SNOW− plot.
We could not estimate the effect of doubled snow density on the ecosystem. In the natural condition, snow
density was much lower than the observed density in our
SNOW+ treatment. This would affect the soil temperature and consequently N availability.
4.5 Comparison with other regions

Our study site is located in a continuous permafrost zone
with an extreme continental subarctic climate. It has
extremely cold long winters with a small amount of snowfall and dry and hot short summers. Compared to otherregions, it is a rather complex study area because it is as
cold as high arctic tundra but as dry as alpine tundra, andcontains abundant forests. Groffman et al. (2001) proposed a hypothesis whereby global warming will cause
less snow cover during winter and therefore less thermal
insulation, which will lead to the “colder soils in a warmer
world” phenomenon. They conducted a snow removal
experiment in a northern hardwood forest in the US andreported only mild soil freezing (− 4 °C) in winter, indicating a greater potential for freeze–thaw cycles during
winter. Active growth of soil microbes during winterand/or the snowmelt period was reported following
snow reduction experiments in northern forests (Groffman et al. 2001; Durán et al. 2014; Sorensen et al. 2016;
Watanabe et al. 2019). On the other hand, at our site,
the soil in the CONTROL plot was frozen for more than
3months and the soil temperature was below − 14 °C.
Moreover, the soil temperature at the SNOW− plot wasless than − 19 °C soon after snow removal (Fig. 3). Such
low temperatures could cause mechanical or cellularroot damage. In our experiment, the production of soilammonium at the SNOW− plot was delayed until theend of July (Fig. 11c). The low soil temperature at the
SNOW− plot observed during winter might damage not
only the roots but also the soil microbes. In addition,
freeze–thaw cycles in spring (Fig. 4a) may also damage
soil microbes.
Owing to these problems, snow removal in the experiments caused a delay in needle elongation during theearly growing season. It has previously been reported
that low spring soil temperatures affect the ecosystem.
In a boreal forest in Finland, the bud burst of Norway
spruce was delayed for 2 weeks in a spring frost experiment, in which insulation by 15 cm of hay was applied,
resulting in prolonged soil frost (Sutinen et al. 2015).
This indicates that spring soil frost affects the length of
the growing season. In our experiment, we observed thatearlier snowmelt at the SNOW− plot did not hasten needle flushing, but slowed needle elongation, although theShakhmatov et al. Progress in Earth and Planetary Science(2022) 9:3

soil temperature was higher than that at the CONTROL
plot. This result contradicts the diagram illustrated inFig. 1, where shorter period for snowmelt cased an earlier
start of the growing season. Our results showed the later
start of growing season, despite the shorter period ofsnowmelt and higher soil temperature. Our study site ischaracterized by extremely low air temperature and dry
conditions in winter, as well as a short snowmelt season.
Therefore, it is very important for plants to obtain water,
instead of warming the soil during the needle flushing
period.
In the present study, we observed soil inorganic N onlyin summer season. Additional studies to investigate soil
microbial activity, especially during winter and spring,are required in the future.

5 Conclusions
A snow cover manipulation experiment was conductedin December 2015 at the Spasskaya Pad Scientific Forest Station in the Eastern Siberia taiga forest ecosystem. The snow removal (SNOW−) plot showed a moresignificant effect of manipulation than the other plots.
Soon after the manipulation, the SNOW− plot showed
extreme cold soil temperature. Needle elongation was
delayed in the SNOW− plot, despite earlier snowmelt
and a higher soil temperature during the growing season.
Production of soil inorganic N was also delayed in the
SNOW− plot, and the larch needle N content was lower
than that in other plots until July. Soil ammonium at the
SNOW− plot was eventually produced in August, which
resulted in a comparable needle N content to the otherplots. Dry soil conditions in the SNOW− plot, during
the snowmelt period and early growing season, caused
higher soil temperature, and also caused freeze–thaw
cycles in spring, resulting in soil N production.
The study duration was only 1 year, however, weobserved a clear response of larch trees to snow coverchanges, including changes in phenology (needle opening and elongation). Moreover, longer experiments may
cause changes to the ecosystem, such as photosynthesis and production. It is also difficult to predict possible
scenarios for long-term ecosystem change because otherfactors, such as overwetting or permafrost degradation,
should also be assessed.
Acknowledgements
We acknowledge Dr. A. Kononov, R. Petrov, E. Starostin, and the othermembers of the IBPC for supporting our fieldwork at Spasskaya Pad Scientific
Forest Station, and H. Shimada and M. Borisov for their assistance in summer
and winter fieldwork. We also thank Y. Hoshino, S. Nunohashi, H. Kudo, and A.
Alekseeva for their support in laboratory work and logistics.
Authors’ contributions
RS conducted the experiments and wrote the manuscript, and SH ana‑lyzed the soil inorganic N. TM organized all processes for field works and AS

Page 14 of 16

designed the experiments, and wrote the manuscript. All authors read andapproved the final manuscript.
Funding
This work was supported by GRENE Arctic Climate Change Research Project
funded by the Ministry of Education, Culture, Sports, Science and TechnologyJapan (MEXT), and Belmont Forum Arctic program COPERA project funded by
Japan Science and Technology Agency.
Availability of data and materials
Please contact author for data requests.
Declaration
Competing interests
The authors declare no competing interests.
Author details
Graduate School of Environmental Science, Hokkaido University, Kita 10 Nishi
5, Kita‑ku, Sapporo 060‑0810, Japan. 2 Institute for Biological Problems of Cryo‑
lithozone, Siberian Branch of Russian Academy of Sciences, 41 Lenin Avenue,
Yakutsk, Sakha, Russia 677000. 3 Arctic Research Center, Hokkaido University,
Kita 21 Nishi 11, Kita‑ku, Sapporo 001‑0021, Japan. 4 Faculty of Environmental
Earth Science, Hokkaido University, Kita 10 Nishi 5, Kita‑ku, Sapporo 060‑0810,
Japan.

1

Received: 26 July 2021 Accepted: 9 December 2021

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