Northern peatlands represent one of the largest carbon pools in the
biosphere, but the carbon they store is increasingly vulnerable to
perturbations from climate and land-use change. Meteorological observations
taken directly at peatland areas in Siberia are unique and rare, while
peatlands are characterized by a specific local climate. This paper presents
a hydrological and meteorological dataset collected at the Mukhrino
peatland, Khanty-Mansi Autonomous Okrug – Yugra, Russia, over the period of
8 May 2010 to 31 December 2019. Hydrometeorological data were collected from
stations located at a small pine–shrub–Sphagnum ridge and Scheuchzeria–Sphagnum hollow at ridge–hollow
complexes of ombrotrophic peatland. The monitored meteorological variables
include air temperature, air humidity, atmospheric pressure, wind speed and
direction, incoming and reflected photosynthetically active radiation, net
radiation, soil heat flux, precipitation (rain), and snow depth. A
gap-filling procedure based on the Gaussian process regression model with an
exponential kernel was developed to obtain continuous time series. For the
record from 2010 to 2019, the average mean annual air temperature at the
site was -1.0∘C, with the mean monthly temperature of the
warmest month (July) recorded as 17.4 ∘C and for the coldest
month (January) -21.5∘C. The average net radiation was about
35.0 W m-2, and the soil heat flux was 2.4 and 1.2 W m-2 for the
hollow and the ridge sites, respectively.
The presented data are freely available through Zenodo
(10.5281/zenodo.4323024, Dyukarev et al.,
2020), last access: 15 December 2020) and can be used in coordination with
other hydrological and meteorological datasets to examine the
spatiotemporal effects of meteorological conditions on local hydrological
responses across cold regions.
Introduction
The availability of hydrometeorological data is limited in northern
latitudes because of a sparse monitoring network, harsh weather, and the
high cost of experiments and instrument maintenance in these environments
(Rasouli et al., 2019). The number of stations that record a complete
hydrometeorological dataset in the northern latitudes is limited and
declining (Laudon et al., 2017). Weather stations located directly in
peatland areas are unique and rare, while peatlands are characterized by a
specific local climate (Worrall et al., 2019; Kiselev et al., 2019;
Koronatova et al., 2018).
Northern peatlands developed mostly after the last deglaciation in the
circum-Arctic region and represent one of the largest carbon pools in the
biosphere (Yu, 2012). West Siberian peatlands are wetlands representing a
long-term carbon dioxide sink and global methane source since the early
Holocene (Sheng et al., 2004). Peatlands clearly play a significant role in
global carbon cycling, and the carbon they store is increasingly vulnerable
to perturbations from climate and land-use change (Amesbury et al., 2019).
Temperature is the most important long-term driver of peat accumulation in
northern peatlands, and excessive moisture is deemed a necessary condition
for peatland development, maintenance, and carbon preservation (Loisel et
al., 2021). Increased global warming, such as the increased temperature and
resulting water table drawdown, may imbalance peatland carbon cycles,
resulting in a large feedback to the global climate (Packalen et al., 2015;
Samson et al., 2018; Dyukarev et al., 2019).
Large peatland systems in Western Siberia occupy about 28 % of the area
(Sheng et al., 2004; Terent'eva et al., 2017), and continued observations of
atmospheric conditions and upper soil layers are therefore of great
importance. Modern hydrometeorological records for the northern part of
Russia are rare (i.e., Beer et al., 2013; Heimann et al., 2014; Boike et al.,
2019), and they are primarily related to Arctic sites. Hydrometeorological
data are required for the study of the ecosystem–atmosphere exchange
(Alekseychik et al., 2017; Holl et al., 2019), biochemical processes in peat
(Szajdak et al., 2016; Djukic et al., 2018), hydrology (Bleuten et al.,
2020), and microbiology including mycology (Filippova and Lapshina, 2019).
Mukhrino field station (MFS – http://mukhrinostation.com, last access: 4 May 2021) was established in 2009
as part of the UNESCO chaired Environmental Dynamics and Global Climate
Change (EDCC) of the Yugra State University (Khanty-Mansiysk, Russia). It is
equipped with modern facilities allowing the conduct of year-round long-term
scientific research, scientific excursions, workshops, symposia, and other
events at the national and international levels.
MFS became part of INTERACT – International Network for Terrestrial Research
and Monitoring in the Arctic (https://eu-interact.org/field-sites/mukhrino-field-station/, last access: 4 May 2021) in 2012 and
has developed its infrastructure in line with the network activities. The
station is regularly visited by international research groups through the
INTERACT Transnational Access (TA) working package to conduct field work at
the station. About 30 international research projects were conducted at MFS
through INTERACT TA during the last 5 years. The participation in INTERACT
is propelling the development of other functions of Arctic stations, e.g., monitoring standardization, data management, communication, safety
regulations, science outreach, and other activities, thus developing the
station according to international best practices.
MFS is located in the central part of Western Siberia in the middle taiga
biogeographic zone, 30 km to the southwest of Khanty-Mansiysk, on the left
upper terrace of Irtysh River (near the confluence with Ob River) at the
Mukhrino peatland (Fig. 1). The wide area to the southwest is represented
by the paludified peatland and lake landscape of the Kondinskaya
Nyzmennost interspersed by forests along the rivers. The Mukhrino peatland
is located at the northernmost part of it, bordering with the Ob River
floodplain and distinguished from the other surroundings by an oval shape
about 10×5 km in size. The MFS research polygon is located in the
northeast part of the peatland and covers an area of about 1 km2,
providing a system of walking boards with a total length of about 2 km, an
energy supply complex, and permanent monitoring plots for peatland ecosystem
studies with hydrometeorological equipment.
Different aspects of Mukhrino peatland were described in a series of
publications over the history of MFS (Lapshina et al., 2015). The study of
hydrological dynamics and fire history for the last millennium (Lamentowicz
et al., 2015, 2016) revealed that Mukhrino peatland was
wet until the Little Ice Age, when drought was recorded. The hydrological
model of a peatland is provided by Bleuten et al. (2020). The net ecosystem
exchange studies reported that the native peatland complex acts as a net sink for
the carbon dioxide (Alekseychik et al., 2017; Dyukarev et al., 2019). The
present stage of the peatland development is represented by a raised
oligotrophic bog with a mosaic of ridge–hollows, oligo-mesotrophic fens, and
treed bog micro-landscapes. A few secondary lakes up to 300 m in width are
located in the most waterlogged areas, and the central part of the peatland
is occupied by a wide watercourse. The average peat depth is 3.3 m, with the
longest core depth (located at an ancient alley) reaching about 5 m (Bleuten
et al., 2020). The most abundant peat type is Sphagnum peat, with pH 3.5–5
and electric conductivity from 0 to 200 µSmm-2 (Sabrekov et
al., 2011).
The vegetation comprises oligotrophic communities dominated by various
Sphagnum species. The highest levels with the ground water below 30 cm (about a
third of the peatland area) are covered by pine–dwarf shrub–Sphagnum communities
(so-called “ryams”), dominated by Pinus sylvestris and P. sibirica and several dwarf shrubs
(Chamaedaphne calyculata, Ledum palustre, Andromeda polifolia, Vaccinium uliginosum, V. oxycoccus). Herb species, such as Rubus chamaemorus, Carex globularis, Eriophorum vaginatum, and Drosera spp. are scarce in diversity and
density. The dominating species of Sphagnum here is S. fuscum, with other species (S. magellanicum, S. angustifolium, S. capillifolium) in
admixture. The pine–dwarf shrub–Sphagnum communities also participate in
ridge–hollow complexes and their variations, the most abundant landscapes
of Mukhrino peatland, with minor differences in plant composition. The lower
positions of the landscape with a ground water level of 0–15 cm are covered by
graminoid–Sphagnum communities. The dwarf shrubs are represented by Andromeda polifolia and Oxycoccus palustris. The herbs
include several species: Scheuchzeria palustris, Carex limosa, Eriophorum russeolum, and Drosera spp. Several hydrophilic Sphagnum species are
dominant in the moss cover: S. balticum, S. papillosum, S. jensenii, S. majus, and S. lindbergii. The most waterlogged conditions along peatland
watercourses contain sparse vegetation from several floating species like
Menyanthes trifoliata and Sphagnum majus.
The hydrometeorological complex described in the paper is located inside the
irregular ridge–hollow complex with nearly equal proportions of the
pine–dwarf shrub–Sphagnum ridges and graminoid–Sphagnum hollows. The trees' mean
height at the ridges is about 3 m, with sparse trees reaching up to 10 m
height. Waterlogged areas, lakes, or streams with open water are absent in
the near vicinity (but exist about 500 m away).
List of sensors, parameters, and installation site at Mukhrino field
station, 2010–2019.
nParameterEquipmentRidgeHollow1Air temperature and humidity at 2 mRotronic HC2A-S3112Atmospheric pressureCampbell Scientific CS105 PTB101b1–3Wind speed and direction at 2 mYoung wind monitor 05103–14Wind speed and direction at 10 mYoung wind monitor 051031–5Incoming PARLi-Cor LI-190R116Reflected PARLi-Cor LI-190R117Net radiation balanceKipp & Zonen NRLite118Ground heat fluxHukseflux heat flux sensor HFP01SC219Precipitation (summer)HOBO data logging rain gauge RG3-M–110Snow depthManual observations–111Surface albedoCalculated from Li-Cor LI-190R11Data description
Hydrometeorological data are available for MFS from 2010 to 2019 for two
sites at boreal raised peatland in typical micro-landscape forms. Data on air
temperature, air humidity, atmospheric pressure, wind speed and direction,
incoming and outgoing shortwave radiation, net radiation, and soil heat flux
were recorded at three automated weather stations. Two stations were located
at a small pine–shrub–Sphagnum ridge and one station at a Scheuchzeria–Sphagnum hollow. The hollow
represents a wet site with the water level near the surface (0–15 cm), and
the ridge is a relatively dry site with the water level at 20–40 cm depth.
All sensors were connected to four Campbell Scientific data loggers CR10X
with AM16/32A multiplexers to collect data. The data were collected at a scan
rate of 30 s and averaged for 15, 30, and 60 min intervals by data logger
software. Table 1 lists the sensors measuring the meteorology parameters and
their location. The weather stations were assembled and tested by In Situ
Instrument AB (Sweden).
An air temperature and humidity probe was covered by a naturally ventilated
radiation shield Rotronic AC1000. An atmospheric pressure sensor was mounted
inside the enclosure case for the data logger. Wind speed and direction
sensors were installed on a 10 m mast at the ridge site and a 2 m tripod at
the hollow site. The distance between the mast and tripod is about 15 m. Net
radiation and upward and downward photosynthetically active radiation (PAR)
sensors were mounted at each site on a 2 m support crossarm CM200 with a
leveling fixture. Two soil heat flux sensors were installed at the ridge
site to cover the spatial variability of fluxes due to the inhomogeneous
micro-landscape. The soil heat flux sensors were initially installed within
the moss layer at a depth of 10 cm and checked in 2015 and moved again to a
10 cm position. In 2020 the heat flux sensors were found at a depth of 20 cm
due to the growth of mosses and increase in the dead moss layer thickness. A
self-calibrating procedure was applied every 3 min each hour for
calibration of the soil heat flux sensors. The heat flux values generated
during the self-calibration process were excluded from time averaging.
Liquid precipitation was measured by an unshielded tipping-bucket rain gauge
deployed at the surface level after the disappearance of the snow cover. The
automated measurements in 2010–2013 were accompanied by routine manual
meteorological observations of air temperature and humidity and
precipitation. Measurements of the snow depth were made daily from November
to April in 2011–2014 using permanently installed snow depth lines with a 1 cm scale. Snow precipitation was measured manually using a rain gauge of
Tretyakov construction, and the frequency of measurements varied from a day
to a week during the whole period of observations.
Some other characteristics were recorded by the weather stations, such as
the standard error of the wind direction, average soil temperature in the
0–20 cm layer, and battery voltage, but are not discussed here. Soil
temperature measurements at five depths down to 50 cm were made using a
Hukseflux thermal sensor STP01 at four sites (two at ridges and two at
hollows). Soil surface temperature was measured using an averaging soil
thermocouple probe TCAV (Campbell Sci. Inc.). Soil temperature data are not
presented here due to the high level of noisy disturbed data.
Number of days with hydrometeorological data at Mukhrino field
station, 2010–2019.
Data processing
All time series data were collected and stored in the data loggers at the
weather stations. Several times a year data were manually downloaded,
rearranged, and archived at the Yurga State University. Before 2020 raw data
were publicly available through a shared Google Drive directory (Mukhrino
Weather Station, 2020; Mukhrino Field Station, 2020). Due to a power system
malfunction, the weather stations operated in 2010–2012 for less than half
the year. There were data missing each year, and the number of full days with
available data is shown in Fig. 2. The weather stations recorded data at 15 min intervals in 2010–2011, hourly intervals in 2012–2013, and half-hourly intervals
since 2014 but are reported hourly in this paper. The missing data were
denoted by “NA”. Raw data were thoroughly checked for errors and erroneous
data were removed. Soil heat flux sensors produce unnatural spikes in
measurement data so these spikes were removed from the data. If the
deviation from the moving average is greater than 5 standard deviations for
a centered time window of 3 d, the data point is discarded. The
rejected data were denoted by “NA”.
Factory calibration coefficients were applied to the data of the PAR and net
radiation sensors. All the other sensors' calibration coefficients were
implemented in the data recording software in the data logger. Surface
albedo was calculated as the ratio of incoming and reflected PAR for values
of incoming PAR exceeding 30 µmolm-2s-1.
Data gap-filling
The number of missing observation data in the early period of automated
station operation is high. Therefore, different methods for the gap-filling
procedure were tested. Continuous weather data on various meteorological
characteristics are required to produce a continuous gap-free data set.
List of meteorological variables from ERA5-Land reanalysis used for
gap-filling and minimal, maximal, and average values for 2010–2019. mwe: meter
of water equivalent.
nVariable, unitnVariable, unit12 m temperature, ∘C24Skin reservoir content, mwe2Skin temperature, ∘C25Runoff, m s-132 m dew point temperature, ∘C26Surface runoff, m s-14Relative air humidity, %27Sub-surface runoff, m s-1510 m U wind component, m s-128Snow cover, %610 m V wind component, m s-129Snow depth, m7Wind speed at 10 m, m s-130Snow depth water equivalent, mwe8Wind direction at 10 m, ∘31Snow albedo9Surface pressure, hPa32Snow density, kg m-310Total precipitation, mm h-133Temperature of snow layer, ∘C11Surface solar radiation downwards, W m-234Snowfall, mwe s-112Surface thermal radiation downwards, W m-235Snowmelt, mwe s-113Surface net solar radiation, W m-236Snow evaporation, mwe s-114Surface net thermal radiation, W m-237Leaf area index (LAI), high vegetation, m2 m-215Forecast albedo38LAI, low vegetation, m2 m-216Surface sensible heat flux, W m-239Soil temperature (ST) level 1, ∘C17Surface latent heat flux, W m-240ST level 2, ∘C18Potential evaporation, mwe s-141ST level 3, ∘C19Evaporation (EV), mwe s-142ST level 4, ∘C20EV from bare soil, mwe s-143Volumetric soil water (VSW) layer 1, %21EV from open water surfaces, mwe s-144VSW layer 2, %22EV from the top of canopy, mwe s-145VSW layer 3, %23EV from vegetation transpiration, mwe s-146VSW layer 4, %
The fourth-generation reanalysis ERA5 was chosen as a source of continuous
meteorological data. The ERA5 dataset showed the best performance with
NASA's most recent satellite-based dataset (Hennermann, 2019). ERA5 updates
ERA-Interim using the most recent ECMWF model (Hersbach et al., 2018),
adopting a four-dimensional variational data assimilation system (4D-Var).
It improves the correction of satellite observations and ground-based radar
(Beck et al., 2019). Hourly data for 46 meteorological parameters (see Table 2) were provided by the ECMWF downloaded from the Climate Data Store
(Muñoz-Sabater, 2020) for the period from January 2010 to
December 2019.
The dataset has a spatial resolution of 0.1∘× 0.1∘, which approximately corresponds to 11.1 km in latitude and
5.4 km in longitude for the MFS area. The ERA5-Land time series for a grid
point with coordinates 60.9∘ N, 68.7∘ E was used as
reference continuous meteorological data. It is clear that direct comparison
of local observation data with the global reanalysis product is senseless,
because the data sets have completely different origins and purposes.
Nonetheless, ERA5-Land reanalyses reproduce local weather conditions with
reasonable accuracy. The differences in the time series of observed and
reanalysis data are high, but the linear correlation is good (Berg et al.,
2018; Kharyutkina et al., 2019).
Monthly averaged air temperature (a) and water vapor pressure (b)
at the hollow (1) and the ridge (2) in January and July. Bars show standard
deviations for daily data.
Monthly averaged diurnal course of incoming and reflected
photosynthetically active radiation (a) (µmolm-2s-1) and
net radiation balance, soil heat flux (b) (W m-2) at hollow and ridge
in January, April, July, and October. Mean values for 2010–2019. Legend: (a) 1: incoming PAR, hollow; 2: incoming PAR, ridge; 3: reflected PAR,
hollow; 4: reflected PAR, ridge. (b) 1: net radiation, hollow; 2: net
radiation, ridge; 3: soil heat flux, hollow; 4: soil heat flux, ridge,
site 1; 5: soil heat flux, ridge, site 2.
Daily snow depth for 2010–2019.
Annual wind rose for 2010–2019 at 10 m (a) and 2 m (b). Legend for wind speed: 1: 0.5–2 m s-1; 2: 2–5 m s-1; 3: >5 m s-1. Wind direction for wind speed below
0.5 m s-1 was not accounted for in plotting.
Several regression models were tested for the gap-filling procedure. The
model performance was estimated using the root-mean-squared error (RMSE)
value. Model parameters were estimated on the training set, and its
performance was assessed with the validation set. The model used for
validation is based on 75 % of the data. The final model is trained using
the full data set. Regression models were optimized using the Regression
Learner toolbox from MATLAB. Regression Learner performs supervised
machine learning by supplying a known set of observations of input data
(predictors) and known responses. The list of tested regression models
includes linear; interaction linear; robust linear; stepwise linear;
quadratic, fine/medium/coarse tree, support vector machine regression with
linear, quadratic, cubic, and Gaussian kernel; Gaussian process regression
with rational quadratic; squared exponential; Matern 5/2; and exponential
kernel (The MathWorks Inc., 2019). It was found that the Gaussian process
regression model exponential kernel gives the minimal RMSE for all
observation time series.
Wind direction and wind speed observation data were recalculated into
meridional and zonal (U and V) wind components. Relative air humidity was
recalculated into water vapor pressure. Before model training, missing snow
depth data when ERA5 reanalysis indicated the absence of snow cover was set
to zero. Missing incoming/reflected PAR data was set to zero for the nighttime. Nighttime periods were determined as the time when downward solar
radiation from ERA5 reanalysis is zero.
Models were trained for 19 time series of meteorological parameters,
including air temperature, air absolute humidity, incoming and reflected
PAR, net radiation, and U and V wind components for both the ridge and hollow
sites. Regression models for atmospheric pressure and snow depth were
trained using observation data for a single site. Three models were trained
against soil heat flux observation data at the ridge (two sites) and the
hollow (one site).
Average, minimal, and maximal errors for model data for 2010–2019.
ME: mean error; MAE: mean absolute error; RMSE: root-mean-squared
error.
nIDVariableUnitMinMaxMeanMEMAERMSE1taHAir temperature hollow∘C-45.0132.76-1.05-2.09×10-42.83×10-12.09×10-32taRAir temperature ridge∘C-43.9532.86-0.98-8.89×10-42.05×10-11.61×10-33vpHVapor pressure hollowkPa02.700.64-1.14×10-65.61×10-43.92×10-64vpR'Vapor pressure ridgekPa02.630.633.27×10-64.89×10-43.69×10-65iparHIncoming PAR hollowµmolm-2s-101520.8190.23.06×10-33.11×10-11.16×10-46iparRIncoming PAR ridgeµmolm-2s-101587.3192.79.56×10-32.94×10-11.22×10-47rparHReflected PAR hollowµmolm-2s-101283.049.72.25×10-21.27×1008.01×10-48rparRReflected PAR ridgeµmolm-2s-101087.536.71.14×10-29.26×10-16.94×10-49nrHNet radiation hollowW m-2-166.3668.634.69.52×10-51.12×10-19.10×10-510nrRNet radiation ridgeW m-2-203.6661.535.31.42×10-41.31×10-11.14×10-412shfHSoil heat flux hollowW m-2-24.030.72.41.25×10-45.80×10-34.24×10-511shfR1Soil heat flux ridge 1W m-2-72.6108.61.5-7.41×10-41.91×10-21.24×10-413shfR2Soil heat flux ridge 2W m-2-33.732.70.9-3.27×10-46.95×10-34.88×10-514w10UU wind at 10 mm s-1-10.014.10.5-6.23×10-68.03×10-25.96×10-415w10VV wind at 10 mm s-1-11.611.90.1-2.22×10-58.03×10-36.24×10-516w2UU wind at 2 mm s-1-7.19.70.2-2.75×10-61.72×10-31.16×10-517w2VV wind at 2 mm s-1-5.68.10.2-3.98×10-52.49×10-21.76×10-418prsAtmospheric pressurekPa98.1108.0103.25.85×10-51.30×10-38.29×10-619sdpSnow depthcm095.025.46.15×10-46.25×10-33.62×10-4
All 46 parameters from the ERA5-Land reanalysis were used as input variables
for the models. The model data have an extremely high linear correlation
coefficient (r>0.99) with the observation data. Long-term mean,
range, and errors for the model data are shown in Table 3. Comparisons of the
observed and modeled time series, residuals, and probability distributions
are given in the Supplement Figs. S1 to S19. Mean, mean absolute,
and root-mean-squared errors for all the modeled time series are small
(Table 3), and therefore the model data can be used to interpolate the
observations into the gaps. The probability distributions of the observed
and model data (Figs. S1–S19) are very close. Extremely small errors for
model data were obtained due to a large number of input discontinuous
variables (46) from ERA5-Land reanalysis.
Gap-filled time series were constructed from all the available data of
observations, replacing the missing data with model values. Negative model
data for incoming and reflected PAR were set to zero. Filling the gaps in
data of the precipitation time series is a very complex task and was not
solved in the present research. Comparison of the liquid precipitation data
with total precipitation from ERA5-Land reanalyses is shown in Fig. S20 in
the Supplement. Figure S21 illustrates the calculated albedo
variations.
Code availability
The codes used to develop the database are interconnected mixtures of MATLAB, Python, and Basic scripts that are difficult to make available on a public repository. Please contact the authors for any related questions.
Data availability
The database presented and described in this article is available for
download from Zenodo 10.5281/zenodo.4323024
(Dyukarev et al., 2020). Gap-filled, quality-controlled, and raw observation
data are provided in separate files in CSV format.
Data examples
Figures 3–6 illustrate the annual, seasonal and diurnal variations in the
hydrometeorological parameters observed at MFS. The monthly air temperature
varies from 13.8 to 17.4 ∘C in July and from -27.8 to -17.3∘C in January (Fig. 3a), whereas the absolute temperature minimum
was -45.0∘C at 22:00 LT (GMT+5) on 21 December 2016, and the absolute
temperature maximum was 32.9 ∘C at 16:00 LT (GMT+5) on 5 August 2016. The
average, minimal, and maximal values for all the observed variables are shown
in Table 3.
The air humidity in winter is much lower than in summer. The monthly water
vapor pressure varies from 0.05 to 0.16 kPa in January and from 1.22 to 1.69
in July (Fig. 3b). The differences between measurements of air parameters
obtained at the ridge and the hollow sites are insignificant. The two sites
are closely situated, and intense air mixing equalizes the air conditions.
The incoming PAR registered at both sites (Fig. 4a) has a maximum at noon,
and the value of the maximum rises from December to July. The amount of
reflected PAR is closely related to the state of the surface. The albedo
for the PAR range (the ratio of reflected and incoming PAR) in summer is
about 0.03 and 0.06 at the hollow and ridge sites, respectively. Extremely
small albedo values are related with the spectral range of the PAR sensor.
The PAR range albedo can be useful for characterizing the vegetation
greenness. The albedo in winter at snow-covered surfaces is about 0.95 at
the hollow site and 0.8 at the ridge site, where small dark branches of
trees are present. The net radiation balance has close maximal values at
both sites (Fig. 4b), but the diurnal course of net radiation at the ridge
is shifted 1 h later compared with the hollow site. The January net
radiation is negative and varies within a range from -8 to -18 W m-2
during the day. The daily averaged soil heat flux is negative from October to
March. The maximal heat flux into the soil was observed in June at
approximately 18:00 local time. The amplitude of diurnal variations in soil
heat flux at the hollow is 2–3 times higher than at the ridge. The soil
heat flux sensors at the ridge were located under the porous mat of weakly
decomposed dead mosses isolating the peat layers from heating.
The snow cover onset date varies from 9 October 2014 to 3 November 2010 (Fig. 5). Maximal snow storage was recorded on 18 March 2013 when the
snow depth reached 95 cm. The winter of 2010–2011 was the season with the
weakest snowpack. The snow cover at the end of winter on 16 February 2011
was only 64 cm. Complete melting of the snow can take place between 16 April and 19 May depending on the year. The average duration of the snow cover
period is 191 d. South-southwest winds prevail at the observation site
(Fig. 6), but winds with speeds above 5 ms-1 are mostly of northwest origin.
The median wind speed value at 10 m is 1.8 ms-1, while at 2 m above the
surface it is only 1.0 ms-1. The wind rose structure is similar for all
observation years, except 2017 and 2019.
The automated weather station at the MFS was rebuilt in October 2020. All
the sensors were connected to a new data logger (CR1000X) through four
multiplexers. A four-channel net radiometer CNR1 (Kipp & Zonen) was
installed for measuring the incoming short-wave, incoming long-wave,
surface-reflected short-wave, and outgoing long-wave radiation. A new
rain gauge MPDO-500.120 Volna (MeraPribor) with heater will allow winter and
summer precipitation to be registered. We will continue to update these data
sets for use in baseline studies as well as to assist in identifying
important processes and parameters through conceptual or numerical modeling.
The supplement related to this article is available online at: https://doi.org/10.5194/essd-13-2595-2021-supplement.
Author contributions
ED, NF, NV, EZ, and EL cleaned, organized, and
corrected the data and wrote the first draft of the paper. ED and NV
developed the gap-filling procedure. NS, DK, IF, AA, and VA designed and
built the instrumental stations, collected data, managed the data collection
over the last decade, and contributed to the writing of the manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
Any reference to specific equipment types or
manufacturers is for informational purposes and does not represent a product
endorsement.
Acknowledgements
The research was carried out within the grant of the Tyumen regional government in accordance with the Program of the World-Class West Siberian Interregional Scientific and Educational Center (national project “Nauka”). Fieldwork support by MFS staff, Yaroslav Solomin, and Alexey Dmitrichenko was essential in accurate data collection in adverse conditions.
Financial support
This research has been supported by the Russian Fund for Basic Researches (grant nos. 15-44-00091, 18-05-00306, and 18-44-860017) and by the Yugra State University (grant no. 17-02-07/58 from 14 February 2020). The Mukhrino field station infrastructure development was supported by the INTERACT project – International Network for Terrestrial Research and Monitoring in the Arctic (grant nos. 730938 and 871120).
Review statement
This paper was edited by Ge Peng and reviewed by two anonymous referees.
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