Most of the world's permafrost is located in the
Arctic, where its frozen organic carbon content makes it a potentially
important influence on the global climate system. The Arctic climate appears
to be changing more rapidly than the lower latitudes, but observational data
density in the region is low. Permafrost thaw and carbon release into the
atmosphere, as well as snow cover changes, are positive feedback mechanisms
that have the potential for climate warming. It is therefore particularly
important to understand the links between the energy balance, which can vary
rapidly over hourly to annual timescales, and permafrost conditions, which
changes slowly on decadal to centennial timescales. This requires long-term
observational data such as that available from the Samoylov research site in
northern Siberia, where meteorological parameters, energy balance, and
subsurface observations have been recorded since 1998. This paper presents
the temporal data set produced between 2002 and 2017, explaining the
instrumentation, calibration, processing, and data quality control.
Furthermore, we present a merged data set of the parameters, which were
measured from 1998 onwards. Additional data include a high-resolution digital
terrain model (DTM) obtained from terrestrial lidar laser scanning. Since the
data provide observations of temporally variable parameters that influence
energy fluxes between permafrost, active-layer soils, and the atmosphere
(such as snow depth and soil moisture content), they are suitable for
calibrating and quantifying the dynamics of permafrost as a component in
earth system models. The data also include soil properties beneath different
microtopographic features (a polygon centre, a rim, a slope, and a trough),
yielding much-needed information on landscape heterogeneity for use in land
surface modelling.
For the record from 1998 to 2017, the average mean annual air temperature
was -12.3∘C, with mean monthly temperature of the warmest month
(July) recorded as 9.5 ∘C and for the coldest month (February)
-32.7∘C. The average annual rainfall was 169 mm. The depth of
zero annual amplitude is at 20.75 m. At this depth, the temperature has
increased from -9.1∘C in 2006 to -7.7∘C in 2017.
The presented data are freely available through the PANGAEA
(10.1594/PANGAEA.891142) and Zenodo
(https://zenodo.org/record/2223709, last access: 6 February 2019) websites.
Introduction
Permafrost, which is defined as ground that remains frozen continuously for
2 years or more, underlies large parts of the land surface in the Northern
Hemisphere, amounting to about 15 million km2 (Aalto et al., 2018;
Brown et al., 1998; Zhang et al., 2000). The temperature range and the water
and ice content of the upper soil layer of seasonally freezing and thawing
ground (the active layer) determine the biological and hydrological
processes that operate within this layer. Warming of permafrost over the
last few decades has been reported from many circum-Arctic boreholes
(Biskaborn et al., 2019; Romanovsky et al., 2010). Warming and thawing of
permafrost and an overall reduction in the area that it covers have been
predicted under future climate change scenarios by the CMIP5 climate models,
but at widely varying rates (Koven et al., 2012; McGuire et al., 2018).
Continued observations, not only of the thermal state of permafrost but also
of the multiple other types of data required to understand the changes to
permafrost, are therefore of great importance. The data required include
information on conditions at the upper boundary of the soil (specifically on
snow cover), on atmospheric conditions, and on various subsurface state
variables (such as, e.g. soil volumetric liquid water content and soil
temperature). The seasonal snow cover in Arctic permafrost regions can
blanket the land surface for many months of the year and has an important
effect on the thermal regime of permafrost-affected soils (Langer et al.,
2013). The soil's water content determines not only its hydrological and
thermal properties, but also the energy exchange (including latent heat
conversion or release) and biogeochemical processes.
In view of these dependencies, the data sets presented here, including snow
cover and the thermal state of the soil and permafrost, together with
meteorological data, will be of great value (i) for evaluating permafrost
models or land surface models, (ii) for satellite calibration and validation
(cal/val) missions, (iii) in continuing baseline studies for future trend
analysis (for example, of the permafrost's thermal state), and (iv) for
biological or biogeochemical studies.
The Samoylov research site in the Lena River delta of the Russian Arctic has
been investigated by the Alfred Wegener Institute Helmholtz Center for Polar
and Marine Research (AWI), in collaboration with Russian and German academic
partners, since 1998. The land surface characteristics and basic climate
parameter data collected between 1998 and 2011 have been previously
published in Boike et al. (2013). Major developments in earth system models,
for example through the European PAGE21 project (http://www.page21.org, last access: 6 February 2019), the
Permafrost Carbon Network projects (http://www.permafrostcarbon.org, last access: 6 February 2019), satellite
calibration and validation missions, and observations through the Global
Terrestrial Network on Permafrost (GTN-P) have subsequently led to sustained
interest from a broader modelling community in the data obtained.
Samoylov research site. (a) Location of Samoylov Island in
the Lena River delta, northeastern Siberia (Landsat-7 ETM+ GeoCover 2000).
(b) Location of instrumentation and measurement sites.
(c) The research site under summer conditions (September 2017) and
(d) spring conditions (April 2014; photo by Torsten Sachs).
(e) Digital terrain model obtained by terrestrial laser scanning
(TLS) in September 2017, and (f) relative heights of vegetation
derived from TLS data acquired in September 2017. Further details of the
methods of TLS data processing are provided in Appendix H.
In this publication we provide information on the research site and full
documentation of the data set collected between 2002 and 2017, which can be
used for forcing and validation of earth system models (see e.g. Chadburn et
al., 2015, 2017; Ekici et al., 2014, 2015).
We present data that incorporate subsurface thermal and hydrologic
components of heat flux as well as of snow cover properties and
meteorological data from the Samoylov research site that are similar to the data
published previously for a Spitsbergen permafrost site (Boike et al., 2018a).
Site description
The Samoylov research site is located within the continuous permafrost zone
on Samoylov Island in the Lena River delta, Siberia (Fig. 1). It has been
used for intensive monitoring of soil temperatures and meteorological
conditions since 1998 (Boike et al., 2013).
The region is characterized by an Arctic continental climate with low mean
annual air temperatures of below -12∘C, very cold minimum winter
air temperatures (below -45∘C), and summer air temperatures
that can exceed 25 ∘C, with a thin snow cover and a summer water
balance equilibrated between precipitation input and evapotranspiration
(Boike et al., 2013).
The study area of the Lena River delta has permafrost to depths of between
400 and 600 m (Grigoriev, 1960). The active-layer thawing period starts at
the end of May and the active-layer thickness reaches a maximum at the end of
August–beginning of September. Marked warming of this area over the last 200 years has been inferred from temperature reconstruction using deep borehole
permafrost temperature measurements in the delta and the broader Laptev Sea
region (Kneier et al., 2018).
Samoylov Island is located within a deltaic setting and consists of a flood
plain in the western part of the island and a Holocene terrace characterized
by ice-wedge polygonal tundra and larger waterbodies in the eastern part
(Fig. 1).
The area is generally characterized by ice-rich organic alluvial deposits,
with an average ice content in the upper metre of more than 65 % by volume
for the Holocene terrace and of about 35 % for the flood plain deposits
(Zubrzycki et al., 2013). The Holocene terrace is dominated by ice wedge
polygons, so that a considerable volume of the upper soil layer (0–10 m) is
characterized by excess ground ice (Kutzbach et al., 2004). Degradation of
ice wedges, as observed throughout the Arctic (Liljedahl et al., 2016),
occurs at only a few, localized parts of the research site (Kutzbach, 2006).
The recent work by Nitzbon et al. (2018) shows that the spatial variability
in the types of ice-wedge polygons observed at this study area can be linked
to the spatial variability in the hydrological conditions. Furthermore,
wetter hydrological conditions have a destabilizing effect on ice wedges and
enhance degradation.
The total mapped area of the polygonal tundra on Samoylov Island (excluding
the floodplain) is composed of 58 % dry tundra, 17 % wet tundra, and
25 % water surfaces, of which 10 % is overgrown water and 15 % is open
water (Muster et al., 2012, Fig. 3a). The landscape is characterized by
polygonal tundra, i.e. a complex mosaic of low- and high-centred polygons
(with moist to dry polygonal ridges and wet depressed centres) and larger
waterbodies (Muster, 2013; Muster et al., 2012). The polygonal tundra
microtopography, polygon rims, slopes, and depressed centres are clearly
distinguishable. Depressed polygon centres are typically water saturated or
have water levels above the ground surface (shallow ponds). High-centred
polygons have inverse microtopography, i.e. drier elevated centres and wet
surrounding troughs. Polygonal ponds and troughs make up about 35 % of the
total water surface area on the island (Boike et al., 2013).
Previous research based at the research site has focused on greenhouse gas
cycling (Abnizova et al., 2012; Knoblauch et al., 2018,
2015; Kutzbach et al., 2004, 2007; Langer et al., 2015;
Runkle et al., 2013; Sachs et al., 2010, 2008; Wille et al.,
2008; Holl et al., 2019), aquatic biology (Abramova et al., 2017), upscaling of land surface
characteristics and parameters from ground-based data to remote-sensing data
(Cresto Aleina et al., 2013; Muster et al., 2013, 2012), and
hydrology (Boike et al., 2008b; Fedorova et al., 2015; Helbig et al., 2013).
Data from a few years have also been used in earth system modelling (Chadburn
et al., 2015, 2017; Ekici et al., 2014, 2015)
and for modelling land surface, snow, and permafrost processes (Gouttevin et
al., 2018; Langer et al., 2016; Westermann et al., 2016,
2017; Yi et al., 2014; Aas et al., 2019). Table 1 summarizes the characteristics of the
research site, based on data in previous publications and additional data
included in this paper.
Site description parameters for earth system model input. Values
have been computed and compiled for the Samoylov research site and
surrounding areas.
VariableValueSourceSurface characteristicsSummer albedo0.15–0.2Langer et al. (2011a)Summer Bowen ratio0.35–0.50Langer et al. (2011a)Summer roughness length (mm)1×10-3 (from eddy covariance data)Langer et al. (2011a)Snow propertiesSnow albedoSpring period prior to melt: 0.8 (2007, 2008)Langer et al. (2011a)End of the snow ablation26 April–18 June (1998–2017)Boike et al. (2013), this paperRange of snow depths (end of season before ablation) (m) recorded by the SR50 sensor (thus disregarding spatial variability in snow depth)0.09–0.7 (1999–2017)Boike et al. (2013), which includes two locations: 1999–2002 polygon rim; 2003–2017 polygon centreEnd of season snow density (kg m-3) (different year and different methods)175–225 (field measurement) 190±10 (field measurement) 264±24 (based on X-ray microtomography and direct numerical simulations)Boike et al. (2013) Langer et al. (2011b) Gouttevin et al. (2018)Snow heat capacity (MJ m-3 K-1)0.39±0.02Langer et al. (2011b)Snow thermal conductivity (W m-1 K-1) (bulk value for snowpack overlying vegetation/grass)0.22±0.03 (fitted from temperature profiles) 0.22±0.01 (based on X-ray microtomography and direct numerical simulations)Langer et al. (2011b) Gouttevin et al. (2018)Soil propertiesSoil classificationComplex of Glacic Aquiturbels, Typic Aquiturbels, and Histic Aquorthels according to USDA Soil TaxonomyKutzbach et al. (2004)Surface organic layer thickness (cm)0–15 (bare to vegetated tundra areas; up to 20 in wetter areas)Boike et al. (2013)Soil texture (below surface organic layer)Sand to silt with organic peat layers of varying depthsBoike et al. (2013); Appendix F for single profilesThawed soil thermal conductivity (W m-1 K-1)0.14±0.08 (dry peat) 0.60±0.17 (wet peat) 0.72±0.08 (saturated peat)Langer et al. (2011a)Thawed soil heat capacity (MJ m-3 K-1)0.9±0.5 (dry peat) 3.4±0.5 (wet peat) 3.8±0.2 (saturated peat)Langer et al. (2011a)Frozen soil thermal conductivity (W m-1 K-1)0.46±0.25 (dry peat) 0.95±0.23 (wet peat) 1.92±0.19 (saturated peat)Langer et al. (2011b)Soil bulk density (kg m-3)Depth average: 0.75×103Boike et al. (2013); Appendix FSoil carbon content (g g-1)0.01–0.22Boike et al. (2013)Organic carbon stock (kg C m-2)24 (for 0–100 cm) Chadburn et al. (2017) (spatial average); Appendix FSaturated hydraulic conductivity (m s-1)463×10-6 (moss layer) 0.3×10-6 (mineral layer) 10.9×10-6130×10-6Helbig et al. (2013) Ekici et al. (2015) Boike et al. (2008b)
Continued.
VariableValueSourceClapp–Hornberger exponent (b factor)∼4 (organic layer, typical for organic/peat) ∼4.5 (mineral layer, typical for sandy loam)Beringer et al. (2001)Porosity (volumetric water content at saturation)0.95–0.99 (organic layer) 0.5–0.7 (mineral layer)Boike et al. (2013)Van Genuchten parameters: α (mm-1)sandy loam: 6 peat/organic:10Yang and You (2013)Van Genuchten parameters: n (unit-free)sandy loam: 1.3 peat/organic: 10Dettmann et al. (2014)Vegetation characteristicsVegetation height (cm)(based on field measurements)Wet tundra at polygon centres and on margins of polygonal ponds: moss and lichen stratum is 5, vascular plant stratum is 30. Moist (dry) tundra at polygon rims and in high-centre polygons: moss and lichen stratum is 5, vascular plants stratum is 20. Centres of polygonal and interpolygonal ponds: moss stratum is 20–45, vascular plants stratum is 30.Knoblauch et al. (2015), Kutzbach et al. (2004), Spott (2003), this paperVegetation height (cm)(estimates from terrestrial laser scanning) (1) Derived as mean vegetation height within a radius of 2.5 – centre: mean is 5.4, standard deviation is 2.0 – rim: mean is 4.6, standard deviation is 2.1 (2) Derived as maximum vegetation height (99th percentile) within a radius of 2.5 – centre: mean is 11.7, standard deviation is 4.5 – rim: mean is 10.7, standard deviation is 5.2This paper (Appendix H)Vegetation fractional coverage (%) sensors, parameters, and instrument characteristics for the automated timeWet tundra at polygon centres and on margins of polygonal ponds: moss and lichen stratum 95, vascular plants stratum 33–55 Moist (dry) tundra at polygon rims and in high-centre polygons: moss and lichen stratum is 95, vascular plants stratum is 30 Centres of polygonal and interpolygonal ponds: moss stratum is 95, vascular plants stratum is 0–20Knoblauch et al. (2015), Kutzbach et al. (2004), Spott (2003)Vegetation typeComplex of G3 and W2 according to CAVM-Team (2005). Moist (dry) tundra at polygon rims and in high-centre polygons: Hylocomium splendens–Dryas punctata community. Wet tundra at polygon centres and on margins of polygonal ponds: Drepanocladus revolvens–Meesia triquetra–Carex chordorrhiza community Centres of polygonal and interpolygonal ponds: Scorpidium scorpioides–Carex aquatilis–Arctophila fulva.Boike et al. (2013), Knoblauch et al. (2015), Kutzbach et al. (2004), this paperMax. leaf area index (LAI) in summer (does not include moss)0.3 (derived from MODIS)Chadburn et al. (2017)Root depth (cm)30 (centre, rim)Kutzbach et al. (2004)LandscapeLandscape typeLowland polygonal tundra, mosaic of wet and moist sitesKutzbach (2006), Kutzbach et al. (2004)Bioclimate subzonesSubzone DCAVM-Team (2005)Data description
This paper presents, for the first time, a complete data archive and
descriptions in the form of the following data sets: (i) a full range of
meteorological, soil thermal, and hydrologic data from the research site
covering the period between 2002 and 2017 (Fig. 2), (ii) high spatial
resolution data from terrestrial laser scanning of the research site
completed in 2017, with resulting data sets for a digital terrain model and
for vegetation height, (iii) time-lapse camera images, and (iv) a data set
containing specially compiled or processed data sets for those parameters
that were measured in the period from 1998 to 2002, thus extending the
record to form a long-term data set, as initiated in Boike et al. (2013).
The processing and level structure is described in detail in Sect. 4.
Additional data such as soil properties and soil carbon content are also
included in this paper in order to provide a complete set of data and
parameters suitable for earth system, conceptual, and land surface modelling.
All of these data are archived in the PANGAEA data libraries (Boike et al.,
2018b, c, d) and the measuring principles and analysis are described in this
paper.
Time series (daily mean values) of Samoylov data presented in this
paper: (a–i) meteorological data and (j–p) soil data.
Seasonal average active-layer thaw depth (o) was measured at the
150 data points on the Samoylov CALM grid. Further details on the sensors and
periods of operation are given in Table 2.
Data logging between 2002 and 2013 at the research site was powered by a
solar panel and a wind turbine generator, and the data were retrieved manually
during site visits once or twice a year, when visual inspections were also
made of the sensors. Data gaps prior to 2013 resulted mainly from problems
with the site's energy supply, such as problems with the solar or wind charge
controller. No other gap filling has been undertaken, but previous
publications (e.g. Langer et al., 2013) suggest that reanalysis data, such
as ERA-Interim, could be used for this purpose. In Chadburn et al. (2017), a
method for correcting reanalysis data to better represent the site is
described and applied. The gap-free meteorological data set that was produced
and used in Chadburn et al. (2017) is now available on the PANGAEA database
(Burke et al., 2018), making it easy for modellers to begin running the
Samoylov site and therefore to make good use of our data.
Mean annual, maximum, and minimum permafrost temperatures at
different depths between 2006 and 2017, as recorded in the Samoylov Island
borehole. Mean annual temperatures are based on the period 1 September 2006
to 31 August 2007 and 1 September 2016 to 31 August 2017. Maximum and
minimum annual variations are based on the same time period and computed from
mean daily temperatures. The upper 3.5 m below the surface is shaded in grey
since we recommend not using these data for active-layer thermal processes.
Since 2013 the research site has been connected to the main electricity
supply of the new Russian research station, resulting in a much improved data
collection with almost no data gaps.
Details of the sensors used are provided in the following sections, as well
as descriptions of the data quality and cleaning routine (Sect. 4). The
instruments can be divided into aboveground sensors (meteorological) and
below-ground sensors (e.g. soil sensors). Further detailed information on
the sensors can be found in Table 2, which summarizes all of the instruments
and relevant parameters, as well as in the appendices B to H (metadata,
description of instruments, and calculations of final parameters). Figure 2
presents a time series of selected parameters measured between 2002 and
2017.
List of sensors, parameters, and instrument characteristics for the
automated time series data from the Samoylov research site, 2002–2017.
Positive heights are above the ground surface; negative heights are below
the ground surface. Sensor names refer to the original manufacturer brand
name (e.g. the Vaisala PTB110 air pressure sensor is distributed by Campbell
Scientific as model CS106). Integration methods are average (avg), sample
(spl), and sum.
VariableSensor (number of sensors if >1)Period of operation Height (m)UnitMeasuring intervalIntegration methodAccuracy (±)Spectral rangefromtoAboveground sensors Air temperature (A)Vaisala HMP155A (2)Sep 2017now0.5, 2.0∘C30 savg 30 min(0.226–0.0028×T) ∘C (-80 to 20 ∘C), (0.055+0.0057×T) ∘C (20 to 60 ∘C)Air temperature (A)Rotronic MP103A/Rotronic MP340/Vaisala HMP45A (2)Aug 2002Sep 20170.5, 2.0∘C20 s (Aug 2002–Jul 2005), 15 s (Jul 2005–Jun 2009), 10 min (Jun 2009–Jul 2009), 10 s (Jul 2009–Jul 2010), 30 s (Jul 2010–Sep 2017)avg 60 min (Aug 2002–Jun 2009), avg 30 min (Jun 2009–Jul 2009), avg 60 min (Jul 2009–Jul 2010), avg 30 min (Jul 2010–Sep 2017)0.5 ∘C (-40 to 60 ∘C)/0.5 ∘C (-40 to 60 ∘C)/0.2 ∘C (20 ∘C), linear increase: 0.5 ∘C (-40∘C), 0.4 ∘C (60 ∘C)Air temperature (B)Campbell Scientific PT100 (2)Aug 2013now0.5, 2.0∘C30 savg 30 min<0.15∘C (-100∘C), <0.1∘C (0 ∘C), <0.19∘C (100 ∘C)Relative humidityVaisala HMP155A (2)Sep 2017now0.5, 2.0%30 savg 30 min(1.4+0.032×RH) % (-60 to -40∘C), (1.2+0.012×RH) % (-40 to -20∘C), (1.0+0.008×RH) % (-20 to 40 ∘C)Relative humidityRotronic MP103A/Rotronic MP340/Vaisala HMP45A (2)Aug 2002Sep 20170.5, 2.0%20 s (Aug 2002–Jul 2005), 15 s (Jul 2005–Jun 2009), 10 min (Jun 2009–Jul 2009), 10 s (Jul 2009–Jul 2010), 30 s (Jul 2010–Sep 2017)avg 60 min (Aug 2002–Jun 2009), avg 30 min (Jun 2009–Jul 2009), avg 60 min (Jul 2009–Jul 2010), avg 30 min (Jul 2010–Sep 2017)2 % (0 to 90 %, 20 ∘C), 3 % (90 % to 100 %, 20 ∘C)
VariableSensor (number of sensors, if >1)Period of operation Height (m)UnitMeasuring intervalIntegration methodAccuracy (±)Spectral rangefromtoPrecipitation (liquid)R. M. Young Company 52203Jul 2010now0.35mm30 ssum 30 min2 % (≤25 mm h-1)Precipitation (liquid)Environmental Measurements ARG100Aug 2002Oct 20090.4mm20 s (Aug 2002–Jul 2005), 15 s (Jul 2005–Jun 2009), 10 s (Jun 2009–Oct 209)sum 60 min (Aug 2002–Jun 2009), sum 30 min (Jun 2009–Jul 2010)4 % (25 mm h-1), 8 % (133 mm h-1)Snow depthCampbell Scientific SR50Aug 2002now1.23 (Aug 2002–Jul 2015), 1.07 (Jul 2015–now)m60 minspl 60 min0.4 % (of distance to snow surface)Wind directionR. M. Young Company 05103Aug 2002now3∘20 s (Aug 2002–Jul 2005), 15 s (Jul 2005–Jun 2009), 10 s (Jun 2009–Jul 2010), 30 s (Jul 2010–now)avg 60 min (Aug 2002–Jun 2009), avg 30 min (Jun 2009–now)3∘Wind speedR. M. Young Company 05103Aug 2002now3m s-120 s (Aug 2002–Jul 2005), 15 s (Jul 2005–Jun 2009), 10 s (Jun 2009–Jul 2010), 30 s (Jul 2010–now)avg 60 min (Aug 2002–Jun 2009), avg 30 min (Jun 2009–now)0.3 m s-1Time-lapse photographyCampbell Scientific CC640Sep 2006now2.2px1 day (at 12:00 local time/UTC+9)Time-lapse photographyCampbell Scientific CC5MPXAug 2015now3px60 min (from 11:00 to 14:00 local time/UTC+9)Below-ground sensors Soil temperatureCampbell Scientific 107 (32)Aug 2002now-0.01 to -2.71∘C10 minavg 60 min<1.0∘C (-40 to +56∘C), <0.5∘C (-38 to +52∘C), <0.1∘C (-23 to +48∘C)
Continued.
VariableSensor (number of sensors, if >1)Period of operation Height (m)UnitMeasuring intervalIntegration methodAccuracy (±)Spectral rangefromtoSoil/permafrost temperatureRBR Thermistor chain (24)Aug 2006now0 to -26.75∘C60 minspl 60 min0.1 ∘CSoil bulk electrical conductivityCampbell Scientific TDR100 + CS605 (20)Aug 2002now-0.05 to -0.70S m-160 minspl 60 minSoil volumetric liquid water contentCampbell Scientific TDR100 + CS605 (20)Aug 2002now-0.05 to -0.70%60 minspl 60 minGround heat fluxHukseflux HFP01 (2)Aug 2002now-0.05, -0.06W m-210 minavg 60 min-15 % to +5 % (12 h total)Water levelCampbell Scientific CS616Jul 2010now-0.115cm30 savg 30 min2.5 % (≤0.5 dS m-1, bulk density ≤1.55 g cm-3, 0 % to 50 % θl)Water levelCampbell Scientific CS625Jul 2007Jul 2010-0.15cm10 min (Jul 2007–Jul 2009), 10 s (Jul 2009–Jul 2010)avg 60 min (Jul 2007–Jul 2009), avg 30 min (8–12 Jul 2009), avg 60 min (Jul 2009–Jul 2010)2.5 % (≤0.5 dS m-1, bulk density ≤1.55 g cm-3, 0 % to 50 % θl)Meteorological station data
The standard meteorological variables described in this section were
averaged over various intervals (Table 2) with the averages, sums, and
individual values all being saved hourly until 2009 and half-hourly
thereafter. The sampling intervals changed as a result of different logger
and sensor setups and different available power sources. Sensors were
connected directly to data loggers. A number of different data logger models
from Campbell Scientific were used over the years (CR10X between 2002 and
2009, CR200 between 2007 and 2010, and CR1000 since 2009), together with an
AM16/32A multiplexer.
Description of data level and quality control for data flags. Most
data are flagged automatically; some are occasionally flagged manually (Flag 3:
maintenance, Flag 6: plausibility). Online data transfer is not currently
operational but is planned for the future.
FlagMeaningDescriptionONLOnline dataData from online stations, daily download, used for online status checkRAWRaw dataBase data from offline stations, 3-monthly backup of online data, used for maintenance check in the fieldLV0Level 0Standardized format with data in equal time steps (UTC), filled with NA for data gapsLV1Level 1Quality-controlled data including flags; quality control includes maintenance periods, physical plausibility, spike/constant value detection, sensor drifts, and snow on sensor detectionLV2Level 2Modified data compiled for special purposes such as combined data series from multiple sensors and gap-filled data0Good dataAll quality tests passed1No dataMissing value2System errorSystem failure led to corrupted data, e.g. due to power failure, sensors being removed from their proper location, broken or damaged sensors, or the data logger saving error codes3MaintenanceValues influenced by the installation, calibration, and cleaning of sensors or programming of the data logger; information from field protocols of engineers4Physical limitsValues outside the physically possible or likely limits5GradientValues unlikely because of prolonged constant periods or high/low spikes; test within each individual series6PlausibilityValues unlikely in comparison with other series or for a given time of the year; flagged manually by engineers7Decreased accuracyValues with reduced sensor accuracy, e.g. identified if freezing soil does not show a temperature of 0 ∘C8Snow coveredGood data, but the sensor is snow coveredAir temperature, relative humidity
Air temperature and relative humidity were measured at 0.5 and 2 m above
the ground (starting with hourly averages at 2.0 m until 30 June 2009 and at
0.5 m until 26 July 2010, with half-hourly averages thereafter) using
Rotronic and Vaisala air temperature and relative humidity probes protected
by unventilated shields (Fig. B1 and Table 2). According to the sensor's
manuals, the HMP45 sensors have a measurement limit of -39.2∘C,
but we recorded data down to -39.8∘C. During extreme cold-air
temperature periods, for example between 1 February and 15 March, 2013,
constant air temperature values were recorded at the sensor's output limit.
These data periods were manually flagged (Flag 6: consistency; Table 3)
using a lower temperature limit of -39.5∘C.
Also of importance is the decrease in accuracy of the air temperature and
humidity data with decreasing temperature and moisture content. For example,
the accuracy for the HMP45A sensor at 20 ∘C is ±0.2∘C, but at -40∘C it is ±0.5∘C.
Campbell Scientific PT100 temperature sensors were installed on 22 August
2013 alongside the temperature and humidity probes, at the same heights but
in separate unventilated shields, in order to circumvent this problem. Since
17 September 2017 Vaisala HMP155A air temperature and relative humidity
probes were installed, which enable the full range of temperatures (below -40∘C). The uncertainty in all the temperature measurements ranges
between 0.03 and 0.5 ∘C, depending on the sensors used; the
uncertainty in the relative humidity measurements ranges between 2 % and
3 %. The measurement heights were not adjusted with respect to the snow
surface during periods of snow cover accumulation or ablation. The lower
probes (at 0.5 m) were only completely snow covered during 2 months of the
2017 winter season (16 April–11 June 2017), as observed in photographic
images, and therefore this time period is flagged in the data series (Flag 8: snow covered; Table 3).
Wind speed and direction
The wind speed and direction were measured using a propeller anemometer
(R. M. Young Company 05103, Fig. B2), which was calibrated towards
geographic north. This was done by orienting the centre line of
the sensor towards true north (using a GPS reference point) and then
rotating the sensor base until the data logger indicated 0∘. The
averaged wind direction, its standard deviation, and the wind speed were all
recorded at hourly intervals until 30 June 2009 and at half-hourly intervals
thereafter. Since August 2015, wind maximum and minimum wind speed are also
recorded. The mean wind speeds and directions were calculated using every
value recorded during the measurement interval. The standard deviation of
the wind direction was calculated using the algorithm provided by the
Campbell Scientific data logger.
Radiation
The net radiation was measured between 2002 and 2009 using a Kipp & Zonen
NR Lite net radiometer; outgoing longwave radiation was also measured using
a Kipp & Zonen CG1 pyrgeometer. Since 2009, various four-component
radiometers were used (Table 2). The averaged values were stored at hourly
intervals until 30 June 2009 and at half-hourly intervals thereafter.
Further details of the measuring periods and the specifications for the
different sensors can be found in Table 2. Although all radiation sensors
were checked for condensation, dirt, physical damage, hoar frost, and snow
coverage during the regular site visits, the instruments were largely
unattended and their accuracy is therefore estimated to have been ±10 %. Our quality analysis also includes flagging the data during those
periods in which shortwave incoming radiation was lower than shortwave
outgoing radiation by 10 W m-2 using Flag 6
(plausibility, values unlikely in comparison with other sensor series or for
a given time of the year). Between 30 June 2009 and 21 July 2017, less than
1 % of the data were flagged. Since August 2014 a Kipp & Zonen CNR4
four-component radiation sensor is operative, together with a CNF4
ventilation unit to prevent condensation (Fig. B3). The additional heating
available for the CNR4 sensor was never used.
Rainfall
Unheated and unshielded tipping bucket rain gauges (Environmental
Measurements ARG100 and R. M. Young Company model 52203) were installed
directly on the ground on 31 August 2002 (ARG100) and 26 July 2010 (52203).
The Environmental Measurements ARG100 liquid precipitation probe was damaged
during the winter of 2009–2010. By installing the gauge close to the ground,
the risk of wind-induced tipping of the bucket, which would lead to false
data records, can be reduced (as observed by Boike et al., 2018a). Due to the
typically low snow heights, the risk of snow coverage of the instrument is
also very low.
The instruments measure only liquid precipitation (rainfall) and not winter
snowfall. The tipping buckets were checked regularly during every summer by
pouring a known volume of water into the bucket and carrying out frequent
visual inspections for dirt or snow during each site visit. These
calibration data are flagged with Flag 3 (maintenance periods).
Snow depth
The snow depth around the station has been continuously monitored since 2002
using a Campbell Scientific SR50 sonic ranging sensor (Fig. B4). The
sensor measures the distance between the sensor and an object or surface, which
could be the upper surface of the snow (in winter) or the water surface,
ground surface, or vegetation (in summer). On 17 July 2015 a metal plate was
placed directly beneath the ultrasonic beam to reduce the amount of noise in
the reflected signal due to surface vegetation (Fig. B4). The acoustic
distance data obtained from the sonic sensor were temperature-corrected
using the formula provided by the manufacturer (Appendix C) using the air
temperature measured at the Samoylov meteorological station.
To obtain the snow depth, the distance of the sensor from the surface was
recorded over the summer and the mean was calculated. The recorded (corrected)
winter distances are then subtracted from this mean (previous) summer value
to obtain snow depth. Due to seasonal thawing, the ground surface can subside
by a few centimetres over the summer season (and therefore no longer be set
to zero), resulting in negative heights for the ground-surface level being
computed. In contrast, vegetation growth and higher water levels (e.g. as
observed in 2017) will result in positive heights. The distance measurements
collected during the snow-free season are not removed from the series or
corrected, since they provide potentially useful information about these
processes.
The SR50 sensor acquires data over a discoidal surface with a radius that
ranges from 0.23 m (0.17 m2) in snow-free conditions to 0.19 m (0.12 m2) with 20 cm of snow. This footprint disk is located in the centre of
a low-centred polygon for which the spatial variability of snow has been
investigated by Gouttevin et al. (2018). The microtopography of this
polygonal tundra (characterized by rims, slopes and polygon centres) was
identified as a profound driver of spatial variability in snow depth: at
maximum accumulation in 2013 rims typically had 50 % less snow cover and
slopes 40 % more snow cover than polygon centres. However, the snow cover
within each topographical unit also exhibited spatial variability on a
decimetre scale (Gouttevin et al., 2018), probably resulting from underlying
micro-relief (notably vegetation tussocks) and processes such as wind
erosion. This variability can affect the representativity of the
SR50-measured snow depth data and visual data obtained from time-lapse
photography can therefore be extremely important (see next section).
Time-lapse photography of snow cover and land surface
In order to monitor the timing and pattern of snow melt an automated camera
system (Campbell Scientific CC640) was set up in September 2006 to
photograph the land surface in the area in which the instruments were
located (Figs. B5 to B7). The images are used as a secondary check on the
snow cover figures obtained from the depth sensor and are also valuable for
monitoring the spatial variability of snow cover across polygon
microtopography. During the polar night the image quality was found to be
somewhat reduced and a second camera with a better resolution (Campbell
Scientific CC5MPX) was therefore installed in August 2015 to record
high-quality images in low-light conditions over the winter period.
Atmospheric pressure
A Vaisala PTB110 sensor in a vented box was installed next to the data
loggers at the meteorological station (Fig. B1) in August 2014 to measure
atmospheric pressure.
Water levels
The suprapermafrost ground-water level, i.e. water level of the seasonally
thawed active layer above the permafrost table within one polygon, was
estimated using Campbell Scientific CS616 and CS625 water content
reflectometer probes installed vertically in the soil and air, with the
sensor's ends standing upright (Appendix D). The advantage of this method is
that the sensor can remain in the soil during freezing and subzero
temperatures, whereas pressure transducers need to be removed over winter and
then reinstalled. For the unfrozen periods, the soil was measured by a
dielectric device is a mixture of air, water, and soil particles. The sensor
outputs a signal period measurement from which the bulk dielectric number is
usually calculated. The dielectric number (also referred to as the relative
permittivity or dielectric constant) is then used to calculate the volumetric
water content using an empirical polynomial calibration provided by the
manufacturer. We use the signal period output of the CS616 and CS625 water
content reflectometer probes (Campbell Scientific, 2016) and a site-specific
calibration and convert these values to water level
with respect to the sensor
base (Appendix D).
Subsurface data on permafrost and the active layerInstrument installation at the soil station and soil sampling
In order to take into account any possible effects of heterogeneity in
vegetation and microtopography at the research site (e.g. due to the
presence of polygons), instruments for measuring the soil's thermal and
hydrologic dynamics (Table 2) were installed at a number of different
positions within a low-centred polygon.
Instrument installation and soil sampling in 2002
A new measurement station was established in August 2002, with instruments
installed in four profiles (Appendices B2 and F). Four pits were dug through
the active layer and into the permafrost (Figs. B8 and B9), one at the
peak of the elevated polygon rim (BS-1), one on the slope (BS-2), a third in
the depressed centre (BS-3), and one above the ice wedge (Wille et al.,
2003).
The surface was carefully cut and the excavated soil stockpiled separately
according to depth and soil horizon in order to be able to restore the
original profile following instrument installation. The soil material is
generally stratified fluviatile (and aeolian) sands and loams, with layers
of peat. The BS-1 and BS-2 soil profiles are classified as Typic Aquiturbels, while the BS-3
soil profile is classified as Typic Historthel, according to US Soil Taxonomy (Soil Survey
Staff, 2010). The thaw depth was between 17 and 40 cm thick at the time of
instrument installation.
Sensors were installed to cover the entire depth range of the profile, i.e.
from the very top, through the active layer and into the permafrost soil.
The sensors were positioned according to the soil horizons so that every
horizon in the profile contained at least one probe.
Sensors were installed horizontally into the undisturbed soil profile face
beneath different microtopographical features and the pits were then
backfilled (Figs. B10 and B13).
Soil samples were collected before instrument installation so that physical
parameters could be analyzed. Soil properties within the soil profiles,
including the soil organic carbon (OC) content, nitrogen (N) content, soil
textures, bulk densities, and porosities can be found in Appendix F.
The Typic Aquiturbels from the peak and the slope of the polygon rim show cryoturbation
features due to the formation of thermal contraction polygons. The Typic Historthel in the
polygon centre, on the other hand, does not have any cryoturbation features
and is characterized by peat accumulation under waterlogged conditions.
(Fig. F1).
Soil temperature
Soil temperature sensors were installed over vertical 1-D profiles in 2002
beneath a polygon centre, slope, and rim. A measurement chain of temperature
sensors was also installed in the ice wedge down to a depth of 220 cm. Their
positions are shown in Fig. B13. The temperatures were initially measured
using Campbell Scientific 107 thermistors connected to a Campbell Scientific
CR10X data logger with a Campbell Scientific AM416 multiplexer. Campbell
Scientific's worst-case example, with all errors considered to be
additive, is given as ±0.3∘C between -25 and 50 ∘C.
The average deviation from 0 ∘C determined through ice bath
calibration prior to installation was 0.008 ∘C (maximum:
1.0 ∘C; minimum: -0.56∘C, standard deviation:
0.33 ∘C). The sensors cannot be recalibrated once they have been
installed. Phase change temperatures during spring thaw and fall refreezing
are stable (the zero-curtain effect in freezing and thawing soils of
periglacial regions). Assuming that freezing point depression (due to the
soil type and soil water composition) does not change significantly from year
to year, these periods can be used to evaluate sensor stability. Between 2002
and 2009 the data logger and multiplexer were not replaced, which resulted in
a reduced accuracy of up to ±0.7∘C during the winter
freeze-back periods in 2009 for two of the sensors near to the surface
(centre of the polygon at -1 cm, rim of the polygon at -2 cm below the
ground surface, respectively). The zero-curtain period during fall–winter,
where temperatures in the ground are stabilized at 0 ∘C during phase
change, offers an accuracy test for sensors that cannot be retrieved. For the
remaining sensors the accuracy was better, up to ±0.5∘C. The
affected data are flagged in the data series (Flag 7: decreased accuracy;
Table 3). The data quality improved greatly following the installation of a
new data logger and multiplexer system (Campbell Scientific CR1000 data
logger, AM16/32A multiplexer) in 2010 and the maximum offset at 0 ∘C
during freeze-back was ±0.3∘C.
Soil dielectric number, volumetric liquid water content, and bulk electrical
conductivity
Time-domain reflectometry (TDR) probes were installed horizontally in three
soil profiles adjacent to the temperature probes. The fourth profile in the
ice wedge only records temperature data (see Sect. 3.2.2., Figs. B11 and
B13). The TDR probes automatically record hourly measurements of bulk
electrical conductivity (from 25 July 2010 only), and the dielectric number,
obtained by measuring the amplitude of the electromagnetic wave over very
long time periods and the ratio of apparent probe length to real probe
length (the La/L ratio), corresponding to the square root of the
dielectric number. A Campbell Scientific TDR100 reflectometer was used
together with an SDMX50 coaxial multiplexer, custom-made 20 cm TDR probes
(Campbell Scientific CS605) connected to a Campbell Scientific CR10X data
logger between 2002 and 2010 and to a Campbell Scientific CR1000 data logger
thereafter. All TDR probes were checked for offsets following the method
described in Heimovaara and de Water (1993) and in Campbell Scientific's
TDR100 manual (Campbell Scientific, 2015). The calibration delivered a probe
offset of 0.085 (an apparent length value used to correct for the portion of
the probe rods that is covered with epoxy), which was used instead of the
value of 0.09 suggested by Campbell Scientific. The dielectric number
ε (dimensionless) and the computed volumetric liquid water
values θl (volume/volume) in frozen and unfrozen soil are
provided as part of the time series data set. The calculation for volumetric
liquid water content takes into account four phases of the soil medium (air,
water, ice, and mineral) and uses the mixing model from Roth et al. (1990)
(Appendix C).
The data are generally continuous and of high quality, and the absolute
accuracy is estimated to be better than 5 %. This is estimated from the
maximum deviation of calculated volumetric liquid water content below and
above the physical limits (between 0 % and 1 % or 0 % and 100 %). A probe located at
0.37 m depth beneath the polygon rim showed a shift of about 3 % (up and
down) in the volumetric liquid water content during the summers of 2009,
2013, and 2014, for which we could not find any technical explanation. This
shift is flagged in the data series (Flag 6: consistency; Table 3).
Time-domain reflectometry was also used to measure the bulk soil impedance,
which is related to the soil's bulk electrical conductivity (BEC). These
data were used to infer the electrical conductivity of soil water and solute
transport over a 12-month period in the active layer of a permafrost
soil (Boike et al., 2008a). The impedance can be determined from the
attenuation of the electromagnetic wave travelling along the TDR probe after
all multiple reflections have ceased and the signal has stabilized. The bulk
conductivities were recorded hourly using the TDR setup described above in
this section. Because no calibration was done, and the TDR probes were
custom made to 20 cm, a probe constant (Kp) of 1 was used for BEC
waveform retrieval; Campbell Scientific suggests a Kp for the CS605
probes of 1.74. Measurements of electrical conductivity and the dielectric
number were affected by irregular spikes and possibly also by a sensor drift
similar to that in the soil temperature measurements and thus flagged until
August 2015 (Flag 6). Data quality improved significantly after August 2015
when the Campbell Scientific coaxial SDMX50 multiplexers were exchanged for
SDM8X50 and the electrical grounding system was improved. The dielectric
numbers, computed volumetric liquid water contents, and soil bulk electrical
conductivities can be found in the time series data set.
Ground heat flux
Two Hukseflux HFP01 heat flux plates were installed on 24 August 2002 and
recorded ground heat flux at 0.06 (rim) and 0.11 m (centre) depth since then
(Fig. B12). The manufacturer's calibration values were used to record heat
flux in W m-2 (Hukseflux, 2016). Downward fluxes are positive and occur
during spring and summer, while upward heat fluxes are negative and typically
occur during fall and winter.
Permafrost temperature
The monitoring of essential climate variables (ECVs) for permafrost has
been delegated to the Global Terrestrial Network on Permafrost (GTN-P), which
was developed in the 1990s by the International Permafrost Association under
the World Meteorological Organization. The GTN-P has established permafrost
temperature and active-layer thickness as ECVs in (1) the TSP (Thermal
State of Permafrost) data set and (2) the CALM (Circumpolar Active Layer
Monitoring) monitoring programme (Romanovsky et al., 2010; Shiklomanov et al.,
2012). A 27 m deep borehole was drilled in March 2006 with the objective to
establish permafrost temperature monitoring (Fig. 1, Appendix E). A 4 m
long metal pipe (diameter 13 cm; extending 0.5 m above and 3.5 m below the
surface) was used for stability and to prevent the inflow of water during
summer season when the upper ground is thawed. In August 2006, 24 thermistors (RBR
thermistor chain with an RBR XR-420 logger) were installed,
one at the ground surface and 23 between 0.75 and 26.75 m depth, inside a
PVC tube (Fig. E2). A second PVC tube was inserted into the borehole and
the remaining air space in the borehole was backfilled with dry sand.
Temperatures were recorded at hourly intervals, with no averaging; no data
were recorded between September 2008 and April 2009. We recommend that the
temperature data from the sensors at the ground surface, at 0.75, 1.75 and
2.75 m depths should not be used due to the possibility of it having been
affected by the metal access pipe. The data from these sensors have not been
flagged as they are of high quality, but they may not provide an accurate
reflection of the actual temperatures. They show above-zero temperatures
down to 1.75 m during summer in contrast to the active-layer soil
temperatures (Fig. 2). In contrast, the CALM active-layer thaw never exceeded
> 0.8 m since 2002 at all grid locations.
The second PVC tube was used for comparison measurements at the same depths
in the borehole. The differences between the calibrated reference
thermometer (PT100) showed values between ±0.03 and ±0.33∘C (Appendix E, Table E1).
The data record shows that depth of zero annual amplitude (ZAA, where
seasonal temperature changes are negligible, ≤0.1∘C) is
located below 20.75 m. At 26.75 m, temperatures fluctuate with a maximum of
0.05 ∘C. The annual mean temperatures between the start and end
of the time series, as well as minimum and maximum temperatures, are
displayed in Fig. 3 (trumpet curve). The permafrost warms at all
depths within this 10-year period, but is most pronounced at the surface. At 2.75 m, the mean annual temperature increased by 5.7 ∘C (from -9.2 to
-3.5∘C), at 10.75 m by 2.8 ∘C (from -9.0 to -6.2∘C), and at ZAA of 20.75 m by 1.3 ∘C (from -9.1 to
-7.7∘C).
Active-layer thaw depth
Active-layer thaw depth measurements have been carried out since 2002 at 150 points over a 27.5×18 m measurement grid (Boike et al., 2013,
Fig. 12; Wille et al., 2003, 2004), by pushing a steel probe
vertically into the soil to the depth at which frozen soil provides firm
resistance. The data are recorded at regular time intervals, usually between
June–July and the end of August, when the research site is visited. The data
set shows that thawing of the active layer continues until mid-September in
some years (e.g. in 2010 and 2015). Large interannual variations in maximum
active-layer thaw depths are recorded at the end of August, ranging between
the largest mean thaw depth of about 0.57 m (2011) and the smallest mean of 0.41 m (2016).
Timeline for all of the parameters recorded at the Samoylov
research site between 1998 and 2017. Green bars represent aboveground
sensors; brown bars represent sensors installed below the ground surface.
Dark brown and dark green colouring indicates a data set described in this
paper (2002–2017); light brown and light green colouring indicates a
previously described data set (1998–2011; Boike et al., 2013). Continuous
data (light and dark coloured data sets, e.g. wind speed and direction) are
combined in the level 2 product as one continuous data series for the period
1998–2017. Details of parameters for all sensors can be found in Table 2.
Note that the colour bars describe the sensor installation period, but data
might not be available in the published data set due to sensor
malfunction or failure. Note that the measuring period for the Vaisala HMP155A
only started on 17 September, 2017, which is why the bar appears very thin.
Recording of all parameters is still continuing at present.
To assist in the interpretation of active-layer thickness data, surface
elevation change measurements (subsidence measurements) have been collected
since 2013 at three locations (two wet centres, one rim) using reference
rods installed deep in the permafrost (Fig. 1). These measurements show
that a net subsidence of about 15 cm occurred between 2013 and 2017 at the
rim, and smaller subsidence (-1 and -3 cm) at the wet centres. A net
subsidence of between -1.4 and -19.4 cm between 2013 and 2017 was reported by
Antonova et al. (2018) for the Yedoma region of the Lena River delta.
Subsidence monitoring will in future be incorporated into the observational
programme on Samoylov Island so that active-layer thaw depths can be more
accurately interpreted taking into account surface changes due to subsurface
excess ice melt.
Data quality control
An overview of the periods of instrumentation and parameters is provided in
Fig. 4. Quality control was carried out as outlined in Boike et al. (2018a) for the
data set compiled from the Bayelva site, which is located on Spitsbergen.
Quality control on observational data aimed to detect missing data and
errors in the data in order to provide the highest possible standard of
accuracy. In addition to the automated processing, all data have been
visually controlled and outliers have been manually detected, but it cannot
be ruled out that there are still unreasonable values present which are not
flagged accordingly. We differentiate level 0, level 1, and level 2
data (Table 3). Level 0 are data with equal time steps (UTC), and data gaps are
filled with NA and standardized into one file format. These data, as well as
raw data, are stored internally at AWI and are not archived in PANGAEA.
Level 1 data have undergone extensive quality control and are flagged with
regards to equipment maintenance periods, physical plausibility,
spike/constant value detection, and sensor drift (Table 3). Level 2 data are
compiled for special purposes and may include combinations of data series
from multiple sensors and gap filling. Examples in this paper of level 2
data are soil temperature and meteorological data (air temperature,
humidity, wind speed, and net radiation) recorded between 1998 and 2002 (Boike
et al., 2013) that have been combined with a data set since 2002 into a
single data series in order to obtain a long-term picture (documentation of
source data is provided in the PANGAEA data archives).
Nine types of quality control (flags) have been used (Table 3). Data are
flagged to indicate where no data are available, or system errors, or to
provide information on system maintenance or consistency checks based on
physical limits, gradients, and plausibility.
Due to the failure of some sensors that cannot be retrieved for repair or
recalibration (e.g. sensors installed in the ground), the initial accuracy
and precision of the sensors may not always be maintained. In the case of
soil temperature sensor accuracy can be estimated by analysis of
temperatures relative to the fall zero-curtain effect, assuming that the
soil water composition is similar from year to year. Our temperature data
have been checked against the fall zero-curtain effect and information on
any reduction in accuracy is flagged in the data set (Flag 7: decreased
accuracy; Table 3). These checks are essential if subtle warming trends are
to be detected and interpreted. The suitability of flagged data therefore
depends on what it is to be used for and the accuracy required.
The local differences between the sensor locations from 1998 and 2002 (even
though less than 50 m apart), as well as differences between sensor
types and accuracies, need to be considered when interpreting longer-term
records. For example, relative air humidity data show marked differences
between the earlier data set (1998–1999) compared to the later data set
(starting in 2002). Net radiation between 1998 and 2009 showed lower values
during the summer periods compared to the summer periods between 2009 and
2017. One reason could be the change in sensor types: during the first
period, a net radiation sensor was in place, whereas during the second
period a four-component radiation sensor was used.
The data sets presented herein can be downloaded from
PANGAEA (https://www.pangaea.de/, last access: 6 February 2019) and
Zenodo (https://zenodo.org/, last access for all Zenodo links: 6 February 2019), which
provides a data set view and download statistics.
Data (including links to subsets) can be found on either repository using the
following links:
Measurements in soil and air at Samoylov (2002–2017) (Boike et al.,
2018b):
10.1594/PANGAEA.891142, https://zenodo.org/record/2223709.
TLS measurements at Samoylov (2017) (Boike et al., 2018c):
10.1594/PANGAEA.891157, https://zenodo.org/record/2222569.
Time-lapse camera pictures at Samoylov (2006–2018) (Boike et al.,
2018d): 10.1594/PANGAEA.891129, https://zenodo.org/record/2222454.
Permafrost temperature and active-layer thaw depth data are also available
through the Global Terrestrial Network for Permafrost (GTN-P) database
(http://gtnpdatabase.org, last access: 6 February 2019).
Summary and outlook
The climate of the period between 1998 and 2017 can be characterized as
follows: the average mean annual air temperature is -12.3∘C,
with mean monthly temperature of the warmest month (July) recorded as 9.5 ∘C and for the coldest month (February) as -32.7∘C.
The average annual rainfall was 169 mm and the average annual winter snow
covers 0.3 m (2002–2017; no data are available prior to 2002 for snow
cover), with a maximum snow depth of 0.8 m recorded in 2017. Since the
installation in 2006, permafrost has warmed by 1.3 ∘C at the zero
annual amplitude depth at 20.75 m. Permafrost in the Arctic has been warming
and the rate of warming at this borehole is one of the highest recorded
(Biskaborn et al., 2019). Mean annual permafrost temperatures have been
increasing over the recording period at all depths, but the end-of-season
active-layer thaw depth shows a marked interannual variation. Further
analysis is required to disentangle the relationships between meteorological
drivers, permafrost warming, and active-layer thaw depths at this research
site. The data sets described in and distributed through this paper
provide a basis for analyzing this relationship at one particular research
site and a means of parameterizing earth system modelling over a long
observational period. The newly collated data set will allow multi-year
model validation and evaluation that includes the small-scale
microtopographic effects of permafrost-affected polygonal ground. Landscape
heterogeneity (e.g. in soil moisture) is particularly poorly
represented in earth system models and yet exerts a strong influence on the
greenhouse gas balance (e.g. Kutzbach et al., 2004; Sachs et al., 2010). As
such, this data set allows the distinction between microtopographic units
(wet vs. dry) to be incorporated into modelling. This makes this an
important data set for modellers. 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 modelling.
Symbols and abbreviations
αGeometry of the medium in relation to the orientation of the applied electrical field (Roth et al., 1990)εbBulk dielectric number (Ka), also referred to as relative permittivityεlTemperature-dependent dielectric number of liquid waterεiDielectric number of iceεsDielectric number of soil matrixεaDielectric number of airθlVolumetric liquid water contentθiVolumetric ice contentθsVolumetric soil matrix fractionθaVolumetric air fractionθtotTotal volumetric water content (liquid water and ice)ρ‾bulkAverage dry bulk density (kg m-3)ΦPorosity (%)avgAverageBECBulk electrical conductivity (S m-1)CALMCircumpolar Active Layer MonitoringCAVMCircumpolar Arctic Vegetation MapCDbulkBulk carbon density (kg m-3)DsnSnow depth (m)DsnrawRaw snow depth obtained from the sensor (m)ECVsEssential climate variablesGNSSGlobal Navigation Satellite SystemGTN-PGlobal Terrestrial Network on PermafrostKpProbe constantLaLApparent length of the TDR probes (TDR data logger output)MODISModerate Resolution Imaging SpectroradiometerNMass fraction of nitrogen in soil (%)OCMass fraction of organic carbon in soil (%)SOCCSoil organic carbon content (kg m-2)SPSignal period (µs)splSampleTDRTime-domain reflectometryTfFreezing temperature (∘C)TLSTerrestrial laser scanningUSDAUnited States Department of AgricultureWLWater level (m)ZAAZero annual amplitude
Metadata description and photos of meteorological, soil, and
permafrost stations and instrumentationMeteorological station
Samoylov meteorological station setup, August 2002–present
(72.37001∘ N, 126.48106∘ E). Photo taken in August 2015.
The two long radiation shields (left side of tower) at heights of 0.5 and
2 m house the combined temperature and relative humidity probes (two Vaisala
HMP155A sensors were installed on 17 September 2017) and the two shorter
shields (right side of tower) at the same heights contain Campbell Scientific
PT100 sensors (installed on 22 August 2013) to measure air temperature only.
The data logger (Campbell Scientific CR1000, installed on 30 June 2009),
multiplexer (Campbell Scientific AM16/32A, installed on 27 July 2010) and
barometric pressure sensor (Vaisala PTP110, installed on 22 August 2014) are
located in the white box at the back of the tower. The wind monitor and
radiation sensor are shown in the figures below (Figs. B2 and B3).
Young 05103 wind monitor for measuring wind direction and speed,
installed on 31 August 2002.
Kipp & Zonen CNR4 radiation sensor (including CNF4 ventilation
unit) for measuring incoming and outgoing shortwave and longwave radiation
installed on 22 August 2014.
Campbell Scientific SR50 snow depth sensor, installed on 24 August
2002. An aluminum plate was installed on the ground surface beneath the
sensor beam on 17 July 2015.
Cameras for time-lapse photography of snow cover and land surface
pointing towards the polygon field: a Campbell Scientific CC5MPX at the top
(since 4 August 2015) and a Campbell Scientific CC640 below (since
1 September 2006). Photo taken in 2016.
Examples of photos taken by the cameras used for time-lapse
photography (Fig. B5) showing summer field conditions. (a) Left
photo taken by the Campbell Scientific CC640 camera (at a height of 2.2 m)
and (b) right photo taken by the Campbell Scientific CC5MPX camera
(at a height of 3 m) on 7 August 2017.
Examples of photos taken by cameras used for time-lapse photography
(Fig. B5) showing winter field conditions. (a) Left photo taken by
the Campbell Scientific CC640 camera (at a height of 2.2 m) and
(b) right photo taken by the Campbell Scientific CC5MPX camera (at a
height of 3 m) on 4 April 2017.
Soil station
Meteorological station and soil station (consisting of sensors
installed along 1-D profiles within polygon centre, rim, slope, and ice
wedge) with cameras for time-lapse photography pointing towards both stations
for snow and surface observations.
Samoylov research site in September 2017, showing locations of
meteorological station and soil station (consisting of sensors installed
along 1-D profiles within polygon centre, rim, slope, and ice wedge).
White/grey tubes have been placed on the surface to indicate the locations of
the subsurface sensors and their respective microtopographic locations
(polygon centre, rim, slope, and ice wedge).
Research site after instrument installation in soil pits and
subsequent refilling, August 2002. Cable strings indicate locations of
centre, slope, and rim profiles.
Soil volumetric liquid water content sensors (rim): 20 Campbell
Scientific CS605 TDR probes, which are connected to a Campbell Scientific
TDR100 time-domain reflectometer, installed on 24 August 2002.
Hukseflux HFP01 ground heat-flux sensors (a: centre,
b: rim), installed on 24 August 2002.
Diagram showing the sensor distribution below the polygon's centre,
slope, rim and inside the ice wedge, as installed on 24 August 2002.
Descriptions of soil profiles and data from these profiles are provided in
Appendix F.
Calculation and correction of soil and meteorological parametersCalculation of soil volumetric liquid water content using TDR
The apparent dielectric numbers were converted into liquid water content
(θl) using the semi-empirical mixing model in Roth et al. (1990). Frozen soil was treated as a four-phase porous medium composed of a
solid (soil) matrix and interconnected pore spaces filled with water, ice,
and air.
The TDR method measures the ratio of apparent to physical probe rod length
(LaL), which is equal to the square root of the bulk dielectric
number (εb).
The bulk dielectric number is then calculated from the volumetric fractions
and the dielectric numbers of the four phases using
εb=θlεlα+θiεiα+θsεsα+θaεaα1α.
A value of 0.5 was used for α. It is not possible to distinguish between changes in the liquid water
content and changes in the ice content with only one measured parameter
(εb). Equation (C1) was therefore rewritten in terms of the
total water content (θtot) and the porosity (Φ) as
θi=θtot-θl.
Note that Equation C2 assumes the densities of liquid and frozen water to be
the same, which is clearly incorrect for free phases and probably also in
the pore space of soils. However, the density ratio can be absorbed into the
dielectric number εi, which we do below. The resulting
fluctuation of εi is presumed to be small compared to
other uncertainties.
Porosity values for different depths and locations used for the
calculation of volumetric liquid water content. Values were estimated using
measured laboratory values, soil texture/horizon characteristics and TDR
values at maximum saturation (porosity).
We use
θs=1-ϕ
and
θa=ϕ-θl-θi=ϕ-θtot
to obtain the equation
εb=θlεlα+θtot-θlεiα+1-ϕεsα+ϕ-θtotεaα1α.
For temperatures above a threshold freezing temperature (T>Tf),
all water is assumed to be unfrozen (θtot=θl). Equation (C5) then reduces to the
following:
θlT>Tf=εbα-εsα+ϕεsα-εaαεlα-εaα.
For temperatures equal to or below the threshold freezing temperature (T≤Tf) it was assumed that the total water content (θtot)
remained constant and only the ratio between volumetric liquid water content
(θl) and volumetric ice content (θi) changed. This
is a rather bold assumption as freezing can lead to high gradients of matric
potential, as well as to moisture redistribution. However, since the
dielectric number of ice is much smaller than the dielectric number of
liquid water, the error in liquid water content measurements is still
acceptable (which is not the case for ice content measurements). Under these
assumptions we obtained the following equation for calculating the liquid
water content of a four-phase mixture:
θlT≤Tf=εbα-εsα+ϕεsα-εaα+θtotεaα-εiαεlα-εiα.
The error of the volumetric water content measurements using TDR probes was
estimated to be between 2 % and 5 %, which is in agreement with Boike and
Roth (1997).
The availability of reliable temperature data is crucial in this approach.
The liquid water content is first calculated for all times that the soil
temperature was above the freezing threshold, using Eq. (C5). When the
soil temperature was below the freezing threshold the water content was determined
immediately prior to the onset of freezing and used as the
total water content (θtot) for calculating the liquid water
content during the frozen interval with Eq. (C7).
Since water in a porous medium does not necessarily freeze at 0 ∘C but at a temperature that depends on the soil type and water content,
estimating the threshold temperature is a crucial part of this approach. If
the freezing characteristic curve is known for the material, then the
threshold temperature can be determined from the soil volumetric liquid
water content. To avoid interpretations of frequent freezing and thawing due
to soil temperature measurement errors, short-term temperature fluctuations
were smoothed by calculating the mean of a moving window with an adjustable
width. The smoothed temperatures were then used to trigger the switch from
one equation to the other, rather than using the original temperature time
series.
The porosity values for volumetric liquid water content calculations were
obtained from laboratory measurements (Appendix F) and adjusted for probe
location if necessary.
Snow depth correction for air temperature
The acoustic distance sensor (Campbell Scientific SR50) measures the elapsed
time between emission and return of the ultrasonic pulse. The raw distance
Dsnraw obtained from the sensor was temperature corrected using the
speed of sound at 0 ∘C and the air temperature at 2 m height
(Tair_200) in Kelvin (K), using the formula provided by the
manufacturer (Campbell Scientific, 2007):
Dsn=Dsnraw×Tair_200(K)273.15.
Metadata description and photos of installations for water-level
measurements
A measurement system was installed in a polygon centre 3 m southeast of the
meteorological station tower at 72.37001∘ N, 126.48106∘ E to allow changes in the water level to be recorded without requiring the
presence of any personnel. A major disadvantage of using a common pressure
transducer sensor to measure the water level is that such a device cannot
withstand the long frozen Arctic winter and is therefore not suitable for
use when the presence of personnel is limited due to expedition schedules
being restricted to the summer period. A setup that can remain installed and
withstand the cold winter temperatures therefore has a great advantage.
Scheme of setup of water-level measurement in the polygon centre:
(a) length of the parallel measurement rods (30 cm) of the Campbell
Scientific CS616–CS625 sensors, (b) distance from the sensor base to
the ground surface (CS625 is -15 cm; CS616 is -11.5 cm),
(c) Campbell Scientific T109 probes at depths of -1, -3, and
-6 cm below the surface, (d) height of the well above the ground
surface (45.5 cm), (e) length of the well (70 cm),
(f) distance from the top of the well to the water pressure
measurement level of the Schlumberger Mini-Diver. The difference between the
ground level at CS616 and Mini-Diver locations is 3 cm. Blue line
illustrates water level, grey line the ground surface.
We apply vertically installed soil moisture probes to estimate water level,
as described in Thomsen et al. (2000). Our sensors remained permanently in
the soil with the circuit board at the base of the sensor and the
parallel-connected rods pointing upwards. The base of the sensor marks the
lowest measurable water level. For the water content reflectometer we
measured the distance from the ground surface to the base of the sensor,
where the measurement rods are connected (Fig. D1), to compute water level
below the ground surface. From 2007 to 2010 a Campbell Scientific CR200 data
logger was connected to a Campbell Scientific CS625 probe (15 cm below the
ground surface) to record the water level and two Campbell Scientific T109
sensors (1 and 6 cm below the surface) for temperature measurements.
Since 2010 the setup has been connected to the main Campbell Scientific
CR1000 logger of the meteorological station and the CS625 probe was
therefore exchanged for a Campbell Scientific CS616 probe, installed 11.5 cm
below the ground surface. Due to a change in data loggers in the summer of
2010, we have two setups with minor differences in the measurement probes
and their installation depths, which is detailed below and visualized in
Fig. D1. The difference between the two water content reflectometers is
the electrical output voltage, which had to be changed in order to meet the
requirements of the logger. A third T109 probe was also installed 3 cm below
ground surface in 2010. This setup is still in operation. These temperature
data are only used to distinguish between periods of frozen and unfrozen
surface conditions. The unfrozen period, for which water levels were
computed, was defined as the period for which soil temperatures at 6 cm
below the surface are >0.4∘C during spring, and
>0.1∘C during fall. Below these temperatures, no
water-level data are provided.
To obtain a better field calibration of the water content reflectometer a
Schlumberger Mini-Diver pressure water-level sensor was installed in a well
in the same polygon for 68 days of the non-frozen vegetation period in 2016.
Measurements obtained from the Mini-Diver were compensated for changes in air
pressure using data from the meteorological station's barometric pressure
sensor (Vaisala PTB110).
Calculation and correction of water-level measurements
The measured output, signal period (SP) from the Campbell Scientific CS616
or CS625 probes were converted into the height of the water level above the
sensor base (WL) using two polynomial functions derived from an empirical
field experiment to determine the correlation between the results from the
CS616–CS625 probes and those from a Mini-Diver.
The two regressions represent different water-level regimes (low and higher
water levels) recorded by the CS616–CS625 sensor. The results of this
experiment showed a low accuracy for very low water levels (1.5 cm or less
above the sensor base) resulting in output periods of SP <19µs, which were excluded from the data series. For values >19µs, the following formulas are applied to obtain WL data from the
CS616 and CS625 probe output:
WL=0.01831394SP3-1.2398SP2+28.84699187SP-224.41499308
for SP <27µs and
WL=0.06194726SP2-1.7673294SP+13.66709591
for SP >27µs. Note that WL for the equations above is
given in centimetres.
The mean deviation of the calculated WL values from the values measured with
the Mini-Diver was (0.034 cm) with a standard deviation of 0.29 cm (number of
values: 2679).
Campbell Scientific CS616 vertical probe installation. Water-level
measurement is done manually with a ruler.
Installation for water-level measurements using a permeable ground
water measurement tube with a Schlumberger Mini-Diver.
Note that WL is given relative to the sensor base in the time series data and
reported in metres. To obtain water level relative to the ground surface
(WLgs) from level 1 data, the following calculation is suggested for CS616:
WLgs=WL-0.115m(from27July2010untilnow).
For CS625,
WLgs=WL-0.15m(from25July2007until26July2010).
Special post-processing of the CS625 sensor readings was carried out from
6 July 2009 to 26 July 2010, as no probe output periods were logged
over this period. Instead, volumetric liquid water content (θl)
was stored on the CR200 logger and calculated from the CS625 probe output
using a formula from the sensor's manual (Campbell Scientific, 2016).
The θl, values were converted to SP values using formula (D5):
SP=39.12153154θl3-61.59657836,θl2+56.7054971θl+15.37001712
We compared the calculated WL with manual distance measurements taken in the
field over the years (n=12). The largest differences between TDR-derived
and manual measurements was 2 cm. This includes all measurement errors, such
as sensor movement (probes are not anchored into the permafrost, they can
potentially move with the seasonal heaving, subsiding of the active layer),
and difficulties in defining the ground surface (which is covered by mosses and
grasses).
Metadata description and photos of the borehole, 2006
A borehole was drilled at 72.36941∘ N, 126.47612∘ E into
the permafrost during the spring of 2006. Drilling started with 146 mm
diameter to 4 m depth and continued with 132 mm diameter to 26.75 m depth. A 4 m long metal stand pipe (diameter 13 cm) was used for stability
and to prevent the inflow of water during the summer season into the borehole.
The metal pipe extends 0.5 m above and 3.5 m below the ground surface
(Figs. E1 and E2).
A thermistor chain with 24 temperature sensors (RBR thermistor chain with an
XR-420 logger) was inserted into a close-fitting PVC tube (4 cm inside,
5 cm outside diameter) and installed in the borehole on 21 August 2006, down
to a depth of 26.75 m (Fig. E2).
A second PVC tube with the same dimensions as the first tube was also
inserted into the borehole to permit additional (geophysical and
calibration) measurements to be made in the future. The remaining air space
in the borehole was backfilled with dry sand. The outside metal pipe (used
for drilling and to prevent inflow of water), which stands 0.5 m above the ground
surface, was closed at the top, and was covered with a wooden shield which
was renewed in 2015.
The accuracy of the temperature sensors of the thermistor chain is reported
by RBR to be ±0.005∘C between -5 and 35 ∘C. However, direct comparison with a high-precision reference
PT100 temperature sensor (certified to be accurate to ±0.01∘C between -20 and 30 ∘C) at six different depths in
the borehole between 9 and 17 August 2014 showed the accuracy of the RBR
XR-40 temperature sensors to be approximately ±0.03∘C at
depths ≥8.75 m (Table E1). The deviation increased with decreasing
depth; e.g. between -7.75 and -1.75 m the deviation was ±0.33∘C and at -0.75 m it was ±0.65∘C. This
increase in deviation towards the surface may be because (a) the chain was
installed in sand, whereas the calibration thermometer was in air and could
therefore possibly have been affected by air circulation, or (b) the
temperature gradient becomes steeper with decreasing depth below the surface
and thus small differences between the measuring heights of the two sensors
will have a larger impact on temperatures as the surface is approached. The
offset of the reference thermometer at exactly 0 ∘C was 0.01 ∘C, and the average statistical accuracy (Uk=2) is given by
the manufacturer as 0.1083 ∘C. During calibration in the borehole
the temperature was given time to stabilize (i.e. until the recorded
temperature change was less than ±0.03∘C) before being
recorded (Table E1).
Location of the 2006 borehole showing the proximity of the borehole
to a small lake. The metal pipe extends 0.5 m above the ground surface.
Thermistor setup showing their depths within the borehole, as
installed in 2006. The metal pipe extends 0.5 m above ground surface. Note
the differences in scale above and below the ground surface.
Continuous measurements have been obtained since mid-August 2006 from sensor
depths of 0.00, 0.75, 1.75, 2.75, 3.75, 4.75, 5.75, 6.75, 7.75, 8.75, 9.75,
10.75, 11.75, 12.75, 13.75, 14.75, 15.75, 16.75, 17.75, 18.75, 20.75, 22.75,
24.75, and 26.75 m below the ground surface. We recommend that the
temperature data from the three sensors at 0, 0.75, 1.75 and 2.75 m depths
should not be used due to the possibility of them having been affected by
the metal access pipe. The data from these sensors have not been flagged as
they are of high quality, but they may not provide an accurate reflection of
the actual soil temperatures.
In situ calibration of thermometers in the borehole between 9 and
17 August 2014. Comparison measurements were made in the 27 m borehole using
a certified PT100 thermometer (Service für Messtechnik Geraberg
DTM 3000).
Construction of the new Russian Samoylov Island Research Station started in
September 2011 and was completed in summer 2012. The new research station
included a water supply from a nearby lake. The water supply system (Figs. E3 and E4) is an aboveground structure that is likely to affect the wind
and hence the accumulation of snow on the tundra surface. Visual inspection
in the vicinity of the borehole in April 2016 suggested an increased snow
accumulation around this location since construction of the water supply
system. A new borehole was drilled in April 2018 down to 61 m, far away from
the research station and associated structures. A new temperature chain was
installed in the early summer of 2018 to provide deeper permafrost data, as
well as observations from a second borehole.
Location of the 2006 borehole (wooden box) showing the proximity of
the new water supply system (a silver metal structure extending
above the tundra surface since 2013).
Borehole location (wooden box on right side) with the new Samoylov
research station and water supply system (silver metal structure extending
above the tundra surface).
Data from soil profiles
The organic carbon density in bulk soil CDbulk (kg m-3) was
calculated using the mass fraction of organic carbon in soil OC, the average
dry bulk density ρ‾bulk, and the following
formula:
Soil data from the BS-1 (polygon rim) and BS-3 (polygon centre) soil
pits, which were sampled and had instruments installed in 2002. The location
of the soil profiles is described in Wille et al. (2003) and shown in
Figs. B8 and B9. Photos of the soil profiles can be seen in Fig. F1 below.
Grain size classification is according to Folk (1954), where S is sand, s is
sandy, Z is silt, z is silty, M is mud, m is muddy, C is clay, and c is
clayey. Other abbreviations used are OC for the mass fraction of organic
carbon in soil, N for the mass fraction of nitrogen in soil,
CDbulk for the organic carbon density in bulk soil, and SOCC for
the soil organic carbon content for each soil horizon. The soil horizon
designations are according to the USDA Soil Survey Staff (2010). The BS-1 and
BS-2 (polygon slope) soil profiles are classified as
Typic Aquiturbels and the BS-3 soil profile is classified as
Typic Historthel according to the US Soil Taxonomy (Soil Survey
Staff, 2010). Analysis of the physical properties of soil was done according
to DIN 19683 (1973). OC and N were determined following the removal of
inorganic carbon and dry combustion at 900 ∘C (DIN ISO 10694).
CDbulk=OC×ρ‾bulk.
The organic carbon content for each soil horizon SOCC (kg m-2) was
calculated using the mass fraction of organic carbon in soil OC, the average
dry bulk density ρ‾bulk, the horizon thickness, and
the following formula:
SOCC=OC×ρ‾bulk×horizonthickness=CDbulk×horizonthickness.
Photographs of soil profiles (a) BS-1 (Typic Aquiturbel) at the peak of a
polygon rim and (b) BS-3 (Typic Historthel) at a polygon
centre. Designations of soil horizons according to US Soil Taxonomy (Soil
Survey Staff, 2010). Horizon labels are positioned at the upper boundary of
the respective horizon.
Names of the variables and units for data files
Overview of all variables published as a
time series. Some variables have _center, _rim and _slope as their
location index (Sect. 3.2). Additional level 2 data are published for the
variables air temperature, relative humidity, precipitation, wind speed and
direction, net radiation, soil temperatures, and soil volumetric liquid water
content, which is indicated by _lv2 in the column name. If an air temperature sensor is covered
by snow and thus measures snow temperature, this is indicated by a Flag 8 in
the data.
VariableColumn nameUnitsAir/snow-covered air temperatureTair_(height in cm)∘CRelative humidityRH_(height in cm)%Atmospheric pressurePAkPaIncoming shortwave radiationSwInW m-2Outgoing shortwave radiationSwOutW m-2Incoming longwave radiationLwInW m-2Outgoing longwave radiationLwOutW m-2Net radiationRadNetW m-2Wind speedVwind_(height in cm)m s-1Wind speed maximumVwind_max_(height in cm)m s-1Wind speed minimumVwind_min_(height in cm)m s-1Wind directionDirwind_(height in cm)∘Wind direction standard deviationDirwind_sd_(height in cm)∘Active-layer thaw depthDal_(ID)cmSoil/permafrost temperatureTs_(depth in cm)∘CSoil bulk electrical conductivityCond_(depth in cm)S m-1Soil dielectric numberE2_(depth in cm)–Soil volumetric liquid water contentVwc_(depth in cm)–Ground heat fluxG_(depth in cm)W m-2Precipitation (liquid)PrecmmSnow depthDsnmWater levelWLmTerrestrial laser scanning – analysis of 2017 data
3-D point cloud data were acquired for several polygons around the
meteorological, soil and CALM sites by terrestrial laser scanning (TLS) on
12 September 2017, using a RIEGL VZ-400 3-D TLS instrument. According to the
manufacturer's specifications, the TLS instrument measures 3-D coordinates
with an accuracy of 5 mm and a precision of 3 mm (RIEGL LMS, 2017). We
captured the full extent of the research site, which has dimensions of
approximately 70×70 m, from 10 scan positions with a horizontal and
vertical point spacing of 3 mm at 10 m measurement range. The single-point
clouds were registered into a common coordinate system using five cylindrical
reflectors placed around the research site during the TLS data acquisition so
that they were visible from all scan positions. Mean residual distances per
scan position between the cylindrical reflectors amounted to 1.6 cm, with a
standard deviation of 0.8 cm.
The registered 3-D point cloud data set was georeferenced using high-accuracy
global positioning measurements recorded with a global navigation satellite
system (GNSS). We obtained GNSS measurements in static-phase observation mode
with a Leica Viva GS10 as the base station receiver and a GS15 mobile rover
unit (Leica Geosystems, 2012a, b). According to the manufacturer's
specifications (Leica Geosystems, 2012a), this mode achieves a measurement
accuracy of 3 mm horizontally and 3.5 mm vertically with respect to the
local reference frame established by the base station. The scan positions
were georeferenced and registered using the RiSCAN PRO software
(version 2.1.1; RIEGL LMS, 2016).
The raw data set was filtered using a statistical outlier removal (SOR; Rusu
and Cousins, 2011) to remove spatially isolated points as outliers from the
point cloud, with the number of neighbours set to 10 and the standard
deviation multiplier threshold to 1.0.
A digital terrain model (DTM) representing the ground-surface elevation was
derived from this preprocessed data set. To determine the ground-surface
elevation the 3-D TLS points were first classified into ground and non-ground
points. For this we used a minimum approach, classifying all points within a
search radius of 2.5 cm that were at less than
1.0 cm vertical distance from the minimum point
elevation as ground points. This vertical distance threshold is included to
take into account position uncertainties of the TLS acquisition. The ground
points in the 3-D TLS data set are subsequently rasterized into the final DTM
(with a cell size of 5.0 cm) using a robust moving-plane interpolation
strategy (TU Wien, 2016).
For evaluation purposes the DTM was compared to 27 GNSS measurements of the
ground surface that were obtained during the TLS data acquisition. The data
sets were compared by taking the difference between GNSS-based elevation
measurements and the corresponding DTM pixel values. Statistical analysis of
these differences in ground-surface elevation yielded a mean difference of
3.7 cm, a median difference of 1.7 cm, and a standard deviation of 5.1 cm.
Differences were mainly within the accuracy ranges of TLS point cloud
registration and GNSS positioning. Larger positive differences (>2.0 cm) indicated an overestimation of ground-surface elevation in the TLS
point cloud. Where dense, short vegetation is present, an error is introduced
to the estimated ground-surface elevation as the laser beam does not hit the
ground surface at every local area in the site. This is to be expected,
particularly for larger distances from the scan positions, as the incidence
angle from the TLS instrument has a direct effect on the penetration depth
of the laser beam (Marx et al., 2017).
Relative height above the ground surface was derived as vertical distance of
TLS points to the ground surface. The DTM was used to calculate the vertical
distance to the ground surface for every 3-D point in the TLS point cloud. A
raster of relative height values was generated using the 99th percentile of
the relative height attribute per raster cell, with a cell size of 5.0 cm.
Furthermore, a raster of mean relative heights above the ground surface was
generated that could provide an estimate of the vegetation height and volume
within each 5 cm raster cell. With regard to the vegetation height values
derived from the TLS data, it should be noted that the heights could be
underestimated when compared to actual field measurements, for which there
are two possible explanations. Firstly, overestimation of the ground-surface
elevation (where the laser beam does not fully penetrate the vegetation)
reduces the calculated relative (vegetation) height. Secondly, the sampling
of the laser-scanning process with the given 3-D point spacing implies an
uncertainty in the maximum height being recorded at every local position.
This applies in particular to grass-covered surfaces, where individual
blades are not necessarily hit by the laser beam at their highest point.
Both of these effects can result in reduced vegetation heights in a
TLS-based approach, e.g. compared to length measurements of individual
sedges in the field.
The modular programme system OPALS (version 2.3.0; Pfeifer et al., 2014) was
used for the point cloud analyses of ground-surface elevation and relative
height above the ground surface.
JB initiated and set up the long term observational site
on Samoylov together with LK and CW. Data collection was done by JB, CW, NB, and PS, with the support of MG, DB, and AM.
KA collected the terrestrial lidar scanning data
and analysed the data. IG, SC, and ML contributed to the modelling sections, EB
and SC provided the Samoylov driving data. JB wrote the paper with inputs
from the co-authors and coordinated the analysis and contributions from all
co-authors.
The authors declare that they have no conflict of
interest.
Acknowledgements
Logistical support was provided by the Russian–German
Samoylov Research Base (1998–2012) and the Russian Samoylov Island Research
Station (2013–2017). Field support, including data collection, was provided
by Konstanze Piel, Steffen Frey, Günter Stoof, and Waldemar Schneider.
We gratefully acknowledge the funding received from the Helmholtz
Association's ACROSS (Advanced Remote Sensing – Ground Truth Demo and Test
Facilities) project.
Edited by: Kirsten Elger
Reviewed by: Richard L. H. Essery and Nick Brown
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