Across the Qinghai–Tibet Plateau (QTP) there is a narrow
engineering corridor with widely distributed slopes called the
Qinghai–Tibet Engineering Corridor (QTEC), where a variety of important
infrastructures are concentrated. These facilities are transportation routes
for people, materials, energy, etc. from inland China to the Tibet Autonomous Region. From
Golmud to Lhasa, the engineering corridor covers 632 km of permafrost
containing the densely developed Qinghai–Tibet Railway and Qinghai–Tibet Highway, as well
as power and communication towers. Slope failure in permafrost regions, caused
by permafrost degradation, ground ice melting, etc., affects the engineering
construction and permafrost environments in the QTEC. We implement a variety
of sensors to monitor the hydrological and thermal deformation between
permafrost slopes and permafrost engineering projects in the corridor. In
addition to soil temperature and moisture sensors, the global navigation
satellite system (GNSS), terrestrial laser scanning (TLS), and unmanned
aerial vehicles (UAVs) were adopted to monitor the spatial distribution and
changes in thermal deformation. An integrated dataset of
hydrological and thermal deformation in permafrost engineering and slopes in the
QTEC from the 1950s to 2020, including meteorological and ground
observations, TLS point cloud data, and RGB and thermal infrared (TIR)
images, can be of great value for estimating the hydrological and thermal impact
and stability between engineering and slopes under the influence of climate
change and engineering disturbance. The dataset and code were uploaded to
the Zenodo repository and can be accessed through
https://zenodo.org/communities/qtec (last access: 23 June 2021), including meteorological and ground
observations at 10.5281/zenodo.5009871 (Luo et al.,
2020d), TLS measurements at 10.5281/zenodo.5009558 (Luo
et al., 2020a), UAV RGB and TIR images at
10.5281/zenodo.5016192 (Luo et al., 2020b), and R code
for permafrost indices and visualisation at
10.5281/zenodo.5002981 (Luo et al., 2020c).
Introduction
Permafrost is frozen soil or rock containing ice, where organic material
remains at or below 0 ∘C for at least 2 consecutive years, and it
occurs mostly in the northern extreme of Northern Hemisphere, Alaska, and the Qinghai–Tibet
Plateau (QTP) (Wang et al., 2018; Zhang et al., 1999). As a typical
mountain permafrost region, permafrost slopes occur widely across the QTP.
There exists a narrow engineering corridor on the QTP, where a variety of
important infrastructures are concentrated, called the Qinghai–Tibet
Engineering Corridor (QTEC) (Luo et al., 2018b; Zhang et al., 2015). These
facilities are transportation routes for people, materials, energy, etc.
from inland China to the Tibet Autonomous Region, and the QTEC is several hundred metres to
several kilometres wide. From Golmud to Lhasa across the QTP, more than 1120 km of the engineering corridor contains the densely developed Qinghai–Tibet
Highway (QTH), Golmud–Lhasa pipeline, Lanzhou–Xining–Lhasa fibre-optic
cable, Qinghai–Tibet Railway (QTR), and 440 kV Qinghai–Tibet DC networking
system, which were completed and opened in 1954, 1977, 1997, 2006, and 2013,
respectively (Jin et al., 2008). There are more than six major linear
projects in the QTEC, covering 632 km of permafrost and approximately 550 km
of continuous permafrost with widely distributed slopes (Luo et
al., 2018b). Inevitably, parts of this infrastructure are built on these
slopes (Luo et al., 2018c; Jin et al., 2008).
Against the background of global climate change and increasing human
activities, permafrost degradation is remarkable, resulting in an increase
in the number of permafrost disasters (Huggel et al., 2010; Streletskiy et
al., 2019; Bessette-Kirton and Coe, 2020; Patton et al., 2019). In engineering
around permafrost slopes, permafrost disasters have widely occurred (Ma
et al., 2006; Guo and Sun, 2015; Yu et al., 2020). Slope-instability-type
disasters tend to occur frequently, thereby causing direct or potential harm
to the engineering projects in this region, which has become the main
problem affecting the safe operation and service performance of these
engineering projects while increasing the difficulty and cost of engineering
maintenance (Niu et al., 2015; Wirz et al., 2015). Engineering in
permafrost areas will inevitably destroy the surface energy and water
balance, resulting in higher temperatures at the top of the permafrost
(TTOP) and a lower permafrost table (Zhang et al., 2020; Liu et al.,
2020; Zhao et al., 2020). Permafrost engineering occurs on slopes, which
changes the circulation of surface water and supra-permafrost groundwater,
causing thermal erosion (Mu et al., 2018; Chang et al., 2015). Permafrost
is affected not only by the construction and operation of engineering but
also by the long-term heat impact of climate change (Wicky and Hauck,
2017; Gruber and Haeberli, 2007). In particular, high temperatures and highly
ice-rich permafrost with a mean annual air temperature (MAAT) above -1.0∘C and an ice volume content higher than 25 % are more
sensitive to climate change and disturbances in engineering activities
(Wu and Zhang, 2008; Patton et al., 2019). In the past 60 years,
the MAAT of the seasonal and island permafrost areas along the QTEC has
increased by 0.3 to 0.5 ∘C, and the MAAT in the continuous
permafrost area has increased by 0.1 to 0.3 ∘C (Obu et al.,
2019; Luo et al., 2018c; Wu et al., 2007). The QTP is a large-scale amplifier
of global change, experiencing warming above the global average. If the air
temperature rises to 2.6 ∘C, permafrost with a mean annual ground
temperature (MAGT) higher than -1.0∘C will degrade to seasonal
frozen soil after 50 years (Luo et al., 2018c; Wu et al., 2002).
Furthermore, the permafrost slope in the freezing (thawing) process is
subject to frost heave (thawing subsidence), which leads to deformation
and even destruction of the foundation of engineering facilities, thereby
affecting the normal use of these engineering facilities (Streletskiy et
al., 2019; Yu et al., 2020; Ma et al., 2017). Warming of the climate and
operation of permafrost projects around slopes have caused the ground
temperature to rise. On ice-rich slopes, melting underground ice due to
rising temperatures reduces the cohesion and angle of internal friction
between the active layer and underground ice and becomes extremely unstable
under the influence of gravity (Yuan et al., 2017). The locations
of these slopes near permafrost engineering projects, such as railways and
highways, thaw slumps, frost heaves, landslides, rockfalls, may cause
serious damage to permafrost engineering (Niu et al., 2015; Luo et al.,
2018a).
The Chinese National Highway Network Plan, released in 2004, is aimed at
building the Beijing–Lhasa Expressway, which will be the only expressway to
enter Tibet. The Beijing–Tibet Expressway will also be built within this
narrow QTEC and will run parallel to or cross-positioned with the already
crowded corridor (Ma et al., 2017). The interaction between the dense
layout of various permafrost engineering facilities and permafrost slopes
cannot be ignored. The stability and potential disaster of permafrost slopes
constitute an area of increased interest of the international permafrost
community, but studies on the spatiotemporal dynamics on unstable permafrost
slopes in the QTEC are still lacking. A thorough understanding of the impact
of a variety of the permafrost engineering infrastructure (QTH, QTR,
power and communication towers, etc.) that is densely distributed on or around
permafrost slopes is lacking (Wang et al., 2020; Ma et al., 2019).
Therefore, it is important to evaluate the interaction and influence between
permafrost slopes and permafrost engineering by integrating
hydrological and thermal deformation monitoring (Luo et al., 2018a).
Terrestrial laser scanning (TLS) with the global navigation satellite system
(GNSS) was used to monitor the deformation of permafrost slopes (Luo et
al., 2017; Arenson et al., 2016) and steep bedrock permafrost (Weber
et al., 2019) to construct a high-resolution digital terrain model of the
permafrost region (Boike et al., 2019). GNSS can be used as the datum
point and control point of TLS, helping TLS point cloud data establish a
georeferenced coordinate system and improving the accuracy of comparative
analysis of multiple TLS data. Unmanned aerial vehicles (UAVs) can be
equipped with visible digital, thermal infrared (TIR), and multispectral
sensors. In addition to obtaining the topographic and landform features of
the two frozen soil slopes, it can also estimate the spatial distribution of
the ground surface temperature on permafrost slopes and evaluate the thermal
influence of nearby engineering infrastructure (Luo et al., 2018a).
UAVs can be used to monitor vegetation and terrain information in permafrost
regions (Léger et al., 2019). Sluijs et al. (2018) also
adopted UAVs to quantify the deformation of thaw slumps and to estimate the
transfer volumes of sediment.
We provide an integrated dataset of the hydrological and thermal deformation
covering permafrost engineering and slope areas in the QTEC. In addition to
using soil temperature and moisture sensors to monitor in situ hydrothermal
changes, GNSS, TLS, and UAVs were also adopted to observe the spatial
distribution of thermal deformation. This synthesis dataset for permafrost
engineering and slopes includes measured air and ground temperatures and
moisture, MAAT, mean annual ground surface
temperature (MAGST), TLS point cloud data, and RGB and TIR images. To
fully understand and leverage existing datasets and to allow for full
transparency and repeatability, we provide comprehensive information and
metadata, including complete documentation of the dataset and technical
methods.
Site description
The study area is located in the Kunlun Mountain Pass (KMP) in the QTEC,
where the QTH, QTR, and power and communication towers are crisscrossed
(Luo et al., 2018a). They are densely situated across a width of 200 m,
and a variety of projects occur on permafrost slopes, connected by supporting
bridges or laying towers, or directly on roadbeds. The KMP is located in the
hinterland of the QTP and in the middle of the East Kunlun Mountains,
adjacent to the giant main fault in the northern part of the plateau that
controls its formation and development (Wu et al., 2017). In the KMP,
thick early Quaternary sediments were deposited, and the sedimentary strata
recorded the history of tectonic evolution in the northern part of the QTP.
The MAAT in the KMP is -4∘C, and the MAGST ranges from -2.4 to 3.2 ∘C at a
depth of approximately 15 m below the ground surface. The monthly air
temperature ranges from a minimum of -28∘C in winter to a
maximum of 18 ∘C in summer. The mean annual total precipitation
ranges from 300 to 500 mm, and precipitation occurs most frequently from May
to September, accounting for 80 % of the annual total precipitation, with
the highest precipitation occurring from July to August. Permafrost is well
developed with a high ice content, and the active layer thickness (ALT) is
more than 3 m, which is generally present above 4200 m in the region.
Permafrost can be found in mountains and basins, but most of the valleys do
not contain permafrost. Through drilling data near the study area, the
permafrost thickness ranges from 46 to 112 m, the temperature gradient
within the frozen soil is 3.45 ∘C/100 m, and the geothermal
gradient below the frozen soil is 3.9 ∘C/100 m (Luo et al.,
2019; Yang et al., 2017).
Two slopes (slopes A and B) within the KMP were selected (Fig. 1). Slope A
(35∘39′4′′ N, 94∘03′46′′ E, 4759 m a.s.l.) is located along the side of the QTH, while the QTR, supported by
bridges, runs across slope B (35∘38′45′′ N, 94∘03′48′′ E, 4770 m a.s.l.), and the QTH is next to slope B. A large
number of similar permafrost slopes have been found along the QTEC, but a few
projects are densely distributed on permafrost slopes. Therefore, the study
area is considered to be an excellent place for observing the interaction of
hydrological and thermal deformation effects between engineering operations and
permafrost slopes.
The active layer is mainly composed of clay from the top of the soil to a
depth of 270 cm, including wet silty clay from 0 to 50 cm, loose silty clay
from 50 to 120 cm, compacted clay from 120 to 180 cm, saturated clay mud
from 180 to 230 cm, loose silty clay with gravel from 230 to 250 cm, and
thick underground ice from 250 to 270 cm. According to the soil profile, the
types of permafrost on the above two slopes can be classified as ice-rich
permafrost. The vegetation type in this area is a typical alpine desert
steppe. The vegetation on slope A is sparse, at a coverage lower than 5 %,
and most of the vegetation is gathered at the top of the slope, while slope B contains almost no vegetation.
From 2014–2017, four deformation monitoring campaigns using TLS with GNSS
and soil hydrothermal in situ monitoring were deployed. Moreover, a UAV
system equipped with RGB and TIR sensors was adopted to monitor the
spatiotemporal changes in the ecological environment and ground surface
temperature of the slopes. The deployment of these instruments provides an
integrated dataset for hydrological and thermal deformation monitoring of
permafrost slopes and the various frozen soil projects (QTR, QTH, and
power and communication towers) located on or near them.
Geography of the study area, including the two permafrost
slopes A and B, the Qinghai–Tibet Highway and Qinghai–Tibet Railway, and power and communication
towers.
Data descriptionMeteorological observations
Observation of meteorological factors was conducted at two permanent
meteorological stations (Golmud and Wudaoliang) and one field meteorological
station (Xidatan) (Table 1), which were retrieved from the National
Meteorological Information Center (NMIC, http://data.cma.cn, last access: 22 June 2021). The two
weather stations are located on the north and south sides of the two slopes
and are also the closest national weather stations to the two slopes. The
meteorological database includes the daily mean, maximum (max), and minimum
(min) air temperatures; wind speed; observed and corrected precipitation;
evaporation; air humidity; atmospheric pressure; sunshine duration; daily
mean, max, and min ground surface temperatures; and the soil temperature at
different depths (i.e., 5, 10, 15, 20, 40, 50, 80, 160, and 320 cm) from the
1950s to 2020. Golmud and Wudaoliang are two national reference
meteorological stations in China, while Xidatan is a national general field
station. Although these two national meteorological stations are far from
the above two slopes, the data obtained from these meteorological stations
are very valuable in this harsh environment. Their data can be combined with
the data obtained from the Xidatan field station (Zhao,
2018) to analyse the spatiotemporal dynamics of the permafrost slopes in the
corridor.
Meteorological station Golmud (36∘25′ N, 94∘54′ E, 2808 m a.s.l.) is located in the urban area of Golmud, south
of the two slopes, with few surrounding buildings. This weather station area
is located in the seasonal frozen soil zone. The distance between the
station and the two slopes is approximately 115 km.
The meteorological station Xidatan (35∘43′ N, 94∘08′ E,
4538 m a.s.l.) is located in the northern part
of the two slopes and is the closest field weather station to the two
slopes. The distance between the station and the two slopes is approximately
9.7 km. The Xidatan field station is 300 m away from the QTH and is located
at the northernmost end of the permafrost in the QTEC. The vegetation type
on the underlying surface is an alpine meadow. Figure 2 shows the data for
this station. Observation data were retrieved from the National Tibetan
Plateau Data Center (NTPDC, https://data.tpdc.ac.cn/, last access: 22 June 2021).
The meteorological station Wudaoliang (35∘13′ N, 93∘05′ E,
4612 m a.s.l.) is located to the north of the
two slopes in a continuous permafrost zone, next to the 109 National Highway
along the QTH. The distance between the station and the two slopes is
approximately 101 km.
List of meteorological observation data, where n/a
indicates not applicable. Automated observations were conducted at the
Golmud and Wudaoliang stations in 2000 and 2001, respectively.
SIDStation nameLongitudeLatitudeElevationAutomatic station modelStart yearEnd yearSource52818Golmud94∘54′36∘25′2808Vaisala Milos 50019552020NMICXDTMSXidatan94∘08′35∘43′4538n/a20142018NTPDC52908Wudaoliang93∘05′35∘13′4612Vaisala Milos 50019562020NMIC
Time series (daily mean values) of the Xidatan field
station from 2014 to 2018: (a–e) meteorological observation data.
Ground observations
Changes in soil temperature and humidity can be used to indicate water and
heat transfer processes, which will strongly affect the physical and
mechanical properties of frozen soil and will further affect the stability
of slopes. The ground temperature and moisture data from near the surface to
within 270 cm in the study area were recorded. In situ ground observations
were deployed starting in July 2013 in the active layer of slope A at 11 depths (1, 30, 63, 80, 100, 123, 140, 175, 205, 235, and 260 cm) using
thermocouple probes (105T, Campbell Scientific) to measure the soil
temperature and using 11 time domain reflectometer (TDR) probes (model
CS615-L, Campbell Scientific) at 11 depths (10, 20, 48, 74, 91, 110, 135,
157, 190, 220, 245, and 270 cm) to measure the soil volumetric water content
from 2014 to 2019 (Fig. 3). The TDR probes were mounted horizontally along
the soil profile next to the temperature probes at the different soil
depths, and measurements were recorded once per hour. Since the soil
undergoes a freezing period from refilling to compaction, the 2013 data are
not analysed. A Campbell Scientific CR1000 data logger is used to connect
the ground temperature and volumetric water content probes. Figure 4 shows
the kriging interpolation data of the soil temperature and volumetric water
content at the different depths from 2014 to 2019 in the study area. The two
permanent meteorological stations also contain ground observation data. Soil
moisture with a soil temperature below 0 ∘C is beyond the scope
of instrument monitoring. Monitoring soil moisture under frozen conditions
has always been a technical difficulty. Therefore, soil moisture data below
0 ∘C are not available.
Ground sensor installation.
Soil temperature and volumetric water content from 2014
to 2019: (a) soil temperature (∘C) and (b) soil moisture (%).
TLS and GNSS
Deformation monitoring was performed through TLS with a network real-time
kinematic (RTK) service provided by the National Geodetic Control Network
(NGCN) for the China Geodetic Coordinate System 2000 (CGCS 2000) at
permanent reference stations for the GNSS (Fig. 5a and b). A FARO
Focus3D X130 3D laser scanner and six Trimble 5700 GNSS systems were
deployed at the study site between May 2014 and October 2015. Two GNSSs were
adopted as datum points 30 km outside the study area (Fig. 5c and d), and
another four GNSSs were deployed as reference points around one of the
slopes. According to the manufacturer's specifications, the FARO TLS
instrument measures 3D coordinates with a distance accuracy up to ±1 mm, and the ranging error is ±2 mm. The Trimble 5700 GNSS systems
achieve a measurement accuracy of ±5 mm + 0.5 ppm root mean square
(rms) horizontally and ±5 mm + 1 ppm rms vertically for static and
FastStatic GPS surveying, respectively.
TLS observation with the GNSS from 2014 to 2015: (a) datum station of the GNSS, (b) TLS observation with the GNSS
and white reference sphere set, (c) datum station 454F, and (d) datum station
455F.
Since May and October are the transition periods of the freeze–thaw cycle,
we conducted monitoring campaigns in the months of May and October between
May 2014 and October 2015. The successive three freeze–thaw phases are
referred to as the first thaw (2 May to 10 October 2014), first
freeze (10 October 2014 to 3 May 2015), and second thaw (3 May to
4 October 2015). The above two slopes mainly manifested melting collapse
during thawing and frost heave during freezing, but frost heave dominated after several
freeze–thaw cycles (Figs. 6 and 7). A full slope
scan requires approximately 30 scan positions to generate 3D point cloud
data. Each scan requires the placement of six white reference spheres at the
study site, and they are visible at all scan locations. Two to three of
these reference spheres are moved for the next scan. Six reference sphere
data points can be combined with the GNSS data to georeference, register,
align, and mosaic the point cloud data of the TLS instrument with FARO SCENE
and Geomagic Studio software. TLS monitoring data show that during the
thawing period, the slopes were dominated by subsidence, while during the
freezing period, the slopes were dominated by frost heave. After multiple
thawing cycles and one freezing cycle, the slopes also exhibit frost heave
characteristics (Luo et al., 2019).
As a supplement to the TLS point cloud data, we prepared Sentinel-1
deformation data during the freeze–thaw stages in the study area from 2014
to 2020 using interferometric synthetic aperture radar (InSAR) technology.
Deformation of slope A during the first thaw, first
freeze, and second thaw: (a) first thaw, (b) first freeze, and (c) second thaw.
Deformation of slope B during the first thaw, first
freeze, and second thaw: (a) first thaw, (b) first freeze, and (c) second thaw.
UAV with multisensors
The change in frozen soil is greatly affected by the temperature. To monitor
the heat exchange between the two slopes and the engineering infrastructure
around them, a UAV system with mounted RGB and TIR sensors was adopted
(Luo et al., 2018a). The DJI Inspire 1 UAV system (DJI, Inc., Shenzhen,
China) weighs approximately 2.85 kg, including propellers and batteries, and
is equipped with a Zenmuse X3 RGB camera and a Zenmuse XT TIR sensor. The
camera and sensor weigh 221 and 270 g, respectively. The UAV is also
equipped with GPS and an inertial measurement unit (IMU) to measure the
geographical and flight positions, respectively. The TIR sensor has a
resolution of 336 × 256 pixels, a thermal sensitivity of <0.05∘C at f/1.0, a field of view of 17∘ (H) × 13∘ (V), a focal length of 19 mm, and a spectral response in the
electromagnetic spectrum. The above TIR sensor has a range from 7.5 to 13.5 µm. A mobile phone equipped with a flight control system app is used to
control the flight of the UAV, in addition to DJI GO and Pix4D capture
software. Both instruments adopt vertical angles to capture images, with RGB
overlap rates above 75 % and TIR overlap rates above 80 %.
Figure 8 shows the UAV flight path over the slopes. The two permafrost
slopes were subjected to four flight experiments with UAV-mounted RGB and
TIR sensors in 2016 and 2017. The TIR flight experiments lasted from morning
to afternoon, with intervals of 1 to 2 h (Table 2). The RGB datasets
were processed with pix4dmapper software to generate digital surface model
(DSM) and orthorectification images. The TIR images were processed with the
software program FLIR Tools (FLIR System Inc., USA) (Fig. 9), and ground
surface temperature differences were analysed to determine the effects of
the different permafrost engineering operations on the slopes (Fig. 10).
This study analyses the thermal impact of engineering operations on
permafrost slopes. The projects and the slope were divided according to a
width of 2 m, and then the surface temperature of the project and the
temperature between different zones of the surrounding slope were compared.
When these temperature differences appear at the first break point, this is
the largest thermal impact of the project on the slope. The distance between
the heat-affected zone and the project is the maximum range of thermal
influence (Luo et al., 2018a). The results show that the QTH has the
greatest thermal impact on permafrost slopes, followed by the QTR and
finally the power and communication towers.
UAV flight time from 2016 to 2017.
Flight dateFlight timeHeightSlopeSensoryyyymmddhh:mmm2016041713:36–13:5620–120Slopes A and BRGB2016083010:18–13:55120Slopes A and BRGB2017082211:26–13:46120Slopes A and BRGB2016083012:47–12:5230Slope ATIR2017072211:00–15:51150Slopes A and BTIR2017082310:30–17:25150Slopes A and BTIR
UAV field observations from 2016 to 2017: (a) UAV under
observation, (b) GNSS and rectangular white and black cardboard, and (c) UAV
flight path over the two permafrost slopes.
Analysis of the ground surface temperature using UAVs
with TIR sensors, with the Qinghai–Tibet Railway as an example.
Ground surface temperature using UAVs with TIR sensors:
(a) Qinghai–Tibet Highway, (b) power and communication tower, (c) slope A, and (d) slope B.
Data quality control
The meteorological data have undergone quality control. First, all
suspicious and incorrect data were manually re-examined and corrected. For
example, a new column of “Corrected_P” has been added to
the precipitation data based on the original data, and this column of data
is the result of the manual revision. Ultimately, all feature data are
marked with a quality control code (Table 3). In terms of the instantaneous
meteorological values, if data are missing due to collector or communication
issues, the terminal directly generates missing data when the terminal
commands the data input, and the corresponding quality control identifier is
8. If there are no missing data, the quality control is assessed to be
incorrect at the terminal. When the command data are output, values are
still generated, and the corresponding quality control identifier is 2, but
the erroneous data do not participate in the subsequent related calculations
or statistics. After quality control, the availability of the various
weather elements is usually higher than 99 %, and the correct data
transmission rate approaches 100 %. However, the meteorological data of
Xidatan field station and the ground data of the study area are manually
sorted and verified, and no standardised quality control is adopted.
The use of GNSS and white reference spheres can improve the accuracy of TLS
monitoring, while the use of GNSS and rectangular white and black cardboard
can improve the accuracy of UAV monitoring (Luo et al., 2019). By
measuring the ground reference and control points of the TLS, the GNSS is
used to ensure the orientation and registration of the different 3D datasets
in the common coordinate system. The spherical shape achieves the maximum
scanning efficiency in all directions and has proven to be the most
effective laser scanning target, which can be used in conjunction with the
GNSS datum points (454F and 455F) and control points to register, align, and
mosaic the TLS data. These GNSS instruments collect data on the ground
reference points to ensure maximum geospatial accuracy, and they are subject
to stringent ground controls to reference and calibrate the 3D FARO laser
scanner. Moreover, the 3D laser scanner and GNSS obtain continuous,
high-precision spatial deformation data on the slopes, and we can compare
the spatial changes over time through the GNSS network. These targets are
used for registration and as georeferences and checkpoints. Therefore, their
positioning accuracy directly affects the accuracy of the data processing
results. Data preprocessing is proposed to determine the scope of the slope,
filter any noise points, and repair data gaps. Semiautomatic and manual
processing is conducted to filter the noise points and repair the gaps in
the point cloud datasets (Luo et al., 2019). Due to the high
moisture content in the lower part of the slope, monitoring is easily
disturbed by vibrations, resulting in noise. At the top of slope A, large
wild animals such as wild donkeys were observed. Therefore, the deformation
of the slope is also affected by the trampling of wild animals and should be
taken into account in the analysis.
Quality control codes for the meteorological station data.
The variable names are suffixed with _QC.
Quality controlDescriptioncode0Correct data, no modification1Suspicious data, no modification2Error data, no modification3Missing data, no modification4Data with revised values5Originally suspicious data, has been modified6Originally error data, has been modified8Originally missing data, has been modified9No data quality controlCode and data availability
All data and R code presented in this paper are available from Zenodo
(https://zenodo.org/communities/qtec; last access for all Zenodo links: 23 June 2021), which provides a dataset view and download statistics. The data
and code are open access (including links to the subsets) and can be found on
either repository via the following links:
Meteorological and ground observations:
10.5281/zenodo.5009871 (Luo et al., 2020d);
TLS measurements:
10.5281/zenodo.5009558 (Luo et al., 2020a);
UAV RGB and TIR images:
10.5281/zenodo.5016192 (Luo et al., 2020b);
R code of permafrost indices and visualisation:
10.5281/zenodo.5002981 (Luo et al., 2020c).
Summary and outlook
Slope failure in permafrost regions, caused by permafrost degradation and
ground ice melting, affects the engineering infrastructure and permafrost
environment in the QTEC. Most of the current studies are based on the
interaction between individual engineering projects and permafrost slopes by
means of multipoint monitoring, interpolation, or simulation, but the dense
layout of the various projects and the fragile and sensitive permafrost
slopes in the corridor have rarely been previously studied as a whole. The
permafrost slopes and various projects (QTR, QTH, and power and communication
towers) located in the corridor are chosen as the research objects, and 3D
terrain change monitoring technology using TLS and GNSS, low-altitude remote
sensing technology using UAV-based visible and TIR, and in situ monitoring
technology were deployed, combined with image mosaics, three-dimensional
modelling, and spatial analysis. This dataset contains both site and space
features on both the surface and underground horizons, including the ground
hydrothermal state, spatial ground surface temperature, slope deformation,
and meteorological data, thus establishing a comprehensive monitoring
dataset for the QTP permafrost slopes and their surroundings (QTR, QTH, etc.). The
dataset will be of great value to examine the hydrological and thermal
deformation of permafrost slopes under the influence of climate change and
engineering disturbances, as well as to reveal the mutual feedback between
the slopes and engineering infrastructure, evaluate the potential hazards of
long-term stability and safety operation of the engineering infrastructure
and slopes, and provide data support for the safety range and layout of the
proposed permafrost engineering infrastructure.
Abbreviations
CGCSChina Geodetic Coordinate SystemDSMDigital surface modelGNSSGlobal navigation satellite systemInSARInterferometric synthetic aperture radarKMPKunlun Mountain PassMAATMean annual air temperatureMAGSTMean annual ground surface temperatureMAGTMean annual ground temperatureNGCNNational Geodetic Control NetworkNMICNational Meteorological Information CenterQTECQinghai–Tibet Engineering CorridorQTHQinghai–Tibet HighwayQTPQinghai–Tibet PlateauQTRQinghai–Tibet RailwayRTKReal-time kinematicTDRTime domain reflectometryTIRThermal infraredTLSTerrestrial laser scanningTTOPTemperature at the top of the permafrostUAVUnmanned aerial vehicle
Drilling data source
The location of two drilled boreholes.
Classification of frozen soil
The frozen soil distribution in the study area.
Pictures of the field site and selected instrument
details
Slopes A and B, with the QTH and QTR clearly visible.
Slope B. The railways and power and communication towers are
clearly visible.
Digital surface model of the study area Using RGB
images
DSM by the UAV: (a) slope A and (b) slope B.
Names of the variables and units for the data files
Overview of the meteorological and ground observation
data. The suffixes _MIN, _MAX, _AVG, and _QC indicate the minimum, maximum, average value,
and quality control code of the variable, respectively, while 32766, NA, and
NAN indicate null values. The suffix of TotalPrecip with “20_8”, “8_20”, and “20_20” are the total
precipitation from 20:00 to 08:00 UTC/GMT+8 the next day, 08:00 to 20:00, and 20:00 to
20:00 the next day, respectively. The suffixes of evaporation with
“SmallEvaporators” and “LargeEvaporators” are the data monitored by the
small and large evaporator, respectively. The suffix GT with a number
indicates the ground temperature in centimetres.
Variable nameDescriptionUnitTemperatureAir temperature∘CWindWind speedm/sWindDirectionWind direction16 directionsExtreme_WindExtreme wind speedm/sWindDirection_Extreme_WindWind direction with extreme wind speed16 directionsTotalPrecipPrecipitationmmCorrected_PCorrected precipitationmmEvaporationEvaporationmmHumidityAir humidity%PressAtmospheric pressurehPaSunshineSunshine durationhGTGround temperature∘CPhysical Parameters of Engineering Infrastructure
Physical parameters of the engineering infrastructure near the
permafrost slopes. Two types of foundations are applied in the construction
of the power and communication towers. a The cone cylinder
spread footing. b The drilled shaft.
EngineeringVariableValueHighwaySubgrade height2.0–2.5 mRoad width9.0–10.0 mRailwayTrack width6.5 mBridge diameter4.5 mBridge height-16.5 mTowerBuried base depth3.7–5.8 mBase width of foundationa3.5–6.4 mDiameter of pilesb∼ 1.0 mLength of piles7.0–16.0 mPermafrost Indices From 1955 to 2020
Mean annual air temperature (MAAT, ∘) and mean
annual ground surface temperature (MAGST, ∘) at the meteorological
stations Golmud and Wudaoliang.
The supplement related to this article is available online at: https://doi.org/10.5194/essd-13-4035-2021-supplement.
Author contributions
LL, YZ, YS, and LW initiated and set up the
ground observation, TLS, GNSS, and UAV field experiments. LL, YZ, and ZZ
compiled the database, performed the analysis, generated the figures, and
wrote the paper. All authors contributed to the database compilation and
assisted in writing the paper.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
The authors acknowledge the Hoh Xil National Nature Reserve for their support and permission to undertake this project. Reviews from Jan Beutel and an anonymous referee provided valuable comments that helped to improve the paper substantially. We thank the handling editor Kirsten Elger for constructive feedback and suggestions.
Financial support
This research has been supported by the National Natural Science Foundation of China (grant no. 41871065), the National Science Fund for Distinguished Young Scholars (grant no. 41825015), the Key Research Project of Frontier Science of Chinese Academy of Sciences (grant no. QYZDJ-SSW-DQC040), the Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDA19090122), and the CAS “Light of West China” Program.
Review statement
This paper was edited by Kirsten Elger and reviewed by Jan Beutel and one anonymous referee.
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