In:
Earth System Science Data, Copernicus GmbH, Vol. 13, No. 8 ( 2021-08-20), p. 4035-4052
Abstract:
Abstract. 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 https://doi.org/10.5281/zenodo.5009871 (Luo et al.,
2020d), TLS measurements at https://doi.org/10.5281/zenodo.5009558 (Luo
et al., 2020a), UAV RGB and TIR images at
https://doi.org/10.5281/zenodo.5016192 (Luo et al., 2020b), and R code
for permafrost indices and visualisation at
https://doi.org/10.5281/zenodo.5002981 (Luo et al., 2020c).
Type of Medium:
Online Resource
ISSN:
1866-3516
DOI:
10.5194/essd-13-4035-2021
DOI:
10.5194/essd-13-4035-2021-supplement
Language:
English
Publisher:
Copernicus GmbH
Publication Date:
2021
detail.hit.zdb_id:
2475469-9
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