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).
Highlights
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)
This synthesis dataset for permafrost engineering and slopes includes measured air and ground temperatures and moisture, mean annual air temperature (MAAT), mean annual ground surface temperature (MAGST), terrestrial laser scanning (TLS) point cloud data, and RGB and thermal infrared (TIR) images
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 global navigation satellite system (GNSS) (Fig. 5a and b)
Summary
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). 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). In addition to using soil temperature and moisture sensors to monitor in situ hydrothermal changes, GNSS, TLS, and UAVs were 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
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