Articles | Volume 14, issue 11
https://doi.org/10.5194/essd-14-5093-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/essd-14-5093-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A newly integrated ground temperature dataset of permafrost along the China–Russia crude oil pipeline route in Northeast China
State Key Laboratory of Frozen Soil Engineering, Northwest
Institute of Eco-Environment and Resources, Chinese Academy of Sciences,
Lanzhou 730000, China
Da Xing'anling Observation and Research Station of Frozen-Ground
Engineering and Environment, Northwest Institute of Eco-Environment and
Resources, Chinese Academy of Sciences, Jagdaqi, Inner Mongolia 165000,
China
School of Engineering Science, University of Chinese Academy of
Sciences, Beijing 100049, China
Wei Ma
State Key Laboratory of Frozen Soil Engineering, Northwest
Institute of Eco-Environment and Resources, Chinese Academy of Sciences,
Lanzhou 730000, China
Da Xing'anling Observation and Research Station of Frozen-Ground
Engineering and Environment, Northwest Institute of Eco-Environment and
Resources, Chinese Academy of Sciences, Jagdaqi, Inner Mongolia 165000,
China
School of Engineering Science, University of Chinese Academy of
Sciences, Beijing 100049, China
Fei Wang
CORRESPONDING AUTHOR
State Key Laboratory of Frozen Soil Engineering, Northwest
Institute of Eco-Environment and Resources, Chinese Academy of Sciences,
Lanzhou 730000, China
Faculty of Civil Engineering and Mechanics, Jiangsu University,
Zhenjiang 212013, China
Huijun Jin
CORRESPONDING AUTHOR
State Key Laboratory of Frozen Soil Engineering, Northwest
Institute of Eco-Environment and Resources, Chinese Academy of Sciences,
Lanzhou 730000, China
Da Xing'anling Observation and Research Station of Frozen-Ground
Engineering and Environment, Northwest Institute of Eco-Environment and
Resources, Chinese Academy of Sciences, Jagdaqi, Inner Mongolia 165000,
China
School of Civil Engineering and Permafrost Institute, Northeast
Forestry University, Harbin 150040, China
Alexander Fedorov
Melnikov Permafrost Institute, Russian Academy of Sciences, Yakutsk
677010, Russia
Dun Chen
State Key Laboratory of Frozen Soil Engineering, Northwest
Institute of Eco-Environment and Resources, Chinese Academy of Sciences,
Lanzhou 730000, China
Da Xing'anling Observation and Research Station of Frozen-Ground
Engineering and Environment, Northwest Institute of Eco-Environment and
Resources, Chinese Academy of Sciences, Jagdaqi, Inner Mongolia 165000,
China
Gang Wu
State Key Laboratory of Frozen Soil Engineering, Northwest
Institute of Eco-Environment and Resources, Chinese Academy of Sciences,
Lanzhou 730000, China
Da Xing'anling Observation and Research Station of Frozen-Ground
Engineering and Environment, Northwest Institute of Eco-Environment and
Resources, Chinese Academy of Sciences, Jagdaqi, Inner Mongolia 165000,
China
School of Engineering Science, University of Chinese Academy of
Sciences, Beijing 100049, China
Yapeng Cao
State Key Laboratory of Frozen Soil Engineering, Northwest
Institute of Eco-Environment and Resources, Chinese Academy of Sciences,
Lanzhou 730000, China
Da Xing'anling Observation and Research Station of Frozen-Ground
Engineering and Environment, Northwest Institute of Eco-Environment and
Resources, Chinese Academy of Sciences, Jagdaqi, Inner Mongolia 165000,
China
School of Engineering Science, University of Chinese Academy of
Sciences, Beijing 100049, China
Yu Zhou
State Key Laboratory of Frozen Soil Engineering, Northwest
Institute of Eco-Environment and Resources, Chinese Academy of Sciences,
Lanzhou 730000, China
Da Xing'anling Observation and Research Station of Frozen-Ground
Engineering and Environment, Northwest Institute of Eco-Environment and
Resources, Chinese Academy of Sciences, Jagdaqi, Inner Mongolia 165000,
China
School of Engineering Science, University of Chinese Academy of
Sciences, Beijing 100049, China
Yanhu Mu
State Key Laboratory of Frozen Soil Engineering, Northwest
Institute of Eco-Environment and Resources, Chinese Academy of Sciences,
Lanzhou 730000, China
Da Xing'anling Observation and Research Station of Frozen-Ground
Engineering and Environment, Northwest Institute of Eco-Environment and
Resources, Chinese Academy of Sciences, Jagdaqi, Inner Mongolia 165000,
China
School of Engineering Science, University of Chinese Academy of
Sciences, Beijing 100049, China
Yuncheng Mao
School of Civil Engineering, Northwest Minzu University, Lanzhou
730000, China
Jun Zhang
School of Civil Engineering, Henan Polytechnic University, Jiaozuo
454000, China
Kai Gao
State Key Laboratory of Frozen Soil Engineering, Northwest
Institute of Eco-Environment and Resources, Chinese Academy of Sciences,
Lanzhou 730000, China
Da Xing'anling Observation and Research Station of Frozen-Ground
Engineering and Environment, Northwest Institute of Eco-Environment and
Resources, Chinese Academy of Sciences, Jagdaqi, Inner Mongolia 165000,
China
School of Engineering Science, University of Chinese Academy of
Sciences, Beijing 100049, China
Xiaoying Jin
School of Civil Engineering and Permafrost Institute, Northeast
Forestry University, Harbin 150040, China
Ruixia He
State Key Laboratory of Frozen Soil Engineering, Northwest
Institute of Eco-Environment and Resources, Chinese Academy of Sciences,
Lanzhou 730000, China
Da Xing'anling Observation and Research Station of Frozen-Ground
Engineering and Environment, Northwest Institute of Eco-Environment and
Resources, Chinese Academy of Sciences, Jagdaqi, Inner Mongolia 165000,
China
Xinyu Li
School of Civil Engineering, Harbin Institute of Technology, Harbin
150090, China
Yan Li
State Key Laboratory of Frozen Soil Engineering, Northwest
Institute of Eco-Environment and Resources, Chinese Academy of Sciences,
Lanzhou 730000, China
Da Xing'anling Observation and Research Station of Frozen-Ground
Engineering and Environment, Northwest Institute of Eco-Environment and
Resources, Chinese Academy of Sciences, Jagdaqi, Inner Mongolia 165000,
China
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To apply a deep learning (DL) algorithm to electromagnetic (EM) methods, subsurface resistivity models and/or the corresponding EM responses are often required. To date, there are no standardized EM datasets, which hinders the progress and evolution of DL methods due to data inconsistency. Therefore, we present a large-scale physics-driven model database of geologically plausible and EM-resolvable subsurface models to incorporate consistency and reliability into DL applications for EM methods.
Médéric Gravelle, Guy Wöppelmann, Kevin Gobron, Zuheir Altamimi, Mikaël Guichard, Thomas Herring, and Paul Rebischung
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We produced a reanalysis of GNSS data near tide gauges worldwide within the International GNSS Service. It implements advances in data modelling and corrections, extending the record length by about 7 years. A 28 % reduction in station velocity uncertainties is achieved over the previous solution. These estimates of vertical land motion at the coast supplement data from satellite altimetry or tide gauges for an improved understanding of sea level changes and their impacts along coastal areas.
Michal Kruszewski, Gerd Klee, Thomas Niederhuber, and Oliver Heidbach
Earth Syst. Sci. Data, 14, 5367–5385, https://doi.org/10.5194/essd-14-5367-2022, https://doi.org/10.5194/essd-14-5367-2022, 2022
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The authors assemble an in situ stress magnitude and orientation database based on 429 hydrofracturing tests that were carried out in six coal mines and two coal bed methane boreholes between 1986 and 1995 within the greater Ruhr region (Germany). Our study summarises the results of the extensive in situ stress test campaign and assigns quality to each data record using the established quality ranking schemes of the World Stress Map project.
Andrea Rovida, Andrea Antonucci, and Mario Locati
Earth Syst. Sci. Data, 14, 5213–5231, https://doi.org/10.5194/essd-14-5213-2022, https://doi.org/10.5194/essd-14-5213-2022, 2022
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EPICA is the 1000–1899 catalogue compiled for the European Seismic Hazard Model 2020 and contains 5703 earthquakes with Mw ≥ 4.0. It relies on the data of the European Archive of Historical Earthquake Data (AHEAD), both macroseismic intensities from historical seismological studies and parameters from regional catalogues. For each earthquake, the most representative datasets were selected and processed in order to derive harmonised parameters, both from intensity data and parametric catalogues.
Suqin Zhang, Changhua Fu, Jianjun Wang, Guohao Zhu, Chuanhua Chen, Shaopeng He, Pengkun Guo, and Guoping Chang
Earth Syst. Sci. Data, 14, 5195–5212, https://doi.org/10.5194/essd-14-5195-2022, https://doi.org/10.5194/essd-14-5195-2022, 2022
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Alessandro Cicoira, Samuel Weber, Andreas Biri, Ben Buchli, Reynald Delaloye, Reto Da Forno, Isabelle Gärtner-Roer, Stephan Gruber, Tonio Gsell, Andreas Hasler, Roman Lim, Philippe Limpach, Raphael Mayoraz, Matthias Meyer, Jeannette Noetzli, Marcia Phillips, Eric Pointner, Hugo Raetzo, Cristian Scapozza, Tazio Strozzi, Lothar Thiele, Andreas Vieli, Daniel Vonder Mühll, Vanessa Wirz, and Jan Beutel
Earth Syst. Sci. Data, 14, 5061–5091, https://doi.org/10.5194/essd-14-5061-2022, https://doi.org/10.5194/essd-14-5061-2022, 2022
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This paper documents a monitoring network of 54 positions, located on different periglacial landforms in the Swiss Alps: rock glaciers, landslides, and steep rock walls. The data serve basic research but also decision-making and mitigation of natural hazards. It is the largest dataset of its kind, comprising over 209 000 daily positions and additional weather data.
Xiaoli Chang, Huijun Jin, Ruixia He, Yanlin Zhang, Xiaoying Li, Xiaoying Jin, and Guoyu Li
Earth Syst. Sci. Data, 14, 3947–3959, https://doi.org/10.5194/essd-14-3947-2022, https://doi.org/10.5194/essd-14-3947-2022, 2022
Short summary
Short summary
Based on 10-year observations of ground temperatures in seven deep boreholes in Gen’he, Mangui, and Yituli’he, a wide range of mean annual ground temperatures at the depth of 20 m (−2.83 to −0.49 ℃) and that of annual maximum thawing depth (about 1.1 to 7.0 m) have been revealed. This study demonstrates that most trajectories of permafrost changes in Northeast China are ground warming and permafrost degradation, except that the shallow permafrost is cooling in Yituli’he.
Alice C. Frémand, Julien A. Bodart, Tom A. Jordan, Fausto Ferraccioli, Carl Robinson, Hugh F. J. Corr, Helen J. Peat, Robert G. Bingham, and David G. Vaughan
Earth Syst. Sci. Data, 14, 3379–3410, https://doi.org/10.5194/essd-14-3379-2022, https://doi.org/10.5194/essd-14-3379-2022, 2022
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This paper presents the release of large swaths of airborne geophysical data (including gravity, magnetics, and radar) acquired between 1994 and 2020 over Antarctica by the British Antarctic Survey. These include a total of 64 datasets from 24 different surveys, amounting to >30 % of coverage over the Antarctic Ice Sheet. This paper discusses how these data were acquired and processed and presents the methods used to standardize and publish the data in an interactive and reproducible manner.
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Short summary
A permafrost monitoring network was established along the China–Russia crude oil pipeline (CRCOP) route at the eastern flank of the northern Da Xing'anling Mountains in Northeast China. The resulting datasets fill the gaps in the spatial coverage of mid-latitude mountain permafrost databases. Results show that permafrost warming has been extensively observed along the CRCOP route, and local disturbances triggered by the CRCOPs have resulted in significant permafrost thawing.
A permafrost monitoring network was established along the China–Russia crude oil pipeline...
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