This data includes the soil microbial composition data in permafrost of different ages in Barrow area of the Arctic. It can be used to explore the response of soil microorganisms to the thawing in permafrost of different ages. This data is generated by high through-put sequencing using the earth microbiome project primers are 515f – 806r. The region amplified is the V4 hypervariable region, and the sequencing platform is Illumina hiseq PE250; This data is used in the articles published in cryosphere, Permafrost thawing exhibits a greater influence on bacterial richness and community structure than permafrost age in Arctic permafrost soils. The Cryosphere, 2020, 14, 3907–3916, https://doi.org/10.5194/tc-14-3907-2020https://doi.org/10.5194/tc-14-3907-2020 . This data can also be used for the comparative analysis of soil microorganisms across the three poles.
The Qinghai-Tibetan Plateau (QTP), the largest high-altitude and low-latitude permafrost zone in the world, has experienced rapid permafrost degradation in recent decades, and one of the most remarkable resulting characteristics is the formation of thermokarst lakes. Such lakes have attracted significant attention because of their ability to regulate carbon cycle, water, and energy fluxes. However, the distribution of thermokarst lakes in this area remains largely unknown, hindering our understanding of the response of permafrost and its carbon feedback to climate change.Based on more than 200 sentinel-2A images and combined with ArcGIS, NDWI and Google Earth Engine platform, this data set extracted the boundary of thermokarst lakes in permafrost regions of the Qinghai-Tibet Plateau through GEE automatic extraction and manual visual interpretation.In 2018, there were 121,758 thermokarst lakes in the permafrost area of the Qinghai-Tibet Plateau, covering an area of 0.0004-0.5km², with a total area of 1,730.34km² respectively.The cataloging data set of Thermokarst Lakes provides basic data for water resources evaluation, permafrost degradation evaluation and thermal karst study on the Qinghai-Tibet Plateau.
CHEN Xu, MU Cuicui, JIA Lin, LI Zhilong, FAN Chenyan, MU Mei, PENG Xiaoqing, WU Xiaodong WU Xiaodong
A comprehensive understanding of the permafrost changes in the Qinghai Tibet Plateau, including the changes of annual mean ground temperature (Magt) and active layer thickness (ALT), is of great significance to the implementation of the permafrost change project caused by climate change. Based on the CMFD reanalysis data from 2000 to 2015, meteorological observation data of China Meteorological Administration, 1 km digital elevation model, geo spatial environment prediction factors, glacier and ice lake data, drilling data and so on, this paper uses statistics and machine learning (ML) method to simulate the current changes of permafrost flux and magnetic flux in Qinghai Tibet Plateau The range data of mean ground temperature (Magt) and active layer thickness (ALT) from 2000 to 2015 and 2061 to 2080 under rcp2.6, rcp4.5 and rcp8.5 concentration scenarios were obtained, with the resolution of 0.1 * 0.1 degree. The simulation results show that the combination of statistics and ML method needs less parameters and input variables to simulate the thermal state of frozen soil, which can effectively understand the response of frozen soil on the Qinghai Tibet Plateau to climate change.
Ni Jie, WU Tonghua WU Tonghua WU Tonghua
These datasets include mean annual ground temperature (MAGT) at the depth of zero annual amplitude, active layer thickness (ALT), the probability of the permafrost occurrence, and the new permafrost zonation based on hydrothermal condition for the period of 2000-2016 in the Northern Hemisphere with an 1-km resolution by integrate unprecedentedly large amounts of field data (1,002 boreholes for MAGT and 452 sites for ALT) and multisource geospatial data, especially remote sensing data, using statistical learning modelling with an ensemble strategy, and thus more accurate than previous circumpolar maps.
RAN Youhua, LI Xin, CHENG Guodong, CHE Jinxing, Juha Aalto, Olli Karjalainen, Jan Hjort, Miska Luoto, JIN Huijun, Jaroslav Obu, Masahiro Hori, YU Qihao, CHANG Xiaoli
The widely definition of seasonally frozen ground include seasonally frozen layer (seasonally frozen ground regions) and seasonally thaw layer (active layer in permafrost regions). So the area extent of seasonally frozen ground occupied more than 80% land surface over Northern Hemisphere. Soil freeze/thaw cycle is one special character of seasonally frozen ground, which covers area extent, depth, time duration, variation of soil freeze/thaw. These changes in seasonally frozen ground have substantial impacts on energy, water and carbon exchange between the atmosphere and the land surface, surface and sub-surface hydrologic processes, vegetation growth, the ecosystem, carbon dioxide cycle, agriculture, and engineering constructuion, as a whole.Based on the observations from sites, CRU air temperature, we used the Stefan solution to calculate the spatial distribution of active layer thickness and soil freeze depth during 1971-2000. These results are helpful to further study the physical mechanism between seasonally frozen ground and climate change, eco-hydrology process.
PENG Xiaoqing, ZHANG Tingjun
This data set is the distribution data of permafrost and underground ice in Qilian Mountains. Based on the existing borehole data, combined with the Quaternary sedimentary type distribution data and land use data in Qilian mountain area, this paper estimates the distribution of underground ice from permafrost upper limit to 10 m depth underground. In this data set, 374 boreholes in Qilian mountain area are used, and the indication function of Quaternary sedimentary type to underground ice storage is considered, so it has certain reliability. This data has a certain scientific value for the study of permafrost and water resources in Qilian Mountains. In addition, it has a certain promotion value for the estimation of underground ice reserves in the whole Qinghai Tibet Plateau.
This dataset contains measurements of L-band brightness temperature by an ELBARA-III microwave radiometer in horizontal and vertical polarization, profile soil moisture and soil temperature, turbulent heat fluxes, and meteorological data from the beginning of 2016 till August 2019, while the experiment is still continuing. Auxiliary vegetation and soil texture information collected in dedicated campaigns are also reported. This dataset can be used to validate the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite based observations and retrievals, verify radiative transfer model assumptions and validate land surface model and reanalysis outputs, retrieve soil properties, as well as to quantify land-atmosphere exchanges of energy, water and carbon and help to reduce discrepancies and uncertainties in current Earth System Models (ESM) parameterizations. ELBARA-III horizontal and vertical brightness temperature are computed from measured radiometer voltages and calibrated internal noise temperatures. The data is reliable, and its quality is evaluated by 1) Perform ‘histogram test’ on the voltage samples (raw-data) of the detector output at sampling frequency of 800 Hz. Statistics of the histogram test showed no non-Gaussian Radio Frequency Interference (RFI) were found when ELBAR-III was operated. 2) Check the voltages at the antenna ports measured during sky measurements. Results showed close values. 3) Check the instrument internal temperature, active cold source temperature and ambient temperature. 3) Analysis the angular behaviour of the processed brightness temperatures. -Temporal resolution: 30 minutes -Spatial resolution: incident angle of observation ranges from 40° to 70° in step of 5°. The area of footprint ranges between 3.31 m^2 and 43.64 m^2 -Accuracy of Measurement: Brightness temperature, 1 K; Soil moisture, 0.001 m^3 m^-3; Soil temperature, 0.1 °C -Unit: Brightness temperature, K; Soil moisture, m^3 m^-3; Soil temperature, °C/K
Bob Su, WEN Jun
This data includes the ground temperature data of the source area of the Yellow River The main model of Permafrost Distribution in the source area of the Yellow River is constructed based on the permafrost boreholes and the measured ground temperature data. The temperature value of the permafrost on the sunny slope terrain is adjusted separately, and the fine-tuning model under the sunny slope terrain is established. The simulation results of the boreholes participating in the model construction are compared with the measured results, and the results show that the model is involved in the construction of the model The results show that the model is feasible to simulate the spatial distribution pattern of permafrost annual average ground temperature in the source area of the Yellow River
SHENG Yu, LI Jing
The distribution data of permafrost in the source area of the Yellow River is established based on the annual average ground temperature model of permafrost in the source area of the Yellow River. The annual average ground temperature of 0 ℃ is taken as the standard and boundary for dividing seasonal frozen soil and permafrost. Compared with the available permafrost maps of the source region of the Yellow River (1:3 million) and the permafrost background survey project of the Qinghai Tibet Plateau (1:1 million), the data set is based on the measured data of the Yellow River source area, which has higher consistency with the measured data, and the simulation accuracy of the permafrost distribution map is the highest. The data set can be used to verify the distribution of permafrost in the source area of the Yellow River, as well as to study the frozen soil environment.
SHENG Yu, LI Jing
This dataset is derived from the paper: Ding, J., Wang, T., Piao, S., Smith, P., Zhang, G., Yan, Z., Ren, S., Liu, D., Wang, S., Chen, S., Dai, F., He, J., Li, Y., Liu, Y., Mao, J., Arain, A., Tian, H., Shi, X., Yang, Y., Zeng, N., & Zhao, L. (2019). The paleoclimatic footprint in the soil carbon stock of the Tibetan permafrost region. Nature Communications, 10(1), 4195. doi:10.1038/s41467-019-12214-5. This data contains R code and a new estimate of Tibetan soil carbon pool to 3 m depth, at a 0.1° spatial resolution. Previous assessments of the Tibetan soil carbon pools have relied on a collection of predictors based only on modern climate and remote sensing-based vegetation features. Here, researchers have merged modern climate and remote sensing-based methods common in previous estimates, with paleoclimate, landform and soil geochemical properties in multiple machine learning algorithms, to make a new estimate of the permafrost soil carbon pool to 3 m depth over the Tibetan Plateau, and find that the stock (38.9-34.2 Pg C) is triple that predicted by ecosystem models (11.5 ± 4.2 Pg C), which use pre-industrial climate to initialize the soil carbon pool. This study provides evidence that illustrates, for the first time, the bias caused by the lack of paleoclimate information in ecosystem models. The data contains the following fields: Longitude (°E) Latitude (°N) SOCD (0-30cm) (kg C m-2) SOCD (0-300cm) (kg C m-2) GridArea (k㎡) 3mCstcok (10^6 kg C)
DING Jinzhi, WANG Tao
The data includes the distribution data of underground ice in permafrost layer in the source area of the Yellow River. Based on the field data of 105 boreholes, such as landform and genetic type, permafrost temperature distribution, lithology composition and water content, the permafrost layer in the source area of the Yellow River is estimated to be 3.0-10.0 M The results show that the average ice content per cubic meter of soil in the source area of the Yellow River is close to the estimated value of underground ice storage in permafrost regions of the Qinghai Tibet Plateau calculated by Zhao Lin et al. The data is also of great significance for frozen soil prediction, evaluation of landscape stability in permafrost regions, and regional changes of topography, vegetation and hydrology caused by environmental changes.
SHENG Yu, WANG Shengting
Active layer thickness in mountians shows strong spatial heterogeneity mainly due to the complex terrain. In this data set, the active layer thickness in the upper reaches of Heihe River Basin is systematically investigated by ground-penetrating radar (GPR) and other traditional methods. Compared with other direct measurement methods, the error is about 8 cm, indicating a high reliability. This data set can provide detailed field data for understanding the active layer thickness in this area and can provide evaluation datasets for the land surface model, especially for permafrost research.
The data includes continuous and discontinuous permafrost and seasonally frozen ground distributed in the Qilian Mountains. Based on the field investigation, borehole drillings along the highway as well as previous data collected from the documentations, the lower limits of permafrost and the formula of the lower limits of permafrost in the Qilian Mountains is obtained by regression analysis. The digital elevation model (DEM) data is the SRTM (Shuttle Radar Topography Mission) jointly measured by NASA and NIMA. After the data being transformed into GCS WGS 1984 coordinate system, it is resampled into 100 m spatial resolution. The altitude of 3000 m was used to define the area of the Qilian Mountains. With the aid of ArcGIS platform and the support of DEM data, the permafrost distribution map of the Qilian Mountains with a resolution of 100 m is simulated. The lower limits of permafrost obtained by the regression analysis passed the significance test. According to the 548 existing borehole data points, the verification accuracy of permafrost area is 90.11%. The data can be used to estimate the ground ice content and the amount of water released from permafrost degradation.
The data include continuous permafrost area, discontinuous permafrost area and seasonal permafrost area. Based on the field scientific investigation, road survey drilling points and the previous data of the lower boundary elevation of permafrost, the formula of the lower boundary elevation of permafrost is obtained by regression. The DEM data is the SRTM (Shuttle Radar Topography Mission) data jointly measured by NASA and NIMA. After the data is transformed into GCS · WGS · 1984 coordinate system, it is resampled into 100m spatial resolution. The altitude of the data is 3000m to define the Qilian mountain area. With the aid of ArcGIS platform and the support of DEM data, the permafrost distribution map of Qilian Mountain with a resolution of 100m is simulated. The lower bound model obtained by regression has passed the significance test. According to the 548 existing borehole data points, the verification accuracy of permafrost area is 90.11%. The data can be used to estimate the underground ice content and the amount of water released from permafrost degradation.
The data includes the runoff components of the main stream and four tributaries in the source area of the Yellow River. In 2014-2016, spring, summer and winter, based on the measurement of radon and tritium isotopic contents of river water samples from several permafrost regions in the source area of the Yellow River, and according to the mass conservation model and isotope balance model of river water flow, the runoff component analysis of river flow was carried out, and the proportion of groundwater supply and underground ice melt water in river runoff was preliminarily divided. The quality of the data calculated by the model is good, and the relative error is less than 20%. The data can provide help for the parameter calibration of future hydrological model and the simulation of hydrological runoff process.
The data set of hydrogeological elements in the typical frozen soil area of Qilian Mountain mainly includes groundwater type, water richness (single water inflow or single spring flow), main rivers and tributaries, spring water (falling springs, spring groups, large springs, Mineral spring distribution), borehole (pressure water borehole, submerged borehole, gravity flow borehole distribution), fault zone (compressive fracture, tensile fracture), angle unconformity boundary, parallel unconformity boundary, west branch of upper Heihe River The boundary of the watershed, the seasonal frozen soil area and the permafrost distinguish the boundary, the distribution of modern glaciers and swamps. This data set of hydrogeological elements can provide background information for the hydrological ecological process and hydrogeological environment in cold regions. This data comes from the vectorization of four 1: 200,000 hydrogeological maps (Qilian, Yenigou, Qilian, and Sunan) and reintegrates the groundwater types. With higher resolution, the data can provide background information for the research on the evolution of water and soil resources and environmental changes in the source area of the Pan-Third Pole River.
The ground temperature, moisture and ice content at various depth (0 cm, 4 cm, 10 cm, 20 cm, 40 cm, 80 cm, 120 cm, 160 cm, 240 cm, 400 cm, 600 cm, 900 cm, 1200 cm, 1400 cm, 1500 cm) was generated through the SHAW model, which was evaluated by observations at AWS stations and WSN in the study area and could be used in research relevant on soil freezing and thawing.
Chinese Cryospheric Information System is a comprehensive information system for the management and analysis of cryospheric data over China. The establishment of Chinese Cryospheric Information System is to meet the needs of earth system science, and provide parameters and verification data for the development of response and feedback models of permafrost, glacier and snow cover to global changes under GIS framework. On the other hand, the system collates and rescues valuable cryospheric data to provide a scientific, efficient and safe management and analysis tool. Chinese Cryospheric Information System selected three regions with different spatial scales as its main research areas to highlight the research focus. The research area along the Qinghai-Tibet highway is mainly about 700 kilometers long from Xidatan to Naqu, and 20 to 30 kilometers wide on both sides of the highway. The datasets of the Tibetan highway contains the following types of data: 1. Cryosphere data.Including: snow depth distribution. 2. Natural environment and resources.Include: Digital elevation topography (DEM) : elevation elevation, elevation zoning, slope and slope direction; Fundamental geology: Quatgeo 3. Boreholes: drilling data of 200 boreholes along the qinghai-tibet highway. Engineering geological profile (CAD) : lithologic distribution, water content, grain fraction data, etc 4. Model of glacier mass equilibrium distribution along qinghai-tibet highway: prediction of frozen soil grid data. The graphic data along the qinghai-tibet highway includes 13 map scales of 1:250,000.The grid size is 100×100m. For details, please refer to the documents (in Chinese): "Chinese Cryospheric Information System design. Doc", "Chinese Cryospheric Information System data dictionary. Doc", "Database of the Tibetan highway. Doc".
In April 2014 and may 2016, 21 Lakes (7 non thermal lakes and 14 thermal lakes) were collected in the source area of the Yellow River (along the Yellow River) respectively. The abundance of hydrogen and oxygen allogens was measured by Delta V advantage dual inlet / hdevice system in inno tech Alberta laboratory in Victoria, Canada. The isotope abundance was expressed in the form of δ (‰) (relative to the average seawater abundance in Vienna) ）Test error: δ 18O: 0.1 ‰, δ D: 1 ‰. The data also includes Lake area and lake basin area extracted from Landsat 2017 image data in Google Earth engine.
This data set includes the concentration and distribution data of main persistent organic pollutants in the environmental media of Sanjiangyuan area. The samples were collected in May 2018, covering Sanjiangyuan Nature Reserve and its surrounding areas. The sample was prepared by Soxhlet extraction purification concentration and other pretreatment steps, and then determined by gas chromatography ion trap mass spectrometry. The target compounds include organochlorine pesticides, polychlorinated biphenyls, polycyclic aromatic hydrocarbons, etc. During sample pretreatment, mirex and pcb-30 were added as recovery markers. The internal standards for sample testing are PCNB and PCB-209. After calculation, the recovery of samples is generally between 60% - 101%.
GONG Ping, WANG Xiaoping