The thickness of the active layer of the three pole permafrost combines two sets of data products. The main reference data is the annual value of the active layer thickness from 1990 to 2015 generated by GCM model simulation. The data format of this data set is netcdf4 format, with a spatial resolution of 0.5 ° and a temporal resolution of years. The reference correction data set is the average value of active layer thickness from 2000 to 2015 simulated by statistical and machine learning (ML) methods. The data format is GeoTIFF format, the spatial resolution is 0.1 °, and the data unit is m. Through post-processing operations such as data format conversion, spatial interpolation, data correction, etc., this research work generates the permafrost active layer thickness data in netcdf4 format, with a spatial resolution of 0.1 °, a temporal resolution of years, a time range of 1990-2015, and a data unit of CM.
YE Aizhong
The original data of carbon flux in the three pole permafrost region are generated by GCM model simulation, and the original data are from http://www.cryosphere.csdb.cn/portal/metadata/5abef388-3f3f-4802-b3de-f4d233cb333b 。 This data set contains the prediction of future scenarios under different representative concentration paths (RCPs) in the next 2046-2065 years, including rcp2.6 scenario, rcp4.5 scenario and rcp8.5 scenario. The original data include parameters representing carbon flux such as NPP and GPP in the permafrost region of the Qinghai Tibet Plateau. The data format is netcdf4 format, with a spatial resolution of 0.5 ° and a temporal resolution of years. Through data format conversion, spatial interpolation and other post-processing operations, the NPP and GPP data in permafrost region in netcdf4 format are generated. The spatial resolution is 0.1 °, the time resolution is years, the time range is 2046-2065, and the data unit is gc/m2yr.
YE Aizhong
The original thickness data of the active layer of the three pole permafrost are generated by GCM model simulation, and the original data are from http://www.cryosphere.csdb.cn/portal/metadata/5abef388-3f3f-4802-b3de-f4d233cb333b 。 This data set contains the prediction of future scenarios under different representative concentration paths (RCPs) in the next 2046-2065 years, including rcp2.6 scenario, rcp4.5 scenario and rcp8.5 scenario. The content of the original data is the thickness of the active layer in the permafrost area of the Qinghai Tibet Plateau. The data format is netcdf4, with a spatial resolution of 0.5 ° and a temporal resolution of years. Through data format conversion, spatial interpolation and other post-processing operations, the active layer thickness in permafrost area in netcdf4 format is generated, with a spatial resolution of 0.1 °, a time resolution of years, a time range of 2046-2065, and the unit is cm.
YE Aizhong
The original data of the three pole permafrost range are generated by GCM model simulation, and the original data are from http://www.cryosphere.csdb.cn/portal/metadata/5abef388-3f3f-4802-b3de-f4d233cb333b 。 This data set contains the prediction of future scenarios under different representative concentration paths (RCPs) in the next 2046-2065 years, including rcp2.6 scenario, rcp4.5 scenario and rcp8.5 scenario. The original data content is the spatial range of permafrost and seasonal frozen soil in the Qinghai Tibet Plateau. The data format is netcdf4 format, with a spatial resolution of 0.5 ° and a temporal resolution of years. Through data format conversion, spatial interpolation and other post-processing operations, this research work generates the permafrost range data in netcdf4 format, with a spatial resolution of 0.1 °, a time resolution of years, and a time range of 2046-2065. Permafrost is represented by 1, and seasonal permafrost is represented by 0.
YE Aizhong
As an important part of the global carbon pool, Arctic permafrost is one of the most sensitive regions to global climate change. The rate of warming in the Arctic is twice the global average, causing rapid changes in Arctic permafrost. The NDVI change data set of different types of permafrost regions in the Northern Hemisphere from 1982 to 2015 has a temporal resolution of every five years, covers the entire Arctic Rim countries, and a spatial resolution of 8km. Based on multi-source remote sensing, simulation, statistics and measured data, GIS method and ecological method are used to quantify the regulation and service function of permafrost in the northern hemisphere to the ecosystem, and all the data are subject to quality control.
WANG Shijin
This data provides the data on carbon dioxide emissions on the Tibetan Plateau during 2014-2101, and the data is derived from the CMIP6 ScenarioMIP Comparison Plan. We provided three future socio-economic sharing pathways of carbon dioxide emissions: SSP126, SSP370, SSP585. The data from 2014 to 2101 were extracted for the grid points on the Qinghai-Tibet Plateau, and the data accuracy was 0.9x1.25 degrees. The txt file contains three columns, the first column is latitude, the second column is longitude, and the third column is the annual carbon dioxide flux in kg m-2 s-1. The carbon dioxide emissions under different future scenarios of the Tibetan Plateau provided in this datasets can provide reference for site observation and numerical simulations.
LV Yaqiong
Soil freezing depth (SFD) is necessary to evaluate the balance of water resources, surface energy exchange and biogeochemical cycle change in frozen soil area. It is an important indicator of climate change in the cryosphere and is very important to seasonal frozen soil and permafrost. This data is based on Stefan equation, using the daily temperature prediction data and E-factor data of canems2 (rcp45 and rcp85), gfdl-esm2m (rcp26, rcp45, rcp60 and rcp85), hadgem2-es (rcp26, rcp45 and rcp85), ipsl-cm5a-lr (rcp26, rcp45, rcp60 and rcp85), miroc5 (rcp26, rcp45, rcp60 and rcp85) and noresm1-m (rcp26, rcp45, rcp60 and rcp85), The data set of annual average soil freezing depth in the Qinghai Tibet Plateau with a spatial resolution of 0.25 degrees from 2007 to 2065 was obtained.
PAN Xiaoduo, LI Hu
Freezing (thawing) index refers to the sum of all temperatures less than (greater than) 0 ℃ in a year. Surface freezing (thawing) index is an important parameter to measure the time and capacity of surface freezing (thawing), which can reflect the characteristics of regional freezing and thawing environment. Based on the modis-lst data product, which comes from the national Qinghai Tibet Plateau science data center, the data in the Sanjiang River Basin are read by MATLAB language, and combined with the calculation of freezing (thawing index) formula, the spatial distribution data set of surface freezing and thawing index of dynamic environmental factors outside the Sanjiang River basin (average from 2003 to 2015) is obtained. This data set can better reflect the ability of surface freezing and thawing in the Sanjiang River Basin, so as to reflect the characteristics of regional freezing and thawing environment, It provides important external dynamic environmental factors for the development of freeze-thaw landslide.
LIU Minghao
This data set takes the freezing index calculated by the long-time scale (1901-2016) temperature provided by UEA-CRU and UDEL as the input data, calculates the soil freezing depth of Yarlung Zangbo River Basin through Stefan empirical formula, and interpolates the 30-year scale average soil freezing depth data set output by simulation. This data set takes the freezing index calculated by the long-time scale (1901-2016) temperature provided by UEA-CRU and UDEL as the input data, calculates the soil freezing depth of Yarlung Zangbo River Basin through Stefan empirical formula, and interpolates the 30-year scale average soil freezing depth data set output by simulation.
LIU Lei , LUO Dongliang , WANG Lei
The vegetation type map was created by the random forest (RF) classification approach, based on 319 ground-truth samples, combined with a set of input variables derived from the visible, infrared, and thermal Landsat-8 images. According to vegetation characteristics, four types include alpine swamp meadow (ASM), alpine meadow (AM), alpine steppe (AS), and alpine desert (AD) were classified in this map. Based on a spatial resolution of 30 m, the map can provide more detailed vegetation information.
ZHOU Defu, ZOU Defu, ZOU Defu, Zhao Lin, ZHAO Lin, Liu Guangyue, LIU Guangyue, Du Erji, DU Erji, LI Zhibin , LI Zhibin, Wu Tonghua, WU Xiaodong, CHEN Jie CHEN Jie
The monitoring section is located in the high plain of chumar River (dk1043 + 500-dk1067 + 022). The frozen soil under the subgrade at the section is mainly multi frozen soil, ice saturated frozen soil and thick underground ice, belonging to the low-temperature basically stable multi-year frozen soil subregion (zone III). A total of 5 monitoring sections are arranged in this section, including 2 plain soil subgrade sections, 1 block stone subgrade, 1 block stone slope protection subgrade and 1 U-shaped block stone subgrade section respectively. 4-5 test holes are arranged in each section, with a test depth of 15 ~ 20m, and the deepest hole in the section is 40m. The main element of monitoring is permafrost ground temperature, and the monitoring period is from 2003 to 2021. This data is based on Permafrost Engineering The temperature measuring probe made by the State Key Laboratory was obtained through field monitoring. Every year, the monitoring data of each monitoring section is collected on site through cr3000 data acquisition instrument. Through certain quality control, including eliminating the data when the sensor does not fully adapt to the soil environment and the systematic error caused by sensor failure. The corrected final data is stored in Excel file. The field data obtained has been reviewed by many people, and the data integrity and accuracy have reached more than 95%. The data can provide a reference for the long-term stability evaluation of block stone subgrade.
NIU Fujun
The data set mainly includes the investigation data set of geological disasters, pavement diseases and bridge and culvert diseases along Qinghai Tibet highway g109, Qinghai Tibet railway and Xinzang highway G219. The investigation time is August 12, 2020 - August 19, 2020, and July 26, 2021 - August 15, 2021. The survey objects are South Asia channel and Himalayan Mountain project. The types of diseases investigated mainly include geological disasters induced by freeze-thaw (rockfall, dangerous rock mass, debris flow gully and debris slope), pavement crack diseases, loose diseases, pit diseases, subgrade deformation diseases, bridge and culvert diseases, etc. The method of manual investigation shall be adopted to observe the damage of various diseases, and the quantity (range), damage degree and location of various damage types of pavement, bridge and culvert and geological disasters shall be recorded in detail as required. The data set can provide a basis for a comprehensive understanding of the freeze-thaw diseases of South Asia channel and Himalayan mountain projects and related research.
LI Guoyu
The maximum freezing depth is an important index of the thermal state of seasonal frozen ground. Due to global warming, the maximum freezing depth of seasonal frozen ground continues to decline. The maximum freezing depth data set of five provinces in Northwest China, Tibet and surrounding areas from 1961 to 2020 was released, with a spatial resolution of 1 km. The data set is a support vector regression (SVR) model based on the measured data of maximum freezing depth from 2001 to 2010 and spatial environmental variables, which simulates the maximum freezing depth in Northwest China, Tibet and surrounding areas from 1961 to 2020. The validation results show that the SVR model has good spatial generalization ability, and there is a high consistency between the predicted value and the observed value of the maximum soil freezing depth. The determination coefficients of the simulation results in the four periods of 1980s, 1990s, 2000s and 2010s are 0.77, 0.83, 0.73 and 0.71 respectively. The percentile range of the prediction results shows that the simulation results have good stability. Based on this data set, it is found that the maximum soil freezing depth in Northwest China continues to decline, among which Qinghai has the fastest decline rate, with an average decline of 0.53 cm every decade. The data set provides data support for the study of seasonal frozen soil in Northwest China, High Mountain Asia and the Third Pole.
WANG Bingquan, RAN Youhua
This biophysical permafrost zonation map was produced using a rule-based GIS model that integrated a new permafrost extent, climate conditions, vegetation structure, soil and topographic conditions, as well as a yedoma map. Different from the previous maps, permafrost in this map is classified into five types: climate-driven, climate-driven/ecosystem-modified, climate-driven/ecosystem protected, ecosystem-driven, and ecosystem-protected. Excluding glaciers and lakes, the areas of these five types in the Northern Hemisphere are 3.66×106 km2, 8.06×106 km2, 0.62×106 km2, 5.79×106 km2, and 1.63×106 km2, respectively. 81% of the permafrost regions in the Northern Hemisphere are modified, driven, or protected by ecosystems, indicating the dominant role of ecosystems in permafrost stability in the Northern Hemisphere. Permafrost driven solely by climate occupies 19% of permafrost regions, mainly in High Arctic and high mountains areas, such as the Qinghai-Tibet Plateau.
RAN Youhua, M. Torre Jorgenson, LI Xin, JIN Huijun, Wu Tonghua, Li Ren, CHENG Guodong
Based on gipl1.0 permafrost spatial distribution model, combined with the existing basic data, including climate change, soil types, and vegetation data, the permafrost and seasonal permafrost characteristics of Sichuan Tibet railway are simulated. The data result is 500m spatial resolution grid, including the maximum depth of permafrost and the maximum freezing depth of seasonal permafrost. The results are verified by drilling data. The data date is 2001-20192041-20602081-2100 (20-year average), in which the water body and glacier area are excluded from the calculation range through the mask (null value). The climate data is monthly mean, other data remain unchanged in the process of simulation, and the spatial resolution is 500m. Data sources and "woeldc" lim:https :// www.worldclim.org/ , DEM and vegetation soil: https://data.tpdc.ac.cn/zh-hans/ ”According to the characteristics of different data sources, the authenticity and consistency of the original data are checked and standardized; The permafrost model is used to simulate the permafrost and seasonal frozen soil. The output results are ground temperature and active layer (maximum frozen depth). The simulation results are verified with the borehole ground temperature. Finally, the spatial data set is mapped by ArcGIS. Make digital processing operation standard. In the process of processing, the operators are required to strictly abide by the operation specifications, and the special person is responsible for the quality review. The data integrity, logical consistency, position accuracy, attribute accuracy, edge connection accuracy and current situation are all in line with the requirements of relevant technical regulations and standards formulated by the State Bureau of Surveying and mapping. The data can provide necessary data support for the later research on the freezing (thawing) depth of the corridor of Sichuan Tibet project.
YIN Guoan
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.
KONG Weidong
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 Chengyan, MU Mei, PENG Xiaoqing, 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
These datasets include mean annual ground temperature (MAGT) at the depth of zero annual amplitude (approximately 3 m to 25 m), 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 Qinghai Tibet Plateau is known as "the third pole of the Earth". The long-term and large-scale observation data of permafrost is of great significance to understand the changes and effects of Permafrost on the Qinghai-Xizang Plateau (QXP). Especially in such a cold and anoxic area, the extreme shortage of data resources greatly limits the development, improvement and validation of various remote sensing inversion algorithms, as well as the earth system simulation and scientific research of the QXP. In the past few decades, our research team has established a synthesis network in the permafrost region of the QXP. For the first time, the database systematically integrates the long-time series observation data of 6 automatic meteorological stations, 12 active layer sites and 84 boreholes. In the process of data collection and processing, all observation data have been strictly controlled. The data set will be released to scientists with multi-disciplinary backgrounds (e.g., cryosphere, hydrology, ecology and meteorology), which will greatly promote the validation, development and improvement of hydrological model, land surface process model and climate model of the QXP.
Zhao Lin, ZHAO Lin, ZHOU Defu, ZOU Defu, ZOU Defu, Wu Tonghua, Du Erji, DU Erji, Liu Guangyue, LIU Guangyue, Xiao Yao, Li Ren, Pang Qiangqiang, Qiao Yongping, WU Xiaodong, SUN Zhe, Xing Zangping, Zhao Yonghua, Shi Jianzong, Xie Changwei, Wang Lingxiao, Wang Chong, CHENG Guodong
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
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.
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.
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.
CAO Bin CAO Bin
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.
WAN Chengwei
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.
SUN Ziyong
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.
ZHANG Yanlin
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".
LI Xin
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.
WAN Chengwei
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
This data set uses SMMR (1979-1987), SSM / I (1987-2009) and ssmis (2009-2015) daily brightness temperature data, which is generated by double index (TB V, SG) freeze-thaw discrimination algorithm. The classification results include four types: frozen surface, melted surface, desert and water body. The data covers the source area of three rivers, with a spatial resolution of 25.067525 km. It is stored in geotif format in the form of ease grid projection. Pixel values represent the state of freezing and thawing: 1 for freezing, 2 for thawing, 3 for deserts, 4 for water bodies. Because all TIF files in the dataset describe the scope of Sanjiangyuan National Park, the row and column number information of these files is unchanged, and the excerpt is as follows (where the unit of cellsize is m): ncols 52 nrows 28 cellsize 25067.525 nodata_value 0
The spatial-temporal distribution map of topographic shadows in the upper reaches of Heihe River (2018), which is calculated based on the SRTM DEM and the solar position (http://www.esrl.noaa.gov/gmd/grad/solcalc/azel.html). The spatial resolution is 100 m and the time resolution is 15 min. The datased can be used in the fields of ecological hydrology and remote sensing research. Using the observed solar radiation at several automatic weather stations in the upper reaches of Heihe River, the accuracy of the calculation results is verified. Results show that the dataset can accurately capture the temporal and spatial changes of the topographic shadow at the stations, and the time error is within 20 minutes.
ZHANG Yanlin
The permafrost stability map was created based on the classification system proposed by Guodong Cheng (1984), which mainly depended on the inter-annual variation of deep soil temperature. By using the geographical weighted regression method, many auxiliary data was fusion in the map, such as average soil temperature, snow cover days, GLASS LAI, soil texture and organic from SoilGrids250, soil moisture products from CLDAS of CMA, and FY2/EMSIP precipitation products. The permafrost stability data spatial resolution is 1km and represents the status around 2010. The following table is the permafrost stability classification system. The data format is Arcgis Raster.
RAN Youhua
Mean annual ground temperature (MAGT) at a depth of zero annual amplitude and permafrost thermal stability type are fundamental importance for engineering planning and design, ecosystem management in permafrost region. This dataset is produced by integrating remotely sensed freezing degree-days and thawing degree-days, snow cover days, leaf area index, soil bulk density, high-accuracy soil moisture data, and in situ MAGT measurements from 237 boreholes for the 2010s (2005-2015) on the Tibetan Plateau (TP) by using an ensemble learning method that employs a support vector regression (SVR) model based on distance-blocked resampling training data with 200 repetitions. Validation of the new permafrost map indicates that it is probably the most accurate of all available maps at present. The RMSE of MAGT is approximately 0.75 °C and the bias is approximately 0.01 °C. This map shows that the total area of permafrost on the TP is approximately 115.02 (105.47-129.59) *104 km2. The areas corresponding to the very stable, stable, semi-stable, transitional, and unstable types are 0.86*104 km2, 9.62*104 km2, 38.45*104 km2, 42.29*104 km2, and 23.80*104 km2, respectively. This new dataset is available for evaluate the permafrost change in the future on the TP as a baseline. More details can be found in Ran et al., (2020) that published at Science China Earth Sciences.
RAN Youhua, LI Xin
This data is a 5km monthly hydrological data set, including grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation). This data is a 5km monthly hydrological data set, including grid runoff and evaporation (if evaporation is less than 0, it means condensation; if runoff is less than 0, it means precipitation is less than evaporation).
WANG Lei
The freeze/thaw status of the near-surface soil is the water-ice phase transition that occurred at the top soil layer. It is an important indicator as a giant on-off “switch” of the land surface processes including water, energy, and carbon exchanges between the land surface and atmosphere. The freeze/thaw status is an essential variable for understanding how the ecosystem responds to and affects global changes. This dataset is based on the AMSR-E, AMSR2 passive microwave brightness temperature data and MODIS optical remote sensing data. The freeze-thaw discriminant function algorithm and downscaling algorithm are used to generate the global near-surface soil freeze-thaw status with a spatial resolution of grids at 0.05° from 2002 to 2017. The dataset can be used for the analysis of the spatial distribution and trend changes of global freeze-thaw cycles, such as the freeze/thaw onset dates and duration. It provides data support for understanding the interaction mechanism between the land surface freeze-thaw cycle and the land-atmosphere exchanges under the context of global changes.
Zhao Tianjie, ZHANG Ziqian
Permafrost regions occupy about 46% of the exposed land area on the Tibetan Plateau (TP). Permafrost is a hidden phenomenon that cannot be easily observed, and its distribution is hence heavily dependent on in-situ observations. Four methods are used to derive permafrost presence or absence over the TP, including borehole temperature, soil pit, ground surface temperature, and ground-penetrating radar surveys. There are a total of 626 sites of permafrost presence or absence contained in the inventory. In order to apply the permafrost presence or absence inventory more broadly, the degree of confidence in the data is estimated and provided in the inventory. The inventory provided a baseline for the presence or absence of pernmafrost at point scale on the TP, and could be additionally used for permafrost simulation evalution.
CAO Bin, CAO Bin, ZHANG Tingjun, WU Qingbai, ZHAO Lin, ZHOU Defu ZOU Defu ZOU Defu
Based on a recently developed inventory of permafrost presence or absence from 1475 in situ observations, we developed and trained a statistical model and used it to compile a high‐resolution (30 arc‐ seconds) permafrost zonation index (PZI) map. The PZI model captures the high spatial variability of permafrost distribution over the QTP because it considers multi- ple controlling variables, including near‐surface air temperature downscaled from re‐ analysis, snow cover days and vegetation cover derived from remote sensing. Our results showed the new PZI map achieved the best performance compared to avail- able existing PZI and traditional categorical maps. Based on more than 1000 in situ measurements, the Cohen's kappa coefficient and overall classification accuracy were 0.62 and 82.5%, respectively. Excluding glaciers and lakes, the area of permafrost regions over the QTP is approximately 1.54 (1.35–1.66) ×106 km2, or 60.7 (54.5– 65.2)% of the exposed land, while area underlain by permafrost is about 1.17 (0.95–1.35) ×106 km2, or 46 (37.3–53.0)%.
CAO Bin CAO Bin
Sentine-1 SAR data were used to monitor the permafrost of Biuniugou in Heihe River Basin of Qinghai-Tibet Plateau. Based on the Sentine-1 SAR image of Bison Valley from 2014 to 2018, the active layer thickness in the study area was estimated by using the small baseline set time series InSAR (DSs-SBAS) frozen soil deformation monitoring method based on distributed radar target, combined with SAR backscattering coefficient, MODIS surface temperature and Stefan model. The results show that the thickness of active layer is between 0.8 m and 6.6 m, with an average of about 3.3 M. It is of great significance to carry out large-scale and high-resolution monitoring.
JIANG Liming
Global warming and human activities have led to the degradation of permafrost and the collapse of permafrost, which have seriously affected the construction of permafrost projects and the ecological environment. Based on high-resolution satellite images, the permafrost of oboling in Heihe River Basin of Qinghai Tibet Plateau is taken as the research area, and the object-oriented classification technology of machine learning is used to extract the thermal collapse information in the research area. The results show that from 2009 to 2019, the number of thermal collapse increased from 12 to 16, and the total area increased from 14718.9 square meters to 28579.5 square meters, nearly twice. The combination of high spatial resolution remote sensing and object-oriented classification method has a broad application prospect in the monitoring of thermal thawing and collapse of frozen soil.
JIANG Liming
This data is a simulated output data set of 5km monthly hydrological data obtained by establishing the WEB-DHM distributed hydrological model of the source regions of Yangtze River and Yellow River, using temperature, precipitation and pressure as input data, and GAME-TIBET data as verification data. The dataset includes grid runoff and evaporation (if the evaporation is less than 0, it means deposition; if the runoff is less than 0, it means that the precipitation in the month is less than evaporation). This data is a model based on the WEB-DHM distributed hydrological model, and established by using temperature, and precipitation (from itp-forcing and CMA) as input data, GLASS, MODIA, AVHRR as vegetation data, and SOILGRID and FAO as soil parameters. And by the calibration and verification of runoff，soil temperature and soil humidity, the 5 km monthly grid runoff and evaporation in the source regions of Yangtze River and Yellow River from 1998 to 2017 was obtained. If asc can't open normally in arcmap, please delete the blacks space of the top 5 lines of the asc file.
WANG Lei
Frozen soil refers to a soil or rock mass with a temperature lower than or equal to 0 ° C and containing ice. It is particularly sensitive to temperature and its physical and mechanical properties change significantly with temperature. The frost heaving deformation and melt settlement deformation of frozen soil are the most common frozen soil disasters. Their occurrence is mainly caused by the change of the inherent temperature of frozen soil due to the frozen soil engineering activities. Therefore, the protection of frozen soil is mainly to protect the temperature of frozen soil. , to maintain it in the closest state before the engineering activities. The main method for obtaining the temperature of the frozen land is to embed the temperature measuring cable. Through the data acquisition function of the CR3000, the resistance value of the temperature measuring cable is obtained at different times, and the temperature value is calculated by the correspondence between the calibration coefficient and the resistance value. According to the sensitive characteristics of frozen soil to temperature, the change of ground temperature can reflect the change of climate, and can also analyze the influence mechanism and degree of human activities on the stability of frozen soil in combination with other factors, so as to guide the later engineering activities. Upgrading and upgrading of frozen soil protection measures.
CHEN Ji
Qinghai Tibet Plateau is the largest permafrost area in the world. At present, some permafrost distribution maps have been compiled. However, due to the limited data sources, unclear standards, insufficient verification and lack of high-quality spatial data sets, there is great uncertainty in drawing Permafrost Distribution Maps on TP. Based on the improved medium resolution imaging spectrometer (MODIS) surface temperature (LSTS) model of 1 km clear sky mod11a2 (Terra MODIS) and myd11a2 (Aqua MODIS) product (reprocessing version 5) in 2003-2012, the data set simulates the distribution of permafrost and generates the permafrost map of Qinghai Tibet Plateau. The map was verified by field observation, soil moisture content and bulk density. Permafrost attributes mainly include: seasonally frozen ground, permafrost and unfrozen ground. The data set provides more detailed data of Permafrost Distribution and basic data for the study of permafrost in the Qinghai Tibet Plateau.
ZHAO Lin
This dataset includes the ground surface temperature in the Qilian Mountains on the Qinghai-Tibet Plateau during 1980-2013. This dataset was obtained from the ERA-interim reanalysis product. The ERA-interim system includes a 4-dimensional variational analysis (4D-Var). The quality of the data has been improved using the bias correction of satellite data. The spatial resolution of the dataset is 0.125°. The dataset includes the grid data of the ground surface temperature in the Qilian Mountains during the past 30 years, and may provide a basic data for relevant studies such as climatic change, ecosystem succession, and earth system models.
WU Xiaodong
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