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), simulated and output through the WEB-DHM distributed hydrological model of the Indus River basin, with temperature, precipitation, barometric pressure, etc. as input data.
WANG Lei, LIU Hu
The Qinghai Tibet Engineering Corridor starts from Golmud in the north and ends at Lhasa in the south. It passes through the core area of the Qinghai Tibet Plateau and is an important channel connecting the mainland and Tibet. Permafrost temperature is not only an important index to study ground thermal state in permafrost regions, but also a key factor to be considered in permafrost engineering construction. The core of GIPL1.0 is the Kudryavtesv method, which considers the thermophysical properties of snow cover, vegetation and different soil layers. However, Yin found that compared with the Kudryavtesv method, the accuracy of TTOP model was higher. Therefore, the model was improved in combination with the freezing/thawing index. Through the verification of field monitoring data, it was found that the simulation error of permafrost temperature was less than 1 ℃. Therefore, the improved GIPL1.0 model is used to simulate the permafrost temperature of the Qinghai Tibet project corridor, and predict the future permafrost temperature under the SSP2-4.5 climate change scenario.
NIU Fujun
Retrogressive thaw slumps (RTSs) are slope failures caused by the thawing of ice-rich permafrost. Once developed, they usually retreat at high speeds (meters to tens of meters) towards the upslope direction, and the mudflow may destroy infrastructure and release carbon stored in frozen ground. RTSs are frequently distributed in permafrost areas and increase dramatically but lack investigation. Qinghai Tibet Engineering Corridor crosses the permafrost, links the inland and the Tibet. However, in this critical area, we lack knowledge of the distribution and impact of RTSs. To compile the first comprehensive inventory of RTSs, this study uses an iterative semi-automatic method based on deep learning and manual inspection to delineate RTSs in 2019 images. The images from PlanetScope CubeSat have a resolution of 3 meters, have four bands, cover a corridor area of approximately 54,000 square kilometers. The method combines the high efficiency and automation of deep learning and the reliability of the manual inspection to map the entire region ninth, which minimize the missings and misidentification. The manual inspection is based on geomorphic features and temporal changes (2016 to 2020) of RTSs. The inventory which includes 875 RTSs with their attributes, including identification, Longitude and Latitude, possibilities and time, provides a benchmark dataset for quantifying permafrost degradation and its impact.
XIA Zhuoxuan, HUANG Lingcao, LIU Lin
This data is generated based on meteorological observation data, hydrological station data, combined with various assimilation data and remote sensing data, through the preparation of the Qinghai Tibet Plateau multi-level hydrological model system WEB-DHM (distributed hydrological model based on water and energy balance) coupling snow, glacier and frozen soil physical processes. The time resolution is monthly, the spatial resolution is 5km, and the original data format is ASCII text format, Data types include 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 in the month). If the asc cannot be opened normally in arcmap, please top the first 5 lines of the asc file.
WANG Lei, CHAI Chenhao
Based on the CMIP6 model data (see Table 1 for the model list), the distribution and thickness of frozen soil in the Qinghai Tibet Plateau and the circum Arctic region, as well as the terrestrial ecosystem carbon flux (total primary productivity GPP and ecosystem carbon source sink NEP) data in the frozen soil area under different climate change scenarios (including SSP126, SSP245 and SSP585) in the historical period (1990-2014) and the future (2046-2065) are estimated, with a spatial resolution of 1 ° × 1°。 Among them, the distribution of frozen soil is estimated under the future climate warming scenario by using the spatial constraint method (Chadburn et al., 2017), based on the probability of frozen soil occurrence under different temperature gradients at the current stage, and combined with the future temperature change simulated by the Earth system model. For the change of active layer thickness, the sensitivity of active layer thickness to temperature change estimated by remote sensing at this stage is used to constrain the change of active layer thickness simulated by the Earth System Model, so as to correct the error of the model in simulating the thickness of frozen soil active layer. The future permafrost carbon flux is the multi model ensemble average of the Earth system model simulation results. The simulation results show that the permafrost in the Qinghai Tibet Plateau will be significantly degraded under the future climate change scenario. With the future temperature rise, the continuous permafrost regions will be shown as carbon sources, but the temperature rise will promote the growth of vegetation, and the carbon sink capacity in the discontinuous permafrost regions will be enhanced. Similar to the Qinghai Tibet Plateau, the permafrost around the Arctic will also be generally degraded in the future, and the future climate warming will promote the growth of vegetation in the Arctic, thus enhancing regional carbon sinks.
WANG Tao, LIU Dan , WEI Jianjun
The active layer thickness in the Wudaoliang permafrost region of the Qinghai Tibet Plateau is retrieved based on the seasonal deformation obtained by SBAS-InSAR technology and ERA5-Land spatio-temporal multi-layer soil moisture data corrected by variational mode decomposition method. The time range of the is 2017-2020, and the spatial resolution is 1km. This data can be used to study the change of the active layer thickness in the permafrost region of the Qinghai Tibet Plateau and analyze its interaction with climate change, water cycle and energy cycle. It is significance to understand the permafrost degradation, environment evolution and the impact of permafrost degradation on ecology and climate.
LU Ping , HAO Tong , LI Rongxing
The Qinghai-Tibet Engineering Corridor runs from Golmud to Lhasa. It passes through the core region of the Qinghai-Tibet Plateau and is an important passage connecting the interior and Tibet. The active layer thickness (ALT) is not only an important index to study the thermal state of ground in permafrost region, but also a key factor to be considered in the construction of permafrost engineering. The core of GIPL1.0 is kudryavtesv method, which takes into account the thermophysical properties of snow cover, vegetation and different soil layers. However, Yin Guoan et al. found that compared with kudryavtesv method, the accuracy of TTOP model is higher, so they improved the model in combination with freezing / thawing index. Through verification of field monitoring data, it was found that the simulation error of ALT is less than 50cm. Therefore, the ALT in the Qinghai Tibet project corridor is simulated by using the improved GIPL1.0 model, and the future ALT under the ssp2-4.5 climate change scenario is predicted.
NIU Fujun
Freeze-thaw index is an important sensitive indicator of climate change, and is also widely used in the study of frozen soil changes. The research on the spatial distribution characteristics and time variation trend of freezing and thawing index in the global scope can provide a basis for the global frozen soil environment assessment, engineering construction and coping with climate change. This data set is based on the daily temperature observation data of more than 14000 stations covering the global land from 1973 to 2021 to calculate the air freezing index (FI) and air melting index (TI). The freezing/thawing index is the cumulative value of the daily average temperature below/above 0 ℃ during the freezing/thawing period. Considering that the index calculation should cover the whole freezing/thawing period and ensure the continuity of the calculation period, the northern hemisphere takes July 1 of that year to June 30 of the next year as a freezing period, and takes January 1 to December 30 of that year as a melting period, while the southern hemisphere has the opposite freezing/thawing period. The stations with missing survey years were not filled, which, on the one hand, avoided the uncertainty error caused by interpolation on the results, and on the other hand, retained the authenticity and accuracy of the data as much as possible. The study of global freeze-thaw index can effectively and comprehensively understand the near surface heat state, and can provide important support for exploring the changes of freeze-thaw state.
PENG Xiaoqing, CHEN Cong , MU Cuicui
This data set contains the observation constrained permafrost distribution over the Tibetan Plateau under medium emission scenario (SSP245) at the end of the 21st century (2080-2099). The future permafrost distribution was estimated using the spatial-constrain approach following Chadburn et al. (2017). We developed the permafrost-MAAT relationship using the current permafrost distribution map at 1km spatial resolution (Zou et al., 2017) and mean annual air temprature (MAAT) derived from CMFD dataset. This spatial relationship was than driven by the projected temperture under SSP245 scenario from 10 Earth System Models from CMIP6 to etimate the future permafrost distribution by the end of the 21st century (2080-2099). This observation constrained permafrost distribution indicates future permafrost loss under equilibrium state, and has high relevance to international climate negotiations which are framed in term of climate stabilization.
WEI Jianjun , LIU Dan , WANG Tao
The data set of ecological adjustment value of Arctic permafrost change from 1982 to 2015, with the time resolution of 1982, 2015 and the change rate of two phases, covers the entire Arctic tundra area, with the spatial resolution of 8km. Based on multi-source remote sensing, simulation, statistics and measured data, and combined with GIS and ecological methods, it quantifies the adjustment service value of Arctic permafrost to the ecosystem, The unit price refers to the correlation (0.35) between the active layer thickness and NDVI changes after excluding precipitation and snow water equivalent, and the grassland ecosystem service value (the unit price of tundra ecosystem service is based on 1/3 of the grassland ecosystem service value).
WANG Shijin
Zoige Wetland observation point is located at Huahu wetland (102 ° 49 ′ 09 ″ E, 33 ° 55 ′ 09 ″ N) in Zoige County, Sichuan Province, with an initial altitude of 3435 m. The underlying surface is the alpine peat wetland, with well-developed vegetation, water and peat layer. This data set is the meteorological observation data of Zoige Wetland observation point from 2017 to 2019. It is obtained by using Kipp&Zonen CNR4, Vaisala HMP155A, PTB110 and other instruments. The time resolution is half an hour, mainly including wind speed, wind direction, air temperature, relative humidity, air pressure, downward short wave radiation, downward long wave radiation.
MENG Xianhong, LI Zhaoguo
This is the predicted future permafrost hazard level data sets and the original data for Figure 2, 3, and 4 in Communications Earth & Environment publication (2022, 3, 238.doi: 10.1038/s43247-022-00568-6).
RAN Youhua, CHENG Guodong, DONG Yuanhong , LI Xin
The seasonal thaw layer in permafrost regions, namely the active layer, is an important part of the study of seasonal frozen soil, and its changes are also affected by climate change. The change of active layer thickness has a profound impact on the energy transfer between earth and atmosphere, water cycle, carbon cycle, surface and underground hydrological processes, and vegetation growth. By collecting the long time series active layer thickness of 347 stations in the Northern Hemisphere and the temperature data output by dozens of CMIP5, the author constructs the E-factor of the permafrost region in the Northern Hemisphere through Stefan equation; Finally, the spatial distribution of the active layer thickness in the permafrost region of the Northern Hemisphere and the future prediction under different climate scenarios are obtained by coupling the melting index. It is found that the observed value is significantly correlated with the simulated value, the correlation coefficient R=0.84 (P<0.01), the average percentage error is 4.7%, the average deviation error is -11.7 cm, and the root mean square error is 64 cm. This data product can be used in the research of frozen soil and climate change, frozen soil carbon cycle, frozen soil ecological hydrological process, frozen soil engineering, etc.
PENG Xiaoqing
The dataset include the current (2000-2016) extent of permafrost, seasonally frozen ground, and unfrozen ground, as well as decadal change of MAGT and active layer thickness in Third Pole support the analysis of publication in Advances in Climate Change Research (Ran et al., 2022).
RAN Youhua, LI Xin, CHE Tao, WANG Bingquan, CHENG Guodong
From 1982 to 2015, the NDVI change data sets of different types of permafrost regions in the northern hemisphere have a temporal resolution of once every five years, covering the entire Arctic countries with a spatial resolution of 8km. Based on multi-source remote sensing, simulation, statistics and measured data, the regulation and service functions of Permafrost on Ecosystem in the northern hemisphere are quantified by using GIS and ecological methods, All the data are under quality control.
WANG Shijin
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
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 dataset is the remote sensing image data ofGF-1 satellite in the Qinghai-Tibet engineering corridor obtained by China High Resolution Earth Observation Center. After the fusion processing of multispectral and panchromatic bands, the image data with a spatial resolution of 2 m is obtained. In the process of obtaining ground vegetation information, the classification technology of combining object-oriented computer automatic interpretation and manual interpretation is adopted, The object-oriented classification technology is to collect adjacent pixels as objects to identify the spectral elements of interest, make full use of high-resolution panchromatic and multispectral data space, texture and spectral information to segment and classify, and output high-precision classification results or vectors. In actual operation, the image is automatically extracted by eCognition software. The main processes are image segmentation, information extraction and accuracy evaluation. After verification with the field survey, the overall extraction accuracy is more than 90%.
NIU Fujun
Firstly, the freeze thaw index is calculated by using the resampled crunep data, and then the permafrost area of circum-Arctic is predicted by the frozen number model after snow depth correction. The simulated pan Arctic permafrost area from 2000 to 2015 is 19.96 × 106 km2。 Places inconsistent with the distribution of Pan Arctic permafrost provided by the existing international snow and Ice Data Center are mainly located in island permafrost areas.
NIU Fujun
According to the inducing factors of potential thermal melting disasters (mainly thermal melting landslides) in the pan Arctic, including temperature (freezing and Thawing Environment), rainfall, snow cover, soil type, topography and landform, and underground ice content, based on the basic data provided by the big data resource database of the earth, machine learning methods (logic regression, random forest, artificial neural network, support vector machine, etc.) are adopted, and the currently interpreted thermal melting landslides in the northern hemisphere are taken as training samples, Finally, the zonation map of thermal melt disaster susceptibility (occurrence probability) in the pan Arctic was obtained. According to the sensitivity of driving factors, it is found that climate factors (temperature and rainfall) have the largest contribution to the occurrence and distribution of thermal melt disasters, followed by slope factors, and ice content and radiation also have a high contribution.
NIU Fujun
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
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
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
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 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
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
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
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
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
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 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
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
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
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
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
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
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.
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 active layer is one of the main characteristics of permafrost. It melts in warm season and freezes in cold season, showing seasonal changes. The change of ground temperature of active layer will directly affect the change of temperature of permafrost, thus affecting the stability of permafrost.The monitoring station of this data set is located at 92 °E, 35 ° N, with an elevation of 4,600 M. The monitoring site is flat, the vegetation type is alpine meadow, and the monitoring instrument is DT500 series data acquisition instrument. The monitoring of ground temperature is carried out at 5 depths below the surface, 10 cm, 20 cm, 40 cm, 80 cm and 160cm respectively. The time interval of this data set is 1 day, which is the average value of data once every 30 minutes.Data are stable and continuous during the period.Scientific subjects such as thermal change process and change mechanism of active layer are carried out by combining data of soil heat flux and soil moisture.
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
This dataset is Meteorologic Elements Dataset of XDT on Qinghai-Tibet Plateau 2014-2018. Meteorologic elements including: 2m air temperature(℃), 2m air humidity(%), precipitation(mm), 2m wind speed(m/s), global radiation(w/㎡). The data are from the XiDaTan monitoring site(site code: XDTMS) of Cryosphere Research Station on Qinghai-Tibat Plateau, Chinese Academy of Sciences(CRS-CAS). These daily data was calculated from the original monitoring data(monitoring frequency is 30min). The missing part of the daily data was marked by NAN, which were manually collated and verified. The missing period was from 2017-7-7 to 2017-10-3.
ZHAO Lin
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
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
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
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 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 and AMSR2 passive microwave brightness temperature data, and the freeze-thaw discriminant function algorithm is used to generate the global near-surface soil freeze-thaw status with a spatial resolution of grids at 0.25° from 2002 to 2019. 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
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
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 Tibetan Plateau is known as “The World’s Third Pole” and “The Water Tower of Asia”. A relatively accurate map of the frozen soil in the Tibetan Plateau is therefore significant for local cold region engineering and environmental construction. Thus, to meet the engineering and environmental needs, a decision tree was established based on multi-source remote sensing data (elevation, MODIS surface temperature, vegetation index and soil moisture) to divide the permafrost and seasonally frozen soil of the Tibetan Plateau. The data are in grid format, DN=1 stands for permafrost, and DN=2 stands for seasonally frozen soil. The elevation data are from the 1 km x 1 km China DEM (digital elevation model) data set (http://westdc.westgis.ac.cn); the surface temperature is the yearly average data based on daily data estimated by Bin Ouyang and others using the Sin-Linear method. The estimation of the daily average surface temperature was based on the application of the Sin-Linear method to MODIS surface products, and to reduce the time difference with existing frozen soil maps, the surface temperature of the study area in 2003 was used as the information source for the classification of frozen soil. Vegetation information was extracted from the 16-day synthetic product data of Aqua and Terra (MYD13A1 and MOD13A1) in 2003. Soil moisture values were obtained from relatively high-quality ascending pass data collected by AMSR-E in May 2003. Therefore, based on the above data, the classification threshold of the decision tree was obtained using the Map of Frozen Soil in the Tibetan Plateau (1:3000000) and Map of the Glaciers, Frozen Soil and Deserts in China (1:4000000) as the a priori information. Based on the prosed method, the frozen soil types on the Tibetan Plateau were classified. The classification results were then verified and compared with the surveyed maps of frozen soil in the West Kunlun Mountains, revised maps, maps of hot springs and other existing frozen soil maps related to the Tibetan Plateau. Based on the Tibetan Plateau frozen soil map generated from the multi-source remote sensing information, the permafrost area accounts for 42.5% (111.3 × 104 km²), and the seasonally frozen soil area accounts for 53.8% (140.9 × 104 km²) of the total area of the Tibetan Plateau. This result is relatively consistent with the prior map (the 1:3000000 Map of Frozen Soil in the Tibetan Plateau). In addition, the overall accuracy and Kappa coefficient of the different frozen soil maps show that the frozen soil maps compiled or simulated by different methods are basically consistent in terms of the spatial distribution pattern, and the inconsistencies are mainly in the boundary areas between permafrost areas and seasonally frozen soil areas.
NIU Fujun, YIN Guoan
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 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
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
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 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
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
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
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
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 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
Field description: Num_code (Frozen soil attribute code) Combo (Permafrost properties) extent (Extent of frozen ground) content (Ice content) Attributes comparison are as follows: (1) Comparison table of frozen soil properties: 0 (No information) 1 - chf (Continuous permafrost extent with high ground ice content and thick overburden) 2 - dhf (Discontinuous permafrost extent with high ground ice content and thick overburden) 3 - shf (Sporadic permafrost extent with high ground ice content and thick overburden) 4 - ihf (Isolated patches of permafrost extent with high ground ice content and thick overburden) 5 - cmf (Continuous permafrost extent with medium ground ice content and thick overburden) 6 - dmf (Discontinuous permafrost extent with medium ground ice content and thick overburden) 7 - smf (Sporadic permafrost extent with medium ground ice content and thick overburden) 8 - imf (Isolated patches of permafrost extent with medium ground ice content and thick overburden) 9 - clf (Continuous permafrost extent with low ground ice content and thick overburden) 10 - dlf (Discontinuous permafrost extent with low ground ice content and thick overburden) 11 - slf (Sporadic permafrost extent with low ground ice content and thick overburden) 12 - ilf (Isolated patches of permafrost extent with low ground ice content and thick overburden) 13 - chr (Continuous permafrost extent with high ground ice content and thin overburden and exposed bedrock) 14 - dhr (Discontinuous permafrost extent with high ground ice content and thin overburden and exposed bedrock) 15 - shr (Sporadic permafrost extent with high ground ice content and thin overburden and exposed bedrock) 16 - ihr (Isolated patches of permafrost extent with high ground ice content and thin overburden and exposed bedrock) 17 - clr (Continuous permafrost extent with low ground ice content and thin overburden and exposed bedrock) 18 - dlr (Discontinuous permafrost extent with low ground ice content and thin overburden and exposed bedrock) 19 - slr (Sporadic permafrost extent with low ground ice content and thin overburden and exposed bedrock) 20 - ilr (Isolated patches of permafrost extent with low ground ice content and thin overburden and exposed bedrock) 21 - g (Glaciers) 22 - r (Relict permafrost) 23 - l (Inland lakes) 24 - o (Ocean/inland seas) 25 - ld (Land) (2) Comparison table of frozen soil scope c = continuous (90-100%) d = discontinuous (50- 90%) s = sporadic (10- 50%) i = isolated patches (0 - 10%) (3) Ice content comparison table h = high (>20% for "f" landform codes) (>10% for "r" landform codes) m = medium (10-20%) l = low (0-10%)
National Snow and Ice Data Center（NSIDC）, WU Lizong
These data are digitized for the Geocryological Regionalization and Classification Map of the Frozen Soil in China (1:10 million) (Guoqing Qiu et al., 2000; Youwu Zhou et al., 2000), adopting a geocryological regionalization and classification dual series system. The geocryological regionalization system and classification system are used on the same map to reflect the commonality and individuality of the formation and distribution of frozen soil at each level. The geocryological regionalization system consists of three regions of frozen soil: (1) the frozen soil region of eastern China; (2) the frozen soil region of northwestern China; and (3) the frozen soil region of southwestern China (Tibetan Plateau). Based on the three large regions, 16 regions and several subregions are further divided. In the division of the geocryological boundary in the frozen soil area, the boundary between major regions I and III mainly consults the results of Bingyuan Li (1987). The boundary between major regions II and III is the northern boundary of the Tibetan Plateau, which is the Kunlun Mountains-Altun Mountains-Northern Qilian Mountains and the piedmont line. The boundary between major regions I and II is in the area of Helan Mountain-Langshan Mountain. The boundary of the secondary region is divided by the geomorphological conditions in regions II and III. However, in region I, it is mainly divided by the ratio of the annual temperature range A to the annual mean temperature T, and the frozen depths of various regions are taken into consideration. The classification system is divided into 8 types based on the continuity of frozen soil, the time of existence of frozen soil and the seasonal frozen depth. The various classifications of boundaries are mainly taken from the "Map of Snow, Ice and Frozen Ground in China" (1:4 million) (Yafeng Shi et al., 1988) and consult some new materials, whereas the seasonal frozen soil boundary is mainly based on the weather station data. The definitions of each classification are as follows: (1) Large permafrost: the continuous coefficient is 90%-70%; (2) Large-island permafrost: the continuous coefficient is 70%-30%; (3) Sparse island-shaped permafrost: the continuous coefficient is <30%; (4) Permafrost in the mountains; (5) Medium-season seasonal frozen soil: the maximum seasonal frozen depth that can be reached is >1 m; (6) Shallow seasonal frozen soil: the maximum seasonal frozen depth that can be reached is <1 m; (7) Short-term frozen soil: less than one month of storage time; and (8) Nonfrozen soil. According to the data, China's permafrost areas sum to approximately 2.19 × 106 km², accounting for 22.83% of China's territory. Among those areas, the mountain permafrost is found over 0.42×106 km2, which is 4.39% of the territory of China. The seasonal frozen soil area is approximately 4.76×106 km², accounting for 49.6% of China's territory, and the instantaneous frozen soil area is approximately 1.86×106 km², i.e., 19.33% of China's territory. For more information, please see the references (Youwu Zhou et al., 2000).
GUO Dongxin, QIU Guoqing
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
The source of the data is a 1:2500000-scale map series, "Geocryological Map of Russia and Neighboring Republics", published by Russia from 1991 to 1996, which is labelled in Russian and includes a total of 16 images. In 1998, Zaitsev and others translated it into English. In this study, seven of the images were digitized: 1) Distribution of frozen and unfrozen ground, 2) Mean annual temperature of unfrozen ground at the depth of zero annual amplitude (note that there is some uncertainty because the depth of zero amplitude is not provided, and data on this parameter is generally lacking), 3) Thickness of permafrost, 4) Depth from the surface and thickness of relict permafrost, 5) Distribution of permafrost containing cryopegs, 6) Thickness of permafrost containing cryopegs, 7) Distribution of permafrost with depth. 1. The data include multiple vector layers: (1) permafrost distribution, (2) permafrost temperature, (3) permafrost thickness, (4) permafrost formation conditions, and (5) the correction image. 2. The permafrost distribution map includes the following fields: AREA, PERIMETER, FROZEN_, FROZEN_ID: POLY_, POLY_, RINGS_OK, RINGS_NOK, A, FROZEN_SOI (frozen soil layer), and temperature. FROZEN_SOI are the Chinese and English representations of the type of frozen soil, respectively. 4. Frozen soil properties: Frozen soil Continuous predominantly unfrozen 1-5 Continuous permafrost -3- -5 Continuous unfrozen ground 4-6 Discontinuous permafrost 0.5- -2 Predominantly continuous permafrost -1- -3 Predominantly unfrozen ground 1-3 5. Projection information: PROJCS["Asia_North_Equidistant_Conic", GEOGCS["GCS_North_American_1927", DATUM["North_American_Datum_1927", SPHEROID["Clarke_1866",6378206.4,294.9786982]], PRIMEM["Greenwich",0.0], UNIT["Degree",0.0174532925199433]], PROJECTION["Equidistant_Conic"], PARAMETER["False_Easting",0.0], PARAMETER["False_Northing",0.0], PARAMETER["longitude_of_center",100.0], PARAMETER["Standard_Parallel_1",15.0], PARAMETER["Standard_Parallel_2",58.3], PARAMETER["latitude_of_center",60.0], UNIT["Meter",1.0]]
Yershow
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
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
In the permafrost area of the upper reaches of Heihe River, 11 numbered typical boreholes are selected, and the thickness values of permafrost and seasonal permafrost are calculated by the temperature interpolation of boreholes. The 0 degree isothermal surface is set as the bottom plate of permafrost and seasonal permafrost. The data include borehole number, longitude and latitude, thickness of frozen soil and type of frozen soil.
ZHANG Tingjun, GAO Tanguang
This map was compiled by Li Xin and others in 2008 in order to re-count the permafrost area in China and based on the analysis of the existing permafrost map in China. It consists of three parts, of which the Qinghai-Tibet Plateau part uses the simulated permafrost map of the Qinghai-Tibet Plateau (Nanzhuo Copper, 2002), the northeast part comes from the "14 million map of China's Glacier, Frozen Soil and Desert" (Institute of Environment and Engineering in Cold and Arid Regions, Chinese Academy of Sciences, 2006), and the other part uses the map of China's permafrost zoning and types (1: 10 million) (Zhou Youwu and others, 2000). More Information References (Institute of Environment and Engineering in Cold and Arid Regions, Chinese Academy of Sciences, 2006; Nanzhuo Copper, 2002; Zhou Youwu et al., 2000; Li et al, 2008）。
LI Xin, NAN Zhuotong, ZHOU Youwu
Chinese Cryospheric Information System is a comprehensive information system for the management and analysis of Chinese Cryospheric data. The establishment of Chinese Cryospheric Information System is to meet the needs of earth system science, to provide parameters and validation data for the development of response and feedback model of frozen soil, glacier and snow cover to global change under GIS framework; on the other hand, it is to systemically sort out and rescue valuable cryospheric data, to provide a scientific, efficient and safe management and division for it Analysis tools. The basic datasets of the Tibet Plateau mainly takes the Tibetan Plateau as the research region, ranging from longitude 70 -- 105 ° east and latitude 20 -- 40 ° north, containing the following types of data: 1. Cryosphere data. Includes: Permafrost type (Frozengd), (Fromap); Snow depth distribution (Snowdpt) Quatgla (Quatgla) 2. Natural environment and resources. Includes: Terrain: elevation, elevation zoning, slope, slope direction (DEM); Hydrology: surface water (Stram_line), (Lake); Basic geology: Quatgeo, Hydrogeo; Surface properties: Vegetat; 4. Climate data: temperature, surface temperature, and precipitation. 3. Socio-economic resources (Stations) : distribution of meteorological Stations on the Tibetan Plateau and it surrounding areas. 4. Response model of plateau permafrost to global change (named "Fgmodel"): permafrost distribution data in 2009, 2049 and 2099 were projected. Please refer to the following documents (in Chinese): "Design of Chinese Cryospheric Information System.doc", "Datasheet of Chinese Cryospheric Information System.DOC", "Database of the Tibetan Plateau.DOC" and "Database of the Tibetan Plateau 2.DOC".
LI Xin
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
China long-sequence surface freeze-thaw dataset——decision tree algorithm (1987-2009), is derived from the decision tree classification using passive microwave remote sensing SSM / I brightness temperature data. This data set uses the EASE-Grid projection method (equal cut cylindrical projection, standard latitude is ± 30 °), with a spatial resolution of 25.067525km, and provides daily classification results of the surface freeze-thaw state of the main part of mainland China. The data set is stored by year and consists of 23 folders, from 1987 to 2009. Each folder contains the day-to-day surface freeze-thaw classification results for the current year. It is an ASCII file with the naming rule: SSMI-frozenYYYY ***. Txt, where YYYY represents the year and *** represents the Julian date (001 ~ 365 / 366). The freeze-thaw classification result txt file can be opened and viewed directly with a text program, and can also be opened with ArcView + Spatial Analyst extension module or Arcinfo's Asciigrid command. The original frozen and thawed surface data was derived from daily passive microwave data processed by the National Snow and Ice Data Center (NSIDC) since 1987. This data set uses EASE-Grid (equivalent area expandable earth grid) as a standard format . China's surface freeze-thaw long-term sequence data set-The decision tree algorithm (1987-2009) attributes consist of the spatial-temporal resolution, projection information, and data format of the data set. Spatio-temporal resolution: the time resolution is day by day, the spatial resolution is 25.067525km, the longitude range is 60 ° ~ 140 ° E, and the latitude is 15 ° ~ 55 ° N. Projection information: Global equal-area cylindrical EASE-Grid projection. For more information about EASE-Grid projection, see the description of this projection in data preparation. Data format: The data set consists of 23 folders from 1987 to 2009. Each folder contains the results of the day-to-day surface freeze-thaw classification of the year, and is stored as a txt file on a daily basis. File naming rules: For example, SMI-frozen1994001.txt represents the surface freeze-thaw classification results on the first day of 1994. The ASCII file of the data set is composed of a header file and a body content. The header file consists of 6 lines of description information such as the number of rows, the number of columns, the coordinates of the lower left point of the x-axis, the coordinates of the lower left point of the y-axis, the grid size, and the value of the data-less area. Array, with columns as the priority. The values are integers, from 1 to 4, 1 for frozen, 2 for melting, 3 for desert, and 4 for precipitation. Because the space described by all ASCII files in this data set is nationwide, the header files of these files are unchanged. The header files are extracted as follows (where xllcenter, yllcenter and cellsize are in m): ncols 308 nrows 166 xllcorner 5778060 yllcorner 1880060 cellsize 25067.525 nodata_value 0 All ASCII files in this data set can be opened directly with a text program such as Notepad. Except for the header file, the main content is a numerical representation of the surface freeze-thaw state: 1 for frozen, 2 for melting, 3 for desert, and 4 for precipitation. If you want to display it with an icon, we recommend using ArcView + 3D or Spatial Analyst extension module to read it. During the reading process, a grid format file will be generated. The displayed grid file is the graphic representation of the ASCII code file. Reading method: [1] Add 3D or Spatial Analyst extension module in ArcView software, and then create a new View; [2] Activate View, click the File menu, select the Import Data Source option, the Import Data Source selection box pops up, select ASCII Raster in Select import file type: in this box, and a dialog box for selecting the source ASCII file automatically pops up Find any ASCII file in the data set and press OK; [3] Type the name of the Grid file in the Output Grid dialog box (a meaningful file name is recommended for later viewing), and click the path where the Grid file is stored, press Ok again, and then press Yes (to select an integer) Data), Yes (call the generated grid file into the current view). The generated file can be edited according to the Grid file standard. This completes the process of displaying the ASCII file as a Grid file. [4] During batch processing, you can use ARCINFO's ASCIIGRID command to write an AML file, and then use the Run command to complete in the Grid module: Usage: ASCIIGRID <in_ascii_file> <out_grid> {INT | FLOAT}
LI Xin
The data are a digitized permafrost map along the Qinghai-Tibet Highway (1:600,000) (Boliang Tong, et al. 1983), which was compiled by Boliang Tong, shude Li, Jueying bu, and Guoqing Qiu from the Cold and Arid Regions Environmental and Engineering Research Institute of the Chinese Academy of Sciences (originally called the Lanzhou Institute of Glaciology and Cryopedology, Chinese Academy of Sciences) in 1981. The map aims to reflect the basic laws of permafrost distribution along the highway and its relationship with the main natural environmental factors. The basic data for the compilation of the map include hydrogeological and engineering geological survey results and maps along the Qinghai-Tibet Highway(1:200000) (First Hydrogeological Engineering Geological Brigade of Qinghai Province, Institute of Geomechanics of the Academy of Geological Science), the cryopedological research results of the Institute of Glaciology and Cryopedology of Chinese Academy of Sciences since 1960 in nine locations along the Qinghai-Tibet Highway (West Datan, Kunlun pass basin, Qingshuihe, Fenghuohe, Tuotuohe, the Sangma Basin, Buquhe, Tumengela, and Liangdaohe) and drilling data of the Golmud-Lhasa oil pipeline and aerial topographic data of the work area. Taking the 1:200000 topographic map as the working base map, a permafrost map was compiled, which was then downscaled to a 1:600000 map to ensure the accuracy of the map. To make up for the lack of data in a larger area along the line, the characteristics and principles of the frozen soils found in the nine frozen soil research points along the highway were applied to areas with the same geologic and geographical conditions; meanwhile, aerial photographs were used as supplements to the freeze-thaw geology and frozen soil characteristics. The permafrost map along the Qinghai-Tibet Highway (1:600,000) includes the annual average temperature contour map along the Qinghai-Tibet Highway (1:7,200,000) and the permafrost map along the Qinghai-Tibet Highway (1:600,000). The permafrost map along the Qinghai-Tibet Highway also contains information on permafrost types, lithology, frozen soil phenomena, types of through-melting zones, classification of frozen soil engineering, and geological structural fractures. These data contain only digitized permafrost information. The spatial coverage is from Daxitan on the Qinghai-Tibet Highway in the north to Sangxiong in the south and is nearly 800 kilometers long and 40-50 kilometers wide. The data set includes a vectorized and a scanned map of the permafrost map along the Qinghai-Tibet Highway. The attribute information of the map is as follows. A-1; Continuous permafrost; >0°C; remained as a frozen soil layer and isolation layer A-2; Continuous permafrost; 0~-0.5°C; 0-25 m A-3; Continuous permafrost; -0.5~-1.5°C; 25-60 m A-4; Continuous permafrost; -1.5~-3.5°C; 60-120 m A-5;Continuous permafrost;<-3.5°C;>120 m B-1; Island permafrost ground; Seasonal Frozen Ground; B-2; Continuous permafrost; >0°C; remained as a frozen soil layer and isolation layer B-3; Island permafrost extent; 0~-0.5°C; 0-25 m B-4; Island permafrost extent; -0.5~-1.5°C; 25-60 m B-5; Island permafrost extent; -1.5~-3.5°C; 60-120 m
TONG Boliang, LI Shude, BO Jueying, QIU Guoqing
The distribution map of permafrost and ground-ice around the Arctic is the only data map of permafrost compiled by the international permafrost association in collaboration with permafrost research institutes of several countries in 1997. The map describes the distribution and properties of permafrost and subsurface ice conditions in the northern hemisphere (20°N to 90°N). Permafrost was divided into continuous (90-100%), discontinuous (50-90%), sporadic (10-50%), island (<10%) and non-permafrost by continuous division of permafrost scope. The subsurface ice abundance at the top 20 m is divided by the percentage of ice volume (>20%, 10-20%, <10% and 0%). Published ESRI-shape files are based on 1:10 million paper maps (Brown et al. 1997). The map can be used in related research such as global climate change, polar resource development and environmental protection. The China section is shown in thumbnail. See the reference for more information (Heginbottom et al. 1993). The format of the data is the ESRI shapefile, you can download it on the snow and ice data center (http://nsidc.org/data/ggd318.html).
O. Ferrians, J. A. Heginbottom, E. Melnikov
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 frozen soil type map of Kazakhstan (1:10,000,000) includes three .shp vector layers: 1, Polyline ranges.shp, indicating the extent of frozen soil; 2, Polygon kaz_perm.shp, frozen soil; 3, An attribute description Word file. The kaz_perm attribute table includes four fields: ID, REGION, SUBREGION, M_RANGE. Comparison of the main attributes: First, Area I. Altai-TienShan Second, Region: High mountains I.1. Altai, I.2. Saur-Tarbagatai, I.3.Dzhungarskyi, I.4. Northern Tien Shan, I.5. Western Tien Shan Intermountain depressions I.6. Zaysanskyi, I.7. Alakulskyi, I.8. Iliyskyi II. Western Siberian Second, Region: Planes II.1. Northern Kazakhstanskyi V. Western Kazakhstanskaya III. Kazakh small hills area IV. Turanskaya: IV.1. Turgayskyi IV.2. Near Aaralskyi IV.3. Chuysko-Syrdaryinskyi IV.4. South-Balkhashskyi V. Western Kazakhstanskaya: V.1. Mugodzhar-Uralskyi V.2. Near Caspian V.3. manghyshlak-Ustyrtskyi Third, Sub-region: I.1.1. Western Altai I.1.2. South Altai I.1.3. Kalbinskyi I.2.1. Tarbagatayskyi I.2.2. Saurskyi I.3.1. Nortern Dzhungarskyi I.3.2. Western Dzhungarskyi I.3.3. Southern Dzhungarskyi I.4.1. Kirgizskyi Alatau I.4.2. Zailiyskyi-Kungeyskyi I.4.3. Ketmenskyi I.4.4. Bayankolskyi I.5.1. Karatauskyi I.5.2. Talaso-Ugamskyi The layer projection information is as follows: GEOGCS["GCS_WGS_1984", DATUM["WGS_1984", SPHEROID["WGS_1984", 6378137.0, 298.257223563]], PRIMEM["Greenwich", 0.0], UNIT["Degree",0.0174532925199433]] Different regions feature different frozen soil attributes, and the specific attribute information can be found in the Word file.
Sergei Marchenko
A map of the frozen soil distribution in the Republic of Mongolia is digitized from the National Atlas of the Republic of Mongolia (Sodnom and Yanshin, 1990). This data set describes the distribution and general properties of permafrost, seasonally frozen soil, and low-temperature phenomena in the Republic of Mongolia. Two plates were specifically digitized. The first plate, with a scale of 1:12,000,000, describes four general frozen soil regions: (1) continuous and discontinuous permafrost; (2) island-like and sparse island-like permafrost; (3) sporadic permafrost; and (4) seasonally frozen soil. The second plate, with a scale of 1:4,500,000, describes 14 different terrain types. The terrain types are divided based on elevation, annual average temperature, permafrost thickness, melting depth, and freezing depth of seasonally frozen soil. The locations of the six types of low-temperature phenomena in Mongolia are also included: pingos, ice cones, hot karst, detachment failures, solifluction, and cryoplatation processes. The data are provided in the ESRI shape file format and can be downloaded from the US Ice and Snow Data Center.
A. L.Yanshin, Sodnom
This dataset is the spatial distribution map of the marshes in the source area of the Yellow River near the Zaling Lake-Eling Lake, covering an area of about 21,000 square kilometers. The data set is classified by the Landsat 8 image through an expert decision tree and corrected by manual visual interpretation. The spatial resolution of the image is 30m, using the WGS 1984 UTM projected coordinate system, and the data format is grid format. The image is divided into five types of land, the land type 1 is “water body”, the land type 2 is “high-cover vegetation”, the land type 3 is “naked land”, and the land type 4 is “low-cover vegetation”, and the land type 5 is For "marsh", low-coverage vegetation and high-coverage vegetation are distinguished by vegetation coverage. The threshold is 0.1 to 0.4 for low-cover vegetation and 0.4 to 1 for high-cover vegetation.
KHROMOVA Tatiana,
The 2008 national remote sensing annual average surface temperature and freezing index is a 5 km instantaneous surface temperature data product based on MODIS Aqua/Terra four times a day by Ran Youhua et al. (2015). A new method for estimating the annual average surface temperature and freezing index has been developed. The method uses the average daily mean surface temperature observed by LST in morning and afternoon to obtain the daily mean surface temperature. The core of the method is how to recover the missing data of LST products. The method has two characteristics: (1) Spatial interpolation is carried out on the daily surface temperature variation observed by remote sensing, and the spatial continuous daily surface temperature variation obtained by interpolation is utilized, so that satellite observation data which is only once a day is applied; (2) A new time series filtering method for missing data is used, that is, the penalty least squares regression method based on discrete cosine transform. Verification shows that the accuracy of annual mean surface temperature and freezing index is only related to the accuracy of original MODIS LST, i.e. the accuracy of MODIS LST products is maintained. It can be used for frozen soil mapping and related resources and environment applications.
RAN Youhua, LI Xin
Overviewing the various frozen soil maps in China, there are great differences in the classification systems, data sources, and mapping methods. These maps represent the stage of understanding of the permafrost distribution of China in the past half century. To reflect the distribution and area of frozen soil in our country more reasonably, we have made a new frozen soil distribution map based on the analysis of the existing frozen soil maps. The map combines several existing maps of permafrost and the simulation results of a permafrost distribution model on the Tibetan Plateau. It unifies the acquisition time of data from various parts of the country and reflects the distribution of permafrost in our country around 2000. In the new frozen soil map, the distributions of various types of frozen soil are determined according to the following principles. 1. The base map uses the Geocryological Regionalization and Classification Map of the Frozen Soil in China (1:10 000 000) (Guoqing Qiu et al., 2000). The distribution of permafrost and instantaneous frozen soil in the high mountains outside the Tibetan Plateau follows the original map; the boundaries of seasonal frozen soil and instantaneous frozen soil, instantaneous frozen soil and nonfrozen soil remain unchanged, too. The distribution of permafrost on the Tibetan Plateau and in the high latitudes of the Northeast is updated with the following results. 2. The distribution of high-altitude permafrost and alpine permafrost in the Tibetan Plateau region is updated using the simulation results of Zhuotong Nan et al. (2002). This model uses the measured average annual ground temperature data of 76 boreholes along the Qinghai-Tibet Highway to perform regression statistical analysis and obtains the relationship between annual mean geothermal data with latitude and elevation. Based on this relationship, combined with the GTOPO30 elevation data (global 1-km digital elevation model data developed under the leadership of the US Geological Survey's Earth Resources Observation and Technology Center), the average annual ground temperature distribution over the entire Tibetan Plateau is simulated, the average annual ground temperature is 0.5 C, and it is used as the boundary between permafrost and seasonal frozen soil. 3. The distribution of permafrost at high latitudes in the Northeast is based on the latest results from Jin et al. (2007). Jin et al. (2007) analyze the average annual precipitation and soil moisture in Northeast China over the past few decades and conclude that the relationship between the southern boundary of permafrost in Northeast China and the annual average temperature has not changed substantially in the past few decades. 4. Alpine permafrost distribution in other regions is updated with the Map of the Glaciers, Frozen Ground and Deserts in China (1:4 million) (Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, 2006). In terms of classification systems, the current existing frozen soil maps use continuous standards for the division of permafrost, but the specific definition of continuity is very different. Many studies have shown that the continuity criterion is a concept closely related to scale, it is not suitable for the classification of permafrost at high altitude (Guodong Cheng, 1984; Cheng et al., 1992), and it cannot be applied to the permafrost distribution model that uses grid as the basic simulation unit. In this paper, we abandon the continuity criteria and take the existence of frozen soil in the mapping unit (grid or region). The new frozen soil map divides China's frozen soil into several categories: (1) High latitude permafrost; (2) High altitude permafrost; (3) Plateau permafrost; (4) Alpine permafrost; (5) Medium-season seasonal frozen soil: the maximum seasonal freezing depth that can be reached is >1 m; (6) Shallow seasonal frozen soil: the maximum seasonal freezing depth that can be achieved is <1 m; (7) Instant frozen soil: less than one month of storage time; and (8) Nonfrozen soil. For a specific description of the data, please refer to the explanatory documents and citations.
RAN Youhua, LI Xin
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
The borehole is about 7km away from Jiagedaqi City （50.47°N, 124.23°E), located in a wetland with about 80cm-thick peaty soil. There are three boreholes, and one is 2m away from the pipe center and 20m deep, the second is 16.6m away and 20m deep, and the third is 50m away from the second pipeline and 60 m deep. Based on the temperature borehole with a diameter of 40 mm and depths of 20 to 60 m, the ground temperature along the China-Russia Crude Oil Pipeline was measured using the thermistor sensor, which was assembled by State Key Laboratory of Frozen Soil Engineering, and calibrated with an accuracy of ±0.05℃. Therefore, the critical characteristic parameters such as ground stratigraphy, temperature of permafrost, surface temperature and active layer thickness were obtained. During the period from October 2014 to October 2017, ground temperatures in the T1 and T2 boreholes were collected manually. The ground temperatures in T3 was collected automatically and continuously since 12 June of 2018. Then the continuous and complete record of ground temperature data uploaded to the specified server (fixed IP address) by the wireless transmission module utilizing cellular networks. From these measured data along the China-Russia Crude Oil Pipeline route, the development characteristics and historical evolution of permafrost, and its response to the climate change can be analyzed.
LI Guoyu
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.
This data is obtained by spatial interpolation and permafrost simulation through the surface temperature at 0 cm of nine stations in and outside the source area of the upper reaches of Heihe River. In the figure, 1 represents seasonal frozen soil and 2 represents permafrost. The data is in TIFF format, WGS-84 is used for projection, and the spatial range is 37.7263n-39.0976n, 98.5769e-101.1608e.
GE Shemin
As the main parameter in the land surface energy balance, surface temperature indicates the degree of land-atmosphere energy and water transfer and is widely used in research on climatology, hydrology and ecology. In the study of frozen soil, climate is one of the decisive factors for the existence and development of frozen soil. The surface temperature is the main climatic factor affecting the distribution of frozen soil and affects the occurrence, development and distribution of frozen soil. It is the upper boundary condition for modelling frozen soil and is significant to the study of hydrological processes in cold regions. The data set was based on the DEM and observation station data of the Tibetan Plateau Engineering Corridor and analysed the changing trend of surface temperature on the Tibetan Plateau from 2000 to 2014. Using the surface temperature data products MOD11A1/A2 and MYD11A1/A2 of MODIS aboard Terra and Aqua, the surface temperature information under cloud cover was reconstructed based on the spatio-temporal information of the images. The reconstruction information and surface temperature representativeness problems were analysed using information obtained from 8 sites, including the Kunlun Mountains (wetland, grassland), Beiluhe (grassland, meadow), Kaixinling (meadow, grassland), and Tanggula Mountain (meadow, wetland). According to the correlation coefficient (R2), root-mean-square error (RMSE), mean absolute error (MAE) and mean deviation (MBE), the following results were obtained: (1) the reconstruction accuracy of MODIS surface temperature under cloud cover is higher when it is based on spatio-temporal information; (2) the weighted average representation is the best when generalizing four observations of Terra and Aqua. By analysing the reconstruction of MODIS surface temperature information and representativeness problems, the average annual MODIS surface temperature data of the Tibetan Plateau and the engineering corridor from 2000 to 2010 were obtained. According to the data set, the surface temperature from 2000 to 2010 also experienced volatile rising trends from 2000 to 2010, which is basically consistent with the changing trend of the climate change in the permafrost regions of the Tibetan Plateau and the Qinghai-Tibet Engineering Corridor.
NIU Fujun, YIN Guoan
The past frozen soil map of the Tibetan Plateau was based on a small number of temperature station observations and used a classification system based on continuity. This data set used the geographically weighted regression model (GWR) to synthesize MODIS surface temperature, leaf area index, snow cover ratio and multimodel soil moisture forecast products of the National Meteorological Information Center through spatiotemporal reconstruction. In addition, precipitation observations of more than 40 meteorological stations, the precipitation products of FY2 satellite observations and the multiyear average temperature observation data of 152 meteorological stations from 2000 to 2010 were integrated to simulate the average temperature data of the Tibetan Plateau, and the permafrost thermal condition classification system was used to classify permafrost into several types: Very cold, Cold, Cool, Warm, Very warm, and Likely thawing. The map shows that, after deducting lakes and glaciers, the total area of permafrost on the Tibetan Plateau is approximately 1,071,900 square kilometers. Verification shows that this map has higher accuracy. It can provide support for future planning and design of frozen soil projects and environmental management.
RAN Youhua, LI Xin
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
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
The active layer is one of the main characteristics of permafrost. It melts in warm season and freezes in cold season, showing seasonal changes. The amount of water content in the active layer has certain influence on the temperature of the permafrost, thus affecting the stability of the permafrost.The data set is mainly composed of active layer moisture data. The monitoring station is located at 92°E, 34°N, with an elevation of 4600m. The monitoring site is flat, the vegetation type is alpine meadow, and the water probe used by Beilouhe Meteorological Station is CS615. The data set is used to monitor water at 5 depths below the surface, 10 cm, 20 cm, 40 cm, 80 cm and 160cm. The time interval of the data set is 1 day and is 30 minutes.Mean value of data once, data is stable and continuous during monitoring.By combining the data of soil heat flux and frozen soil temperature, the thermal change process and mechanism of active layer can be carried out.
CHEN Ji
The map is "1:4 Million Ice, Snow and Frozen Soil Map of China" compiled by Mr. Shi Yafeng and Mr. Meadson. The working map compiled by the map is "Chinese Pinyin Edition of the People's Republic of China", which retains the water system and mountain annotation of the map and adds some mountain annotation. The compilation of frozen soil map is based on the actual data of frozen soil survey and exploration, interpretation of remote sensing data, temperature conditions and topographic characteristics that affect the formation and distribution of frozen soil. The height of glacier snow line is expressed by isolines. Seasonal snow accumulation and seasonal icing are based on the data of 1600 meteorological observation stations and the results of many years of investigation in China. They are expressed by isoline notation and symbols. The selection of cold (periglacial) phenomena is a representative and schematic representation observed on the spot. The boundary line between permafrost and non-permafrost is mapped by calculation based on the field data, and its comprehensive degree is relatively high (Tö pfer, 1982) "China Ice and Snow Frozen Soil Map" reflects the scale, types and characteristics of distribution of glaciers, snow cover, frozen soil and periglacial, as well as its value in scientific research and the prospect of utilization and prevention in production practice. It shows our achievements in glacier and frozen soil research in the past 30 years.
SHI Yafeng, MI Desheng
The compilation basis of frozen soil map includes: (1) frozen soil field survey, exploration and measurement data; (2) aerial photo and satellite image interpretation; (3) topo300 1km resolution ground elevation data; (4) temperature and ground temperature data. Among them, the distribution of permafrost in the Qinghai Tibet Plateau adopts the research results of nanzhuo Tong et al. (2002). Using the measured annual average ground temperature data of 76 boreholes along the Qinghai Tibet highway, regression statistical analysis is carried out to obtain the relationship between the annual average ground temperature and latitude, elevation, and based on this relationship, combined with the gtopo30 elevation data (developed under the leadership of the center for earth resources observation and science and technology, USGS) Global 1 km DEM data) to simulate the annual mean ground temperature distribution over the whole Tibetan Plateau. Taking the annual average ground temperature of 0.5 ℃ as the boundary between permafrost and seasonal permafrost, the boundary between discontinuous Permafrost on the plateau and island Permafrost on the plateau is delimited by referring to the map of ice and snow permafrost in China (1:4 million) (Shi Yafeng et al., 1988); in addition, the division map of Permafrost on the big and small Xing'an Mountains in the Northeast (Guo Dongxin et al., 1981), the distribution map of permafrost and underground ice around the Arctic (b According to rown et al. 1997) and the latest field survey data, the Permafrost Boundary in Northeast China has been revised; the Permafrost Boundary in Northwest mountains mostly uses the boundary defined in the map of ice and snow permafrost in China (1:4 million) (Shi Yafeng et al., 1988). According to the data, the area of permafrost in China is about 1.75 × 106km2, accounting for about 18.25% of China's territory. Among them, alpine permafrost is 0.29 × 106km2, accounting for about 3.03% of China's territory. For more information, please refer to the specification of "1:4 million map of glacial and frozen deserts in China" (Institute of environment and Engineering in cold and dry areas, Chinese Academy of Sciences, 2006)
WANG Tao
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
1. Data overview: This data set is the data set of frozen depth of permafrost observed artificially in qilian station from January 1, 2013 to December 31, 2013, and observed at 08 o 'clock every day. 2. Data content: The data content is the frozen depth data set of the tundra.The frozen depth (length) of the water in the inner rubber tube is used as a record to determine the freezing level and the upper and lower depth of the frozen layer according to the freezing position and length of the water in the frozen pot.In centimeters (cm), round off the whole number and round off the decimal.Observe once a day at 0:8. 3. Space and time range: Geographical coordinates: longitude: 99° 53’e;Latitude: 38°16 'N;Height: 2981.0 m
CHEN Rensheng, HAN Chuntan, SONG Yaoxuan, LIU Junfeng, YANG Yong, LIU Zhangwen
1. Data overview: This data set is the data set of frozen depth of permafrost observed artificially in qilian station from January 1, 2012 to December 31, 2012, and observed at 08 o 'clock every day. 2. Data content: The data content is the frozen depth data set of the tundra.The frozen depth (length) of the water in the inner rubber tube is used as a record to determine the freezing level and the upper and lower depth of the frozen layer according to the freezing position and length of the water in the frozen pot.In centimeters (cm), round off the whole number and round off the decimal.Observe once a day at 0:8. 3. Space and time range: Geographical coordinates: longitude: 99° 53’e;Latitude: 38°16 'N;Height: 2981.0 m
CHEN Rensheng, SONG Yaoxuan, HAN Chuntan, LIU Junfeng, YANG Yong
1. Data overview: this data set is the data set of artificial observation of frozen soil depth at Qilian station from January 1, 2011 to December 31, 2011, at 08:00 every day. 2. Data content: data content is frozen depth data set of permafrost. Frozen soil observation uses the frozen depth (length) of water poured into the rubber inner tube as a record. According to the position and length of water frozen in the permafrost buried in the soil, the frozen layer and its upper and lower limit depths are measured. In centimeters (CM), rounded to the nearest whole number. Observe once every day at 0.8 o'clock. 3. Space time scope: geographic coordinates: longitude: 99 ° 53 ′ E; latitude: 38 ° 16 ′ n; altitude: 2981.0m
HAN Chuntan, SONG Yaoxuan, LIU Junfeng, YANG Yong, QING Wenwu, LIU Zhangwen
As an important parameter of permafrost research, the freezing-thawing index is of great significance to the research of permafrost, and it is also an important index for the research of climate change.The cumulative value of daily air temperature or surface soil temperature at a given time. This data is based on the daily surface temperature observation data of 15 regular meteorological stations in the heihe valley of China meteorological administration, and the annual surface freezing-thawing index of each meteorological station from 1960 to 2006 is calculated.
ZHANG Tingjun
The data is the monthly average spatial distribution of frozen soil in Heihe River Basin from 2000 to 2009. Based on the grid temperature data of Heihe River Basin from 2000 to 2009, the freezing and thawing state of surface soil is divided into three kinds: unfreezing state, incomplete freezing state and complete freezing state. Complete freezing means that the soil is completely frozen in the whole month. Incomplete freezing refers to soil freezing days ≤ 30 days but ≥ 1 day in a month, and the soil has freeze-thaw cycle. Non freezing means that the soil will not freeze this month. The data is in the form of grid, which can be opened in ArcGIS. 1 represents unfrozen state, 2 represents unfrozen state, 3 represents fully frozen state
PENG Xiaoqing, ZHANG Tingjun
The data set includes 1. permaice (map of frozen soil types), 2. subsea (subsea boundary vectorgraph), 3. treeline (timberline vectorgraph), 4. nhipa (grid map) and 5. llipa (grid map). Permaice includes the following attribute fields: Num_code (frozen soil attribute code), Combo (frozen soil attribute), extent (frozen soil coverage) and content (ice content). The attribute comparison is as follows. (1) Frozen soil attribute comparison table: 0 （No information） 1 - chf （Continuous permafrost extent with high ground ice content and thick overburden） 2 - dhf （Discontinuous permafrost extent with high ground ice content and thick overburden） 3 - shf （Sporadic permafrost extent with high ground ice content and thick overburden） 4 - ihf （Isolated patches of permafrost extent with high ground ice content and thick overburden） 5 - cmf （Continuous permafrost extent with medium ground ice content and thick overburden） 6 - dmf （Discontinuous permafrost extent with medium ground ice content and thick overburden） 7 - smf （Sporadic permafrost extent with medium ground ice content and thick overburden） 8 - imf （Isolated patches of permafrost extent with medium ground ice content and thick overburden） 9 - clf （Continuous permafrost extent with low ground ice content and thick overburden） 10 - dlf （Discontinuous permafrost extent with low ground ice content and thick overburden） 11 - slf （Sporadic permafrost extent with low ground ice content and thick overburden） 12 - ilf （Isolated patches of permafrost extent with low ground ice content and thick overburden） 13 - chr （Continuous permafrost extent with high ground ice content and thin overburden and exposed bedrock） 14 - dhr （Discontinuous permafrost extent with high ground ice content and thin overburden and exposed bedrock） 15 - shr （Sporadic permafrost extent with high ground ice content and thin overburden and exposed bedrock） 16 - ihr （Isolated patches of permafrost extent with high ground ice content and thin overburden and exposed bedrock） 17 - clr （Continuous permafrost extent with low ground ice content and thin overburden and exposed bedrock） 18 - dlr （Discontinuous permafrost extent with low ground ice content and thin overburden and exposed bedrock） 19 - slr （Sporadic permafrost extent with low ground ice content and thin overburden and exposed bedrock） 20 - ilr （Isolated patches of permafrost extent with low ground ice content and thin overburden and exposed bedrock） 21 - g （Glaciers） 22 - r （Relict permafrost） 23 - l （Inland lakes） 24 - o （Ocean/inland seas） 25 - ld （Land） （2）The frozen soil coverage attribute comparison table c = continuous (90-100%) d = discontinuous (50-90%) s = sporadic (10-50%) i = isolated patches (0-10%) （3）The ice content comparison table h = high (>20% for "f" landform codes) (>10% for "r" landform codes) m = medium (10-20%) l = low (0-10%) ------------------------------------------------------------ Projection of the shapefiles is: PROJCS["Sphere_ARC_INFO_Lambert_Azimuthal_Equal_Area", GEOGCS["GCS_Sphere_ARC_INFO", DATUM["Sphere_ARC_INFO", SPHEROID["Sphere_ARC_INFO",6370997.0,0.0]], PRIMEM["Greenwich",0.0], UNIT["Degree",0.0174532925199433]], PROJECTION["Lambert_Azimuthal_Equal_Area"], PARAMETER["False_Easting",0.0], PARAMETER["False_Northing",0.0], PARAMETER["longitude_of_center",180.0], PARAMETER["latitude_of_center",90.0], UNIT["Meter",1.0]] Projection for the raster (*.byte) files is: Projection: Lambert Azimuthal Units: meters Spheroid: defined Major Axis: 6371228.00000 Minor Axis: 6371228.000 Parameters: radius of the sphere of reference: 6371228.00000 longitude of center of projection: 0 latitude of center of projection: 90 false easting (meters): 0.00000 false northing (meters): 0.00000
O. Ferrians, J. A. Heginbottom, E. Melnikov, ZHANG Tingjun, RAN Youhua
These data are a digitization of the frozen soil distribution map of the Map of the Glaciers, Frozen Ground and Deserts in China (1:4,000,000). Considering the unification with the global frozen soil classification system, the permafrost is divided into the following five types: (1) Discontinuous permafrost: continuous coefficient 50%-90% (2) Island permafrost: continuous coefficient <50% (3) Plateau discontinuous permafrost: continuous coefficient 50%-90% (4) Plateau island permafrost: continuous coefficient 50%-90% (5) Mountain permafrost The compilation basis of the frozen soil map includes (1) the measured field survey data and exploration of frozen soil; (2) aerial image and satellite image interpretation; (3) TOPO30 1-km resolution ground elevation data; and (4) and temperature and ground temperature data. The distribution of frozen soil on the Tibetan Plateau adopted the research results of Zhuotong Nan et al. (2002). Using the average annual temperature data of 76 boreholes along the Qinghai-Tibet Highway, a statistical regression analysis was performed to obtain the relation between annual mean ground temperature, latitude and elevation. Based on the relation combined with GTOPO30 elevation data (global 1-km digital elevation model data developed by the Earth Resources Observation and Technology Center of the U.S Geological Survey), the annual average ground temperature distribution over the entire Tibetan Plateau was simulated. Taking the annual average ground temperature of 0.5 °C as the boundary between permafrost and seasonal frozen soil and the Map of Snow Ice and Frozen Ground in China (1:4,000,000) (Yafeng Shi, et al., 1988) as a reference, the boundary between the plateau discontinuous permafrost and plateau island permafrost was determined. In addition, taking the Distributions Map of Permafrost in Daxiao Hinganling Northeast China (Dongxin Guo, et al. 1981), the Distribution Map of Permafrost and Ground Ice in Circum-Arctic (Brown et al. 1997) and the latest field data as references, the permafrost boundary of northeast China has been revised; the mountain permafrost boundary in the northwest mostly adopted the boundary delineated in the Map of Snow Ice and Frozen Ground in China (1:4,000,000) (Yafeng Shi, et al., 1988). According to this data set, permafrost area in China is approximately 1.75×106 km2, accounting for 18.25% of the territory of China, among which the mountain permafrost area is 0.29×106 km2, which accounts for 3.03% of the territory of China. For more information, please refer to the Map of the Glaciers, Frozen Ground and Deserts in China (1:4,000,000) specification (Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, 2006).
WANG Tao, SHI Yafeng, GUO Dongxin
This dataset uses daily temperature data from SMMR (1978-1987), SSM/I (1987-2009) and SSMIS (2009-2015). It is generated by the dual-index (TB, 37v, SG) freeze-thaw discrimination algorithm. The classification results include the frozen surface, the thawed surface, the deserts and water bodies. The data coverage is the main part of China’s mainland, with a spatial resolution of 25.067525 km via the EASE-Grid projection method, and it is stored in ASCIIGRID format. All the ASCII files in this data set can be opened directly with a text program such as Notepad. Except for the head file, the body content is numerically characterized by the freeze/thaw status of the surface soil: 1 for frozen, 2 for thawed, 3 for desert, and 4 for precipitation. If you want to use the icon for display, we recommend using the ArcView + 3D or Spatial Analyst extension module for reading; in the process of reading, a grid format file will be generated, and the displayed grid file is the graphical expression of the ASCII file. The read method comprises the following. [1] Add the 3D or Spatial Analyst extension module to the ArcView software and then create a new View. [2] Activate View, click File menu, and select the Import Data Source option. When the Import Data Source selection box pops up, select ASCII Raster in the Select import file type box. When the dialog box for selecting the source ASCII file automatically pops up, click to find any ASCII file in the data set, and then press OK. [3] Type the name of the Grid file in the Output Grid dialog box (it is recommended that a meaningful file name is used for later viewing) and click the path to store the Grid file, press OK again, and then press Yes (to select integer data) and Yes (to put the generated grid file into the current view). The generated files can be edited according to the Grid file standard. This completes the process of displaying an ASCII file into a Grid file. [4] In the batch processing, the ASCIGRID command of ARCINFO can be used to write AML files, and then use the Run command to complete the process in the Grid module: Usage: ASCIIGRID <in_ascii_file> <out_grid> {INT | FLOAT}. The production of this data is supported by the following Natural Science Foundation Projects: Environmental and Ecological Science Data Center of West China (90502010), Land Data Assimilation System of West China (90202014) and Active and Passive Microwave Radiation Transmission Simulation and Radiation Scattering Characteristics of the Frozen Soil (41071226).
LI Xin
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