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
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