The data includes the runoff components of the main stream and four tributaries in the source area of the Yellow River. In 2014-2016, spring, summer and winter, based on the measurement of radon and tritium isotopic contents of river water samples from several permafrost regions in the source area of the Yellow River, and according to the mass conservation model and isotope balance model of river water flow, the runoff component analysis of river flow was carried out, and the proportion of groundwater supply and underground ice melt water in river runoff was preliminarily divided. The quality of the data calculated by the model is good, and the relative error is less than 20%. The data can provide help for the parameter calibration of future hydrological model and the simulation of hydrological runoff process.
The data set of hydrogeological elements in the typical frozen soil area of Qilian Mountain mainly includes groundwater type, water richness (single water inflow or single spring flow), main rivers and tributaries, spring water (falling springs, spring groups, large springs, Mineral spring distribution), borehole (pressure water borehole, submerged borehole, gravity flow borehole distribution), fault zone (compressive fracture, tensile fracture), angle unconformity boundary, parallel unconformity boundary, west branch of upper Heihe River The boundary of the watershed, the seasonal frozen soil area and the permafrost distinguish the boundary, the distribution of modern glaciers and swamps. This data set of hydrogeological elements can provide background information for the hydrological ecological process and hydrogeological environment in cold regions. This data comes from the vectorization of four 1: 200,000 hydrogeological maps (Qilian, Yenigou, Qilian, and Sunan) and reintegrates the groundwater types. With higher resolution, the data can provide background information for the research on the evolution of water and soil resources and environmental changes in the source area of the Pan-Third Pole River.
Chinese Cryospheric Information System is a comprehensive information system for the management and analysis of cryospheric data over China. The establishment of Chinese Cryospheric Information System is to meet the needs of earth system science, and provide parameters and verification data for the development of response and feedback models of permafrost, glacier and snow cover to global changes under GIS framework. On the other hand, the system collates and rescues valuable cryospheric data to provide a scientific, efficient and safe management and analysis tool. Chinese Cryospheric Information System selected three regions with different spatial scales as its main research areas to highlight the research focus. The research area along the Qinghai-Tibet highway is mainly about 700 kilometers long from Xidatan to Naqu, and 20 to 30 kilometers wide on both sides of the highway. The datasets of the Tibetan highway contains the following types of data: 1. Cryosphere data.Including: snow depth distribution. 2. Natural environment and resources.Include: Digital elevation topography (DEM) : elevation elevation, elevation zoning, slope and slope direction; Fundamental geology: Quatgeo 3. Boreholes: drilling data of 200 boreholes along the qinghai-tibet highway. Engineering geological profile (CAD) : lithologic distribution, water content, grain fraction data, etc 4. Model of glacier mass equilibrium distribution along qinghai-tibet highway: prediction of frozen soil grid data. The graphic data along the qinghai-tibet highway includes 13 map scales of 1:250,000.The grid size is 100×100m. For details, please refer to the documents (in Chinese): "Chinese Cryospheric Information System design. Doc", "Chinese Cryospheric Information System data dictionary. Doc", "Database of the Tibetan highway. Doc".
In April 2014 and may 2016, 21 Lakes (7 non thermal lakes and 14 thermal lakes) were collected in the source area of the Yellow River (along the Yellow River) respectively. The abundance of hydrogen and oxygen allogens was measured by Delta V advantage dual inlet / hdevice system in inno tech Alberta laboratory in Victoria, Canada. The isotope abundance was expressed in the form of δ (‰) (relative to the average seawater abundance in Vienna) ）Test error: δ 18O: 0.1 ‰, δ D: 1 ‰. The data also includes Lake area and lake basin area extracted from Landsat 2017 image data in Google Earth engine.
This data set includes the concentration and distribution data of main persistent organic pollutants in the environmental media of Sanjiangyuan area. The samples were collected in May 2018, covering Sanjiangyuan Nature Reserve and its surrounding areas. The sample was prepared by Soxhlet extraction purification concentration and other pretreatment steps, and then determined by gas chromatography ion trap mass spectrometry. The target compounds include organochlorine pesticides, polychlorinated biphenyls, polycyclic aromatic hydrocarbons, etc. During sample pretreatment, mirex and pcb-30 were added as recovery markers. The internal standards for sample testing are PCNB and PCB-209. After calculation, the recovery of samples is generally between 60% - 101%.
GONG Ping WANG Xiaoping
Active layer thickness in mountians shows strong spatial heterogeneity mainly due to the complex terrain. In this data set, the active layer thickness in the upper reaches of Heihe River Basin is systematically investigated by ground-penetrating radar (GPR) and other traditional methods. Compared with other direct measurement methods, the error is about 8 cm, indicating a high reliability. This data set can provide detailed field data for understanding the active layer thickness in this area and can provide evaluation datasets for the land surface model, especially for permafrost research.
The data includes continuous and discontinuous permafrost and seasonally frozen ground distributed in the Qilian Mountains. Based on the field investigation, borehole drillings along the highway as well as previous data collected from the documentations, the lower limits of permafrost and the formula of the lower limits of permafrost in the Qilian Mountains is obtained by regression analysis. The digital elevation model (DEM) data is the SRTM (Shuttle Radar Topography Mission) jointly measured by NASA and NIMA. After the data being transformed into GCS WGS 1984 coordinate system, it is resampled into 100 m spatial resolution. The altitude of 3000 m was used to define the area of the Qilian Mountains. With the aid of ArcGIS platform and the support of DEM data, the permafrost distribution map of the Qilian Mountains with a resolution of 100 m is simulated. The lower limits of permafrost obtained by the regression analysis passed the significance test. According to the 548 existing borehole data points, the verification accuracy of permafrost area is 90.11%. The data can be used to estimate the ground ice content and the amount of water released from permafrost degradation.
The data include continuous permafrost area, discontinuous permafrost area and seasonal permafrost area. Based on the field scientific investigation, road survey drilling points and the previous data of the lower boundary elevation of permafrost, the formula of the lower boundary elevation of permafrost is obtained by regression. The DEM data is the SRTM (Shuttle Radar Topography Mission) data jointly measured by NASA and NIMA. After the data is transformed into GCS · WGS · 1984 coordinate system, it is resampled into 100m spatial resolution. The altitude of the data is 3000m to define the Qilian mountain area. With the aid of ArcGIS platform and the support of DEM data, the permafrost distribution map of Qilian Mountain with a resolution of 100m is simulated. The lower bound model obtained by regression has passed the significance test. According to the 548 existing borehole data points, the verification accuracy of permafrost area is 90.11%. The data can be used to estimate the underground ice content and the amount of water released from permafrost degradation.
This data set uses SMMR (1979-1987), SSM / I (1987-2009) and ssmis (2009-2015) daily brightness temperature data, which is generated by double index (TB V, SG) freeze-thaw discrimination algorithm. The classification results include four types: frozen surface, melted surface, desert and water body. The data covers the source area of three rivers, with a spatial resolution of 25.067525 km. It is stored in geotif format in the form of ease grid projection. Pixel values represent the state of freezing and thawing: 1 for freezing, 2 for thawing, 3 for deserts, 4 for water bodies. Because all TIF files in the dataset describe the scope of Sanjiangyuan National Park, the row and column number information of these files is unchanged, and the excerpt is as follows (where the unit of cellsize is m): ncols 52 nrows 28 cellsize 25067.525 nodata_value 0
The spatial-temporal distribution map of topographic shadows in the upper reaches of Heihe River (2018), which is calculated based on the SRTM DEM and the solar position (http://www.esrl.noaa.gov/gmd/grad/solcalc/azel.html). The spatial resolution is 100 m and the time resolution is 15 min. The datased can be used in the fields of ecological hydrology and remote sensing research. Using the observed solar radiation at several automatic weather stations in the upper reaches of Heihe River, the accuracy of the calculation results is verified. Results show that the dataset can accurately capture the temporal and spatial changes of the topographic shadow at the stations, and the time error is within 20 minutes.
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.
Data scarcity is a major obstacle for high-resolution mapping of permafrost on the Tibetan Plateau (TP). This study produces a new permafrost stability distribution map for the 2010s (2005-2015) derived from the predicted mean annual ground temperature (MAGT) at a depth of zero annual amplitude (10 - 25 m) 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 on the 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. This map shows that the total area of permafrost on the TP, excluding glaciers and lakes, 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 map is of fundamental importance for engineering planning and design, ecosystem management, and evaluation of the permafrost change in the future on the TP as a baseline.
RAN Youhua LI Xin
The near-surface freeze-thaw affects the water and energy exchanges mode and efficiency between the land and atmosphere. The transition of the freeze/thaw state affects the pattern of runoff concentration, which has an important impact on regional and global water cycle. Based on the remote sensing data of AMSR-E/2 passive microwave sensors and MODIS optical sensor, this data set uses the discriminant function algorithm and its downscaling method to produce a global mapping of near-surface freeze-thaw states with higher spatial resolution. This product covers the time period from 2002 to 2018 (daily), and spatial coverage is global scale (spatial resolution of 0.05°). It can be used to analyze the start/end time of global near-surface freeze/thaw states, the duration of freezing/thawing and their changing trends, and provide data support for studying the mechanism of water cycle and energy exchanges in the context of global change.
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).
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)%.
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.
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.
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.
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.
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.