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
The Map of Permafrost on the Qinghai-Tibet Plateau (1:3,000,000) (Shude Li and Guodong Cheng, 1996) was made by the State Key Laboratory of Frozen Soil Engineering, LIGG, CAS (currently called the Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences). It was based on first-hand information from the study of frozen soil and previous research papers and literature. By detailed study and consultation of aerial photographs, satellite images, the Permafrost Map along the Qinghai-Tibet Highway (1:600,000) (Boliang Tong, et al., 1983), Geomorphological Map of the Qilian Mountains (1:1,000,000) (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 1985), Natural Landscape Map of Qinghai-Tibetan Plateau (1:3,000,000) (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 1990), Quaternary Glacial Distribution Map of the Qinghai-Tibetan Plateau (1:3,000,000) (Bingyuan Li and Jijun Li, 1991), Frozen Soil Remote Sensing Map of the Western Channel Project of the South-North Water Diversion in the Region of the Tongtian-Yalong Rivers (1:500,000) (Lanzhou Institute of Glaciology and Cryopedology, Chinese Academy of Sciences, 1995), and Map of Snow, Ice, Frozen Ground in China (1:4,000,000) (Yafeng Shi and Desheng Mi, 1988), with editing on 1,000,000 aerial survey topographic maps, and the 1:3,000,000 Map of Permafrost on the Qinghai-Tibetan Plateau was then generated. It was later digitized by Zhuotong Nan of the Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences. The data include: 1) Digitized distribution map of frozen soil on the Qinghai-Tibetan Plateau 2) Scanned map of frozen soil map on the Qinghai-Tibetan Plateau The types of frozen soil in the digitized frozen soil map include: 0. Seasonally frozen ground; seasonal frozen soil 1. Permafrost 2. Island permafrost; 3. Continuous permafrost;
CHENG Guodong, LI Shude, NAN Zhuotong, TONG Boliang
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
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
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
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
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
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
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 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
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
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
The dataset of ground truth measurements synchronizing with Envisat ASAR was obtained in No.2 quadrate of the A'rou foci experimental area on Oct. 17, 2007 during the pre-observation period. The Envisat ASAR data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 23:04 BJT. The quadrate was divided into 3×3 subsites, with each one spanning a 30×30 m2 plot. 25 sampling points were chosen, including centers and corners of each subsites. Simultaneous with the satellite overpass, numerous ground data were collected, soil volumetric moisture by ML2X; soil volumetric moisture, soil conductivity, soil temperature, and the real part of soil complex permittivity by WET soil moisture sensor; the surface radiative temperature by the hand-held infrared thermometer; soil gravimetric moisture, volumetric moisture, and soil bulk density after drying by the cutting ring (100cm^3). Meanwhile, vegetation parameters as height, coverage and water content were also observed. Surface roughness was detailed in the "WATER: Surface roughness dataset in the A'rou foci experimental area". Those provide reliable ground data for retrieval and validation of soil moisture and freeze/thaw status from active remote sensing approaches.
BAI Yunjie, HAO Xiaohua, LI Hongyi, LI Xin, LI Zhe
The dataset of ground truth measurements synchronizing with Envisat ASAR was obtained in No. 1 and 2 quadrates of the A'rou foci experimental area on Oct. 18, 2007 during the pre-observation period. The Envisat ASAR data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:17 BJT. Both the quadrates were divided into 3×3 subsites, with each one spanning a 30×30 m2 plot. 25 sampling points were chosen, including centers and corners of each subsites. Simultaneous with the satellite overpass, numerous ground data were collected, soil volumetric moisture, soil conductivity, the soil temperature, and the real part of soil complex permittivity by the WET soil moisture sensor; the surface radiative temperature by the hand-held infrared thermometer; soil gravimetric moisture, volumetric moisture, and soil bulk density after drying by the cutting ring (100cm^3). Meanwhile, vegetation parameters as height, coverage and water content were also observed. Surface roughness was detailed in the "WATER: Surface roughness dataset in the A'rou foci experimental area". Those provide reliable ground data for retrieval and validation of soil moisture and freeze/thaw status from active remote sensing approaches.
BAI Yunjie, HAO Xiaohua, LI Hongyi, LI Xin, LI Zhe
The project “The impact of the frozen soil environment on the construction of the Qinghai-Tibet Railway and the environmental effects of the construction” is part of the “Environmental and Ecological Science in West China” programme supported by the National Natural Science Foundation of China. The person in charge of the project is Wei Ma, a researcher at the Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences. The project ran from January 2002 to December 2004. Data collected in this project included the following: Monitoring data of the active layer in the Beiluhe River Basin (1) Description of the active layer in the Beiluhe River Basin (2) Subsurface moisture data from the Beiluhe River Basin, 2002.9.28-2003.8.10 (Excel file) * Site 1 - Grassland moisture data * Site 2 – Removed turf moisture data * Site 3 - Natural turf moisture data * Site 4 - Gravel moisture data * Site 5 - Insulation moisture data (3) Subsurface temperature data from the Beiluhe River Basin, 0207-0408 Excel file * Temperature data for the ballast surface * Temperature data for insulation materials * Temperature data for a surface without vegetation * Temperature data for a grassland surface * Temperature data for a grit and pebble surface Data on the impact of construction on the ecological environment were obtained at Fenghuoshan, Tuotuohe, and Wudaoliang. Sample survey included plant type, abundance, community coverage, total coverage, aboveground biomass ratio and soil structure. The moisture content at different depths of the soil was detected using a time domain reflectometer (TDR). A set of soil samples was collected at a depth of 0-100 cm at each sample site. An EKKO100 ground-penetrating radar detector was used to continuously sample 1-1.5 km long sections parallel to the road to determine the upper limit depth of the frozen soil. 3. Predicted data: The temperature of the frozen soil at different depths and times was predicted in response to temperature increases of 1 degree and 2 degrees over the next 50 years based on initial surface temperatures of -0.5, -1.5, -2.5, -3.5, and -4.5 degrees. 4. The frozen soil parameters of the Qinghai-Tibet Railway were as follows: location, railway mileage, total mileage (km), frozen soil type mileage, mileage of zones with an average temperature conducive to permafrost, frozen soil with high temperatures and high ice contents, frozen soils with high temperatures and low ice contents, frozen soils with low temperatures and high ice contents, frozen soils with low temperatures and low ice contents, and melting area.
MA Wei, WU Qingbai
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
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 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
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
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
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 scanned picture of the Map of Snow Ice and Frozen Ground in China (1:4,000,000) (Shi Yafeng, Meidesheng, 1988) is geometrically corrected and then digitized in the data set, and by taking altitude and latitude into account in combination with the continuity of permafrost, the frozen soil is divided into the predominant permafrost of high-latitude permafrost, island talik permafrost and island permafrost; high-altitude permafrost and mountain permafrost (including Altai, Tianshan Mountain, Qilian Mountain, Hengduan, the Himalayas and Taibai Mountain in East China, Huanggangliang and Changbai Mountain), and the plateau permafrost (the Tibetan Plateau), which is divided into predominant permafrost and island permafrost; and seasonal frozen soil, instantaneous frozen soil and nonfrozen areas.
SHI Yafeng, MI Desheng
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
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
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 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
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
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 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
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
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
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
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
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
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
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 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 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 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
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 High Asia region is an area sensitive to global changes in mid-latitude regions and is a hotspot for research. The lakes in the territory are scattered, and the lake freeze-thaw process is one of the key factors sensitive to global change. Due to the large difference in the dielectric constant between ice and water, satellite-borne passive microwave remote sensing is weather insensitive and has a high revisiting rate; thus, it can achieve rapid monitoring of the freeze-thaw state of lakes. According to the area ratio of the lake and the land surface in the sub-pixels of passive microwave radiometer data, this data set represents the lake brightness temperature information of the pixel (sub-pixel level) by applying the hybrid pixel decomposition method in order to monitor the lake freeze-thaw process in the High Asia region. Thus, by adopting a variety of passive microwave data, time series of lake brightness temperature and freeze-thaw status were obtained for a total of 51 medium to large lakes from 2002 to 2016 in the High Asia region. Using cloudless MODIS optical products as validation data, three lakes of different sizes in different regions of High Asia, i.e., Hoh Xil Lake, Dagze Co Lake, and Kusai Lake, were selected for freeze-thaw detection validation. The results indicated that the lake freeze-thaw parameters obtained by microwave and optical remote sensing were highly consistent, and the correlation coefficients reached 0.968 and 0.987. This data set contained the time series brightness temperature of lakes and the freeze-thaw parameters of lake ice, which could be used to further invert the characteristic parameters of lakes and enhance the understanding of lake ice freezing and thawing in the High Asia region. This database will be useful in the assessment of climatic and environmental changes in the High Asia region and in global climatic change response models. The data set consists of two parts: the passive microwave remote sensing brightness temperature data set of 51 lakes in the High Asia region from 2002 to 2016, with an observation interval of 1 to 2 days, and the lake ice freeze-thaw data set obtained by estimation of the lake brightness temperature. The files are the lake brightness temperature data via the nearest neighbour method and pixel decomposition in the form of a .zip file (12 MB) and the lake freeze-thaw data set for 51 lakes in the High Asia region from 2002 to 2016 in the form of an .xls file (0.1 MB).
QIU Yubao
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
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
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.
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
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
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
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
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