The soil moisture data set of China based on microwave data assimilation contains three layers of soil moisture data (0-5 cm, 5-20 cm, and 20-100 cm) from 2002 to 2011. The data adopted the automatically calibrating parameters land data assimilation system (ITPLDAS) developed by Yang et al. (2007), the land surface process model SiB2 was then driven by the high spatiotemporal resolution ground meteorological element data set (ITP-forcing data set) in China, the brightness temperature observed by the AMSR-E satellite was assimilated, and, finally, three layers of soil moisture data were obtained. Soil moisture content accuracy: ± 5% VWC (evaluation accuracy of Naqu and Maqu on the Tibetan Plateau). Data file name: Soil-Moisture_from_ITPLDAS_daily_0.25deg_v2.1.nc Description of data content variables: The file mainly consists of five variables: lon, lat, lev, time and www; www (time, lev, lat, and lon) is the soil moisture content (missing value: -999.0), where lon, lat, lev, and time are the four dimensions of longitude, latitude, depth and time, respectively; Variable unit description: Soil water volume content (www): m3/m3 p.s: The ncdump –h command can be used to directly view the head file information.
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).
The China Meteorological Forcing Dataset (CMFD) is a high spatial-temporal resolution gridded near-surface meteorological dataset that was developed specifically for studies of land surface processes in China. The dataset was made through fusion of remote sensing products, reanalysis dataset and in-situ observation data at weather stations. Its record starts from January 1979 and keeps extending (currently up to December 2018) with a temporal resolution of three hours and a spatial resolution of 0.1°. Seven near-surface meteorological elements are provided in CMFD, including 2-meter air temperature, surface pressure, specific humidity, 10-meter wind speed, downward shortwave radiation, downward longwave radiation and precipitation rate.
This data set is an upgraded version of the “Long-term Sequence Data Set of China Snow Depth". The source data of the dataset differ from those of the previous version. Because AMSR-E stopped running in 2011, snow depth from 2008 to 2018 is extracted using the brightness temperature of the SSMI/S sensor. This dataset provides daily data of snow depth distribution in China from January 1, 1979, to December 31, 2018, with a spatial resolution of 0.25 degrees. The original data used to invert the snow depth dataset are the daily passive microwave brightness temperature data (EASE-Grid) from SMMR (1979-1987), SSM/I (1987-2007) and SSMI/S (2008-2018) processed by the National Snow and Ice Data Center (NSIDC). Because the three sensors are mounted on different platforms, there is a certain system inconsistency in the obtained data. The time consistency of the brightness temperature data is improved by cross-calibrating the brightness temperatures of different sensors. The snow depth inversion is then performed using the algorithm specifically modified for China by Dr. Tao Che based on the Chang algorithm. For the specific inversion method, please refer to the data specification, “Long-term Sequence Data Set of China Snow Depth (1979-2018) Introduction. doc". The data set is a latitude and longitude projection, with one file each day, the naming convention of which is year + day; for example, 1990001 represents the first day of 1990, and 1990207 represents the 207th day of 1990. For a detailed data description, please refer to the data file.
Vegetation functional type (PFT) is a combination of large plant species according to the ecosystem function and resource utilization mode of plant species. Each planting functional type shares similar plant attributes, which simplifies the diversity of plant species into the diversity of plant function and structure.The concept of vegetation-functional has been advocated by ecologists especially ecosystem modelers.The basic assumption is that globally important ecosystem dynamics can be expressed and simulated through limited vegetative functional types.At present, vegetation-functional model has been widely used in biogeographic model, biogeochemical model, land surface process model and global dynamic vegetation model. For example, the land surface process model of the national center for atmospheric research (NCAR) in the United States has changed the original land cover information into the applied vegetation-functional map (Bonan et al., 2002).Functional vegetation has been used in the dynamic global vegetation model (DGVM) to predict the changes of ecosystem structure and function under the global change scenario. 1. Functional classification system of vegetation 1 Needleleaf evergreen tree, temperate 2 Needleleaf evergreen tree, boreal 3 Needleleaf deciduous tree 4 Broadleaf evergreen tree, tropical 5 Broadleaf evergreen tree, temperate 6 Broadleaf deciduous tree, tropical 7 Broadleaf deciduous tree, temperate 8 Broadleaf deciduous tree, boreal 9 Broadleaf evergreen shrub, temperate 10 Broadleaf deciduous shrub, temperate 11 Broadleaf deciduous shrub, boreal 12 C3 grass, arctic 13 C3 grass 14 C4 grass 15 Crop 16 Permanent wetlands 17 Urban and built-up lands 18 Snow and ice 19 Barren or sparsely vegetated lands 20 Bodies of water 2. Drawing method China's 1km vegetation function map is based on the climate rules of land cover and vegetation function conversion proposed by Bonan et al. (Bonan et al., 2002).Ran et al., 2012).MICLCover land cover map is a blend of 1:100000 data of land use in China in 2000, the Chinese atlas (1:10 00000) the type of vegetation, China 1:100000 glacier map, China 1:10 00000 marshes and MODIS land cover 2001 products (MOD12Q1) released the latest land cover data, using IGBP land cover classification system.The evaluation shows that it may be the most accurate land cover map on the scale of 1km in China.Climate data is China's atmospheric driven data with spatial resolution of 0.1 and temporal resolution of 3 hours from 1981 to 2008 developed by he jie et al. (2010).The data incorporates Princeton land-surface model driven data (Sheffield et al., 2006), gewex-srb radiation data (Pinker et al., 2003), TRMM 3B42 and APHRODITE precipitation data, and observations from 740 meteorological stations and stations under the China meteorological administration.According to the evaluation results of RanYouhua et al. (2010), GLC2000 has a relatively high accuracy in the current global land cover data set, and there is no mixed forest in its classification system. Therefore, the mixed forest in the MICLCover land cover diagram USES GLC2000 (Bartholome and Belward, 2005).The information in xu wenting et al., 2005) was replaced.The data can be used in land surface process model and other related researches.
DEM is the English abbreviation of Digital Elevation Model, which is the important original data of watershed topography and feature recognition.DEM is based on the principle that the watershed is divided into cells of m rows and n columns, the average elevation of each quadrilateral is calculated, and then the elevation is stored in a two-dimensional matrix.Since DEM data can reflect local topographic features with a certain resolution, a large amount of surface morphology information can be extracted through DEM, which includes slope, slope direction and relationship between cells of watershed grid cells, etc..At the same time, the surface flow path, river network and watershed boundary can be determined according to certain algorithm.Therefore, to extract watershed features from DEM, a good watershed structure pattern is the premise and key of the design algorithm. Elevation data map 1km data formed according to 1:250,000 contour lines and elevation points in China, including DEM, hillshade, Slope and Aspect maps. Data set projection: Two projection methods: Equal Area projection Albers Conical Equal Area (105, 25, 47) Geodetic coordinates WGS84 coordinate system
This data set includes the microwave brightness temperatures obtained by the spaceborne microwave radiometer SSM/I carried by the US Defense Meteorological Satellite Program (DMSP) satellite. It contains the twice daily (ascending and descending) brightness temperatures of seven channels, which are 19H, 19V, 22V, 37H, 37V, 85H, and 85V. The Specialized Microwave Imager (SSM/I) was developed by the Hughes Corporation of the United States. In 1987, it was first carried into the space on the Block 5D-/F8 satellite of the US Defense Meteorological Satellite Program (DMSP) to perform a detection mission. In the 10 years from when the DMSP soared to orbit in 1987 to when the TRMM soared to orbit in 1997, the SSM/I was the world's most advanced spaceborne passive microwave remote sensing detection instrument, having the highest spatial resolution in the world. The DMSP satellite is in a near-polar circular solar synchronous orbit; the elevation is approximately 833 km, the inclination is 98.8 degrees, and the orbital period is 102.2 minutes. It passes through the equator at approximately 6:00 local time and covers the whole world once every 24 hours. The SSM/I consists of seven channels set at four frequencies, and the center frequencies are 19.35, 22.24, 37.05, and 85.50 GHz. The instrument actually comprises seven independent, total-power, balanced-mixing, superheterodyne passive microwave radiometer systems, and it can simultaneously measure microwave radiation from Earth and the atmospheric systems. Except for the 22.24 GHz frequency, all the frequencies have both horizontal and vertical polarization states. Some Eigenvalues of SSM/I Channel Frequency (GHz) Polarization Mode (V/H) Spatial Resolution (km * km) Footprint Size (km) 19V 19.35 V 25×25 56 19H 19.35 H 25×25 56 22V 22.24 V 25×25 45 37V 37.05 V 25×25 33 37H 37.05 H 25×25 33 85V 85.50 V 12.5×12.5 14 85H 85.50 H 12.5×12.5 14 1. File Format and Naming: Each group of data consists of remote sensing data files, .JPG image files and .met auxiliary information files as well as .TIM time information files and the corresponding .met time information auxiliary files. The data file names and naming rules for each group in the SSMI_Grid_China directory are as follows: China-EASE-Fnn-ML/HaaaabbbA/D.ccH/V (remote sensing data); China-EASE-Fnn -ML/HaaaabbbA/D.ccH/V.jpg (image file); China-EASE-Fnn-ML/HaaaabbbA/D.ccH/V.met (auxiliary information document); China-EASE-Fnn-ML/HaaaabbbA/D.TIM (time information file); and China-EASE- Fnn -ML/HaaaabbbA/D.TIM.met (time information auxiliary file). Among them, EASE stands for EASE-Grid projection mode; Fnn represents carrier satellite number (F08, F11, and F13); ML/H represents multichannel low resolution and multichannel high resolution; A/D stands for ascending (A) and descending (D); aaaa represents the year; bbb represents the Julian day of the year; cc represents the channel number (19H, 19V, 22V, 37H, 37V, 85H, and 85V); and H/V represents horizontal polarization (H) and vertical polarization (V). 2. Coordinate System and Projection: The projection method is an equal-area secant cylindrical projection, and the double standard latitude is 30 degrees north and south. For more information on EASE-GRID, please refer to http://www.ncgia.ucsb.edu/globalgrids-book/ease_grid/. If you need to convert the EASE-Grid projection method into a geographic projection method, please refer to the ease2geo.prj file, which reads as follows. Input Projection cylindrical Units meters Parameters 6371228 6371228 1 /* Enter projection type (1, 2, or 3) 0 00 00 /* Longitude of central meridian 30 00 00 /* Latitude of standard parallel Output Projection GEOGRAPHIC Spheroid KRASovsky Units dd Parameters End 3. Data Format: Stored as binary integers, each datum occupies 2 bytes. The data that are actually stored in this data set are the brightness temperatures *10, and after reading the data, they need to be divided by 10 to obtain true brightness temperature. 4. Data Resolution: Spatial resolution: 25 km, 12.5 km (SSM/I 85 GHz); Time resolution: day by day, from 1978 to 2007. 5. The Spatial Coverage: Longitude: 60°-140° east longitude; Latitude: 15°-55° north latitude. 6. Data Reading: Each group of data includes remote sensing image data files, .JPG image files and .met auxiliary information files. The JPG files can be opened with Windows image and fax viewers. The .met auxiliary information files can be opened with notepad, and the remote sensing image data files can be opened in ENVI and ERDAS software.
This data set contains a total of 717 files, among which the station.txt file mainly describes the site information of 716 stations. The columns correspond to longitude, latitude and elevation. The other 716 files denoted by station number correspond to the data of the 716 stations. The columns in the files are year, month, day and daily average photosynthetically active radiation. The data are the estimations on the basis of the conventional meteorological elements observed by the China Meteorological Administration: temperature, humidity, pressure and sunshine hours. (1) Algorithm and model introduction: This model develops a parameterization scheme for atmospheric broadband transmittance of the photosynthetically active radiation (PAR) band. It takes into account four decay processes in clear weather, which are aerosol scattering and absorption, water vapor absorption, ozone absorption, and Rayleigh scattering. On this basis, the estimation scheme of surface PAR under clear sky conditions is established. At the same time, sunshine hours are used as indicators to measure the influence of clouds on radiation, and its influence on surface PAR is parameterized to estimate the surface PAR under full sky conditions. It is verified that the root mean square error of the estimated results of the data set is less than 14 W/m² .
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.