Based on the vulnerability assessment framework of "exposure sensitivity adaptability", the vulnerability assessment index system of agricultural and pastoral areas in Qinghai Tibet Plateau was constructed. The index system data includes meteorological data, soil data, vegetation data, terrain data and socio-economic data, with a total of 12 data indicators, mainly from the national Qinghai Tibet Plateau scientific data center and the resource and environmental science data center of the Chinese Academy of Sciences. Based on the questionnaire survey of six experts in related fields, the weight of the indicators is determined by using the analytic hierarchy process (AHP). Finally, four 1km grid data are formed involving ecological exposure, sensitivity, adaptability and ecological vulnerability in the agricultural and pastoral areas of the Qinghai Tibet Plateau. The data can provide a reference for the identification of ecological vulnerable areas in the Qinghai Tibet Plateau.
This study takes the land resources in the Qinghai-Tibet Plateau as the evaluation object, and clarifies the current situation in the region suitable for agriculture, forestry, animal husbandry production and the quantity, quality and distribution of the reserve land resources. Through field investigations, collect relevant data from the study area, and combine relevant literature and expert experience to determine the evaluation factors (altitude, slope, annual precipitation, accumulated temperature, sunshine hours, soil effective depth, texture, erosion, vegetation type, NDVI). The grading and standardization are carried out, and the weights of each evaluation factor are determined by principal component analysis. The weighted index and model are used to determine the total score of the evaluation unit. Finally, the ArcGis natural discontinuity classification method is used to obtain the Qingshang Plateau. And the grades of farmland, forestry and grassland suitability drawings of the Qinghai-Tibet Plateau with a resolution of 90m were given. Finally, the results are verified and analyzed.
The data set contains agricultural economic data of all counties and regions in the Tibetan Plateau in 1980-2015, and covering the total number of households and total population in rural areas, agricultural population, rural labor force, cultivated land, paddy field area, the dry land area, power of agricultural machinery, agricultural vehicles, mechanical ploughing area, irrigation area, consumption of chemical fertilizers electricity use, gross output value of agriculture, forestry, animal husbandry and fishery, the output of cattle, pig, sheep, meat, poultry, and fish, the sown area of grain, the output of grain, cotton, oil and all kinds of crops, and characteristic agricultural products and livestock production and other relevant data.The data came from the statistical yearbook of the provinces included in the Tibetan Plateau.The data are of good quality and can be used to analyze the socio-economic and agricultural development of qinghai-tibet plateau.
The ASTER Global Digital Elevation Model (ASTER GDEM) is a global digital elevation data product jointly released by the National Aeronautics and Space Administration of America (NASA) and the Ministry of Economy, Trade and Industry of Japan (METI). The DEM data were based on the observation results of NASA’s new generation of Earth observation satellite, TERRA, and generated from 1.3 million stereo image pairs collected by ASTER (Advanced Space borne Thermal Emission and Reflection Radio meter) sensors, covering more than 99% of the land surface of the Earth. These data were downloaded from the ASTER GDEM data distribution website. For the convenience of using the data, based on framing the ASTER GDEM data, we used Erdas software to splice and prepare the ASTER GDEM mosaic of the Tibetan Plateau. This data set contains three data files: ASTER_GDEM_TILES ASTERGDEM_MOSAIC_DEM ASTERGDEM_MOSAIC_NUM The ASTER GDEM data of the Tibetan Plateau have an accuracy of 30 meters, the raw data are in tif format, and the mosaic data are stored in the img format. The raw data of this data set were downloaded from the ASTERGDEM website and completely retained the original appearance of the data. ASTER GDEM was divided into several 1×1 degree data blocks during distribution. The distribution format was the zip compression format, and each compressed package included two files. The file naming format is as follows: ASTGTM_NxxEyyy_dem.tif ASTGTM_NxxEyyy_num.tif xx is the starting latitude, and yyy is the starting longitude. _dem.tif is the dem data file, and _num.tif is the data quality file. ASTER GDEM TILES: The original, unprocessed raw data are retained. ASTERGDEM_MOSAIC_DEM: Inlay the dem.tif data using Erdas software, and parameter settings use default values. ASRERGDEM_MOSAIC_NUM: Inlay the num.tif data using Erdas software, and parameter settings use default values. The original raw data are retained, and the accuracy is consistent with that of the ASTERGDEM data distribution website. The horizontal accuracy of the data is 30 meters, and the elevation accuracy is 20 meters. The mosaic data are made by Erdas, and the parameter settings use the default values.
The Qinghai Tibet Plateau is a sensitive region of global climate change. Land surface temperature (LST), as the main parameter of land surface energy balance, characterizes the degree of energy and water exchange between land and atmosphere, and is widely used in the research of meteorology, climate, hydrology, ecology and other fields. In order to study the land atmosphere interaction over the Qinghai Tibet Plateau, it is urgent to develop an all-weather land surface temperature data set with long time series and high spatial-temporal resolution. However, due to the frequent cloud coverage in this region, the use of existing satellite thermal infrared remote sensing land surface temperature data sets is greatly limited. Compared with the daily 1 km spatial resolution all-weather land surface temperature data set (2003-2018) V1 in Western China released in 2019, this data set (V2) adopts a new generation method, namely satellite thermal infrared remote sensing reanalysis data integration method (RTM) based on the new land surface temperature time decomposition model. The main input data of the method are Aqua MODIS LST products and GLDAS data, and the auxiliary data include vegetation index and surface albedo provided by satellite remote sensing. This method makes full use of the high frequency and low frequency components of land surface temperature and the spatial correlation of land surface temperature provided by satellite thermal infrared remote sensing and reanalysis data. When MODIS land surface temperature was used as reference value, the MBE of trims land surface temperature in daytime and at night were -0.28 K and -0.29 K, respectively, and the corresponding STD were 1.25 K and 1.36 K, respectively, indicating the high consistency between them. Three stations (dhsar, dhz05) and Heihe River (d166) were used for verification. For AR, DSL and HZZ stations, the systematic deviation between trims and measured land surface temperature is small, and MBE is between - 0.45 K and 0.39 K. Among the three stations, the accuracy of trims is the highest at DSL station with RMSE of 2.12 K, and the lowest at HZZ station. The RMSE is 3.31 K. There is no significant difference between clear sky and non clear sky in 6 stations. Under clear sky conditions, trims LST and measured LST at day / night are highly consistent at six stations, with R2 of 0.93 ~ 0.97 / 0.93 ~ 0.98; MBE of -0.42 ~ 0.25 K / - 0.35 ~ 0.19 K; RMSE of 1.03 ~ 2.28 K / 1.05 ~ 2.05 K. Under the condition of non clear sky, the MBE of daytime / nighttime trims is -0.55 ~ 1.42 K / - 0.46 ~ 1.27 K, and the RMSE is 2.24 ~ 3.87 K / 2.03 ~ 3.62 K. Compared with the V1 version of the data, the two kinds of all-weather land surface temperature show the characteristics of seamless (i.e. no missing value) in the spatial dimension, and in most areas, the spatial distribution and amplitude of the two kinds of all-weather land surface temperature are highly consistent with MODIS land surface temperature. However, in the region where the brightness temperature of AMSR-E orbital gap is missing, the V1 version of land surface temperature has a significant systematic underestimation. The mass of trims land surface temperature is close to that of V1 version outside AMSR-E orbital gap, while the mass of trims is more reliable inside the orbital gap. Therefore, it is recommended that users use V2 version.
This data set records the statistical data of the administrative divisions and the names of States, prefectures, cities, counties and districts in Qinghai Province from 1998 to 2000. The data are divided by industry, region, affiliation and registration type. The data were collected from the annual statistical inspection of Qinghai Province issued by Qinghai Provincial Bureau of statistics. The data set consists of four tables Administrative divisions and names of States, prefectures, cities, counties and districts.xlsx Administrative divisions and names of States, prefectures, cities, counties and districts, 1998.xls Administrative divisions and names of States, prefectures, cities, counties and districts, 1999.xls Administrative divisions and names of States, prefectures, cities, counties and districts, 2000.xls The data table structure is the same. For example, there are nine fields in the 1998 data table of administrative divisions and names of States, prefectures, cities and counties Field 1: Region Field 2: number of county administrative units Field 3: name of county administrative unit Field 4: sub district office Field 5: Town Field 6: Rural Township Government Field 7: Village Committee Field 8: Residents Committee Field 9: family Committee
The data set records the basic situation of counties (cities) in Qinghai Province from 2013 to 2014. The data is divided by year, and the statistical area covers 46 counties and cities, including Xining, Haidong, Hainan, Huangnan, Yushu, Guoluo, Haixi, Haibei, etc. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set contains two data tables, namely: basic situation of county (city) (2013). XLS, basic situation of county (city) (2014). XLS. The data table structure is the same. For example, the data table in 2013 has five fields: Field 1: Region Field 2: administrative area Field 3: number of villages Field 4: number of towns Field 5: number of sub district offices
The data set records the density of railway and highway transportation lines in Qinghai Province from 1952 to 2004, and the data is divided by year. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set contains seven data tables: Railway and highway transportation line density 1952-1998.xls, railway and highway transportation line density 1952-1999.xls, railway and highway transportation line density 1952-2000.xls, railway and highway transportation line density 1952-2001.xls, railway and highway transportation line density 1952-2002.xls, railway and highway transportation line density 1952-2003.xls The density of railway and highway transportation lines in 1952-2004.xls. The data table structure is the same. For example, there are three fields in the data table from 1952 to 1998 Field 1: year Field 2: Railway Field 3: Highway
The data set records the statistical data of highway bridges in Qinghai Province in main years. The data are divided by row year, and grouped by aperture and service life. The data are collected from the statistical yearbook of Qinghai Province issued by the Bureau of statistics of Qinghai Province. The data set contains a data table, which is: Main Years: Highway Bridge 1990-2004.xls. In 2004, the super large bridge was re classified according to the national standard. There are six fields in the data table from 1990 to 2004 Field 1: Grand Bridge Field 2: Bridge Field 3: Medium Bridge Field 4: small bridge Field 5: tunnel Field 6: permanent Field 7: Highway Tunnel
1) Data content (including elements and significance) This data set contains information of flow direction, accumulation of vector river network of Lancang Mekong River Basin. 2) Data sources and processing methods In this data set, the remote sensing stream buring (RSSB) method (Wang et al., 2021) is adopted, and the high-precision elevation model MERIT-DEM and Sentinel-2 optical imagery are fused. 3) Data quality description Validations show that this data set has high spatial accuracy (Wang et al, 2021). 4) Data application achievements and Prospects This data set provides basic information of river networks, which can be used for hydrological model, land surface model, earth system model, as well as for mapping and spatial statistical analysis.