The data set contains the observation data of the 10m tower automatic meteorological station on December 31, 2015 on January 1, 2015 at solstice.The station is located in east garden town, huailai county, hebei province.The latitude and longitude of the observation point is 115.7880E, 40.3491N, and the altitude is 480m. The automatic weather station is installed on a 10m tower, the acquisition frequency is 30s, and the output time is 10min.The observation factors include air temperature and relative humidity (5m), and the direction is due north.The wind speed (10m), the wind direction (10m), the direction is due to the north;Air pressure (installed in waterproof box);Rainfall (10m);The four-component radiation (5m), the direction is due to the south;The infrared surface temperature (5m), the arm is facing south, and the probe is facing vertically downward.The soil temperature and humidity probe was buried at 1.5m to the south of the meteorological tower. The buried depth of the soil temperature probe was 0cm, 2cm, 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm. The buried depth of the soil moisture sensor was 2cm, 4cm, 10cm, 10cm, 10cm, 10cm, 20cm, 80cm, 120cm and 160cm.The average soil temperature was buried 2,4 cm underground.Soil hot flow plates (3) are buried in the ground 6cm. Processing and quality control of observation data :(1) ensure 144 data per day (every 10min). If data is missing, it will be marked by -6999;(2) eliminate the moments with duplicate records;(3) data that is obviously beyond the physical meaning or the range of the instrument is deleted;(4) the format of date and time is unified, and the date and time are in the same column.For example, the time is: 10:30 on June 10, 2015.Data missing due to damage of charging controller from May 30 to June 5 and October 1 to October 9.Soil heat flux G1 due to the heat flux plate problem, the data of April 19 solstice on May 20 was missing. Data released by the automatic weather station include:Date/Time, air temperature and humidity observation (Ta_5m, RH_5m) (℃, %), wind speed (Ws_10m) (m/s), wind direction (WD) (°), pressure (hpa), precipitation (Rain) (mm), four-component radiation (DR, UR, DLR, ULR, Rn) (W/m2), surface radiation temperature (IRT_1, IRT_2) (℃),Soil heat flux (Gs_1, Gs_2, Gs_3) (W/m2), multi-layer soil moisture (Ms_2cm, Ms_4cm, Ms_10cm, Ms_20cm, Ms_40cm, Ms_80cm, Ms_120cm, Ms_160cm) (%), multi-layer soil temperature (Ts_2cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_40cm, Ts_80cm, Ts_120cm, Ts_160cm) (℃), average soil temperature TCAV (℃). Please refer to Guo et al. (2020) for information of observation test or site, and Liu et al. (2013) for data processing.
The data set contains the observation data of the vorticity correlator of 10m tower on December 31, 2015 from January 1, 2015 to solstice.Station is located in huailai county, hebei province, east garden town, under the surface of irrigated corn.The latitude and longitude of the observation point is 115.7880E, 40.3491N, and the altitude is 480m.The acquisition frequency of vortex correlativity instrument is 10Hz, the frame height is 5m, the ultrasonic direction is due to the north, and the distance between the ultrasonic anometer (CSAT3) and the CO2/H2O analyzer (Li7500A) is 15cm. The released data is the 30-minute data obtained from the post-processing of the original collected 10Hz data with Eddypro software. The main steps of the processing include: outfield value elimination, delay time correction, coordinate rotation (secondary coordinate rotation), frequency response correction, ultrasonic virtual temperature correction and density (WPL) correction.Quality assessment for each intercompared to at the same time, mainly is the atmospheric stability (Δ st) and turbulent characteristics of similarity (ITC) test.The 30min pass value output after processing was also screened :(1) the data when the instrument was wrong was removed;(2) data of 1h before and after precipitation were excluded;(3) the missing rate of 10Hz original data is more than 10% every 30min;(4) the observed data of weak turbulence at night were excluded (u* less than 0.1m/s).The average period of observation data was 30 minutes, 48 data a day, and the missing data was marked as -6999.May 14 solstice May 20 and May 24 solstice June 6 due to power converter damage, data missing. The observation data released by vortex correlator include:Date/Time for the Date/Time, wind Wdir (°), Wnd horizontal wind speed (m/s), standard deviation Std_Uy lateral wind speed (m/s), ultrasonic virtual temperature Tv (K), the water vapor density H2O (g/m3), carbon dioxide concentration CO2 (mg/m3), friction velocity Ustar) (m/s), the length of cloth hoff, sensible heat flux Hs (W/m2), latent heat flux LE (W/m2), carbon dioxide flux Fc (mg/(m2s)), the quality of the sensible heat flux identifier QA_Hs, the quality of the latent heat flux identifier QA_LE.The quality of the sensible heat and latent heat, carbon dioxide flux identification is divided into three (quality id 0: (Δ st < 30, the ITC < 30);1: (Δ st < 100, ITC < 100);The rest are 2).The meaning of data time, such as 0:30 represents the average between 0:00 and 0:30;The data is stored in *.xls format. Please refer to Guo et al, 2020 for information of observation test or site, and Liu et al. (2013) for data processing.
The data set contains the observation data of large aperture scintillator from January 1, 2015 to December 31, 2015. Two large aperture scintillation meters, bls450 and zzlas, are installed respectively. The site is located in donghuayuan Town, Huailai County, Hebei Province. The longitude and latitude of the observation point are 115.7880e, 40.3491n and 480m above sea level. The effective height of the large aperture scintillator is 14m, the optical path length is 1870m, the longitude and latitude of the transmitter are 115.8023e, 40.3596n, and the longitude and latitude of the receiver are 115.7825e and 40.3522n. The acquisition frequency of bls450 and zzlas is 5Hz and 1Hz respectively, with an average output of 1min. The original data of large aperture scintillator is 1 min, and the released data is 30 min average data after processing and quality control. The sensible heat flux is mainly obtained by iterative calculation based on Monin obkhov similarity theory and combined with automatic weather station data. In the process of iterative calculation, for bls450, the stability function of thiermann and Grassl, 1992 is selected; for zzlas, the stability function of Andreas, 1988 is selected. The main quality control steps include: (1) eliminating the data of cn2 saturation; (2) eliminating the data with weak demodulation signal intensity; (3) eliminating the data of precipitation time and one hour before and after; (4) eliminating the data of weak turbulence under stable conditions (U * less than 0.1m/s). Several explanations about the published data are as follows: (1) the Las data is mainly bls450, and the missing time is supplemented by zzlas observation, and the missing time is marked with - 6999. (2) Data header: date / time: date / time, cn2: structure parameter of air refraction index (m-2 / 3), H_ Las: sensible heat flux (w / m2). The meaning of data time, for example, 0:30 represents the average of 0:00-0:30; the data is stored in *. XLS format. Guo et al, 2020 is used for site introduction and Liu et al, 2013 for data processing
The Qinghai Tibet Plateau belongs to the plateau mountain climate. The precipitation, its seasonal distribution and the change of precipitation forms have been one of the hot spots in the global climate change research. The data includes precipitation data of Qinghai Tibet Plateau, with spatial resolution of 1km * 1km, temporal resolution of month and year, and time coverage of 2000, 2005, 2010 and 2015. The data are obtained by Kring interpolation of meteorological data of National Meteorological Science Information Center. The data can be used to analyze the temporal and spatial distribution of precipitation over the Qinghai Tibet Plateau. In addition, the data can also be used to analyze the temporal and spatial variation of precipitation over the Qinghai Tibet Plateau, which is of great significance to the study of the ecological environment of the Qinghai Tibet Plateau.
This dataset is the boundary vector data of the prefecture-level administrative units in the Qinghai-Tibet Plateau in 2015. The data is in the Shapefile format and includes provincial-level administrative units such as the Tibet Autonomous Region, Qinghai Province, Gansu Province, Yunnan Province, and Xinjiang Uygur Autonomous Region in the Qinghai-Tibet Plateau. The 38 prefecture-level administrative units can be used for the geographical background research of the urbanization and ecological environment interaction stress of the Qinghai-Tibet Plateau. It is the basic geographic data for the statistics of urbanization indicators such as social, economic and population levels of the Qinghai-Tibet Plateau. The data is obtained by means of data capture and collected through the administrative interface data acquisition API interface provided by the high-tech map. The data set uses the geographic coordinate system of WGS84.
1:100000 vegetation map of Heihe River Basin, the regional scope is subject to the Heihe river boundary of Huangwei Committee, the area is about 14.29 × 104km2, the data format is GIS vector format, this version is version 3.0. The data is mainly based on ground observation data, integrated with all kinds of remote sensing data, 1:1 million vegetation map, climate, terrain, landform, soil data mapping, and compiled by cross validation. The classification standard, legend unit and system of vegetation map of the people's Republic of China (1:1000000), 2007 are adopted, including vegetation type group, vegetation type, formation and sub formation. The new version mainly unifies the codes of the new formation (74 codes in total, distinguishing the formation and the sub formation). 9 vegetation type groups, 22 vegetation types and 74 formations (sub formations) in version 2.0 were changed into 9 vegetation type groups, 22 vegetation types and 67 formations (7 sub formations). The data includes versions 2.0 and 3.0
The Qinghai Tibet Plateau belongs to the plateau mountain climate. The temperature and its seasonal variation have been one of the hot spots in the global climate change research. The data includes the temperature data of Qinghai Tibet Plateau, with spatial resolution of 1km * 1km, temporal resolution of month and year, and time coverage of 2000, 2005, 2010 and 2015. The data are obtained by Kring interpolation on the data of national weather station in Qinghai Tibet Plateau. The data can be used to analyze the temporal and spatial distribution of air temperature in the Qinghai Tibet Plateau. In addition, the data can also be used to analyze the law of temperature change with time in the Qinghai Tibet Plateau, which is of great significance to the study of the ecological environment of the Qinghai Tibet Plateau.
Photosynthetic effective radiation absorption coefficient photosynthetically active radiation component is an important biophysical parameter. It is an important land characteristic parameter of ecosystem function model, crop growth model, net primary productivity model, atmosphere model, biogeochemical model and ecological model, and is an ideal parameter for estimating vegetation biomass. The data set contains the data of photosynthetically active radiation absorption coefficient in Qinghai Tibet Plateau, with spatial resolution of 500m, temporal resolution of 8D, and time coverage of 2000, 2005, 2010 and 2015. The data source is MODIS Lai / FPAR product data mod15a2h (C6) on NASA website. The data are of great significance to the analysis of vegetation ecological environment in the Qinghai Tibet Plateau.
The Administrative boundary dataset is the base in the global change research, and it is important for the whole project.At present, DIVA-GIS is the basic source of administrative boundary. Whole national administrative boundary shapefiles were downloaded from DIVA-GIS. Based on the official administrative units (municipalities) as the basic units, the administrative units at the higher level (provincial level) where the municipalities are located are stored and reserved as the research objects.If the provincial unit area of the node has exceeded 10,000 square kilometers, the provincial unit will be retained as the research area. At the same time, if the provincial unit area of the node is small, then considering the political and economic impact of the provincial level and its surrounding areas, neighboring provincial units are also combined by merging and retaining to at least 10,000 square kilometers as the research object. Finally, the administrative region data of all 31 key node regions (Abbas, Alexander, Ankara, Astana, Bangkok, Chittagong, Colombo, Dhaka, Djibouti, Ekaterinburg, Gwadar, Hambantota, Karachi, Kolkata, Kuantan, Kyaukpyu, Maldives, Mandalay, Melaka, Minsk, Mumbai, Novosibirsk, Piraeus, Rayong, Sihanouk, Tashkent, Teheran, Valencia, Vientiane, Warsaw, Yangon) are produced. This data set serves as the research basis for all remote sensing data and provides baseline data for the project. This dataset can be updated in real time according to the official or government information of the node.
Based on 2015 ESA global land cover data (ESA GlobCover, 300 m grid), combined with the Tsinghua university global land cover data (FROM GLC, 30 m grid), NASA MODIS global land cover data (MCD12Q1, 300 m grid), the United States Geological Survey (USGS global land data (GFSAD30, 30 m), Japanese global forest data (PALSAR/PALSAR - 2, 25 m), we build the LUCC classification system in the Belt and Road's region and the rest of the data transformation rules of the classification system. We also build the land cover classification confidence function and the rules of fusing land classification to finish the integration and modification of land cover products and finally completed the land use data in the Belt and Road's region V1.0 (64 + 1 countries, 2015, 1 km x 1 km grid, the first level classification).