This dataset includes data recorded by the Hydrometeorological observation network obtained from the automatic weather station (AWS) at the observation system of Meteorological elements gradient of A’rou shady slope station between 8 August, 2013, and 31 December, 2013. The site (100.411° E, 37.984° N) was located on a cold grassland surface on the shady slope, which is near south of A’rou township, Qilian county, Qinghai Province. The elevation is 3536 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP45AC; 5 m, north), wind speed and direction profile (010C/020C; 10 m, north), air pressure (278; in the tamper box on the ground), rain gauge (TE525M; 10 m), four-component radiometer (CNR4; 6 m, south), two infrared temperature sensors (SI-111; 6 m, south, vertically downward), soil heat flux (HFP01; 3 duplicates, -0.06 m), soil temperature profile (109-L; 0, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (CS616; -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), and two photosynthetically active radiation (PQS-1; 6 m, south, one vertically downward and one vertically upward). The observations included the following: air temperature and humidity (Ta_5 m; RH_5 m) (℃ and %, respectively), wind speed (Ws_10 m) (m/s), wind direction (WD_10 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m2), infrared temperature (IRT_1 and IRT_2) (℃), soil heat flux (Gs_1, Gs_2 and Gs_3) (W/m2), soil temperature (Ts_0 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm) (℃), soil moisture (Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm) (%, volumetric water content), and photosynthetically active radiation of upward and downward (PAR_up and PAR_down) (μmol/ (s m-2)). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2013-9-10 10:30. (6) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for hydrometeorological observation network or sites information), Liu et al. (2011) (for data processing) in the Citation section.
0 2020-04-10
GIMMS (glaobal inventory modelling and mapping studies) NDVI data is the latest global vegetation index change data released by NASA C-J-Tucker and others in November 2003. The dataset includes the global vegetation index changes from 1981 to 2006, the format is ENVI standard format, the projection is ALBERS, and its time resolution is 15 days and its spatial resolution is 8km. GIMMS NDVI data recorded the changes of vegetation in 22a area in the format of satellite data. 1. File format: The GIMMS-NDVI dataset contains all rar compressed files with a 15-day interval from July 1981 to 2006. After decompression, it includes an XML file, an .HDR header file, an .IMG file, and a .JPG image file. 2. File naming: The naming rules for compressed files in the NOAA / AVHRR-NDVI data set are: YYMMM15a (b) .n **-VIg_data_envi.rar, where YY-year, MMM-abbreviated English month letters, 15a-synthesized in the first half of the month, 15b-synthesized in the second half of the month, **-Satellite. After decompression, there are 4 files with the same file name, and the attributes are: XML document, header file (suffix: .HDF), remote sensing image file (suffix: .IMG), and JPEG image file. In this data set, the user uses the remote sensing image file with the suffix .IMG to analyze the vegetation index. Remote sensing image files with suffix of .IMG and .HDF used by users to analyze vegetation indices can be opened in ENVI and ERDAS software. 3. The data header file information is as follows: Coordinate System is: PROJECTION ["Albers_Conic_Equal_Area"], PARAMETER ["standard_parallel_1", 25], PARAMETER ["standard_parallel_2", 47], PARAMETER ["latitude_of_center", 0], PARAMETER ["longitude_of_center", 105], PARAMETER ["false_easting", 0], PARAMETER ["false_northing", 0], UNIT ["Meter", 1]] Pixel Size = (8000.000000000000000, -8000.000000000000000) Corner Coordinates: Upper Left (-3922260.739, 6100362.950) (51d20'23.06 "E, 46d21'21.43" N) Lower Left (-3922260.739, 1540362.950) (71d16'1.22 "E, 8d41'42.21" N) Upper Right (3277739.261, 6100362.950) (151d 8'57.22 "E, 49d 9'35.37" N) Lower Right (3277739.261, 1540362.950) (133d30'58.46 "E, 10d37'13.35" N) Center (-322260.739, 3820362.950) (101d22'21.08 "E, 35d42'18.02" N) Band 1 Block = 900x1 Type = Int16, ColorInterp = Undefined Computed Min / Max = -16066.000,11231.000 4. Conversion relationship between DN value and NDVI NDVI = DN / 1000, divided by 10000 after 2003 The NDVI value should be between [-1,1]. Data outside this interval represent other features, such as water bodies.
0 2020-06-08
This data set contains the observation data of vorticity correlativity from January 1, 2016 to December 31, 2016 of the super station at the upper reaches of heihe hydrometeorological observation network.The station is located in caoban village, aru township, qilian county, qinghai province.The longitude and latitude of the observation point are 100.4643e, 38.0473n and 3033m above sea level.The rack height of the vortex correlativity meter is 3.5m, the sampling frequency is 10Hz, the ultrasonic orientation is due north, and the distance between the ultrasonic wind speed and temperature meter (CSAT3) and CO2/H2O analyzer (Li7500A) is 15cm. The original observation data of the vortex correlativity instrument is 10Hz, and the published data is the 30-minute data processed by Eddypro software. The main processing steps include: outliers, delay time correction, coordinate rotation (quadratic 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 by Eddypro software was also screened :(1) data when instrument error was eliminated;(2) data of 1h before and after precipitation are excluded;(3) remove the data with a missing rate of more than 10% in the original 10Hz data within every 30 minutes;(4) the observation data of weak turbulence at night (u* less than 0.1m/s) were excluded.The average observation period was 30 minutes, 48 data per day, and the missing data was marked as -6999.Suspicious data caused by instrument drift and other reasons are marked with red font, in which Li7500A calibration data of vorticity system from April 30 to May 1 is missing;When 10Hz data is missing due to a problem with the memory card storage data (11.9-11.24), the data will be replaced by 30min flux data output by the collector. The published observational data 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 (℃), the water vapor density H2O (g/m3), carbon dioxide concentration CO2 (mg/m3), friction velocity Ustar) (m/s), Mr. Hoff length L (m), 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,Quality indicator for co2 flux QA_Fc.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 is 2).The meaning of data time, such as 0:30 represents the average of 0:00-0:30;The data is stored in *.xls format. For information of hydrometeorological network or station, please refer to Liu et al. (2018), and for observation data processing, please refer to Liu et al. (2011).
0 2020-04-10
The dataset of ground truth measurements synchronizing with Envisat ASAR was obtained in the saline plot B, the alfalfa plot D and the barley plot E of the Linze grassland foci experimental area on May 24, 2008. The data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:34 BJT. The quadrate was divided into 6×6 subsites, with each one spanning a 120×120 m2 plot. Corner points were chosen. Simultaneous with the satellite overpass, numerous ground data were collected, soil gravimetric moisture, volumetric moisture, and soil bulk density after drying by the cutting ring (100cm^3), the mean soil temperature from 0-5cm by the probe thermometer, and the land surface radiative temperature measured three times by the hand-held infrared thermometer in plot B; soil moisture, soil conductivity, the soil temperature, and the real part of soil complex permittivity by WET, the mean soil temperature from 0-5cm by the probe thermometer, and the land surface radiative temperature measured three times by the hand-held infrared thermometer in plot D; the soil temperature, soil moisture, the loss tangent, soil conductivity, the real part and the imaginary part of soil complex permittivity by the POGO soil sensor, the mean soil temperature from 0-5cm by the probe thermometer, and the land surface radiative temperature measured three times by the hand-held infrared thermometer in plot E. Data were archived in Excel file. Those provide reliable ground data for retrieval and validation of soil moisture and alinity content with active microwave remote sensing approaches. See WATER: Dataset of setting of the sampling plots and stripes in the Linze station foci experimental area for more information.
0 2019-05-23
This dataset includes component temperatures measured by the thermal imager at the Mixed Forest and Sidaoqiao stations between 23 July and 18 August, 2014. The Mixed Forest (101.1335 °E, 41.9903 °N, 874 m.a.s.l.) and Sidaoqiao (101.1374 °E, 42.0012 °N, 873 m.a.s.l.) stations were located in the downstream of the Heihe River basin, Dalaihubu Town, Ejin Banner, Inner Mongolia. At the Mixed Forest station, a Testo 890-2 thermal imager (Testo Inc., Germany) with a resolution of 640 × 480 pixels was employed to acquire brightness temperature images. The imager was manually operated from a 10-m height platform of the tower between 10:00-16:00 (China Standard Time, CST) with an observation interval of 1-h on cloudless days. On August 4th observations were acquired between 11:00 and 17:00 at an interval of 10-min to match observations acquired with an airborne TIR imager. The ground based imager was pointed to five viewing directions (southeast-SE, east-E, northeast-NE, northwest-NW, and southwest-SW) and was inclined 25°–45° below the horizon depending on viewing direction. At Sidaoqiao station, a Testo 875-2i imager (Testo Inc., Germany) with a resolution of 160 × 120 pixels was manually operated from a 10-m high platform to acquire brightness temperature images in directions SW, SE, NE, and NW. Depending on the targets in each viewing direction, the imager was inclined to 30°–45° below the horizon. Observations at Sidaoqiao and Mixed Forest stations were almost synchronous. Furthermore, visible images were taken simultaneously with the aforementioned two TIR imagers (2048 × 1536 pixels for Testo 890-2 and 640 × 480 pixels for Testo 875-2i).
0 2020-10-13
I. Overview The Yellow River is the second longest river in our country. The problem of the Yellow River's sediment has attracted the attention of people all over the world. The watershed is an important natural unit. Using the SRTM-DEM and ASTER-GEDEM data sets as the data source, under the ArcGIS software platform, the method of combining river burning method and river scalar method is used to extract the upper reaches of the Yellow River basin. The boundary of the basin from the source area of the Yellow River to the upper reaches of the Yellow River in Hekou Town. Ⅱ. Data processing description Using SRTM-DEM and ASTER-GDEM issued by the United States as data sources, under the ArcGIS software platform, the method of combining river burning method and river scalar method was used to extract the upper reaches of the Yellow River basin. Because the ratio of the rivers from the Three Lakes Estuary to Hekou Town is extremely small, there is a certain error in the boundary of the basin. Ⅲ. Data content description The map is stored in ArcGIS and .shp files. The river basin boundary spans five provinces (autonomous regions) of Qinghai, Sichuan, Gansu, Ningxia, and Inner Mongolia, with a total area of 55.06 × 104 km2. Ⅳ. Data usage description Watershed boundary is an important natural unit for hydrology, soil erosion, and non-point source pollution research. By extracting watershed boundaries, the migration range of soil erosion and non-point source pollution can be delineated.
0 2020-06-05
The 1:1,000,000 road data set of the North Pole includes the Arctic_Major_Routes, the Arctic_Minor_Routes, the Arctic_railway vector space data and the related attribute data: road Name and Type. The data comes from the 1:100,000 ADC_WorldMap global data set，The data through topology, warehousing and other data quality inspection，It's most comprehensive, current and seamless geographic digital data for the whole earth. The world map coordinate system is latitude and longitude, WGS84 datum surface，Arctic specific projection parameters（North_Pole_Stereographic）.
0 2019-09-15
The data set contains the vortex correlativity instrument observation data of zhangye wetland station in the middle reaches of heihe hydrometeorological observation network from January 1, 2017 to December 31, 2017.The site is located in zhangye city, gansu province.The latitude and longitude of the observation point is 100.44640E, 38.97514N, and the altitude is 1460.00m.The height of the vortex correlation instrument is 5.2m, the sampling frequency is 10Hz, the ultrasonic direction is due to the north, and the distance between the ultrasonic wind speed and temperature instrument (Gill) and the CO2/H2O analyzer (Li7500A) is 25cm. The original observation data of vorticity correlativity is 10Hz, and the released data is the data of 30 minutes processed by Eddypro software. The main steps of its processing include: outfield value elimination, delay time correction, Angle 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 by Eddypro software was also screened.(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.Suspicious data caused by instrument drift and other reasons are marked in red. From April 3 to 12, due to instrument calibration, data is missing.When 10Hz data is missing due to a memory card storage problem, the data is replaced by 30min flux data output from the collector. Observations published 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 (℃), the water vapor density H2O (g/m3), carbon dioxide concentration CO2 (mg/m3), friction velocity Ustar) (m/s), Mr. Hoff length L (m), 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,Carbon dioxide flux mass identification QA_Fc.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 Li et al. (2013) for hydrometeorological network or site information, and Liu et al. (2011) for observation data processing.
0 2020-03-05
This dataset contains the automatic weather station (AWS) measurements from site No.17 in the flux observation matrix from 12 May to 17 September, 2012. The site (100.36972° E, 38.84510° N) was located in an orchard in Daman irrigation district, which is near Zhangye, Gansu Province. The elevation is 1559.63 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity (HMP45C; 5 m, towards north), air pressure (PTB110; 2 m), rain gauge (52203; 10 m), wind speed and direction (034B; 10 m, towards north), a four-component radiometer (CNR1; 6 m, towards south), two infrared temperature sensors (SI-111; 6 m, vertically downward), soil temperature profile (109; 0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.6, and -1.0 m), soil moisture profile (CS616; -0.02, -0.04, -0.1, -0.2, -0.4, -0.6, and -1.0 m), and soil heat flux (HFP01; 3 duplicates with one below the vegetation and the other between plants, 0.06 m). One of the infrared temperature sensors (IRT_2) was adjusted to a zenith angle of 50° after 6 August. The observations included the following: air temperature and humidity (Ta_5 m and RH_5 m) (℃ and %, respectively), air pressure (press, hpa), precipitation (rain, mm), wind speed (Ws_10 m, m/s), wind direction (WD_10 m, °), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation; W/m^2), infrared temperature (IRT_1 and IR_2, ℃), soil heat flux (Gs_1, below the vegetation; Gs_2 and Gs_3, W/m^2), soil temperature profile (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_60 cm, and Ts_100 cm, ℃), and soil moisture profile (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_60 cm, and Ms_100 cm, %). The data processing and quality control steps were as follows. (1) The AWS data were averaged over intervals of 10 min; therefore, there were 144 records per day. The missing data were filled with -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) In this dataset, the time of 0:10 corresponds to the average data for the period between 0:00 and 0:10; the data were stored in *.xlsx format. (5) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), Xu et al. (2013) (for data processing) in the Citation section.
0 2019-09-15
Based on the historical documents, the changes of water resources management organization and management system in Heihe River Basin are sorted out. In this paper, the historical records of water resource management institutions, official positions and their positions, water resource management laws and regulations, and water affairs contradictions in the Heihe River Basin since the Western Han Dynasty are reviewed. From the Western Han Dynasty to the 1950s.
0 2020-03-07
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