This dataset contains the data of the meteorological element gradient observation system of the Sidaoqiao superstation downstream of the Heihe Hydrometeorological Observation Network from January 1, 2014 to December 31, 2014. The site is located in Sidaoqiao, Dalaihu Town, Ejin Banner, Inner Mongolia. The underlying surface is Tamarix. The latitude and longitude of the observation point is 101.1374E, 42.0012N, and the altitude is 873m. The air temperature, relative humidity and wind speed sensors are respectively set at 5m, 7m, 10m, 15m, 20m and 28m, with 6 layers facing the north; the wind direction sensor is set at 15m, facing the north; the barometer is installed in the waterproof box. The tipping bucket rain gauge is installed at 28m; the four-component radiometer is installed at 10m, facing south; two infrared thermometers are installed at 10m, facing south, the probe orientation is vertically downward; two photosynthetically active radiometers are installed At 10m, facing south, and the probe is vertically upward and downward respectively; the soil moisture sensor is installed 2m on the south side of the tower body, and the soil heat flow plates (self-correcting type) (3 pieces) are buried in turn in the ground 6cm deep; The average soil temperature sensor TCAV is buried in the ground 2cm, 4cm; the soil temperature probe is buried in the ground surface 0cm and underground 2cm, 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm; soil moisture sensors are buried in the underground 2cm, 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm. Observed items include: wind speed (WS_5m, WS_7m, WS_10m, WS_15m, WS_20m, WS_28m) (unit: m/s), wind direction (WD_15m) (unit: degree), air temperature and humidity (Ta_5m, Ta_7m, Ta_10m, Ta_15m, Ta_20m, Ta_28m and RH_5m, RH_7m, RH_10m, RH_15m, RH_20m, RH_28m) (unit: centigrade, percentage), pressure (unit: hectopascal), precipitation (Rain) (unit: mm), four-component radiation (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watts/square meter), surface radiation temperature (IRT_1, IRT_2) (unit: centigrade), up and down photosynthetically active radiation (PAR_U_up, PAR_U_down) (unit: micromol/square Msec), average soil temperature (TCAV) (unit: centigrade), soil heat flux (Gs_1, Gs_2, Gs_3) (unit: watts/square meter), soil moisture (Ms_2cm, Ms_4cm, Ms_10cm, Ms_20cm, Ms_40cm, Ms_80cm) , Ms_120cm, Ms_160cm) (unit: volumetric water content, percentage), soil temperature (Ts_0cm, Ts_2cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_40cm, Ts_80cm, Ts_120cm, Ts_160cm) (unit: centigrade). Processing and quality control of the observation data: (1) ensure 144 data per day (every 10 minutes), when there is missing data, it is marked by -6999; From September 8, 2014 to November 8, due to the sensor problems, the data is missing; on May 9, 2014, the soil moisture probe was re-buried, and the data before and after is inconsistent; (2) eliminate the moment with duplicate records; (3) delete the data that is obviously beyond the physical meaning or the range of the instrument; (5) the format of date and time is uniform, and the date and time are in the same column. For example, the time is: 2014-9-10 10:30; (6) the naming rules are: AWS+ site name. For hydrometeorological network or site information, please refer to Li et al. (2013). For observation data processing, please refer to Liu et al. (2011).
0 2019-09-15
The project on the impact of agricultural development in northwest Lvzhou on watershed scale water cycle and eco-environmental effects belongs to the major research program of "Environmental and Ecological Science in Western China" sponsored by the National Natural Science Foundation. The person in charge is Professor Kang Shaozhong of Northwest China Agriculture and Forestry University. The project runs from January 2003 to December 2005. Data collected from this project: soil experimental data of Shiyang River Basin, including: 1. Saturated hydraulic conductivity (excel table): includes four fields: number, sampling point, measured value and saturated hydraulic conductivity. 2. Conductivity (excel table): including number, sampling point, measured value, temperature, temperature correction value and conductivity. 3. Original indoor infiltration data (excel table): including number, time, cumulative value and reading. 4. Field Infiltration Data (excel Form): Including Number, Time, Cumulative Value and Reading. 5. Sampling point of horizontal infiltration data (excel form): including time, measuring cylinder (ml), wetting peak (ml), wet weight, dry weight, box weight and distance. 6. soil particle analysis (excel form): including numbers, > 0.25 mm, < 0.05 mm, < 0.01 mm, < 0.005 mm, < 0.001 mm. 7. Soil moisture characteristic curve (excel table): including soil weight and drying weight when the pressure of pressure membrane instrument is 0,0.05,0.1,0.3,0.5,0.8,1.5,3,5,14.4. 8. Organic matter (excel form): including number, sampling point, amount of soil taken (G), titration amount (ml) 9. Sampling Point Coordinates (excel Form)
0 2020-04-02
This data mainly includes ten day runoff data of Yingluo gorge and Zhengyi gorge in Heihe River Basin, among which the time range of Yingluo gorge data is 1944-2010 and Zhengyi gorge data is 1947-2010. Source: Heihe River Basin Authority. Data unit: 100 million cubic meters / 10 days. Data format: Excel "Yingluo gorge 2" and "Yingluo gorge 2 (2)" in the data table are the ten day runoff data of Yingluo gorge, the same as "Yingluo gorge" in the data table, and Yingluo gorge 2 (2) contains the chart.
0 2020-09-14
The dataset of ground truth measurement synchronizing with the airborne microwave radiometers (L&K bands) mission was obtained in the Linze station foci experimental area on Jul. 8, 2008. Observation items included: (1) soil moisture (0-5cm) measured by the cutting ring method (50cm^3) in P1 to P6 strips (17 sample points each). Photos were taken. The preprocessed soil volumetric moisture data were archived as Excel files. (2) surface radiative temperature measured by three handheld infrared thermometer (5# and 6# from Cold and Arid Regions Environmental and Engineering Research Institute, and one from Institute of Geographic Sciences and Natural Resources, which were all calibrated) from P1 to P6 strips. There are 34 sample points in total and each was repeated three times synchronizing with the airplane. Photos were taken. Data were archived as Excel files. See the metadata record “WATER: Dataset of setting of the sampling plots and stripes in the Linze station foci experimental area” for more information of the quadrate locations.
0 2019-05-23
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.
0 2020-05-06
This dataset includes data recorded by the Heihe integrated observatory network obtained from an observation system of Meteorological elements gradient of A’rou Superstation from January 1 to December 31, 2018. The site (100.464° E, 38.047° N) was located on a cold grassland surface in the Caodaban village, A’rou Town, Qilian County, Qinghai Province. The elevation is 3033 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP45C; 1, 2, 5, 10, 15 and 25 m, towards north), wind speed profile (010C; 1, 2, 5, 10, 15 and 25 m, towards north), wind direction profile (020C; 2 m, towards north), air pressure (CS100; 2 m), rain gauge (TE525M; 5 m, towards south), four-component radiometer (CNR4; 5 m, towards south), two infrared temperature sensors (SI-111; 5 m, towards south, vertically downward), photosynthetically active radiation (PAR-LITE; 5 m, towards south, vertically upward), soil heat flux (HFP01SC; 3 duplicates, -0.06 m, 2 m in the south of tower), a TCAV averaging soil thermocouple probe (TCAV; -0.02, -0.04 m, 2 m in the south of tower), soil temperature profile (109; 0, -0.02, -0.04, -0.06, -0.1, -0.15, -0.2, -0.3, -0.4, -0.6, -0.8, -1.2, -1.6, -2, -2.4, -2.8 and -3.2 m, 3 duplicates in -0.04 m and -0.1 m), and soil moisture profile (CS616; -0.02, -0.04, -0.06, -0.1, -0.15, -0.2, -0.3, -0.4, -0.6, -0.8, -1.2, -1.6, -2, -2.4, -2.8 and -3.2 m, 3 duplicates in -0.04 m and -0.1 m). The observations included the following: air temperature and humidity (Ta_1 m, Ta_2 m, Ta_5 m, Ta_10 m, Ta_15 m and Ta_25 m; RH_1 m, RH_2 m, RH_5 m, RH_10 m, RH_15 m and RH_25 m) (℃ and %, respectively), wind speed (Ws_1 m, Ws_2 m, Ws_5 m, Ws_10 m, Ws_15 m and Ws_25 m) (m/s), wind direction (WD_2 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) (℃), photosynthetically active radiation (PAR) (μmol/(s m-2)), average soil temperature (TCAV, ℃), soil heat flux (Gs_1, Gs_2 and Gs_3) (W/m2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm_1, Ts_4 cm_2, Ts_4 cm_3, Ts_6 cm, Ts_10 cm_1, Ts_10 cm_2, Ts_10 cm_3, Ts_15 cm, Ts_20 cm, Ts_30 cm, Ts_40 cm, Ts_60 cm, Ts_80 cm, Ts_120 cm, Ts_160 cm, Ts_200 cm, Ts_240 cm, Ts_280 cm and Ts_320 cm) (℃), and soil moisture (Ms_2 cm, Ms_4 cm_1, Ms_4 cm_2, Ms_4 cm_3, Ms_6 cm, Ms_10 cm_1, Ms_10 cm_2, Ms_10 cm_3, Ms_15 cm, Ms_20 cm, Ms_30 cm, Ms_40 cm, Ms_60 cm, Ms_80 cm, Ms_120 cm, Ms_160 cm, Ms_200 cm, Ms_240 cm, Ms_280 cm and Ms_320 cm) (%, volumetric water content). 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 average soil temperature was rejected during February 16 to March 31 and April 15 to May 20 because of broken of the sensor line; Soil heat flux were wrong occasionally during November to December. 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: 2018-9-10 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 sites information), Liu et al. (2011) for data processing) in the Citation section.
0 2020-07-25
This data set includes the continuous observation data set of soil texture, roughness and surface temperature measured by the vehicle borne microwave radiometer and synchronous measurement from November 24-25, 2013 in the desert of Minle County, Zhangye City, Gansu Province. The surface temperature and humidity include four layers of temperature sensor at the soil depth of 1cm, 5cm, 10cm, 20cm, and the observation of soil temperature and soil moisture data at the soil depth of 0-5cm. The time frequency of routine observation of soil temperature and humidity is 5 minutes. Data details: 1. Time: November 24-25, 2013 2. data: Brightness temperature: observed by vehicle mounted multi frequency passive microwave radiometer, including 6.925, 18.7 and 36.5ghz V polarization and H polarization data (10.65ghz band damage, 18.7ghz h polarization damage) Soil temperature: use sensor installed on dt80 to measure 1cm, 5cm, 10cm, 20cm soil temperature Soil moisture: use h-probe sensor to measure 0-5cm soil moisture, the probe can measure 0-5cm soil temperature at the same time Soil texture: soil samples measured in Beijing Normal University Soil roughness: measured by roughness meter provided by northeast geography 3. Data size: 2.3m 4. Data format:. Xls
0 2020-03-14
The data is the distribution map of 100,000 deserts in the Tarim River Basin. This data uses 2000 TM images as the data source to interpret, extract and revise, and uses remote sensing and geographic information system technology in combination with the mapping requirements of 1: 100,000 scale to carry out thematic mapping of deserts, sands and gravelly Gobi. Data attribute table: area (area), perimeter (perimeter), ashm_ (sequence code), class (desert code), ashm_id (desert code), of which desert code is as follows: flowing sand 2341010, semi-flowing sand 2341020, semi-fixed sand 2341030, Gobi desert 2342000, saline-alkali land 2343000
0 2020-09-15
This data is from "China 1:100,000 land use data".China 1:100,000 land use data was constructed in three years based on Landsat MSS, TM and ETM remote sensing data by using satellite remote sensing as a means to organize remote sensing science and technology teams from 19 institutes affiliated to the Chinese academy of sciences (cas) in the "eighth five-year plan" major application project "national macro survey and dynamic research on remote sensing of resources and environment". According to the 1:100,000 landuse data of gansu province, a hierarchical land cover classification system is adopted, which divides the whole country into 6 primary categories (arable land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 secondary categories.It is the most accurate land use data product in China and has played an important role in national land resource survey, hydrological and ecological research.
0 2020-03-31
The dataset of intensive runoff observations was obtained by the cup type current meter (made in Chongqing Hydrological Instrument Factory) in the Binggou watershed foci experimental area from Jan. 17, 2008 to Dec. 31, 2009. Data directions included: (1) the regular observation before Mar. 14, 2008, once per day; the intensive observation from Mar. 15, 2008 to Apr. 1, 2008, 7-8 times per day and even hourly for some intensive observations (2) three times (9, 14 and 19 BJT) per day from May 3, 2008 to Sep. 17, 2008; from Sep. 17, 2008 on, two times (9 and 18 BJT) per day; the water runoff by evenly spaced method, 20cm, 40cm and 80cm based on different situations The data were named after WATER_Runoff_BG_yyyymmdd-yyyymmdd.csv (WATER_Runoff_BG for Ginggou, yyyymmdd-yyyymmdd for the observation time). The missing data were marked "None".
0 2019-05-23
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