The No. 8 hydrological section is located at Gaotai Heihe River Bridge (39 ° 23′22 .93 ″ N, 99 ° 49′37 .29″ E, 1347 m a.s.l.) in the middle reaches of the Heihe River Basin, Zhangye, Gansu Province. The dataset contains observations from the No.8 hydrological section from 17 June, 2012, to 24 November, 2012. The width of this section is 130 meters. The water level was measured using SR50 ultrasonic range and the discharge was measured using cross-section reconnaissance by the StreamPro ADCP. The dataset includes the following sections: Water level (recorded every 30 minutes) and Discharge. The data processing and quality control steps were as follows: 1) The water level data which collected from the hydrological station were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. 2) Data out the normal range records were rejected. 3) Unphysical data were rejected. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), He et al. (2016) (for data processing) in the Citation section.
0 2019-09-15
The No. 4 hydrological section is located at Wujin Heihe River Bridge (100.433° E, 39.065° N, 1431 m) in the midstream of the Heihe River Basin, Zhangye city, Gansu Province. The dataset contains recorded by the No.4 hydrological section from 10 June, 2012 to 10 August, 2012, and from 6 September, 2013 to 31 December, 2013. The width of this section is 58 meters and the cross-sectional area is unstable because of human factors. The water level was measured using an HOBO pressure range and the discharge was measured using cross-section reconnaissance by the StreamPro ADCP. The dataset includes the following parameters: water level (recorded every 30 minutes) and discharge. The missing and incorrect (outside the normal range) data were replaced with -6999. For more information, please refer to Li et al. (2013) (for hydrometeorological observation network or sites information), He et al. (2016) (for data processing) in the Citation section.
0 2019-09-13
The NDVI data of GIMMS (glaobal modelling and mapping studies) is the latest global vegetation index change data released by NASA c-j-tucker et al in November 2003. This data set includes the changes in the vegetation index of the long time series of the qaidam basin from 1981 to 2006. The format is the standard ENVI format, and the projection is ALBERS. The temporal resolution is 15 days and the spatial resolution is 8km.GIMMS NDVI data recorded the vegetation changes in 22a region in the format of satellite data. 1. File format: The gimms-ndvi data set contains all the.rar compressed files with a 15-day interval from July 1981 to 2006, including one XML document, one.hdr header file, one.img file, and one.jpg image file after unzipped. 2. File name: The naming rule for compressed files in NOAA/ avhrr-ndvi data set is: YYMMM15a(b). N ** -vig_data_envi.After unzipping, there are four files with the same file name and attributes: XML document, header file (suffix:.hdf), remote sensing image file (suffix:.img) and JPEG image file. Remote sensing image files with suffixes.img and.hdf, which are used by users to analyze vegetation index, can be opened in ENVI and ERDAS software.
0 2020-06-05
The 1:1 million wetland data of Guangdong Province (2000) is cut from the "1:1 million wetland data of China". "China 1:100,000 wetland data" mainly reflects the information of marshes and wetlands throughout the country in the 2000s, and is represented by geographical coordinates in decimal scale. The main contents include: types of marshes and wetlands, types of water supply, types of soil, types of main vegetation, and geographical regions.The information classification and coding standard of China sustainable development information sharing system was implemented.Data source of this database: 1:20 swamp map (internal version), 1:500 000 swamp map (internal version) of qinghai-tibet plateau, 1:100 000 swamp survey data and 1:400 000 swamp map of China;The processing steps are as follows: data source selection, preprocessing, marshland element digitization and coding, data editing and processing, establishment of topological relationship, edge-to-edge processing, projection transformation, connection with attribute database such as geographical name and acquisition of attribute data.
0 2020-06-09
The datasets of “Land Cover Map of Heihe River Basin” provide monthly land cover classification data in 2012-2013. The HJ-1/CCD data with both high spatial resolution (30 m) and high temporal (2 days) frequency was used to construct the time series data. The NDVI curves from the time series HJ-1/CCD data can depict the variation of typical land surface. Different land use type has different NDVI curve. Rules were set to extract every land use type information. The datasets of “Land Cover Map of Heihe River Basin” hold the traditional land use types including water bodies, urban and built-up, croplands, evergreen coniferous forests, deciduous broadleaf forests and so on. Crop type classification (including maize, spring wheat, highland barely, rape and so on), snow and ice and glaciers information updates, make the datasets more detailed. Compared with previous land cover map and other products, the classification result of the datasets is visually bette. Especially in middle stream, the accuracy of crop classification is quite high compared with the data from the ground campaign. The accuracy of land cover map of the datasets in 2012 was evaluated using very high spatial resolution remote sensing data within Google Earth and data from campaign, and the overall accuracy can be as high as 92.19%. In a word, the datasets of “Land Cover Map of Heihe River Basin” is not only high in overall accuracy, but also more detailed in crop fine classification. Furthermore, it updated some new classes like glaciers and snow. The datasets of “Land Cover Map of Heihe River Basin” are consequently the classification datasets with the highest accuracy and most detailed information up to now.
0 2019-09-15
The data set contains data from the meteorological gradient observation system of sidaqiao super station downstream of heihe hydrometeorological observation network from January 1, 2017 to December 31, 2017.The station is located in the four Bridges of dalaihubu town, ejin banner, Inner Mongolia.The latitude and longitude of the observation point are 101.1374e, 42.0012n, and 873m above sea level.Air temperature, relative humidity and wind speed sensors are installed at 5m, 7m, 10m, 15m, 20m and 28m, with a total of 6 layers, facing due north.The wind sensor is installed at 15m, facing due north;The barometer is installed in the waterproof box;Dump-type rain gauge installed at 28m;The four-component radiometer is installed at 10m, facing due south;The two infrared thermometers are installed at 10m, facing due south, and the probe is facing vertically down.The two photosynthetic effective radiometers are installed at a location of 10m, facing due south, with the probes pointing vertically up and down, respectively.Part of the soil sensor is installed at 2m to the south of the tower body, in which the soil heat flow plate (self-calibration formal) (3 pieces) is successively buried at 6cm underground;The average soil temperature sensor TCAV is buried 2cm and 4cm underground.The soil temperature probe is buried at 0cm on the surface and 2cm, 4cm, 10cm, 20cm, 40cm, 80cm, 120cm, 160cm and 200cm underground.The soil moisture sensors were embedded in the ground at 2cm, 4cm, 10cm, 20cm, 40cm, 80cm, 120cm, 160cm and 200cm. The observation items are: 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: Celsius, percentage), air pressure (Press) (unit:Hundred mpa), precipitation (Rain) (unit: mm), the radiation of four component (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watts per square meter), the surface radiation temperature (IRT_1, IRT_2) (unit: c), up and down the photosynthetic active radiation (PAR_U_up, PAR_U_down) (unit: second micromoles/m2), the average soil temperature (TCAV) (unit: c), soil heat flux (Gs_1, Gs_2, Gs_3) (unit:W/m2), soil moisture (Ms_2cm, Ms_4cm, Ms_10cm, Ms_20cm, Ms_40cm, Ms_80cm, Ms_120cm, Ms_160cm, Ms_200cm) (unit: volume water content, percentage), soil temperature (Ts_0cm, Ts_2cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_40cm, Ts_80cm, Ts_120cm, Ts_160cm, Ts_200cm) (unit: Celsius). 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 part marked by red letter in the data is the data in question;(5) the format of date and time is uniform, and the date and time are in the same column.For example, the time is: 2017-9-10-10:30;(6) the naming rule is: AWS+ site name. 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 data on river water level and flow velocity at No.8 in the intensive runoff observation in the middle reaches of Heihe River runoff from January 1, 2014 to December 31, 2014. The observation point is located at Heihe Bridge, Gaotai County, Zhangye City, Gansu Province. The riverbed is sediment and the section is stable. The latitude and longitude of the observation point is N39°23'22.93", N 99°49'37.29", the altitude is 1347 meters, and the river channel width is 210 meters. The water level observation is measured by SR50 ultrasonic range finder with a frequency of 30 minutes. The data declaration includes the following two parts: Water level observation, observation frequency 30 minutes, unit (cm); data covering time period from January 1, 2014 to December 31, 2014; Flow observation, unit (m3); monitoring flow and obtaining water level flow curve according to different water levels. The process of the runoff changing is obtained by observing the water level process. The No. 8 point-Gaotaiqiao section only monitored the water level because the water body of the wetland park basically stopped flowing. The missing data is uniformly represented by the string -6999. For hydrometeorological network or site information, please refer to Li et al. (2013). For observation data processing, please refer to He et al. (2016).
0 2019-09-11
1. Data overview: This data set is the scale artificial evaporation dish and precipitation data of qilian station from January 1, 2013 to December 31, 2013. The artificial evaporator is a 20cm standard evaporator, and the precipitation is a 20cm standard rain gauge. 2. Data content: (1) the evaporation capacity is measured at 20:00 every day with 20 special measuring cups;It is before a day commonly 20 when measure clear water 20 millimeter with special measure cup (original quantity) pour into implement inside, 24 hours hind namely in the same day 20 hour, again measure the water inside implement (allowance), its reduce quantity is evaporation quantity.Namely: evaporation = original quantity - residual quantity.If there is precipitation between 20:00 of the previous day and 20:00 of the same day, the calculation formula is: evaporation = original quantity + precipitation - residual quantity. (2) precipitation is generally observed in two stages, namely once at 8 o 'clock and once at 20 o 'clock each day. In the rainy season, observation periods are increased, and additional measurements are needed when the rainfall is large.The daily rainfall is divided into 8 a.m. of each day, and the precipitation from 8 a.m. to 8 a.m. of the next day is the precipitation of the current day.If it is rain, measure it with 20 special measuring cups. When it snows, only use the outer tube as snow bearing equipment, and then weigh it with an electronic balance (shenyang longteng es30k-12 type electronic balance, the minimum sensible amount is 0.2g). 3. Space and time range: Geographical coordinates: longitude: 99° 53’e; Latitude: 38°16 'N; Height: 2981.0 m
0 2020-03-11
The dataset of survey at the poplar sampling plot was obtained in the Linze station foci experimental area. Observation items included: (1) soil profile moisture and temperature (0-5cm, 0-5cm, 10-20cm, 20-40cm and 40-60cm) with photos measured twice by the cutting ring method (50cm^3, each layer), once by ML2X Soil Moisture Tachometer and the probe thermometer (15cm, twice each layer) on Jun. 3, 2008. Data were archived as Excel files. (2) shallow layer soil moisture (0-5cm) measured once by the cutting ring method (50cm^3, once each point) and twice by ML2X Soil Moisture Tachometer on Jun. 4, 2008. 13 points were selected and data were archived as Excel files. (3) LAI by TRAC on Jul. 20, 2008. Data were archived as Excel files. (4) roughness measured by the roughness plate together with the digital camera. 18 points were selected and data were archived in JPG format format. (5) forest investigation of Populus gansuensis from Jun. 5-13, 2008: coordinates, the diameter at breast height and the crown size by the measuring tape, full height by TruPulse200. 408 trees were selected and 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 2019-09-12
The data set contains the observation data of meteorological elements from the Dashalong Station,,which is located along the upper reaches of the Heihe Hydro-meteorological Observation Network, and the data set covers data from January 1, 2017 to December 31, 2017. The station is located in Shalong Beach area on the west side of Qilian County, Qinghai Province. The underlying surface is swamp meadow. The latitude and longitude of the observation point is 98.9406E, 38.8399N, and the altitude is 3739m. The air temperature and relative humidity sensors are erected 5 meters above the ground, facing North; the barometer is installed in the pick-proof box on the ground; the tipping bucket rain gauge is erected 10 meters above the ground; the wind speed and direction sensor is set 10 meters above the ground, facing North; the four-component radiometer is installed 6 meters above the ground, facing South; two infrared thermometers are installed 6 meters above the ground, facing South, and the probe orientation is vertical downward; the soil temperature probes are buried respectively at 0cm on the ground surface, 4cm、10cm、20cm、40cm、80cm、120cm and 160cm under the ground, they are located 2 meters from the meteorological tower in the South; the soil moisture sensors are buried 4cm、10cm、20cm、40cm、80cm、120cm and 160cm under the ground, 2 meters from the meteorological tower in the South; the soil heat flow boards (3 pieces) are buried 6cm under the ground, 2 meters from the meteorological tower in the South. Observed items include: air temperature and humidity (Ta_5m, RH_5m) (unit: Celsius, percentage), air pressure (Press) (unit: hectopascal), precipitation (Rain) (unit: mm), wind speed (WS_10m) (unit: meter / sec), wind direction (WD_10m) (unit: degree), four-component radiation (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watt / square meter), surface radiation temperature (IRT_1, IRT_2) (unit: Celsius) , soil heat flux (Gs_1, Gs_2, Gs_3) (unit: watt / square meter), soil temperature (Ts_0cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_40cm, Ts_80cm, Ts_120cm, Ts_160cm) (unit: Celsius), soil moisture (Ms_4cm , Ms_10cm, Ms_20cm, Ms_40cm, Ms_80cm, Ms_120cm, Ms_160cm) (unit: volumetric water content, percentage). Processing and quality control of observation data: (1) Ensure 144 data per day (every 10 minutes), if there is missing data, it is marked as -6999. (2) Eliminate moments with duplicate records; (3) Remove data that is significantly beyond physical meaning or beyond the measuring range of the instrument; (4) Data marked by red is debatable; (5) The formats of the date and time are uniform, and the date and time are in the same column. For example, the time is: 2017-9-10 10:30; (6) The naming rule is: AWS + site name. For hydro-meteorological network or site information, please refer to Liu et al. (2018). For observation data processing, please refer to Liu et al. (2011).
0 2020-04-10
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