The No. 7 hydrological section is located at Pingchuan Heihe River Bridge (39 ° 20′2.03 ″ N, 100° 5′49.63″ E, 1375 m a.s.l.) in the middle reaches of the Heihe River Basin, Zhangye, Gansu Province. The dataset contains observations from the No.7 hydrological section from 13 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
This data includes the coverage data set of vegetation in one growth cycle in five stations of Daman super station, wetland, desert, desert and Gobi, and the biomass data set of maize and wetland reed in one growth cycle in Daman super station. The observation time starts from May 10, 2014 and ends on September 11, 2014. 1 coverage observation 1.1 observation time 1.1.1 super station: the observation period is from May 10 to September 11, 2014. Before July 20, the observation is once every five days. After July 20, the observation is once every 10 days. A total of 17 observations are made. The specific observation time is as follows:; Super stations: May 10, 15, 20, 25, 30, 10, 15, 20, 20, 30, 30, 30, 30, 30, 7, 10, 10, 10, 10, 10, 15 1.1.2 other four stations: the observation period is from May 20 to September 15, 2014, once every 10 days, and 11 observations have been made in total. The specific observation time is as follows:; Other four stations: May 10, 2014, May 20, 2014, May 30, 2014, June 10, 2014, June 20, 2014, June 30, July 10, 2014, July 20, August 5, 2014, August 17, 2014, September 11, 2014 1.2 observation method 1.2.1 measuring instruments and principles: The digital camera is placed on the instrument platform at the front end of the simple support pole to keep the shooting vertical and downward and remotely control the camera measurement data. The observation frame can be used to change the shooting height of the camera and realize targeted measurement for different types of vegetation. 1.2.2 design of sample Super station: take 3 plots in total, the sample size of each plot is 10 × 10 meters, take photos along two diagonal lines in turn each time, take 9-10 photos in total; Wetland station: take 2 sample plots, each plot is 10 × 10 meters in size, and take 9-10 photos for each survey; 3 other stations: select 1 sample plot, each sample plot is 10 × 10 meters in size, and take 9-10 photos for each survey; 1.2.3 shooting method For the super station corn and wetland station reed, the observation frame is directly used to ensure that the camera on the observation frame is far higher than the vegetation crown height. Samples are taken along the diagonal in the square quadrat, and then the arithmetic average is made. In the case of a small field angle (< 30 °), the field of view includes more than 2 ridges with a full cycle, and the side length of the photo is parallel to the ridge; in the other three sites, due to the relatively low vegetation, the camera is directly used to take pictures vertically downward (without using the bracket). 1.2.4 coverage calculation The coverage calculation is completed by Beijing Normal University, and an automatic classification method is adopted. For details, see article 1 of "recommended references". By transforming RGB color space to lab space which is easier to distinguish green vegetation, the histogram of green component A is clustered to separate green vegetation and non green background, and the vegetation coverage of a single photo is obtained. The advantage of this method lies in its simple algorithm, easy to implement and high degree of automation and precision. In the future, more rapid, automatic and accurate classification methods are needed to maximize the advantages of digital camera methods. 2 biomass observation 2.1 observation time 2.1.1 corn: the observation period is from May 10 to September 11, 2014, once every 5 days before July 20, and once every 10 days after July 20. A total of 17 observations have been made. The specific observation time is as follows:; Super stations: May 10, 15, 20, 25, 30, 10, 15, 20, 20, 30, 30, 30, 30, 30, 7, 10, 10, 10, 10, 10, 15 2.1.2 Reed: the observation period is from May 20 to September 15, 2014, once every 10 days, and 11 observations have been made in total. The specific observation time is as follows:; 2014-5-10、2014-5-20、2014-5-30、2014-6-10、2014-6-20、2014-6-30、2014-7-10、2014-7-20、2014-8-5、2014-8-17、2014-9-11 2.2 observation method Corn: select three sample plots, and select three corn plants that represent the average level of each sample plot for each observation, respectively weigh the fresh weight (aboveground biomass + underground biomass) and the corresponding dry weight (85 ℃ constant temperature drying), and calculate the biomass of unit area corn according to the plant spacing and row spacing; Reed: set two 0.5m × 0.5m quadrats, cut them in the same place, and weigh the fresh weight (stem and leaf) and dry weight (constant temperature drying at 85 ℃) of reed respectively. 2.3 observation instruments Balance (accuracy 0.01g), oven. 3 data storage All the observation data were recorded in the excel table first, and then stored in the excel table. At the same time, the data of corn planting structure was sorted out, including the plant spacing, row spacing, planting time, irrigation time, except for the parent time, harvesting time and other relevant information.
0 2020-03-14
This data set contains meteorological observation data of meteorological elements from January 1, 2017 to December 31, 2017 at guokou station on heihewen meteorological observation network.The station is located in da dong shu pass, qilian county, qinghai province.The latitude and longitude of the observation point are 100.2421E, 38.0142N, and 4148m above sea level.The published data include air temperature and relative humidity sensors set up at 5m, facing due north;The barometer is installed in an anti-skid box on the ground;The inverted bucket rain gauge is installed at 2m;Wind speed and direction sensors are set at 10m, facing due north;The four-component radiometer is at the meteorological tower 6m, facing due south;The two infrared thermometers are installed at the position of 6m, facing south, and the probe is facing vertically downward.The soil temperature probe is buried at 0cm on the surface and 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm underground.The soil water probe is buried in the ground 4cm, 10cm, 20cm, 40cm, 80cm, 120cm and 160cm.The soil hot plate is buried 6cm underground, due south of 2m from the weather tower. Observation items are: air temperature and humidity (Ta_5m, RH_5m) (unit: c, percentage), pressure (Press) (unit: hundred mpa), precipitation (Rain) (unit: mm), wind speed (WS_10m) (unit: m/s), wind (WD_10m) (unit: degrees), 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), soil heat flux (Gs_1, Gs_2, Gs_3) (unit: watts/m2), 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_40cm, Ms_80cm, Ms_120cm, Ms_160cm) (unit: volume water content, percentage). 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 Liu et al. (2018) for hydrometeorological network or site information, and Liu et al. (2011) for observation data processing.
0 2020-04-10
The data is the dataset of the road distribution in the qaidam river basin, scale: 250,000, projection: longitude and latitude, mainly including the spatial distribution and attribute data of the main roads in the qaidam river basin, attribute fields: code (road code), Name (road classification).
0 2020-04-06
The data set contains the slope aspect (resolution: 30 m) factor affecting soil erosion on the Loess Plateau and the slope aspect data extracted from the elevation data of the Loess Plateau. Each theme map is divided into frames according to the 1:250000 scale standard map cartography method, and the frames are denoted by the 1:250000 scale standard map cartography number. The geographical coordinate is WGS1984; the accuracy can meet the requirements of regional scale hydrology and soil erosion analysis and forecasting.
0 2020-06-03
Arctic 1:100,000 mountains data set includes vector spatial distribution data and related attribute data of the Arctic_Mountains in the arctic range: Name, Name of the country where the mountains are located (CNTRY_NAME), abbreviation of the country where the mountains are located (CNTRY_CODE), LATITUDE, LONGITUDE). 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，Antarctic specific projection parameters（North_Pole_Stereographic）.
0 2019-09-14
"Heihe River Basin Ecological hydrological comprehensive atlas" is supported by the key project of Heihe River Basin Ecological hydrological process integration research. It aims at data arrangement and service of Heihe River Basin Ecological hydrological process integration research. The atlas will provide researchers with a comprehensive and detailed background introduction and basic data set of Heihe River Basin. The soil quality map of Heihe River Basin is one of the atlas of land surface, with scale of 1:2500000, positive axis and equal volume conic projection, and standard latitude of 25 47 n. Data sources: soil texture data of Heihe River Basin, road data of Heihe River Basin in 2010, administrative boundary data of one million Heihe River Basin in 2008, residential area data of Heihe River Basin in 2009 and 100000 river data of 2009.
0 2020-03-05
The dataset of LAI measurements was obtained by LI-3000, the protractor and the ruler in the Yingke oasis and Huazhaizi desert steppe foci experimental areas on May, 20, 24, 25, 28 and 31, Jun. 6, 11, 12, 14, 16, 21 and 27, Jul. 2 and 9, 2008. The maximum leaf length and width of maize and wheat, the leaf angle, length and width of each section (one leaf was divided into 3 sections) were measured. And also the plant height, leaf base height, the crop spacing, the canopy height, row spacing and ridge spacing were measured. Two representative plants would be taken back for indoor observation for the stem length, stem width, stem circumference, and leaf area by LAI3000. Data were archived in Excel format.
0 2019-09-11
This data was derived from "1: 100,000 Land Use Data of China". Based on Landsat MSS, TM and ETM remote sensing data, 1: 100,000 Land Use Data of China was compiled within three years by a remote sensing scientific and technological team of 19 research institutes affiliated to the Chinese Academy of Sciences, which was organized by the “Remote Sensing Macroinvestigation and Dynamic Research on the National Resources and Environment", one of the major application programs in Chinese Academy of Sciences during the "Eighth Five-year Plan". This data adopts a hierarchical land cover classification system, which divides the country into 6 first-class categories (cultivated land, forest land, grassland, water area, urban and rural areas, industrial and mining areas, residential land and unused land) and 31 second-class categories. This is the most accurate land use data product in our country at present. It has already played an important role in national land resources survey, hydrology and ecological research.
0 2020-10-12
Leaf area index, also known as leaf area coefficient, refers to the multiple of the total area of plant leaves in the land area per unit land area. Leaf area index is an important structural parameter of ecosystem, which is used to reflect the number of plant leaves, the change of canopy structure, the life activity of plant community and its environmental effect, to provide structured quantitative information for the description of material and energy exchange on the canopy surface, and to balance the energy of carbon accumulation, vegetation productivity and the interaction between soil, plant and atmosphere, Vegetation remote sensing plays an important role. The leaf area index and other indexes of Picea crassifolia forest in Pailugou watershed were measured by plant canopy imager CI - 110
0 2020-03-10
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