This dataset contains the flux measurements from the Dashalong station eddy covariance system (EC) in the upper reaches of the Heihe hydrometeorological observation network from 12 August to 31 December, 2013. The site (98.941° E, 38.840° N) was located in the swamp meadow, Qilian County in Qilian Province. The elevation is 3739 m. The EC was installed at a height of 4.5 m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (CSAT3 & Li7500) was 0.15 m. The raw data acquired at 10 Hz were processed using the Edire post-processing software (University of Edinburgh, http://www.geos.ed.ac.uk/abs/research/micromet/EdiRe/), including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC), as proposed by Foken and Wichura [1996]: class 1 (level 0: Δst<30 and ITC<30), class 2 (level 1: Δst<100 and ITC<100), and class 3 (level 2: Δst>100 and ITC>100), representing high-, medium-, and low-quality data, respectively. In addition to the above processing steps, the half-hourly flux data were screened using a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 3% of the 30 min raw record; and (4) data were rejected at night when the friction velocity (u*) was less than 0.1 m/s. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m^3), CO2 mass density (CO2, mg/m^3), friction velocity (ustar, m/s), stability (z/L), sensible heat flux (Hs, W/m^2), latent heat flux (LE, W/m^2), carbon dioxide flux (Fc, mg/ (m^2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. 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
Data overview: This set of data mainly includes six prefecture level cities and 16 counties (Ganzhou District, Gaotai County, Shandan County, Minle County, Linze County, Sunan Yugu Autonomous County, Jinta County, Subei Mongolian Autonomous County, Suzhou District, Yumen City, Jiayuguan City, Yongchang County, Qilian County, Alxa Left Banner, Ejina Banner, Alxa Right Banner) in Heihe River Basin ）The 12 social and economic data are: GDP, output value of primary industry, output value of secondary industry, output value of tertiary industry, per capita GDP, per capita disposable income of urban residents, per capita net income of rural residents, fixed asset investment, total retail sales of social consumer goods, fiscal revenue, fiscal expenditure, and total grain output (including all kinds of work) Output of the product). It is divided into county level and township level. The data period is 2000-2009.
0 2020-10-12
The dataset of ground truth measurements synchronizing with Landsat TM was obtained in the Biandukou foci experimental area from 11:10-13:30 on Mar. 17, 2008. Those provide reliable ground data for objects modelling and background modelling, remote sensing image simulation and scaling. Simultaneous with the satellite overpass, numerous ground data were collected, spectrum (ASD Fieldspec FRTM (Boulder, Co, USA), 350nm-2500nm, 3nm for the visible near-infrared band and 10nm for the shortwave infrared band), the surface temperature, atmospheric parameters, the soil profile gravimetric moisture (0-1cm, 1-3cm and 3-5cm), the shallow layer frost depth and the soil roughness in C1, G1, W1, W2, B1 and B2, mostly the grassland, the wheat stubble land, the deep plowed land and the rape stubble land. The quadrates of 90m×90m and 450m×450m were compartmentalized into 81 subgrids of 10m×10m and 50m×50m. Based on the resolution of 30m×30m and 150m×150m, the influence of adjacent eight pixels on the center pixel was studied. Section lines of each subgrid were adopted to acquire the pixel spectrum, which were measured more than once for the mean value. The spectrum data were archived in the ASCII format, with the first five rows as the file header and the following two columns as wavelength (nm) and reflectance (percentage) respectively. The .txt file was not reflectance but intermediate file for further calculation. Raw data were binary files direct from ASD (by ViewSpecPro). The surface radiative temperature and the physical temperature were measured by the handheld infrared thermometer. Besides, the cover type was also recorded. The data can be opened by Microsoft Office. Atmospheric parameters were measured by CE318 to retrieve the total optical depth, aerosol optical depth, Rayleigh scattering coefficient, column water vapor in 936 nm, and various parameters at 550nm to obtain horizontal visibility with the help of MODTRAN or 6S. Those provide reliable data for atmosphere correction of the same period in this area. The gravimetric soil moisture (samples from 0-1cm, 1-3cm and 3-5cm) was measured by the microwave drying method. The frost depth by the chopstick and the ruler. The soil was considered frozen when it was hard and with ice crystal. The data can be opened by Microsoft Office. Nine data files were included, TM data, CE318 data, B1, B2, C1, G1, W1 and W2.
0 2019-09-12
The data set contains observation data of cosmic-ray instrument (crs) from January 1, 2017 to December 31, 2017. The site is located in the farmland of Daman Irrigation District, Zhangye, Gansu Province, and the underlying surface is cornfield. The latitude and longitude of the observation site is 100.3722E, 38.8555N, the altitude is 1556 meters. The bottom of the instrument probe is 0.5 meter from the ground, and the sampling frequency is 1 hour. The original observation items of the cosmic-ray instrument include: voltage Batt (V), temperature T (°C), relative humidity RH (%), air pressure P (hPa), fast neutron number N1C (number / hour), thermal neutron number N2C (number / hour), fast neutron sampling time N1ET (s) and thermal neutron sampling time N2ET (s). The data was released after being processed and calculated. The data includes: Date Time, P (pressure hPa), N1C (fast neutrons one/hour), N1C_cor (pressure-corrected fast neutrons one/hour) and VWC ( soil water content %), it was processed mainly by the following steps: 1) Data Screening There are four criteria for data screening: (1) Eliminating data with a voltage less than or equal to 11.8 volts ; (2) Eliminating data with a relative humidity greater than or equal to 80%; (3) Eliminating data with a sampling time interval not within 60 ± 1 minute; (4) Eliminating data with fast neutrons that vary by more than 200 in one hour. In addition, missing data is supplemented with -6999. 2) Air Pressure Correction The original data is corrected by air pressure according to the fast neutron pressure correction formula mentioned in the instrument manual, and the corrected fast neutron number N1C_cor is obtained. 3) Instrument Calibration In the process of calculating soil moisture, it is necessary to calibrate the N0 in the calculation formula. N0 is the number of fast neutrons under the situation with low antecedent soil moisture . Usually, soil samples in the source area are used to obtain measured soil moisture (or obtained by relatively dense soil moisture wireless sensors) θm (Zreda et al. 2012) and the fast neutron correction data N in corresponding time periods, then NO can be obtained by reversing the formula. Here, the instrument is calibrated according to the Soilnet soil moisture data in the source region of the instrument, and the relationship between the soil volumetric water content θv and the fast neutron is established. The data of June 26-27, and July 16-17, respectively, which have obvious differences in dry and wet conditions, were selected. The data from June 26 to 27 showed low soil moisture content, so the average of the three values of 4 cm, 10 cm and 20 cm was used as the calibration data, and the variation range was 22% to 30%; meanwhile , the data from July 16 to 17 showed high soil moisture content, so the average of the two values of 4cm and 10 cm was used as the calibration data, and the variation range was 28% - 39%, and the final average N0 was 3597. 4) Soil Moisture Calculation According to the formula, the hourly soil water content data is calculated. Please refer to Liu et al. (2018) for information of hydrometeorological network or site, and Zhu et al. (2015) for observation data processing.
0 2020-04-10
On 4 July 2012 (UTC+8), a TASI sensor boarded on the Y-12 aircraft was used to obtain the thermal-infrared hyperspectral image, which is located in the observation experimental area, Linze region and Heihe riverway. The relative flight altitude is 1000 meters. The wavelength of TASI is 8-11.5 μm with a spatial resolution of 3 meters. Through the ground sample points and atmospheric data, the data are recorded in surface radiance processed by geometric correction and atmospheric correction. Land surface temperature (LST) data was retrieved by temperature/emissivity separation algorithm.
0 2019-09-13
According to the principle of optimization of water diversion scheme and the economic, social and ecological development status of Heihe River Basin, the following three optimization schemes of water diversion scheme are proposed. In Scheme 1, the water consumption in the middle reaches is 630 million m3 in each coming year. In Scheme 2, the water consumption in the middle reaches is 180 million m3 and 60 million m3 in 90% and 75% coming years respectively. In Scheme 3, when the water consumption in Yingluo Gorge is more than 1.9 billion m3, the water consumption in excess of 1.9 billion m3 is distributed by 40% in the middle reaches and 60% in the lower reaches. At the same time, in order to maintain the annual average inflow of 1.58 billion m3 from Yingluo Gorge, 950 million m3 from Zhengyi Gorge, and when the inflow of Yingluo Gorge is less than 1.29 billion m3, 60% of the inflow of less than 1.29 billion m3 will be distributed in the middle reaches and 40% in the lower reaches.
0 2020-06-03
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 2020-04-01
This data set includes the 2014 observation data of 9 water net nodes in the 5.5km × 5.5km observation matrix (red box in the thumbnail) of Yingke / Daman irrigation area in the middle reaches of Heihe River. The nine nodes contain 4cm and 10cm two-layer hydro probe II probes to observe the main variables such as soil moisture, soil temperature, conductivity and complex permittivity; the si-111 infrared temperature probe is set up at 4m height to observe the surface radiation infrared temperature of the underlying surface. The observation time frequency is 5 minutes. This data set can provide spatiotemporal continuous observation data set for remote sensing estimation of key water and heat variables of heterogeneous surface, remote sensing authenticity test, ecological hydrology research, irrigation optimization management and other research. Please refer to "2014 middle reaches of Heihe River waternet data document 20141231. Docx" for details
0 2020-08-17
This data is based on the observation of corn in the middle reaches of heihe river irrigated area. The observation instrument is licor-6400 XTR and the site is selected near the HiWATER combined test superstation.The photosynthesis parameters of maize were observed through uncontrolled experiments and controlled experiments (controlling carbon dioxide and light intensity) from June 22, 2012 to August 24, 2012.
0 2019-12-27
This data is the NCEP/DOE reanalysis data of 6h interval nested downscaling by WRF model in northwest China to a horizontal resolution of 12km, 364 grid points in the east-west direction, 251 grid points in the north-south direction and 31 layers in the vertical direction. The simulation time starts from 1979-01-01,06:00:00 and ends at 2013-12-31,23:00:00. The parameterization schemes of the model are as follows: Kain Frisch cumulus convection scheme, WSM3 cloud microphysics scheme, RRTM long wave scheme, Dudhia short wave scheme, Noah land surface model, YSU planetary boundary layer scheme. The file naming rules in the data set are: wrf_t2_YYYY.nc and wrf_rain_YYYY.nc, where YYYY is the annual abbreviation, t2 is the 2m temperature (unit ℃), and rain is the total surface precipitation (unit mm).
0 2020-03-28
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