This data includes animal products and labor prices; economic income structure, level and per capita net income; economic expenditure structure, productive and living expenditure structure; population composition, labor and household head age and education level; pasture area, grade, suitable stocking capacity; , livestock sheds, human and animal drinking water, pastoral roads, fence construction scale; maintenance scale, and livestock structure.
2020-03-31
This data set contains observation data of vegetation ecological properties in the middle and lower reaches of heihe river from January 1, 2015 to July 31, 2017. It contains 355 data, among which 208 are populus eupoplar and 147 are tamarisk.Ecological attributes include 4 groups of ecological parameters and a total of 15 categories of 74 indicators, as follows: Vegetation structure parameters (25 indicators in 5 categories) : Coverage: total coverage, three-layer coverage, average diameter of canopy; Height: three-layer height, canopy thickness, litter thickness, moss thickness, maximum root depth; Density: layer density and average diameter of trees; Leaf area index: maximum leaf area index and minimum leaf area index of three layers of trees and grass; Phenological stage: leaf spreading stage, leaf filling stage, leaf deciduous stage, complete deciduous stage. Vegetation productivity parameters (16 indicators in 3 categories) : Aboveground biomass: total biomass, three-layer stem biomass, leaf biomass; Root biomass: root biomass, 0-5, 5-15, 15-30, 30-50, 50-100, 100-250cm fine root biomass; Other biomass: litter layer, moss layer biomass and carbon storage. Physiological and ecological parameters (24 indicators in 4 categories) : Biomass distribution: proportion of rhizome and leaf distribution; Element content: carbon content of roots and leaves, carbon - nitrogen ratio, carbon content of litters, carbon content of moss; Blade shape: specific leaf area, blade length and width, leaf inclination; Characteristics of gas exchange: leaf water potential, net photosynthetic rate, stomatal conductance, transpiration rate, air temperature, intercellular CO2 concentration, photosynthetic effective radiation, etc. Hydrological parameters of vegetation (3 categories and 9 indicators) : Redistribution of rainfall: maximum interception, canopy interception, rain penetration, trunk flow Yield flow: yield flow, yield coefficient; Evaporation: plant transpiration, soil evaporation, soil evaporation depth.
2020-03-31
The data set contains data of three stations in the middle reaches: (1) the eddy related flux observation data of point 4 in the flux observation matrix from May 31 to September 17, 2012. The station is located in the Yingke irrigation area of Zhangye City, Gansu Province, and the underlying surface is the village. The longitude and latitude of the observation point are 100.35753e, 38.87752n and 1561.87m above sea level. The height of the eddy correlator is 4.2m (after August 19, the height of the eddy correlator is adjusted to 6.2m), the sampling frequency is 10Hz, the ultrasonic direction is due north, and the distance between the ultrasonic anemometer and the CO2 / H2O analyzer is 17cm. (2) Eddy related flux data of point 12 in the flux observation matrix from May 28 to September 21, 2012. The site is located in the farmland of Daman irrigation area, Zhangye City, Gansu Province, with corn as the underlying surface. The longitude and latitude of the observation point are 100.36631e, 38.86515n and 1559.25m above sea level. The height of the eddy correlator is 3.5m, the sampling frequency is 10Hz, the ultrasonic direction is north, and the distance between the ultrasonic anemometer and the CO2 / H2O analyzer is 15cm. (3) Eddy related flux data of point 14 in the flux observation matrix from May 30 to September 21, 2012. The site is located in the farmland of Yingke Irrigation District, Zhangye City, Gansu Province, with corn as the underlying surface. The longitude and latitude of the observation point are 100.35310e, 38.85867n and 1570.23m above sea level. The height of the eddy correlator is 4.6m, the sampling frequency is 10Hz, the ultrasonic direction is north, and the distance between the ultrasonic anemometer and the CO2 / H2O analyzer is 15cm. The original observation data of the eddy correlator is 10Hz. The published data is the 30 minute data processed by the edire software. The main processing steps include: outliers elimination, delay time correction, coordinate rotation (secondary coordinate rotation), frequency response correction, ultrasonic virtual temperature correction and density (WPL) correction, etc. At the same time, the quality evaluation of each flux value is mainly the test of atmospheric stability (Δ st) and turbulence similarity (ITC). The 30min flux value output by edire software was also screened: (1) data in case of instrument error; (2) data in 1H before and after precipitation; (3) data with loss rate greater than 3% in every 30min of 10Hz original data; (4) observation data with weak turbulence at night (U * less than 0.1M / s). The average period of observation data is 30 minutes, 48 data in a day, and the missing data is marked as - 6999. Suspicious data caused by instrument drift and other reasons shall be identified with red font. The published observation data include: date / time, wind direction WDIR (?), horizontal wind speed wnd (M / s), standard deviation of lateral wind speed STD uuy (M / s), ultrasonic virtual temperature TV (℃), water vapor density H2O (g / m3), carbon dioxide concentration CO2 (mg / m3), friction velocity ustar (M / s), stability Z / L (dimensionless), sensible heat flux HS (w / m2), latent heat flux Le (w / m2), two Carbon dioxide flux FC (mg / (M2S)), quality mark of sensible heat flux QA ﹤ HS, quality mark of latent heat flux QA ﹐ Le, quality mark of carbon dioxide flux QA ﹐ FC. The quality identification of sensible heat, latent heat and carbon dioxide flux is divided into three levels (quality identification 0: (Δ st < 30, ITC < 30); 1: (Δ st < 100, ITC < 100); the rest is 2). The meaning of data time, for example, 0:30 represents the average of 0:00-0:30; data is stored in *. XLS format. For station information, please refer to Liu et al. (2015), and for observation data processing, please refer to Liu et al. (2011) and Xu et al. (2013).
2020-03-28
Based on the data information provided by the data management center of Heihe project, the daily humidity data of 21 regular meteorological observation stations in Heihe River Basin and its surrounding areas and 13 national reference stations around Heihe River were collected and calculated. The spatial stability analysis is carried out to calculate the coefficient of variation. If the coefficient of variation is greater than 100%, the geographical weighted regression is used to calculate the relationship between the station and the geographical terrain factors, and the monthly humidity distribution trend is obtained; if the coefficient of variation is less than or equal to 100%, the common least square regression is used to calculate the relationship between the station humidity value and the geographical terrain factors (latitude, longitude, elevation, slope, aspect, etc.) The residual after removing the trend was fitted and corrected by HASM (high accuracy surface modeling method). Finally, the monthly average humidity distribution of the Heihe River Basin in 1961-2010 is obtained by adding the trend surface results and the residual correction results. Time resolution: monthly average humidity for many years from 1961 to 2010. Spatial resolution: 500M.
2020-03-28
The station data information of 21 regular meteorological observation stations in Heihe River Basin and surrounding areas and 13 national benchmark stations around Heihe River provided by Heihe plan data management center are used to make statistics and collation of daily wind speed and calculate the monthly wind speed data of 1961-2010 for many years. The spatial stability analysis is carried out to calculate the variation coefficient. If the variation coefficient is greater than 100%, the geographical weighted regression is used to calculate the relationship between the station and the geographical terrain factors, and the monthly wind speed distribution trend is obtained; if the variation coefficient is less than or equal to 100%, the common least square regression is used to calculate the relationship between the station wind speed value and the geographical terrain factors (longitude and latitude, elevation, slope, aspect, etc.) The trend of monthly wind speed distribution is obtained, and the residual after removing the trend is fitted and corrected by HASM (high accuracy surface modeling method). Finally, the monthly average wind speed distribution of the Heihe River Basin in 1961-2010 is obtained by adding the trend surface results and the residual correction results. Time resolution: monthly average wind speed for many years from 1961 to 2010. Spatial resolution: 500M.
2020-03-28
Based on the data of 21 regular meteorological observation stations in Heihe River Basin and its surrounding areas and 13 national benchmark stations around Heihe River provided by the data management center of Heihe plan, the daily sunshine hours are statistically sorted out and the monthly sunshine hours data of 1961-2010 for many years are calculated. The spatial stability analysis is carried out to calculate the variation coefficient. If the variation coefficient is greater than 100%, the geographical weighted regression is used to calculate the relationship between the station and the geographical terrain factors, and the monthly sunshine hours distribution trend is obtained; if the variation coefficient is less than or equal to 100%, the ordinary least square regression is used to calculate the sunshine hours and the geographical terrain factors (longitude, latitude, elevation, slope, aspect, etc.) of the station ）The distribution trend of sunshine hours per month is obtained, and the residuals after removing the trend are fitted and corrected by HASM (high accuracy surface modeling method). Finally, the monthly average sunshine hours distribution of the Heihe River Basin in 1961-2010 is obtained by adding the trend surface results and the residual correction results. Time resolution: monthly average sunshine hours for many years from 1961 to 2010. Spatial resolution: 500M.
2020-03-28
The routine meteorological observation data set of four times a day provided by the data management center of Heihe plan is adopted, including 13 stations. The daily evaporation was statistically sorted out, and the monthly evaporation data of 2000-2009 years was calculated. The spatial stability analysis is carried out to calculate the coefficient of variation. If the coefficient of variation is greater than 100%, the geographical weighted regression is used to calculate the relationship between the station and the geographical terrain factors, and the monthly evaporation distribution trend is obtained; if the coefficient of variation is less than or equal to 100%, the common least square regression is used to calculate the relationship between the station evaporation value and the geographical terrain factors (latitude, longitude, elevation, slope, aspect, etc.) After the trend is removed, the residuals are fitted and corrected by HASM (high accuracy surface modeling method). Finally, the monthly average evaporation distribution of the Heihe River Basin in 1961-2010 is obtained by adding the trend surface results and the residual correction results. Time resolution: monthly average evaporation in 2000-2009. Spatial resolution: 500M.
2020-03-28
Select the soil mechanical composition data of 0-20cm depth of soil surface, select the optimal spatial prediction mapping method of soil composition data, and make the spatial distribution data product of soil texture (particle size composition). The American system classification is used as the standard of soil particle classification. The source data of this data set comes from the soil sampling data integrated by the data center of cold and dry areas and the major research plan integration project of Heihe River Basin (spatial interpolation and dynamic simulation analysis of vegetation and environmental elements in the upper reaches of Heihe River basin / approval No. 91325204).
2020-03-28
Select the soil mechanical composition data with a depth of 0-20cm on the surface of the soil, select the optimal spatial prediction mapping method for soil composition data, and make the spatial distribution data product of soil texture (particle size composition). The classification standard of soil particle size is American classification. The source data of this data set are from the data center of cold and drought regions, soil physical properties-soil bulk density and mechanical composition data set soil sampling profile data of Tianlaochi watershed in Qilian mountain.
2020-03-28
Adopt aster with 30 meter resolution provided by Heihe project data management center GDEM data and 90 meter resolution SRTM data are two sets of grid data, as well as multi-source point data. These point data include radar point cloud elevation data in the middle and upper reaches; elevation data extracted from soil sample points and vegetation sample in the data management center of Heihe plan; elevation data extracted from climate and hydrological stations; and elevation sample data measured by the research group. By using the HASM scaling up algorithm, the grid data of different sources and different precision are fused with the elevation point data to obtain the high-precision DEM data of Heihe River Basin. First of all, the accuracy of two groups of grid data is verified by using various point data. According to the results of accuracy verification, different grid data are used as the trend surface of data fusion in different regions. The residuals of various point data and trend surface are calculated, and the residual surface is obtained by interpolation with HASM algorithm, and the trend surface and residual surface are superposed to obtain the final DEM surface. The spatial resolution is 500 meters.
2020-03-28
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