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
Two sets of grid data, aster GDEM data with a resolution of 30 meters and SRTM data with a resolution of 90 meters provided by the data management center of Heihe project, as well as point data from multiple sources, are used. By using the HASM scaling algorithm, the grid data of different sources and different precision are fused with the elevation point data to obtain the high precision slope 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 slope surface. The spatial resolution is 500 meters.
2020-03-28
Two sets of grid data, aster GDEM data with a resolution of 30 meters and SRTM data with a resolution of 90 meters provided by the data management center of Heihe project, as well as point data from multiple sources, are used. 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 slope direction 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 slope surface. The spatial resolution is 500 meters.
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).
2020-03-27
According to the principle of soil landscape model, the key hydrological parameters spatial distribution map data products are made by digital soil mapping method. The source data of this data set comes from the soil profile data integrated by the major research plan integration project of Heihe River Basin (soil data integration and soil information product generation of Heihe River Basin, 91325301). Scope: Heihe River Basin; Projection: WGS · 1984 · Albers / Albers · conic · equal · area; Spatial resolution: 90m; Data format: TIFF; Data content: spatial distribution of saturated water content, field water capacity, wilting water content and saturated conductivity Prediction method: enhanced regression tree Environmental variables: main soil forming factors Dataset content: Pr_0kpsm.tif: saturated water content (unit:%) Pr_33kp SM. TIF: field capacity (unit:%) X1500kp sm.tif: wilting water content (unit:%) SHC sm.tif: saturated hydraulic conductivity (unit: KS / (mm · min-1))
2020-03-27
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