This dataset contains the flux measurements from site No.7 eddy covariance system (EC) in the flux observation matrix from 29 May to 18 September, 2012. The site (100.36521° E, 38.87676° N) was located in a cropland (maize surface) in Yingke irrigation district, which is near Zhangye, Gansu Province. The elevation is 1556.39 m. The EC was installed at a height of 3.8 m; 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&Li7500A) was 0.15 m. 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 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. Moreover, 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), which was proposed by Foken and Wichura : 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 in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data 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; the missing data were replaced with -6999. Moreover, 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 *.xlsx format. For more information, please refer to Liu et al. (2016) (for multi-scale observation experiment or sites information), Xu et al. (2013) (for data processing) in the Citation section.
LIU Shaomin LI Xin XU Ziwei
Snow is a significant component of the ecosystem and water resources in high-mountain Asia (HMA). Therefore, accurate, continuous, and long-term snow monitoring is indispensable for the water resources management and economic development. The present study improves the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites 8 d (“d” denotes “day”) composite snow cover Collection 6 (C6) products, named MOD10A2.006 (Terra) and MYD10A2.006 (Aqua), for HMA with a multistep approach. The primary purpose of this study was to reduce uncertainty in the Terra–Aqua MODIS snow cover products and generate a combined snow cover product. For reducing underestimation mainly caused by cloud cover, we used seasonal, temporal, and spatial filters. For reducing overestimation caused by MODIS sensors, we combined Terra and Aqua MODIS snow cover products, considering snow only if a pixel represents snow in both the products; otherwise it is classified as no snow, unlike some previous studies which consider snow if any of the Terra or Aqua product identifies snow. Our methodology generates a new product which removes a significant amount of uncertainty in Terra and Aqua MODIS 8 d composite C6 products comprising 46 % overestimation and 3.66 % underestimation, mainly caused by sensor limitations and cloud cover, respectively. The results were validated using Landsat 8 data, both for winter and summer at 20 well-distributed sites in the study area. Our validated adopted methodology improved accuracy by 10 % on average, compared to Landsat data. The final product covers the period from 2002 to 2018, comprising a combination of snow and glaciers created by merging Randolph Glacier Inventory version 6.0 (RGI 6.0) separated as debris-covered and debris-free with the final snow product MOYDGL06*. We have processed approximately 746 images of both Terra and Aqua MODIS snow containing approximately 100 000 satellite individual images. Furthermore, this product can serve as a valuable input dataset for hydrological and glaciological modelling to assess the melt contribution of snow-covered areas. The data, which can be used in various climatological and water-related studies, are available for end users at https://doi.org/10.1594/PANGAEA.901821 (Muhammad and Thapa, 2019).
Sher Muhammad Sher Muhammad
This dataset is the Fractional Vegetation Cover observation in the artificial oasis experimental region of the middle stream of the Heihe River Basin. The observations lasted for a vegetation growth cycle from May 2012 to September 2012 (UTC+8). Instruments and measurement method: Digital photography measurement is implemented to measure the FVC. Plot positions, photographic method and data processing method are dedicatedly designed. Details are described in the following: 0. In ﬁeld measurements, a long stick with the camera mounted on one end is beneﬁcial to conveniently measure various species of vegetation, enabling a larger area to be photographed with a smaller ﬁeld of view. The stick can be used to change the camera height; a ﬁxed-focus camera can be placed at the end of the instrument platform at the front end of the support bar, and the camera can be operated by remote control. 1. For row crop like corn, the plot is set to be 10×10 m2, and for the orchard, plot scale is 30×30 m2. Shoot 9 times along two perpendicularly crossed rectangular-belt transects. The picture generated of each time is used to calculate a FVC value. “True FVC” of the plot is then acquired as the average of these 9 FVC values. 2. The photographic method used depends on the species of vegetation and planting pattern: Low crops (<2 m) in rows in a situation with a small ﬁeld of view (<30 ), rows of more than two cycles should be included in the ﬁeld of view, and the side length of the image should be parallel to the row. If there are no more than two complete cycles, then information regarding row spacing and plant spacing are required. The FVC of the entire cycle, that is, the FVC of the quadrat, can be obtained from the number of rows included in the ﬁeld of view. 3. High vegetation in rows (>2 m) Through the top-down photography of the low vegetation underneath the crown and the bottom-up photography beneath the tree crown, the FVC within the crown projection area can be obtained by weighting the FVC obtained from the two images. Next, the low vegetation between the trees is photographed, and the FVC that does not lie within the crown projection area is calculated. Finally, the average area of the tree crown is obtained using the tree crown projection method. The ratio of the crown projection area to the area outside the projection is calculated based on row spacing, and the FVC of the quadrat is obtained by weighting. 4. FVC extraction from the classiﬁcation of digital images. Many methods are available to extract the FVC from digital images, and the degree of automation and the precision of identiﬁcation are important factors that affect the efﬁciency of ﬁeld measurements. This method, which is proposed by the authors, has the advantages of a simple algorithm, a high degree of automation and high precision, as well as ease of operation.
MU Xihan HUANG Shuai MA Mingguo
This data set includes a monthly composite of 30 m × 30 m surface vegetation coverage products in the Qilian Mountain Area in 2019. In this paper, the maximum value composition (MVC) method is used to synthesize monthly NDVI products and calculate FVC by using the reflectance data of Landsat 8 and sentinel 2 red and near infrared channels. The data is monthly synthesized by Google Earth engine cloud platform, and the index is calculated by the model. The missing pixels are interpolated with good quality, which can be used in environmental change monitoring and other fields.
SUN Ziyong CHANG Qixin
Zhangye basin mainly includes 20 irrigation areas. Under the restriction of water diversion, the surface water consumption of the irrigation area is under control, but the groundwater exploitation is increased, resulting in the groundwater level drop in the middle reaches, resulting in potential ecological environment risks. Due to the complex and frequent exchange of surface water and groundwater in the study area, it is possible to realize the overall water resource saving by optimizing the utilization ratio of surface water and groundwater in each irrigation area. In this project, on the premise of not changing the water demand of the middle reaches irrigation area, the two problems of maximizing the outflow of Zhengyi Gorge (given groundwater reserve constraint) and maximizing the outflow of Zhengyi Gorge (given groundwater reserve constraint) are studied.
ALOS PRISM dataset includes 13 scenes; one covers the A'rou foci experimental area on Mar. 19, 2008, one covers the Haichaoba on Mar. 19, 2008, one covers the Biandukou foci experimental area on Apr. 17, 2008, and one covers the Linze grassland and Linze station foci experimental areas on Apr. 22, 2008. The data version is LB2, which was released after radiometric correction and geometric correction.
Japan Aerospace Exploration Agency
This data is from the central station of environmental monitoring in gansu province. The data includes three observation elements, namely sulfur dioxide, nitrogen dioxide and inhalable particles, which are published on the network. The data format is a text file. The first column is the city name, the second column is sulfur dioxide, the third column is nitrogen dioxide, the fourth column is pm10, and the fifth column is the observation date. The data included lanzhou, jiayuguan, jinchang, baiyin, tianshui, qingyang, pingliang, dingxi, longnan, wuwei, zhangye, jiuquan and linxia. This data will be updated automatically and continuously according to the data source.
Gansu environmental monitoring center station
Data Overview: Zhangye's channels are divided into five levels: dry, branch, bucket, agricultural and Mao channels, of which the agricultural channels are generally unlined. Mao channels are field projects, so the three levels of dry, branch and bucket channels and a small part of agricultural channels are mainly collected. The irrigation canal system data includes 2 main canals (involving multiple irrigation districts), 157 main canals (within a single irrigation district), 782 branch canals and 5315 dou canals, with a total length of 8, 745.0km. Data acquisition process: remote sensing interpretation and GPS field measurement are adopted for data acquisition of irrigation canal system. Direct GPS acquisition channel is the most effective method, but the workload of GPS acquisition channel is too large, and we only verify the measurement in some irrigation areas. The main method is to first collect the manual maps of irrigation districts drawn by each water pipe. Most of these maps have no location, only some irrigation districts such as Daman and Shangsan have been located based on topographic maps, and some irrigation districts in Gaotai County have used GPS to locate some channels. Referring to the schematic diagram of the irrigation district, channel spatial positioning is carried out based on Quikbird, ASTER, TM remote sensing images and 1: 50000 topographic maps. For the main canal and branch canal, due to the obvious linear features on remote sensing images and the general signs on topographic maps, it can be located more accurately. For Douqu, areas with high-resolution images can be located more accurately, while other areas can only be roughly located according to fuzzy linear features of images and prompt information of irrigation district staff, with low positioning accuracy. Each water management office simultaneously provides channel attribute data, which is one-to-one corresponding to spatial data. After the first draft of the channel distribution map is completed, it is submitted twice to the personnel familiar with the channel distribution of each water pipe for correction. The first time is mainly to eliminate duplication and leak, and the second time is mainly to correct the position and perfect the attribute data. Description of data content: The fields in the attribute table include code, district and county name, irrigation area name, channel whole process, channel name, channel type, location, total length, lined, design flow, design farmland, design forest and grass, real irrigation farmland, real irrigation forest and grass, water right area, and remarks. Code example: G06G02Z15D01, where the first letter represents the county name, the 2nd and 3rd numbers represent the county (district) number, the 4th to 6th characters represent the trunk canal code, the 7th to 9th characters represent the branch canal code, and the 10th to 12th characters represent the dou canal code.
In 2007 and 2008, Landsat data set 49 scenes, covering the entire black river basin. The acquisition time is:2007-08-12, 2007-09-23, 2008-01-05, 2008-02-06, 2008-03-17, 2008-03-25, 2008-05-10, 2008-05-19, 2008-05-28, 2008-06-04, 2008-07-07, 2008-07-15, 2008-07-22, 2008-07-23, 2008-08-16, 2008-08-30,2008-09-08, 2008-09-15, 2008-09-17, 2008-10-01, 2008-10-10, 2008-10-19, 2008-10-26, 2008-11-02, 2008-11-04, 2008-11-18, 2008-11-20, 2008-11-27, 2008-12-06, 2008-12-13, 2008-12-14. The product is class L1 and has been geometrically corrected.It includes 4 scenes of TM image and 45 scenes of ETM+ image. The Landsat satellite remote sensing data set of heihe integrated remote sensing joint experiment was obtained through free download.
The data set includes ASTER GDEM data and its Mosaic. ASTER Global DEM (ASTER GDEM) is a Global digital elevation data product jointly released by NASA and Japan's ministry of economy, trade and industry (METI) on June 29, 2009. The DEM data is based on the observation results of NASA's new earth observation satellite TERRA.It is produced by the ASTER(Advanced Space borne Thermal Emission and Reflection Radio meter) sensor, which collects 1.3 million stereo image data, covering more than 99% of the earth's land surface.The data has a horizontal accuracy of 30 m (95% confidence) and an elevation accuracy of 7-14 m (95% confidence).This data is the third global elevation data, which is significantly higher than previous SRTM3 DEM and GTOPO30 data. We from NASA's web site (http://wist.echo.nasa.gov/api) to download the data of heihe river basin, and through the data center to distribute.The data distributed by the center completely retains the original appearance of the data without any modification to the data.If users need details about ASTER GDEM preparation process, please refer to the data documents of metadata connections, or visit http://www.ersdac.or.jp/GDEM/E/3.html or directly from https://lpdaac.usgs.gov/ reading and ASTER Global DEM related documents. ASTER GDEM is divided into several data blocks of 1×1 degree in distribution, and the distribution format is zip compression format. Each compressed file includes three files. The file naming format is as follows: ASTGTM_NxxEyyy_dem.tif ASTGTM_NxxEyyy_num.tif reademe.pdf Where xx is the starting latitude and yyy is the starting longitude._dem. Tif is the dem data file, _num. Tif is the data quality file, and reademe is the data description file. In order to facilitate users to use the data, on the basis of the fractional ASTER GDEM data, we splice fractional SRTM data to prepare the ASTER GDEM Mosaic map of the black river basin, which retains all the original features of ASTER GDEM without any resamulation. This data includes two files: heihe_aster_gdem_mosaic_dem.img Heihe_Aster_GDEM_Mosaic_num. Img The data is stored in the format of Erdas image, where the file _dem.img is the dem data file and the file _num. Img is the data quality file.
National Aeronautics and Space Administration
The Landsat TM Mosaic Image of the Heihe River Basin can be effectively applied to monitoring land-use change of the basin, which reflects the current situation of the Heihe River Basin in 2010, and provides a reliable basis for ecological planning and restoration. This mosaic image collected the TM images released by the USGS for free in 2010 (data from July to September 2010, totally 21 scenes, the maximum cloud amount is less than 10%), and the preprocessed images were geometrically registered by topographic maps(polynomial geometry correction method), then a geometrically-corrected digital mosaic map was generated, which was of high quality after a certain accuracy evaluation. The images were stored in ERDAS IMG format, and the most abundant bands 5, 4 and 3 combination, with three colors: red, green, and blue were selected to generate a color composite image. The combined composite image not only is similar to natural color, which is more in accordance with people's visual habits, but also can fully display the differences in image features because of the rich amount of information.
LP DAAC User Services
Terra (EOS am-1), the flagship of the EOS earth observation series, was the first satellite to be launched on December 18, 1999.ASTER is primarily used for high-resolution observations of surface radiation balance. Compared with Landsat series satellites, ASTER has improved spectral and spatial resolution, and significantly increased short-wave infrared and thermal infrared bands.ASTER has a total of 14 wavebands, including 3 visible and near-infrared wavebands, 5 short-wave infrared wavebands and 5 thermal infrared wavebands. The resolution is 15m, 30m and 90m respectively, and the scanning width is 60km, 30m and 90m respectively.Heihe river basin ASTER remote sensing image data set through the international cooperation data from NASA's web site (https://wist.echo.nasa.gov/). Data naming rules as follows: assuming that the name of the ASTER image for "ASTL1B0103190215190103290064", then ASTL1B said ASTER L1B products, 003 on behalf of the version number namely VersionID, (010319) represents the next 6 digits observation date will be March 19, 2001, followed by six digits (021519) represents the observation time (02:15:19), followed by the last six digits (010329) representing the processing date is March 29, 2001, the last four digits (0064) representing the four-digit sequence code. At present, there are 258 scents of ASTER data in heihe river basin.The acquisition time is:2000-04-25, 2000-04-27 (2 scenes), 2000-05-04, 2000-05-15 (4 scenes), 2000-05-20 (9 scenes), 2000-05-29 (3 scenes), 2000-05-31 (2 scenes), 2000-06-12, 2000-06-14 (5 scenes), 2000-06-21 (3 scenes), 2000-06-30 (8 scenes), 2000-07-18, 2000-07-23 (3 scenes), 2000-08-03 (4 scenes),2000-08-08 (9 scenes), 2000-08-17 (7 scenes), 2000-08-19 (4 scenes), 2000-08-26 (3 scenes), 2000-09-02 (4 scenes), 2000-10-02 (7 scenes), 2000-10-04 (6 scenes), 2000-10-29 (3 scenes), 2000-11-21, 2001-02-18 (2 scenes), 2001-02-25, 2001-03-11 (5 scenes), 2001-03-22 (4 scenes),2001-03-27 (4 scenes), 2001-03-29 (9 scenes), 2001-04-07 (2 scenes), 2001-04-12 (2 scenes), 2001-04-14 (6 scenes), 2001-07-10, 2001-07-12 (8 scenes), 2001-07-21 (8 scenes), 2001-08-13 (8 scenes), 2001-08-20 (7 scenes), 2001-08-22, 2001-08-27 (2 scenes), 2001-08-29,2001-09-03 (2 scenes), 2001-11-15 (7 scenes), 2002-02-01, 2002-03-30 (2 scenes), 2002-04-17 (2 scenes), 2002-05-24, 2002-06-04 (6 scenes), 2002-06-09, 2002-06-13, 2002-06-25, 2002-08-14 (3 scenes), 2002-09-29, 2002-10-19 (2 scenes), 2002-11-11 (2 scenes),2002-12-29 (4 scenes), 2003-04-18, 2003-05-24 (2 scenes), 2003-07-25, 2003-07-30, 2003-8-10 (5 scenes), 2003-08-12, 2003-08-17, 2003-09-09 (11 scenes), 2003-09-13 (4 scenes), 2003-10-15, 2003-10-18, 2003-10-29 (9 scenes), 2003-11-30, 2004-03-14, 2005-03-20,2005-06-05, 2005-08-11, 2007-10-22, 2007-11-14, 2007-11-23, 2007-12-04, 2008-01-28, 2008-02-13, 2008-05-03 (4 scenes), 2008-05-05, 2008-05-17, 2008-06-04 (2 scenes), 2008-06-13.
National Aeronautics and Space Administration
Landsat 5 was launched in March 1984 and has been in orbit for 16 years. The thematic mapper (TM) sensor on Landsat 5 consists of seven bands, all of which have a resolution of 30m except for band 6, which has a resolution of 120m. Currently, there are 23 TM data sets in heihe river basin.The obtained time was 1987-08-15, 1987-09-14, 1987-10-09, 1988-06-28, 1989-05-09, 1990-07-30, 1990-08-21 (2 scenes), 1990-08-28, 1990-08-30, 1990-09-15 (2 scenes), 1991-09-02, 1995-08-19, 1995-08-21, 2002-06-13,2003-09-12, 2007-09-23, 2008-03-17, 2008-07-07, 2008-07-23. The product is class L1 and has been geometrically corrected.
LP DAAC User Services
This set of data mainly includes the demographic data of 12 counties in 6 prefecture-level cities of Qinghai, Gansu and Inner Mongolia in Heihe River Basin, covering the time period of 2000-2009. The data source is the local statistical yearbook, which mainly includes: Statistical Bureau of Suzhou District. Statistical Yearbook of Suzhou. 2004-2009; Yumen Statistical Bureau. Yumen Statistical Yearbook. 2000-2008; Jinta County Statistical Bureau. Jinta County Statistical Yearbook. 2004-2009; Gaotai Statistical Bureau. Gaotai Statistical Yearbook. 2000-2007; Shandan County Statistical Bureau. Shandan County Statistical Yearbook. 2000-2009; Sunan Yugur Statistical Bureau. Statistical Yearbook of Sunan Yugur Autonomous County. 2004-2009; Minle County Statistical Bureau. Minle County Statistical Yearbook. 2004-2009; Shandan County Statistical Bureau. Shandan County Statistical Yearbook. 2000-2009; Linze County Statistical Bureau. Linze County Statistical Yearbook. 2000-2009; Ejin Banner Statistical Bureau. Ejin Banner Statistical Yearbook. 1990-2005; Qilian County Statistical Bureau. Qilian County National Economic Statistics. 2004-2009; Part of the data of Zhangye City comes from the basic social and economic situation of townships of Zhangye City in 2005. Data of Jiayuguan City is derived from the CNKI statistical data database of China National Knowledge Infrastructure, and only contains some county-level data. Data Content Description: The data mainly includes three population indicators of 12 counties in the basin, including Ganzhou District, Gaotai County, Shandan County, Minle County, Linze County, Sunan Yugur Autonomous County, Jinta County, Sunzhou District and Yumen City, Jiayuguan City, Qilian County, and Ejin Banner. The population indicators are permanent population, agricultural population and non-agricultural population at the end of the year. It is divided into two levels: county level and township level. The statistics currently available are: County level: Ejina Banner: 2006-2009: resident population, agricultural population, non-agricultural population at the end of each year Ganzhou District: 2009: agricultural population, non-agricultural population of the year; Gaotai County: 2009: agricultural population, non-agricultural population of the year; Sunan: 2000-2009: permanent population, agricultural population, non-agricultural population at the end of each year; Minle County: 2009: permanent population, agricultural population, non-agricultural population at the end of the year; Linze: 2009: permanent population, agricultural population, non-agricultural population at the end of the year; Yumen City: 2000-2005: permanent population, agricultural population, non-agricultural population at the end of each year; Township level: Ejin Banner: 2000-2005: permanent population, agricultural population, non-agricultural population at the end of the year; Ganzhou District: 2000-2008: permanent population, agricultural population, non-agricultural population at the end of the year; 2009: resident population at the end of the year; Gaotai County: 2000-2004, 2006, 2007: permanent population, agricultural population, non-agricultural population at the end of the year; 2009: resident population at the end of the year; Shandan County: 2000-2007: permanent population, agricultural population, non-agricultural population at the end of the year; 2009: resident population at the end of the year; Minle County: 2000-2008: permanent population, agricultural population, non-agricultural population at the end of the year; Jinta County: 2004-2009: permanent population, agricultural population, non-agricultural population at the end of the year; Yumen City: 2006-2008: permanent population, agricultural population, non-agricultural population at the end of the year; Suzhou District 2004-2009: permanent population, agricultural population, non-agricultural population at the end of the year; Qilian County: 2004-2009: permanent population, agricultural population, non-agricultural population at the end of the year; Permanent population at the end of the year, agricultural population, non-agricultural population County level township level county level township level county level township level Ejin Banner:2006-2009 2000-2005 2006-2009 2000-2005 2006-2009 2000-2005 Ganzhou District 2000-2009 2009 2000-2008 2009 2000-2008 Gaotai County 2000-2004、 2006、2007、2009 2009 2000-2004、 2006、2007 2009 2000-2004、 2006、2007 Shandan County 2000-2007、2009 2000-2007 2000-2007 Sunan County 2000-2009 2000-2009 2000-2009 Minle County 2009 2000-2008 2009 2000-2008 2009 2000-2008 Linze County 2009 2009 2009 Jinta County 2004-2009 2004-2009 2004-2009 Sunzhou District 2004-2009 2004-2009 2004-2009 Qilian County 2004-2009 2004-2009 2004-2009 Yumen City 2000-2005 2006-2008 2000-2005 2006-2008 2000-2005 2006-2008
ASAR (Advanced Synthetic Aperture Radar) is a Synthetic Aperture Radar sensor mounted on ENVISAT satellite. It operates in c-band with a wavelength of 5.6 cm and features multi-polarization, variable observation Angle and wide-range imaging. Heihe river basin of ENVISAT ASAR remote sensing data sets mainly through central Europe "dragon plan" project, the data to the Image mode, cross polarization (Alternating Polarisation) model with wide is given priority to, the spatial resolution of 30 meters. ENVISAT ASAR data 404 scenes are currently available in heihe river basin, including 82 scenes in APP mode, 7 scenes in IMP mode and 315 scenes in WSM mode. The acquisition time is: APP can choose the polarization mode, the time range is from 2007-08-15 to 2007-12-23, 2008-01-02 to 2008-12-20, 2009-02-15 to 2009-09-06; IMP imaging mode, time range from 2009-06-19 to 2009-07-12; WSM wide format, time range from 2005-12-05 to 2005-12-31,2006-01-06 to 2006-12-31, 2007-01-01 to 2007-12-30, 2008-01-01 to 2008-12-28, 2009-03-13 to 2009-05-22. Product level is L1B, without geometric correction, is amplitude data.
The DEM elevation model data set of 1km in heihe river basin generates DEM grid data based on China 1:250,000 digital contour line and elevation point data interpolation released by national basic geographic information center (http://ngcc.sbsm.gov.cn). The data includes Albers projection and longitude and latitude coordinates.A large amount of surface morphology information can be extracted from DEM, including the slope, slope direction and the relationship among cells of the watershed grid, which is an important source data for watershed research.
National Basic Geographic Information Center
The geomorphic data of Heihe River are from the geomorphic Atlas of the people's Republic of China (1:1 million). This data is based on remote sensing image and other multi-source data integration and update. The main data used and referenced include: 1) remote sensing image data: TM and 2000's around 1990's nationwide About ETM image; 2) historical geomorphic map: 15 published 1 million geomorphic maps, two sets of 1:4 million geomorphic maps in China, 500000 or 1 million geomorphic sketches in all provinces and cities in China; 3) basic geographic data: 1:250000 basic geographic data and 250000 DEM data in China; 4) geological data: 1:500000 geological map in China; 5) relevant thematic maps: land use map, vegetation map and land resource map And so on. The interpretation method adopts the human-computer interaction method based on ArcGIS, and is carried out according to the interpretation sequence of hierarchical classification: the first layer: plain and mountain; the second layer: basic geomorphic types (28); the third layer: 10 genetic types; the fourth layer: secondary genetic types; the fifth layer: morphological difference classification types; the sixth layer: secondary morphological difference classification types; the seventh layer: slope, slope The eighth layer is the type of geomorphic material determined by material composition or lithology; the ninth layer is the combination of 1-7 layers of map spots. There are 441 geomorphic types and codes. Data fields include: fenfu (view frame number), name (attribute), class (code), sname (administrative division).
The data includes the county-level data of characteristic agriculture distribution in the Qinghai Tibet Plateau, which lays the foundation for the spatial distribution and development of characteristic agriculture in the Qinghai Tibet Plateau.
MA Rui HU Yalu
一. data description The data included the precipitation, river water and groundwater in the small calabash valley from July to September 2015 2H, 18O, with a sampling frequency of 2 weeks/time. 二. Sampling location (1) the precipitation sampling point is located in the ecological hydrology station of the institute of cold and dry regions, Chinese academy of sciences, with the latitude and longitude of 99 ° 53 '06.66 "E, 38 ° 16' 18.35" N. (2) the sampling point of the river is located at the outlet flow weir of haugugou small watershed in the upper reaches of the heihe river, with the latitude and longitude of 99 ° 52 '47.7 "E and 38 ° 16' 11" N.The water sampling point number 2 position for heihe river upstream hoist ditch Ⅱ area exports, latitude and longitude 99 ° 52 '58.40 "E, 38 ° 14' 36.85" N. (3) underground water spring and well water sampling points.The sampling point of spring water is located at 20m to the east of the outlet of the basin, with the latitude and longitude of 99°52 '50.9 "E, 38°16' 11.44" N. The well water sampling point is located near the intersection of east and west branches, with the latitude and longitude of 99 ° 52 '45.38 "E, 38 ° 15' 21.27" N. 三. Test method The δ2H and δ18O values of the samples were measured by PICARRO L2130-i ultra-high precision liquid water and water vapor isotope analyzer. The results were expressed by the test accuracy value of v-smow relative to the international standard substance, and the measurement accuracy was 0.038‰ and 0.011‰, respectively.
MA Rui XING Wenle
1、 Data Description: the data includes the samples of anions and anions of river water and groundwater in hulugou small watershed from July to September 2015 for test and analysis. The sampling frequency is once every two weeks. 2、 Sampling location: (1) there are two river water sampling points. One is located at the outlet flow weir of hulugou small watershed in the upper reaches of Heihe River, with latitude and longitude of 99 ° 52 ′ 47.7 ″ E and 38 ° 16 ′ 11 ″ n. The second sampling point of the river is located at the outlet of hulugou area II at the upper reaches of Heihe River, with the longitude and latitude of 99 ° 52 ′ 58.40 ″ E and 38 ° 14 ′ 36.85 ″ n. (2) Underground water spring and well water sampling points are 20 m to the east of the drainage basin outlet, with longitude and latitude of 99 ° 52 ′ 50.9 ″ E and 38 ° 16 ′ 11.44 ″ n. The well water sampling point is located near the intersection of the East and West Branch ditches, with the longitude and latitude of 99 ° 52 ′ 45.38 ″ E and 38 ° 15 ′ 21.27 ″ n. 3、 Test method: the cation of sample is tested by inductively coupled plasma atomic emission spectrometer (ICP-AES), the test accuracy is 0.05mg/l, and the anion is tested by ion chromatograph (ics1100), the test accuracy is 0.002mg/l.
MA Rui HU Yalu