This dataset contains the automatic weather station (AWS) measurements from site No.10 in the flux observation matrix from 1 June to 17 September, 2012. The site (100.39572° E, 38.87567° N) was located in a cropland (maize surface) in Yingke irrigation district, which is near Zhangye, Gansu Province. The elevation is 1534.73 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity (HMP155; 5 m, towards north), rain gauge (TE525M; 10 m), wind speed (03001; 10 m, towards north), a four-component radiometer (CNR1; 6 m, towards south), two infrared temperature sensors (SI-111; 6 m, vertically downward), soil temperature profile (109ss-L; 0, -0.02, and -0.04 m), soil moisture profile (CS616; 0.02, 0.04 m), and soil heat flux (HFP01; 3 duplicates with one below the vegetation and the other between plants, 0.06 m). The observations included the following: air temperature and humidity (Ta_5 m and RH_5 m) (℃ and %, respectively), precipitation (rain, mm), wind speed (Ws_10 m, m/s), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation; W/m^2), infrared temperature (IRT_1 and IR_2, ℃), soil heat flux (Gs_1, below the vegetation; Gs_2 and Gs_3, W/m^2), soil temperature profile (Ts_0 cm, Ts_2 cm, and Ts_4 cm, ℃), and soil moisture profile (Ms_2 cm and Ms_4 cm, %). The data processing and quality control steps were as follows. (1) The AWS data were averaged over intervals of 10 min; therefore, there were 144 records per day. The missing data were filled with -6999. (2) Duplicate records were rejected. (3) Unphysical data were rejected. (4) In this dataset, the time of 0:10 corresponds to the average data for the period between 0:00 and 0:10; the data were stored in *.xlsx format. (5) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. 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.
0 2019-09-15
The research project on land surface data assimilation system in western China belongs to the major research plan of "environment and ecological science in western China" of the national natural science foundation. the person in charge is Li Xin, researcher of the institute of environment and engineering in cold and arid regions of the Chinese academy of sciences. the project runs from January 2003 to December 2005. One of the data collected in this project is the reanalysis data of surface climate factors in western China in 2002. This data set is generated based on the daily 1 × 1 provided by the National Environmental Prediction Center (NCEP). However, the re-analysis of the data has the following problems: (1) the temporal and spatial resolution is not high enough (the horizontal resolution is 1 degree and the time is 6 hours); (2) The low-level errors in plateau areas are large; (3) The data are standard isosurface data and need interpolation. The 2002 reanalysis data set of surface climate elements in western China was generated by combining NCEP reanalysis data and MM5 model by Dr. Longxiao and Professor Qiu Chongjian of Lanzhou University using Newton relaxation data assimilation method (Nudging), including 10m horizontal and vertical wind speed (m/s), 2m air temperature (k), 2m mixing ratio, surface pressure (Pa), upstream and downstream short wave and long wave radiation (w/m2), convective precipitation and large scale precipitation (mm/s) at 0.25 degree per hour throughout 2002. I. preparation background The quality of the driving data seriously affects the ability of the land surface model to simulate the land surface state, so a very important component of the land surface modeling research is the driving data used to drive the land surface model. No matter how realistic these models are in describing the surface process, no matter how accurate the boundary and initial conditions they input, if the driving data are not accurate, they cannot get the results close to reality. Land surface models are so dependent on the quality of externally provided data that any error in these externally provided data will seriously affect the ability of land surface models to simulate soil moisture, runoff, snow cover and latent heat flux. These externally provided data include: precipitation, radiation, temperature, wind field, humidity and pressure. The 2002 reanalysis data set of surface climate elements in western China uses Newton relaxation data assimilation method (Nudging) to combine NCEP reanalysis data and MM5 model to generate driving data with higher spatial and temporal resolution suitable for complex terrain in western China. Second, the basic parameters of the operation mode 1. Using the US PSU/NCAR mesoscale model MM5 as a simulation model; The selection of simulation grid domain: center (32°N, 90°E), grid distance of 36km, number of horizontal grid points of 131*151, vertical resolution of 25 layers, and mode top of 100hPa；; 2. The data used for initialization are 1 * 1 GRIB grid data of NCEP in the United States. 3. The time step is 120s. Third, the physical process 1. physical process treatment of cloud and precipitation: Grell cumulus cloud parameterization scheme is adopted for sub-grid scale precipitation, and Reisner mixed phase microphysical explicit scheme is adopted for distinguishable scale precipitation; 2. MRF parameterization scheme is adopted for planetary boundary layer process. 3. the radiation process adopts CCM2 radiation scheme. IV. File Format and Naming It is stored in a monthly folder and contains 24 hours of data every day. The naming rules are as follows: 2002***&.forc, where * * * is Julian day and 2002***& is time (in hours), where. forc is the file extension. V. data format Stored in binary floating point type, each data takes up 4 bytes.
0 2020-03-29
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.
0 2020-03-31
The aerosol optical thickness data of the Arctic Alaska station is based on the observation data products of the atmospheric radiation observation plan of the U.S. Department of energy at the Arctic Alaska station. The data coverage time is updated from 2017 to 2019, with the time resolution of hour by hour. The coverage site is the northern Alaska station, with the longitude and latitude coordinates of (71 ° 19 ′ 22.8 ″ n, 156 ° 36 ′ 32.4 ″ w). The source of the observed data is retrieved from the radiation data observed by mfrsr instrument. The characteristic variable is aerosol optical thickness, and the error range of the observed inversion is about 15%. The data format is NC format. The aerosol optical thickness data of Qomolangma station and Namuco station in the Qinghai Tibet Plateau is based on the observation data products of Qomolangma station and Namuco station from the atmospheric radiation view of the Institute of Qinghai Tibet Plateau of the Chinese Academy of Sciences. The data coverage time is from 2017 to 2019, the time resolution is hour by hour, the coverage sites are Qomolangma station and Namuco station, the longitude and latitude coordinates are (Qomolangma station: 28.365n, 86.948e, Namuco station Mucuo station: 30.7725n, 90.9626e). The source of the observed data is retrieved from the radiation data observed by mfrsr instrument. The characteristic variable is aerosol optical thickness, and the error range of the observed inversion is about 15%. The data format is TXT.
0 2020-05-25
The dataset of surface roughness measurements was obtained in the reed plot A, the saline plots B and C of the Linze grassland foci experimental area on Jun. 7, 18 and 25, 2008. All the quadrates were divided into 4×4 subsites, with each one spanning a 120×120 m2 plot. With the roughness plate 110cm long and the measuring points distance 1cm, the samples were collected from south to north and from east to west, respectively. The coordinates of the sample would be got with the help of ArcView; and after geometric correction, surface height standard deviation (cm) and correlation length (cm) could be acquired based on the formula listed on pages 234-236, Microwave Remote Sensing, Vol. II. The original photos of each sampling point, surface height standard deviation (cm) and correlation length (cm) were included this dataset. The roughness data were initialized with the sample name, which was followed by the serial number, the name of the file, standard deviation and correlation length. Each .txt file is matched with one sample photo and standard deviation and correlation length represent the roughness. In addition, the length of 101 needles is also included for further checking.
0 2019-09-13
The section data of the upper reaches of Heihe River mainly show the structure and cross section distribution characteristics of the terrace of Heihe River. These data are mainly obtained through field investigation and measurement. The data include the forest farm section and raft section near Qilian County in the upper reaches of Heihe River, and the Heihekou section in Yingluoxia.
0 2020-03-11
The dataset of airborne imaging spectrometer (OMIS-II) mission was obtained in the Dayekou watershed flight zone on Jun. 4, 2008. Data after radiometric correction and calibration and geometric approximate correction were released. The flying time of each route was as follows: {| ! id ! flight ! file ! starttime ! lat ! long ! alt ! image linage ! endtime ! lat ! long ! alt |- | 1 || 4-13 || 2008-06-04_13-19-02_DATA.BSQ || 13:23:45 || 38.542 || 100.382 || 4624.5 || 3125 || 13:27:13 || 38.493 || 100.230 || 4617.5 |- | 2 || 4-12 || 2008-06-04_13-30-55_DATA.BSQ || 13:31:21 || 38.494 || 100.214 || 4644.9 || 2912 || 13:34:35 || 38.543 || 100.370 || 4626.3 |- | 3 || 4-11 || 2008-06-04_13-38-17_DATA.BSQ || 13:39:14 || 38.551|| 100.381 || 4616.2 || 3051 || 13:42:38 || 38.500 || 100.221 || 4656.5 |- | 4 || 4-10 || 2008-06-04_13-46-20_DATA.BSQ || 13:47:09 || 38.502 || 100.212 || 4640.3 || 2866 || 13:50:20 || 38.550 || 100.365 || 4633.4 |- | 5 || 4-9 || 2008-06-04_13-54-02_DATA.BSQ || 13:55:01 || 38.558 || 100.374 || 4644.3 || 2897 || 13:58:14 || 38.511 || 100.223 || 4628.4 |- | 6 || 4-8 || 2008-06-04_14-01-56_DATA.BSQ || 14:01:51 || 38.511 || 100.209 || 4644.6 || 2751 || 14:04:54 || 38.558 || 100.359 || 4655.7 |- | 7 || 4-7 || 2008-06-04_14-08-36_DATA.BSQ || 14:09:28 || 38.568 || 100.373 || 4630.5 || 2995 || 14:12:48 || 38.519 || 100.218 || 4642.8 |- | 8 || 4-6 || 2008-06-04_14-16-30_DATA.BSQ || 14:16:38 || 38.521 || 100.209 || 4650.1 || 2705 || 14:19:38 || 38.568 || 100.357 || 4652.9 |- | 9 || 4-5 || 2008-06-04_14-23-20_DATA.BSQ || 14:24:25 || 38.576 || 100.367 || 4649.0 || 2958 || 14:27:42 || 38.526 || 100.210 || 4673.5 |- | 10 || 4-4 || 2008-06-04_14-31-24_DATA.BSQ || 14:31:09 || 38.527 || 100.199 || 4631.3 || 2817 || 14:34:17 || 38.576 || 100.353 || 4641.7 |- | 11 || 4-3 || 2008-06-04_14-37-59_DATA.BSQ || 14:39:55 || 38.579 || 100.346 || 4599.6 || 2555 || 14:42:46 || 38.536 || 100.210 || 4612.0 |- | 12 || 4-2 || 2008-06-04_14-46-28_DATA.BSQ || 14:46:20 || 38.535 || 100.194 || 4620.5 || 2869 || 14:49:31 || 38.583 || 100.345 || 4639.2 |- | 13 || 4-1 || 2008-06-04_14-53-13_DATA.BSQ || 14:55:36 || 38.594 || 100.364 || 4621.2 || 3018 || 14:58:58 || 38.544 || 100.206 || 4606.9 |}
0 2019-05-23
This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Dunhuang Station from January 1 to December 31, 2018. The site (93.708° E, 40.348° N) was located on a wetland in the Dunhuang west lake, Gansu Province. The elevation is 990 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (4m and 8 m, towards north), wind speed and direction profile (windsonic; 4m and 8 m, towards north), air pressure (1 m), rain gauge (4 m), infrared temperature sensors (4 m, towards south, vertically downward), soil heat flux (-0.05 and -0.1m ), soil soil temperature/ moisture/ electrical conductivity profile (below the vegetation in the south of tower, -0.05 and -0.2 m), photosynthetically active radiation (4 m, towards south), four-component radiometer (4 m, towards south), sunshine duration sensor(4 m, towards south). The observations included the following: air temperature and humidity (Ta_4 m, Ta_8 m; RH_2 m, RH_4 m, RH_8 m) (℃ and %, respectively), wind speed (Ws_4 m, Ws_8 m) (m/s), wind direction (WD_4 m, WD_8 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), soil heat flux (Gs_0.05m, Gs_0.1m) (W/m^2), soil temperature (Ts_0.05m, Ts_0.2m) (℃), soil moisture (Ms_0.05m, Ms_0.2m) (%, volumetric water content), soil conductivity (Ec_0.05m, Ec_0.2m)(μs/cm), sun time(h). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The data were missing during Jan. 23 to Jan. 24 because of collector failure; the data during Mar. 17 and May 24 were wrong because of the tower body tilt; The air humidity data were rejected due to program error. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018-6-10 10:30.
0 2019-09-15
The data set includes estimated data on the SOS (start of season) and the EOS (end of season) of vegetation in Sanjiangyuan based on the MODIS 16-day synthetic NDVI product (MOD13A2 collection 6). Two common phenological estimation methods were adopted: the threshold extraction method based on polynomial fitting (the term “poly” was included in the file names) and the inflection point extraction method based on double logistic function fitting (the term “sig” was included in the file names). These data can be used to analyse the relationship between vegetation phenology and climate change. The temporal coverage ranges from 2001 to 2014, and the spatial resolution is 1 km.
0 2019-09-14
The dataset of airborne LiDAR mission in the Dayekou watershed flight zone on Jun. 23, 2008 included peak pulse data (*.LAS), full waveform data (.lgc), CCD photos, DEM, DSM and DOM. The flight routes were as follows: {| ! flight route ! startpoint lat ! startpoint lon ! endpoint lat ! endpoint lon ! altitude (m) ! length (km) ! photos |- | 8 || 38°32′52.25″ || 100°12′35.26″ || 38°30′25.65″ || 100°18′31.76″ || 3650 || 9.7 || 34 |- | 9 || 38°32′57.99″ || 100°12′39.09″ || 38°30′31.59″ || 100°18′35.14″ || 3650 || 9.7 || 34 |- | 10 || 38°33′03.74″ || 100°12′42.91″ || 38°30′40.25″ || 100°18′31.88″ || 3650 || 9.5 || 34 |- | 11 || 38°33′12.80″ || 100°12′38.68″ || 38°30′46.10″ || 100°18′35.47″ || 3650 || 9.8 || 35 |- | 12 || 38°33′18.55″ || 100°12′42.51″ || 38°30′54.86″ || 100°18′31.99″ || 3650 || 9.6 || 35 |- | 13 || 38°33′24.30″ || 100°12′46.34″ || 38°31′00.95″ || 100°18′34.98″ || 3650 || 9.5 || 36 |- | 14 || 38°33′30.05″ || 100°12′50.16″ || 38°31′09.54″ || 100°18′31.92″ || 3650 || 9.3 || 35 |- | 15 || 38°33′35.80″ || 100°12′53.99″ || 38°31′15.47″ || 100°18′35.29″ || 3750 || 9.3 || 35 |- | 16 || 38°33′41.55″ || 100°12′57.82″ || 38°31′21.66″ || 100°18′38.05″ || 3750 || 9.3 || 35 |- | 17 || 38°33′47.30″ || 100°13′01.65″ || 38°31′27.25″ || 100°18′42.27″ || 3750 || 9.3 || 35 |- | 19 || 38°34′02.11″ || 100°13′01.25″ || 38°31′45.61″ || 100°18′33.27″ || 3750 || 9.1 || 45 |- | 20 || 38°34′07.86″ || 100°13′05.07″ || 38°31′51.54″ || 100°18′36.64″ || 3750 || 9.1 || 45 |- | 21 || 38°34′13.61″ || 100°13′08.90″ || 38°32′00.12″ || 100°18′33.60″ || 3750 || 8.9 || 45 |- | 22 || 38°34′19.36″ || 100°13′12.73″ || 38°32′05.45″ || 100°18′38.44″ || 3750 || 8.9 || 45 |- | 23 || 38°34′25.10″ || 100°13′16.56″ || 38°32′14.72″ || 100°18′33.72″ || 3750 || 8.7 || 45 |- | 24 || 38°34′30.85″ || 100°13′20.39″ || 38°32′20.48″ || 100°18′37.52″ || 3750 || 8.7 || 45 |- | 25 || 38°34′36.60″ || 100°13′24.22″ || 38°32′26.24″ || 100°18′41.32″ || 3750 || 8.7 || 45 |- | 26 || 38°34′45.66″ || 100°13′19.98″ || 38°32′31.98″ || 100°18′45.15″ || 3750 || 8.9 || 45 |}
0 2019-09-11
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