Pan-third-polar environmental change and green silk road construction

Brief Introduction: Pan-third-polar environmental change and green silk road construction

Number of Datasets: 1212

  • Remote sensing products of vegetation parameters in Heihe River Basin (2020)

    Remote sensing products of vegetation parameters in Heihe River Basin (2020)

    The dataset includes the remote sensing products of normalized vegetation index, vegetation coverage, net primary productivity, grassland biomass, forest volume and vegetation parameters of Heihe River Basin from May 2020 to October 2020, with a spatial resolution of 10m. In this data set, gaofen-1, gaofen-6, sentry, ziyuan-3 and other remote sensing data sources are used, combined with meteorological, ground monitoring and other basic data, and vegetation parameter inversion algorithms and models such as band ratio method, mixed pixel decomposition model, CASA model are used to generate monthly vegetation index remote sensing products of Qilian mountain key point region in growing season. This data set provides data support for the diagnosis and dynamic assessment of regional eco-environmental problems by building a high spatial-temporal resolution eco-environmental monitoring data set based on high resolution satellites.

    2021-06-29 260 12

  • Remote sensing products of vegetation parameters in key areas of Qilian Mountains (2020)

    Remote sensing products of vegetation parameters in key areas of Qilian Mountains (2020)

    The dataset includes the remote sensing products of normalized vegetation index, vegetation coverage, net primary productivity, grassland biomass, forest volume and vegetation parameters of Heihe River Basin from May 2020 to October 2020, with a spatial resolution of 10m. In this data set, gaofen-1, gaofen-6, sentry, ziyuan-3 and other remote sensing data sources are used, combined with meteorological, ground monitoring and other basic data, and vegetation parameter inversion algorithms and models such as band ratio method, mixed pixel decomposition model, CASA model are used to generate monthly vegetation index remote sensing products of Qilian mountain key point region in growing season. This data set provides data support for the diagnosis and dynamic assessment of regional eco-environmental problems by building a high spatial-temporal resolution eco-environmental monitoring data set based on high resolution satellites.

    2021-06-29 335 6

  • Human activity data of key regions in Qilian Mountains (2020)

    Human activity data of key regions in Qilian Mountains (2020)

    This data set is the human activity data in the key areas of Qilian Mountain in 2020. Based on the data of mining, illegal house renovation, new roads, land leveling and ecological restoration in the key areas of Qilian Mountain, the Gaofen-1, Gaofen-2 and ZY3 high-resolution remote sensing images to compare the changes before and after statistical analysis. in the key areas of Qilian Mountain, the changes of land types are investigated and verified block by block; in the areas with suspicious maps, re-interpretation and verification; in the areas with unreflecting images, field verification is carried out to collect relevant data, check and correct the location. At the same time, it further checks the attribute information of mining, illegal house renovation, new roads, land leveling and ecological restoration in the key areas of Qilian Mountain in 2020, and unifies the input and editing of the patches and their attributes, forming the data set of 2m spatial resolution human activities in the key areas of Qilian Mountain in 2020, realizing the current situation and timeliness of ecological management in the key areas of Qilian Mountain, and providing data support for the monitoring of human activities in the key areas of Qilian Mountain in 2020.

    2021-06-25 273 6

  • 30 m land cover classification product data set of Qilian Mountain Area in 2020 (V2.0)

    30 m land cover classification product data set of Qilian Mountain Area in 2020 (V2.0)

    This dataset contains land cover products in Qilian Mountain Area in 2020. The dataset was produced based on the product in 2019 using change monitoring method on the Google Earth Engine platform using Landsat series data. The overall accuracy of this product is above 85%. This is a continuation of the products from 1985-2019.

    2021-06-25 495 0

  • Product data set of 30 m human activity parameters in Qilian Mountain Area in 2020 (V2.0)

    Product data set of 30 m human activity parameters in Qilian Mountain Area in 2020 (V2.0)

    This data set includes 30 m cultivated land and construction land distribution products in Qilian Mountain Area in 2020. The product comes from the land cover classification product of 30 m in Qilian Mountain Area in 2020. The land cover classification products of 30m in 2020 areproduced using change detection method based on the land cover classification product of 2019 in Google Earth engine platform with the Landsat series data . The overall accuracy of the product is better than 85%. This product is a continuation of the human activity parameter product from 1985 to 2019,which also can be downloaded from this website.

    2021-06-25 497 0

  • Landsat-based continuous monthly 30m NDVI Dataset in Qilian mountain area in 2020 (V1.0)

    Landsat-based continuous monthly 30m NDVI Dataset in Qilian mountain area in 2020 (V1.0)

    This data set includes the monthly synthesis of 30m*30m surface vegetation index products in Qilian mountain area in 2019. Max value composition (MVC) method was used to synthesize monthly NDVI products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.

    2021-06-24 523 14

  • Landsat-based continuous monthly 30m LAI Dataset in Qilian mountain area in 2020 (V1.0)

    Landsat-based continuous monthly 30m LAI Dataset in Qilian mountain area in 2020 (V1.0)

    This data set includes the monthly synthesis of 30m LAI products in Qilian mountain area in 2020. Max value composition (MVC) method was used to synthesize monthly LAI products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.

    2021-06-24 459 3

  • Landsat-based continuous monthly 30m FVC Dataset in Qilian mountain area in 2020 (V1.0)

    Landsat-based continuous monthly 30m FVC Dataset in Qilian mountain area in 2020 (V1.0)

    This data set includes the monthly synthesis of 30m*30m surface vegetation index products in Qilian mountain area in 2020. Max value composition (MVC) method was used to synthesize monthly FVC products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.

    2021-06-24 617 14

  • Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (eddy covariance system of the Subalpine shrub, 2020)

    Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (eddy covariance system of the Subalpine shrub, 2020)

    This dataset contains the flux measurements from the Subalpine shrub eddy covariance system (EC) belonging to the Qinghai Lake basin integrated observatory network from January 1 to December 31 in 2020. The site (100°6'3.62"E, 37°31'15.67" N ) was located near Dasi, Shaliuhe Town, Gangcha County, Qinghai Province. Data missing due to instrument failure. The elevation is 3495m. The EC was installed at a height of 2.5m, 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 (Gill&Li7500A) was about 0.17 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, 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): class 1-3 (high quality), class 4-6 (good), class 7-8 (poor, better than gap filling data), class9 (rejected). 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 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. The released data contained the following variables: DATE/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/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (z/L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), 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). The quality marks of sensible heat flux, latent heat flux and carbon flux are divided into three levels (quality marks 0 have good data quality, 1 have good data quality and 2 have poor data quality). 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. Detailed information can be found in the suggested references.

    2021-06-22 375 19

  • Dataset of agricultural water resources vulnerability in Central Asia (V1.0)

    Dataset of agricultural water resources vulnerability in Central Asia (V1.0)

    In order to investigate the variation characteristics of agricultural water resources vulnerability in Central Asia, an index system was established with 18 indicators from three components, namely exposure, sensitivity and adaptation, according to the scheme of vulnerability assessment. Based on the socio-economic, topography, land cover and soil data, agricultural water resources vulnerability were calculated using the Equal-Weights and Principal Component Analysis (PCA) method. Each original raster data is resampled, starting from the upper-left corner of the original grid, and extending to the adjacent right and lower grids in turn, and every four grids (0.5 °) are merged into one grid, taking the median data as the center point value corresponding to four grid of geographic coordinates. The extreme values of the grids could be eliminated. The data sets includes 1992-1996, 1997-2001, 2002-2006, 2007-2011, 2012-2017and 1992-2017with a spatial resolution of 0.5°*0.5°. It is expected to provide basic data support for agricultural water supply and demand, development and utilization analysis in five central Asian countries.

    2021-06-21 607 25

  • Water index in the Qilian Mountain Area in 2020

    Water index in the Qilian Mountain Area in 2020

    This dataset contains the ground surface water (including liquid water, glacier and perennial snow) distribution in Qilian Mountain Area in 2020. The dataset was produced based on classical Normalized Difference Water Index (NDWI) extraction criterion and manual editing. Landsat images collected in 2020 were used as basic data for water index extraction. Sentinel-2 images and Google images were employed as reference data for adjusting the extraction threshold. The dataset was stored in SHP format and attached with the attributions of coordinates and water area. Consisting of 1 season, the dataset has a temporal resolution of 1 year and a spatial resolution of 30 meters. The accuracy is about 1 pixel (±30 meter). The dataset directly reflects the distribution of water bodies within the Qilian Mountain in 2020, and can be used for quantitative estimation of water resource.

    2021-06-17 262 0

  • Glacier boundary in Qilian Mountains (V2.0, 2019)

    Glacier boundary in Qilian Mountains (V2.0, 2019)

    This dataset contains the glacier outlines in Qilian Mountain Area in 2019. The dataset was produced based on classical band ratio criterion and manual editing. Chinese GF series images collected in 2019 were used as basic data for glacier extraction. Google images and Map World images were employed as reference data for manual adjusting. The dataset was stored in SHP format and attached with the attributions of coordinates, glacier ID and glacier area. Consisting of 1 season, the dataset has a spatial resolution of 2 meters. The accuracy is about 1 pixel (±2 meter). The dataset directly reflects the glacier distribution within the Qilian Mountain in 2019, and can be used for quantitative estimation of glacier mass balance and the quantitative assessment of glacier change’s impact on basin runoff.

    2021-06-15 348 0

  • Glacier inventory of Qilian Mountain Area (2020)

    Glacier inventory of Qilian Mountain Area (2020)

    This dataset contains the glacier outlines in Qilian Mountain Area in 2020. The dataset was produced based on classical band ratio criterion and manual editing. Chinese GF series images collected in 2020 were used as basic data for glacier extraction. Google images and Map World images were employed as reference data for manual adjusting. The dataset was stored in SHP format and attached with the attributions of coordinates, glacier ID and glacier area. Consisting of 1 season, the dataset has a spatial resolution of 2 meters. The accuracy is about 1 pixel (±2 meter). The dataset directly reflects the glacier distribution within the Qilian Mountain in 2020, and can be used for quantitative estimation of glacier mass balance and the quantitative assessment of glacier change’s impact on basin runoff.

    2021-06-15 419 0

  • Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (Large aperture scintillometer of A'rou Superstation, 2020)

    Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (Large aperture scintillometer of A'rou Superstation, 2020)

    This dataset contains the flux measurements from the large aperture scintillometer (LAS) at Arou Superstation in the Heihe integrated observatory network from January 18 to December 31 in 2020. There were two types of LASs at Arou Superstation: BLS900 and RR-RSS460, produced by Germany and China, respectively. The north tower was set up with the RR-RSS460 receiver and the BLS900 transmitter, and the south tower was equipped with the RR-RSS460 transmitter and the BLS900 receiver. The site (north: 100.471° E, 38.057° N; south: 100.457° E, 38.038° N) was located in Caodaban village of A’rou town in Qilian county, Qinghai Province. The underlying surface between the two towers was alpine meadow. The elevation is 3033 m. The effective height of the LASs was 13.0 m, and the path length was 2390 m. The data were sampled 1 minute at both BLS900 and RR-RSS460. The raw data acquired at 1 min intervals were processed and quality controlled. The data were subsequently averaged over 30 min periods, in which sensible heat flux was iteratively calculated by combining Cn2 with meteorological data according to the Monin-Obukhov similarity theory. The main quality control steps were as follows: (1) The data were rejected when Cn2 exceeded the saturated criterion (BLS900: Cn2>7.25E-14, RR-RSS460: Cn2>7.84E-14). (2) The data were rejected when the demodulation signal was small (BLS900: Average X Intensity<1000; RR-RSS460: Demod>-20mv). (3) The data were rejected when collected during precipitation. (4) The data were rejected if collected at night when weak turbulence occurred (u* was less than 0.1 m/s). In the iteration process, the universal functions of Thiermann and Grassl (1992) and Andreas (1988) were selected for BLS900 and RR-RSS460, respectively. Detailed can refer to Liu et al. (2011, 2013). Due to instrument adjustment and inadequate power supply, the date of missing data for the large aperture scintillator is:2020.09.25-2020.10.16. Several instructions were included with the released data. (1) The data were primarily obtained from BLS900 measurements, and missing flux measurements from the BLS900 instrument were substituted with measurements from the RR-RSS460 instrument. The missing data were denoted by -6999. Due to the problems of storing and wireless transmission. (2) The dataset contained the following variables: Date/time (yyyy/m/d h:mm), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H, W/m^2). In this dataset, a time of 0:30 corresponds to the average data for the period between 0:00 and 0:30, and the data were stored in *.xlsx format. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) (for data processing) in the Citation section.

    2021-06-11 431 15

  • Daily MODIS-based Land Surface Evapotranspiration Dataset of 2020 in Qilian Mountain Area (ETHi-merge V1.0)

    Daily MODIS-based Land Surface Evapotranspiration Dataset of 2020 in Qilian Mountain Area (ETHi-merge V1.0)

    This dataset contains daily land surface evapotranspiration products of 2020 in Qilian Mountain area. It has 0.01 degree spatial resolution. The dataset was produced based on Gaussian Process Regression (GPR) method by fusing six satellite-derived evapotranspiration products including RS-PM (Mu et al., 2011), SW (Shuttleworth and Wallace., 1985), PT-JPL (Fisher et al., 2008), MS-PT (Yao et al., 2013), SEMI-PM (Wang et al., 2010a) and SIM (Wang et al.2008). The input variables for the evapotranspiration products include MODIS products, MERRA meteorological data, and China Meteorological Forcing Dataset.

    2021-06-09 532 36

  • Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of mixed forest station, 2020)

    Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of mixed forest station, 2020)

    This dataset contains the flux measurements from the mixed forest station eddy covariance system (EC) in the downstream reaches of the Heihe integrated observatory network from January 1 to December 31 in 2020. The site (101.1335° E, 41.9903° N) was located in the Sidaoqiao County, in Ejina Banner in Inner Mongolia Autonomous Region . The elevation is 874 m. The EC was installed at a height of 3.2 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 (CSAT3B & Li7500DS) was 0.17 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, 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): class 1-3 (high quality), class 4-6 (good), class 7-8 (poor, better than gap filling data), class9 (rejected). 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 collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 10% of the 30 min raw record. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. The water vapor density data were rejected when the negative values occurred. Data during May 8 to 20, 2020 were missing due to instrument malfunction. 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/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), 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. Detailed information can be found in the suggested references. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) for data processing) in the Citation section.

    2021-06-09 418 39

  • Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (an observation system of Meteorological elements gradient of Alpine meadow and grassland ecosystem Superstation, 2020)

    Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (an observation system of Meteorological elements gradient of Alpine meadow and grassland ecosystem Superstation, 2020)

    This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient of the Alpine meadow and grassland ecosystem Superstation from Janurary 1 to December 31 in 2020. The site (98°35′41.62″E, 37°42′11.47″N) was located in the alpine meadow and alpine grassland ecosystem, near the SuGe Road in Tianjun County, Qinghai Province. The elevation is 3718m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 3, 5, 10, 15, 20, 30, and 40 m, towards north), wind speed and direction profile (windsonic; 3, 5, 10, 15, 20, 30, and 40 m, towards north), air pressure (PTB110; 3 m), rain gauge (TE525M; 10m of the platform in west by north of tower), four-component radiometer (CNR4; 6m, towards south), two infrared temperature sensors (SI-111; 6 m, towards south, vertically downward), photosynthetically active radiation (PQS1; 6 m, towards south, each with one vertically downward and one vertically upward, soil heat flux (HFP01; 3 duplicates below the vegetation; -0.06 m), soil temperature profile (109; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m), soil moisture profile (CS616; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, Ta_10 m, Ta_15 m, Ta_20 m, Ta_30 m, and Ta_40 m; RH_3 m, RH_5 m, RH_10 m, RH_15 m, RH_20 m, RH_30 m, and RH_40 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, Ws_10 m, Ws_15 m, Ws_20 m, Ws_30 m, and Ws_40 m) (m/s), wind direction (WD_3 m, WD_5 m, WD_10 m, WD_15 m, WD_20 m, WD_30m, and WD_40 m) (°), precipitation (rain) (mm), air pressure (press) (hpa), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)), 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), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_5cm、Ts_10cm、Ts_20cm、Ts_40cm、Ts_80cm、Ts_120cm、Ts_200cm、Ts_300cm、Ts_400cm) (℃), soil moisture (Ms_5cm、Ms_10cm、Ms_20cm、Ms_40cm、Ms_80cm、Ms_120cm、Ms_200cm、Ms_300cm、Ms_400cm) (%, volumetric water content). 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 missing data were denoted by -6999. (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: 2018/8/31 10:30. Moreover, suspicious data were marked in red.

    2021-06-08 465 32

  • Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (an observation system of Meteorological elements gradient of Yulei station on Qinghai lake, 2020)

    Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (an observation system of Meteorological elements gradient of Yulei station on Qinghai lake, 2020)

    This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient of Yulei station on Qinghai lake from Janurary 1 to December 31, 2020. The site (100° 29' 59.726'' E, 36° 35' 27.337'' N) was located on the Yulei Platform in Erlangjian scenic area, Qinghai Province. The elevation is 3209m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 12 and 12.5 m above the water surface, towards north), wind speed and direction profile (windsonic; 14 m above the water surface, towards north) , rain gauge (TE525M; 10m above the water surface in the eastern part of the Yulei platform ), four-component radiometer (NR01; 10 m above the water surface, towards south), one infrared temperature sensors (SI-111; 10 m above the water surface, towards south, vertically downward), photosynthetically active radiation (LI190SB; 10 m above the water surface, towards south), water temperature profile (109, -0.2, -0.5, -1.0, -2.0, and -3.0 m). The observations included the following: air temperature and humidity (Ta_12 m, Ta_12.5 m; RH_12 m, RH_12.5 m) (℃ and %, respectively), wind speed (Ws_14 m) (m/s), wind direction (WD_14 m) (°) , 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_1) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), water temperature (Tw_20cm、Tw_50cm、Tw_100cm、Tw_200cm、Tw_300cm) (℃). 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. As the lake water freezes in winter, the water temperature probe is withdrawn, so there is no water temperature data record during October 19, 2020 to December 31, 2020. The missing data were denoted by -6999. (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: 2018-1-1 10:30. Moreover, suspicious data were marked in red.

    2021-06-08 382 31

  • Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (an observation system of Meteorological elements gradient of the temperate steppe, 2020

    Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (an observation system of Meteorological elements gradient of the temperate steppe, 2020

    This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient from Janurary 1 to December 31 in 2020. The site (100°14'8.99"E, 37°14'49.00"N) was located in Sanjiaocheng sheep breeding farm, Gangcha County, Qinghai Province. The elevation is 3210m.The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 3, 5, 10 m, towards north), wind speed and direction profile (windsonic; 3, 5, 10m, towards north), air pressure (PTB110; 3 m), rain gauge (TE525M; towards north), four-component radiometer (CNR4; 6m, towards south), two infrared temperature sensors (SI-111; 6 m, towards south, vertically downward), photosynthetically active radiation (PQS1; 6 m, towards south, each with one vertically downward and one vertically upward, soil heat flux (HFP01; 3 duplicates below the vegetation; -0.06 m), soil temperature profile (109; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -5.00m), soil moisture profile (CS616; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -5.00m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, Ta_10 m; RH_3 m, RH_5 m, RH_10 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, Ws_10 m) (m/s), wind direction (WD_3 m, WD_5 m, WD_10 m) (°), precipitation (rain) (mm), air pressure (press) (hpa), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)), 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), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_5cm、Ts_10cm、Ts_20cm、Ts_40cm、Ts_80cm、Ts_120cm、Ts_200cm、Ts_300cm、Ts_400cm) (℃), soil moisture (Ms_5cm、Ms_10cm、Ms_20cm、Ms_40cm、Ms_80cm、Ms_120cm、Ms_200cm、Ms_300cm、Ms_400cm) (%, volumetric water content). 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 missing data were denoted by -6999. (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: 2018/8/31 10:30.

    2021-06-08 403 13

  • Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (an observation system of Meteorological elements gradient of Subalpine shrub, 2020

    Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (an observation system of Meteorological elements gradient of Subalpine shrub, 2020

    This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient of the Subalpine shrub from Janurary 1to December 31, 2020. The site (100°6'3.62"E, 37°31'15.67") was located in the subalpine shrub ecosystem, near the Gangcha County, Qinghai Province. The elevation is 3495m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP155; 3, 5 and 10 m, towards north), wind speed and direction profile (windsonic; 3, 5 and 10 m, towards north), air pressure (PTB110; 3 m), rain gauge (TE525M; 2 m of the platform in west by north of tower), four-component radiometer (CNR4; 6m, towards south), two infrared temperature sensors (SI-111; 6 m, towards south, vertically downward), photosynthetically active radiation (PQS1; 6 m, towards south, each with one vertically downward and one vertically upward, soil heat flux (HFP01; 3 duplicates below the vegetation; -0.06 m), soil temperature profile (109; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m), soil moisture profile (CS616; -0.05、-0.10、-0.20、-0.40、-0.80、-1.20、-2.00、-3.00 and -4.00m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_5 m, and Ta_10 m; RH_3 m, RH_5 m, and RH_10 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_5 m, and Ws_10 m) (m/s), wind direction (WD_3 m, WD_5 m and WD_10 m) (°), precipitation (rain) (mm), air pressure (press) (hpa), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)), 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), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_5cm、Ts_10cm、Ts_20cm、Ts_40cm、Ts_80cm、Ts_120cm、Ts_200cm、Ts_300cm、Ts_500cm) (℃), soil moisture (Ms_5cm、Ms_10cm、Ms_20cm、Ms_40cm、Ms_80cm、Ms_120cm、Ms_200cm、Ms_300cm、Ms_500cm) (%, volumetric water content). 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 missing data were denoted by -6999. (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: 2018/8/31 10:30. Moreover, suspicious data were marked in red.

    2021-06-08 392 31