The China-Mongolia-Russia Economic Corridor is confronted with security problems related with global warming, mostly including the increasingly serious of degradation of permafrost and land desertification. On one hand, frozen soil degradation has caused frequent disasters such as debris flow, flood, ice and snow damage along the China-Mongolia-Russia transportation and pipeline, which will cause water and soil erosion followed by exposed pipes in frozen soil, in particular in summer. On the other hand, desertification will drive the ecological environment more vulnerable with the compound hazards of soil erosion and sandstorms occurring frequently. Therefore, this dataset will hopefully provide basic climate data for the research on the climate change and its impacts on permafrost and desertification for the China-Mongolia-Russia Economic Corridor. The original data is extracted from ERA5- Land surface climate reanalysis data (ERA5 – Land) (source: https://cds.climate.copernicus.eu). We adopted the inverse distance weight (IDW) method to interpolate the original data with the spatial resolution of 10 km. Based on this dataset, the spatial and temporal distribution pattern of climatic factors are outlined over the past 40 years for the corridor.
ZHANG Xueqin
1) Data content: multi-model ensemble mean wind speed at 200 hPa and 850 hPa during the Last Glacial Maximum, mid-Holocene and pre-industrial period (reflecting high and low level westerlies), 850 hPa meridional and zonal winds (reflecting the East Asian monsoon circulation) and zonal mass streamfunction (reflecting Walker circulation); 2) Data sources: monthly data simulated by multiple climate models from the second and third stages of the international Paleoclimate Modelling Intercomparison Project; processing methods: multi-model equal weight arithmetic mean, monthly climate average; 3) Data application: used for the study of paleoclimate change and dynamic mechanism.
TIAN Zhiping, WANG Na
When using the 3DVAR for data assimilation, it is necessary to use error covariance to determine the contribution of background field and observation. Among them, the background field error covariance depends not only on the atmospheric prediction model (such as resolution, parameterization scheme, etc.), but also on the simulation area. Based on the Weather Forecast and Research (WRF) model, this data is estimated by NMC method through the simulation of the Central Asian Great Lakes region (27 km horizontal resolution) in 2017. The variables include stream function, velocity potential function, temperature, relative humidity and surface pressure. This data can be applied to the study and application of data assimilation in the Central Asia Great Lakes region based on WRF model.
YAO Yao
The China Meteorological Forcing Dataset (CMFD) is a high spatial-temporal resolution gridded near-surface meteorological dataset that was developed specifically for studies of land surface processes in China. The dataset was made through fusion of remote sensing products, reanalysis dataset and in-situ observation data at weather stations. Its record starts from January 1979 and keeps extending (currently up to December 2018) with a temporal resolution of three hours and a spatial resolution of 0.1°. Seven near-surface meteorological elements are provided in CMFD, including 2-meter air temperature, surface pressure, specific humidity, 10-meter wind speed, downward shortwave radiation, downward longwave radiation and precipitation rate.
YANG Kun, HE Jie, WENJUN TANG , LU Hui, QIN Jun , CHEN Yingying, LI Xin
The dataset of CMA operational meteorological stations observations in the Heihe river basin were provided by Gansu Meteorological Administration and Qinghai Meteorological Administration. It included: (1) Diurnal precipitation, sunshine, evaporation, the wind speed, the air temperature and air humidity (2, 8, 14 and 20 o'clock) in Mazongshan, Yumen touwnship, Dingxin, Jinta, Jiuquan, Gaotai, Linze, Sunan, Zhangye, Mingle, Shandan and Yongchang in Gansu province (2) the wind direction and speed, the temperature and the dew-point spread (8 and 20 o'clock; 850, 700, 600, 500, 400, 300, 250, 200, 150, 100 and 50hpa) in Jiuquan, Zhangye and Mingqin in Gansu province and Golmud, Doulan and Xining in Qinghai province (3) the surface temperature, the dew point, the air pressure, the voltage transformation (3 hours and 24 hours), the weather phenomena (the present and the past), variable temperatures, visibility, cloudage, the wind direction and speed, precipitation within six hours and unusual weather in Jiuquan, Sunan, Jinta, Dingxin, Mingle, Zhangye, Gaotai, Shandan, Linze, Yongchang and Mingqin in Gansu province and Tuole, Yeniugao, Qilian, Menyuan, Xining, Gangcha and Huangyuan in Qinhai province.
Gansu meteorological bureau, Qinghai Meteorological Bureau
The data set includes the average wind speed data of main areas in Qinghai Province from 1988 to 2016 such as Xining, Haidong, Menyuan, Huangnan, Hainan, Guoluo, Yushu and Haixi. The data were derived from the Qinghai Society and Economics Statistical Yearbook and the Qinghai Statistical Yearbook. The accuracy of the data is consistent with that of the statistical yearbook. The data table records the monthly and annual average wind speed in eight regions of Qinghai. Unit: m / s The data set is mainly applied in geography and socioeconomic research.
Qinghai Provincial Bureau of Statistics
1) The data set is composed of global atmospheric reanalysis data jointly produced by the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR). These grid data are generated by reanalysing the global meteorological data from 1948 to present by applying observation data, forecasting models and assimilation systems. The data variables include surface, near-surface (.995 sigma layer) and multiple meteorological variables in different barospheres, such as precipitation, temperature, relative humidity, sea level pressure, geopotential height, wind field, heat flux, etc. 2) The coverage time is from 1948 to 2018, and the data from 1948 to 1957 are non-Gaussian grid data. The data cover the whole world. The spatial resolution is a 2.5° latitude by 2.5° longitude grid. The vertical resolution is a 17-layer standard pressure barosphere, with layer boundaries at 1000, 925, 850, 700, 600, 500, 400, 300, 250, 200, 150, 100, 70, 50, 30, 20, and 10 hPa, and 28 sigma levels. Some variables are calculated for 8 layers (omega) or 12 layers (humidity), with temporal resolutions of 6 hours, daily, monthly or a long-term monthly average (from 1981 to 2010). The daily data are obtained by averaging the daily values of 0Z, 6Z, 12Z and 18Z. 3) Missing values are assigned a value of -9.99691e+36f. The data are stored in the .nc format with the file name var.time.stat.nc, and each file includes data on latitude, longitude, time, and atmospheric variables. For detailed data specifications, please visit http://www.esrl.noaa.gov/pad/data.
National Oceanic and Atmospheric Administration, National Center for Atmospheric Research
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 August 31 to December 24, 2018. 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) (°), 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_1 and IRT_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), photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)). 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.
Li Xiaoyan
This dataset contains the flux measurements from the Guazhou station eddy covariance system (EC) in the middle reaches of the Heihe integrated observatory network from September 24 to December 31 in 2018. The site (95.673E, 41.405N) was located in a desert in Liuyuan Guazhou, which is near Jiuquan city in Gansu Province. The elevation is 2016 m. The EC was installed at a height of 4.0 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 (CSAT3&Li7500A) 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 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. 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/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). 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. (2011) for data processing) in the Citation section.
ZHANG Renyi
This dataset includes data recorded by the Heihe integrated observatory network obtained from an observation system of Meteorological elements gradient of Daman Superstation from January 1 to December 31, 2018. The site (100.372° E, 38.856° N) was located on a cropland (maize surface) in the Daman irrigation, which is near Zhangye city, Gansu Province. The elevation is 1556 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (AV-14TH;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 (CS100; 2 m), rain gauge (TE525M; 2.5 m, 8 m in west of tower), four-component radiometer (PIR&PSP; 12 m, towards south), two infrared temperature sensors (IRTC3; 12 m, towards south, vertically downward), photosynthetically active radiation (LI190SB; 12 m, towards south, vertically upward; another four photosynthetically active radiation, PQS-1; two above the plants (12 m) and two below the plants (0.3 m), towards south, each with one vertically downward and one vertically upward), soil heat flux (HFP01SC; 3 duplicates with G1 below the vegetation; G2 and G3 between plants, -0.06 m), a TCAV averaging soil thermocouple probe (TCAV; -0.02, -0.04 m), soil temperature profile (AV-10T; 0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (CS616; -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m). 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) (°), 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_1 and IRT_2) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), average soil temperature (TCAV, ℃), soil heat flux (Gs_1, below the vegetation; Gs_2, and Gs_3, between plants) (W/m^2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm) (℃), soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm) (%, volumetric water content), above the plants photosynthetically active radiation of upward and downward (PAR_U_up and PAR_U_down) (μmol/ (s m-2)), and below the plants photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m-2)). 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 meterological data during September 17 and November 7 and TCAV data after November 7 were wrong because the malfunction of datalogger. 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: 2018-6-10 10:30. 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.
LI Xin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei
Based on the long-term observation data of each field station in the alpine network and overseas stations in the pan third polar region, a series of data sets of meteorological, hydrological and ecological elements in the pan third polar region are established; the inversion of data products such as meteorological elements, lake water quantity and quality, aboveground vegetation biomass, glacial and frozen soil changes are completed through enhanced observation and sample site verification in key regions; based on the IOT Network technology, the development and establishment of multi station network meteorological, hydrological, ecological data management platform, to achieve real-time access to network data and remote control and sharing. The data includes the daily meteorological observation data sets (air temperature, precipitation, wind direction and speed, relative humidity, air pressure, radiation and evaporation) of the Qinghai Tibet Plateau in 2014-2017 from 17 stations of China Alpine network. The data of the three river sources are missing.
ZHU Liping,
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 October 23 to December 31, 2019. 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. The other data in addition to the four-component radiation data during January 1 to October 12 were missing because the malfunction of datalogger. 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.
Li Xiaoyan
This dataset includes data recorded by the Qinghai Lake integrated observatory network obtained from an observation system of Meteorological elements gradient from April 26 to December 31 in 2019. The site (100°14'8.99"E, 37°14'49.00"N) was located in the south of 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.
Li Xiaoyan
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 September 3 in 2018 to December 31 in 2019. 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.
Li Xiaoyan
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 April 28 to December 31, 2019. The site (100°6'3.62"E, 37°31'15.67"N) 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.
Li Xiaoyan
This dataset contains the flux measurements from site No.16 eddy covariance system (EC) in the flux observation matrix from 6 June to 17 September, 2012. The site (100.36411° E, 38.84931° N) was located in a cropland (maize surface) in Daman irrigation district, which is near Zhangye, Gansu Province. The elevation is 1564.31 m. The EC was installed at a height of 4.9 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 (Gill&Li7500) was 0.2 m. Raw data acquired at 10 Hz were processed using the Eddypro post-processing software (Li-Cor Company, http://www.licor.com/env/products/ eddy_covariance/software.html), including spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, angle of attack 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 [1996]: 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
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 [1996]: 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
This dataset is derived from the Nagqu Station of Plateau Climate and Environment (31.37N, 91.90E, 4509 a.s.l), Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences. The ground is flat, with open surrounding terrain. An uneven growth of alpine steppe, with a height of 3–20 cm. The observation time of this dataset is from January 1, 2014 to December 31, 2017. The observation elements primarily included the wind speed, air temperature, air relative humidity, air pressure, downward shortwave radiation, precipitation, evaporation, latent heat flux and CO2 flux. The precipitation , evaporation and CO2 flux data are daily cumulative values, and the other variables are daily average values. The observed data are generally continuous, but some data are missing due to power supply failure, and the missing data in this dataset are marked as NAN.
HU Zeyong, GU Lianglei, SUN Fanglei, WANG Shujin
The hydrological ecological process at the loess basin scale and its response to global climate change is a project of the Major Research plan of the National Natural Science Foundation of China - Environmental and Ecological Science in Western China. The project is led by liu wenzhao, a researcher from the institute of water and soil conservation, ministry of water resources, Chinese academy of sciences. The project runs from January 2003 to December 2005. The project submitted data: The CLIGEN parameter and output dataset of the Loess Plateau: It was generated during the evaluation and improvement of the practicality of the weather generator CLIGEN in the Loess Plateau. The dataset includes parameter data files for driving CLIGEN and 100-year daily weather data files generated by running CLIGEN from 71 meteorological stations on the Loess Plateau. The 71 sites are distributed in 7 provinces (Shanxi, Shanxi, Gansu, Inner Mongolia, Ningxia, Henan, and Qinghai). Each file is individually saved in ASCII format and can be opened for viewing with text programs. This data set is generated based on long-term serial daily meteorological data measured by 71 meteorological stations on the Loess Plateau. Daily meteorological parameters include: precipitation, maximum, minimum, and average temperature, solar radiation, relative humidity, wind speed and direction. The data comes from the China Meteorological Science Data Sharing Service Network and the Loess Plateau Soil and Water Conservation Database. Among them, solar radiation data is available at only 12 sites on the Loess Plateau. The solar radiation parameters at other sites are generated by kriging space interpolation. The dew point temperature is calculated using the average temperature and relative humidity.
LIU Wenzhao
The data set contains meteorological observations from Guoluo Station from January 1, 2017, to December 31, 2017, and includes temperature (Ta_1_AVG), relative humidity (RH_1_AVG), vapour pressure (Pvapor_1_AVG), average wind speed (WS_AVG), atmospheric pressure (P_1), average downward longwave radiation (DLR_5_AVG), average upward longwave radiation (ULR_5_AVG), average net radiation (Rn_5_AVG), average soil temperature (Ts_TCAV_AVG), soil water content (Smoist_AVG), total precipitation (Rain_7_TOT), downward longwave radiation (CG3_down_Avg), upward longwave radiation (CGR3_up_Avg), average photosynthetically active radiation (Par_Avg), etc. The temporal resolution is 1 hour. Missing observations have been assigned a value of -99999.
HU Linyong
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