This dataset contains the flux measurements from the Xiyinghe station eddy covariance system (EC) in the middle reaches of the Heihe integrated observatory network from January 1 to December 27 in 2021. The site (101.853E, 37.561N) was located on a alpine meadow in the Menyuan, Qinghai Province. The elevation is 3639 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.
ZHAO Changming, ZHANG Renyi
This dataset contains the flux measurements from the Minqin station eddy covariance system (EC) in the middle reaches of the Shiyanghe integrated observatory network from January 1 to December 27 in 2021. The site (103.668E, 39.208N) was located on a alpine meadow in the Wuwei, Gansu Province. The elevation is 1020 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) incomplete 30 min data were rejected when the missing data constituted more than 3% of the 30 min raw record; and 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.
ZHAO Changming, ZHANG Renyi
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 January 1 to December 27 in 2021. 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. 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.
ZHAO Changming, ZHANG Renyi
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 January 1 to December 27 in 2021. The site (102.73E, 36.692N) was located in a desert in Liuyuan Guazhou, which is near Jiuquan city in Gansu Province. The elevation is 2903 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. 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.
ZHAO Changming, ZHANG Renyi
This dataset contains the flux measurements from the Sidalong station eddy covariance system (EC) in the middle reaches of the Heihe integrated observatory network from January 1 to Dec 19 in 2021. The site (99.926E, 38.428N) was located on a forest in the Kangle Sunan, which is near Zhangye city, Gansu Province. The elevation is 3146 m. The EC was installed at a height of 4.0 m above the canopy , 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.
ZHAO Changming, ZHANG Renyi
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 Xiyinghe Station from January 1 to December 31, 2021. The site (101.853E, 37.561N) was located in Wuwei, Gansu Province. The elevation is 3614m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (2, 4, and 8 m, towards north), wind speed and direction profile (windsonic; 2, 4, and 8 m, towards north), air pressure (1.5 m), rain gauge (4 m), four-component radiometer (4 m, towards south), infrared temperature sensors (4 m, towards south, vertically downward), photosynthetically active radiation (4 m, towards south), soil heat flux (-0.05 m and -0.1m in south of tower), soil temperature/ moisture/ electrical conductivity profile (-0.05, -0.2 and -0.4 m in south of tower), sunshine duration sensor (4 m, towards south). The observations included the following: air temperature and humidity (Ta_1_2_1, Ta_1_4_1, and Ta_1_8_1; RH_1_2_1, RH_1_4_1and RH_1_8_1) (℃ and %, respectively), wind speed (WS_1_2_1, WS_1_4_1 and WS_1_8_1) (m/s), wind direction (WD_1_2_1, WD_1_4_1 and WD_1_8_1) (°), air pressure (PA_1_1_1) (hpa), precipitation (P_1_4_1) (mm), four-component radiation (SWIN_1_4_1, incoming shortwave radiation; SWOUT_1_4_1, outgoing shortwave radiation; LWIN_1_4_1, incoming longwave radiation; LWOUT_1_4_1 outgoing longwave radiation; Rn_1_4_1, net radiation) (W/m^2), infrared temperature (TC_1_4_1) (℃), photosynthetically active radiation (PPFD_1_4_1) (μmol/ (s/m^2)), soil heat flux (SHF_1_5_1, SHF_1_10_1) (W/m^2), soil temperature (TS_1_5_1, TS_1_20_1 and TS_1_40_1) (℃), soil moisture (SWC_1_5_1, SWC_1_20_1 and SWC_1_40_1) (%, volumetric water content), soil water potential (SWP_1_5_1, SWP_1_20_1 and SWP_1_40_1)(kpa) , soil conductivity (EC_1_5_1, EC_1_20_1 and EC_1_40_1)(μs/cm), Sun_time_1_4_1 (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. Missing or abnormal data is replaced by – 6999. The air pressure data were rejected because of 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: 2021-6-10 10:30.
ZHAO Changming, ZHANG Renyi
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 Suganhu Station from January 1 to December 31, 2021. The site (94.125° E, 38.992° N) was located on a wetland in the Suganhu west lake, Gansu Province. The elevation is 2823 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (4m and 8m, towards north), wind speed and direction profile (windsonic; 4m and 8m, towards north), air pressure (1m), rain gauge (4m), infrared temperature sensors (4 m, towards south, vertically downward), soil heat flux (-0.05 and -0.1m ), soil temperature/ moisture/ electrical conductivity profile (below the vegetation in the south of tower, -0.1, -0.2 and -0.4m), 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_1_4_1, Ta_1_8_1; RH_1_4_1, RH_1_8_1) (℃ and %, respectively), wind speed (WS_1_4_1, WS_1_8_1) (m/s), wind direction (WD_1_4_1, WD_1_8_1) (°), air pressure (PA_1_1_1) (hpa), precipitation (P_1_4_1) (mm), four-component radiation (SWIN_1_4_1, incoming shortwave radiation; SWOUT_1_4_1, outgoing shortwave radiation; LWIN_1_4_1, incoming longwave radiation; LWOUT_1_4_1 outgoing longwave radiation; RN_1_4_1, net radiation) (W/m^2), infrared temperature (TC_1_4_1) (℃), photosynthetically active radiation (PPFD_1_4_1) (μmol/ (s m-2)), soil heat flux (SHF_1_5_1, SHF_1_10_1) (W/m^2), soil temperature (TS_1_10_1, TS_1_20_1, TS_1_40_1) (℃), soil moisture (SWC_1_10_1, SWC_1_20_1, SWC_1_40_1) (%, volumetric water content), soil conductivity (EC_1_10_1, EC_1_20_1, EC_1_40_1)(μs/cm), Sun_time_1_4_1 (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. Missing or abnormal data is replaced 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: 2021-6-10 10:30.
ZHAO Changming, ZHANG Renyi
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 Sidalong Station from January 1 to December 31, 2021. The site (99.926°E, 38.428°N) was located on a forest in the Kangle Sunan, which is near Zhangye city, Gansu Province. The elevation is 3146 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (1, 2, 13, 24, and 48 m), wind speed and direction profile (windsonic; 1, 2, 13, 24, and 48 m), air pressure (1.5 m), rain gauge (24 m), infrared temperature sensors (4 m and 30m, vertically downward), photosynthetically active radiation (4 m and 30m), soil heat flux (-0.05 m and -0.1m), soil temperature/ moisture/ electrical conductivity profile -0.05, -0.1m, -0.2m, -0.4m and -0.6mr), four-component radiometer (30 m, towards south), sunshine duration sensor(30 m, towards south). The observations included the following: air temperature and humidity (Ta_1_1_1, Ta_1_2_1, Ta_1_13_1, Ta_1_24_1 and Ta_1_48_1; RH_1_1_1, RH_1_2_1, RH_1_13_1, RH_1_24_1 and RH_1_48_1) (℃ and %, respectively), wind speed (WS_1_1_1, WS_1_2_1, WS_1_13_1, WS_1_24_1, and WS_1_48_1) (m/s), wind direction (WD_1_1_1, WD_1_2_1, WD_1_13_1, WD_1_24_1, and WD_1_48_1) (°), air pressure (PA_1_1_1) (hpa), precipitation (P_1_24_1) (mm), four-component radiation (SWIN_1_30_1, incoming shortwave radiation; SWOUT_1_30_1, outgoing shortwave radiation; LWIN_1_30_1, incoming longwave radiation; LWOUT_1_30_1, outgoing longwave radiation; RN_1_30_1, net radiation) (W/m^2), infrared temperature (TC_1_4_1, TC_1_30_1) (℃), photosynthetically active radiation (PPFD_1_4_1, PPFD_1_30_1) (μmol/ (s m^2)), soil heat flux (SHF_1_5_1, SHF_1_10_1) (W/m^2), soil temperature (TS_1_5_1, TS_1_10_1, TS_1_20_1, TS_1_40_1 and TS_1_60_1) (℃), soil moisture (SWC_1_5_1, SWC_1_10_1, SWC_1_20_1, SWC_1_40_1 and SWC_1_60_1) (%, volumetric water content),soil water potential (SWP_1_5_1, SWP_1_10_1, SWP_1_20_1, SWP_1_40_1 and SWP_1_60_1)(kpa), soil conductivity (EC_1_5_1, EC_1_10_1, EC_1_20_1, EC_1_40_1 and EC_1_60_1)(μs/cm), Sun_time_1_30_1 (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. Missing or abnormal data is replaced 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: 2021-6-10 10:30.
ZHAO Changming, ZHANG Renyi
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 Minqin Station from January 1 to December 31, 2021. The site (103.668E, 39.208N) was located in Minqin, Gansu Province. The elevation is 1020 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (4 and 8 m, towards north), wind speed and direction profile (windsonic; 4, and 8 m, towards north), air pressure (1.5 m), rain gauge (4 m), four-component radiometer (4 m, towards south), infrared temperature sensors (4 m, towards south, vertically downward), photosynthetically active radiation (4 m, towards south), soil heat flux (-0.05 m and -0.1m in south of tower), soil temperature/ moisture/ electrical conductivity profile (-0.1 and -0.2 m in south of tower), sunshine duration sensor (4 m, towards south). The observations included the following: air temperature and humidity (Ta_1_4_1, Ta_1_8_1; RH_1_4_1, RH_1_8_1) (℃ and %, respectively), wind speed (WS_1_4_1, WS_1_8_1) (m/s), wind direction (WD_1_4_1, WD_1_8_1) (°), air pressure (PA_1_1_1) (hpa), precipitation (P_1_4_1) (mm), four-component radiation (SWIN_1_4_1, incoming shortwave radiation; SWOUT_1_4_1, outgoing shortwave radiation; LWIN_1_4_1, incoming longwave radiation; LWOUT_1_4_1, outgoing longwave radiation; Rn_1_4_1, net radiation) (W/m^2), infrared temperature (TC_1_4_1) (℃), photosynthetically active radiation (PPFD_1_4_1) (μmol/ (s/m^2)), soil heat flux (SHF_1_5_1, SHF_1_10_1) (W/m^2), soil temperature (TS_1_10_1, TS_1_20_1) (℃), soil moisture (SWC_1_10_1, SWC_1_10_1) (%, volumetric water content), soil water potential (SWP_1_10_1 , SWP_1_20_1)(kpa) , soil conductivity (EC_1_10_1, EC_1_20_1) (μs/cm), Sun_time_1_4_1 (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. Missing or abnormal data is replaced 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: 2021-6-10 10:30.
ZHAO Changming, ZHANG Renyi
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 Linze Station from January 1 to December 31, 2021. The site (100.062° E, 39.238° N) was located on a cropland (maize surface) in the Guzhai Xinghua, which is near Zhangye city, Gansu Province. The elevation is 1402 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (4 and 8 m, towards north), wind speed and direction profile (windsonic; 4 and 8 m, towards north), air pressure (1 m), rain gauge (4 m), four-component radiometer (4 m, towards south), infrared temperature sensors (4 m, towards south, vertically downward), photosynthetically active radiation (4 m, towards south), soil heat flux (2 duplicates below the vegetation; -0.05 and -0.1m in south of tower), soil temperature/ moisture/ electrical conductivity profile (-0.05 and -0.2m), sunshine duration sensor (4 m, towards south). The observations included the following: air temperature and humidity (Ta_1_4_1, Ta_1_8_1; RH_1_4_1, RH_1_8_1) (℃ and %, respectively), wind speed (WS_1_4_1, WS_1_8_1) (m/s), wind direction (WS_1_4_1, WS_1_8_1) (°), air pressure (PA_1_1_1) (hpa), precipitation (P_1_4_1) (mm), four-component radiation (SWIN_1_4_1, incoming shortwave radiation; SWOUT_1_4_1, outgoing shortwave radiation; LWIN_1_4_1, incoming longwave radiation; LWOUT_1_4_1 outgoing long wave radiation; RN_1_4_1, net radiation) (W/m^2), infrared temperature (TC_1_4_1) (℃), photosynthetically active radiation (PPFD_1_4_1) (μmol/ (s m-2)), soil heat flux (SHF_1_5_1, SHF_1_10_1) (W/m^2), soil temperature (TS_1_5_1, TS_1_20_1) (℃), soil moisture (SWC_1_5_1, SWC_1_20_1) (%, volumetric water content), soil water potential(SWP_1_5_1, SWP_1_20_1), soil conductivity (EC_1_5_1, EC_1_20_1) (μs/cm), Sun_time_1_4_1 (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. Missing or abnormal data is replaced 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: 2021-10 10:30.
ZHAO Changming, ZHANG Renyi
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 Liancheng Station from January 4 to December 31, 2021. The site (102.737E, 36.692N) was located on a forest in the Tulugou national forest park, which is near Liancheng city, Gansu Province. The elevation is 2903 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (4 and 8 m, towards north), wind speed and direction profile (windsonic; 4 and 8 m, towards north), air pressure (1.5 m), rain gauge (2 m), four-component radiometer (4m, towards south), infrared temperature sensors (4m, towards south, vertically downward), photosynthetically active radiation (4m, towards south), soil heat flux (2 duplicates below the vegetation; -0.05 and -0.1m in south of tower), soil temperature/ moisture/ electrical conductivity profile (below the vegetation;-0.05 and -0.1m in south of tower), sunshine duration sensor(4 m, towards south). The observations included the following: air temperature and humidity (Ta_1_4_1 and Ta_1_8_1; RH_1_4_1 and RH_1_8_1) (℃ and %, respectively), wind speed (WS_1_4_1 and WS_1_8_1) (m/s), wind direction (WD_1_4_1 and WD_1_8_1) (°), air pressure (PA_1_1_1) (hpa), precipitation (P_1_4_1) (mm), four-component radiation (SWIN_1_4_1, incoming shortwave radiation; SWOUT_1_4_1, outgoing shortwave radiation; LWIN_1_4_1, incoming longwave radiation; LWOUT_1_4_1, outgoing longwave radiation; Rn_1_4_1, net radiation) (W/m^2), infrared temperature (TC_1_4_1) (℃), photosynthetically active radiation (PPFD_1_1_1) (μmol/ (s m-2)), soil heat flux (SHF_1_5_1, SHF_1_10_1) (W/m^2), soil temperature (TS_1_5_1, TS_1_10_1) (℃), soil moisture (SWC_1_5_1, SWC_1_10_1) (%, volumetric water content), soil water potential (SWP_1_5_1, SWP_1_10_1)(kpa), soil conductivity (EC_1_5_1, EC_1_10_1)(μs/cm), Sun_time_1_4_1 (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. Missing or abnormal data is replaced by – 6999. 2021.6.13-3021.9.8, the data is missing because the wire is bitten off. 8m wind speed and direction sensor failure; 5 and 10cm soil temperature/ moisture/ electrical conductivity sensor failure; 5 and 10cm soil water potential sensor failure; 4m infrared temperature sensor failure. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) 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: 2021-8-20 10:30.
ZHAO Changming, ZHANG Renyi
This dataset includes data recorded by Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Dayekou Station from January 1 to December 31, 2021. The site (100.286° E, 38.556° N) was located on a glassland in the Dayekou, which is near Zhangye city, Gansu Province. The elevation is 2694 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (8 m), air pressure (2 m), rain gauge (2 m), infrared temperature sensors (2 m, towards south, vertically downward), soil heat flux (below the vegetation, -0.05 m; towards south), soil temperature/moisture/electrical conductivity profile (-0.05m) photosynthetically active radiation (2 m, towards south), four-component radiometer (2 m, towards south), sunshine duration sensor(2 m, towards south). The observations included the following: air temperature and humidity (Ta_1_8_1; RH_1_8_1) (℃ and %, respectively), wind speed (WS_1_8_1) (m/s), wind direction (WD_1_8_1) (°), air pressure (PA_1_1_1) (hpa), precipitation (P_1_4_1) (mm), four-component radiation (SWIN_1_4_1, incoming shortwave radiation; SWOUT_1_4_1, outgoing shortwave radiation; LWIN_1_4_1, incoming longwave radiation; LWOUT_1_4_1, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (℃), photosynthetically active radiation (PPFD_1_4_1) (μmol/ (s m^2)), soil heat flux (SHF_1_5_1) (W/m^2), soil temperature (TS_1_20_1)(℃), soil moisture (SWC_1_20_1)(%, volumetric water content), soil water potential (SWP_1_20_1)(kpa), soil conductivity (EC_1_20_1)(μs/cm). 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. Missing or abnormal data is replaced 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: 2021-6-10 10:30.
ZHAO Changming, ZHANG Renyi
Photosynthetically active radiation (PAR) is fundamental physiological variable driving the process of material and energy exchange, and is indispensable for researches in ecological and agricultural fields. In this study, we produced a 35-year (1984-2018) high-resolution (3 h, 10 km) global grided PAR dataset with an effective physical-based PAR model. The main inputs were cloud optical depth from the latest International Satellite Cloud Climatology Project (ISCCP) H-series cloud products, the routine variables (water vapor, surface pressure and ozone) from the ERA5 reanalysis data, aerosol from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) products and albedo from Moderate Resolution Imaging Spectroradiometer (MODIS) product after 2000 and CLARRA-2 product before 2000. The grided PAR products were evaluated against surface observations measured at seven experimental stations of the SURFace RADiation budget network (SURFRAD), 42 experimental stations of the National Ecological Observatory Network (NEON), and 38 experimental stations of the Chinese Ecosystem Research Network (CERN). The instantaneous PAR was validated at the SURFRAD and NEON, and the mean bias errors (MBEs) and root mean square errors (RMSEs) are 5.6 W m-2 and 44.3 W m-2, and 5.9 W m-2 and 45.5 W m-2, respectively, and correlation coefficients (R) are both 0.94 at 10 km scale. When averaged to 30 km, the errors were obviously reduced with RMSEs decreasing to 36.3 W m-2 and 36.3 W m-2 and R both increasing to 0.96. The daily PAR was validated at the SURFRAD, NEON and CERN, and the RMSEs were 13.2 W m-2, 13.1 W m-2 and 19.6 W m-2, respectively at 10 km scale. The RMSEs were slightly reduced to 11.2 W m-2, 11.6 W m-2, and 18.6 W m-2 when upscaled to 30 km. Comparison with the other well-known global satellite-based PAR product of the Earth's Radiant Energy System (CERES) reveals that our PAR product was a more accurate dataset with higher resolution than the CRERS. Our grided PAR dataset would contribute to the ecological simulation and food yield assessment in the future.
TANG Wenjun
The temporal resolution of temperature and radiation data in Central Asia is monthly scale, and the spatial resolution is 0.5 degree and 0.05 degree, respectively. The GCS_WGS_1984 projection coordinate system was used. Among them, the downward short wave radiation, air temperature and vapor pressure data of GLDAS, surface temperature / emissivity data of MOD11C3, surface albedo data of MCD43C3 and ASTER_GEDv4.1 are used for radiation data calculation; the temperature data was calculated by MOD06_ L2 cloud products and MOD07_ L2 atmospheric profile data was calculated. This data is based on the advanced remote sensing algorithm and makes full use of the current high-precision remote sensing data and products, which is different from the traditional climate model for the estimation of climate elements. The data can be used to analyze the spatial and temporal variation characteristics of water resources in Central Asia, analyze the supply-demand relationship of agricultural water resources and evaluate the development potential of water resources.
SONG Jinxi, JIANG Xiaohui
1) The Qinghai Tibet plateau surface meteorological driving data set (2019-2020) includes four meteorological elements: land surface temperature, mean total precipitation rate, mean surface downward long wave radiation flux and mean surface downward short wave radiation flux. 2) The data set is based on era5 reanalysis data, supplemented by MODIS NDVI, MODIS DEM and fy3d mwri DEM data products. The era5 reanalysis data were downscaled by multiple linear regression method, and finally generated by resampling. 3) All data elements of the Qinghai Tibet plateau surface meteorological driving data set (2019-2020) are stored in TIFF format. The time resolution includes (daily, monthly and annual), and the spatial resolution is unified as 0.1 ° × 0.1°。 4) This data is convenient for researchers and students who will not use such assimilated data in. NC format. Based on the long-term observation data of field stations of the alpine network and overseas stations in the pan third pole region, a series of data sets of meteorological, hydrological and ecological elements in the pan third pole region are established; Complete the inversion of meteorological elements, lake water quantity and quality, aboveground vegetation biomass, glacier and frozen soil change and other data products through intensive observation in key areas and verification of sample plots and sample points; Based on the Internet of things technology, a multi station networked meteorological, hydrological and ecological data management platform is developed to realize real-time acquisition, remote control and sharing of networked data.
ZHU Liping, DU Baolong
Surface solar irradiance (SSI) is one of the products of FY-4A L2 quantitative inversion. It covers a full disk without projection, with a spatial resolution of 4km and a temporal resolution of 15min (there are 40 observation times in the whole day since 20180921, except for the observation of each hour, there is one observation every 3hr before and after the hour）, and the spectral range is 0.2µ m～5.0 µ m. The output elements of the product include total irradiance, direct irradiance on horizontal plane and scattered irradiance, the effective measurement ranges between 0-1500 w / m2. The qualitative improvement of FY-4A SSI products in coverage, spatial resolution, time continuity, output elements and other aspects makes it possible to further carry out its fine application in solar energy, agriculture, ecology, transportation and other professional meteorological services. The current research results show that the overall correlation of FY-4A SSI product in China is more than 0.75 compared with ground-based observation, which can be used for solar energy resource assessment in China.
SHEN Yanbo, HU Yueming, HU Xiuqing
As an important part of global semi-arid grassland, adequately understanding the spatio-temporal variability of evapotranspiration (ET) over the temperate semi-arid grassland of China (TSGC) could advance our understanding of climate, hydrological and ecological processes over global semi-arid areas. Based on the largest number of in-situ ET measurements (13 flux towers) within the TSGC, we applied the support vector regression method to develop a high-quality ET dataset at 1 km spatial resolution and 8-day timescale for the TSGC from 1982 to 2015. The model performed well in validation against flux tower‐measured data and comparison with water-balance derived ET.
LEI Huimin
The data set collected long-term monitoring projects from multiple stations for atmosphere, hydrology and soil in the North Tibetan Plateau. The data set consisted of monitoring data obtained from the automatic weather station (AWS) and the atmospheric boundary layer tower (PBL) in the field. The sensors for temperature, humidity and pressure were provided by Vaisala of Finland; the sensors for wind speed and direction were provided by Met One of America, the radiation sensors were provided by APPLEY of America and EKO of Japan; the gas analyzers were provided by Licor of America; the soil water content instrument, ultrasonic anemometers and data collectors were provided by CAMPBELL of America. The observation system was maintained by professionals regularly (2-3 times a year), the sensors were calibrated and replaced, and the collected data were downloaded and reorganized. The data set was processed by forming a time continuous sequence after the raw data were quality-controlled. It met the accuracy level of the original meteorological observation data of the National Weather Service and the World Meteorological Organization (WMO). The quality control included the elimination of the missing data and the systematic error caused by the failure of the sensor.
HU Zeyong
Photosynthetic effective radiation absorption coefficient photosynthetically active radiation component is an important biophysical parameter. It is an important land characteristic parameter of ecosystem function model, crop growth model, net primary productivity model, atmosphere model, biogeochemical model and ecological model, and is an ideal parameter for estimating vegetation biomass. The data set contains the data of photosynthetically active radiation absorption coefficient in Qinghai Tibet Plateau, with spatial resolution of 500m, temporal resolution of 8D, and time coverage of 2000, 2005, 2010 and 2015. The data source is MODIS Lai / FPAR product data mod15a2h (C6) on NASA website. The data are of great significance to the analysis of vegetation ecological environment in the Qinghai Tibet Plateau.
FANG Huajun, Ranga Myneni
This data set includes meteorological data observed by the carbon flux station in the Guoluo Army Ranch in Qinghai. The temporal coverage is from 2005 to 2009, and the temporal resolution is 1 day. Meteorological and carbon flux data observation methods: vorticity-related observation instruments were used for automatic recording; biomass observation method: harvest method, weighing in a 60-degree oven for 48 hours. Both carbon flux and meteorological data were automatically recorded by the instruments and manually checked. During the data observation process, the operation of the instrument and the selection of the observation objects were in strict accordance with professional requirements, and the data could be applied to plant leaf photosynthetic parameter simulation and productivity estimation. This data contains observation items as follows: Temperature °C Precipitation mm Wind speed m/s Soil temperature at 5 cm depth °C Photosynthetically active radiation µmol/m²s Total radiation W/m²
ZHAO Xinquan
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