This dataset contains the flux measurements from the Qinghai Lake eddy covariance system (EC) belonging to the Qinghai Lake basin integrated observatory network from January 1 to December 31 in 2021. 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 EC was installed at a height of 16.1m, 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.
Li Xiaoyan
This dataset contains the flux measurements from the Alpine meadow and grassland ecosystem Superstation superstation eddy covariance system (EC) belonging to the Qinghai Lake basin integrated observatory network from January 1 to October 31 in 2021. 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 EC was installed at a height of 4.5 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 (CSAT3A &EC150) 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. Data during December 18 to December 24, 2018 were missing due to the data collector failure. 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.
Li Xiaoyan
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 October 13 in 2021. 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.
Li Xiaoyan
This dataset contains the flux measurements from the temperate steppe eddy covariance system (EC) belonging to the Qinghai Lake basin integrated observatory network from January 1 to October 14 in 2021. 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 3210sm. The EC was installed at a height of 2.5 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 (CSAT3A &EC150) 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. Data during December 18 to December 24, 2018 were missing due to the data collector failure. 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.
Li Xiaoyan
As a key component of the earth's energy balance system, surface longwave downward radiation (LWDR) is of great significance to the study of ecology and climate change. With the continuous improvement of remote sensing estimation accuracy and spatial-temporal resolution and accuracy of reanalysis data, remote sensing and reanalysis data fusion will be a new way to further improve the reliability and spatial-temporal continuity of key parameters such as surface radiation. Considering the difference in spatial-temporal resolution and local regional accuracy of current multi-source LWDR data, the study combines the measured data of stations around the world, spatio-temporal fusion of remote sensing observation data (CERES) with reanalysis data ERA5 and GLDAS, and develops a high-precision surface longwave downward radiation dataset covering the world from 2000 to 2020 with a spatial-temporal resolution of 1h/0.25 °. The correlation coefficient (R), mean deviation error (bias) and root mean square error (RMSE) of the newly developed dataset and the site measured data verified on the land surface are 0.97, -0.95 Wm-2 and 22.38 Wm-2, respectively; On the ocean surface, it is 0.99, -0.88 Wm-2 and 10.96 Wm-2, respectively. In particular, compared with the existing data, the new dataset shows better accuracy and stability in the middle and low latitudes and complex terrain areas.
WANG Tianxing, WANG Shiyao
1) Data content: the 2020 updated database file of the Central Asia Great Lakes region database, which contains the observation data of the total radiation in the ecological stations of the Central Asia Great Lakes region in 2020. 2) Data source and processing method: the data are from the observation data of 6 ecological stations (station numbers: 1130, 1131, 1132, 1133, 1134, 1137) without processing. 3) Data quality description: this data is site data with a time resolution of 1 minute. The data quality control process includes two steps (1) internal consistency check; (2) Time consistency check. 4) Data application achievements and prospects: this data is the basic observation data, which is an important annual supplement to the database of the Great Lakes region of Central Asia, and can provide data support for subsequent research fields such as meteorology, ecology, hydrology and environment, and support the development of project research.
LIU Tie
1) Data content: database file of Central Asia Great Lakes region, including observation data of total radiation elements in basic ecological stations of Central Asia Great Lakes region from 2020 to 2021. 2) Data source and processing method: the data are from the observation data of 8 ecological stations (station numbers: 1130, 1131, 1132, 1133, 1134, 1135, 1137, 1138) without processing. 3) Data quality description: this data is site data, and the time resolution is every 1 minute. The data quality control process includes two steps (2) internal consistency check; (2) Time consistency check. 4) Data application achievements and prospects: this data is the basic observation data, which is an important part of the database of the Great Lakes region in Central Asia. It can provide data support for subsequent research fields such as meteorology, ecology, hydrology and environment, and support the development of project research.
LIU Tie
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 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 Suganhu 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 (94.12E, 38.99N was located in a desert in Suganhu, which is in Gansu Province. The elevation is 2823 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 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 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 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 the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Guazhou Station from January 1 to December 31, 2021. The site (95.673E, 41.405N) was located on a desert in the Liuyuan Guazhou, which is near Jiuquan city, Gansu Province. The elevation is 2014 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (2, 4, 8, 16, 32, and 48 m, towards north), wind speed and direction profile (windsonic; 2, 4, 8, 16, 32, and 48 m, towards north), air pressure (1.5 m), rain gauge (4 m), 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, -0.1m, -0.2m, -0.4m, -0.6m and -0.8m in south of tower), 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_2_1, Ta_1_4_1, Ta_1_8_1, Ta_1_16_1, Ta_1_32_1 and Ta_1_48_1; RH_2 m, RH_1_2_1, RH_1_4_1, RH_1_8_1, RH_1_16_1, RH_1_32_1, and RH_1_48_1) (℃ and %, respectively), wind speed (WS_1_2_1, WS_1_4_1, WS_1_8_1, WS_1_16_1, WS_1_32_1 and WS_1_48_1) (m/s), wind direction (WD_1_2_1, WD_1_4_1, WD_1_8_1, WD_1_16_1, WD_1_32_1 and WD_1_48_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_10_1, TS_1_20_1, TS_1_40_1, TS_1_60_1 and TS_1_80_1) (℃), soil moisture (SWC_1_5_1, SWC_1_10_1, SWC_1_20_1, SWC_1_40_1, SWC_1_60_1 and SWC_1_80_1) (%, volumetric water content),soil water potential (SWP_1_5_1, SWP_1_10_1, SWP_1_20_1, SWP_1_40_1, SWP_1_60_1 and SWP_1_80_1)(kpa), soil conductivity (EC_1_5_1, EC_1_10_1, EC_1_20_1, EC_1_40_1, EC_1_60_1 and EC_1_80_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 Dunhuang Station from January 1 to December 31, 2021. The site (93.709° E, 40.348° N) was located on a wetland in the Dunhuang west lake, Gansu Province. The elevation is 994 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (4m and 8 m, towards north), wind speed and direction profile (windsonic; 4m and 8 m, towards north), air pressure (1 m), rain gauge (4 m), infrared temperature sensors (4 m, towards south, vertically downward), soil heat flux (-0.05 and -0.1m ), soil temperature/ moisture/ electrical conductivity profile (below the vegetation in the south of tower, -0.05 and -0.2 m), photosynthetically active radiation (4 m, towards south), four-component radiometer (4 m, towards south), sunshine duration sensor(4 m, towards south). The observations included the following: air temperature and humidity (Ta_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_5_1、TS_1_20_1) (℃), soil moisture (SWC_1_5_1、SWC_1_20_1) (%, volumetric water content), soil conductivity (SWC_1_5_1、SWC_1_20_1)(μs/cm), sun time(Sun_time_1_4_1). 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 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
This meteorological data is the basic meteorological data of air temperature, relative humidity, wind speed, precipitation, air pressure, radiation, soil temperature and humidity observed in the observation site (86.56 ° e, 28.21 ° n, 4276m) of the comprehensive observation and research station of atmosphere and environment of Qomolangma, Chinese Academy of Sciences from 2019 to 2020. Precipitation is the daily cumulative value. All data are observed and collected in strict accordance with the instrument operation specifications, and some obvious error data are eliminated when processing and generating data The data can be used by students and scientific researchers engaged in meteorology, atmospheric environment or ecology (Note: when using, it must be indicated in the article that the data comes from Qomolangma station for atmospheric and environmental observation and research, Chinese Academy of Sciences (QOMS / CAS))
XI Zhenhua
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 data are fy-4a ground solar radiation products in Qinghai Tibet Plateau, including GHI \ DNI \ dif The channels involved in FY4 surface solar incident radiation inversion algorithm include six channels of imager visible light, near-infrared and short wave infrared: ch1 (0.45-0.49 μ m), CH2 (0.55-0.75 μ m), CH3 (0.75-0.90 μ m), CH4 (1.36-1.39 μ m), CH5 (1.58-1.64 μ m) and ch6 (2.1-2.35 μ m). The regression model relied on by the algorithm needs to be established through radiative transfer simulation and statistical analysis in advance. The regression model defines the regression relationship between the surface solar incident radiation and the multi-channel radiation observation of the imager, which is a function of the solar observation geometry and the most important influence parameters (cloud, aerosol, water vapor content, surface albedo, surface altitude, etc.). The algorithm uses the short wave radiation observation from channel 1 to channel 6 of FY-4 satellite imager to obtain the instantaneous state parameter information of atmosphere and surface, and obtains the surface altitude information from the surface elevation data. After determining the instantaneous atmospheric and surface states, combined with the solar angle and observation angle, according to the previously established regression model data, multi-dimensional linear interpolation is carried out to obtain the inversion products of surface solar incident radiation.
SHEN Yanbo, HU Yueming, HU Liqin
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
Central Asia (referred to as CA) is among the most vulnerable regions to climate change due to the fragile ecosystems, frequent natural hazards, strained water resources, and accelerated glacier melting, which underscores the need of high-resolution climate projection datasets for application to vulnerability, impacts, and adaption assessments. We applied three bias-corrected global climate models (GCMs) to conduct 9-km resolution dynamical downscaling in CA. A high-resolution climate projection dataset over CA (the HCPD-CA dataset) is derived from the downscaled results, which contains four static variables and ten meteorological elements that are widely used to drive ecological and hydrological models. The static variables are terrain height (HGT, m), land use category (LU_INDEX, 21 categories), land mask (LANDMASK, 1 for land and 0 for water), and soil category (ISLTYP, 16 categories). The meteorological elements are daily precipitation (PREC, mm/day), daily mean/maximum/minimum temperature at 2m (T2MEAN/T2MAX/T2MIN, K), daily mean relative humidity at 2m (RH2MEAN, %), daily mean eastward and northward wind at 10m (U10MEAN/V10MEAN, m/s), daily mean downward shortwave/longwave flux at surface (SWD/LWD, W/m2), and daily mean surface pressure (PSFC, Pa). The reference and future periods are 1986-2005 and 2031-2050, respectively. The carbon emission scenario is RCP4.5. The results show the data product has good quality in describing the climatology of all the elements in CA, which ensures the suitability of the dataset for future research. The main feature of projected climate changes in CA in the near-term future is strong warming (annual mean temperature increasing by 1.62-2.02℃) and significant increase in downward shortwave and longwave flux at surface, with minor changes in other elements. The HCPD-CA dataset presented here serves as a scientific basis for assessing the impacts of climate change over CA on many sectors, especially on ecological and hydrological systems.
QIU Yuan
Surface downward radiation (SDR), including shortwave downward radiation (SWDR) and longwave downward radiation (LWDR), is of great importance to energy and climate studies. Considering the lack of reliable SDR data with a high spatiotemporal resolution in the East Asia-Pacific (EAP) region, we derived SWDR and LWDR at 10-min and 0.05° resolutions for this region from 2016-2020 based on the next-generation geostationary satellite Himawari-8 (H-8). The SDR product is unique in terms of its all-sky features, high accuracy and high resolution levels. The cloud effect is fully considered in the SDR product, and the influence of high aerosol loadings and topography on the SWDR are considered. Compared to benchmark products of the radiation, such as Clouds and the Earth’s Radiant Energy System (CERES) and the European Centre for Medium-Range Weather Forecasts (ECMWF) next-generation reanalysis (ERA5), and the Global Land Surface Satellite (GLASS), not only is the resolution of the new SDR product notably much higher but the product accuracy is also higher than that of those products. In particular, hourly and daily root mean square errors of hourly and daily of the new SWDR are 104.9 and 31.5 Wm-2, respectively, which are much smaller than those of CERES (at 121.6 and 38.6 Wm-2, respectively), ERA5 (at 176.6 and 39.5 Wm-2, respectively) and GLASS (daily of 36.5 Wm-2). Meanwhile, RMSEs of hourly and daily values of the new LWDR are 19.6 and 14.4 Wm-2, respectively, which are comparable to that of CERES and ERA5, and even better over high altitude regions.
LETU Husi, WANG Tianxing, DU Yihan
This dataset contains the fluxes and meteorological data of Weishan (Gaoying) flux site of Tsinghua University from May 17, 2005 to September 26, 2006. The site (116.0542° E, 36.6487° N, 30 m above sea level) was built on March 18, 2005 and is located in Xiaozhuang Town, Chiping District, Liaocheng City, Shandong Province. It belongs to Weishan Irrigation District along the lower Yellow River. The local climate is characterized as temperate monsoons, with an average annual temperature of 13.8 ℃, an average annual precipitation of 553mm, most of which occurs between June and October, and an average annual potential evaporation of 1950mm. The soil type is silt loam. For the soil of the top 5 cm, the average saturated soil water content, field capacity and wilting point in volumetric values are 0.43, 0.33 and 0.10 m3m-3, respectively. The height of the flux tower is 10m, and the area within about 1 km radius around the flux tower is largely homogeneous winter wheat-summer maize rotation cropland. The winter wheat is generally sown in mid-October and harvested in early June of the following year, while the summer maize is usually planted directly into the stubbles of wheat at the same location immediately after the harvest of wheat and is harvested in late September to early October. See the file named “Supplementary data_WeishanGaoying20052006.xlsx” for specific sowing, harvesting and irrigation dates. The surface flux data is measured by the eddy covariance system, which is composed of a three-dimensional sonic anemometer (CSAT3, Campbell Scientific, Inc., Logan, UT, USA) and an open-path infrared gas analyzer (IRGA) (LI-7500, LI-COR, Inc., Lincoln, NE, USA) with an installation height of 3.7m. The 30-minute net ecosystem carbon exchange (NEE), latent heat flux (LE) and sensible heat flux (H) data were obtained after the raw 10Hz data were processed by Eddypro software. The preprocessing steps included despiking, double coordinate rotation, 30-min block averaging, time lag compensation, spectral corrections, the Webb-Pearman-Leuning (WPL) density correction, a quality check using the “0-1-2 system”. Then the 30-min data were screened as follows: (1) remove bad quality fluxes with quality flag 2; (2) limit H and LE to - 200 ~ 500 W m-2 and - 200 ~ 800 W m-2, respectively; (3) the data during the precipitation events were excluded. Then, REddyproc software is used to filter the data under low turbulence mixing conditions (i.e. filter the flux data according to the friction wind speed u*), fill the gaps in the time series, and then the NEE was divided into ecosystem respiration (Reco) and gross primary production (GPP) by the nighttime partitioning method. The published dataset includes: year, month, day, time, atmospheric pressure (P), infrared surface temperature (Tsurf), wind speed (Ws), wind direction (Wd), air temperature (Tair) and relative humidity (rH) at 2m, downward short wave radiation (Rsd), upward short wave radiation (Rsu), downward long wave radiation (Rld), upward long wave radiation (Rlu), Net radiation (Rn), incident photosynthetically active radiation (PAR_dn), reflected photosynthetically active radiation (PAR_up), precipitation (precip), groundwater level (GW), 5cm/10cm/20cm/40cm/80cm/160cm soil water content (soil_VW_ 5cm / 10cm / 20cm / 40cm / 80cm / 160cm) and soil temperature (soil_T_5cm / 10cm / 20cm / 40cm / 80cm / 160cm), soil heat flux at 5cm depth (soil_ G) , raw data of net ecosystem carbon exchange (NEE_raw), raw data of latent heat flux (LE_raw), raw data of sensible heat flux (H_raw), net ecosystem carbon exchange after gap filling (NEE_ f) , latent heat flux after gap filling (LE_f), sensible heat flux after gap filling (H_f), ecosystem respiration imputation (Reco_f), gross primary productivity (GPP_f). The data are stored in .xlsx format at 30-minute intervals. Null values in the dataset are represented by NA. Please refer to Lei and Yang (2010a, 2010b) for detailed information of this site and the observation instruments.
LEI Huimin
Accurate evapotranspiration (ET) estimation is important for understanding hydrological cycle and water resources management in the cropland. Based on eight flux sites within the North China Plain (NCP) and the surrounding area, which were integrated together for the first time, we applied support vector regression method to develop ET dataset for the cropland in NCP from 2001 to 2015 with 1km spatial resolution and eight-day temporal interval.
LEI Huimin
Accurate evapotranspiration (ET) estimation is important for understanding hydrological cycle and water resources management in the cropland. Based on eight flux sites within the North China Plain (NCP) and the surrounding area, which were integrated together for the first time, we applied support vector regression method to develop ET dataset for the cropland in NCP from 1982 to 2015 with 1/12° spatial resolution and eight-day temporal interval.
LEI Huimin
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
This dataset includes the observed surface incident solar radiantion, and sunshine duration derived soalr radiation, and their homogenized series at 156 meteorological stations in Japan from 1870 to 2015. According to Yang's method, the surface solar radiation is calculated from the observed sunshine duration hours, and then the breakpoints of unnatural factors in the data series are adjusted by RH test homogenization method, so as to obtain the regional homogenized monthly solar radiation data set in Japan.
MA Qian, HE Yanyi, WANG Kaicun, SU Liangyuan
The data set contains the eddy correlator observation data of xiyinghe station of Lanzhou University cold and arid area scientific observation network of Lanzhou University from January 1, 2020 to December 31, 2020. The station is located in taola village, Xianmi Township, Menyuan County, Haibei, Qinghai, with alpine meadow on the underlying surface. The longitude and latitude of the observation point are 101.855e, 37.561n and the altitude is 3616m. The frame height of eddy correlator is 4m, the sampling frequency is 10Hz, the ultrasonic direction is due north, and the distance between ultrasonic anemometer (csat3) and CO2 / H2O analyzer (li7500a) is 17cm. The original observation data of eddy correlator is 10Hz, and the released data is the 30 minute data processed by eddypro software. The main processing steps include: field value elimination, delay time correction, coordinate rotation (quadratic coordinate rotation), frequency response correction, ultrasonic virtual temperature correction and density (WPL) correction. At the same time, the quality of each flux value is evaluated, mainly the atmospheric stability（ Δ St) and turbulence similarity characteristics (ITC). The 30min flux value output by eddypro software is also screened: (1) eliminate the data when the instrument is wrong; (2) Eliminate the data with a loss rate of more than 10% every 30min in the 10Hz original data. The average period of observation data is 30 minutes, 48 data a day, and the missing data is marked as - 6999. Data from September 10 to October 22 are missing. The published observation data include: date / time, wind direction WDIR (°), horizontal wind speed wnd (M / s), standard deviation of lateral wind speed STD_ Uy (M / s), ultrasonic virtual temperature TV (℃), water vapor density H2O (g / m3), carbon dioxide concentration CO2 (mg / m3), friction velocity ustar (M / s), Obukhov length L (m), sensible heat flux HS (w / m2), latent heat flux Le (w / m2), carbon dioxide flux FC (mg / (M2S)), quality identification QA of sensible heat flux_ HS, quality identification of latent heat flux QA_ Le, quality identification QA of carbon dioxide flux_ Fc。 The quality identification of sensible heat, latent heat and carbon dioxide flux is divided into nine levels (quality identification 1-3 has good data quality, 4-6 has good data quality, 7-8 has poor data quality (better than interpolated data), and 9 has poor data quality). The meaning of data time, for example, 0:30 represents the average of 0:00-0:30; The data is stored in *. XLS format.
ZHAO Changming, ZHANG Renyi
The data set includes the eddy correlator observation data of suganhu station of Lanzhou University cold and arid area scientific observation network of Lanzhou University from January 1, 2020 to December 31, 2020. The station is located in Sugan lake, Gansu Province, with wetland on the underlying surface. The longitude and latitude of the observation point are 94.12e, 38.99n and 2823m above sea level. The frame height of eddy correlator is 4m, the sampling frequency is 10Hz, the ultrasonic direction is due north, and the distance between ultrasonic anemometer (csat3) and CO2 / H2O analyzer (li7500a) is 17cm. The original observation data of eddy correlator is 10Hz, and the released data is the 30 minute data processed by eddypro software. The main processing steps include: field value elimination, delay time correction, coordinate rotation (quadratic coordinate rotation), frequency response correction, ultrasonic virtual temperature correction and density (WPL) correction. At the same time, the quality of each flux value is evaluated, mainly the atmospheric stability（ Δ St) and turbulence similarity characteristics (ITC). The 30min flux value output by eddypro software is also screened: (1) eliminate the data when the instrument is wrong; (2) Eliminate the data with a loss rate of more than 10% every 30min in the 10Hz original data. The average period of observation data is 30 minutes, 48 data a day, and the missing data is marked as - 6999. The published observation data include: date / time, wind direction WDIR (°), horizontal wind speed wnd (M / s), standard deviation of lateral wind speed STD_ Uy (M / s), ultrasonic virtual temperature TV (℃), water vapor density H2O (g / m3), carbon dioxide concentration CO2 (mg / m3), friction velocity ustar (M / s), Obukhov length L (m), sensible heat flux HS (w / m2), latent heat flux Le (w / m2), carbon dioxide flux FC (mg / (M2S)), quality identification QA of sensible heat flux_ HS, quality identification of latent heat flux QA_ Le, quality identification QA of carbon dioxide flux_ Fc。 The quality identification of sensible heat, latent heat and carbon dioxide flux is divided into nine levels (quality identification 1-3 has good data quality, 4-6 has good data quality, 7-8 has poor data quality (better than interpolated data), and 9 has poor data quality). The meaning of data time, for example, 0:30 represents the average of 0:00-0:30; The data is stored in *. XLS format.
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 Mar 26 to Dec 31 in 2020. 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
The data set contains the observation data of vortex correlator at Minqin station of cold and arid area scientific observation network of Lanzhou University from January 1, 2020 to December 31, 2020. The station is located in Minqin County, Wuwei City, Gansu Province, between Badain Jaran Desert and Tengger Desert in Western China. The longitude and latitude of the observation point are 103.668e, 39.208n and 1020m above sea level. The frame height of eddy correlator is 4m, the sampling frequency is 10Hz, the ultrasonic direction is due north, and the distance between ultrasonic anemometer (csat3) and CO2 / H2O analyzer (li7500a) is 17cm. The original observation data of eddy correlator is 10Hz, and the released data is the 30 minute data processed by eddypro software. The main processing steps include: field value elimination, delay time correction, coordinate rotation (quadratic coordinate rotation), frequency response correction, ultrasonic virtual temperature correction and density (WPL) correction. At the same time, the quality of each flux value is evaluated, mainly the atmospheric stability（ Δ St) and turbulence similarity characteristics (ITC). The 30min flux value output by eddypro software is also screened: (1) eliminate the data when the instrument is wrong; (2) Eliminate the data with a loss rate of more than 10% every 30min in the 10Hz original data. The average period of observation data is 30 minutes, 48 data a day, and the missing data is marked as - 6999. The published observation data include: date / time, wind direction WDIR (°), horizontal wind speed wnd (M / s), standard deviation of lateral wind speed STD_ Uy (M / s), ultrasonic virtual temperature TV (℃), water vapor density H2O (g / m3), carbon dioxide concentration CO2 (mg / m3), friction velocity ustar (M / s), Obukhov length L (m), sensible heat flux HS (w / m2), latent heat flux Le (w / m2), carbon dioxide flux FC (mg / (M2S)), quality identification QA of sensible heat flux_ HS, quality identification of latent heat flux QA_ Le, quality identification QA of carbon dioxide flux_ Fc。 The quality identification of sensible heat, latent heat and carbon dioxide flux is divided into nine levels (quality identification 1-3 has good data quality, 4-6 has good data quality, 7-8 has poor data quality (better than interpolated data), and 9 has poor data quality). The meaning of data time, for example, 0:30 represents the average of 0:00-0:30; The data is stored in *. XLS format.
ZHAO Changming, ZHANG Renyi
The data set contains the eddy correlator observation data of Guazhou station of Lanzhou University cold and arid area scientific observation network of Lanzhou University from January 1, 2020 to December 31, 2020. The station is located in Liuyuan Town, Guazhou County, Jiuquan, Gansu Province, with desert on the underlying surface. The longitude and latitude of the observation point are 95.673e, 41.405n, and the altitude is 2014m. The frame height of eddy correlator is 4m, the sampling frequency is 10Hz, the ultrasonic direction is due north, and the distance between ultrasonic anemometer (csat3) and CO2 / H2O analyzer (li7500a) is 17cm. The original observation data of eddy correlator is 10Hz, and the released data is the 30 minute data processed by eddypro software. The main processing steps include: field value elimination, delay time correction, coordinate rotation (quadratic coordinate rotation), frequency response correction, ultrasonic virtual temperature correction and density (WPL) correction. At the same time, the quality of each flux value is evaluated, mainly the atmospheric stability（ Δ St) and turbulence similarity characteristics (ITC). The 30min flux value output by eddypro software is also screened: (1) eliminate the data when the instrument is wrong; (2) Eliminate the data 1H before and after precipitation; (3) Eliminate the data with a loss rate of more than 10% every 30min in the 10Hz original data. The average period of observation data is 30 minutes, 48 data a day, and the missing data is marked as - 6999. The published observation data include: date / time, wind direction WDIR (°), horizontal wind speed wnd (M / s), standard deviation of lateral wind speed STD_ Uy (M / s), ultrasonic virtual temperature TV (℃), water vapor density H2O (g / m3), carbon dioxide concentration CO2 (mg / m3), friction velocity ustar (M / s), Obukhov length L (m), sensible heat flux HS (w / m2), latent heat flux Le (w / m2), carbon dioxide flux FC (mg / (M2S)), quality identification QA of sensible heat flux_ HS, quality identification of latent heat flux QA_ Le, quality identification QA of carbon dioxide flux_ Fc。 The quality identification of sensible heat, latent heat and carbon dioxide flux is divided into nine levels (quality identification 1-3 has good data quality, 4-6 has good data quality, 7-8 has poor data quality (better than interpolated data), and 9 has poor data quality). The meaning of data time, for example, 0:30 represents the average of 0:00-0:30; The data is stored in *. XLS format. For observation data processing, please refer to Liu et al. (2011).
ZHAO Changming, ZHANG Renyi
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.
Li Xiaoyan
This dataset contains the flux measurements from the Qinghai Lake eddy covariance system (EC) belonging to the Qinghai Lake basin integrated observatory network from January 1 to December 31 in 2020. Due to the terrible climate in winter and spring, those instruments need maintenance in time. However, the Covid-19 blocked our maintenance, those data in January 1 to April 6 and November 1 to December 31 in 2020 were missing. The effective range of latent heat flux is -500~500 W/m2. The negative value may be caused by condensed water. 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 EC was installed at a height of 16.1m, 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.
Li Xiaoyan
This dataset contains the flux measurements from the temperate steppe eddy covariance system (EC) belonging to the Qinghai Lake basin integrated observatory network from January 1 to December 31 in 2020, but instrument failure and COVID-2019 resulted in lack of data from April 13 to July 20. 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 3210sm. The EC was installed at a height of 2.5 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 (CSAT3A &EC150) 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. Data during December 18 to December 24, 2018 were missing due to the data collector failure. 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.
This dataset contains the flux measurements from the Alpine meadow and grassland ecosystem Superstation superstation eddy covariance system (EC) belonging to the Qinghai Lake basin integrated observatory network from January 1 to December 31 in 2020，but instrument failure and COVID-2019 resulted in lack of data from February 1 to June 27. 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 EC was installed at a height of 4.5 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 (CSAT3A &EC150) 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. Data during December 18 to December 24, 2018 were missing due to the data collector failure. 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.
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
Terrestrial actual evapotranspiration (ETa) is an important component of terrestrial ecosystems because it links the hydrological, energy, and carbon cycles. However, accurately monitoring and understanding the spatial and temporal variability of ETa over the Tibetan Plateau (TP) remains very difficult. Here, the multiyear (2000-2018) monthly ETa on the TP was estimated using the MOD16-STM model supported by datasets of soil properties, meteorological conditions, and remote sensing. The estimated ETa correlates very well with measurements from 9 flux towers, with low root mean square errors (average RMSE = 13.48 mm/month) and mean bias (average MB = 2.85 mm/month), and strong correlation coefficients (R = 0.88) and the index of agreement values (IOA = 0.92). The spatially averaged ETa of the entire TP and the eastern TP (Lon > 90°E) increased significantly, at rates of 1.34 mm/year (p < 0.05) and 2.84 mm/year (p < 0.05) from 2000 to 2018, while no pronounced trend was detected on the western TP (Lon < 90°E). The spatial distribution of ETa and its components were heterogeneous, decreasing from the southeastern to northwestern TP. ETa showed a significantly increasing trend in the eastern TP, and a significant decreasing trend throughout the year in the southwestern TP, particularly in winter and spring. Soil evaporation (Es) accounted for more than 84% of ETa and the spatial distribution of temporal trends was similar to that of ETa over the TP. The amplitudes and rates of variations in ETa were greatest in spring and summer. The multi-year averaged annual terrestrial ETa (over an area of 2444.18×103 km2) was 376.91±13.13 mm/year, equivalent to a volume of 976.52±35.7 km3/year. The average annual evapotranspirated water volume over the whole TP (including all plateau lakes, with an area of 2539.49×103 km2) was about 1028.22±37.8 km3/year. This new estimated ETa dataset is useful for investigating the hydrological impacts of land cover change and will help with better management of watershed water resources across the TP.
MA Yaoming, CHEN Xuelong,
This dataset contains the flux measurements from the large aperture scintillometer (LAS) at Huailai station. There were two types of LASs: German BLS450 and zzLAS. The observation periods were from January 1 to December 31, 2019. The site ( (north: 115.7825° E, 40.3522° N; south: 115.7880° E, 40.3491° N) was located in the Donghuahuan town of Huailai city, Hebei Province. The elevation is 480 m. The underlying surface between the two towers contains mainly maize. The effective height of the LASs was 14 m; the path length was 1870 m. Data were sampled at 1 min intervals. Raw data acquired at 1 min intervals were processed and quality-controlled. The data were subsequently averaged over 30 min periods. The main quality control steps were as follows. (1) The data were rejected when Cn2 was beyond the saturated criterion. (2) Data were rejected when the demodulation signal was small. (3) Data were rejected within 1 h of precipitation. (4) Data were rejected at night when weak turbulence occurred (u* was less than 0.1 m/s). The sensible heat flux was iteratively calculated by combining with meteorological data and based on Monin-Obukhov similarity theory. There were several instructions for the released data. (1) The data were primarily obtained from BLS450 measurements; missing flux measurements from the BLS450 were filled with measurements from the zzLAS. Missing data were denoted by -6999. (2) The dataset contained the following variables: data/time (yyyy-mm-dd hh:mm:ss), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H_LAS, W/m^2). (3) 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. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) (for data processing) in the Citation section.
LIU Shaomin, XU Ziwei
This data set is a national high-resolution solar radiation data set covering 34 years (1983.7-2017.6), with a resolution of 10 km. The data unit is W / m2. The data set is developed by merging the global high-resolution (3 hours, 10 km) surface solar radiation data set (1983-2017) with isccp-hxg cloud products as the main input, with ground based sunshine duration derived surface solar raidation data from 2261 meteorological stations in China by using the geographic weighted regression method. The validation results show that this dataset can provide more accurate simulation of long-term variability of surface solar radiation than that of gewex-srb, cmsaf-clara-a2 and the isccp-hxg based surface solar radiation product. This data can provide favorable data support for the application and research of long-term change of hydrology in land surface process simulation.
FENG Fei, WANG Kaicun
This data set is the data set of climate elements in Hoh Xil area of Qinghai Province, covering the data of 14 observation stations, recording the climate observation data in 1990 in detail. Hoh Xil area in Qinghai Province has a high terrain with an average altitude of over 5000m. The climate is cold, the air is thin and the natural environment is bad. The vast area is still no man's land, known as "forbidden zone for human beings". Due to less interference from human activities, most of the area still maintains its original natural state. Its special geographical location, crustal structure and natural environment, as well as the unique composition of the biological flora, have been the focus of domestic surgical circles. The original data of the data set is digitized from the book "natural environment of Hoh Xil, Qinghai Province". The climate observation data include solar radiation, temperature, precipitation, air pressure, wind speed, etc. This data set provides basic data for the study of Hoh Xil area in Qinghai Province, and has reference value for the research in related fields.
LI Bingyuan
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 August 29 to December 31 in 2019. 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 data set includes the monthly average actual evapotranspiration of the Tibet Plateau from 2001 to 2018. The data set is based on the satellite remote sensing data (MODIS) and reanalysis meteorological data (CMFD), and is calculated by the surface energy balance system model (SEBS). In the process of calculating the turbulent flux, the sub-grid scale topography drag parameterization scheme is introduced to improve the simulation of sensible and latent heat fluxes. In addition, the evapotranspiration of the model is verified by the observation data of six turbulence flux stations on the Tibetan Plateau, which shows high accuracy. The data set can be used to study the characteristics of land-atmosphere interaction and the water cycle in the Tibetan Plateau.
HAN Cunbo, MA Yaoming, WANG Binbin, ZHONG Lei, MA Weiqiang*, CHEN Xuelong, SU Zhongbo
1) Data content (including elements and significance): 21 stations (Southeast Tibet station, Namucuo station, Zhufeng station, mustag station, Ali station, Naqu station, Shuanghu station, Geermu station, Tianshan station, Qilianshan station, Ruoergai station (northwest courtyard), Yulong Xueshan station, Naqu station (hanhansuo), Haibei Station, Sanjiangyuan station, Shenzha station, gonggashan station, Ruoergai station（ Chengdu Institute of biology, Naqu station (Institute of Geography), Lhasa station, Qinghai Lake Station) 2018 Qinghai Tibet Plateau meteorological observation data set (temperature, precipitation, wind direction and speed, relative humidity, air pressure, radiation and evaporation) 2) Data source and processing method: field observation at Excel stations in 21 formats 3) Data quality description: daily resolution of the site 4) Data application results and prospects: Based on long-term observation data of various cold stations in the Alpine Network and overseas stations in the pan-third pole region, a series of datasets of meteorological, hydrological and ecological elements in the pan-third pole region were established; Strengthen observation and sample site and sample point verification, complete the inversion of meteorological elements, lake water quantity and quality, above-ground vegetation biomass, glacial frozen soil change and other data products; based on the Internet of Things technology, develop and establish multi-station networked meteorological, hydrological, Ecological data management platform, real-time acquisition and remote control and sharing of networked data.
ZHU Liping,
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