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

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

Number of Datasets: 1359

  • Annual variation characteristic value of runoff at the major hydrological stations of the Yarlung Zangbo River (1956-2000)

    Annual variation characteristic value of runoff at the major hydrological stations of the Yarlung Zangbo River (1956-2000)

    This dataset contains the annual variation of runoff from the major hydrological stations in the Yarlung Zangbo River (annual average runoff volume, annual extremum ratio, coefficient of variation, etc.). It can be used to study the hydrological characteristics of the Yarlung Zangbo River. The original data are the national hydrological station data, and the quality requirements are the same as the national standards. Spatial Coverage: 4 hydrological stations in the main streams of the Yarlung Zangbo River basin, which are Lazi, Nugesha, Yangcun and Nuxia. This data sheet has five fields. Field 1: Station Name Field 2: Annual average runoff volume Field 3: Annual Extreme Ratio Field 4: Coefficient of variation Field 5: Data Series Length

    2022-09-19 6173 542

  • Dataset of gridded daily precipitation in China (Version 2.0) (1961-2013)

    Dataset of gridded daily precipitation in China (Version 2.0) (1961-2013)

    The National Meteorological Information Center Meteorological Data Room has detected, controlled and corrected the quality of 2474 national-level ground stations' basic meteorological data and formed a set of high-quality, national and provincial ground-based basic data files. On the basis of the basic ground data of the precipitation data files, the thin-plate spline method is used, introducing the digital elevation data to eliminate the influence of the elevation on the precipitation precision under the unique terrain conditions in China. A dataset of 0.5°×0.5° grid values for the surface precipitation in China since 1961 is established. It provides a data basis for accurately describing the trends and magnitudes of precipitation changes in China. One of two data sources for the development of “Dataset of Gridded Daily Precipitation in China (Version 2.0)” was 1) the monthly and daily precipitation data of 2474 national-level stations in the country archived by the Meteorological Data Room for nearly 50 years. The information comes from the monthly information of the “Monthly Report of the Surface Meteorological Record” reported by the climate data processing departments of all the provinces, municipalities and autonomous regions. That information is collected, organized and strictly checked and reviewed by the National Meteorological Information Center. Since the establishment of the station, many stations in the country have undergone historical changes such as business reform and station migration. In 1961, the total number of stations had stabilized above 2,000, and the number of backstage stations in the late 1970s reached 2,400. 2) The second data source was a Chinese range of 0.5°×0.5° digital elevation model data DEMs generated by GTOP030 data (resolution 30′′×30′′) resampling. For the quantitative analysis and evaluation of the data, please see the Dataset of Gridded Daily Precipitation in China - Data Specification.

    2022-09-13 10856 922

  • Daily standard weather station dataset in Sanjiangyuan region (1981-2015)

    Daily standard weather station dataset in Sanjiangyuan region (1981-2015)

    The files in this data set are named as: 1. Pressure of the station: SURF_CLI_CHN_MUL_DAY-PRS-10004-SITEID.TXT 2. Temperature: SURF_CLI_CHN_MUL_DAY-TEM-12001-SITEID.TXT 3. Relative humidity: SURF_CLI_CHN_MUL_DAY-RHU-13003-SITEID.TXT 4. Precipitation: SURF_CLI_CHN_MUL_DAY-PRE-13011-SITEID.TXT 5. Evaporation: SURF_CLI_CHN_MUL_DAY-EVP-13240-SITEID.TXT 6. Wind direction and wind speed: SURF_CLI_CHN_MUL_DAY-WIN-11002-SITEID.TXT 7. Sunshine: SURF_CLI_CHN_MUL_DAY-SSD-14032-SITEID.TXT 8.0cm Ground Temperature: SURF_CLI_CHN_MUL_DAY-GST-12030-0cm-SITEID.TXT Detailed format descriptions for each data file are given in the SURF_CLI_CHN_MUL_DAY_FORMAT.doc file. The meteorological site information contained in this data set is as follows: Site_id lat lon ELV name_En 52754 37.33 100.13 8301.50 Gangcha 52833 36.92 98.48 7950.00 Uran 52836 36.30 98.10 3191.10 Dulan 52856 36.27 100.62 2835.00 Chabcha 52866 36.72 101.75 2295.20 Xining 52868 36.03 101.43 2237.10 Guizhou 52908 35.22 93.08 4612.20 Wu Daoliang 52943 35.58 99.98 3323.20 Xinghai 52955 35.58 100.75 8120.00 Guinan 52974 35.52 102.02 2491.40 Tongren 56004 34.22 92.43 4533.10 Toto River 56018 32.90 95.30 4066.40 Zaduo 56021 34.13 95.78 4175.00 Qumalai 56029 33.02 97.02 3681.20 Yushu 56033 34.92 98.22 4272.30 Maddo 56034 33.80 97.13 4415.40 Qingshui River 56038 32.98 98 98.10 9200.00 Shiqu 56 043 34.47 100.25 3719.00 Golo 56 046 33.75 99.65 3967.50 Dari 56065 34.73 101.60 8500.00 Henan 56 067 33.43 101.48 3628.50 Jiuzhi 56074 34.00 102.08 3471.40 Marqu 56080 35.00 102.90 2910.00 Hezuo 56106 31.88 93.78 4022.80 Suoxian 56116 31.42 95.60 3873.10 Ding Qing 56125 32.20 96.48 3643.70 Xiangqian 56128. 31.22. 96.60. 3810.00 Leiwuqi 56 137 31.15 97.17 3306.00 Changdu 56151 32.93 100.75 8530.00 Banma 56152 32.28 100.33 8893.90 Saida

    2022-09-13 10083 264

  • Xinjiang water resources bulletin (2006-2012)

    Xinjiang water resources bulletin (2006-2012)

    The water resources bulletin is a comprehensive annual report reflecting the annual situation of water resources and its development and utilization and important water information, providing timely water resources information for the macro decision-making of relevant departments.The contents of the communique mainly include: precipitation, surface water and groundwater resources in xinjiang, water storage in water conservancy facilities, water consumption in xinjiang (production, ecological and domestic water), wastewater discharge (various industrial types), water quality evaluation of primary and secondary water systems, and flood disasters.This data comes from the xinjiang water resources department. Relying on the intensive basic hydrological monitoring sample points, it reflects the high level of true reliability of this data.This kind of data is suitable for inter-annual optimal allocation and scheduling of water resources.

    2022-09-13 5906 448

  • A literature-based eddy covariance carbon exchange dataset on the Tibetan Plateau

    A literature-based eddy covariance carbon exchange dataset on the Tibetan Plateau

    (1) This is a literature-based eddy covariance carbon exchange dataset on the Tibetan Plateau, including air temperature, soil temperature, precipitation, ecosystem productivity and other parameters. (2) The data set is based on the field measured data of vorticity, and adopts the internationally recognized standard processing method of vorticity related data. The basic process includes: outlier elimination coordinate rotation WPL correction storage item calculation precipitation synchronization data elimination threshold elimination outlier elimination U * correction missing data interpolation flux decomposition and statistics. This data set also contains the model simulation data calibrated based on the vorticity correlation data set. (3) the data set has been under data quality control, and the data missing rate is 37.3%, and the missing data has been supplemented by interpolation. (4) The data set has scientific value for understanding carbon sink function of alpine wetland, and can also be used for correction and verification of mechanism model.

    2022-09-13 755 612

  • Resilience of population growth in countries along the Belt and Road (2000-2019)

    Resilience of population growth in countries along the Belt and Road (2000-2019)

    Population growth resilience reflects the level of resilience of population growth in the countries along the belt and road, and the higher the value, the stronger the resilience of population growth in the countries along the belt and road. The data on the resilience of population growth is prepared by referring to the World Bank's statistical database, using the year-on-year changes in the population of countries along the Belt and Road from 2000 to 2019, taking into account the year-on-year changes in each indicator, and through comprehensive diagnosis based on sensitivity and adaptability analysis. The resilience of population growth product.

    2022-09-13 674 190

  • Resilience of population age structure in countries along the Belt and Road (2000-2019)

    Resilience of population age structure in countries along the Belt and Road (2000-2019)

    Population age structure resilience reflects the level of population age structure resilience in the countries along the Belt and Road. The World Bank's statistical database was used to prepare the data on the resilience of the population age structure of the countries along the Belt and Road. Based on the sensitivity and adaptability analysis, a comprehensive diagnosis was made based on the year-on-year change of each indicator, and the product on the resilience of population age structure was prepared.

    2022-09-13 720 214

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

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

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

    2022-09-05 1419 10

  • Geo risk index  along the " Belt and Road Initiative" (2017)

    Geo risk index along the " Belt and Road Initiative" (2017)

    "One belt, one road" along the lines of risk rating, credit risk rating and Moodie's national sovereignty rating reflects the structure of sovereign risk in every country. The rating of Moodie's national sovereignty is from the highest Aaa to the lowest C level, and there are twenty-one levels. Data source: organized by the author. Data quality is good. The rating level is divided into two parts, including investment level and speculation level. AAA level is the highest, which is the sovereign rating of excellent level. It means the highest credit quality and the lowest credit risk. The interest payment has sufficient guarantee and the principal is safe. The factors that guarantee the repayment of principal and interest are predictable even if they change. The distribution position is stable. C is the lowest rating, indicating that it cannot be used for real investment.

    2022-08-16 2838 5

  • Data of soil organic matter in Qinghai-Tibet Plateau (1979-1985)

    Data of soil organic matter in Qinghai-Tibet Plateau (1979-1985)

    The data include soil organic matter data of Tibetan Plateau , with a spatial resolution of 1km*1km and a time coverage of 1979-1985.The data source is the soil carbon content generated from the second soil census data.Soil organic matter mainly comes from plants, animals and microbial residues, among which higher plants are the main sources.The organisms that first appeared in the parent material of primitive soils were microorganisms.With the evolution of organisms and the development of soil forming process, animal and plant residues and their secretions become the basic sources of soil organic matter.The data is of great significance for analyzing the ecological environment of Tibetan Plateau

    2022-08-03 3763 92

  • Analysis data set of climate change in the middle and lower reaches of Shiyang River Based on Lake Sediment Records

    Analysis data set of climate change in the middle and lower reaches of Shiyang River Based on Lake Sediment Records

    This data is the sediment record of Qingtu Lake in the middle and lower reaches of Shiyang River Basin, including sediment indicators of qth01 and qth02 Lake profiles. Shiyang River Basin is located in 100 ° 57'~ 104 ° 57' e, 37 ° 02'~ 39 ° 17' n, with a total length of more than 300 kilometers and a total area of 4.16 × 104km2。 The basin is located in the transitional zone between the northwest arid region and the eastern monsoon region, and has a unique climate model. Modern climatological research shows that the hydrological changes in this region are intense, the ecosystem is fragile, and it is very sensitive to global climate change. The two profiles qth01 and qth02 involved in this data have geographical coordinates of 39 ° 03 ′ n 103 ° 40 ′ E and an altitude of 1309m. The depth of the profile is 692cm (qth01) and 736cm (qth02) respectively. AMS14C radiocarbon dating was carried out in the dating Laboratory of Peking University and pretreated in the pretreatment Laboratory of Lanzhou University. The dating samples should try to avoid the layers and sand layers with more plant roots. Radiocarbon 14C dates were calibrated using oxcal v4.4.2 and intcal20 atmospheric profiles. The mineral composition of sediment was determined by x'pert Pro MPD, and the particle size of sediment was determined by Mastersizer 2000 laser diffraction particle size analyzer. The above experiments were completed in the key experiment of the Ministry of western environmental education of Lanzhou University. Grain size data qth01 and qth02 profiles are sampled and measured at 2cm intervals, mineral data qth01 is sampled and measured at 10cm intervals, and qth02 is sampled and measured at 20cm intervals. The fluctuation of grain size and mineral content shows the significant climate change since the Holocene in the middle and lower reaches of Shiyang River, and the climate was relatively dry in the early Holocene (11.0 - 7.4 cal. kyr BP); The middle Holocene (7.4 - 4.7 cal. kyr BP) was in a climate suitable period; In the late Holocene (4.7 - 0 cal. kyr BP), the trend of aridity was obvious, and this aridity became intensified after 1.6 cal. kyr BP.

    2022-08-01 137 21

  • The human activity dataset in key area of Qilian Mountaion (2021)

    The human activity dataset in key area of Qilian Mountaion (2021)

    This data set is the data set of human activities in key areas of Qilian Mountains in 2021, with a spatial resolution of 2m. This data set focuses on the monitoring of mining, urban expansion, cultivated land development, hydropower construction and tourism development in key areas of Qilian Mountains. Through high-resolution remote sensing images, the changes before and after statistics are compared. The map spots of land type change in Qilian mountain area were investigated and verified block by block; Reinterpret and verify the plots with suspicious mapping; For the land type that cannot be reflected by the image, verify the land type on the spot, collect relevant data, check and correct the position. At the same time, further check the attribute information of the monitoring content of key areas in the Qilian Mountains in 2021, input and edit the map spots and their attributes in a unified way, form a human activity data set in the Qilian Mountains in 2021, realize the current situation and timeliness of ecological governance in the Qilian Mountains, and provide data support for human activity monitoring in key areas in the Qilian Mountains.

    2022-07-06 2627 108

  • Daily 0.01°×0.01° Land Surface Soil Moisture Dataset of the Qinghai-Tibet Plateau (2005、2010、2015、2017and 2018) (SMHiRes, V1)

    Daily 0.01°×0.01° Land Surface Soil Moisture Dataset of the Qinghai-Tibet Plateau (2005、2010、2015、2017and 2018) (SMHiRes, V1)

    This dataset contains daily 0.01°×0.01° land surface soil moisture products in the Qinghai-Tibet Plateau in 2005, 2010, 2015, 2017, and 2018. The dataset was produced by utilizing the multivariate statistical regression model to downscale the “SMAP Time-Expanded 0.25°×0.25° Land Surface Soil Moisture Dataset in the Qinghai-Tibet Plateau (SMsmapTE, V1)”. The auxiliary datasets participating in the multivariate statistical regression include GLASS Albedo/LAI/FVC, 1km all-weather surface temperature data in western China by Ji Zhou, and Lat/Lon information.

    2022-07-05 5147 440

  • Monthly 0.01°×0.01° Land Surface Soil Moisture Dataset of the Qinghai-Tibet Plateau (2005、2010 and 2015) (SMHiRes, V1)

    Monthly 0.01°×0.01° Land Surface Soil Moisture Dataset of the Qinghai-Tibet Plateau (2005、2010 and 2015) (SMHiRes, V1)

    This dataset contains monthly 0.05°×0.05° land surface soil moisture products in Qilian Mountain Area in 2005, 2010 and 2015. The dataset was produced by utilizing the multivariate statistical regression model to downscale the “AMSR-E and AMSR2 TB-based SMAP Time-Expanded Daily 0.25°×0.25° Land Surface Soil Moisture Dataset in Qilian Mountain Area (SMsmapTE, V1)”. The auxiliary datasets participating in the multivariate statistical regression include GLASS Albedo/LAI/FVC, 1km all-weather surface temperature data in western China by Ji Zhou and Lat/Lon information.

    2022-07-04 3857 604

  • Daily 0.05°×0.05° land surface soil moisture dataset of Qilian Mountain area (2017, SMHiRes, V1)

    Daily 0.05°×0.05° land surface soil moisture dataset of Qilian Mountain area (2017, SMHiRes, V1)

    This dataset contains daily 0.05°×0.05° land surface soil moisture products in Qilian Mountain Area in 2017. The dataset was produced by utilizing the multivariate statistical regression model to downscale the “AMSR-E and AMSR2 TB-based SMAP Time-Expanded Daily 0.25°×0.25° Land Surface Soil Moisture Dataset in Qilian Mountain Area (SMsmapTE, V1)”. The auxiliary datasets participating in the multivariate statistical regression include GLASS Albedo/LAI/FVC, 1km all-weather surface temperature data in western China by Ji Zhou and Lat/Lon information.

    2022-07-04 2897 598

  • 30 m land cover classification product data set of Qilian Mountain Area in 2021 (V3.0)

    30 m land cover classification product data set of Qilian Mountain Area in 2021 (V3.0)

    This data set is a 30m land cover classification product in the Qilian Mountains in 2021. This product is based on the land cover classification product in 2021, based on the Landsat series data and strong geodetic data processing capability of Google Earth engine platform, and is produced by using the ideas and methods of change detection. The overall accuracy is better than 85%. This product is the continuation of land cover classification products from 1985 to 2020. Land cover classification products from 1985 to 2020 can also be downloaded from this website. Among them, the land use products from 1985 to 2015 are five years and one period, and the land use products from 2015 to 2021 are one year and one period.

    2022-06-30 405 0

  • Product data set of 30 m human activity parameters in Qilian Mountain Area in 2021 (V3.0)

    Product data set of 30 m human activity parameters in Qilian Mountain Area in 2021 (V3.0)

    This data set includes 30 m cultivated land and construction land distribution products in Qilian Mountain Area in 2021. The product comes from the land cover classification product of 30 m in Qilian Mountain Area in 2021. The overall accuracy of the product is better than 85%.

    2022-06-30 563 38

  • Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (eddy covariance system of Yulei station on Qinghai lake, 2021)

    Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (eddy covariance system of Yulei station on Qinghai lake, 2021)

    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.

    2022-06-30 911 52

  • Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (eddy covariance system of Alpine meadow and grassland ecosystem Superstation, 2021)

    Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (eddy covariance system of Alpine meadow and grassland ecosystem Superstation, 2021)

    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.

    2022-06-30 710 45

  • Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (eddy covariance system of the Subalpine shrub, 2021)

    Qilian Mountains integrated observatory network: Dataset of Qinghai Lake integrated observatory network (eddy covariance system of the Subalpine shrub, 2021)

    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.

    2022-06-30 684 31