1.Data of climatic elements in the Qinghai Tibet Plateau (annual average temperature 1990-2015)

    This data set is the data set of climate factors in the Qinghai Tibet Plateau from 1990 to 2015. It records the spatial distribution change of annual average temperature in the past 25 years. The data is in TIF grid format, with a spatial resolution of 1km and an annual average temperature unit of 0.1C. The data comes from the daily observation data of meteorological stations on the Qinghai Tibet Plateau, which is generated by time aggregation calculation and spatial interpolation processing. As an important climate factor, the data set can be used to study the annual average temperature change and climate change of the Qinghai Tibet Plateau. As the climate background of the ecological environment change of the Qinghai Tibet Plateau, it provides data support for the study of urbanization and ecological environment interaction stress Bracing.

    DU Yunyan YI Jiawei

    51 3 Download Online 2019-11-07

    2.Data set of climatic elements of the Qinghai Tibet Plateau (rainfall 1990-2015)

    This data set is the data set of climatic factors in the Qinghai Tibet Plateau from 1990 to 2015, which records the spatial distribution change of annual rainfall every five years in the past 25 years. The data is in TIF grid format, with spatial resolution of 1km and annual rainfall unit of 0.1mm. The data comes from the daily observation data of meteorological stations on the Qinghai Tibet Plateau, which is generated by time aggregation calculation and spatial interpolation processing. As an important climate factor, the data set can be used to study the interannual rainfall change and climate change on the Qinghai Tibet Plateau. As the climate background of the ecological environment change on the Qinghai Tibet Plateau, it can provide data support for the study of the interactive stress between urbanization and ecological environment Bracing.

    DU Yunyan YI Jiawei

    29 3 Download Online 2019-11-07

    3.Lake water temperature, hydrometeorology and lake evaporation at Paiku Co in the central Himalayas (2015-2018)

    HOBO water temperature loggers (U22-001, Onset Corp., USA) were used to monitor changes in water temperature with an accuracy of ±0.2 oC. Two water temperature profiles were installed in Paiku Co’s southern (0-42 m in depth) and northern (0-72 m in depth) basins (Fig. 1). In the southern basin, water temperature was monitored at the depths of 0.4 m, 5m, 10 m, 15 m, 20 m, 30 m and 40 m. In the northern basin, water temperature was monitored at the depths of 0.4 m, 10 m, 20 m, 40 m, 50 m, 60 m and 70 m. To investigate local hydro-meteorology at Paiku Co, air temperature and specific humidity over the lake were monitored since June 2015 by using HOBO air temperature and humidity loggers (U12-012, Onset Corp., USA). The logger was installed in an outcrop ~2 m above the lake surface at the north part of the lake (Fig. 2). Lake evaporation was calculated using the energy budget (Bowen-ratio) method。

    LEI Yanbin

    doi:10.11888/Hydro.tpdc.270287 88 3 Download Online 2019-11-04

    4.List and distribution database of Alpine Periglacial plants (2018)

    The checklist and distribution database of alpine subnival plants mainly includes the collection information and identification information of alpine subnival plants. Between them, the collection information document includes species name, genus name, family name, habitat, altitude, longitude and latitude, collector and collection time; while the identification information document includes species name, genus name, family name, determinavit and identification time. The collected information in the database comes from the first-hand data in the field, while the identification information comes from the identification results of famous botany experts in the world. The quality of data in database is high. It can not only be used in the study of flora and regionalization, but also lay a solid foundation for the study of plant diversity, ecosystem and global climate change response.

    SUN Hang

    doi:10.11888/Ecolo.tpdc.270240 76 3 Application Offline 2019-10-16

    5.Human activity data set of Qinghai Tibet province (2000-2017) (v1.0)

    The data includes 30 items of data in four categories: basic information, comprehensive economy, agriculture and industry, education, health and social security in Qinghai Province and Tibet Autonomous Region. It covers the basic data reflecting human activities, such as population, employees, industrial output value, agricultural machinery power, facility agriculture, etc. of the main county administrative units of the Qinghai Tibet Plateau. The data are sorted out according to the statistical yearbook data of China's counties from 2001 to 2018. For the convenience of application, the data of Qinghai and Tibet are independently tabulated and included in the data of each year. The data can be used to analyze human activities and social and economic development in the county, as well as agricultural and rural development and change process.

    WANG zhaofeng

    doi:10.11888/Socioeco.tpdc.270241 104 11 Download Online 2019-10-16

    6.Temperature and precipitation grid data of the Qinghai Tibet Plateau and its surrounding areas in 1998-2017Grid data of annual temperature and annual precipitation on the Tibetan Plateau and its surrounding areas during 1998-2017

    Data description: This dataset includes the grid data of annual temperature and annual precipitation on the Tibetan Plateau from 1998 to 2017. It is the basic data for study of climate change and its impact on the ecological environment. Data source and processing: The meta data was aquired from the temperature and precipitation daily data of China's ground high-density stations (above 2,400 national meteorological stations) based on the latest compilation of the National Meteorological Information Center's basic data. After removing the missing stations, the software's thin plate spline method in ANUSPLIN was used to perform spatial interpolation, in order to generate grid data with spactial resolution of 1 km on the Tibetan Plateau . Data application: This data can be used to indentify the impact of climate change on the ecological environment.

    DING mingjun

    doi:10.11888/Meteoro.tpdc.270239 147 17 Download Online 2019-10-16

    7.Grain Size Data Set of Luanhaizi Lake (0-859 cm)

    Luanhaizi Borehole (LHZ18) was acquired by Huang Xiaozhong Research Group of Lanzhou University in August 2018. This data is 0-859 cm grain size data of the core of Luanhaizi Lake in Qilian Mountains. Grain size analysis was carried out at 0-4 m according to one sample, and grain size analysis was carried out at on ssample interval at the depth of 4-8.6 m, totaling 390 data were obtained. The experiment was completed in the Key Laboratory of the Ministry of Environmental Education of Lanzhou University, and grain size analysis was carried out with Mastersizer 2000 instrument. The data reflected the grain size change of the lake sediment, which is very important for the study of long-time series eolian activities in the Qinghai-Tibet Plateau.

    ZHANG Jun REN Xiuxiu HUANG Xiaozhong WANG Jiale SUN Mingjie XIANG Lixiong

    doi:10.11888/Paleoenv.tpdc.270284 129 0 Application Offline 2019-10-14

    8.Geochemical Data Set of Lacustrine Core in Luanhaizi Lake (0-859 cm)

    Luanhaizi borehole (LHZ18) was obtained by huangxiaozhong research group of Lanzhou University in August 2018. This data is geochemical element data of 0-859 cm core of Luanhaizi Lake in Qilian Mountains. The experiment was completed in the Key Laboratory of Western Ministry of environmental education of Lanzhou University. This data provides long series and high-resolution geochemical element content. The data comes from core scanning, continuous elemental content changes were obtained 0-829 cm through element change and the field records. The data provided long-time-scale elemental content changes of lakes in Qilian Mountains, and played an important role in the study of paleoclimate and Paleoenvironment in the long time series of the Qinghai-Tibetan Plateau.

    HUANG Xiaozhong ZHANG Jun WANG Jiale REN Xiuxiu SUN Mingjie XIANG Lixiong

    doi:10.11888/Paleoenv.tpdc.270285 33 0 Application Offline 2019-10-14

    9.Paleomagnetic data from the lunpola basin

    The Lunpola Basin in the central Tibetan Plateau is situated along the southern margin of the east-west stretched Banggong-Nujiang suture belt between the Qiangtang Terrane and the Lhasa Terrane. The thick and continuous Cenozoic sediments in the basin can provide great potential for understanding the tectonic uplift, paleoaltimetry, erosion and depositional history of the Tibetan Plateau and climate environmental evolution. However, the study of geologic and climatic changes has been hindered by poor age constraints on the Cenozoic sedimentary sequence in the Lunpola Basin, especially its upper part with typical lacustrine oil shale sediments due to the discontinuous or unexposed outcrop caused by erosion or weathering. In this study, we investigated a 658 m-thick Cenozoic continuous lacustrine sedimentary section, Lunpori, from the upper sequence of the central basin. We found two layers of tuffs in this section and then carried out detailed paleomagnetic measurements. Constrained by tie points of U-Pb zircon ages, the observed magnetic zones are well correlated with chrons C5Bn.2n to C6AAn of the Geomagnetic Polarity Time Scale, yielding ages of ~21.2 to 15 Ma for the section. Lithofacies, pollen and fossil records suggest a relative temperate, humid climate prevailing in the Lunpola Basin during the period of Dingqinghu Formation, indicating that the Indian monsoon occurred before ~26 Ma.Through paleomagnetic analysis and testing of fluvial and lacustrine facies strata in Lumpola Basin since Miocene, 22Ma-15Ma magnetic stratigraphic chronology has been obtained.

    Tan Mengqi

    doi:10.11888/Geo.tpdc.270282 50 0 Download Online 2019-10-14

    10.Revised dataset of temperature and precipitation in the Greater Naren River Basin (1951-2016)

    Precipitation and temperature are essential input variables for hydrological models. There are few meteorological stations in the big Naryn Basin of the Syr Darya, which cannot meet the needs of hydrological simulation. Precipitation data in the Syr Darya were collected through online resources and field research. The precipitation gradient in the study area is obtained. Based on the precipitation gradient, the precipitation and temperature grid products (PGMFD) (http://hydrology.princeton.edu/data.pgf.php)were then corrected to get this set of data sets. The year covered by this data is 1951-2016, the spatial precision is 10km, and the time resolution is daily. The more detail information about the correction method can be found in (Generation of High Mountain Precipitation and Temperature Data for a Quantitative Assessment of Flow Regime in the Upper Yarkant Basin in the Karakoram, Kan et al., 2018)

    Fengge Su

    doi:10.11888/Hydro.tpdc.270216 23 0 Download Online 2019-10-11

    11.Spectral characteristics of sample plots in typical countries along the belt (2015)

    Using the Landsat8 OLI images at the summerof 2015, the spectral characteristics of satellite sensors were extracted in the Belt and Road's region. The bands included the band (0.45 - 0.51μm)、band (0.53 - 0.59μm)、band (0.64 - 0.67μm)、band (0.85 - 0.88μm)、band (1.57 - 1.65μm)、band (2.11 - 2.29 μm)、band (10.60 - 11.19 μm)和band (11.50 - 12.51 μm). And the Land cover data of the Belt and Road's region (Version 1.0) (2015) was used to extract the land cover/use at each location. Data includes the format of excel and shp. The data of shp format includes the spatial distribuition and the spectral characteristics of each sampling point.

    XU Erqi

    doi:10.11888/Ecolo.tpdc.270242 63 0 Download Online 2019-09-19

    12.Land use of the Tibet Plateau in 2015 (Version 1.0)

    Based on 2015 ESA global land cover data (ESA GlobCover), combined with the Tsinghua university global land cover data (FROM GLC)、NASA MODIS global land cover data (MCD12Q1)、University of Maryland global land data (UMD)、USGS global land data (IGBP DISCover),we build the LUC classification system in the Tibet Plateau and the rest of the data transformation rules of the classification system. We also build the land cover classification confidence function and the rules of fusing land classification to finish the Integration and modification of land cover products and finally complet the land use data in the Tibet Plateau V1.0.

    XU Eriqi

    doi:10.11888/Geogra.tpdc.270198 729 75 Download Online 2019-09-12

    13.Land cover of core countries of the Belt and Road in 2015 (Version 1.0)

    Based on 2015 ESA global land cover data (ESA GlobCover, 300 m grid), combined with the tsinghua university global land cover data (FROM GLC, 30 m grid)、NASA MODIS global land cover data (MCD12Q1, 300 m grid)、the United States Geological Survey (USGS global land data (GFSAD30, 30 m)、Japanese global forest data (PALSAR/PALSAR - 2, 25 m),we build the LUC classification system in the Belt and Road’s region and the rest of the data transformation rules of the classification system.We also build the land cover classification confidence function and the rules of fusing land classification to finish the Integration and modification of land cover products and finally complet the land use data in the Belt and Road’s region V1.0(64 + 1 countries, 2015, 1 km x 1 km grid, the first level classification).

    XU Erqi

    doi:10.11888/Geogra.tpdc.270197 359 30 Download Online 2019-09-09

    14.Qilian Mountains integrated observatory network: Dataset of the Heihe River Basin integrated observatory network (eddy covariance system of Sidaoqiao superstation, 2018)

    This dataset contains the flux measurements from the Sidaoqiao superstation eddy covariance system (EC) in the downstream reaches of the Heihe integrated observatory network from January 1 to December 31 in 2018. The site (101.1374° E, 42.0012° N) was located in the Ejina Banner in Inner Mongolia Autonomous Region . The elevation is 873 m. The EC was installed at a height of 3.2 m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (CSAT3&Li7500A) was 0.15 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC): class 1-3 (high quality), class 4-6 (good), class 7-8 (poor, better than gap filling data), class9 (rejected). In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 10% of the 30 min raw record. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. Latent heat flux during November 9 to 21, 2018 were missing due to the sensor malfunction of CO2/H2O gas analyzer. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) for data processing) in the Citation section.

    LIU Shaomin LI Xin XU Ziwei

    doi:10.11888/Geogra.tpdc.270180 414 20 Application Offline 2019-05-27

    15.Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Zhangye wetland station, 2018)

    This dataset contains the flux measurements from the Zhangye wetland station eddy covariance system (EC) in the midstream reaches of the Heihe integrated observatory network from January 1 to December 31 in 2018. The site (100.44640° E, 38.97514° N) was located in the Zhangye City in Gansu Province. The elevation is 1460 m. The EC was installed at a height of 5.2 m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (Gill&Li7500A) was 0.25 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. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. Flux data during March 25 to May 10, 2018 were wrong to the sensor malfunction. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (z/L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) for data processing) in the Citation section.

    LIU Shaomin LI Xin XU Ziwei

    doi:10.11888/Geogra.tpdc.270181 348 13 Application Offline 2019-05-27

    16.Qilian Mountains integrated observatory network: Dataset of the Heihe River Basin integrated observatory network (eddy covariance system of Jingyangling station, 2018)

    This dataset contains the flux measurements from the Jingyangling station eddy covariance system (EC) in the upperstream reaches of the Heihe integrated observatory network from August 28 to December 31 in 2018. The site (101.1160E, 37.8384N) was located in the Jingyangling, near Qilian County in Qinghai Province. The elevation is 3750 m. 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 (CSAT3&Li7500) was 0.15 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC): class 1-3 (high quality), class 4-6 (good), class 7-8 (poor, better than gap filling data), class9 (rejected). In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 10% of the 30 min raw record. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. Data during insufficient power supply, data were missing occasionally. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) for data processing) in the Citation section.

    LIU Shaomin LI Xin XU Ziwei

    doi:10.11888/Geogra.tpdc.270182 353 12 Application Offline 2019-05-26

    17.Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of mixed forest station, 2018)

    This dataset contains the flux measurements from the mixed forest station eddy covariance system (EC) in the downstream reaches of the Heihe integrated observatory network from January 1 to December 31 in 2018. The site (101.1335° E, 41.9903° N) was located in the Sidaoqiao County, in Ejina Banner in Inner Mongolia Autonomous Region . The elevation is 874 m. The EC was installed at a height of 3.2 m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (CSAT3&Li7500) was 0.17 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC): class 1-3 (high quality), class 4-6 (good), class 7-8 (poor, better than gap filling data), class9 (rejected). In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 10% of the 30 min raw record. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. Data during February 7 to 11, 2018 were missing due to the power loss. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) for data processing) in the Citation section.

    LIU Shaomin LI Xin XU Ziwei

    doi:10.11888/Geogra.tpdc.270183 383 10 Application Offline 2019-05-26

    18.Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of desert station, 2018)

    This dataset contains the flux measurements from the desert station eddy covariance system (EC) in the downstream reaches of the Heihe integrated observatory network from January 1 to December 31 in 2018. The site (100.9872° E, 42.1135° N) was located in the Ejina Banner in Inner Mongolia Autonomous Region. The elevation is 1054 m. The EC was installed at a height of 4.7 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&Li7500) was 0.15 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC): class 1-3 (high quality), class 4-6 (good), class 7-8 (poor, better than gap filling data), class9 (rejected). In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 10% of the 30 min raw record. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. Data during May 14 to June 26, 2018 were missing due to the data logger malfunction. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) for data processing) in the Citation section.

    LIU Shaomin LI Xin XU Ziwei

    doi:10.11888/Geogra.tpdc.270184 319 11 Application Offline 2019-05-26

    19.Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Huazhaizi station, 2018)

    This dataset contains the flux measurements from the Huazhaizi station eddy covariance system (EC) in the midstream reaches of the Heihe integrated observatory network from January 1 to December 31 in 2018. The site (100.3201° E, 38.7659° N) was located in the Zhangye City in Gansu Province. The elevation is 1731 m. 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 (CSAT3&Li7500A) was 0.15 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC): class 1-3 (high quality), class 4-6 (good), class 7-8 (poor, better than gap filling data), class9 (rejected). In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 10% of the 30 min raw record. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. Data during May 13 to July 12 and July 16 to August 21, 2018 were missing due to the malfunction of CO2/H2O gas analyzer. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (z/L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) for data processing) in the Citation section.

    XU Ziwei CHE Tao Tan Junlei REN Zhiguo ZHANG Yang LIU Shaomin LI Xin

    doi:10.11888/Geogra.tpdc.270185 330 11 Application Offline 2019-05-26

    20.Qilian Mountains integrated observatory network: Dataset of Heihe integrated observatory network (eddy covariance system of Dashalong station, 2018)

    This dataset contains the flux measurements from the Dashalong station eddy covariance system (EC) in the upperstream reaches of the Heihe integrated observatory network from January 1 to December 31 in 2018. The site (98.9406° E, 38.8399° N) was located in the Qilian County in Qinghai Province. The elevation is 3739 m. 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 (CSAT3&Li7500RS) was 0.15 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. Data during September 27 to November 14, 2018 were missing due to the sensor calibration of sonic anemometer. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (z/L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) for data processing) in the Citation section.

    LIU Shaomin LI Xin XU Ziwei

    doi:10.11888/Geogra.tpdc.270186 306 13 Application Offline 2019-05-26