1.Meteorological data of surface environment and observation network in China's cold region (2018)

    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, PENG Ping

    doi: 10.11888/Meteoro.tpdc.270423 4863 16 Requestable 2020-09-08

    2.Hydrological dataset of China alpine region surface process and environmental observation network (2018)

    Based on the long-term observation data of each field station in the alpine network and overseas stations in the pan third polar region, a series of data sets of meteorological, hydrological and ecological elements in the pan third polar region are established; the inversion of data products such as meteorological elements, lake water quantity and quality, aboveground vegetation biomass, glacial and frozen soil changes are completed through enhanced observation and sample site verification in key regions; based on the IOT Network technology, the development and establishment of multi station network meteorological, hydrological, ecological data management platform, to achieve real-time access to network data and remote control and sharing. In 2018, the hydrological data set of surface process and environmental observation network in China's alpine region mainly collects the daily measured hydrological (runoff, water level, water temperature, etc.) data of Qilianshan station, Southeast Tibet station, Zhufeng station, Yulong Xueshan station, Namucuo station, Ali station, mostag and other seven stations.

    ZHU Liping, PENG Ping

    doi: 10.11888/Hydro.tpdc.270026 4585 34 Requestable 2019-08-12

    3.Integration dataset of Tibet Plateau boundary

    This dataset contains five types of boundaries. 1. TPBoundary_ 2500m: Based on ETOPO5 Global Surface Relief, ENVI+IDL was used to extract data at an elevation of 2500m within the longitude (65~105E) and latitude (20~45N) range in the Tibetan Plateau. 2. TPBoundary_ 3000m: Based on ETOPO5 Global Surface Relief, ENVI+IDL was used to extract data at an elevation of 3000m within the longitude (65~105E) and latitude (20~45N) range in the Tibetan Plateau. 3. TPBoundary_ HF (high_frequency): This boundary is defined according to 2 previous studies. Bingyuan Li (1987) had a systematic discussion on the principles for determining the extent of the Tibetan Plateau and the specific boundaries. From the perspective of the formation and basic characteristics of the Tibetan Plateau, he proposed the basic principles for determining the extent of the Tibetan Plateau based on the geomorphological features, the plateau surface and its altitude, while considering the integrity of the mountain. Yili Zhang (2002) determined the extent and boundaries of the Tibetan Plateau based on the new results of research in related fields and years of field practice. He combined information technology methods to precisely locate and quantitatively analyze the extent and boundary location of the Tibetan Plateau, and concluded that the Tibetan Plateau in China extends from the Pamir Plateau in the west to the Hengduan Mountains in the east, from the southern edge of the Himalayas in the south to the northern side of the Kunlun-Qilian Mountains in the north. On April 14, 2017, the Ministry of Civil Affairs of the People's Republic of China issued the Announcement on Adding Geographical Names for Public Use in the Southern Tibetan Region (First Batch), adding six geographical names in the southern Tibetan region, including Wo’gyainling, Mila Ri, Qoidêngarbo Ri, Mainquka, Bümo La, and Namkapub Ri. 4. TPBoundary_ New (2021): Along with the in-depth research on the Tibetan Plateau, the improvement of multidisciplinary research and understanding inside and outside the plateau, and the progress of geographic big data and Earth observation science and technology, the development of the 2021 version of the Tibetan Plateau boundary data by Yili Zhang and et al. was completed based on the comprehensive analysis of ASTER GDEM and Google Earth remote sensing images. The range boundary starts from the northern foot of the West Kunlun Mountain-Qilian Mountain Range in the north and reaches the southern foot of the Himalayas and other mountain ranges in the south, with a maximum width of 1,560 km from north to south; from the western edge of the Hindu Kush Mountains and the Pamir Plateau in the west to the eastern edge of the Hengduan Mountains and other mountain ranges in the east, with a maximum length of about 3,360 km from east to west; the latitude and longitude range is 25°59′30″N~40°1′0″N, 67°40′37″E~104°40′57″E, with a total area of 3,083,400km2 and an average altitude of about 4,320m. Administratively, the Tibetan Plateau is distributed in nine countries, including China, India, Pakistan, Tajikistan, Afghanistan, Nepal, Bhutan, Myanmar, and Kyrgyzstan. 5. TPBoundary_ Rectangle: The rectangle was drawn according to the range of Lon (63~105E) and Lat (20~45N). The data are in latitude and longitude projection WGS84. As the basic data, the boundary of the Tibetan Plateau can be used as a reference basis for various geological data and scientific research on the Tibetan Plateau.

    ZHANG Yili, REN Huixia, PAN Xiaoduo

    doi: 10.11888/Geogra.tpdc.270099 11295 943 Open Access 2019-06-11

    4.Temperature-humidity index (THI) grid products of Qinghai Tibet Plateau (1km, 2017)

    The temperature humidity index (THI) was proposed by J.E. Oliver in 1973. Its physical meaning is the temperature after humidity correction. It considers the comprehensive impact of temperature and relative humidity on human comfort. It is an important index to measure regional climate comfort. On the basis of referring to the existing classification standards of physiological and climatic evaluation indexes, combined with the natural and geographical characteristics of the Qinghai Tibet Plateau and facing the needs of human settlements suitability evaluation in the Qinghai Tibet Plateau, the temperature and humidity index and its suitability zoning results of the Qinghai Tibet Plateau (more than 3000 meters) are developed (including unsuitable, critical suitable, general suitable, relatively suitable and highly suitable).

    FENG Zhiming, LI Peng, LIN Yumei

    doi: 10.11888/Socioeco.tpdc.271745 31 0 Open Access 2021-10-13

    5.Cold and Arid Research Network of Lanzhou university (eddy covariance system of Liancheng station, 2020)

    This dataset contains the flux measurements from the Liancheng Station eddy covariance system (EC) in the middle reaches of the Heihe integrated observatory network from September 14 to December 31 in 2020. 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 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

    doi: 10.11888/Meteoro.tpdc.271608 293 25 Open Access 2021-07-08

    6.Cold and Arid Research Network of Lanzhou university (eddy covariance system of Minqin station, 2019)

    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

    doi: 10.11888/Meteoro.tpdc.270789 2490 95 Open Access 2020-10-25

    7.Cold and Arid Research Network of Lanzhou university (eddy covariance system of Guazhou station, 2020)

    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

    doi: 10.11888/Meteoro.tpdc.271477 218 17 Open Access 2021-06-15

    8.Cold and Arid Research Network of Lanzhou university (eddy covariance system of Guazhou station, 2019)

    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 31 in 2019. The site (95.673E, 41.405N) was located in a desert in Liuyuan Guazhou, which is near Jiuquan city in Gansu Province. The elevation is 2016 m. The EC was installed at a height of 4.0 m, and the sampling rate was 10 Hz. The sonic anemometer faced north, and the separation distance between the sonic anemometer and the CO2/H2O gas analyzer (CSAT3&Li7500A) was 0.17 m. The raw data acquired at 10 Hz were processed using the Eddypro post-processing software, including the spike detection, lag correction of H2O/CO2 relative to the vertical wind component, sonic virtual temperature correction, coordinate rotation (2-D rotation), corrections for density fluctuation (Webb-Pearman-Leuning correction), and frequency response correction. The EC data were subsequently averaged over 30 min periods. The observation data quality was divided into three classes according to the quality assessment method of stationarity (Δst) and the integral turbulent characteristics test (ITC): class 1-3 (high quality), class 4-6 (good), class 7-8 (poor, better than gap filling data), class9 (rejected). In addition to the above processing steps, the half-hourly flux data were screened in a four-step procedure: (1) data from periods of sensor malfunction were rejected; (2) data collected before or after 1 h of precipitation were rejected; (3) incomplete 30 min data were rejected when the missing data constituted more than 3% of the 30 min raw record; and (4) data were rejected at night when the friction velocity (u*) was less than 0.1 m/s. There were 48 records per day, and the missing data were replaced with -6999. Suspicious data were marked in red. The released data contained the following variables: data/time, wind direction (Wdir, °), wind speed (Wnd, m/s), the standard deviation of the lateral wind (Std_Uy, m/s), virtual temperature (Tv, ℃), H2O mass density (H2O, g/m3), CO2 mass density (CO2, mg/m3), friction velocity (ustar, m/s), stability (z/L), sensible heat flux (Hs, W/m2), latent heat flux (LE, W/m2), carbon dioxide flux (Fc, mg/ (m2s)), quality assessment of the sensible heat flux (QA_Hs), quality assessment of the latent heat flux (QA_LE), and quality assessment of the carbon flux (QA_Fc). In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Detailed information can be found in the suggested references.

    ZHAO Changming, ZHANG Renyi

    doi: 10.11888/Meteoro.tpdc.270709 1134 90 Open Access 2020-07-02

    9.Cold and Arid Research Network of Lanzhou university (eddy covariance system of Xiyinghe station, 2019)

    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 31 in 2019. 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

    doi: 10.11888/Meteoro.tpdc.270706 2277 94 Open Access 2020-06-28

    10.Cold and Arid Research Network of Lanzhou university (eddy covariance system of Suganhu station, 2019)

    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 November 29 to December 31 in 2019. 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

    doi: 10.11888/Meteoro.tpdc.270708 1119 85 Open Access 2020-06-26

    11.Cold and Arid Research Network of Lanzhou university (eddy covariance system of Liancheng station, 2019)

    This dataset contains the flux measurements from the Liancheng station eddy covariance system (EC) in the middle reaches of the Heihe integrated observatory network from August 17 to November 1 in 2019. The site (102.737E, 36.692N) was located on a forest in the Tulugou national forest park, which is near Yongdeng city, Gansu Province. The elevation is 2912 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

    doi: 10.11888/Meteoro.tpdc.270790 1632 81 Open Access 2020-06-23

    12.Cold and Arid Research Network of Lanzhou university (eddy covariance system of Minqin station, 2020)

    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

    doi: 10.11888/Meteoro.tpdc.271478 262 23 Open Access 2021-06-15

    13.Cold and Arid Research Network of Lanzhou university (eddy covariance system of Sidalong station, 2020)

    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

    doi: 10.11888/Meteoro.tpdc.271476 229 23 Open Access 2021-06-15

    14.Cold and Arid Research Network of Lanzhou university (eddy covariance system of Suganhu station, 2020)

    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

    doi: 10.11888/Meteoro.tpdc.271475 274 28 Open Access 2021-06-15

    15.Cold and Arid Research Network of Lanzhou university (eddy covariance system of Xiyinghe station, 2020)

    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

    doi: 10.11888/Meteoro.tpdc.271474 284 29 Open Access 2021-06-15

    16.Slope Length and Stepness Factor Dataset of Pan-Third Pole 20 countries (2020, with a resolution of 7.5 arcsecond)

    The Slope Length and Stepness Factor (LS) dataset of Pan-third pole 20 country is calculated based on the free accessed 1 arc second resolution SRTM digital elevation data (Shuttle Radar Topography Mission, SRTM; the website is http://srtm.csi.cgiar.org). After the pre-processing such as pseudo edge removal, filtering and noise removal, the LS factor with 7.5 arc second resolution was calculated with the LS factor algorithm in CSLE model and the LS calculation tool (LS_tool) developed in this project. The LS factor data of Pan-third pole 20 countries is the fundamental data for soil erosion rate calculation based on CSLE, and it also the fuandatmental data for analyzing the erosion topographic characteristics of Pan third pole 20 countries (such as macro distribution and micro pattern of elevation, slope and slope) . The dataset if of great importance for the analysis of geomorphic characteristics and geological disaster characteristics in this area.

    YANG Qinke

    doi: 10.11888/Soil.tpdc.271740 82 8 Open Access 2021-10-08

    17.Soil Erodibility Dataset of Pan-Third Pole 20 countries (2020, with a resolution of7.5 arc second )

    The soil erodibility factor (K) dataset of 20 countries in the Pan-third pole area is calculated based on the soil properties downloaded from website of the international soil reference and Information Center (ISRIC)(https://files.isric.org/soilgrids/latest/data/) with 7.5 arc second resolution, and the dataset used in the calculation include soil clay content (%), silt content (%), and sand content (%), soil organic carbon content (g / kg) and soil texture class. The soil erodibility factor algorithm proposed by Wischmeier (1978) in the second edition of USLE manual was used, and the soil erodibility factor calculation tool (k_tool) was developed, to generat the 1 arc second (about 25m) soil erodibility factor map. The soil erodibility factor data of Pan third pole 20 countries is not only the fundamental input for soil erosion rate calculation based on CSLE, but also the basic data for analyzing soil characteristics.

    YANG Qinke

    doi: 10.11888/Soil.tpdc.271741 101 12 Open Access 2021-10-08

    18.Dataset of rainfall erosivity R-factor with 300m resoluton in 20 countries in key regions(1986-2015)

    1)The datase includes a 30-year (1986-2015) average rainfall erosivity raster data for 20 countries in key regions, with a spatial resolution of 300 meters. 2)The 0.5°×0.5° grid daily rainfall data generated by the Climate Prediction Center (CPC) based on global site data was used to calculate the rainfall erosivity R factor of 20 countries in key regions. 3)The daily rainfall data of 2358 weather stations nationwide from China Meteorological Administration from 1986 to 2015 was used to calculate the R value, and the R value calculated by establishing the CPC data source was rechecked and verified. It is found that the R value calculated by the CPC data system was low, and then it was revised, and the final data obtained was of good quality. 4)Rainfall erosivity R factor can be used as the driving factor of the CSLE model, and the data is of great significance for the simulation of soil erosion in 20 countries in key regions and the analysis of its spatial pattern.

    ZHANG Wenbo

    doi: 10.11888/Soil.tpdc.271739 86 8 Open Access 2021-10-03

    19.Dataset of vegetation coverage and biological measure factor B with 300 m resoluton in 20 countries in key regions(2014-2016)

    1)The dataset includes the grid data of vegetation coverage and biological measure factor B of 20 countries in key regions, with a spatial resolution of 300 meters. 2)The basic data source is the MODIS MOD13Q1 product from 2014 to 2016 with a spatial resolution of 250 m. Based on this, a 24-half month average vegetation coverage raster data during a 3 year period was calculated, and then the soil loss ratio was calculated according to the land type. The, the 24- half months rainfall erosivity was further weighted and averaged to obtain a grid map of vegetation coverage and biological measures B factor. 3)MOD13Q1 remote sensing vegetation data was processed by cloud removal. The calculated B factor was statistically analyzed by landuse types and rationality analyzed. The final data quality is good. 4)The factor B of vegetation coverage and biological measures reflects the impact of surface land use/vegetation coverage on soil erosion, and is of great significance for soil erosion simulation and spatial pattern analysis in 20 key regions.

    ZHANG Wenbo

    doi: 10.11888/Soil.tpdc.271738 89 8 Open Access 2021-10-03

    20.Dataset of Soil Erosion Intensity with 300 m resoluton in 20 countries in key regions( 2015)

    1)The data includes the raster data of soil erosion intensity in 20 countries in key regions in2015, with a spatial resolution of 300 meters. 2)Based on the data of 13,000 survey units in 20 countries in key regions, the Chinese soil erosion prediction model (CSLE) was used to calculate the factors of rainfall erosivity, soil erodibility, slope length, slope gradient, vegetation cover and biological measures, engineering measures and tillage measures. And then the amount of soil erosion was interpolated by soil types, and the a soil erosion intensity map of 20 countries in key regions was then obtained. 3)The rationality of spatial pattern of soil erosion intensity data was analyze, and the data quality was good . 4)Soil erosion intensity data is of great significance for understanding the spatial pattern of soil erosion in 20 countries in key regions and for carrying out soil erosion control.

    ZHANG Wenbo

    doi: 10.11888/Soil.tpdc.271737 61 2 Open Access 2021-10-03