As a typical arid and semi-arid region, Central Asia is subject to varying degrees of hydrothermal constraints and environmental limitations for sustainable land and agricultural development. Analysis and prediction of land use potential is essential to guarantee regional food security and reduce the adverse effects of climate change. This dataset is oriented to the sustainable agricultural development of five Central Asian countries, and the potential evaluation of land use and agroecology from the perspective of land resource development and utilization potential is carried out with dry farming, irrigated agriculture, forestry, and grass-pastoralism as land use targets. The multi-objective land resource development and utilization evaluation factors include climate (heat and water resources), topography, irrigation and water extraction conditions, and soil conditions, which are greater than 10℃ cumulative temperature, average temperature in January, average temperature in July, precipitation, precipitation variation coefficient, elevation, slope, water extraction distance, groundwater level, soil organic matter, soil texture, soil acidity and alkalinity, among which the precipitation variation coefficient is based on The precipitation variation coefficient is based on precipitation conversion, and the slope information is extracted from the elevation data. Variable climate elements including future monthly-scale precipitation, mean temperature, maximum and minimum air temperature, and humidity are derived from bias-corrected and downscaled CMIP6's ACCESS-CM2, BCC-CSM2-MR, CanESM5, CAS-ESM2-0, CESM2-WACCM, EC-Earth3, GFDL-ESM4, KACE-1-0-G multi-model ensemble averaged data with experiments of r1i1p1f1. This data can provide a basis for future land resources development and utilization, agricultural development, etc. in the five Central Asian countries. The data can provide basic data support for the future development and utilization of land resources and agricultural development in five Central Asian countries.
YAO Linlin , ZHOU Hongfei
This section was measured in the north of the Minzhuochaka Lake in the Nagri region. We collected and studied the fusulines, conodonts and smaller foraminifers from the Strata. The conodonts are dominated by Sweetognathus and Mesogondolella species. The fusulines are dominated by Neoschwagerina, Pseudodoliolina, Mesoschubertella. The smaller foraminifers consist mainly of Pachyphloia, Langella, Palaeotextularia and Tetrataxis. From the viewpoint of conodonts, their age is Kungurian. From the viewpoint of fusulines, it suggests a Murgabian age. The coexistence of fusulines and conodonts suggests that the upper Kungurian of International Scale correspond to the Murgabian of Tethyan Scale. This has provided robust evidence to support a correct correlation between the global scale and Tethyan scale of the Permian stage. In paleobiogeography, the present of conodonts and fusulines in the section suggests that the South Qiangtang Block was in a warm-water environment during the Kungurian time. By contrast, the Kungurian faunas in the Lhasa Block are dominated by cool-water taxa without any warm-water fusulines. The discovery of both conodonts and fusulines suggest a different paleobiogeography between the Lhasa and South Qiangtang blocks during the Kungurian time.
ZHANG Yichun
Ochotona curzoniae is a small herbivorous animal peculiar to the Qinghai Tibet Plateau, which mainly inhabits the open alpine meadow, grassland and desert grassland with an altitude of 2800-5000 meters. In this sub project (2019QZKK05010212), plateau pika, a small constant temperature mammal that is extremely sensitive to environmental changes, is proposed to be selected as the representative to compare the differences in morphology, physiology and life history of pika populations at different altitudes on the Qinghai Tibet Plateau and adjacent areas through field surveys. This data set includes individual photos, habitat photos and work photos taken in Qinghai in 2020 and Maduo County, Tibet Autonomous Region in 2021, including more than 10 photos of plateau pika caves and one pika activity video.
ZHANG Xueying
Plateau pika is a key species of the Qinghai Tibet Plateau and an indigenous species formed with the uplift of the Qinghai Tibet Plateau. During the long-term evolution, it has evolved a unique life history strategy to adapt to the extreme environment of the plateau. This sub project (2019QZKK05010410) investigates the distribution area of plateau pika, analyzes its population fluctuation rule and its influencing factors in the context of global climate change, and discusses the ecological significance of plateau pika in the alpine meadow ecosystem. This data set contains the information table of 213 plateau zokor tissue samples collected in Gonghe County, Guinan County, Hainan Prefecture, and Maqin County, Golog Prefecture, Qinghai Province in 2020, including species, collection place, collection time, collection person, sample type and other information. The information table is named after the sub subject number - year - group and opened in excel
QU Jiapeng
In order to find out the current resource quantity, distribution and utilization status of Tibetan yaks and lay a foundation for the conservation and utilization of the diversity of Tibetan yaks, this sub project (2019QZKK05010705) will investigate the genetic resources of yaks and collect tissue samples in Tibet Autonomous Region from 2021-2022, including Chawula yaks (20), Jiangda yaks (21), Uqi like yaks (65), Pali yaks (20), Sibu yaks (20) Tibetan alpine yaks (20 heads). This data set includes 6 tissue sample information tables, photos and videos. The information table records information such as gender, age, weight, body height, sampling place, etc. The photos include individual appearance photos, habitat photos, and work videos.
XIN Jinwei
In order to complete the investigation of Tibetan sheep resources on the Qinghai Tibet Plateau and its surrounding areas and master the current situation of Tibetan sheep resources, the investigation of Tibetan sheep germplasm resources will be carried out in Maqu County and Xiahe County of Gannan Tibetan Autonomous Prefecture in Gansu Province in 2020, and 500 blood and tissue samples will be collected. This data set contains a tissue sample information table, including species, species, collection place, collection time, sample type and other information, which is stored in excel format. Take 100 individual photos, 10 habitat photos, 9 work photos and 2 videos. Photos are stored in jpg format and videos are stored in mp4 format. 50000 genotype data are generated for each individual, and the SNP genome typing data of 500 individuals in total are stored in "ped" and "map" formats.
LI Menghua
In order to describe the distribution pattern of genetic diversity of important livestock and poultry germplasm resources in the Qinghai Tibet Plateau, clarify their related genetic background, and establish a corresponding genetic resource bank. In 2022, the survey of genetic resources of domestic animals will be carried out in Jiulong County, Hongyuan County and Xiangcheng County of Sichuan Province, and 484 blood and tissue samples of sheep, yaks, goats, dogs, pigs and cattle will be collected, 40 sheep feces samples, 2 RNA samples of Tibetan chickens and 3 RNA samples of Tibetan pigs will be collected. This data set includes 1 sample information table and 685 individual photos, 12 work photos, 5 habitat photos and 12 work videos. The sample information table contains basic sample information such as species, varieties, detailed sampling places, sample types, collection time, collectors, and storage methods, which are stored in excel form. Photos are stored in jpg format and videos are stored in MP4 format.
PENG Minsheng
Knowledge the elemental composition of aerosols in remote areas is very important for assessing the impact of human activities. This dataset reports the elemental composition of atmospheric aerosols (TSP) in Ranwu, a remote area in the southeast of the Tibet Plateau, from November 2019 to December 2020. The data include acid soluble and total soluble components. The results of acid soluble components are determined by adding 1% sample volume of nitric acid to react for seven days; The treatment of the total soluble component is to use the mixture of nitric acid and hydrofluoric acid for determination after pressurized digestion. The Chinese loess reference material (GBW07408) is used for quality control. In general, the element concentrations in this area are lower than those in other stations in the southeast of the Qinghai Tibet Plateau, but slightly higher than those in the interior of the plateau (Nam co). The interior of the Tibet Plateau is the main source of elements from the crust, and the typical heavy metal elements are the long-distance transport of pollutants emitted by human activities in South Asia and Southeast Asia. The data supplement the database of aerosol elements in the southeast of the Tibet Plateau.
LI Chaoliu
The data set includes carbon isotope data of different regions of the Tibetan Plateau and different environmental (carbon isotope data of black carbon and organic carbon in aerosols from 10 typical stations of the Qinghai Tibet Plateau, carbon isotope data of black carbon and water insoluble organic carbon in 11 snow pits in different years, and carbon isotope data of water-soluble organic carbon in monsoon precipitation from 11 stations of the Qinghai Tibet Plateau and its surrounding areas), All samples were collected at each site, and the content and δ 13C and Δ 14C data, which can be used to accurately assess the contribution proportion of atmospheric carbon aerosols, carbon particles deposited on glaciers and water-soluble organic carbon in precipitation from fossil fuels and biomass fuels.
LI Chaoliu
This data set covers the contents of black carbon and water insoluble organic carbon in precipitation at Namco Station (2013-2017), Lulang Station (2014-2017), Everest Station (2015-2016) and Lhasa Station (2017-2018, This data can be used to evaluate the temporal and spatial changes of the wet deposition rate of water insoluble carbon particles in typical areas of the Tibetan Plateau, and is an important input data for model simulation.
LI Chaoliu
This data set includes the light absorption data of carbon components in the atmosphere and precipitation at typical stations on the Tibetan Plateau (Ranwu (2018-2021), Namco (2013-2016), Everest (2013-2016), Lulang (2015-2016)). All samples were collected on the spot from various sampling points. The concentrations of black carbon and water-soluble organic carbon, as well as the light absorption data were measured, using the index (MAC value) representing the light absorption capacity, The MAC values of light absorption of water-soluble organic carbon and black carbon are calculated. This data is of great significance for evaluating the radiative forcing of carbon particles in the atmosphere, and is an important basic data input for model simulation.
LI Chaoliu
Glacial lake inventory from 1977-2017, based on Landsat MSS/TM/ETM+/OLI imagery, uses a semi-automatic water body classification method to distinguish between water body and non-water body information, then extracts the lake boundaries and visually checks and manually edits them by comparison with the original Landsat images. The MSS sensor data was used in 1977 with a resolution of 60 m. Image data used after 1987 had a resolution of 30 m.The relationship between glacial meltwater and glacial lake recharge was determined from RGI 6.0 and Google Earth.
KHADKA Nitesh , ZHANG Guoqing
Carbon particles are an important radiative forcing factor in the atmosphere. Their concentration and composition vary greatly in time and space, especially in remote areas. This data set reports the total suspended particulate matter (TSP), total carbon (TC) and water insoluble particulate carbon (IPC) of PM2.5 at two stations in the remote area of the eastern Qinghai Tibet Plateau (Hongyuan) Δ 14C and δ 13C, the area is affected by severe air pollution from southwest China. The contribution rates of TC fossil fuels in TSP and PM2.5 samples are 18.91 ± 7.22% and 23.13 ± 12.52% respectively, which are far lower than those in Southwest China, indicating the importance of non fossil contributions from local sources. TC in TSP samples at study site δ 13C is 27.06 ± 0.96 ‰, which is between long-distance transport sources (such as the southwest region) and local biomass combustion emissions. This data supplements the database of carbon aerosols in the east of the Tibetan Plateau.
LI Chaoliu
The data files include the extent of the Poqui watershed and multiple periods of glacial lake cataloguing. The glacial lake extent in 1964, was obtained using manual outlining methods based on geo-corrected KH-4 data; the glacial lake extent during 1976-2017, based on Landsat MSS/TM/ETM+/OLI imagery, used a semi-automatic water body classification method to distinguish between water body and non-water body information, and then extracted lake boundaries, and visually checked and manually edited by comparison with the original Landsat images.The relationship between glacial meltwater and glacial lake recharge was determined from RGI 6.0 glacier catalogues and Google Earth.
ZHANG Guoqing
The global annual lake ice phenological dataset includes the freeze-up date, break-up date for 74,245 lakes in the northern hemisphere. The dataset is divided into three parts: 1) current data, obtained from MODIS productions through a DLRM model (with parameters provided), covering the period of 2001 to 2020; 2) historical and 3) future simulation data, obtained from the temperature-based lake-specific models, for the periods of 1861-2005 and 2006-2099, respectively. The historical and future simulations were only performed for 30,063 lakes that meet the model conditions and are presented in the dataset.
WANG Xinchi
This dataset contains the flux measurements from the Huailai station eddy covariance system (EC) from April 13 to December 31 in 2021. The site (115.7923° E, 40.3574° N) was located in the maize surface, near Donghuayuan town of Huailai city in Hebei Province. The elevation is 480 m. The EC was installed at a height of 3.5 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&EC150) was 0 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) (class1-9). 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; 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. There were lots of negative values of H2O density in winter where filling by -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. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) for data processing) in the Citation section.
LIU Shaomin, XIAO Qing, XU Ziwei, BAI Junhua
This dataset contains the flux measurements from the large aperture scintillometer (LAS) at Huailai station. There were two types of LASs: German BLS450 and zzLAS. The observation periods were from January 1 to December 31, 2021. The site ( (north: 115.7825° E, 40.3522° N; south: 115.7880° E, 40.3491° N) was located in the Donghuahuan town of Huailai city, Hebei Province. The elevation is 480 m. The underlying surface between the two towers contains mainly maize. The effective height of the LASs was 14 m; the path length was 1870 m. Data were sampled at 1 min intervals. Raw data acquired at 1 min intervals were processed and quality-controlled. The data were subsequently averaged over 30 min periods. The main quality control steps were as follows. (1) The data were rejected when Cn2 was beyond the saturated criterion. (2) Data were rejected when the demodulation signal was small. (3) Data were rejected within 1 h of precipitation. (4) Data were rejected at night when weak turbulence occurred (u* was less than 0.1 m/s). The sensible heat flux was iteratively calculated by combining with meteorological data and based on Monin-Obukhov similarity theory. There were several instructions for the released data. (1) The data were primarily obtained from BLS450 measurements; missing flux measurements from the BLS450 were filled with measurements from the zzLAS. Missing data were denoted by -6999. (2) The dataset contained the following variables: data/time (yyyy-mm-dd hh:mm:ss), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H_LAS, W/m^2). (3) In this dataset, the time of 0:30 corresponds to the average data for the period between 0:00 and 0:30; the data were stored in *.xls format. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) (for data processing) in the Citation section.
LIU Shaomin, XU Ziwei
This dataset obtained from an observation system of Meteorological elements gradient of Huailai station from January 1 to December 31, 2021. The site (115.7923° E, 40.3574° N) was located on a cropland (maize surface) which is near Donghuayuan town of Huailai city, Hebei Province. The elevation is 480 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (3, 5, 10, 15, 20, 30, and 40 m, towards north), wind speed and direction profile (3, 5, 10, 15, 20, 30, and 40 m, towards north), air pressure (in the box), rain gauge (3 m, south of tower), four-component radiometer (4 m, south of tower), two infrared temperature sensors (4 m, south of tower, vertically downward), photosynthetically active radiation (4 m, south of tower, vertically upward), soil heat flux -0.06 m), a TCAV averaging soil thermocouple probe (-0.02, -0.04 m), soil temperature profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m). The observations included the following: air temperature and humidity (Ta_3 m, Ta_10 m, Ta_15 m, Ta_20 m, Ta_30 m, and Ta_40 m; RH_3 m, RH_5 m, RH_10 m, RH_15 m, RH_20 m, RH_30 m, and RH_40 m) (℃ and %, respectively), wind speed (Ws_3 m, Ws_10 m, Ws_15 m, Ws_20 m, Ws_30 m) (m/s), wind direction (WD_10 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT_1 and IRT_2) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), average soil temperature (TCAV, ℃), soil heat flux (Gs) (W/m^2), soil temperature (Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm) (℃), soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm) (%, volumetric water content). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2021-6-10 10:30. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) for data processing) in the Citation section.
LIU Shaomin, XIAO Qing, XU Ziwei, BAI Junhua
This dataset includes data obtained from the automatic weather station (AWS) at the observation system of Meteorological elements of Huailai station between January 1 and December 31, 2021. The site (115.7880° E, 40.3491° N) was located on a maize surface, which is near Donghuayuan Town of Huailai city in Hebei Province. The elevation is 480 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (5 m, north), wind speed and direction profile (10 m, north), air pressure (in the box), rain gauge (10 m), four-component radiometer (5 m, south), two infrared temperature sensors (5 m, south, vertically downward), soil heat flux (-0.06 m), soil temperature profile (0, -0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), soil moisture profile (-0.02, -0.04, -0.1, -0.2, -0.4, -0.8, -1.2, and -1.6 m), and a TCAV averaging soil thermocouple probe (-0.02, -0.04 m). The observations included the following: air temperature and humidity (Ta_5 m; RH_5 m) (℃ and %, respectively), wind speed (Ws_10 m) (m/s), wind direction (WD_10 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m2), infrared temperature (IRT_1 and IRT_2) (℃), soil heat flux (Gs_1, Gs_2 and Gs_3) (W/m2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, Ts_20 cm, Ts_40 cm, Ts_80 cm, Ts_120 cm, and Ts_160 cm) (℃), soil moisture (Ms_2 cm, Ms_4 cm, Ms_10 cm, Ms_20 cm, Ms_40 cm, Ms_80 cm, Ms_120 cm, and Ms_160 cm) (%, volumetric water content), and average soil temperature (TCAV, ℃). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2021-6-10 10:30. (6) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Guo et al. (2020) (for sites information), Liu et al. (2013) (for data processing) in the Citation section.
LIU Shaomin, XU Ziwei
This dataset contains the flux measurements from the Huailai station eddy covariance system (EC) from January 1 to December 31 in 2021. The site (115.7880° E, 40.3491°N) was located in the maize surface, near Donghuayuan town of Huailai city in Hebei Province. The elevation is 480 m. The EC was installed at a height of 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 to 9). 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; 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 (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 Guo et al. (2020) (for sites information), Liu et al. (2013) for data processing) in the Citation section.
LIU Shaomin, XU Ziwei
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