This data set is the long-term concentrations of atmospheric POPs in southeast Tibet, including OCP, PCBs and PAHs. The sampling period in this study was from August 2008 to July 2014. The data was gained from the continuous air monitoring program in STORS. In this program, a low-volume air sampler (~100 L/min) was set at STORS to trap particle- and gas-phase chemicals by a glass fiber filter (GFF, diameter of 9 cm) and polyurethane foam plugs (PUFs, 7.5 cm × 6 cm diameter), respectively. Typically, ~700 m3 of air was collected over a two-week period. Total (gas + particle) phase concentrations are reported. POPs were analyzed at Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Chinese Academy of Sciences. The air samples were Soxhlet-extracted, purified on an aluminium/silica column (i.d. 8 mm), a gel permeation chromatography (GPC) column subsequently. The samples were detected on a gas chromatograph with an ion-trap mass spectrometer (GC-MS, Finnigan Trace GC/PolarisQ) operating under MS–MS mode. A CP-Sil 8CB capillary column (50 m ×0.25 mm, film thickness 0.25 μm) was used for OCPs and PCBs and a DB-5MS column (60 m ×0.25mm, film thickness 0.25 μm) was used for PAHs. Field blanks and procedural blanks were prepared. The recoveries ranged from 64% to 112% for OCPs, and 65% to 92% for PAHs. The reported concentrations were not corrected for recoveries.
This data set is the spatial distribution of soil POPs in the Tibetan Plateau, including OCPs, PCBs, PBDEs and PAHs. Fourty soil samples were taken from remote sites (i.e., away from towns, roads, or other human activity) in 8 soil zones of the Tibetan Plateau in 2007. The samples were collected using a stainless steel hand-held corer.Five cores (0-5 cm), taken over an area of ~100 m2, were bulked together to form one sample. The samples were wrapped in aluminum foil twice and sealed in two plastic bags to minimize the possibility for contamination. All the samples were analyzed at Key Laboratory of Tibetan Environment Changes and Land Surface Processes, Chinese Academy of Sciences. The samples were Soxhlet-extracted, purified on an aluminium/silica column (i.d. 8 mm), a gel permeation chromatography (GPC) column subsequently, and were detected on a gas chromatograph with an ion-trap mass spectrometer (GC-MS, Finnigan Trace GC/PolarisQ) operating under MS–MS mode. A CP-Sil 8CB capillary column (50 m ×0.25 mm, film thickness 0.25 μm) was used for OCPs, PCBs and PBDEs, and a DB-5MS column (60 m ×0.25mm, film thickness 0.25 μm) was used for PAHs. Procedural blanks were prepared. The recoveries ranged from 53% to 130% for OCPs, and 58% to 92% for PAHs. The reported concentrations were not corrected for recoveries.
The data set of supply of agricultural water resources in Central Asian adopts the water balance method to calculate the precipitation and runoff depth on grid scale in five central Asian countries, respectively, and estimate the agricultural water resources supply in five central Asian countries. The data source is mainly the precipitation and runoff data products of NOAH model in GLDAS. Each original raster data of 0.25 ° is resampled, starting from the upper-left corner of the original grid, and extending to the adjacent right and lower grids in turn, and every four grids (0.5 °) are merged into one grid, taking the median data as the center point value corresponding to four grid of geographic coordinates. The extreme values of the grids could be eliminated. The data sets includes three time periods of 2000s (2001-2005), 2010s (2006-2010) and 2015s (2011-2015) with a spatial resolution of 0.5°*0.5°; The data of demand of agricultural water resources in Central Asia include irrigation water requirement of cotton and winter wheat in 2006, 2010 and 2016 over Central Asia. This was calculated by the equation of irrigation water requirement presented by FAO. It is expected to provide basic data support for distributed water cycle simulation, water supply and demand, development and utilization analysis in five central Asian countries.
ZHANG Yongyong YANG Peng TIAN Jing ZHANG Yongqiang
In order to explore how and when turnip was successfully domesticated the Qinghai-Tibet Plateau and what is the relationship between turnip domestication and early human settlement on the Qinghai-Tibetan Plateau and human migration along the ancient Silk Road, the whole genome De Novo sequencing of a self-bred F1 variety on Qinghai-Xizang Plateau was conducted, with the assembled genome size of 409.69 Mb,Contig N50 was 1.21 Mb in June 2018 using Pacbio sequencing. Those data will provide a genetic basis for elucidating the relationship between plant disperse and human activities. As we know, traditional turnip landrace is influenced by human domestication and nature selection. Hopefully, the study will help to understand the impacts of human selection on turnip genetic differentiation, and the adaptation mechanism of turnip in the Qinghai-Tibetan Plateau.
The sustainable development of husbandry industry depends on the conservation of local species, in which the protection of genetic resource is the core. The unique natural environment and long-term artificial selection shape the exclusive characteristics in endemic husbandry animals that well adapt to the local environments in Qinghai and Gansu Provinces. Currently, the introduction of commercial breeds leads to the loss of species diversity of local breeds and challenges the protection of genetic resources. In the present study, extensive field investigations are conducted to assess production performances and species resources, aiming to identify native breeds facing degradations. The achievements of the current study propose the conservative strategies for local domestic animals, which lay the foundation for purification and rejuvenation of endemic species/strains and promote the progression of husbandry industry in Qinghai-Tibetan Plateau and surrounding areas.
To analyzing the demographic history and the genetic mechanism underlying local adaptation of the domestic Equus animals in Qinghai-Tibet Plateau and surrounding regions and building a genetic resources bank of Equus in Pan-Third Pole, we resequenced 236 domestic Equus animal samples collected until 2018, including Tibet horse, Tibet ass, domestic horses and donkeys in the plains. By applying mitochondrial DNA sequencing and D-loop sequencing on 75 samples, including 73 ass and two horses, , a batch of genetic and genome data were generated. It provides basic genetic data to analysis on domestication, immigration and expansion of domestic animals in Qinghai-Tibet Plateau. Meanwhile it helps better understand the adaption of domestic Equus animal to Qinghai-Tibet Plateau environment.
To analyzing the distribution pattern and genetic background of domain domestic animals in Qinghai-Tibet Plateau and surrounding regions and building a genetic resources bank of animals and plants in Pan-Third Pole, we collected 343 domain domestic animal samples in 2018, including Tibet pigs, Tibet dogs, Tibet sheep and Tibet chickens in Yunnan, Sichuan and Tibet Province. By applying mitochondrial DNA sequencing on 159 chickens from northwest Yunnan and southeast Tibet, genome resequencing on 11 wild and domestic pigs and GBS sequencing on 193 domestic cattle, a batch of genetic and genome data were generated. It provides basic genetic data to analysis on domestication, immigration and expansion of domestic animals in Qinghai-Tibet Plateau. Meanwhile it helps better understand the adaption of domestic animals to Qinghai-Tibet Plateau environment.
YIN Tingting PENG Minsheng
Climate records obtained by most instruments are relatively short in time, which limits the study of climate change, necessitating the use of proxy data to extend records to the past. It was not until the late 1940s that atmospheric data of sufficient quality and spatial resolution were available to determine the main patterns of climate change such as the North American Pacific model and the Pacific Decadal Oscillation. The global ice cores are from the north and south poles and the third pole, and there are also mountain glaciers in Alaska. The ice core data obtained in that area are of great significance for revealing the climate in North America and climate change in the Arctic regions at both low and high latitudes. The physical meaning of each variable: First column: time; second column: accumulation rate data; third column: oxygen isotope data value
The Antarctic ice sheet elevation data were generated from radar altimeter data (Envisat RA-2) and lidar data (ICESat/GLAS). To improve the accuracy of the ICESat/GLAS data, five different quality control indicators were used to process the GLAS data, filtering out 8.36% unqualified data. These five quality control indicators were used to eliminate satellite location error, atmospheric forward scattering, saturation and cloud effects. At the same time, dry and wet tropospheric, correction, solid tide and extreme tide corrections were performed on the Envisat RA-2 data. For the two different elevation data, an elevation relative correction method based on the geometric intersection of Envisat RA-2 and GLAS data spot footprints was proposed, which was used to analyze the point pairs of GLAS footprints and Envisat RA-2 data center points, establish the correlation between the height difference of these intersection points (GLAS-RA-2) and the roughness of the terrain relief, and perform the relative correction of the Envisat RA-2 data to the point pairs with stable correlation. By analyzing the altimetry density in different areas of the Antarctic ice sheet, the final DEM resolution was determined to be 1000 meters. Considering the differences between the Prydz Bay and the inland regions of the Antarctic, the Antarctic ice sheet was divided into 16 sections. The best interpolation model and parameters were determined by semivariogram analysis, and the Antarctic ice sheet elevation data with a resolution of 1000 meters were generated by the Kriging interpolation method. The new Antarctic DEM was verified by two kinds of airborne lidar data and GPS data measured by multiple Antarctic expeditions of China. The results showed that the differences between the new DEM and the measured data ranged from 3.21 to 27.84 meters, and the error distribution was closely related to the slope.
The near-surface soil freeze/thaw state characterizes the dormancy and activity of the land surface process. This freeze-thaw interphase can cause a series of complex surface process trajectory pattern mutations, affecting soil hydrothermal characteristics, surface runoff, groundwater supply, and other water cycle process; it also affects climate change through water and energy cycling mechanisms. Based on AMSR-E and AMSR2 passive microwave data, adopting the global near-surface freeze-thaw state (spatial resolution: 0.25°; temporal coverage: 2002-2014) prepared by discriminant algorithm, the data set can be used to analyze the spatial distributions and trend variations of the indexes (such as start/end dates, freeze/thaw duration, and freeze ranges) of global near-surface freeze-thaw cycle. It can also provide data support for understanding the interaction mechanism between land surface freeze-thaw cycle and water and energy exchange processes under the background of global change.
This data set contains the oxygen isotope, dust, anion and accumulation data obtained from the deep ice core drilled in 1992 in the Guliya ice cap, which is located in the west Kunlun Mountains on the Tibetan Plateau. The length of the ice core was 308.6 m. The ice core was cut into samples, 12628 of which were used to measure the oxygen isotope values, 12480 of which were used to measure the dust concentrations, and 9681 of which were used to measure the anion concentrations. Data Resource: National Centers for Environmental Information（http://www.ncdc.noaa.gov/data-access/paleoclimatology-data/datasets/ice-core）. Processing Method: Average. The data set contains 4 tables, namely: oxygen isotope, dust and anion data from different depths in the Guliya ice core, 10-year mean data of oxygen isotopes, dust, anions and net accumulation in the Guliya ice core, 400-year mean data of oxygen isotopes, dust and anions in the Guliya ice core, and chlorine-36 data from different depths. Table 1: Data on oxygen isotopes, dust and anion concentrations at different depths in the Guliya ice core. a. Name explanation Field 1: Depth Field 2: Oxygen isotope value Field 3: Dust concentration (diameter 0.63 to 20 µm) Field 4: Cl- Field 5: SO42- Field 6: NO3- b. Dimensions (unit of measure) Field 1: m Field 2: ‰ Field 3: particles/mL Field 4: ppb Field 5: ppb Field 6: ppb Table 2: 10-year mean oxygen isotope, dust, anion and net accumulation data for the Guliya ice core (0-1989) a. Name explanation Field 1: Start time Field 2: End time Field 3: Oxygen isotope value Field 4: Dust concentration (diameter 0.63 -20 µm) Field 5: Cl- Field 6: SO42- Field 7: NO3- Field 8: Net accumulation b. Dimensions (unit of measure) Field 1: Dimensionless Field 2: Dimensionless Field 3: ‰ Field 4: particles/mL Field 5: ppb Field 6: ppb Field 7: ppb Field 8: cm/year Table 3: 400-year mean oxygen isotope, dust and anion data for the Guliya ice core. a. Name explanation Field 1: Time Field 2: Oxygen isotope Field 3: Dust concentration (diameter 0.63-20 µm) Field 4: Cl- Field 5: SO42- Field 6: NO3- b. Dimensions (unit of measure) Field 1: Millennium Field 2: ‰ Field 3: particles/mL Field 4: ppb Field 5: ppb Field 6: ppb Table 4: Chlorine-36 data at different depths a. Name explanation Field 1: Depth Field 2: 36Cl Field 3: 36Cl error Field 4: Year b. Dimensions (unit of measure) Field 1: m Field 2: 104 atoms g-1 Field 3: % Field 4: Millennium
National Centers for Environmental Information (NCEI)
ET (ET) monitoring is crucial to agricultural water resource management, regional water resource utilization planning and socio-economic sustainable development.The limitations of traditional ET monitoring methods mainly lie in that they cannot observe a large area at the same time and can only be limited to observation points. Therefore, the cost of personnel and equipment is relatively high, and they can neither provide surface ET data, nor provide ET data of different land use types and crop types. Quantitative monitoring of ET can be achieved by using remote sensing. The characteristics of remote sensing information are that it can not only reflect the macroscopic structure characteristics of the earth surface, but also reflect the microscopic local differences. Version 2.0 (second edition) of the surface evapotranspiration data set of the heihe river basin from 2000 to 2013 is based on multi-source remote sensing data and the latest ETWatch model is adopted to estimate the raster image data. Its temporal resolution is monthly scale and the spatial resolution is 1km scale. The data covers the whole basin in millimeters.Data types include monthly, quarterly, and annual data. The projection information of the data is as follows: Albers equal-area cone projection, Central longitude: 110 degrees, First secant: 25 degrees, Second secant: 47 degrees, Coordinates by west: 4000000 meter. File naming rules are as follows: Monthly cumulative ET value file name: heihe-1km_2013m01_eta.tif Heihe represents the heihe river basin, 1km represents the resolution of 1km, 2013 represents the year of 2013, m01 represents the month of January, eta represents the actual evapotranspiration data, and tif represents the data in tif format. Name of quarterly cumulative ET value file: heihe-1km_2013s01_eta.tif Heihe refers to heihe river basin, 1km refers to the resolution of 1km, 2013 refers to 2013, s01 refers to january-march, is the first quarter, eta refers to the actual evapotranspiration data, and tif refers to the data in tif format. Annual cumulative value file name: heihe-1km_2013y_eta.tif Among them, heihe represents heihe river basin, 1km represents the resolution of 1km, 2013 represents the year of 2013, y represents the year, eta represents the actual evapotranspiration data, and tif represents the data in tif format.
The output data of the distributed eco-hydrological model (GBEHM) of the upper reaches of the black river include the spatial distribution data series of 1-km grid. Region: upper reaches of heihe river (yingxiaoxia), time resolution: month scale, spatial resolution: 1km, time period: 2000-2012. The data include evapotranspiration, runoff depth and soil volumetric water content (0-100cm). All data is in ASCII format. See basan.asc file in the reference directory for the basin space range. The projection parameter of the model result is Sphere_ARC_INFO_Lambert_Azimuthal_Equal_Area.
This set of data is the simulation result of the newly developed land eco-hydrological model CLM_LTF.This model is on top of the land-surface process model CLM4.5 developed by NCAR, coupling the groundwater lateral flow module and considering the role of human irrigation. The model runs from 1981 to 2013, with a spatial resolution of 30 arc seconds (0.0083 degrees), a time step of 1,800 seconds, and a simulation range of the heihe river basin.Air force in 1981-2012 is used by the Chinese academy of sciences institute of the qinghai-tibet plateau of qinghai-tibet plateau more layers of data assimilation and simulation center development areas of China high space-time resolution ground meteorological elements drive data set, air is forced to use 2013 national meteorological information center of wind pressure high resolution made by the wet precipitation temperature radiation data set.The land cover data is a 1km land cover grid data set for the MICLCover heihe river basin, and the irrigation data is shown in "monthly 30-arcsecond resolution surface water and groundwater irrigation data set for the heihe river basin 1981-2013" of the scientific data center for cold and dry regions.The mode output is the monthly average. The document is described as follows: Groundwater depth data: heihe_zwt.nc 2cm soil moisture data: heihe_h2osoi_2cm. nc 100cm soil moisture data: heihe_h2osoi_100cm.nc Evaporation data: Heihe_evaptanspiration. Nc The data is in netcdf format.There are three dimensions, which are month, lat, and lon. Where, month is a month, and the value is 0-395, representing each month from 1981 to 2013. Lat is grid latitude information, and lon is grid longitude information. The data is stored in the data variable. The underground water depth data is in m, the soil moisture data is in m^3/m^3, and the evapotranspiration data is in mm/month
The distribution data of Central Asia desert oil and gas fields are in the form of vector data in ". SHP". Including the distribution of oil and gas fields and major urban settlements in the five Central Asian countries. The data is extracted and cut from modis-mcd12q product. The spatial resolution of the product is 500 m, and the time resolution is 1 year. IGBP global vegetation classification scheme is adopted as the classification standard. The scheme is divided into 17 land cover types, among which the urban data uses the construction and urban land in the scheme. The data can provide data support for the assessment and prevention of sandstorm disasters in Central Asia desert oil and gas fields and green town.
Land use data of Astana, with a resolution of 30 meters, was in the form of TIF and the time was 1989.08.06 and 2017.07.26, respectively.Data source GLC, the raw data of its global land cover data comes from Envisat satellite and is captured by MERIS (Medium Resolution Imaging Spectrometer) sensor.There are currently two issues, GlobCover (Global Land Cover Map) and GlobCover (Global Land Cover Product).
HUANG Jinchuan MA Haitao
Water scarcity，food crises and ecological deterioration caused by drought disasters are a direct threat to food security and socio-economic development. Improvement of drought disaster risk assessment and emergency management is now urgently required. This article describes major scientific and technological progress in the field of drought disaster risk assessment. Drought is a worldwide natural disaster that has long affected agricultural production as well as social and economic activities. Frequent droughts have been observed in the Belt and Road area, in which much of the agricultural land is concentrated in fragile ecological environment. Soil relative moisture index is one of the indicators that characterize soil drought. It is the ratio of soil relative humidity to field water holding capacity, which can directly reflect the availability of water for crops.The soil moisture data is obtained from the SMAP remote sensing soil moisture data product through the downscaling method, and the field water holding capacity data comes from the Hamonized World Soil Database (HWSD). For detailed calculation formulas and methods, please refer to: "National Standard for Agricultural Drought Grades of China" No.: GB/T 32136-2015. The data covers 34 key node areas along the Belt and Road.
Water scarcity，food crises and ecological deterioration caused by drought disasters are a direct threat to food security and socio-economic development. Improvement of drought disaster risk assessment and emergency management is now urgently required. This article describes major scientific and technological progress in the field of drought disaster risk assessment. Drought is a worldwide natural disaster that has long affected agricultural production as well as social and economic activities. Frequent droughts have been observed in the Belt and Road area, in which much of the agricultural land is concentrated in fragile ecological environment. The relative moisture index is the difference between the precipitation in a certain period of time and the potential evapotranspiration in the same period and then divided by the potential evapotranspiration in the same period.The precipitation data comes from the downscaling of the TRMM/GPM satellite precipitation data, and the potential evapotranspiration is estimated using the Thornthwaite method. For detailed algorithm, please refer to "National Standard for Meteorological Drought of China" (GB/T 20481-2017). The data only covers 34 key node areas along the Belt and Road.
Evapotranspiration (ET) is the process which changes from liquid or solid to vapor returning to the atmosphere in hydrological cycles since precipitation arrives at the ground. It is usually the sum of evaporation of surface soil moisture and transpiration (T) in plants. It is the key parameter in the study of global change. At present, THE EVAPotranspiration data product of MODIS satellite is an important data source for monitoring the temporal and spatial changes of the surface, and surface evapotranspiration is an important part of water balance in the earth-gas interaction. Book which has high space-time resolution MODIS16 products as the foundation, global land evaporation in area along the whole area separated from 31 key nodes and Laos, Cambodia's railway, China and myanmar oil and gas pipeline and elegant high iron three key verification area ET cutting, estimation, get the key node area of 8 to 16 days ET products, time range is 2000-2016. Is mainly used in the areas related to all the way the surface of water and energy balance in the process of simulation and dynamic monitoring and management of regional water resources rationally, especially to the scientific allocation of water resources and realize the efficient utilization of water resources has important practical significance, to be able to have a purpose of the related research of area along the area to provide data support and reference.
1) It is also called evapotranspiration, which is the sum of leaf emission (transpiration) of plants on the ground and soil evaporation between plants. That is, the water demand of crops in irrigation project. This data set is the monthly data of evapotranspiration in Central Asia; 2) MODIS data, which is calculated by energy balance method; 3) station disk evaporation verification; 4) evapotranspiration is the total water vapor flux transported to the atmosphere by vegetation and the ground as a whole, which mainly includes vegetation transpiration, soil water evaporation and the evaporation of intercepted water or dew. As an important part of energy balance and water cycle, evapotranspiration is not only a shadow The growth, development and yield of ring plants also affect the general circulation of the atmosphere and play a role in regulating the climate