The data set collected long-term monitoring projects from multiple stations for atmosphere, hydrology and soil in the North Tibetan Plateau. The data set consisted of monitoring data obtained from the automatic weather station (AWS) and the atmospheric boundary layer tower (PBL) in the field. The sensors for temperature, humidity and pressure were provided by Vaisala of Finland; the sensors for wind speed and direction were provided by Met One of America, the radiation sensors were provided by APPLEY of America and EKO of Japan; the gas analyzers were provided by Licor of America; the soil water content instrument, ultrasonic anemometers and data collectors were provided by CAMPBELL of America. The observation system was maintained by professionals regularly (2-3 times a year), the sensors were calibrated and replaced, and the collected data were downloaded and reorganized. The data set was processed by forming a time continuous sequence after the raw data were quality-controlled. It met the accuracy level of the original meteorological observation data of the National Weather Service and the World Meteorological Organization (WMO). The quality control included the elimination of the missing data and the systematic error caused by the failure of the sensor.
HU Zeyong
1) Data content: the main ecological environment data retrieved from remote sensing in Pan third polar region, including PM2.5 concentration, forest coverage, Evi, land cover, and CO2; 2) data source and processing method: PM2.5 is from the atmospheric composition analysis group web site at Dalhousie University, and the forest coverage data is from MODIS Vegetation continuum Fields (VCF), CO2 data from ODIAC fossil fuel emission dataset, EVI data from MODIS vehicle index products, and land cover data from ESA CCI land cover. 65 pan third pole countries and regions are extracted, and others are not processed; 3) data quality description: the data time series from 2000 to 2015 is good; 4) data application achievements and prospects: it can be used for the analysis of ecological environment change.
LI Guangdong
This dataset is based on the sixth edition of the MODIS normalized difference vegetation index product (2001-2014) jointly released by NASA EOSDIS LP DAAC and the US Geological Survey USGS EROS. The NDVI has a time resolution of 16 days and a spatial resolution of 0.05 degree. First,the NDVI data products were re-sampled from the spatial resolution of 0.05 degree to 0.5 degree, then the time series of every year was smoothed by the double-logistic method, and the smoothed curvature was calculated. The maximum curvature of spring was selected as the returning green stage of the vegetation in Spring. This data can be used to analyze the temporal and spatial characteristics of the Holarctic vegetation phenology in Spring.
NASA EOSDIS LP DAAC, XU Xiyan
This dataset is the first 1: 100,000 desert spatial database in China based on the graphic data of desert thematic maps. It mainly reflects the geographical distribution, area size, and mobility of sand dunes in China. According to the system design requirements and relevant standards, the input data is standardized and uniformly converted into a standard format for various types of data input. Build a library to run the delivery system. This project uses the TM image in 2000 as the information source, and interprets, extracts, and edits the coverage of the national land use map and TM digital image information in 2000. It uses remote sensing and geographic information system technology to 1: 100,000 Thematic mapping requirements for scale bar maps were made on the desert, sandy land and gravel Gobi in China. The 1: 100,000 desert map across the country can save users a lot of data entry and editing work when they are engaged in research on resources and the environment. Digital maps can be easily converted into layout maps The dataset properties are as follows: Divided into two folders e00 and shp: Desert map name and province comparison table in each folder 01 Ahsm Anhui 02 Bjsm Beijing 03 Fjsm Fujian 04 Gdsm Guangdong 05 Gssm Gansu 06 Gxsm Guangxi Zhuang Autonomous Region 07 Gzsm Guizhou 08 Hebsm Hebei 09 Hensm Henan 10 Hljsm Heilongjiang 11 Hndsm Hainan 12 Hubsm Hubei 13 Jlsm Jilin Province 14 Jssm Jiangsu 15 Jxsm Jiangxi 16 Lnsm Liaoning 17 Nmsm Inner Mongolia Gu Autonomous Region 18 Nxsm Ningxia Hui Autonomous Region 19 Qhsm Qinghai 20 Scsm Sichuan 21 Sdsm Shandong 22 Sxsm Shaanxi Province 23 Tjsm Tianjin 24 Twsm Taiwan Province 25 Xjsm Xinjiang Uygur Autonomous Region 26 Xzsm Tibet Autonomous Region 27 Zjsm Zhejiang 28 Shxsm Shanxi 1. Data projection: Projection: Albers False_Easting: 0.000000 False_Northing: 0.000000 Central_Meridian: 105.000000 Standard_Parallel_1: 25.000000 Standard_Parallel_2: 47.000000 Latitude_Of_Origin: 0.000000 Linear Unit: Meter (1.000000) 2. Data attribute table: area (area) perimeter ashm_ (sequence code) class (desert encoding) ashm_id (desert encoding) 3. Desert coding: mobile sandy land 2341010 Semi-mobile sandy land Semi-fixed sandy land 2341030 Gobi 2342000 Saline land 2343000 4: File format: National, sub-provincial and county-level desert map data types are vector shapefiles and E00 5: File naming: Data organization based on the National Basic Resources and Environmental Remote Sensing Dynamic Information Service System is performed on the file management layer of Windows NT. The file and directory names are compound names of English characters and numbers. Pinyin + SM composition, such as the desert map of Gansu Province is GSSM. The flag and county desert map is the pinyin + xxxx of the province name, and xxxx is the last four digits of the flag and county code. The division of provinces, districts, flags and counties is based on the administrative division data files in the national basic resources and environmental remote sensing dynamic information service operation system.
WANG Jianhua, WANG Yimou, YAN Changzhen, QI Yuan
Desertification is a kind of land degradation with aeolian sands as the main symbol caused by the uncoordinated human-land relationship in arid, semi-arid and some semi-humid regions of northern China. Data source: edited by the China Institute of Glacial and Frozen Desert and coordinated by the Institute of Geography of the Chinese Academy of Sciences. Based on aerial photographs from the 1970s and field research, a 1: 2 million desert map was drawn. Mapping of the 14 million "Map of the People's Republic of China" published in 1971. First, the data set content 1.Desert_Ch_2009 (desert distribution) 2.Dune_hight_Ch_200 (dune height) 3.Gobi_Ch_200 (Gobi) 4.Wind_eroded_land_Ch_200 (wind erosion data) The fields of the desertification attribute table are as follows: (1) Semifixed (semi-fixed dunes): undulating sandy land (2-1), thicket dunes (2-2), parabolic dunes (2-3), beam nest dunes (2-4), sand ridges And dendritic sand ridge (2-5), honeycomb sand dune (2-6), honeycomb sand ridge (2-7), composite sand ridge (2-8) (2) Fixation (fixed dune): flat sandy land (3-1), grassland bush (3-2), sand ridge (3-3), honeycomb sand dune (3-4) (3) Migratory: Crescent sand dunes and dune chains (1-1), Crescent sand ridges and dunes (1-2), Lattice dunes and Lattice dune chains (1-3), Fish scales Sand dunes (1-4), feathery dunes (1-5), pyramid dunes (1-6), composite dunes and dune chains (1-7), composite dunes (1-8), composite Dome-shaped dunes (1-9), chain-shaped sand hills (sand dunes) (1-10), stacked chain-shaped sand hills (1-11), compound ridge-shaped sand hills (1-12), composite chain-shaped Sand Mountain (1-13), Pyramid Sand Mountain (1-14) (4) class_id: encoding of desertification attributes Projection information PROJCS ["Albers", GEOGCS ["GCS_Beijing_1954", DATUM ["Beijing_1954", SPHEROID ["Krasovsky_1940", 6378245.0,298.3]], PRIMEM ["Greenwich", 0.0], UNIT ["Degree", 0.0174532925199433]], PROJECTION ["Albers_Conic_Equal_Area"], PARAMETER ["False_Easting", 0.0], PARAMETER ["False_Northing", 0.0], PARAMETER ["longitude_of_center", 105.0], PARAMETER ["Standard_Parallel_1", 25.0], PARAMETER ["Standard_Parallel_2", 47.0], PARAMETER ["latitude_of_center", 0.0], UNIT ["Meter", 1.0]]
WANG Jianhua
1. The data is digitized in the map of the development degree of desertification in daqintara (1974) from the drawing. The specific information of the map is as follows: * chief editor: zhu zhenda, qiu xingmin * editor: wang yimou * drawing: feng yu-sun, yao fa-fen, wu wei, wang jianhua, wang zhou-long * cartographic unit: desert laboratory, Chinese academy of sciences * publishing house: xi 'an map publishing house, unified isbn: 12461.26 二. The data is stored in ESRI Shapefile format, including the following layers: 1, * desertification development degree map (1974) : desertification1974.shp 2, * double river: river_double-shp 3, * single river: river_single-shp 4, Road: SHP 5, Lake: lake.shp 6, street: Stree. SHP 7, Railway: Railway. SHP 8, forest belt: Tree_networks 9. Residential land: residential. SHP 10. Map: map_margin.shp 三, desertification development degree figure property fields and encoding attribute: (1) desertification degree (Type) : a flow of sand (Semi - shifting Sandy Land), sand form class (Shapes), grass (Grassland), forest Land, Woodland and forest density (W_density), the cultivated Land (Farmland) (2) sand Shapes: Barchan Dunes, Flat Sandy Land, undulated Sandy Land, Vegetated Dunes (3) the grass (Grassland) (4) Woodland: Woodland. (5) woodland density (W_density): Sparse Woodlot (6) Farmland: Dryfarming and Abandoned Farmland, Irrigated Fields
WANG Jianhua, ZHU Zhenda, QIU Xingmin, FENG Yusun, YAO Fafen
The vegetation regulation mechanism project of soil water cycle in arid desert areas belongs to the national natural science foundation "environment and ecological science in western China" major research plan, led by li xinrong, a researcher of the institute of environment and engineering in dry and cold areas, Chinese academy of sciences, with the running time of 2003.1-2005.12. Remittance data of the project: 1. Dataset of observation field of shapotou railway vegetation sand fixation protection system (excel) Plant and soil information in the vegetation-sand fixation zone established in 1956, 1964, 1981 and 1987.Since the establishment of the observation field, long-term soil moisture and vegetation surveys have been conducted. This database records the soil moisture data after the neutron tube installation in August 2002, the vegetation data from 2003 to 2005 (vegetation structure, herb structure, shrub structure, etc.), and the soil physical and chemical properties data (particle size, total N,P2O5,K2O, hydrolyzed N) of the irregular surveys. 2. Physiological data set of desert plant stress (excel) From 2003 to 2005, the physiological and biochemical characteristics of typical plant communities and their dominant species in steppe desert under natural and simulated environmental conditions were analyzed.(including photosynthetic transpiration, fluorescence, biochemistry and other indicators) 3. Soil infiltration and evapotranspiration data set (excel) Precipitation infiltration process, soil water dynamics and evapotranspiration of fixed sand dunes monitored by desert artificial vegetation using TDR and Lysimeters from 2002 to 2005. 4. Data set of comprehensive survey on soil and vegetation in the southeastern margin of tengger desert (excel) In 2003-2004, silver (sichuan), yan (latour) highway, silver (sichuan) (state) highway through the tengger desert area, set up along the road of eight samples, 449 samples of soil conductivity, Ph, organic matter, total nitrogen (content) and vegetation (plants, coverage, average height, biomass, strains, coverage, high average, biomass).
LI Xinrong
The data is the distribution map of 100,000 deserts in Qinghai Lake Basin. This data uses 2000 TM image as the data source for interpretation, extraction and revision. Remote sensing and geographic information system technology are combined with the mapping requirements of a scale of 1: 100,000 to carry out thematic mapping of deserts, sands and gravelly Gobi. Data attribute table: area (area), perimeter (perimeter), ashm_ (sequence code), class (desert code) and ashm_id (desert code), of which the desert code is as follows: mobile sand 2341010, semi-mobile sand 2341020, semi-fixed sand 2341030, Gobi desert 2342000 and saline-alkali land 2343000.
WANG Jianhua, YAN Changzhen
As determined in mid-august 2013, planting species: bubbly spines (different habitats are mid-range intermountain lowland and gobi), red sand (different habitats are mid-range gobi and downstream gobi). Using the brother company of LI - 6400 Portable Photosynthesis System (Portable Photosynthesis System, LI - COR, USA) and LI - 3100 leaf area meter, etc., to the desert plant photosynthetic physiological characteristics were observed. The symbolic meaning of the observed data is as follows: Obs，observation frequency ； Photo ，net photosynthetic rate，μmol CO2•m–2•s–1； Cond stomatal conductance，mol H2O•m–2•s–1 ; Ci, Intercellular CO2 concentration, μmol CO2•mol-1； Trmmol，transpiration rate，mmol H2O•m–2•s–1； Vpdl，Vapor pressure deficit，kPa； Area，leaf area，cm2； Tair，free air temperature ，℃； Tleaf，Leaf temperature，℃； CO2R，Reference chamber CO2 concentration，μmol CO2•mol-1； CO2S，Sample chamber CO2 concentration，μmol CO2•mol-1； H2OR，Reference chamber moisture，mmol H2O•mol-1； H2OS，Sample chamber moisture，mmol H2O•mol-1； PARo，photon flux density，μmol•m–2•s–1； RH-R，Reference room air relative humidity，%； RH-S，Relative humidity of air in sample room，%； PARi，Photosynthetic effective radiation，μmol•m–2•s–1； Press，barometric pressure，kPa； Others are the state parameters of the instrument at the time of measurement.
SU Peixi
Background: this data interchange is the first data interchange of the key project of "integrated study of eco-hydrological processes in heihe basin", "genomics research on drought tolerance mechanism of typical desert plants in heihe basin".The main research targets of the key projects is a typical sand desert plants are Holly, using the current international advanced a new generation of gene sequencing technology to the whole genome sequence and gene transcription of Holly group sequence decoding, so as to explore related to drought resistance gene and gene groups, and transgenic technology in model to verify their drought resistance in plants. Process and content: as genome sequencing requires special sequencing equipment, the project is huge and the process is complex (mainly including genome library construction, sequencing, data analysis and genome assembly), so it needs to be completed by a professional sequencing company.After contacting with sequencing companies, we learned that before sequencing an unknown genome, the size and complexity of the genome should be predicted, which is a necessary prerequisite for designing sequencing schemes and strategies.Therefore, in 2013, we mainly predicted the chromosome composition, genome size and complexity of sand Holly, and successfully established the extraction and purification method of its genomic DNA.The results showed that the plant was diploid, the genome was composed of 9 staining lines (18 lines of diploid), and the genome size was 1.07G.The quality test results of the genomic DNA indicated that the requirements of the obtained DNA complex sequencing have been sent to the sequencing company for library construction and sequencing, which is now in progress.In addition, in order to obtain a large number of uniform plant materials, we have discussed the induction of callus, which has been successful.Due to these reasons, we were unable to complete the genome sequencing and submit the relevant data of sand Holly in accordance with the original plan of the project this year, mainly because we did not count the predicted contents of the genome before. Data usage: the data obtained in this year on ploidy, karyotype composition and genome size of lycopodium SPP.The success of the callus induction provides a high-quality material guarantee for the subsequent transcriptome sequencing and drought-resistance mechanism research experiments, and it is also a new contribution to the cytological and physiological research of the plant.
HE Junxian, GU Lifei
The research project on the breeding strategies of desert plants in hexi region of gansu province belongs to the national natural science foundation "environment and ecological science in western China" major research plan, led by professor an lizhe of lanzhou university. The project runs from January 2004 to December 2007. Remittance data of the project: 1. Effect of super - dry preservation on seeds The data is in Word format and contains a lot of analysis charts. A comparative study was conducted on the changes of vitality of overlord seeds and rhizoma coptidis seeds stored at 45℃, room temperature and 15℃ respectively, and the effects of dampening treatment, artificial aging and ultra-dry treatment on electrical conductivity and physiological activity indexes of seeds were conducted.The details are as follows: Energy change of seeds was preserved at 45℃ FIG. 1 germination rate (%) of overlord seeds stored at 45℃、FIG. 2 germination index of overlord seeds stored at 45℃、FIG. 3 vigor index of the seeds stored at 45℃. Change of seed vigor at room temperature FIG. 4 germination rate (%) of overlord seeds stored at room temperature、FIG. 5 germination index of overlord seeds stored at room temperature、FIG. 6 vigor index of overlord seeds preserved at room temperature. 15℃ preservation of seed vitality changes FIG. 7 germination rate of overlord seeds stored at 15℃ (%)、FIG. 8 germination index of alba seeds stored at 15℃、FIG. 9 vigor index of the seeds stored at 15℃. Changes of seed vigor of rhizoma coryzae at 45℃ FIG. 10 germination rate (%) of rhizoma coptidis seeds stored at 45℃、FIG. 11 germination index of the seeds of rhizoma coryzae at 45℃、FIG. 12 vigor index of seeds of corydalis corydalis preserved at 45℃. Changes of seed vigor of rhizoma coryzae at room temperature FIG. 13 germination rate (%) of rhizoma corydalis seeds preserved at room temperature、FIG. 14 germination index of seeds preserved at room temperature、FIG. 15 vigor index of seeds of corydalis corydalis preserved at room temperature Changes of seed vigor of rhizoma corydalis in 15℃ storage FIG. 16 germination rate (%) of rhizoma coptidis seeds stored at 15℃、FIG. 17 germination index of the seeds of rhizoma coptidis preserved at 15℃、FIG. 18 vigor index of seeds of corydalis sativus preserved at 15℃ Effect of slow wetting treatment on relative conductivity of seeds FIG. 28 changes in the relative conductivity of arrobatus seeds without dampening treatment、FIG. 29 changes of relative conductivity of overlord seeds after slow wetting treatment、FIG. 31 changes of relative electrical conductivity of seeds of rhizoma coryzae after dampening treatment Effects of artificial aging treatment on seed of archaea chinensis l FIG. 34 effects of artificial aging treatment on germination rate of overlord seeds、FIG. 35 effect of artificial aging treatment on seed vigor index、FIG. 36 effects of artificial aging treatment on the relative conductivity of overlord seeds Effects of artificial aging treatment on seeds of coryza sativa l FIG. 37 effect of artificial aging treatment on germination rate of seeds of coryza sativa l、FIG. 38 effect of artificial aging treatment on seed vigor index of rhizoma coryzae、FIG. 39 effects of artificial aging treatment on the relative electrical conductivity of the seeds of coryza sativa l Effects of artificial aging on the content of aldehydes in seeds after 15 days FIG. 52 effects of artificial aging treatment on the content of aldehydes in the seeds after 15 day、FIG. 53 effects of artificial aging treatment on the content of aldehydes in seeds of prunus chinense after 15 days, Effect of super - dry treatment on physiological activity index of seed Table 31 effect of super - dry treatment on physiological activity index of monkshood seed Table 32 influence of hyperdrying treatment on physiological activity index of seeds of coryza sativa l 2. Micromorphological and structural characteristics of the skin of desert plants (including experimental conditions, microscopic images of the skin microstructure and analysis of distribution of 47 plants, genus, species code, list of length and weight of long and short axes of seeds, and list of seed elements)
AN lizhe
The interaction mechanism project between major road projects and the environment in western mountainous areas belongs to the major research plan of "Environment and Ecological Science in Western China" of the National Natural Science Foundation. The person in charge is Cui Peng researcher of Chengdu Mountain Disaster and Environment Research Institute, Ministry of Water Resources, Chinese Academy of Sciences. The project runs from January 2003 to December 2005. Data collected for this project: Engineering and Environmental Centrifugal Model Test Data (word Document): Consists of six groups of centrifugal model test data, namely: Test 1. Centrifugal Model Test of Soil Cutting High Slope (6 Groups) Test 2. Centrifugal Model Experiment of Backpressure for Slope Cutting and Filling (4 Groups) Test 3. Centrifugal Model Experimental Study on Anti-slide Piles and Pile-slab Walls (10 Groups) Test 4. Centrifugal Model Tests for Different Construction Timing of Slope (5 Groups) Test 5. Migration Effect Centrifugal Model Test (11 Groups) Test 6. Centrifugal Model Test of Water Effect on Temporary Slope (8 Groups) The purpose, theoretical basis, test design, test results and other information of each test are introduced in detail.
CUI Peng
"Hydrological ecological economic process coupling and evolution of Heihe River Basin Management under the framework of water rights" (91125018) project data collection 2 - Dunhuang comprehensive plan for rational utilization of water resources and ecological protection (2011-2020) Planning documents mainly include: 1. Current situation and existing problems of regional water resources utilization; 2. Guiding ideology, basic principles and planning objectives; 3. Analysis of economic, social and ecological water demand; 4. Plan for water resources allocation; 5. Construction of water right system; 6. Main engineering measures; 7. Environmental impact arrangement.
The data is the distribution map of 100,000 deserts in the Tarim River Basin. This data uses 2000 TM images as the data source to interpret, extract and revise, and uses remote sensing and geographic information system technology in combination with the mapping requirements of 1: 100,000 scale to carry out thematic mapping of deserts, sands and gravelly Gobi. Data attribute table: area (area), perimeter (perimeter), ashm_ (sequence code), class (desert code), ashm_id (desert code), of which desert code is as follows: flowing sand 2341010, semi-flowing sand 2341020, semi-fixed sand 2341030, Gobi desert 2342000, saline-alkali land 2343000
WANG Jianhua
The data set is the physiological and ecological parameters of the dominant species of each ecosystem in Heihe River Basin. According to the requirements of tesim model, the data set divides Heihe River basin into seven ecosystems: deciduous broad-leaved forest ecosystem (BRD), evergreen coniferous forest ecosystem (CNF), agricultural field ecosystem (CRP), desert ecosystem (DST), meadow grassland ecosystem (MDS) Shrubbery ecosystem (SHB) and grassland ecosystem (STP). Some of the data in this data set are based on the measured data, some are obtained by reference documents, but after verification, they are applied to the Heihe River Basin. For the data in this data, each parameter of each ecosystem has three values, which are the value in the model, the minimum value and the maximum value of this parameter. The data can provide input parameters for the ecological process model, and the data set is still in further optimization.
PENG Hongchun
This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Xiyinghe Station from January 1 to December 31, 2018. The site (101.853E, 37.561N) was located on a alpine meadow in the Menyuan,Qinghai Province. The elevation is 3639 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (2, 4, and 8 m, towards north), wind speed and direction profile (windsonic; 2, 4, and 8 m, towards north), air pressure (1.5 m), rain gauge (4 m), four-component radiometer (4 m, towards south), infrared temperature sensors (4 m, towards south, vertically downward), photosynthetically active radiation (4 m, towards south), soil heat flux (-0.05 m and -0.1m in south of tower), soil soil temperature/ moisture/ electrical conductivity profile (-0.2 and -0.4 m in south of tower), sunshine duration sensor (4 m, towards south). The observations included the following: air temperature and humidity (Ta_2 m, Ta_4 m, and Ta_8 m; RH_2 m, RH_4 m, and RH_8 m) (℃ and %, respectively), wind speed (Ws_2 m, Ws_4 m, and Ws_8 m) (m/s), wind direction (WD_2 m, WD_4 m, and WD_8 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) (℃), photosynthetically active radiation (PAR) (μmol/ (s/m^2)), soil heat flux (Gs_5 cm, Gs_10cm) (W/m^2), soil temperature (Ts_20 cm, Ts_40 cm) (℃), soil moisture (Ms_20 cm, Ms_40 cm) (%, volumetric water content), soil water potential (SWP_20cm , SWP_40cm)(kpa) , soil conductivity (Ec_20cm, Ec_40cm)(μs/cm), sun time (h). 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 meteorological data were missing during Aug. 29 to Oct.18 because of unstable power supply due to battery box flooding; The wind speed and direction profile data were rejected because of sensor failure; The precipitation data were rejected because of program error; The air humidity data before Mar. 2 were rejected due to program error; (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: 2018-6-10 10:30.
ZHAO Changming, ZHANG Renyi
The compilation basis of frozen soil map includes: (1) frozen soil field survey, exploration and measurement data; (2) aerial photo and satellite image interpretation; (3) topo300 1km resolution ground elevation data; (4) temperature and ground temperature data. Among them, the distribution of permafrost in the Qinghai Tibet Plateau adopts the research results of nanzhuo Tong et al. (2002). Using the measured annual average ground temperature data of 76 boreholes along the Qinghai Tibet highway, regression statistical analysis is carried out to obtain the relationship between the annual average ground temperature and latitude, elevation, and based on this relationship, combined with the gtopo30 elevation data (developed under the leadership of the center for earth resources observation and science and technology, USGS) Global 1 km DEM data) to simulate the annual mean ground temperature distribution over the whole Tibetan Plateau. Taking the annual average ground temperature of 0.5 ℃ as the boundary between permafrost and seasonal permafrost, the boundary between discontinuous Permafrost on the plateau and island Permafrost on the plateau is delimited by referring to the map of ice and snow permafrost in China (1:4 million) (Shi Yafeng et al., 1988); in addition, the division map of Permafrost on the big and small Xing'an Mountains in the Northeast (Guo Dongxin et al., 1981), the distribution map of permafrost and underground ice around the Arctic (b According to rown et al. 1997) and the latest field survey data, the Permafrost Boundary in Northeast China has been revised; the Permafrost Boundary in Northwest mountains mostly uses the boundary defined in the map of ice and snow permafrost in China (1:4 million) (Shi Yafeng et al., 1988). According to the data, the area of permafrost in China is about 1.75 × 106km2, accounting for about 18.25% of China's territory. Among them, alpine permafrost is 0.29 × 106km2, accounting for about 3.03% of China's territory. For more information, please refer to the specification of "1:4 million map of glacial and frozen deserts in China" (Institute of environment and Engineering in cold and dry areas, Chinese Academy of Sciences, 2006)
WANG Tao
The dataset investigated the growth status of plants and leaf morphological indexes of single and conjoined red sand and pearl in the middle and lower reaches of heihe river basin in 2013. The growth indexes were crown width, plant height, and biomass of fine roots and thick roots.Leaf shape indicators are: length, width, thickness, and leaf area, volume, etc.The experimental observation indexes are: leaf nitrogen content, water potential, gas exchange data, chlorophyll fluorescence data. Data include: field observation data and explanatory documents.
SU Peixi
By applying Supply-demand Balance Analysis, the water resource supply and demand of the whole river basin and each county or district were calculated, based on which the vulnerability of the water resources system of the basin was evaluated. The IPAT equation was used to set a future water resource demand scenario, setting variables such as future population growth rate, economic growth rate, and unit GDP water consumption to establish the scenario. By taking 2005 as the base year and using assorted forecasting data of population size and economic scale, the future water demand scenarios of various counties and cities from 2010 to 2050 were forecast. By applying the basic structure of the HBV conceptual hydrological model of the Swedish Hydrometeorological Institute, a model of the variation tendency of the basin under climate change was designed. The glacial melting scenario was used as the model input to construct the runoff scenario under climate change. According to the national regulations of the water resources allocation of the basin, a water distribution plan was set up to calculate the water supply comprehensively. Considering of the supply and demand situation, the water resource system vulnerability was evaluated by the water shortage rate. By calculating the (grain production) land pressure index of the major counties and cities in the basin, the balance of supply and demand of land resources under the climate change, glacial melt and population growth scenarios was analyzed, and the vulnerability of the agricultural system was evaluated. The Miami formula and HANPP model were used to calculate the human appropriation of net primary biomass and primary biomass in the major counties and cities for the future, and the vulnerability of ecosystems from the perspective of supply and demand balance was assessed.
YANG Linsheng, ZHONG Fanglei
This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Liancheng Station from January 1 to December 31, 2018. The site (102.833E, 36.681N) was located on a forest in the Tulugou national forest park, which is near Liancheng city, Gansu Province. The elevation is 2912 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (4 and 8 m, towards north), wind speed and direction profile (windsonic; 4 and 8 m, towards north), air pressure (1.5 m), rain gauge (2 m), four-component radiometer (4 m, towards south),infrared temperature sensors (2 m, towards south, vertically downward), photosynthetically active radiation (4 m, towards south), soil heat flux (2 duplicates below the vegetation;-0.05 and -0.1m in south of tower), soil soil temperature/ moisture/ electrical conductivity profile (below the vegetation;-0.05 and -0.1m in south of tower), sunshine duration sensor(4 m, towards south). The observations included the following: air temperature and humidity (Ta_4 m and Ta_8 m; RH_4 m and RH_8 m) (℃ and %, respectively), wind speed (Ws_2 m, Ws_4 m, and Ws_8 m) (m/s), wind direction (WD_2 m, WD_4 m, and WD_8 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) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), soil heat flux (Gs_5 cm, Gs_10 cm) (W/m^2), soil temperature (Ts_5 cm, Ts_10 cm) (℃), soil moisture (Ms_5 cm, Ms_10 cm) (%, volumetric water content), soil water potential (SWP_5cm,SWP_10cm)(kpa), soil conductivity (EC_5cm,EC_10cm)(μs/cm), sun time (h). 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 soil heat flux data were wrong during Jan.1 to May 30 because of rodent damage; The data during May. 30 to July 6 were missing because the power supply failure; The air humidity data were rejected due to program error. (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: 2018-6-10 10:30.
ZHAO Changming, ZHANG Renyi
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