This data set contains the meteorological data of Pengbo irrigation area in Tibet from 2019 to 2022, including rainfall, temperature and relative humidity data, as well as the measured soil moisture and soil temperature data of highland barley, oat and grassland. The data interval is recorded in hours, and the measured time is from 2019 to 2022. The data of soil temperature and soil moisture are relatively detailed, which can reflect the change law of soil moisture and temperature at different time scales of time, day, month, season and year, and can also better meet the calibration and verification requirements of farmland water and heat transport model. The data set also includes crop evapotranspiration data and leakage data, which is helpful to analyze the water consumption of crops in the whole growth period and the water consumption and leakage at different growth stages in the alpine region of Tibet, and plays an important role in clarifying the water balance of different farmland systems. The meteorological, soil moisture, soil temperature, transpiration and leakage data of Pengbo irrigation area in Tibet provided by this data set are helpful to reveal the water transformation process at the farmland scale and irrigation area scale, and fully understand the water and heat transfer process and crop growth state of SPAC system in the high cold region of Tibet.
The North China Plain (NCP), with an area of ~140,000 square kilometers, is among the most important agricultural producing bases in China. In addition to canal irrigation with surface water from the Yellow River, the NCP also needs much groundwater for intensive irrigation. Spatiotemporally continuous and daily evapotranspiration (ET) estimates of high spatial resolution could be valuable for improving our understanding of agricultural water consumption across the NCP, and also for improving water use efficiency for better agricultural water resource management practices over similar regions globally. This ET data set at 1 km spatial resolution and daily timescale across the NCP from Jan 2008 to Dec 2019 was generated using two source energy balance model (TSEB) and data fusion. The accuracy is generally comparable and even higher than published results, with our ET data set featuring spatiotemporal continuity and high spatial resolution for a decade. Furthermore, this data set and associated approaches are valuable for performing daily, monthly, seasonal, interannual, and trend analyses of ET in the NCP and similar regions globally.
ZHANG Caijin , LONG Di
The database is derived from the UAV image data obtained from the field survey conducted by the Qinghai Tibet Plateau farmland ecosystem research team in 2020. The survey area involves the farmland ecosystem concentration areas and counties along Sichuan and Tibet 318, including Litang and Batang in Sichuan, and the areas such as Basu, Linzhi, Gyangze and Bailang in Xigaze in Tibet. The recorded objects include highland barley Traditional crops such as wheat, as well as open field vegetables and greenhouse facilities in some areas; The flight altitude is generally 50-300m, with high resolution. The photographing equipment is Dajiang yu2pro. The picture comes with GIS longitude, latitude, altitude and other information, which can be used for ground reference or correction data of satellite remote sensing.
The database contains the historical data of agricultural production, operation and management of the Qinghai Tibet Plateau, which is collected and sorted from the statistical yearbooks of cities and states in the Qinghai Tibet Plateau over the years, extracted and summarized after electronization; This data includes the data of farmland effective irrigation area in sub counties within the scope of Qinghai Tibet Plateau from 1995 to 2018. The effective irrigation area of farmland is an important indicator of production, operation and management. The Qinghai Tibet Plateau is dominated by typical dry farming. In most areas, agricultural irrigation is dominated by natural precipitation, and the area covered by artificial irrigation is less. This data is of great significance for analyzing the management of water resources utilization and water footprint of farmland ecosystem in the Qinghai Tibet Plateau. The data are collected by county, The results can reach the county scale.
The dataset of Maize yield in Zhangye Basin in the Northern foot of Qilian Mountains (2001-2015) is obtained by downscaling the actual maize yield data based on the simulation results of Hydrological-Ecological Integrated watershed Flow Model (HEIFLOW). HEIFLOW is a three-dimensional distributed eco-hydrological coupling model, integrating the Precipitation-Runoff Modeling System (PRMS) with the Modular Groundwater Flow Model (MODFLOW) and several ecological modules, which can completely describe the hydrological cycle and vegetation ecological process of the basin. For the modeling details of generating this data, please refer to Han et al. (2021), and for the technical details of HEIFLOW model, please refer to Han et al. (2021), Tian et al. (2018), and sun et al. (2018)
ZHENG Yi , HAN Feng , TIAN Yong
This data set includes the social, economic, resource and other relevant index data of Gansu, Qinghai, Sichuan, Tibet, Xinjiang and Yunnan in the Qinghai Tibet Plateau from 2000 to 2015. The data are derived from Gansu statistical yearbook, Qinghai statistical yearbook, Sichuan statistical yearbook, Xizang statistical yearbook, Xinjiang statistical yearbook, Yunnan statistical Yearbook China county (city) socio economic statistical yearbook And China economic network, guotai'an, etc. The statistical scale is county-level unit scale, including 26 county-level units such as Yumen City, Aksai Kazak Autonomous Region and Subei Mongolian Autonomous County in Gansu Province, 41 county-level units such as Delingha City, Ulan county and Tianjun County in Qinghai Province, 46 counties such as Shiqu County, Ruoergai County and ABA County in Sichuan Province, and 78 counties such as Ritu County, Gaize county and bango County in Tibet, 14 counties including Wuqia County, aktao county and Shache County in Xinjiang Province, and 9 counties including Deqin County, Zhongdian county and Fugong County in Yunnan Province; Variables include County GDP, added value of primary industry, added value of secondary industry, added value of tertiary industry, total industrial output value of Industrial Enterprises above Designated Size, total retail sales of social consumer goods, balance of residents' savings deposits, grain output, total sown area of crops, number of students in ordinary middle schools and land area. The data set can be used to evaluate the social, economic and resource status of the Qinghai Tibet Plateau.
This dataset was captured during the field investigation of the Qinghai-Tibet Plateau in June 2021 using uav aerial photography. The data volume is 3.4 GB and includes more than 330 aerial photographs. The shooting locations mainly include roads, residential areas and their surrounding areas in Lhasa Nyingchi of Tibet, Dali and Nujiang of Yunnan province, Ganzi, Aba and Liangshan of Sichuan Province. These aerial photographs mainly reflect local land use/cover type, the distribution of facility agriculture land, vegetation coverage. Aerial photographs have spatial location information such as longitude, latitude and altitude, which can not only provide basic verification information for land use classification, but also provide reference for remote sensing image inversion of large-scale regional vegetation coverage by calculating vegetation coverage.
LV Changhe, ZHANG Zemin
As the roof of the world, the water tower of Asia and the third pole of the world, the Qinghai Tibet Plateau is an important ecological security barrier for China and even Asia. With the rapid development of social economy, human activities have increased significantly, and the impact on the ecological environment is growing. In this paper, eight factors including cultivated land, construction land, National Road, provincial road, railway, expressway, GDP and population density were selected as the threat factors, and the attributes of the threat factors were determined based on the expert scoring method to evaluate the habitat quality of the Qinghai Tibet Plateau, so as to obtain six data sets of the habitat quality of the agricultural and pastoral areas of the Qinghai Tibet Plateau in 1990, 1995, 2000, 2005, 2010 and 2015. The production of habitat quality data sets will help to explore the habitat quality of the Qinghai Tibet Plateau and provide effective support for the government to formulate sustainable development policies of the Qinghai Tibet Plateau.
LIU Shiliang, LIU Yixuan, SUN Yongxiu, LI Mingqi
The data set was obtained from UAV aerial photography during the field investigation of the Qinghai Tibet Plateau in August 2020. The data size is 10.1 GB, including more than 11600 aerial photos. The shooting sites mainly include Lhasa, Shannan, Shigatse and other areas along the road, residential areas and surrounding areas. The aerial photos mainly reflect the local land use / cover type, facility agriculture distribution, grassland coverage and other information. The aerial photos have longitude, latitude and altitude information, which can provide better verification information for land use / cover remote sensing interpretation, and can also be used for vegetation coverage estimation, and provide better reference information for land use research in the study area.
LV Changhe, LIU Yaqun
The airport data of the 34 key areas along One Belt One Road were first collected from the Internet and then re-processed. First, Using several key words about airport, web pages were then collected by Google and Baidu search engine. We analyze the information on the webpage and check the statistics and characteristics of the airport.The core information such as the location, name, type, size and country of each airport in the 34 key node areas is extracted. Based on statistical data and web information, it is finally integrated into a data product of airport infrastructure elements. This data can provide important basic data for the development of socio-economic infrastructure, transportation and other research on key area and regions of the Belt and Road.
GE Yong, LING Feng
Third Pole 1:100,000 airport and runway data set include：airport（Tibet_Airport）and(Tibet_Airport_runways) vector space data set and its attribute name:Airport name(Name）、Name of airport（CNTRY_NAME）、Airport country abbreviation（CNTRY_CODE）、latitude（LATITUDE）、longitude（LONGITUDE）. The data comes from the 1:100,000 ADC_WorldMap global data set，The data through topology, warehousing and other data quality inspection，Data through the topology, into the library，It's comprehensive, up-to-date and seamless geodigital data. The world map coordinate system is latitude and longitude, D_WGS_1984 datum surface
Antarctic 1:100,000 airport distribution data set includes vector space data and related attribute data of airports (Antarctic_Airport) and airport runways (Antarctic_Airport_runways)：Airport Name(Name), airport country Name（CNTRY_NAME）, airport country abbreviation（CNTRY_CODE）, LATITUDE, LONGITUDE. The data comes from the 1:100,000 ADC_WorldMap global data set，The data through topology, warehousing and other data quality inspection，Data through the topology, into the library，It's comprehensive, up-to-date and seamless geodigital data. The world map coordinate system is latitude and longitude, WGS84 datum surface，Antarctic specific projection parameters（South_Pole_Stereographic）.
Arctic 1:100,000 airport distribution data set includes vector space data and related attribute data of airports (Arctic_Airport) and airport runway (Arctic_Airport_runways) in the arctic range: airport Name, airport country Name, airport country abbreviation (CNTRY_CODE), LATITUDE and LONGITUDE. The data comes from the 1:100,000 ADC_WorldMap global data set, which is a comprehensive, up-to-date and seamless geographic digital data after the data quality inspection of topology, warehousing and other data. The world map coordinate system is latitude and longitude, WGS84 datum surface, and the arctic data set is the special projection parameter for the arctic (North_Pole_Stereographic).
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.
This data set includes the biomass and photosynthesis observational data of the highland spring barley experimental plot at the Lhasa Farm Experimental Station and the meteorological data observationally obtained at the Damxung Grass Experimental Station. The time range is 2006-2009. Biomass observation method: The sampling area of each sample is 25 cm*25 cm. Photosynthetic data observation: The instrument is a LiCor-6400. The biomass data are manually entered according to the record book. The photosynthetic data are automatically recorded by the instrument. The average wind speed, prevailing wind direction, temperature, atmospheric pressure and relative humidity in the daily values of meteorological data are averaged over half-hour data. The precipitation and total radiation data are automatically recorded by the observation system. The observation process of biomass data is in strict accordance with the agronomic method, and it can be applied to the estimation of agricultural productivity. In the process of photosynthetic data observation, the operation of the instrument and the selection of the observation object are strictly in accordance with professional requirements and can be used in photosynthetic parameter simulations estimating plant leaf and productivity. The Tibetan Plateau farmland ecosystem observation data includes: 1) aboveground biomass; 2) CO2 response photosynthetic data; 3) light-response photosynthetic data; and 4) daily meteorological data in Damxung Monitoring Point. Data collection locations: Lhasa Agricultural Ecology Experimental Station, Chinese Academy of Sciences, Longitude: 91°20’, Latitude: 29°41’, Altitude: 3688 m and Damxung Alpine Meadow Carbon Flux Observation Station, Longitude: 91°05′, Latitude: 30°25′, Altitude: 4333 m.
The data set include crop leaf stomatal conductance observed at four sample regions, that is the soil moisture control experimental field at Daman county, and the super station, and Shiqiao sample plots at Wuxing village in Zhangye city. 1) Objective Crop leaf stomatal conductance, a key biophysical parameter, was observed as model parameter or a priori knowledge for crop growth model, or evapotranspiration estimation. 2) Measuring instruments Leaf porometer. 3) Measuring site a. the soil moisture control experimental field at Daman county, Twelve soil water treatments are set. The crop leaf stomatal conductance for each treatment is measured on 17, 23 and 29 May, and 3, 9, 14 and 24 June, and 5 and 12 July. b. the Super Station The crop leaf stomatal conductance at the super station is measured on 22 and 28 May, 5, 11, 18, and 25 June, and 1, 8, 15, 22 and 31 July, 9, 15 and 22 August, and 3 and 11 September. c. the Shiqiao sample site The crop leaf stomatal conductance at the Shiqiao village is measured on 17, 22 and 28 May, 4, 11, 17 and 25 June, 1, 8, 15, 22, and 30 July, 8, 16 and 27 August, and 9 September. 4) Data processing The observational data was recorded in the sheets and reorganized in the EXCEL sheets. The time used in this dataset is in UTC+8 Time.
Xu Fengying, Wang Jing, Huang Yongsheng, LI Xin, MA Mingguo
The dataset combined with crop phrenology data and field management data which were investigated near the 13 eddy covariance (EC) stations. 1.1 Objective of investigation Objectives of investigation is to supply assistant information for experiment on EC, meteorology, and biophysics parameter. 1.2 Investigation spots and items Investigation spots include Jiu She of Shiqiao village (EC3), Xiaoman southern road (EC16), Wu She of Five stars village (EC13), Wu She of Xiaoman village (EC14), Er She of Shiqiao village (EC5), Liu She of Zhonghua village (EC11), Liu She of Shiqiao village (EC2), Wu She of JinCheng village (EC7), EC6, Liu She of Jincheng village (EC8), Yi She of Kangning village (EC9), Er She of Kangning village (EC10), and Si She of Jingcheng village (EC12). Investigation items comprise crop type, crop name, seed time, seed type, plant span, row span, field area, germination time, three leaves period, seven leaves period, farming way, farming time, irrigation time, irrigation water volume, fertilization time, fertilization type, and fertilization rate. The time used in this dataset is in UTC+8 Time. 1.3 Data collection Data was collected by using ask-reply approach according to investigation tables.
GE Yingchun, Ma Chunfeng, LI Xin
According to the characteristics of the selected field and its surrounding area, a trime tube is arranged in the corn field, and 5 trime tubes are arranged in a direction perpendicular to the field path. When monitoring soil moisture content in the TDR vertical direction, the unit is every 10cm. Monitor down. Location: N 38 ° 52′27.6 ″ E 100 ° 21′14.0 ″ The submitted data includes the water content of the farmland and its surrounding soil (TDR monitoring) after three irrigations in a selected farmland in Yingke Irrigation District, encrypted monitoring after irrigation, one group every 3 hours within 24 hours, and 3 groups per day for the next 5 days. -10 days are two groups per day, and 10-15 days are one group per day.
HUANG Guanhua, JIANG Yao
The field experiments of water consumption and irrigation water productivity of corn and cotton were arranged in 2012 and 2013, and the field experiments of irrigation water productivity of corn and sunflower under different mulching and cultivation methods were arranged in 2014. The characteristics of water consumption and irrigation water demand of three crops under different soil conditions, as well as the relationship between key soil properties and crop yield and irrigation water productivity were obtained.