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
The data involved three periods of geodetic glacier mass storage change of three Rongbuk glaciers and its debris-covered ice in the Rongbuk Catchment from 1974-2016 (unit: m w.e. a-1). It is stored in the ESRI vector polygon format. The data sets are composed of three periods of glacier surface elevation difference between 1974-2000，2000-2016 and 1974-2006, i.e. DHPRISM2006-DEM1974（DH2006-1974）、DHSRTM2000-DEM1974（DH2000-1974）、DHASTER2016-SRTM2000（DH2016-2000）. DH2006-1974 was surface elevation change between ALOS/PRISMDEM(PRISM2006) and DEM1974, i.e. the DEM1974 was subtracted from PRISM2006, DH2006-1974 =PRISM2006 – DEM1974. The PRISM2006 was generated from stereo pairs of ALOS/PRISM on 4 Dec. 2006. The earlier historical DEM (DEM1974, spatial resolution 25m) was derived from 1:50,000 topographic maps in October 1974(DEM1974,spatial resolution 25m). The uncertainty in the ice free areas of DHPRISM2006-DEM1974 was ±0.24 m a-1. DHSRTM2000-DEM1974（DH2000-1974）was surface elevation change between SRTM DEM(SRTM2000) and DEM1974. The uncertainty in the ice free areas of DHSRTM2000-DEM1974 was ±0.13 m a-1. DHASTER2016-SRTM2000（DH2016-2000）was the surface elevation change between ASTER DEM2016 and SRTM DEM(SRTM2000). The uncertainty in the ice free areas of DHASTER2016-SRTM2000 was ±0.08 m a-1. Glacier-averaged annual mass balance change (m w.e.a-1) was averaged annually for each glacier, which was calculated by DH2006-1974/DH2000-1974/DH2016-2000, glacier coverage area and ice density of 850 ± 60 kg m−3. The attribute data includes Glacier area by Shape_Area (m2), EC2000-1974/EC2016-2000/EC2006-1974, i.e. Glacier-averaged surface elevation change in each period(m a-1), MB2000-1974/ MB2016-2000/MB2006-1974, i.e. Glacier-averaged annual mass balance in each period (m w.e.a-1), and MC2000-1974/ MC2016-2000/MC2006-1974,Glacier-averaged annual mass change in each period(m3 w.e.a-1), Uncerty_EC is the maximum uncertainty of glacier surface elevation change（m a-1）、Uncerty_MB, is the maximum uncertainty of glacier mass balance（m w.e. a-1），Uncerty_MC, is the maximum uncertainty of glacier mass change（m3w.e. a-1）。 MinUnty_EC，is the minimum uncertainty of glacier surface elevation change，MinUnty_MB，is the minimum uncertainty of glacier mass balance（m w.e. a-1），MinUnty_MC is the minimum uncertainty of glacier mass change（m3 w.e. a-1.The data sets could be used for glacier change, hydrological and climate change studies in the Himalayas and High Mountain Asia.
We obtained the whole genome variation data of 30 Tibetan individuals. The SNP typing of 30 samples was carried out by DNA array method, and about 700000 loci (including nuclear genome, mitochondrial DNA and Y chromosome) of each sample were obtained. First, after extracting genomic DNA, DNA amplification, enzymatic fragmentation, precipitation and re suspension were carried out. After the sample was incubated overnight and hybridized with beadchip, the DNA was annealed to obtain a site-specific 50 mer probe, covalently coupled with an Infinium bead type. Then Infinium XT was used to extend the enzyme base to give the allele specificity, and then fluorescent staining was carried out. The fluorescence intensity of the beads was detected by iSCAN system, and the Illumina software automatically performed the analysis and genotype recognition. Finally, the SNP typing results of each sample were obtained. Based on the above data, relevant biological information analysis (mainly including chip site quality control analysis, Y chromosome and mitochondrial DNA haplotype analysis) was carried out. This data is helpful to analyze the genetic structure of Tibetan population from the perspective of nuclear genome, Y chromosome and mitochondrial DNA. By comparing with the data of people around the plateau, we can trace the migration and settlement history of the plateau population comprehensively.
On the basis of the global tropical cyclone track dataset, the global disaster events and losses dataset, the global tide level observation dataset and DEM data, coastline distribution data, land cover information, population and other related data of the Belt and Road, indicators related to the vulnerability of storm surge in each unit are extracted and calculated using 100 meter grid as evaluation unit, such as population density, land cover type, etc. The comprehensive index of storm surge vulnerability is constructed, and the vulnerability index of storm surge is obtained by using the weighted method. Finally, the storm surge vulnerability index is normalized to 0-1, which can be used to evaluate the vulnerability level of storm surge in each assessment unit.
On the basis of the global tropical cyclone track dataset, the global disaster events and losses dataset, the global tide level observation dataset and DEM data, coastline distribution data, land cover information, population and other related data of the Belt and Road, indicators related to the disaster risk and vulnerability of storm surge in each unit are extracted and calculated using10 meter grid as evaluation unit, such as historical intensity of tide level frequency of storm historic arrival, historical loss, population density, land cover type, etc. The comprehensive index of storm surge disaster risk is constructed, and the risk index of storm surge is obtained by using the weighted method. Finally, the storm surge risk index is normalized to 0-1, which can be used to evaluate the risk level of storm surge in each assessment unit. The data set includes 20-year, 50-year, and 100-year corresponding risks.
1. Data overview: This data set is the daily scale groundwater level data of Qilian station from November 1, 2011 to December 31, 2011. In October 2011, two groundwater monitoring wells were arranged in hulugou small watershed. Well 1 is located beside the general control hydrological section of hulugou watershed, with a depth of 12.8m and an aperture of 12cm. Well 2 is located in the east of the Delta, about 100m away from the river, with a depth of 14.7m and an aperture of 12cm. 2. Data content: U20hobo water level sensor is arranged in the groundwater well, which is mainly used to monitor the change of groundwater level and temperature in hulugou small watershed. The data content is the temperature and atmospheric pressure inside the hole, and the data is the daily scale data. 3. Space time scope: Geographic coordinates of well 1: longitude: longitude: 99 ° 53 ′ E; latitude: 38 ° 16 ′ n; altitude: 2974m (near the hydrological section at the outlet of the basin). Geographic coordinates of well 2: longitude: 99 ° 52 ′ E; latitude: 38 ° 15 ′ n; altitude: 3204.1m (east side of the East Branch of the delta).
HAN Chuntan CHEN Rensheng SONG Yaoxuan LIU Junfeng YANG Yong QING Wenwu LIU Zhangwen
1) Data content: multi-model ensemble mean wind speed at 200 hPa and 850 hPa during the Last Glacial Maximum, mid-Holocene and pre-industrial period (reflecting high and low level westerlies), 850 hPa meridional and zonal winds (reflecting the East Asian monsoon circulation) and zonal mass streamfunction (reflecting Walker circulation); 2) Data sources: monthly data simulated by multiple climate models from the second and third stages of the international Paleoclimate Modelling Intercomparison Project; processing methods: multi-model equal weight arithmetic mean, monthly climate average; 3) Data application: used for the study of paleoclimate change and dynamic mechanism.
TIAN Zhiping WANG Na
The Xiadong section locates at the Xiadong village region in Tsochen County, Tibet. The Permian strata in this region includes Largar, Angjie and Xiala formations. The Xiadong Section locates at the north of the Xiadong Village. The section is composed of entirely carbonates with abundant fusulines, smaller foraminifers and corals. The column exhibit the occurrences of fusulines and smaller foraminifers and their biostratigraphy. According to the fusulines, the age of the Xiala Formation at this section is middle Permian age. The fusulines can be subdivided into two assemblages, respectively Chenella changanchiaoensis-Neoschwagerina cheni in the lower and Nankinella-Chusenella assemblage in the upper. The foraminifers are divided into four assemblages, respectively Lasiodiscus tenuis-Palaeotextularia angusta elongata assemblage, Agathammina pusilla-A.vachardi assemblage, Hemigordiopsis-Midiella assemblage and Pachyphloi-Nodosinelloides assemblage.
25 members consisting of researchers from Nanjing Institute of Geology and Palaeontology, CAS and Nanjing University, reporters from Beijing News, technicians from China Unicom, drivers and kitchener undertook the investigation on the Palaeozoic strata and faunas from various regions in northern Tibet from 30 August to 3 October. The expedition areas include areas in northern Selingco, Rejuechaka and Rongma region in northern Nyima County, Wenbu area in southern Nyima County. The objective of the expedition includes: (1) the origin of the Permian limestone blocks within the Bangong-Nujiang suture zones; (2) the Permian-Triassic strata, faunas and floras in the Rejuechaka region, northern Tibet; (3) the Ordovician cephalopods in the Rongma area, Nyima County; (4) the Permian sequence and faunas in the Wenbu area, southern Nyima County. This album contains the full record of the investigation and geological phenomenon. The links in the album can directly link to the video in internet.
25 members consisting of researchers from Nanjing Institute of Geology and Palaeontology, CAS and Nanjing University, reporters from Beijing News, technicians from China Unicom, drivers and kitchener undertook the investigation on the Palaeozoic strata and faunas from various regions in northern Tibet from 5 September to 2 October. The expedition areas include areas in northern Selingco, Rejuechaka and Rongma region in northern Nyima County, Wenbu area in southern Nyima County. In northern Selingco region, the expedition focused on the faunas from the exotic limestone blocks within the Bangong-Nujiang suture zone. In the Rejuechaka region, the expedition attention was paid on the Permian-Triassic successions, sea-level changes, and Permian and Triassic faunas and floras. In the Rongma area, the Ordovician cephalopod and Permian microfossils within the Longmu Co-Shuanghu suture zone was investigated. In the Wenbu area, the research attention was paid on the stratigraphic transition from the ice-houce to green-house conditions during the early Permian time. This document record the full information about the field investigation.
The data set is based on the reflectance of MODIS channels and the observation data of SIF to establish the neural network model, so as to obtain the SIF data with high spatial and temporal resolution, which is often used as a reference for primary productivity. The data is from Zhang et al. (2018), and the specific algorithm is shown in the article. The source data range is global, and the Qinghai Tibet plateau region is selected in this data set. This data integrates the original 4-day time scale data into the monthly data. The processing method is to take the maximum value of the month, so as to achieve the effect of removing noise as much as possible. This data set is often used to evaluate the temporal and spatial patterns of vegetation greenness and primary productivity, which has practical significance and theoretical value.
Land surface temperature (LST) is a key variable for high temperature and drought monitoring and climate and ecological environment research. Due to the sparse distribution of ground observation stations, thermal infrared remote sensing technology has become an important means of quickly obtaining ground temperature over large areas. However, there are many missing and low-quality values in satellite-based LST data because clouds cover more than 60% of the global surface every day. This article presents a unique LST dataset with a monthly temporal resolution for China from 2003 to 2017 that makes full use of the advantages of MODIS data and meteorological station data to overcome the defects of cloud influence via a reconstruction model. We specifically describe the reconstruction model, which uses a combination of MODIS daily data, monthly data and meteorological station data to reconstruct the LST in areas with cloud coverage and for grid cells with elevated LST error, and the data performance is then further improved by establishing a regression analysis model. The validation indicates that the new LST dataset is highly consistent with in situ observations. For the six natural subregions with different climatic conditions in China, verification using ground observation data shows that the root mean square error (RMSE) ranges from 1.24 to 1.58 K, the mean absolute error (MAE) varies from 1.23 to 1.37 K and the Pearson coefficient (R2) ranges from 0.93 to 0.99. The new dataset adequately captures the spatiotemporal variations in LST at annual, seasonal and monthly scales. From 2003 to 2017, the overall annual mean LST in China showed a weak increase. Moreover, the positive trend was remarkably unevenly distributed across China. The most significant warming occurred in the central and western areas of the Inner Mongolia Plateau in the Northwest Region, and the average annual temperature change is greater than 0.1K (R>0:71, P<0:05), and a strong negative trend was observed in some parts of the Northeast Region and South China Region. Seasonally, there was significant warming in western China in winter, which was most pronounced in December. The reconstructed dataset exhibits significant improvements and can be used for the spatiotemporal evaluation of LST in high-temperature and drought-monitoring studies. More detail please refer to Zhao et al (2020). doi.org/10.5281/zenodo.3528024
The Land Surface Temperature in China dataset contains land surface temperature data for China (about 9.6 million square kilometers of land) during the period of 2003-2017, in Celsius, in monthly temporal and 5600 m spatial resolution. It is produced by combing MODIS daily data(MOD11C1 and MYD11C1), monthly data(MOD11C3 and MYD11C3) and meteorological station data to reconstruct real LST under cloud coverage in monthly LST images, and then a regression analysis model is constructed to further improve accuracy in six natural subregions with different climatic conditions.
The Sixth Zhabuye Section locates at the northern part of the Zhabuye salt lake in Zhongba County, Tibet. The Middle Permian carbonates of the Xiala Formation outcropped very well in the region. This section has a thickness of 200 meters and was divided into 6 units. The lower 50 meters of the section is composed of limestones and chert layers. The chert layers in the above 100 meters decreased significantly, but they reappear in the top 35 meters. This section has abundant fusulines and smaller foraminifers. The fusulines has two assemblages, respectively Neoschwagerina majulensis-Kahlerina pachytheca assemblage in the lower and Chusenella quasifera-Codonofusiella nana assemblage in the upper. The foraminifers are divided into three assemblages, respectively Glomomidiellopsis specialisaeformis-Pachyphloia multiseptata assemblage, Lysites biconcavus-Shanita amosi assemblage and Lasiodiscus tenuis-Neoendothyra reicheli assemblage.
The Mujiucuo section locates at the west of the Mujiucuo salt lake in Xainza County, Tibet. The Permian sequences oupcropped very well at the section. The Permian sequence at the section was divided into five formations, respectively Yunzhub, Largar, Angjie, Xiala and Mujiucuo formations. The Yunzhub Formation is composed of sandstone. The upper part of this formation contains limestone interlayers with 8 species of brachiopods. The brachiopods are grouped into the Costatumulus-Bandoproductus assemblage. The Xiala Formation is composed of entirely carbonates. The purplish limestone in the base of Xiala Formation consists of 6 species of brachiopods. They are grouped into the Alispiriferella-Retimarginifera celeteria assemblage. The overlying bed only contains one species Permophricodothyris elegantula. The brachiopods from these beds overall show a Gondwanan type in palaeobiogeography. It suggests that the Lhasa Block located not far away from the Gondwana margin. According to the constraints by fusulines and conodonts, the ages of beds 83, 86 and 87 are Middle Permian whereas those of beds 88 and 89 are late Permian. Brachiopods are found in many beds in the Xiala Formation. They are divided into two separate assemblages, respectively Echinauris opuntia-Neoplicatifera in the lower and Spinomarginifera lopingensis-Chonetinella cymatilis in the upper. Compared with the brachiopods from the Yunzhub Formation and basal Xiala Formation below, both assemblages from the middle and upper part of the Xiala Formation exhibit a pronounced palaeobiogeographical changes.
XU Haipeng ZHANG Yichun
The Mujiucuo section locates at the west of the Mujiucuo salt lake in Xainza County, Tibet. The Permian sequences oupcropped very well at the section. The beds from 83 to 89 consists mainly of bioclastic limestone with abundant fusulines and foraminifers. After careful examination, 13 species of fusulines and 37 species of smaller foraminifers are identified at the section. In terms of the occurrences of those fusulines and smaller foraminifers, the fusulines are subdivided into the lower Nankinella-Chusenella assemblage of Middle Permian and upper Codonofusiella schubertelloides zone of Late Permian. Similarly, the smaller foraminifers are also divided into lower Agathammina vachardi-Hemigordiopsis subglobosa assemblage and upper Glomomidiellopsis xainzaensis-Midiella reicheli assemblage. The dominance of Miliolinids in the section suggests an overall warm-water depositional settings.
The dataset contains the identification lists of fusulines, smaller foraminifers, brachiopods and conodonts from three sections at the Mujiucuo area, Xainza County, Tibet. The Permian strata has very good outcrops at the Mujiucuo region. The Permian strata is composed of the Yunzhub, Largar, Angjie, Xiala and Mujiucuo formations in ascending order. The Yunzhub Formation contains only abundant brachiopods, they show a typical Gondwanan cool-water type. The middle part of the Xiala Formation is composed of medium-bedded limestone with abundant foraminifers, fusulines and brachiopods. The upper part of the Xiala Formation contains abundant conodonts, smaller foraminifers and fusulines. According to the ages of the fossils, the limestone from the northwest Mujiucuo section corresponds to the bed 86 of the Mujiucuo section. The limestones from the short western Mujiucuo section corresponds to bed 89 of the Mujiucuo section. The limestone and dolomites from the No. 2 short section comes from the lower part of the Mujiucuo Formation.
The data set is based on NDVI 3G calculated by GIMMS AVHRR sensor data, which represents the greenness of vegetation. The data is from Zhu et al. (2013), and the specific calculation method is shown in the article. The source data range is global, and the Qinghai Tibet plateau region is selected in this data set. This data integrates the original semi monthly scale data into the monthly data. The processing method is to take the maximum value of two NDVI of a month to achieve the effect of noise removal as far as possible. This data set is one of the most widely used NDVI data, and is often used to evaluate the temporal and spatial patterns of vegetation greenness, which has practical significance and theoretical value.
The data set is based on the Lai 3g calculated by GIMMS AVHRR sensor, which represents the greenness of vegetation. The data is from Chen et al. (2019), and the specific calculation method is shown in the article. The source data range is global, and Tibetan plateau region is selected in this data set. This data integrates the original semi monthly scale data into the monthly data, and the processing method is to take the maximum value of two periods of Lai in a month, so as to achieve the effect of removing noise as much as possible. This data set is one of the most widely used Lai data, and is often used to evaluate the temporal and spatial patterns of vegetation greenness, which has practical significance and theoretical value.
The data set is based on a series of microwave remote sensing data, including Special Sensor Microwave Imager (SSM/I), Advanced Microwave Scanning Radiometer for Earth Observation System (AMSR-E), etc., which can be used as a reference for primary productivity. The data is from Liu et al. (2015), and the specific calculation method is shown in the article. The source data range is global, and Tibetan Plateau region is selected in this data set. This data set is often used to evaluate the temporal and spatial patterns of vegetation greenness and primary productivity, which has practical significance and theoretical value.