The data set contains the stable oxygen isotope data of ice core from 1864 to 2006. The ice core was obtained from Noijinkansang glacier in the south of Southern Tibetan Plateau, with a length of 55.1 meters. Oxygen isotopes were measured using a MAT-253 mass spectrometer (with an analytical precision of 0.05 ‰) at the Key Laboratory of CAS for Tibetan Environment and Land Surface Processes, China. Data collection location: Noijinkansang glacier (90.2 ° e, 29.04 ° n, altitude: 5950 m)
Clay minerals are the weathering products of the parent rocks, which was formed by a series of chemical processes under a specific climate, and they are also widely-used indicators to reconstruct the history of the regional paleochemical weathering process. In this study, we present a detailed mineralogical investigation of 76 clay samples collected from the Lunpori section (21-15 Ma) in the Lunpola Basin by using X-ray diffraction. The results show that illite-smectite mixed layers, illite, chlorite, and kaolinite are the common clay mineral types in this section. The illite-smectite mixed layers and illite are the most abundant ones, which account for 80-90% of the total clay content; while the content of kaolinite and chlorite is relatively low, only occupying ~10-20% of the total clay minerals. The variations of clay mineral content are relatively stable in the Lunpori section, thus indicating that the intensity of regional chemical weathering was less variable during this period.
The ages of glacial traces of the last glacial maximum, Holocene and little ice age in the Westerlies and monsoon areas were determined by Cosmogenic Nuclide (10Be and 26Al) exposure dating method to determine the absolute age sequence of glacial advance and retreat. The distribution of glacial remains is investigated in the field, the location of moraine ridge is determined, and the geomorphic characteristics of moraine ridge are measured. According to the geomorphic location and weathering degree of glacial remains, the relationship between the new and the old is determined, and the moraine ridge of the last glacial maximum is preliminarily determined. The exposed age samples of glacial boulders on each row of moraine ridges were collected from the ridge upstream. This data includes the range of glacier advance and retreat in Karakoram area during climate transition period based on 10Be exposure age method.
"One belt, one road" delineation of the key Asian regional watershed boundaries is based on the following principles: Principle 1: along the Silk Road Principle 2: located in arid and semi-arid areas Principle 3: high water risk Principle 4: watershed integrity 1. Division basis of arid area Food and Agriculture Organization of the United Nations. FAO GEONETWORK. Global map of aridity - 10 arc minutes (GeoLayer). (Latest update: 04 Jun 2015) Accessed (6 Mar 2018). URI: http://data.fao.org/ref/221072ae-2090-48a1-be6f-5a88f061431a.html?version=1.0 2. Water resources risk data: Gassert, F., M. Landis, M. Luck, P. Reig, and T. Shiao. 2014. Aqueduct Global Maps 2.1. Working Paper. Washington, DC: World Resources Institute. 3. Poverty index data: Elvidge C D, Sutton P C, Ghosh T, et al. A global poverty map derived from satellite data. Computers & Geosciences, 2009, 35(8): 1652-1660. https://www.ngdc.noaa.gov/eog/dmsp/download_ poverty.html 4. Basic basin boundary data: (1) Watershed boundaries were derived from HydroSHEDS drainage basins data (Lehner and Grill 2013) based on a grid resolution of 15 arc-seconds (approximately 500 m at the equator), which can be free download via https://hydrosheds.cr.usgs.gov/hydro.php (2) AQUASTAT Hydrological basins: This dataset is developed as part of a GIS-based information system on water resources. It has been published in the framework of the AQUASTAT - programme of the Land and Water Division of the Food and Agriculture Organization of the United Nations. The map is also available in the SOLAW Report 15: “Sustainable options for addressing land and water problems – A problem tree and case studies”. Data can be free download via http://www.fao.org/nr/water/aquamaps/ (3) HydroBASINS: https://www.hydrosheds.org/downloads 5. The GloRiC provides a database of river types and sub-classifications for all river reaches globally. https://www.hydrosheds.org/page/gloric 6. HydroATLAS offers a global compendium of hydro-environmental sub-basin and river reach characteristics at 15 arc-second resolution. https://www.hydrosheds.org/page/hydroatlas It covers an area of 1469400 square kilometers, including the following areas: Nujiang River Basin, Dead Sea basin, Sistan River Basin, Yellow River Basin, Jordan Syria eastern basin, Indus River Basin, Iran inland flow area, urmiya Lake Basin, Shiyang River Basin, hallelud mulgarb River Basin, Lianghe River Basin, Shule River Basin, Heihe River Basin, issekkor Lake Basin, Tata River Basin Limu River Basin, Turpan Hami basin, Ebinur Lake Basin, Junggar basin, Amu Darya River Basin, Manas River Basin, ulungu River Basin, Emin River Basin, Chu River Talas River Basin, Xil River Basin, Ili River Basin, Caspian Sea basin, Lancang River Basin, Yangtze River Basin, Qinghai lake water system, Eastern Qaidam Basin, western Qaidam Basin and Qiangtang plateau District, Yarlung Zangbo River Basin
RAN Youhua WANG Lei ZENG Tian GE Chunmei LI Hu
The distribution data of permafrost in the source area of the Yellow River is established based on the annual average ground temperature model of permafrost in the source area of the Yellow River. The annual average ground temperature of 0 ℃ is taken as the standard and boundary for dividing seasonal frozen soil and permafrost. Compared with the available permafrost maps of the source region of the Yellow River (1:3 million) and the permafrost background survey project of the Qinghai Tibet Plateau (1:1 million), the data set is based on the measured data of the Yellow River source area, which has higher consistency with the measured data, and the simulation accuracy of the permafrost distribution map is the highest. The data set can be used to verify the distribution of permafrost in the source area of the Yellow River, as well as to study the frozen soil environment.
SHENG Yu LI Jing
This data set includes land cover classification products of 30 meters in Qilian mountain area from 1985 to 2019. Firstly, the product uses Landsat-8/OLI to construct the 2015 time series data. According to the different NDVI time series curves of various ground features, the knowledge of different features is summarized, the rules are set to extract different features, and the land cover classification map in 2015 is obtained. The classification system refers to IGBP classification system and from_ LC classification system can be divided into 10 categories: cultivated land, woodland, grassland, shrub, wetland, water body, impervious surface, bare land, glacier and snow. According to the accuracy evaluation of Google Earth HD images and field survey data, the overall accuracy of land cover classification products in 2015 was as high as 92.19%. Based on the land cover classification products in 2015, based on the Landsat series data and strong geodetic data processing ability of Google Earth engine platform, the land cover classification products from 1985 to 2019 are produced by using the idea and method of change detection. By comparing the classification products, it is concluded that the land cover classification products based on Google Earth engine platform have good consistency with the classification products based on time series method. In short, the land cover data set in the core area of Qilian Mountain has high overall accuracy, and the method based on Google Earth engine platform sample training can expand the existing classification products in time and space, and can reflect more land cover type change information in a long time series.
YANG Aixia ZHONG Bo JUE Kunsheng WU Junjun
Among many indicators reflecting climate and environmental change, the stable isotope index of ice core is an indispensable parameter in the study of ice core record, and is one of the most reliable and effective ways to recover the past climate change. Ice core accumulation is a direct record of precipitation on glaciers, and high resolution ice core records ensure the continuity of precipitation records. Therefore, ice core records provide an effective means to recover precipitation changes. The isotope and accumulation of ice cores drilled from the Qinghai Tibet Plateau can be used to reconstruct the changes of temperature and precipitation, which is a good record of climate and environment. This data set provides stable isotope records of hushe ice core in Karakoram area and provides data support for the study of climate change in Qinghai Tibet Plateau.
XU Baiqing WANG Mo
The data includes the distribution data of underground ice in permafrost layer in the source area of the Yellow River. Based on the field data of 105 boreholes, such as landform and genetic type, permafrost temperature distribution, lithology composition and water content, the permafrost layer in the source area of the Yellow River is estimated to be 3.0-10.0 M The results show that the average ice content per cubic meter of soil in the source area of the Yellow River is close to the estimated value of underground ice storage in permafrost regions of the Qinghai Tibet Plateau calculated by Zhao Lin et al. The data is also of great significance for frozen soil prediction, evaluation of landscape stability in permafrost regions, and regional changes of topography, vegetation and hydrology caused by environmental changes.
SHENG Yu WANG Shengting
This dataset contains daily 0.01°×0.01° land surface soil moisture products in the Qinghai-Tibet Plateau in 2005, 2010, 2015, 2017, and 2018. The dataset was produced by utilizing the multivariate statistical regression model to downscale the “SMAP Time-Expanded 0.25°×0.25° Land Surface Soil Moisture Dataset in the Qinghai-Tibet Plateau (SMsmapTE, V1)”. The auxiliary datasets participating in the multivariate statistical regression include GLASS Albedo/LAI/FVC, 1km all-weather surface temperature data in western China by Ji Zhou, and Lat/Lon information.
CHAI Linna ZHU Zhongli LIU Shaomin
This dataset contains land surface soil moisture products with SMAP time-expanded daily 0.25°×0.25°in Qinghai-Tibet Plateau Area. The dataset was produced based on the Random Forest method by utilizing passive microwave brightness temperature along with some auxiliary datasets. The temporal resolution of the product in 1980,1985,1990,1995 and 2000 is monthly, by using SMMR, SSM/I, and SSMIS brightness temperature from 19 GHz V/H and 37 GHz V channels. The temporal resolution of the product between June 20, 2002 and Dec 30, 2018 is daily, by utilizing AMSR-E and AMSR2 brightness temperature from 6.925 GHz V/H, 10.65 GHz V/H, and 36.5 GHz V channels. The auxiliary datasets participating in the Random Forest training include the IGBP land cover type, GTOPO30 DEM, and Lat/Lon information.
CHAI Linna ZHU Zhongli LIU Shaomin
This dataset is derived from the paper: Ding, J., Wang, T., Piao, S., Smith, P., Zhang, G., Yan, Z., Ren, S., Liu, D., Wang, S., Chen, S., Dai, F., He, J., Li, Y., Liu, Y., Mao, J., Arain, A., Tian, H., Shi, X., Yang, Y., Zeng, N., & Zhao, L. (2019). The paleoclimatic footprint in the soil carbon stock of the Tibetan permafrost region. Nature Communications, 10(1), 4195. doi:10.1038/s41467-019-12214-5. This data contains R code and a new estimate of Tibetan soil carbon pool to 3 m depth, at a 0.1° spatial resolution. Previous assessments of the Tibetan soil carbon pools have relied on a collection of predictors based only on modern climate and remote sensing-based vegetation features. Here, researchers have merged modern climate and remote sensing-based methods common in previous estimates, with paleoclimate, landform and soil geochemical properties in multiple machine learning algorithms, to make a new estimate of the permafrost soil carbon pool to 3 m depth over the Tibetan Plateau, and find that the stock (38.9-34.2 Pg C) is triple that predicted by ecosystem models (11.5 ± 4.2 Pg C), which use pre-industrial climate to initialize the soil carbon pool. This study provides evidence that illustrates, for the first time, the bias caused by the lack of paleoclimate information in ecosystem models. The data contains the following fields: Longitude (°E) Latitude (°N) SOCD (0-30cm) (kg C m-2) SOCD (0-300cm) (kg C m-2) GridArea (k㎡) 3mCstcok (10^6 kg C)
DING Jinzhi WANG Tao
The marine- and terrestrial-facies sediments from the southern piedmont of the Himalayan margin recorded the tectonic deformation and environmental evolution of the front edge of continental collision. To better understand the deformation mechanism of the southern Himalayan margin and constrain the continental collision age, we selected the three well exposed outcrop profiles from late Cretaceous to middle Eocene strata in the western Nepal and carried on rock magnetism. All the samples for the Palpa section with depth of 120 m had been performed on mass-specific magnetic susceptibility (χlf), anhysteretic remanent magnetization (ARM), and saturation isothermal remanent magnetization (SIRM). Meanwhile, the isothermal remanent magnetization (IRM) and the hysteresis loops was acquired from the fine sediments, and several important magnetic parameters were determined, including the saturation magnetization (Ms) and saturation remanent magnetization (Mrs).
The development of the southern piedmont of the Himalayan margin and its depositional setting have changed since the tectonic uplift of the Himalaya due to the continental collision of India with Asia, in which the marine- and terrestrial-facies sediments recorded the tectonic deformation and environmental evolution of the front edge of continental collision. To better understand the deformation mechanism of the southern Himalayan margin and constrain the continental collision age, we selected the well an exposed outcrop profile from late Cretaceous to middle Eocene strata in the western Nepal and carried on detailed paleomagnetic studies. At present, all the samples for the Butwal section with depth of 315 m had been performed on the stepwise alternating field demagnetization (AFD) with high-resolution declination and inclination.
This dataset is collected from the paper: Chen, J.*#, Huang, Y.*#, Brachi, B.*#, Yun, Q.*#, Zhang, W., Lu, W., Li, H., Li, W., Sun, X., Wang, G., He, J., Zhou, Z., Chen, K., Ji, Y., Shi, M., Sun, W., Yang, Y.*, Zhang, R.#, Abbott, R. J.*, & Sun, H.* (2019). Genome-wide analysis of Cushion willow provides insights into alpine plant divergence in a biodiversity hotspot. Nature Communications, 10(1), 5230. doi:10.1038/s41467-019-13128-y. This data contains the genome assembly of alpine species Salix brachista on the Tibetan Plateau, it contains DNA, RNA, Protein files in Fasta format and the annotation file in gff format. Assembly Level: Draft genome in chromosome level Genome Representation: Full Genome Reference Genome: yes Assembly method: SMARTdenovo 1.0; CANU 1.3 Sequencing & coverage: PacBio 125.0; Illumina Hiseq X Ten 43.0; Oxford Nanopore Technologies 74.0 Statistics of Genome Assembly: Genome size (bp): 339,587,529 GC content: 34.15% Chromosomes sequence No.: 19 Organellas sequence No.: 2 Genome sequence No.: 30 Maximum genome sequence length (bp): 39,688,537 Minimum genome sequence length (bp): 57,080 Average genome sequence length (bp): 11,319,584 Genome sequence N50 (bp): 17,922,059 Genome sequence N90 (bp): 13,388,179 Annotation of Whole Genome Assembly: Protein：30,209 tRNA：784 rRNA：118 ncRNA：671 Please see attachments for more details of annotation. The tables in the Supplementary Information of this article can also be found in this dataset. The table list is represented in attachments. The accession no. of genome assembly is GWHAAZH00000000 (https://bigd.big.ac.cn/gwh/Assembly/663/show).
CHEN Jiahui YANG Yongping Richard John Abbott SUN Hang
1) Data content (including elements and significance): 21 stations (Southeast Tibet station, Namucuo station, Zhufeng station, mustag station, Ali station, Naqu station, Shuanghu station, Geermu station, Tianshan station, Qilianshan station, Ruoergai station (northwest courtyard), Yulong Xueshan station, Naqu station (hanhansuo), Haibei Station, Sanjiangyuan station, Shenzha station, gonggashan station, Ruoergai station（ Chengdu Institute of biology, Naqu station (Institute of Geography), Lhasa station, Qinghai Lake Station) 2018 Qinghai Tibet Plateau meteorological observation data set (temperature, precipitation, wind direction and speed, relative humidity, air pressure, radiation and evaporation) 2) Data source and processing method: field observation at Excel stations in 21 formats 3) Data quality description: daily resolution of the site 4) Data application results and prospects: Based on long-term observation data of various cold stations in the Alpine Network and overseas stations in the pan-third pole region, a series of datasets of meteorological, hydrological and ecological elements in the pan-third pole region were established; Strengthen observation and sample site and sample point verification, complete the inversion of meteorological elements, lake water quantity and quality, above-ground vegetation biomass, glacial frozen soil change and other data products; based on the Internet of Things technology, develop and establish multi-station networked meteorological, hydrological, Ecological data management platform, real-time acquisition and remote control and sharing of networked data.
ZHU Liping PENG Ping
There are two types of aerosol data in the Tibetan Plateau. Aerosol type data products are the results of aerosol type data fusion by using Meera 2 assimilation data and active satellite CALIPSO products through a series of data preprocessing, quality control, statistical analysis and comparative analysis. The key of the algorithm is to judge the CALIPSO aerosol type. According to CALIPSO aerosol types and quality control, and referring to merra 2 aerosol types, the final aerosol type data (12 kinds) and quality control results were obtained. Considering the vertical and spatial distribution of aerosols, it has high spatial resolution (0.625 ° × 0.5 °) and temporal resolution (month). Aerosol optical depth (AOD) is a visible band remote sensing inversion method developed by ourselves, combined with merra-2 model data and NASA's official product mod04. The data coverage time is from 2000 to 2019, with daily temporal resolution and spatial resolution of 0.1 degree. The retrieval method mainly uses the self-developed APRs algorithm to retrieve the aerosol optical depth over the ice and snow. The algorithm takes into account the BRDF characteristics of the ice and snow surface, and is suitable for the inversion of aerosol optical thickness on the ice and snow. The results show that the relative deviation of the data is less than 35%, which can effectively improve the coverage and accuracy of the polar AOD.
GUANG Jie ZHAO Chuanfeng
Precipitation estimates with ﬁne quality and spatio-temporal resolutions play signiﬁcant roles in understanding the global and regional cycles of water, carbon, and energy. Satellite-based precipitation products are capable of detecting spatial patterns and temporal variations of precipitation at ﬁne resolutions, which is particularly useful over poorly gauged regions. However, satellite-based precipitation products are the indirect estimates of precipitation, inherently containing regional and seasonal systematic biases and random errors. Focusing on the potential drawbacks in generating Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and its recently updated retrospective IMERG in the Tropical Rainfall Measuring Mission (TRMM) era (ﬁnished in July 2019), which were only calibrated at a monthly scale using ground observations, Global Precipitation Climatology Centre (GPCC, 1.0◦/monthly), we aim to propose a new calibration algorithm for IMERG at a daily scale and to provide a new AIMERG precipitation dataset (0.1◦/half-hourly, 2000–2015, Asia) with better quality, calibrated by Asian Precipitation – Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE, 0.25◦/daily) at the daily scale for the Asian applications. Considering the advantages from both satellite-based precipitation estimates and the ground observations, AIMERG performs better than IMERG at different spatio-temporal scales, in terms of both systematic biases and random errors, over mainland China.
1) This data includes the basic meteorological data of Kathmandu center for research and education,CAS-TU in 2019; the parameters are: temperature ℃, relative humidity%, atmospheric pressure kPa, precipitation mm, radiation w / m2, wind speed M / s. Table 2 is a description of the weather station, including the geographical location and underlying surface. 2) Data sources and processing methods: the data are from the hourly data of Kathmandu science and education center, Chinese Academy of Sciences, daily average of temperature, air pressure, radiation and wind speed, and daily sum of rainfall. 3) Data quality description: among these parameters, the quality of air pressure data is poor, and there are many missing data due to instrument failure from June to August in 2019 4) Compared with the data of different regions in South Asia, the meteorological data can be used for postgraduates and scientists with atmospheric science, hydrology, climatology, physical geography and ecology.
The land cover dataset of Pan third pole major cities contains 14 cities (Urumqi, Xining, Lanzhou, Dhaka, Kathmandu, Lucknow, Delhi, Lahore, Islamabad, Kabul, Dushanbe, Tashkent, Bishkek and Almaty) in 2000 / 2010 / 2017, the spatial resolution of this dataset is 30 m. It includes vegetation, cultivated land, artificial surface, water body and others. Based on globeland30, mcd12q1 and globcover2009, the consistent regions were identified and retained. The inconsistent regions were reclassified by deep learning method, and the final classification results were obtained by fusing the above regions. The data has been verified by visual interpretation. The data are applied to the study of construction land dynamics and anthropogenic influence in Pan-Third Pole cities. Data type: grid. Projection mode: UTM projection.
Xin LI Wenfei LUAN
The coverage time of glacier runoff data set in the five major river source areas of the Qinghai Tibet Plateau is from 1971 to 2015, and the time resolution is year by year, covering the source areas of five major rivers (Yellow River source, Yangtze River source, Lancang River source, Nu River source, Yarlung Zangbo River source). The data is based on multi-source remote sensing and measured data. The glacier runoff data is simulated by using the daily scale meteorological data of five major river source areas and their surrounding meteorological stations, the global vegetation products of umd-1km, the igbp-dis soil database, the first and second glacier catalogue data, and the distributed hydrological model vic-cas coupled with the glacier module is used to simulate the glacier runoff data. The simulation results are verified by the site measured data to enhance the quality control. Data indicators include: Glacier runoff (rate of glacier runoff:%), total runoff (mm / a), snow runoff (rate of snow runoff:%), and rainfall runoff rate (rainfall runoff rate:%).