This data set is the distribution data of permafrost and underground ice in Qilian Mountains. Based on the existing borehole data, combined with the Quaternary sedimentary type distribution data and land use data in Qilian mountain area, this paper estimates the distribution of underground ice from permafrost upper limit to 10 m depth underground. In this data set, 374 boreholes in Qilian mountain area are used, and the indication function of Quaternary sedimentary type to underground ice storage is considered, so it has certain reliability. This data has a certain scientific value for the study of permafrost and water resources in Qilian Mountains. In addition, it has a certain promotion value for the estimation of underground ice reserves in the whole Qinghai Tibet Plateau.
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
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
Daily precipitation data was reconstructed for streamflow simulation in the entire UB by combining orographic and linear correction approaches based on 262 gauge observations. The reconstructed precipitation is used to drive the VIC hydrological model linked with a temperature-index model (VIC-Glacier) , and is inversely evaluated by comparing with observed discharge, glacier area changes, and MODIS-based snow cover faction (SCF) data in the upper Brahmaputra Basin.
SUN He SU Fengge
This data includes the ground temperature data of the source area of the Yellow River The main model of Permafrost Distribution in the source area of the Yellow River is constructed based on the permafrost boreholes and the measured ground temperature data. The temperature value of the permafrost on the sunny slope terrain is adjusted separately, and the fine-tuning model under the sunny slope terrain is established. The simulation results of the boreholes participating in the model construction are compared with the measured results, and the results show that the model is involved in the construction of the model The results show that the model is feasible to simulate the spatial distribution pattern of permafrost annual average ground temperature in the source area of the Yellow River
SHENG Yu LI Jing
This data set includes the distribution products of 30 m cultivated land and construction land in Qilian mountain area from 1985 to 2019. The product comes from the land cover classification products of 30m in Qilian mountain area from 1985 to 2019. NDVI products, light data products, DEM products and SAR data of sentry 1 are used in the production of the products. The total accuracy of the product is better than 85%. Among them, the peoducts from 1985-2015 have a 5 year- time resolution, and the other products have a 1 year - time resolution.
YANG Aixia ZHONG Bo JUE Kunsheng WU Junjun
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
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 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
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) 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.
(1) This data set is the carbon flux data set of Shenzha alpine wetland from 2016 to 2019, including air temperature, soil temperature, precipitation, ecosystem productivity and other parameters. (2) The data set is based on the field measured data of vorticity, and adopts the internationally recognized standard processing method of vorticity related data. The basic process includes: outlier elimination coordinate rotation WPL correction storage item calculation precipitation synchronization data elimination threshold elimination outlier elimination U * correction missing data interpolation flux decomposition and statistics. This data set also contains the model simulation data calibrated based on the vorticity correlation data set. (3) the data set has been under data quality control, and the data missing rate is 37.3%, and the missing data has been supplemented by interpolation. (4) The data set has scientific value for understanding carbon sink function of alpine wetland, and can also be used for correction and verification of mechanism model.
The data set involved geodetic annual glacier-averagedmass balance and mass change data at the Ponkar area in Nepal on the Southern slope of the Himalayas from 1974 to 2014. It is stored in the ESRI vector polygon format and is composed of two periods, which includes surface elevation difference between 1974-2000 (DH1974-2000, from KH-9 DEM1974 and SRTM DEM2000), surface elevation difference between 2000-2014 (DH2000-2014,by DinSAR techniques from SRTM DEM2000 and TSX/TDX data in 2014). KH-9 DEM is a DEM of the study area in 1974, which was generated from three scenes of optical stereo pairs from KH-9. Geodetic glacier mass change was calculated by DH above, glacier cover vector data from TPG1976/CGI2/RGI6.0 with ice density of 850 ± 60 kg m−3. The attribute data included: GLIMSId means the glacier code from GLIMS data base, the glacier_area（m2）、Area（km2）, the glacier name, EC74_2000, the surface elevation change rate from 1974 to 2000(m a-1), EC00_2014, the surface elevation change rate from 2000 to 2014 (m a-1), MB74_2000, the geodetic glacier mass balance between 1974 and 2000（m w.e. a-1），MB00_2014, the geodetic glacier mass balance between 2000 and 2014（m w.e. a-1）.MC74_2000, the geodetic glacier mass change from 1974 to 2000 (m3w.e. a-1), MC00_2014, the geodetic glacier mass change from 2000 to 2014(m3w.e. a-1). Ut_EC74_00 is the uncertainty of glacier surface elevation change（m a-1） in 1974-2000、Ut_MB74_00, is the uncertainty of glacier mass balance for each glacier（m w.e. a-1）in 1974-2000，Ut_MC74_00, is the uncertainty of glacier mass change for each glacier（m3w.e. a-1）in 1974-2000. Ut_EC00_14，is the uncertainty of glacier surface elevation change in 2000-2014（m a-1），Ut_MB00_14，is the uncertainty of glacier mass balance for each glacier in 2000-2014（m w.e. a-1），Ut_MC00_14 is the uncertainty of glacier mass change for each glacier in 2000-2014（m3 w.e. a-1）. This data set is used for the study glaciers melting and its hydrological effects in Ponkar area in Nepal in the Southern slope of the Himalayas. It also could be used in studies of climatic change and disasters research in the Himalayas.
The data set involved geodetic annual glacier-averaged mass balance and mass change data atMt.Xixiabangma areasin the Himalayas from 1974 to 2017. It is stored in the ESRI vector polygon format and is composed of two periods, which includes surface elevation difference between 1974-2000 (DH1974-2000, from KH-9 DEM1974 and SRTM DEM2000), surface elevation difference between 2000-2017(DH2000-2017, by DinSAR techniquesfrom SRTM DEM2000 and TSX/TDX data in 2017). KH-9 DEM is a DEM of the study area in 1974, which was generated from three scenes of optical stereo pairs from KH-9. Geodetic glacier mass change was calculated by DH above, glacier cover vector data from TPG1976/CGI2/RGI6.0 with ice density of 850 ± 60 kg m−3. The attribute data included: GLIMSId means the glacier code from GLIMS data base, Area（km2）is the glacier area by km2, area_m2 is glacier area by (m2）, the glacier name, EC74_2000, the surface elevation change rate from 1974 to 2000(m a-1), EC00_2017, the surface elevation change rate from 2000 to 2017 (m a-1), MB74_2000, the geodetic glacier mass balance between 1974 and 2000（m w.e. a-1），MB00_2017, the geodetic glacier mass balance between 2000 and 2017（m w.e. a-1）.MC74_2000, the geodetic glacier mass change from 1974 to 2000 (m3w.e. a-1), MC00_2017, the geodetic glacier mass change from 2000 to 2017(m3 w.e. a-1). Ut_EC74_00 is the uncertainty of glacier surface elevation change（m a-1） in 1974-2000、Ut_MB74_00, is the uncertainty of glacier mass balance for each glacier（m w.e. a-1）in 1974-2000，Ut_MC74_00, is the uncertainty of glacier mass change for each glacier（m3w.e. a-1）in 1974-2000. Ut_EC00_17，is the uncertainty of glacier surface elevation change in 2000-2017（m a-1），Ut_MB00_17，is the uncertainty of glacier mass balance for each glacier in 2000-2017（m w.e. a-1），Ut_MC00_17 is the uncertainty of glacier mass change for each glacier in 2000-2017（m3 w.e. a-1）.This data set is used for the study glaciers melting and its hydrological effects in the Central Himalayas.It also could be used in studies of climatic change and disasters research in the Himalayas.
The data involved geodetic glacier mass change of 71pieces of glaciers during 2000-2014 in the east of the Yigongzangbu, Southeast Tibetan Plateau. It is stored in the ESRI vector polygon format.Glacier-averaged mass balance (m w.e.a-1) was calculated by the surface elevation difference between 2000-2014 ( Dh2000-2014)、glacier coveraged vector data (CGI2/TPG1976/RGI6.0) and ice density of 850 ± 60 kg m−3. Dh2000-2014 is obtained from surface elevation change by D-InSAR technique from a pair of TSX / TDx SAR images on February 7, 2014 and SRTM DEM. CGI2/TPG1976/RGI6.0 were used to extract glacier boundary and GLIMS-ID. SRTM DEM is the reference DEM and datum DEM with spatial resolution 30m. The attribute data includes GLIMS-ID, Area,EC_m_a-1,,MB_m w.e.a-1, MC_m3 w.e.a-1, MC_Gt.a-1, Uncerty_EC, Uncerty_MB, UT_MCm3w.e. a-1. Respectively, EC_m_a-1,,is the glacier-averaged annual elevation change during 2000-2014(m a-1),MB_m w.e.a-1, is glacier-averaged annual mass balance during 2000-2014(m w.e.a-1), MC_m3 w.e.a-1, is glacier-averaged annual mass change during 2000-2014 (m3 w.e.a-1), MC_Gt.a-1,is glacier-averaged annual mass change during 2000-2014 (Gt.a-1)Uncerty_EC is the uncertainty of glacier surface elevation change（±m a-1）、Uncerty_MB ,is the uncertainty of glacier mass balance（±m w.e. a-1），UT_MCm3w.e. a-1, is the uncertainty of glacier mass change（±m3w.e. a-1）。The data sets could be used for glacier change, hydrological and climate change studies in the southeast of Tibetan Plateau.
The data involved two periods of geodetic glacier mass storage change of Naimona’Nyi glaciers in the western of Himalaya from 1974-2013 (unit: m w.e. a-1). It is stored in the ESRI vector polygon format. The data sets are composed of two periods of glacier surface elevation difference between 1974-2000 and 2000-2013, i.e. DHSRTM2000-DEM1974（DH2000-1974）、DHTanDEM2013-SRTM2000（DH2013-2000）. DH2000-1974 was surface elevation change between SRTM2000 and DEM1974, i.e. 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 DH2000-1974 was ±0.13 m a-1. The surface elevation difference between 2000-2013 (DH2000-2013, by DinSAR techniques from SRTM DEM2000 and TSX/TDX data on Oct.17th in 2013) The uncertainty in the ice free areas of DH2013-2000 was ±0.04 m a-1. Glacier-averaged annual mass balance change (m w.e.a-1) was averaged annually for each glacier, which was calculated by DH2000-1974/DH2013-2000, glacier coverage area and ice density of 850 ± 60 kg m−3. The attribute data includes Glacier area by Shape_Area (m2), EC74_00, EC00_13, i.e. Glacier-averaged surface elevation change in 1974-2000 and 2000-2013(m a-1), MB74_00, MB00_13 i.e. Glacier-averaged annual mass balance in 1974-2000 and 2000-2013 (m w.e.a-1), and MC74_00, MC00_13, Glacier-averaged annual mass change in 1974-2000 and 2000-2013 (m3 w.e.a-1), Uncerty_MB, is the uncertainty of glacier-averaged annual mass balance（m w.e. a-1）， Uncerty_MC, is the Maximum uncertainty of glacier-averaged annual 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.
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.