This data set is collected from the supplementary information part of the paper: Yao, T. , Thompson, L. , & Yang, W. . (2012). Different glacier status with atmospheric circulations in tibetan plateau and surroundings. Nature Climate Change, 1580, 1-5. This paper report on the glacier status over the past 30 years by investigating the glacial retreat of 82 glaciers, area reductionof 7,090 glaciers and mass-balance change of 15 glaciers. This data set contains 8 tables, the names and content are as follows: Data list: The data name list of the rest tables; t1: Distribution of Glaciers in the TP and surroundings; t2: Data and method for analyzing glacial area reduction in each basin; t3: Glacial area reduction during the past three decades from remote sensing images in the TP and surroundings; t4: Glacial length fluctuationin the TP and surroundings in the past three decades; t5: Detailed information on the glaciers for recent mass balance measurement in the TP and surroundings; t6: Recent annual mass balances in different regions in the TP; t7: Mass balance of Long-time series for the Qiyi, Xiaodongkemadi and Kangwure Glaciers in the TP. See attachments for data details: Supplementary information.pdf, Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings.pdf.
This dataset is provided by the author of the paper: Huang, R., Zhu, H.F., Liang, E.Y., Liu, B., Shi, J.F., Zhang, R.B., Yuan, Y.J., & Grießinger, J. (2019). A tree ring-based winter temperature reconstruction for the southeastern Tibetan Plateau since 1340 CE. Climate Dynamics, 53(5-6), 3221-3233. In this paper, in order to understand the past few hundred years of winter temperature change history and its driving factors, the researcher of Key Laboratory of Alpine Ecology, Institute of Tibetan Plateau Research, Chinese Academy of Sciences and CAS Center for Excellence in Tibetan Plateau Earth Sciences. Prof. Eryuan Liang and his research team, reconstructed the minimum winter (November – February) temperature since 1340 A.D. on southeastern Tibetan Plateau based on the tree-ring samples taken from 2007-2016. The dataset contains minimum winter temperature reconstruction data of Changdu on the southeastern TP during 1340-2007. The data contains fileds as follows: year Tmin.recon (℃) See attachments for data details: A tree ring-based winter temperature reconstruction for the southeasternTibetan Plateau since 1340 CE.pdf
This dataset is collected from the Supplementary Materials part of the paper "Chen, F.H., Dong, G.H., Zhang, D.J., Liu, X.Y., Jia, X., An, C.B., Ma, M.M., Xie, Y.W., Barton, L., Ren, X.Y., Zhao, Z.J., & Wu, X.H. (2015). Agriculture facilitated permanent human occupation of the Tibetan Plateau after 3600 BP. Science, 347, 248–250.". In this paper, researchers analyzed animal bones, plant remains and other artefacts from 53 sites across the northeastern Tibetan plateau and found that humans began to relocate to the elevations above 4000 masl after the emergence of Barley. According to the study, the prehistoric human expansion into the higher, colder altitudes of the Tibetan plateau took place as the continental temperatures had themselves become colder after 3,600 calendar years before the present, thus, the key impetus of the expansion was agricultural innovation rather than climate change. This dataset contains 4 tables, table names and content are as follows: Data list: The data name list of the rest tables; t1: Calibrated radiocarbon dates and domesticated plant and animal remains from sites investigated on the NETP; t2: Radiocarbon dates of the Paleolithic sites on the Tibetan Plateau; t3: OSL dates of the Paleolithic sites on the Tibetan Plateau. See attachments for data details: Supplementary Materials.pdf, Agriculture Facilitated Permanent Human Occupation of the Tibetan Plateau after 3,600 BP.pdf.
The source of the data is paper: Zhang, J.F., Xu, B.Q., Turner, F., Zhou, L.P., Gao, P., Lü, X.M., & Nesje, A. (2017). Long-term glacier melt fluctuations over the past 2500 yr in monsoonal high asia revealed by radiocarbon-dated lacustrine pollen concentrates. Geology, 45(4), 359-362. In this paper, the researcher of Institute of Tibetan Plateau Research, Chinese Academy of Sciences and CAS Center for Excellence in Tibetan Plateau Earth Sciences, Baiqing Xu, with his postdoctoral fellow, Jifeng Zhang, and collaborators from Peking University and other institutions, propose that the OPE (“old pollen effect”, the offset between the calibrated 14C ages of pollen in lake sediments and the sediment depositional age) as a new indicator of glacier melt intensity and fluctuations by measuring the radiocarbon ages of the sediments of the proglacial lake of Qiangyong Glacier on the southern Tibetan Plateau with multi-methods (bulk organic matter, pollen concentrates and plant residues). This research suggests that hemispheric-scale temperature variations and mid-latitude Westerlies may be the main controllers of the late Holocene glacier variability in monsoonal High Asia. It also shows that the 20th-century glacier melt intensity exceeded that of two historical warm epochs (the Medieval Warm Period, and the Iron/Roman Age Optimum) and is unprecedented at least for the past 2.5 k.y. This data is provided by the author of the paper, it contains long-term glacier melt fluctuations of Qiangyong Glacier over the past 2500 yr reconstructed by the OPE. A 3.06-m-long core (QYL09-4) and a 1.06-m-long parallel gravity core (QY-3) were retrieved by the researchers from the depositional center of Qiangyong Co. Using a new composite extraction procedure, they obtained relatively pure pollen concentrates and plant residue concentrates (PRC; >125 μm) from the finely laminated sediments. Bulk organic matter and the PRC and pollen fractions were used for 14C dating independently. All 14C ages were calibrated with IntCal13 (Reimer et al., 2013). The age-depth model is based on 210Pb and 137Cs ages and five 14C ages of PRC. Only the youngest PRC ages were used for the age-depth model, whereas older ages that produce a stratigraphic reversal and are apparently influenced by redeposited or aquatic plant material were rejected. The deposition model was constructed using the P_Sequence algorithm in Oxcal 4.2 (Bronk Ramsey, 2008). For the calculation of the offset between the calibrated pollen 14C ages and the sediment depositional age, 2σ intervals for interpolated ages according to the deposition model were subtracted from calibrated pollen ages (2σ span), resulting in the age offset between pollen and estimated sediment ages (ΔAgepollen). This data is radiocarbon ages and the calculated ΔAgepollen of core QYL09-4 from a proglacial lake of Qiangyong Glacier. The data contains fields as follows: Lab No. Dating Material Depth (cm) 14C age (yr BP) ∆Agepollen (≥95.4 % yrs) Sediment Age (CE) See attachments for data details: ZhangJF et al. 2017 GEOLOGY_Long-term glacier melt fluctuations over the past 2500 yr on the Tibetan Plateau.pdf.
Qinghai Tibet Plateau is the largest permafrost area in the world. At present, some permafrost distribution maps have been compiled. However, due to the limited data sources, unclear standards, insufficient verification and lack of high-quality spatial data sets, there is great uncertainty in drawing Permafrost Distribution Maps on TP. Based on the improved medium resolution imaging spectrometer (MODIS) surface temperature (LSTS) model of 1 km clear sky mod11a2 (Terra MODIS) and myd11a2 (Aqua MODIS) product (reprocessing version 5) in 2003-2012, the data set simulates the distribution of permafrost and generates the permafrost map of Qinghai Tibet Plateau. The map was verified by field observation, soil moisture content and bulk density. Permafrost attributes mainly include: seasonally frozen ground, permafrost and unfrozen ground. The data set provides more detailed data of Permafrost Distribution and basic data for the study of permafrost in the Qinghai Tibet Plateau.
Snow cover dataset is produced by snow and cloud identification method based on optical instrument observation data, covering the time from 1989 to 2018 (two periods, from January to April and from October to December) and the region of Qinghai-Tibet Plateau (17°N-41°N, 65°E-106°E) with daily product, which takes equal latitude and longitude projection with 0.01°×0.01° spatial resolution, and characterizes whether the ground under clear sky or transparent thin cloud is covered by snow. The input data sources include AVHRR L1 data of NOAA and MetOp serials of satellites, and L1 data corresponding to AVHRR channels taken from TERRA/MODIS. Decision Tree algorithm (DT) with dynamic thresholds is employed independent of cloud mask and its cloud detection emphasizes on reserving snow, particularly under transparency cirrus. It considers a variety of methods for different situations, such as ice-cloud over the water-cloud, snow in forest and sand, thin snow or melting snow, etc. Besides those, setting dynamic threshold based on land-surface type, DEM and season variation, deleting false snow in low latitude forest covered by heavy aerosol or soot, referring to maximum monthly snowlines and minimum snow surface brightness temperature, and optimizing discrimination program, these techniques all contribute to DT. DT discriminates most snow and cloud under normal circumstances, but underestimates snow on the Qinghai-Tibet Plateau in October. Daily product achieves about 95% average coincidence rate of snow and non-snow identification compared to ground-based snow depth observation in years. The dataset is stored in the standard HDF4 files each having two SDSs of snow cover and quality code with the dimensions of 4100-column and 2400-line. Complete attribute descriptions is written in them.
The integration dataset of Tibetan Plateau boundary includes: TPBoundary_2500m：Based on ETOPO5 Global Surface Relief, ENVI+IDL is used to extract the longitude of the Tibetan Plateau (65~105) and the altitude of 2500 meters above the latitude (20~45); TPBoundary_3000m：Based on ETOPO5 Global Surface Relief, ENVI+IDL is used to extract the longitude of the Tibetan Plateau (65~105) and the altitude of 3000 meters above the latitude (20~45); TPBoundary_HF (High Frequency):Li Bingyuan (1987) has conducted a systematic discussion on the principle and specific boundary of determining the scope of the Qinghai-Tibet Plateau. From the perspective of the formation and basic characteristics of plateau geomorphology, Based on the geomorphological features, the plateau surface and its altitude, and considering the integrity of the mountain as the basic principle for determining the plateau range.Zhang Yili (2002) according to the results of new research in related fields and years of field practice, demonstration principles to determine the scope and boundaries of the Tibetan Plateau, Based on the information technology method, the location and boundary position of the Qinghai-Tibet Plateau are accurately located and quantitatively analyzed. It is concluded that the Qinghai-Tibet Plateau is partly in the Pamir Plateau in the west, the Hengduan Mountains in the east, the southern margin of the Himalayas in the south, and the Kunlun Mountains in the north. Mountain - north side of Qilian Mountain. On April 14, 2017, the Ministry of Civil Affairs of the People's Republic of China issued the "Announcement on Supplementing the Public Use of Place Names in the Southern Region of Tibet (First Batch)", adding Wujianling, Mirage, Qu Dengbu, and Mechuca 6 places in southern Tibet such as Baimingla Mountain Pass and Namkam;. TPBoundary_rectangle：According to the range Lon (63~105E) & Lat (20~45N), The data is projected using latitude and longitude WGS84.. Project source: national natural science foundation of China (41571068,41301063) Spatial range and projection mode of data: elevation greater than 2500m, WGS84 projection As the basic data, the boundary of qinghai-tibet plateau can be used as a reference for all kinds of geoscientific research on Qinghai-Tibet Plateau.
The distribution of lakes in space and its change over time are closely related to agricultural, environmental and ecological issues, and are critical factors for human socio-economic development. In the past decades, satellite based remote sensing has been developed rapidly to provide essential data sources for monitoring temporal lakes dynamics with its advantage of rapidness, wide coverage, and lower cost. This dataset was produced from Landsat images using the automated water detection method (Feng et al, 2015). We collected 96,278 Landsat images (about 25 terabytes) that acquired since 2000 with less than 80% cloud contamination in the arid region of central Asia and Tibetan Plateau. Water is detected in each of the image and then aggregated to monthly temporal resolution by taking advantage of the high-performance processing capability and large data storage provided by Global Land Cover Facility (GLCF) at University of Maryland. The results are validated systematically and quantitatively using manually interpreted dataset, which consists of a set of locations collected by a stratified random sampling strategy to effectively represent different spatial-temporal distributions in the region. The validation suggests high accuracy of the results (overall accuracy: 99.45(±0.59); user accuracy: 85.37%±(3.74); produce accuracy: 98.17(±1.05)).
This data is originated from the 1:100,000 national basic geographic database, which was open freely for public by the National Basic Geographic Information Center in November 2017. The boundary of the Qinghai-Tibet Plateau was spliced and clipped as a whole, so as to facilitate the study on the Qinghai-Tibet plateau. This data set is the 1:100,000 administrative boundaries of the qinghai-tibet plateau, including National_Tibet_line、 Province_Tibet、City_Tibet、County_Tibet_poly and County_Tibet_line. Administrative boundary layer (County_Tibet_poly) property name and definition: Item Properties Describe Example PAC Administrative division code 513230 NAME The name of the County line name Administrative boundary layer (BOUL) attribute name and definition: Item Properties Describe Example GB classification code 630200 Administrative boundary layer (County_Tibet_line) attribute item meaning: Item Properties Describe Example GB 630200 Provincial boundary GB 640200 Prefectural, municipal and state administrative boundaries GB 650201 county administrative boundaries (determined)
This dataset is blended by two other sets of data, snow cover dataset based on optical instrument remote sensing with 1km spatial resolution on the Qinghai-Tibet Plateau (1989-2018) produced by National Satellite Meteorological Center, and near-real-time SSM/I-SSMIS 25km EASE-grid daily global ice concentration and snow extent (NISE, 1995-2018) provided by National Snow and Ice Data Center (NSIDC, U.S.A). It covers the time from 1995 to 2018 (two periods, from January to April and from October to December) and the region of Qinghai-Tibet Plateau (17°N-41°N, 65°E-106°E) with daily product, which takes equal latitude and longitude projection with 0.01°×0.01° spatial resolution, and characterizes whether the ground is covered by snow. The input data sources include daily snow cover products generated by NOAA/AVHRR, MetOp/AVHRR, and alternative to AVHRR taken from TERRA/MODIS corresponding observation, and snow extent information of NISE derived from observation by SSM/I or SSMIS of DMSP satellites. The processing method of data collection is as following: first, taking 1km snow cover product from optical instruments as initial value, and fully trusting its snow and clear sky without snow information; then, under the aid of sea-land template with relatively high resolution, replacing the pixels or grids where is cloud coverage, no decision, or lack of satellite observation, by NISE's effective terrestrial identification results. For some water and land boundaries, there still may be a small amount of cloud coverage or no observation data area that can’t be replaced due to the low spatial resolution of NISE product. Blended daily snow cover product achieves about 91% average coincidence rate of snow and non-snow identification compared to ground-based snow depth observation in years. The dataset is stored in the standard HDF4 files each having two SDSs of snow cover and quality code with the dimensions of 4100-column and 2400-line. Complete attribute descriptions is written in them.