This data is derived from the Supplementary Tables of the paper: Chen, F. H., Welker, F., Shen, C. C., Bailey, S. E., Bergmann, I., Davis, S., Xia, H., Wang, H., Fischer, R., Freidline, S. E., Yu, T. L., Skinner, M. M., Stelzer, S., Dong, G. R., Fu, Q. M., Dong, G. H., Wang, J., Zhang, D. J., & Hublin, J. J. (2019). A late Middle Pleistocene Denisovan mandible from the Tibetan Plateau. Nature, 569, 409-412. This research is another breakthrough made by academician Fahu Chen and his team over the years research of human activities and environmental adaptation on the Tibetan Plateau. The research team analyzed the newly discovered hominid mandible fossils in Xiahe County, Gansu Province, China, and identified it belongs to Denisovan of the Tibetan Plateau, which suggested to call Xiahe Denisovan. The team conducted a multidisciplinary analysis of the fossil, including chronology, physique morphology, molecular archaeology, living environment and human adaptation. It is the first Denisovan fossil found outside the Denisova Cave in the Altai Mountains and the earliest evidence of human activity on the Tibetan Plateau (160 kyr BP). This study provides key evidence for further study of Denisovans' physical characteristics and distribution in East Asia, it also provides evidence of a deep evolutionary history of these archaic hominins within the challenging environment of the Tibetan Plateau. This data contains 6 tables, table name and contents are as follows: t1: Distances in mm between meshes generated from CT versus photoscans (PS). t2: Measurements of the Xiahe mandible after reconstruction. t3: Comparative Dental metrics. t4: Comparative crown morphology. t5: Uniprot accession numbers for protein sequences of extant primates used in the phylogenetic analyses. t6: Specimen names and numbers.
Soil bulk density, porosity, water content, water characteristic curve, saturated hydraulic conductivity, particle analysis, infiltration rate, and sampling point location information in the upper reaches of the Heihe River Basin. 1. The data is for 2014 supplementary sampling for 2012, using the ring knife to take the original soil; 2. The soil bulk density is the dry bulk density of the soil and is measured by the drying method. The original ring-shaped soil sample collected in the field was thermostated at 105 ° C for 24 hours in an oven, and the soil dry weight was divided by the soil volume (100 cubic centimeters) , unit: g/cm 3 . 3. Soil porosity is obtained according to the relationship between soil bulk density and soil porosity; 4. Soil infiltration analysis data set, the data is the field experimental measurement data from 2013 to 2014. 5. The infiltration data is measured by “MINI DISK PORTABLE TENSION INFILTROMETER”, and the approximate saturated hydraulic conductivity under a certain negative pressure is obtained. 6. Soil particle size data was measured at the Grain Granulation Laboratory of the Key Laboratory of the Ministry of Education of Lanzhou University. The measuring instrument is a Malvern laser particle size analyzer MS2000. 7. The saturated hydraulic conductivity is measured according to the enamel hair self-made instrument of Yi Yanli (2009). The Marioot bottle was used to maintain the head during the experiment; at the same time, the Ks measured at the time was converted to the Ks value at 10 °C for analysis and calculation. 8. Soil water content data is measured using ECH2O, including 5 layers of soil water content and soil temperature. 9. The water characteristic curve is measured by the centrifuge method: the undisturbed soil of the ring cutter collected in the field is placed in a centrifuge, and each of the speeds is measured at 0, 310, 980, 1700, 2190, 2770, 3100, 5370, 6930, 8200, 11600. The secondary rotor weight is obtained.
This data set comprises the plateau soil moisture and soil temperature observational data based on the Tibetan Plateau, and it is used to quantify the uncertainty of model products of coarse-resolution satellites, soil moisture and soil temperature. The observation data of soil temperature and moisture on the Tibetan Plateau (Tibet-Obs) are from in situ reference networks at four regional scales, which are the Nagqu network of cold and semiarid climate, the Maqu network of cold and humid climate, and the Ali network of cold and arid climate，and Pali network. These networks provided representative coverage of different climates and surface hydrometeorological conditions on the Tibetan Plateau. - Temporal resolution: 1hour - Spatial resolution: point measurement - Measurement accuracy: soil moisture, 0.00001; soil temperature, 0.1 °C; data set size: soil moisture and temperature measurements at nominal depths of 5, 10, 20, 40 - Unit: soil moisture, cm ^ 3 cm ^ -3; soil temperature, °C
Bob Su YANG Kun
This data set 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 data set contains minimum winter temperature reconstruction data of Changdu on the southeastern TP during 1340-2007. See attachments for data details: A tree ring-based winter temperature reconstruction for the southeasternTibetan Plateau since 1340 CE.pdf
HUANG Ru ZHU Haifeng LIANG Eryuan
This data set is collected from the supplementary information part of the paper: Pei, S.P., Niu, F.L., Ben-Zion, Y., Sun, Q., Liu, Y.B., Xue, X.T., Su,J.R., & Shao, Z.G. (2019). Seismic velocity reduction and accelerated recovery due to earthquakes on the Longmenshan fault. Nature Geoscience. 12. 387-392. doi:10.1038/s41561-019-0347-1. This paper studies the structural evolution process of The Longmenshan fault zone located at a pronounced topographic boundary between the eastern margin of the Tibetan plateau and the western Sichuan basin. With the observations on coseismic velocity reductions and the healing phases, it is found that the healing phase of Wenchuan earthquake fracture zone accelerated significantly in response to the Lushan earthquake. This data set contains 3 tables, table names and content are as follows: Data list: The data name list of the rest tables; t1: Data of the four periods (befor Wenchuan earthquake, after Wenchuan earthquake, before Lushan earthquake, after Lushan earthquake); t2: The average velocities with error in Figure 2 in the paper for Wenchuan earthquake (WCEQ) and Lushan earthquake (LSEQ) area. See attachments for data details: Supplementary information.pdf, Seismic velocity reduction and accelerated recovery due to earthquakes on the Longmenshan fault.pdf.
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 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)
National Basic Geographic Information Center
DEM is the English abbreviation of Digital Elevation Model, which is the important original data of watershed topography and feature recognition.DEM is based on the principle that the watershed is divided into cells of m rows and n columns, the average elevation of each quadrilateral is calculated, and then the elevation is stored in a two-dimensional matrix.Since DEM data can reflect local topographic features with a certain resolution, a large amount of surface morphology information can be extracted through DEM, which includes slope, slope direction and relationship between cells of watershed grid cells, etc..At the same time, the surface flow path, river network and watershed boundary can be determined according to certain algorithm.Therefore, to extract watershed features from DEM, a good watershed structure pattern is the premise and key of the design algorithm. Elevation data map 1km data formed according to 1:250,000 contour lines and elevation points in China, including DEM, hillshade, Slope and Aspect maps. Data set projection: Two projection methods: Equal Area projection Albers Conical Equal Area (105, 25, 47) Geodetic coordinates WGS84 coordinate system
Vegetation functional type (PFT) is a combination of large plant species according to the ecosystem function and resource utilization mode of plant species. Each planting functional type shares similar plant attributes, which simplifies the diversity of plant species into the diversity of plant function and structure.The concept of vegetation-functional has been advocated by ecologists especially ecosystem modelers.The basic assumption is that globally important ecosystem dynamics can be expressed and simulated through limited vegetative functional types.At present, vegetation-functional model has been widely used in biogeographic model, biogeochemical model, land surface process model and global dynamic vegetation model. For example, the land surface process model of the national center for atmospheric research (NCAR) in the United States has changed the original land cover information into the applied vegetation-functional map (Bonan et al., 2002).Functional vegetation has been used in the dynamic global vegetation model (DGVM) to predict the changes of ecosystem structure and function under the global change scenario. 1. Functional classification system of vegetation 1 Needleleaf evergreen tree, temperate 2 Needleleaf evergreen tree, boreal 3 Needleleaf deciduous tree 4 Broadleaf evergreen tree, tropical 5 Broadleaf evergreen tree, temperate 6 Broadleaf deciduous tree, tropical 7 Broadleaf deciduous tree, temperate 8 Broadleaf deciduous tree, boreal 9 Broadleaf evergreen shrub, temperate 10 Broadleaf deciduous shrub, temperate 11 Broadleaf deciduous shrub, boreal 12 C3 grass, arctic 13 C3 grass 14 C4 grass 15 Crop 16 Permanent wetlands 17 Urban and built-up lands 18 Snow and ice 19 Barren or sparsely vegetated lands 20 Bodies of water 2. Drawing method China's 1km vegetation function map is based on the climate rules of land cover and vegetation function conversion proposed by Bonan et al. (Bonan et al., 2002).Ran et al., 2012).MICLCover land cover map is a blend of 1:100000 data of land use in China in 2000, the Chinese atlas (1:10 00000) the type of vegetation, China 1:100000 glacier map, China 1:10 00000 marshes and MODIS land cover 2001 products (MOD12Q1) released the latest land cover data, using IGBP land cover classification system.The evaluation shows that it may be the most accurate land cover map on the scale of 1km in China.Climate data is China's atmospheric driven data with spatial resolution of 0.1 and temporal resolution of 3 hours from 1981 to 2008 developed by he jie et al. (2010).The data incorporates Princeton land-surface model driven data (Sheffield et al., 2006), gewex-srb radiation data (Pinker et al., 2003), TRMM 3B42 and APHRODITE precipitation data, and observations from 740 meteorological stations and stations under the China meteorological administration.According to the evaluation results of RanYouhua et al. (2010), GLC2000 has a relatively high accuracy in the current global land cover data set, and there is no mixed forest in its classification system. Therefore, the mixed forest in the MICLCover land cover diagram USES GLC2000 (Bartholome and Belward, 2005).The information in xu wenting et al., 2005) was replaced.The data can be used in land surface process model and other related researches.
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.
ZHENG Zhaojun CHU Duo
The dataset includes soil physical and chemical attributes: pH value, organic matter fraction, cation exchange capacity, root abundance, total nitrogen (N), total phosphorus (P), total potassium (K), alkali-hydrolysable N, available P, available K, exchangeable H+, Al3+, Ca2+, Mg2+, K+ , Na+, horizon thickness, soil profile depth, sand, silt and clay fractions, rock fragment, bulk density, porosity, structure, consistency and soil color. Quality control information (QC) was provided. The resolution is 30 arc-seconds (about 1 km at the equator). The vertical variation of soil property was captured by eight layers to the depth of 2.3 m (i.e. 0- 0.045, 0.045- 0.091, 0.091- 0.166, 0.166- 0.289, 0.289- 0.493, 0.493- 0.829, 0.829- 1.383 and 1.383- 2.296 m) for convenience of use in the Common Land Model and the Community Land Model (CLM). 1.THSCH.nc: Saturated water content of FCH 2.PSI_S.nc: Saturated capillary potential of FCH 3.LAMBDA.nc: Pore size distribution index of FCH 4.K_SCH.nc: Saturate hydraulic conductivity of FCH 5.THR.nc: Residual moisture content of FGM 6.THSGM.nc: Saturated water content of FGM 7.ALPHA.nc: The inverse of the air-entry value of FGM 8.N.nc: The shape parameter of FGM 9.L.nc: The pore-connectivity parameter of FGM 10.K_SVG.nc: Saturated hydraulic conductivity of FGM 11.TH33.nc: Water content at -33 kPa of suction pressure, or field capacity 12.TH1500.nc: Water content at -1500 kPa of suction pressure, or permanent wilting point
DAI Yongjiu SHANGGUAN Wei
A multi-layer soil particle-size distribution dataset (sand, silt and clay content), based on USDA (United States Department of Agriculture) standard for regional land and climate modelling in China. was developed The 1:1,000,000 scale soil map of China and 8595 soil profiles from the Second National Soil Survey served as the starting point for this work. We reclassified the inconsistent soil profiles into the proper soil type of the map as much as possible because the soil classification names of the map units and profiles were not quite the same. The sand, silt and clay maps were derived using the polygon linkage method, which linked soil profiles and map polygons considering the distance between them, the sample sizes of the profiles, and soil classification information. For comparison, a soil type linkage was also generated by linking the map units and soil profiles with the same soil type. The quality of the derived soil fractions was reliable. Overall, the map polygon linkage offered better results than the soil type linkage or the Harmonized World Soil Database. The dataset, with a 1-km resolution, can be applied to land and climate modelling at a regional scale. Data characteristics： projection：projection Coverage: China Resolution: 0.00833 (about 1 km) Data format: FLT, TIFF Value range: 0%-100% Document describing： Floating point raster files include: Sand1. FLT, clay1. FLT -- surface (0-30cm) sand, clay content. Sand2. FLT, clay2. FLT -- content of sand and clay in the bottom layer (30-100cm). PSD. HDR -- header file: Ncols - the number of columns Nrows- rows Xllcorner - latitude in the lower left corner Yllcorner - longitude of the lower left corner Cellsize - cellsize NODATA_value - a null value byteorder - LSBFIRST, Least Significant Bit First TIFF raster files include: Sand1. Tif, clay1. Tif - surface (0-30cm) sand, clay content. Sand2. Tif, clay2. Tif - bottom layer (30-100cm) sand, clay content.
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.
ZHANG Yili REN Huixia PAN Xiaoduo
This data set is based on the evaluation of existing land cover data and the evidence theory，including a 1:100,000 land use map for the year 20 2000、a 1:1,000,000 vegetation map、a 1:1,000,000 swamp-wetland map, a glacier map and a Moderate-Resolution Imaging Spectroradiometer land cover map for China in 2001 (MODIS2001) were merged，Finally, the decision is made based on the principle of maximum trust, and a new 1KM land cover data of China in 2000 with IGBP classification system is produced. The new land cover data not only maintain the overall accuracy of China's land use data, but also supplement the information of vegetation types and vegetation seasons in China's vegetation map, update China's wetland map, add the latest information of China's glacier map, and make the classification system more general.
The Tibetan Plateau (TP) has the largest areas of permafrost terrain in the mid- and low-latitude regions of the world. Some permafrost distribution maps have been compiled but, due to limited data sources, ambiguous criteria, inadequate validation, and deficiency of high-quality spatial data sets, there is high uncertainty in the mapping of the permafrost distribution on the TP. We generated a new permafrost map based on freezing and thawing indices from modified Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperatures (LSTs)、The temperature at the top of permafrost (TTOP) model was applied to simulate the permafrost distribution ， validated this map using various ground-based data sets. The properties of frozen soil include: Seasonally frozen ground、Permafrost、Unfrozen ground. The results provide more detailed information on the permafrost distribution and basic data for use in future research on the Tibetan Plateau permafrost.
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)).
FENG Min CHE Xianghong
The remote sensing monitoring database of land use status in China is a multi-temporal land use status database covering the land area of China, which has been established after many years of accumulation under the support of the National Science and Technology Support Plan and the Key Direction Project of the Knowledge Innovation Project of the Chinese Academy of Sciences. It is the most accurate remote sensing monitoring data product of land use in China at present, which has played an important role in the national land resources survey, hydrology and ecological research. This data set covers the six western provinces in China: Xinjiang, Tibet, Qinghai, Yunnan, Sichuan and Gansu. Based on Landsat TM/ETM remote sensing images in the late 1970s、1980s、1995、2000、2005、2010、2015， 1KM raster data are generated by using the professional software and manual visual interpretation on the basis of vector data. The land use types include six primary land types which are cultivated land, forest land, grassland, water area, residential land and unused land, and 25 secondary types.
The data set was produced based on the SRTM DEM data collected by Space Shuttle Radar terrain mission in 2016, the reference data such as river, lake and other water system auxiliary data , using the arcgis hydrological model to analyze and extract the river network. There are 12 sub-basins over the Tibet Plateau, including AmuDayra、Brahmaputra、Ganges、Hexi、Indus、Inner、Mekong、Qaidam、Salween、Tarim、Yangtze、Yellow. The outer boundary is based on the 2500-metre contour line and national boundaries.
Gridded climatic datasets with fine spatial resolution can potentially be used to depict the climatic characteristics across the complex topography of China. In this study we collected records of monthly temperature at 1153 stations and precipitation at 1202 stations in China and neighboring countries to construct a monthly climate dataset in China with a 0.025° resolution (~2.5 km). The dataset, named LZU0025, was designed by Lanzhou University and used a partial thin plate smoothing method embedded in the ANUSPLIN software. The accuracy of LZU0025 was evaluated based on three aspects: (1) Diagnostic statistics from the surface fitting model during 1951–2011. The results indicate a low mean square root of generalized cross validation (RTGCV) for the monthly air temperature surface (1.06 °C) and monthly precipitation surface (1.97 mm1/2). (2) Error statistics of comparisons between interpolated monthly LZU0025 with the withholding of climatic data from 265 stations during 1951–2011. The results show that the predicted values closely tracked the real true values with values of mean absolute error (MAE) of 0.59 °C and 70.5 mm, and standard deviation of the mean error (STD) of 1.27 °C and 122.6 mm. In addition, the monthly STDs exhibited a consistent pattern of variation with RTGCV. (3) Comparison with other datasets. This was done in two ways. The first was via comparison of standard deviation, mean and time trend derived from all datasets to a reference dataset released by the China Meteorological Administration (CMA), using Taylor diagrams. The second was to compare LZU0025 with the station dataset in the Tibetan Plateau. Taylor diagrams show that the standard deviation, mean and time trend derived from LZU had a higher correlation with that produced by the CMA, and the centered normalized root-mean-square difference for this index derived from LZU and CMA was lower. LZU0025 had high correlation with the Coordinated Energy and Water Cycle Observation Project (CEOP) - Asian Monsoon Project, (CAMP) Tibet surface meteorology station dataset for air temperature, despite a non-significant correlation for precipitation at a few stations. Based on this comprehensive analysis, we conclude that LZU0025 is a reliable dataset. LZU0025, which has a fine resolution, can be used to identify a greater number of climate types, such as tundra and subpolar continental, along the Himalayan Mountain. We anticipate that LZU0025 can be used for the monitoring of regional climate change and precision agriculture modulation under global climate change.
HUANG Wei ZHAO Hong
"1)Data content (including elements and meanings): hydrological data set of 7 rivers of HORN in Pan-Third pole 2)Data source and processing method: 7 rivers of HORN, field observation Excel format 3)Data quality description: site day resolution 4)Data application results and prospects: Based on the long-term observation data of the 17 stations of HORN, establish a series of data series of meteorological, hydrological and ecological elements in the Pan-Earth region; Strengthen observation and sample and sample verification, and complete the inversion of meteorological elements, lake water quantity and water quality, aboveground vegetation biomass, glacier and frozen soil changes; based on Internet of Things technology, develop multi-station networked meteorological, hydrological, The ecological data management platform realizes real-time acquisition and remote control and sharing of networked data.