Numerical test: The climate model used is the regional climate model RegCM4.1. RegCM4.1 developed by the Italian Research Center for Theoretical Physics (ICTP). In the test of regional model simulation, the horizontal resolution of the atmospheric model is 50 km and the vertical direction is 18 layers; Online coupling sand dust module. Sea surface temperature The sea surface temperature interpolated by OISST is used. The test includes two groups: the Middle Paleocene topographic test (MP,~60Ma BP, test name 60ma_regcm4.1_xxx. nc) and the Late Oligocene (LO,~25Ma BP, test name 25ma_regcM4.1_xxx. nc) The MP regional terrain modification test removed the northern part of the plateau and approximately replaced the terrain distribution of Asian land during the 60Ma period. BP regional terrain modification test only removed the terrain of Pamirs Plateau, approximately replacing the terrain distribution of Asian land during the 25Ma period. The sand and dust source areas of the two tests have not changed, and the sand and dust circulation process has been opened online. Output time: All tests were integrated for 22 years, using the average results of the last 20 years of each test. The data can be used to explain the difference of drought evolution in different regions around the plateau.
SUN Hui
Topographic data of China Pakistan Economic Corridor and Tianshan Mountains are produced by Japan Aerospace Exploration Agency (JAXA). The 30m digital elevation model (DEM) (2006-2011) of China Pakistan Economic Corridor and Tianshan Mountains describes the ground elevation information, which is an important raw data for studying and analyzing terrain, watershed and feature recognition. It is widely used in surveying and mapping, hydrology, hydrology, meteorology, geomorphology, geology, soil, engineering construction and other fields of national economy as well as humanities and natural sciences. In terms of flood control and disaster reduction, DEM is the basis for hydrological analysis, such as catchment area analysis, water network analysis, rainfall analysis, flood storage calculation, inundation analysis, etc; At the same time, DEM data can reflect local terrain features with a certain resolution. Through DEM, a large amount of surface morphology information can be extracted, which can be used to draw contour lines, elevation maps, slope maps, water system maps, three-dimensional perspective maps, three-dimensional landscape maps, and used to make orthophotos, three-dimensional terrain models, and map revision. The horizontal resolution of the data is 30m (1 arc second), and the elevation accuracy is 5m. It is one of the most accurate terrain data in the world. The data download address is https://www.eorc.jaxa.jp/ALOS/en/aw3d30/data/ 。
QIU Haijun
The dataset includes three high-resolution DSM data as well as Orthophoto Maps of Kuqionggangri Glacier, which were measured in September 2020, June 2021 and September 2021. The dataset is generated using the image data taken by Dajiang Phantom 4 RTK UAV, and the products are generated through tilt photogrammetry technology. The spatial resolution of the data reaches 0.15 m. This dataset is a supplement to the current low-resolution open-source topographic data, and can reflect the surface morphological changes of Kuoqionggangri Glacier from 2020 to 2021. The dataset helps to accurately study the melting process of Kuoqionggangri Glacier under climate change.
LIU Jintao
The riverbed surface of the main channel in Nyangqu river is composed of gravel particles with wide grain size distribution. there are abundant gravel particles on the beach and riverbed. In this investigation, the bed surface grain size distribution of the main channel and tributaries of the Nyangqu river was measured. This data set contains the information of the five sampling locations in five main channels and two locations in tributaries of the Nyangqu River Basin (Table 1) and the bed surface grain size distribution (Table 2). The sampling locations were generally selected near the cross-section with obvious riverbed. It was considered that water flow through these sections in the straight channel for a long. At the same time, because it was a dry season, the bed grain size distribution on the river beach could be considered as the movement of gravel bedload carried by the last flood season. Therefore, it was considered that the bed grain size distribution in the sampling area on the river beach in the dry season was the bedload size distribution in the flood season. The grain size distributions were measured by the automatic identification method of full particle size based on image processing (e.g., Baserain software), with high identification accuracy of sediment particles is high. It is of great value to the scientific research on the evolution of source to sink process，bedlaod transport, and flood numerical simualtion， as well as the basic research on the flash flood prevention and control.
LUO Ming, HUANG Er, YAN Xufeng, MA Xudong, WANG Lu
The dataset based on synthesized data from 1114 sites across the Tibetan permafrost region which report that paleoclimate is more important than modern climate in shaping current permafrost carbon distribution.A new estimate of modern soil carbon stock to 3m depth on Tibetan permafrost region was derived by machine learning algorithm, including factors such as climate (paleoclimate and modern climate), vegetation, soil (soil thickness and soil physical and chemical properties, etc.) and topography. This dataset shows that ecosystem models clearly underestimated the Tibetan soil carbon stock, due to the absence of paleoclimate effects in the model. Future modelling of soil carbon cycling should include paleoclimate .
DING Jinzhi
The multi-scale dataset of environment and element-at-risk for the Qinghai-Tibet Plateau includes geomorphic data, normalized vegetation index data, annual temperature and rainfall data, and disaster bearing value grade data, covering an area of 6.56 million square kilometers. The data set is mainly prepared for disaster and risk assessment. Due to the huge coverage, the geomorphic data adopts 150m spatial resolution and other data adopts 1000m spatial resolution. Geomorphology, vegetation index, temperature and rainfall data are mainly produced by processing open source data, and disaster bearing value grade data are produced by superposition calculation, comprehensively considering population data, night light index, buildings and surface cover types.
TANG Chenxiao
The Cenozoic strata developed within and around the Tibetan Plateau, contain fruitful information on the tectonic evolution, paleoenvironment and paleoclimate changes. It's very significant on revealing the history of the uplift and deformation of the Tibetan Plateau and its relevant effects on the regional and even global environment and climate. This data set contains several well developed sections, which have been identified by the systematic geological survey. Depending on the tools (e.g. GPS, geological compass) in the fieldwork, we have finished the geological measurements and descriptions of these sections as well as the relevant geological maps. It includes a 90-m loess deposit of the Duikang section in the Linxia basin, several fluvial and lacustrine deposits (such as the 1890-m Dayu section in the Lunpola basin, the 300-m Shuanghe section in the Jianchuan basin, the 252-m Caijiachong section in the Qujing basin) and a 932-m saline lacustrine deposit with gypsolyte of the Jiangcheng section in the Simao basin. This data set provides a solid geological foundation for the following researches on stratigraphic chronology, tectonic evolution, paleoenvironment and paleoclimate, and so forth.
FANG Xiaomin , FANG Xiaomin, YAN Maodu, ZHANG Weilin, ZHANG Dawen
The data was obtained from the 30-second global elevation dataset developed by the US Geological Survey (USGS) and completed in 1996. Downloaded the data from the NCAR and UCAR Joint Data Download Center (https://rda.ucar.edu/datasets/ds758.0/) and redistributed it through this data center. GTOPO30 divides the world into 33 blocks. The sampling interval is 30 arc seconds, which is 0.00833333333333333 degrees. The coordinate reference is WGS84. The DEM is the distance from the sea level in the vertical direction, ie the altitude, in m, the altitude range from -407 to 8752, the ocean depth information is not included here, the negative value is the altitude of the continental shelf; the ocean is marked as -9999, the elevation above the coastline is at least 1; the island less than 1 square kilometer is not considered. In order to facilitate the user's convenience, on the basis of the block data, splice 10 blocks in -10S-90N and 20W-180E without any resampling processing. This data file is DEM_ptpe_Gtopo30.nc
HE Yongli
The sand drift potential data sets of Central Asia in 2017 is in tif format. It covers five countries in Central Asia, including Uzbekistan, Tajikistan, Kyrgyzstan, Kazakhstan and Turkmenistan. The sand drift potential is absolutely drift potential, that is, the sum of the flux in all directions, regardless of the direction of the potential. The data was obtained by GLDAS global three-hour assimilation data extraction calculation. The temporal resolution is month, the spatial resolution is 0.25°, and the time range is 2017. This data set can be used as an important reference data for sand storm disaster assessment.
GAO Xin
The data set is the vegetation coverage in Central Asia including three temperate deserts, the Karakum, Kyzylkum and Muyunkun Deserts, and one of the world's largest arid zones. This is the MODIS-NDVI data set calculated by using the NDVI and the vegetation coverage in arid region. The space and time resolutions are 500 m and 16 days, respectively. The time is from 01, January, 2017 to 18, December, 2017. The data set uses the the Geodetic coordinate system. It can be used for the investigation of the Desert oil and gas field, and oasis cities.
GAO Xin
This data set is the global high accuracy global elevation control point dataset, including the geographic positioning, elevation, acquisition time and other information of each elevation control point. The accuracy of laser footprint elevation extracted from satellite laser altimetry data is affected by many factors, such as atmosphere, payload instrument noise, terrain fluctuation in laser footprint and so on. The dataset extracted from the altimetry observation data of ICESat satellite from 2003 to 2009 through the screening criteria constructed by the evaluation label and ranging error model, in order to provide global high accuracy elevation control points for topographic map or other scientific fields relying on good elevation information. It has been verified that the elevation accuracy of flat (slope<2°), hilly (2°≤slope<6°), and mountain (6°≤slope<25°) areas meet the accuracy requirements of 0.5m, 1.5m, and 3m respectively.
XIE Huan, LI Binbin, TONG Xionghua, TANG Hong, LIU Shijie, JIN Yanmin, WANG Chao, YE Zhen, CHEN Peng, XU Xiong, LIU Sicong, FENG Yongjiu
Ⅰ. Overview This dataset is derived from the global 30m-resolution digital elevation product dataset, which is processed using the data of the first version (v1) of ASTER GDEM. Its spatial resolution is 30m. Due to the influence of clouds, lines, pits, bulges, dams or other anomalies generated by the boundary stacking, there are local anomalies in the first version of the original data of ASTER GDEM, so the digital elevation processed by ASTER GDEM v1 Data products have data anomalies in individual areas, and users need to pay attention to them during use. In addition, this data set can complement the SRTM global 90m resolution elevation dataset. Ⅱ. Data processing description ASTER GDEM is a fully automated method to process and generate ASTER archived data of 1.5 million scenes, including 1,264,118 ASTER DEM data based on independent scenes generated through stereo correlation. After de-cloud processing, residual outliers are removed, and the average value is taken as the final pixel value of ASTER GDEM object area. After correcting the remaining abnormal data, the global ASTER GDEM data was generated by 1°× 1° sharding. Ⅲ. Data content description The dataset covers the entire upper reaches of the Yellow River, and each data file name is generated based on the latitude and longitude of the lower left (southwest) Angle of the fractal geometry center. For example, the lower-left coordinate of the ASTGTM_N40E116 file is 40 degrees north latitude and 116 degrees east longitude. ASTGTM_N40E116_dem and ASTGTM_N40E116_num correspond to digital elevation model (DEM) and quality control (QA) data, respectively. Ⅳ. Data usage description ASTER GDEM data can be calculated and visualized. It has a broad application prospect in various fields, especially in mapping, surface deformation and military fields.Specifically, it mainly includes the following aspects: In scientific research, ASTER GDEM data plays an important role in geology, geophysics, seismic research, horizontal modeling, volcano monitoring and remote sensing image registration.The three-dimensional model of the ground is built by using high-precision digital terrain elevation data, which can be embedded and superimposed with the image of the ground to observe subtle changes of the earth surface. In civil and industrial applications, ASTER GDEM data can be used for civil engineering calculation, dam site selection, land use planning, etc. In communications, digital topographic data can help businesses build better broadcast towers and determine the best location of mobile phone booths.In terms of aviation safety, ASTER GDEM digital elevation data can be used to establish the enhanced aircraft landing alarm system, which greatly improves the aircraft landing safety coefficient. In the military, ASTER GDEM data is the basic information platform of C4ISR (army automatic command system), which is indispensable in the study of battlefield regional structure, combat direction, battlefield preset, combat deployment, troop concentration in projection, protection conditions, logistics support and other aspects.
XUE Xian, DU Heqiang
Through the investigation of tourist spots, tourist routes and tourist areas at different levels, form photos and video data of tourism resources, tourism services and tourism facilities of scenic spots, scenic spots, corridors and important tourism transportation nodes, tourism villages and tourism towns, record the tourism development status, find problems in tourism development, and form corresponding ideas for the construction of world tourism destinations; The data sources are UAV, tachograph and camera, mobile phone and GPS, and are divided into different folders according to scenic spots and data categories; The data has been checked for many times to ensure its authenticity; This data can provide a traceable basis for the construction of world tourism destinations on the Qinghai Tibet Plateau.
SHI Shanshan
On October 24, 2019 and June 9, 2021, the special group conducted UAV flight operations in the debris flow gullies of Jilong gully, Tianmo gully and Guxiang gully in Bomi County, Nyingchi City, Tibet Autonomous Region, and generated the real three-dimensional model and digital surface model (DSM) of the debris flow gully in the demonstration area; In 2020, he worked in Kada village, Bomi county and generated real 3D model, digital surface model, digital orthophoto (DOM) and digital elevation model (DEM); On June 9, 2021, it was operated again in guxianggou to obtain the real 3D model and digital surface model. The spatial resolution of the above products is about 0.1M, and the main processing methods are as follows: (1) The real scene 3D modeling method based on UAV remote sensing can obtain rich texture information and generate dense 3D point clouds by using UAV photogrammetry technology. Combined with automatic real scene 3D modeling technology, the real 3D scene can be obtained. (2) The obtained three-dimensional model is optimized by cavity repair and filtering, which effectively fills the water cavity in the three-dimensional model. (3) Based on 3D modeling, DSM data of the demonstration area can be directly generated in CC. (4) Use mapmatrix to interpolate the polygon of DSM, erase the height of vegetation and other ground objects, and get DEM data. (5) The accuracy of 3D modeling is optimized by pricking operation.
HUANG Fang , PENG Shuying
The data set includes ASTER GDEM data and its Mosaic. ASTER Global DEM (ASTER GDEM) is a Global digital elevation data product jointly released by NASA and Japan's ministry of economy, trade and industry (METI) on June 29, 2009. The DEM data is based on the observation results of NASA's new earth observation satellite TERRA.It is produced by the ASTER(Advanced Space borne Thermal Emission and Reflection Radio meter) sensor, which collects 1.3 million stereo image data, covering more than 99% of the earth's land surface.The data has a horizontal accuracy of 30 m (95% confidence) and an elevation accuracy of 7-14 m (95% confidence).This data is the third global elevation data, which is significantly higher than previous SRTM3 DEM and GTOPO30 data. We from NASA's web site (http://wist.echo.nasa.gov/api) to download the data of heihe river basin, and through the data center to distribute.The data distributed by the center completely retains the original appearance of the data without any modification to the data.If users need details about ASTER GDEM preparation process, please refer to the data documents of metadata connections, or visit http://www.ersdac.or.jp/GDEM/E/3.html or directly from https://lpdaac.usgs.gov/ reading and ASTER Global DEM related documents. ASTER GDEM is divided into several data blocks of 1×1 degree in distribution, and the distribution format is zip compression format. Each compressed file includes three files. The file naming format is as follows: ASTGTM_NxxEyyy_dem.tif ASTGTM_NxxEyyy_num.tif reademe.pdf Where xx is the starting latitude and yyy is the starting longitude._dem. Tif is the dem data file, _num. Tif is the data quality file, and reademe is the data description file. In order to facilitate users to use the data, on the basis of the fractional ASTER GDEM data, we splice fractional SRTM data to prepare the ASTER GDEM Mosaic map of the black river basin, which retains all the original features of ASTER GDEM without any resamulation. This data includes two files: heihe_aster_gdem_mosaic_dem.img Heihe_Aster_GDEM_Mosaic_num. Img The data is stored in the format of Erdas image, where the file _dem.img is the dem data file and the file _num. Img is the data quality file.
National Aeronautics and Space Administration
The SRTM sensor has two bands, namely C-band and X-band. The SRTM we are using now comes from the C-band. The publicly released SRTM digital elevation products include DEM data at three different resolutions: * SRTM1 covers only the continental United States, with a spatial resolution of 1s; * SRTM3 data covers the world with a spatial resolution of 3s. This is the most widely used dataset. The elevation reference of SRTM3 is the geoid of EGM96 and the horizontal reference is WGS84. The nominal absolute elevation accuracy is ± 16m, and the absolute plane accuracy is ± 20m. * SRTM30 data also covers the world, with a resolution of 30s. There are multiple versions of SRTM data. The early SRTM data was completed by NASA's "JPL" (Jet Propulsion Laboratory) ground data processing system (GDPS). The data is called SRTM3- 1. The National Geospatial Intelligence Agency has further processed the data, and the lack of data has been significantly improved. The data is called SRTM3-2. This dataset is mainly the fourth version of SRTM terrain data obtained by CIAT (International Center for Tropical Agriculture) using a new interpolation algorithm. This method better fills the SRTM 90 data hole. The interpolation algorithm comes from Reuter et al. (2007). The data of SRTM is organized as follows: every 5 latitude and longitude grids is divided into a file, which are divided into 24 rows (-60 to 60 degrees) and 72 columns (-180 to 180 degrees). The file naming rule is srtm_XX_YY.zip, where XX indicates the number of columns (01-72), and YY indicates the number of rows (01-24). The resolution of the data is 90 m. Data use: SRTM data uses a 16-bit value to represent the elevation value (-/ + / 32767 meters), the maximum positive elevation is 9000 meters, and the negative elevation (12,000 meters below sea level). -32767 standard for empty data.
CGIAR-CSI
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.
SHANG Cheng
This dataset is the Digital Elevation Model （DEM）in the Qilian Mountain， spatial resolution 30m. This dataset is based on the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model （ASTER-GDEM）. The data set has a vertical accuracy of 20 m and a horizontal accuracy of 30 m. Through the data download, preprocessing and splicing, the 30m×30m DEM data of Qilian Mountain is generated. This data set can extract a large amount of surface morphology information, which is an important basic data for terrain analysis and feature recognition in Qilian Mountain. The data will serve the ecological environment monitoring, ecological environmental protection and treatment project implementation, hydrology and water resources analysis and evaluation in Qilian Mountain area.
QI Yuan, ZHANG Jinlong, ZHOU Shengming, WANG Hongwei
The data is the large-scale DSM data of debris flow gully area generated by using the purchased resource No. 3 satellite remote sensing image of the demonstration area and based on the stereo image pair matching method. The processing method is as follows: (1) Due to the large amount of cloud and shadow noise in the original image, this study uses IDL language to develop and form a special denoising and information completion program for satellite images in cloudy mountainous areas. (2) The emmetropia correction image is used as the left image and the front view correction image is the right image. Envi is used for DSM production. (3) Using aster-dem data with 30 m resolution, at least 4 typical ground control points are selected for geographic correction to ensure that the geographic coordinate error is in the order of 1 ". (4) Cross entropy, root mean square error and area error information entropy are used as accuracy evaluation indexes. Compared with the DSM obtained from the original data, it is verified that the imaging accuracy of the processed DSM is improved.
HUANG Fang , PENG Shuying
Based on the distribution locations of the Qinghai toad-headed lizard (Phrynocephalus vlangalii) collected by field investigation and literature investigation, combined with five climate factors from WorldClim database, the current (1960-1990) and future (2061-2080) climate data were input into the trained species distribution model to predict the current and future suitable habitats. The prediction results shows that the lizard will lose a lot of original habitats under the climate change, and the protection measures for the lizard species should focus on the eastern margin of Qinghai-Tibet Plateau, the northern and eastern parts of Qaidam Basin. The model also predicts that after the climate change, new suitable habitats will appear in areas that were not suitable for the Qinghai toad-headed lizard. However, due to the very limited diffusion ability of reptiles (the maximum annual diffusion distance recorded in the literature is less than 500m), the newly emerging suitable habitats may not be used by the Qinghai toad-headed lizard. Meanwhile, based on the physiological, life history, behavior and morphological data of three altitudinal populations of the Qinghai toad-headed lizard collected by field work, and combined with microclimate data, the physiological consequences of climate change on the Qinghai toad-headed lizard in the current suitable distribution area were predicted by using the mechanism niche model. The prediction results of the model show that, whether in the SSP245 or SSP585 climate change scenarios, the activity time of the lizard will increase in most areas (> 93%) of the current suitable distribution area, and the thermal safety threshold will decrease in all places of the current suitable distribution area. The increase of activity time of high-altitude populations is less than that of low-altitude populations, but the decrease of thermal safety threshold is greater than that of low-altitude populations. The results reveal that climate change may have a greater impact on lizard populations in high altitude areas.
ZENG Zhigao
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