This dataset includes boundary and topographic data of Southeastern Tibetan Plateau (SETP): 1. SETP_ Boundary: we centered on the traditional SETP region (i.e., the Parlung Tsangpo River basin or Bomi County) and used the surrounding river network (e.g., the Yarlung Zangbo-Brahmaputra River, Nujiang-Salween River, and their tributaries) to delineate the boundary of the SETP. This region covers the Eastern Nyainqentanglha Ranges, Eastern Himalayas, and Western Hengduan Mountains and hosts the largest maritime glacier concentration across China. 2. Topographic data: Based on NASADEM provided by NASA Earthdata, we mosaicked the DEM, slope, aspect, profile curvature (profc) and water Mask (SWB) of SETP. 3. Hillshade: We produced the hillshde with a altitude angle of 45° from the NASADEM of SETP.
ZHAO Fanyu, LONG Di, LI Xingdong, HUANG Qi, HAN Pengfei
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
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
Based on China's daily meteorological elements data set and National Geographic basic data, the extreme precipitation, extreme temperature, drought intensity, drought frequency and other indicators in Hengduan Mountain area were calculated by using rclimdex, nspei and bilinear interpolation methods. The data set includes basic data set of disaster pregnant environment, basic data set of extreme precipitation index, basic data set of extreme temperature index, basic data set of drought intensity and frequency. The data set can provide a basic index system for regional extreme high temperature, precipitation and drought risk assessment.
SUN Peng
The data set analyzes the spatial and temporal distribution, impact and loss of typical global flood disasters from 2018 to 2019. In 2018, there were 109 flood disasters in the world, with a death toll of 1995. The total number of people affected was 12.62 million. The direct economic loss was about 4.5 billion US dollars, which was at a low level in the past 30 years. The number of global flood incidents in 2018 was higher in the first half of the year than in the second half of the year, and the frequency of occurrence was higher from May to July. Therefore, based on three typical disaster events such as the hurricane flood in Florence in the United States in 2018, the flooding of the Niger River in Nigeria in 2018, and the Shouguang flood in Shandong Province in 2018, the disaster background, hazard factors, and disaster situation were analyzed. .
JIANG Zijie, JIANG Weiguo, WU Jianjun, ZHOU Hongmin
Basic Geographic Data Set of Resources and Environment in Central and Western Asia Region, includes six parts: administrative divisions map, topographic and geomorphological map, river system maps, precipitation map, temperature map and potential evapotranspiration map. The precipitation and temperature datasets are interpolated based on the ground observations, while the potential evapotranspiration dataset is calculated based on the Penman-Monteith equation. The precipitation, temperature and potential evapotranspiration datasets are resampled from the original 0.5° CRU dataset by using the linear interpolation method in ArcGIS software. This dataset is made based a large number of gauge observations with good quality control and homogeneity check. The results of the related studies (Deng and Chen, 2017; Li et al., 2017; Li et al., 2016) suggested that this dataset is applicable and satisfactory for the climatological studies. The data produced by the key laboratory of remote sensing and GIS, Xinjiang institute of ecology and geography, Chinese Academy of Sciences. Data production Supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDA20030101.
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
Slope data of economic corridors in Silk Road can reflect the degree of steepness of the surface units of the six major economic corridors, the unit is degree (°). The spatial resolution of the data is 0.016 degrees, which is about 1.8km. The longitude range is 12.09°E-180°, and the latitude range is 10.99°S-90°N. The source is derived from the Global Relief Model built by the National Oceanic and Atmospheric Administration of the United States (NOAA). The range is cut by the border of the Silk Road. This data is one of the basic data necessary to assess the risks of natural disasters (including debris flows, landslides, flash floods, etc.) in the six economic corridors. The application frequency will be high and the prospects will be broad.
ZOU Qiang
The data set is the distribution of the average roughness 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 median particle diameter and the vegetation coverage. 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
"Heihe River Basin Ecological hydrological comprehensive atlas" is supported by the key project of Heihe River Basin Ecological hydrological process integration research. It aims at data arrangement and service of Heihe River Basin Ecological hydrological process integration research. The atlas will provide researchers with a comprehensive and detailed background introduction and basic data set of Heihe River Basin. Heihe River Basin water system map is one of the hydrological and water resources part of the atlas, with a scale of 1:2500000, positive axis isometric conic projection and standard latitude of 25 47 n. Data sources: river data of Heihe River Basin, reservoir distribution data of Heihe River Basin, residential area data of Heihe River Basin in 2009, administrative boundary data of one million Heihe River Basin in 2008, Lake data of Heihe River Basin and other basic geographic data. The upper reaches of Heihe River Basin are located in Qilian County, Haibei Tibetan Autonomous Prefecture, Qinghai Province, and the northern foot of Qilian Mountain in Zhangye, Jiuquan City, Sunan and Subei counties of Gansu Province. The middle reaches are located in Shandan, Minle, Ganzhou, Linze, Gaotai, Sunan, Suzhou, Jiayuguan and Yumen counties of Gansu Province. The lower reaches are located in Jinta, Gansu Province, Ejina Banner and Alxa Right Banner of Inner Mongolia, involving three provinces (autonomous regions), 16 cities and counties (District, banner), 56 towns, 45 townships and 4 Sumu. Table 1 shows the information about the administrative divisions of Heihe River Basin.
WANG Jianhua, ZHAO Jun, WANG Xiaomin, FENG Bin
The Shiyang River Basin Information System thematic data set is one of the results of the technical assistance project “Optimization of Desertification Control in Gansu Province” assisted by the Asian Development Bank, including 5 folders including document, investigation_point, maps, photo, and spatial. Each file The folder contains several files. The document folder includes the target design, data processing, thematic summary report, and projection information.The gpspoint folder includes files recorded in shapefile point format sampled by gps according to different purposes.The maps folder contains Chinese, english, and fonts files. Folder, the first two folders represent 14 Chinese and English maps stored in A4 format and pdf format, and fonts contain some special fonts: the photo folder contains field survey digital photos stored in bmp format: spatial The folder contains the dem folder of the digital elevation model, the gansu folder of the outline map of Gansu Province and the Hexi Corridor, the generate folder of the site data file shapefile, the grid folder of the raster data of various geographic features, and the remote sensing image. image folder, meteoHydro folder for original site text data, and vector folder for vector data for various geographic features. The data includes: 1. DEM folder: 100m dem, hillshade, divided into GRID and geotif formats 2. Gansu folder: Gansu border, Hexi border 3. Grid folder: NDVI (vegetation index), lndchange (land transfer matrix), landscape86 (land landscape map in 86 years), landscape2k (land landscape map in 2000), Desertiftype (desert type landscape map), Desersevrt (desert type map ), Annprecip 4. Meteohydro folder: Minqin, Wuwei, Yongchang meteorological data (1) daily daily observation items: Airpress (humidity), Precipitation (radiation), Sunlight (sunlight), Temperature (temperature) ), Wind (wind speed) (2) Months (monthly): Airpress (air pressure), Humidity (humidity), Rain (precipitation), Sunlight (sunlight), Temperature (temperature), Wind (wind speed) (3) tendays: Airpress, Humidity, Rain, Sunlight, Temperature, Wind (4) years (year by year): Precipitation, Temperature 5. Vectro folder: (1) Admwhole (county boundary map), (2) Lake (lake), (3) Hydrasta (hydrological site), (4) Basin (watershed boundary), (5) Landscape2000 (land use 200 (Year), (6) landscape86 (land use 1986), (7) Meteosta (meteorological station), (8) Lakep (reservoir point), (9) Place (residential point), (10) Rainfallcontour (railway), ( 11) Rainfallcontour (rainfall contour map), (12) Road (highway), (13) Stream (water system map), (14) Town (county name), (15) Township (county township boundary), (16) Vegetation (vegetation map) Data projection information: PROJCS ["Albers", GEOGCS ["GCS_Krasovsky_1940", DATUM ["Not_specified_based_on_Krassowsky_1940_ellipsoid", SPHEROID ["Krasovsky_1940", 6378245.0,298.3]], PRIMEM ["Greenwich", 0.0], UNIT ["Degree", 0.0174532925199433]], PROJECTION ["Albers_Conic_Equal_Area"], PARAMETER ["False_Easting", 0.0], PARAMETER ["False_Northing", 0.0], PARAMETER ["longitude_of_center", 105.0], PARAMETER ["Standard_Parallel_1", 25.0], PARAMETER ["Standard_Parallel_2", 47.0], PARAMETER ["latitude_of_center", 0.0], UNIT ["Meter", 1.0]] For detailed data description, please refer to the data file
LI Xin
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