Meteorological data is generally divided into three categories: short-term (i.e. daily), medium-term, and long-term. Among them, daily meteorological data is the most commonly used data, mainly including temperature, precipitation, precipitation type, relative humidity, wind speed, and direction. They are the basic data for meteorological surveys and research, and are an important basis for meteorological forecasting, climate change monitoring, and precipitation forecasting. Daily meteorological data from national standard meteorological stations in Sanjiangyuan and adjacent areas from 1981 to 2015, including eight variables, namely station pressure, temperature, relative humidity, precipitation, evaporation, wind direction, wind speed, sunlight, and 0cm ground temperature. The data is in. txt format.
WANG Xufeng WANG Xufeng
Net Primary Production (NPP) refers to the amount of energy consumed by plant respiration, which is deducted from the fixed energy of plant photosynthesis during the primary production process. The remaining energy can be used for plant growth and reproduction. The production volume is usually expressed as the dry weight of organic matter produced per square meter per year [g/(m2 · a)] or the fixed energy per square meter per year [J/(m2 · a)]. This data comes from MODIS-NPP data - MOD17A3HGF V6 product, which provides annual net primary productivity (NPP) information at a resolution of 500 meters. The annual net primary productivity is determined by the sum of all 8-day net photosynthetic (PSN) products (MOD17A2H) for a given year. The PSN value is the difference between total primary productivity (GPP) and sustained respiration (MR) (GPP MR).
WANG Xufeng
The Sanjiangyuan National Park is located in the hinterland of the Qinghai Tibet Plateau, the third pole of the Earth. It consists of the Yangtze River Source Park, the Yellow River Source Park, and the Lancang River Source Park, with a total area of 123100 square kilometers. The remote sensing snow cover days data product of Sanjiangyuan National Park is based on the daily cloudless snow cover area of 500 meters in China from 2000 to 2020. It is calculated based on the sum of the number of snow cover days observed in a hydrological year, with the hydrological year from September 1 to August 31 of the following year. The range of snow days is 0-365 days or 366 days, with an invalid value of -1. The data format is TIFF, the data projection is WGS84 projection, and the resolution is 500m.
HAO Xiaohua
Mountain glaciers are important freshwater resources in Western China and its surrounding areas. It is at the drainage basin scale that mountain glaciers provide meltwater that humans exploit and utilize. Therefore, the determination of glacierized river basins is the basis for the research on glacier meltwater provisioning functions and their services. Based on the Randolph glacier inventory 6.0, Chinese Glacier Inventories, China's river basin classifications (collected from the Data Centre for Resources and Environmental Sciences, Chinese Academy of Sciences), and global-scale HydroBASINS (www.hydrosheds.org), the following dataset was generated by the intersection between river basins and glacier inventory: (1) Chinese glacierized macroscale and microscale river basins; (2) International glacierized macroscale river basin fed by China’s glaciers; (3) Glacierized macroscale river basin data across High Mountain Asia. This data takes the common river basin boundaries in China and the globe into account, which is poised to provide basic data for the study of historical and future glacier water resources in China and its surrounding areas.
SU Bo
Geladandong region is an important and typical source region of great rivers and lakes in the Qinghai Tibet Plateau. This data set provides DEM covering glaciers in the source region of the Yangtze River and Selin Co with different time scales and resolutions to calculate the seasonal and decadal changes of glacier surface elevation in the source region. This data set includes seven 5-meter resolution TanDEM-X data from July 2016 to 2017, which can be used to calculate the seasonal change of glacier surface elevation; it includes one KH-9 DEM with a resolution of 30m in 1976, five TanDEM-X with a resolution of 30m in 2011, one TanDEM-X in 2014 and three TanDEM-X in 2017 with a resolution of 30m. The data can be used to calculate the change of glacier surface elevation during 1976-2000, 2000-20112011-2017. At the same time, Landsat ETM data are used to extract the glacier outline in 1976and we divide it according to the RGI6.0; The right figure shows the spatial and temporal coverage information of the data set, and the base figure is the orthophoto corrected kh-9 image.
CHEN Wenfeng
The data set is a record of glacier distribution in Hoh Xil region, including three tables: the distribution of modern glaciers in various mountain areas in Hoh Xil region, the distribution of modern glaciers in various river basins in Hoh Xil region, and the distribution of modern glaciers in different mountain height segments in Hoh Xil region. Hoh Xil, located in the hinterland of the Qinghai Tibet Plateau, has an average altitude of more than 5000m and a very cold climate. According to the catalogue of China's glaciers and the author's re statistics on the 1 / 100000 topographic map, 437 modern glaciers are developed in the whole region, covering an area of 1552.39 square kilometers, with ice reserves of 162.8349 cubic kilometers, becoming an important source of water supply for many rivers and lakes in the region. Through this data set, we can know more about the distribution of glaciers in this area.
LI Bingyuan
The Qinghai-Tibetan Plateau (QTP), the largest high-altitude and low-latitude permafrost zone in the world, has experienced rapid permafrost degradation in recent decades, and one of the most remarkable resulting characteristics is the formation of thermokarst lakes. Such lakes have attracted significant attention because of their ability to regulate carbon cycle, water, and energy fluxes. However, the distribution of thermokarst lakes in this area remains largely unknown, hindering our understanding of the response of permafrost and its carbon feedback to climate change.Based on more than 200 sentinel-2A images and combined with ArcGIS, NDWI and Google Earth Engine platform, this data set extracted the boundary of thermokarst lakes in permafrost regions of the Qinghai-Tibet Plateau through GEE automatic extraction and manual visual interpretation.In 2018, there were 121,758 thermokarst lakes in the permafrost area of the Qinghai-Tibet Plateau, covering an area of 0.0004-0.5km², with a total area of 1,730.34km² respectively.The cataloging data set of Thermokarst Lakes provides basic data for water resources evaluation, permafrost degradation evaluation and thermal karst study on the Qinghai-Tibet Plateau.
CHEN Xu, MU Cuicui, JIA Lin, LI Zhilong, FAN Chengyan, MU Mei, PENG Xiaoqing, WU Xiaodong
A comprehensive understanding of the permafrost changes in the Qinghai Tibet Plateau, including the changes of annual mean ground temperature (Magt) and active layer thickness (ALT), is of great significance to the implementation of the permafrost change project caused by climate change. Based on the CMFD reanalysis data from 2000 to 2015, meteorological observation data of China Meteorological Administration, 1 km digital elevation model, geo spatial environment prediction factors, glacier and ice lake data, drilling data and so on, this paper uses statistics and machine learning (ML) method to simulate the current changes of permafrost flux and magnetic flux in Qinghai Tibet Plateau The range data of mean ground temperature (Magt) and active layer thickness (ALT) from 2000 to 2015 and 2061 to 2080 under rcp2.6, rcp4.5 and rcp8.5 concentration scenarios were obtained, with the resolution of 0.1 * 0.1 degree. The simulation results show that the combination of statistics and ML method needs less parameters and input variables to simulate the thermal state of frozen soil, which can effectively understand the response of frozen soil on the Qinghai Tibet Plateau to climate change.
Ni Jie, Wu Tonghua
Based on the vulnerability assessment framework of "exposure sensitivity adaptability", the vulnerability assessment index system of agricultural and pastoral areas in Qinghai Tibet Plateau was constructed. The index system data includes meteorological data, soil data, vegetation data, terrain data and socio-economic data, with a total of 12 data indicators, mainly from the national Qinghai Tibet Plateau scientific data center and the resource and environmental science data center of the Chinese Academy of Sciences. Based on the questionnaire survey of six experts in related fields, the weight of the indicators is determined by using the analytic hierarchy process (AHP). Finally, four 1km grid data are formed involving ecological exposure, sensitivity, adaptability and ecological vulnerability in the agricultural and pastoral areas of the Qinghai Tibet Plateau. The data can provide a reference for the identification of ecological vulnerable areas in the Qinghai Tibet Plateau.
ZHAN Jinyan, TENG Yanmin, LIU Shiliang
This data set contains the statistical information of natural disasters in Qinghai Tibet Plateau in the past 50 years (1950-2002), including drought, snow disaster, frost disaster, hail, flood, wind disaster, lightning disaster, cold wave and strong cooling, low temperature and freezing damage, gale sandstorm, insect disaster, rodent damage and other meteorological disasters. Qinghai and Tibet are the main parts of the Qinghai Tibet Plateau. The Qinghai Tibet Plateau is one of the Centers for the formation and evolution of biological species in China. It is also a sensitive area and fragile zone for the international scientific and technological circles to study climate and ecological environment changes. Its complex terrain conditions, high altitude and severe climate conditions determine that the ecological environment is very fragile, It has become the most frequent area of natural disasters in China. The data were extracted from "China Meteorological Disaster Canon · Qinghai volume" and "China Meteorological Disaster Canon · Tibet Volume", which were manually input, summarized and proofread.
Statistical Bureau Statistical Bureau
The data set is based on the reflectance of MODIS channels and the observation data of SIF to establish the neural network model, so as to obtain the SIF data with high spatial and temporal resolution, which is often used as a reference for primary productivity. The data is from Zhang et al. (2018), and the specific algorithm is shown in the article. The source data range is global, and the Qinghai Tibet plateau region is selected in this data set. This data integrates the original 4-day time scale data into the monthly data. The processing method is to take the maximum value of the month, so as to achieve the effect of removing noise as much as possible. This data set is often used to evaluate the temporal and spatial patterns of vegetation greenness and primary productivity, which has practical significance and theoretical value.
ZHANG Yao
The data set is based on the reflectance of MODIS channels and the observation data of SIF to establish the neural network model, so as to obtain the SIF data with high spatial and temporal resolution, which is often used as a reference for primary productivity. The data is from Zhang et al. (2018), and the specific algorithm is shown in the article. The source data range is global, and the Tibetan plateau region is selected in this data set. This data integrates the original 4-day time scale data into the monthly data. The processing method is to take the maximum value of the month, so as to achieve the effect of removing noise as much as possible. This data set is often used to evaluate the temporal and spatial patterns of vegetation greenness and primary productivity, which has practical significance and theoretical value.
ZHANG Yao
This data set is the data set of climate elements in Hoh Xil area of Qinghai Province, covering the data of 14 observation stations, recording the climate observation data in 1990 in detail. Hoh Xil area in Qinghai Province has a high terrain with an average altitude of over 5000m. The climate is cold, the air is thin and the natural environment is bad. The vast area is still no man's land, known as "forbidden zone for human beings". Due to less interference from human activities, most of the area still maintains its original natural state. Its special geographical location, crustal structure and natural environment, as well as the unique composition of the biological flora, have been the focus of domestic surgical circles. The original data of the data set is digitized from the book "natural environment of Hoh Xil, Qinghai Province". The climate observation data include solar radiation, temperature, precipitation, air pressure, wind speed, etc. This data set provides basic data for the study of Hoh Xil area in Qinghai Province, and has reference value for the research in related fields.
LI Bingyuan
This data set is the data set of Lake elements in Hoh Xil area of Qinghai Province, which records the main lake characteristics and water quality sampling and analysis data in detail. There are many lakes in Hoh Xil area of Qinghai Province, which is one of the concentrated distribution areas of lakes in Qinghai Tibet Plateau. The basic characteristics of Lake Development in this area are: large quantity, many types and complex structure. According to preliminary statistics, there are 107 lakes with an area of more than 1km2, with a total area of 3825km2 and a lake degree of about 0.05. The original data of the data set is digitized from the book "natural environment of Hoh Xil region in Qinghai Province", which includes 35 main lake characteristic data and 60 lake water chemical analysis data. This data set provides basic data for the study of Hoh Xil area in Qinghai Province, and has reference value for the research in related fields.
LI Bingyuan
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 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
There are two types of aerosol data in the Tibetan Plateau. Aerosol type data products are the results of aerosol type data fusion by using Meera 2 assimilation data and active satellite CALIPSO products through a series of data preprocessing, quality control, statistical analysis and comparative analysis. The key of the algorithm is to judge the CALIPSO aerosol type. According to CALIPSO aerosol types and quality control, and referring to merra 2 aerosol types, the final aerosol type data (12 kinds) and quality control results were obtained. Considering the vertical and spatial distribution of aerosols, it has high spatial resolution (0.625 ° × 0.5 °) and temporal resolution (month). Aerosol optical depth (AOD) is a visible band remote sensing inversion method developed by ourselves, combined with merra-2 model data and NASA's official product mod04. The data coverage time is from 2000 to 2019, with daily temporal resolution and spatial resolution of 0.1 degree. The retrieval method mainly uses the self-developed APRs algorithm to retrieve the aerosol optical depth over the ice and snow. The algorithm takes into account the BRDF characteristics of the ice and snow surface, and is suitable for the inversion of aerosol optical thickness on the ice and snow. The results show that the relative deviation of the data is less than 35%, which can effectively improve the coverage and accuracy of the polar AOD.
GUANG Jie, ZHAO Chuanfeng
The surface air temperature dataset of the Tibetan Plateau is obtained by downscaling the China regional surface meteorological feature dataset (CRSMFD). It contains the daily mean surface air temperature and 3-hourly instantaneous surface air temperature. This dataset has a spatial resolution of 0.01°. Its time range for surface air temperature dataset is from 1979 to 2018. Spatial dimension of data: 73°E-106°E, 23°N-40°N. The surface air temperature with a 0.01° can serve as an important input for the modeling of land surface processes, such as surface evapotranspiration estimation, agricultural monitoring, and climate change analysis.
DING Lirong, ZHOU Ji, WANG Wei , MA Jin
Snow is a significant component of the ecosystem and water resources in high-mountain Asia (HMA). Therefore, accurate, continuous, and long-term snow monitoring is indispensable for the water resources management and economic development. The present study improves the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites 8 d (“d” denotes “day”) composite snow cover Collection 6 (C6) products, named MOD10A2.006 (Terra) and MYD10A2.006 (Aqua), for HMA with a multistep approach. The primary purpose of this study was to reduce uncertainty in the Terra–Aqua MODIS snow cover products and generate a combined snow cover product. For reducing underestimation mainly caused by cloud cover, we used seasonal, temporal, and spatial filters. For reducing overestimation caused by MODIS sensors, we combined Terra and Aqua MODIS snow cover products, considering snow only if a pixel represents snow in both the products; otherwise it is classified as no snow, unlike some previous studies which consider snow if any of the Terra or Aqua product identifies snow. Our methodology generates a new product which removes a significant amount of uncertainty in Terra and Aqua MODIS 8 d composite C6 products comprising 46 % overestimation and 3.66 % underestimation, mainly caused by sensor limitations and cloud cover, respectively. The results were validated using Landsat 8 data, both for winter and summer at 20 well-distributed sites in the study area. Our validated adopted methodology improved accuracy by 10 % on average, compared to Landsat data. The final product covers the period from 2002 to 2018, comprising a combination of snow and glaciers created by merging Randolph Glacier Inventory version 6.0 (RGI 6.0) separated as debris-covered and debris-free with the final snow product MOYDGL06*. We have processed approximately 746 images of both Terra and Aqua MODIS snow containing approximately 100 000 satellite individual images. Furthermore, this product can serve as a valuable input dataset for hydrological and glaciological modelling to assess the melt contribution of snow-covered areas. The data, which can be used in various climatological and water-related studies, are available for end users at https://doi.org/10.1594/PANGAEA.901821 (Muhammad and Thapa, 2019).
SHER Muhammad
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