Based on the monthly precipitation data of 262 rain gauges, WRF and ERA5 precipitation data in the Yarlung Zangbo River basin, the daily precipitation data with a resolution of 10km from 1951 to 2020 in the Yarlung Zangbo River basin and seven sub basins are reconstructed using random forest learning algorithm. This data has been verified by the single point of the station and performs well in terms of annual and seasonal changes. And the data has been reverse evaluated by the hydrological model, which is used to drive the VIC hydrological model to simulate the runoff change of Yajiang River basin and each sub basin, and verified by the measured runoff, MODIS and glacier cataloging data. On the basis of the original first edition, this data has considered the spatial distribution characteristics of precipitation, which can better describe the precipitation characteristics in alpine regions.
SUN He
Precipitation over the Tibetan Plateau (TP) known as Asia's water tower plays a critical role in regional water and energy cycles, largely affecting water availability for downstream countries. Rain gauges are indispensable in precipitation measurement, but are quite limited in the TP that features complex terrain and the harsh environment. Satellite and reanalysis precipitation products can provide complementary information for ground-based measurements, particularly over large poorly gauged areas. Here we optimally merged gauge, satellite, and reanalysis data by determining weights of various data sources using artificial neural networks (ANNs) and environmental variables including elevation, surface pressure, and wind speed. A Multi-Source Precipitation (MSP) data set was generated at a daily timescale and a spatial resolution of 0.1° across the TP for the 1998‒2017 period. The correlation coefficient (CC) of daily precipitation between the MSP and gauge observations was highest (0.74) and the root mean squared error was the second lowest compared with four other satellite products, indicating the quality of the MSP and the effectiveness of the data merging approach. We further evaluated the hydrological utility of different precipitation products using a distributed hydrological model for the poorly gauged headwaters of the Yangtze and Yellow rivers in the TP. The MSP achieved the best Nash-Sutcliffe efficiency coefficient (over 0.8) and CC (over 0.9) for daily streamflow simulations during 2004‒2014. In addition, the MSP performed best over the ungauged western TP based on multiple collocation evaluation. The merging method could be applicable to other data-scarce regions globally to provide high quality precipitation data for hydrological research. The latitude and longitude of the left bottom corner across the TP, the number of rows and columns, and grid cells information are all included in each ASCII file.
HONG Zhongkun , LONG Di
Simulation results of four cmip6 models in 2015-2100 under the scenario of shared socio-economic path (SSP) 5-8.5. The selection standard is that the resolution of the four modes is less than 1 °, and there are daily data. Eight variables representing extreme climate are extracted from the original simulation results, which are the extremely high value of daily maximum temperature (TXX), the extremely high value of daily minimum temperature (TNX), the extremely low value of daily maximum temperature (TxN), the extremely low value of daily minimum temperature (TNN), the number of continuous dry days (CDD), the number of continuous wet days (CWD), precipitation intensity (SDII) and the number of heavy precipitation days (r20mm). The time resolution of the data is years, the spatial range is the Qinghai Tibet Plateau, and the time range is 2015-2100.
ZHANG Ran ZHANG Ran
1) Data content (including elements and significance): 19 stations of Alpine network (Southeast Tibet station, Namuco station, Everest station, mustage station, Ali station, Golmud station, Tianshan station, Qilian mountain station, Ruoergai station (2 points in total, Northwest Institute and Chengdu Institute of Biology), Yulong Snow Mountain station and Naqu station (including stations, Qinghai Tibet Institute, Northwest Institute and Geography Institute), Haibei Station, Sanjiangyuan station, Shenza station,, Lhasa station and Qinghai Lake Station) meteorological observation data set of Qinghai Tibet Plateau in 2020 (temperature, precipitation, wind direction and speed, relative humidity, air pressure, radiation and flux) 2) Data source and processing method: Excel format for field observation of 19 stations of Alpine network 3) Data quality description: Daily resolution of the station 4) Data application achievements and prospects: Based on the long-term observation data of field stations of the alpine network and overseas stations in the pan third pole region, a series of data sets of meteorological, hydrological and ecological elements in the pan third pole region are established; Complete the inversion of meteorological elements, lake water quantity and quality, aboveground vegetation biomass, glacier and frozen soil change and other data products through intensive observation in key areas and verification of sample plots and sample points; Based on the Internet of things technology, a multi station networked meteorological, hydrological and ecological data management platform is developed to realize real-time acquisition, remote control and sharing of networked data. In addition, the data set is an update of the meteorological data of the surface environment and observation network in China's high and cold regions (2019).
ZHU Liping
The SZIsnow dataset was calculated based on systematic physical fields from the Global Land Data Assimilation System version 2 (GLDAS-2) with the Noah land surface model. This SZIsnow dataset considers different physical water-energy processes, especially snow processes. The evaluation shows the dataset is capable of investigating different types of droughts across different timescales. The assessment also indicates that the dataset has an adequate performance to capture droughts across different spatial scales. The consideration of snow processes improved the capability of SZIsnow, and the improvement is evident over snow-covered areas (e.g., Arctic region) and high-altitude areas (e.g., Tibet Plateau). Moreover, the analysis also implies that SZIsnow dataset is able to well capture the large-scale drought events across the world. This drought dataset has high application potential for monitoring, assessing, and supplying information of drought, and also can serve as a valuable resource for drought studies.
WU Pute, TIAN Lei, ZHANG Baoqing
This data includes the image data of the second comprehensive field scientific investigation of the Qinghai Tibet Plateau. The image data includes the sample plot photos of the quadrats collected in the nature reserve during the scientific research, the images of forest ecosystem, grassland ecosystem and lake ecosystem in the nature reserve in Northwest Yunnan and Western Sichuan, the vegetation situation, wildlife habitat, and the data of animals, plants and fungi in the reserve. In addition, the image data also includes the sample collection process of the scientific research, the household survey of the scientific research team in the community survey and the image data of the interview with the local protection department. The data comes from UAV and camera shooting, which can provide evidence and reference for scientific research.
SU Xukun
This data is precipitation data, which is the monthly precipitation product of tropical rainfall measurement mission TRMM 3b43. It integrates the main area of the Qinghai Tibet Plateau (25 ~ 40 ° n; 25 ~ 40 ° n); The precipitation data of 332 meteorological stations are from the National Meteorological Information Center of China Meteorological Administration. The reanalysis data set is obtained by the station 3 ° interpolation optimization variational correction method. For the monthly sample data from January 1998 to December 2018, the spatial coverage is 25 ~ 40 ° n; 73 ~ 105 ° e, the spatial resolution is 1 ° * 1 °.
XU Xiangde, SUN Chan
This dataset includes daily minimum temperature (Tmin), maximum temperature (Tmax) and precipitation (PPT) data of NEX-GDDP (NASA Earth Exchange Global Daily Downscaled Projections) (v1.0) over the periods of 2000–2009 and 2090–2099. The unit of Tmax and Tmin is K, and the unit of PPT is kgm-2s-1; the background filling value is -999. This dataset is a subset extraction fromthe original data. The original data was downloaded from https://portal.nccs.nasa.gov/datashare/NEXGDDP/BCSD/ in August 2020; The NEX-GDDP data set is obtained from CMIP5 (Coupled Model Intercomparison Project Phase 5) historical climate and General Circulation Models (General Circulation Models) operating in RCP (Representative Concentration Pathways) 4.5 scenario mode, including 21 atmospheric circulation models; among them, 2000 –2005 is a historical climate scenario, and 2006–2009 and 2090-2099 are RCP 4.5 scenarios. For the description of the original data, please refer to https://www.nccs.nasa.gov/services/data-collections/land-based-products/nex-gddp.
Shen Miaogen, JIANG Nan
The stable oxygen isotope ratio (δ 18O) in precipitation is a comprehensive tracer of global atmospheric processes. Since the 1990s, efforts have been made to study the isotopic composition of precipitation at more than 20 stations located on the TP of the Tibetan Plateau, which are located at the air mass intersection between westerlies and monsoons. In this paper, we establish a database of monthly precipitation δ 18O over the Tibetan Plateau and use different models to evaluate the climate control of precipitation δ 18O over TP. The spatiotemporal pattern of precipitation δ 18O and its relationship with temperature and precipitation reveal three different domains, which are respectively related to westerly wind (North TP), Indian monsoon (South TP) and their transition.
GAO Jing
The Frequency distribution improved and wind-induced undercatch corrected gridded precipitation in Tibetan Plateau(1980-2009) is a dataset suitable for the Tibetan Plateau . It considers the measurement undercatch caused by wind and optimizes the precipitation frequency distribution by adopting an advanced interpolation method. The data is in NETCDF format, with a temporal resolution of 1 day and a horizontal spatial resolution of 10km. The data can be used as a reference data source for numerical model precipitation frequency correction. This dataset uses daily observations from the China Meteorological Administration and GSOD at 164 stations as the data sources. The construction of the dataset is divided into four steps :(1) firstly, quality control is carried out on the gauge data, including the removal of abnormal values and bad values.(2) Doing wind-induced undercatch correction for every precipitation record.(3) A thin-plate splines interpolation algorithm considering altitude as a covariate is used to interpolate the monthly total precipitation, and the ratio of daily and monthly precipitation was interpolated by the Ordinary Kriging method. The dataset with a spatial resolution of 1km was obtained by multiplying the monthly total precipitation and day to month ratio. (4) Aggregating the 1km dataset to 10km spatial resolution to obtain the final data. Compared with the similar international gridded precipitation dataset, this data highlights for it’s wind-induced undercatch correction of gauge precipitation and the optimized interpolation method to make itself have more accurate frequency distribution. The data is suitable for correction of statistical deviation of precipitation output by numerical model or analysis of precipitation frequency characteristics at grid-box. y. It is more suitable for correcting the statistical deviation of precipitation output by numerical model or analyzing the precipitation frequency characteristics on gridded points.
MA Jiapei, LI Hongyi
1) Data content (including elements and significance): 21 stations (Southeast Tibet station, Namucuo station, Zhufeng station, mustag station, Ali station, Naqu station, Shuanghu station, Geermu station, Tianshan station, Qilianshan station, Ruoergai station (northwest courtyard), Yulong Xueshan station, Naqu station (hanhansuo), Haibei Station, Sanjiangyuan station, Shenzha station, gonggashan station, Ruoergai station（ Chengdu Institute of biology, Naqu station (Institute of Geography), Lhasa station, Qinghai Lake Station) 2018 Qinghai Tibet Plateau meteorological observation data set (temperature, precipitation, wind direction and speed, relative humidity, air pressure, radiation and evaporation) 2) Data source and processing method: field observation at Excel stations in 21 formats 3) Data quality description: daily resolution of the site 4) Data application results and prospects: Based on long-term observation data of various cold stations in the Alpine Network and overseas stations in the pan-third pole region, a series of datasets of meteorological, hydrological and ecological elements in the pan-third pole region were established; Strengthen observation and sample site and sample point verification, complete the inversion of meteorological elements, lake water quantity and quality, above-ground vegetation biomass, glacial frozen soil change and other data products; based on the Internet of Things technology, develop and establish multi-station networked meteorological, hydrological, Ecological data management platform, real-time acquisition and remote control and sharing of networked data.
ZHU Liping,
(1) This data set is the carbon flux data set of Shenzha alpine wetland from 2016 to 2019, including air temperature, soil temperature, precipitation, ecosystem productivity and other parameters. (2) The data set is based on the field measured data of vorticity, and adopts the internationally recognized standard processing method of vorticity related data. The basic process includes: outlier elimination coordinate rotation WPL correction storage item calculation precipitation synchronization data elimination threshold elimination outlier elimination U * correction missing data interpolation flux decomposition and statistics. This data set also contains the model simulation data calibrated based on the vorticity correlation data set. (3) the data set has been under data quality control, and the data missing rate is 37.3%, and the missing data has been supplemented by interpolation. (4) The data set has scientific value for understanding carbon sink function of alpine wetland, and can also be used for correction and verification of mechanism model.
Da Wei
1) The data set driven by the surface meteorological elements of the surface meteorological observation data product (2017-2018) of the Qinghai Tibet Plateau includes four elements: near surface temperature, surface precipitation rate, short wave radiation and long wave radiation. 2) The data set is based on the existing Princeton reanalysis data, GLDAS data, gewex-srb radiation data and TRMM Precipitation Data in the world as the background field, and integrates the conventional meteorological observation data of China Meteorological Administration, and is formed by spatial interpolation. 3) The data is TIFF format, the temporal resolution is daily value, and the spatial resolution is 0.1 °. 4) It is convenient for researchers and students who do not use such assimilation data in NC format. Based on the long-term observation data of each field station in the alpine network and overseas stations in the pan third polar region, a series of data sets of meteorological, hydrological and ecological elements in the pan third polar region are established; the inversion of data products such as meteorological elements, lake water quantity and quality, aboveground vegetation biomass, glacial and frozen soil changes are completed through enhanced observation and sample site verification in key regions; based on the IOT Network technology, the development and establishment of multi station network meteorological, hydrological, ecological data management platform, to achieve real-time access to network data and remote control and sharing.
ZHU Liping,
The field observation platform of the Tibetan Plateau is the forefront of scientific observation and research on the Tibetan Plateau. The land surface processes and environmental changes based comprehensive observation of the land-boundary layer in the Tibetan Plateau provides valuable data for the study of the mechanism of the land-atmosphere interaction on the Tibetan Plateau and its effects. This dataset integrates the 2005-2016 hourly atmospheric, soil hydrothermal and turbulent fluxes observations of Qomolangma Atmospheric and Environmental Observation and Research Station, Chinese Academy of Sciences (QOMS/CAS), Southeast Tibet Observation and Research Station for the Alpine Environment, CAS (SETORS), the BJ site of Nagqu Station of Plateau Climate and Environment, CAS (NPCE-BJ), Nam Co Monitoring and Research Station for Multisphere Interactions, CAS (NAMORS), Ngari Desert Observation and Research Station, CAS (NADORS), Muztagh Ata Westerly Observation and Research Station, CAS (MAWORS). It contains gradient observation data composed of multi-layer wind speed and direction, temperature, humidity, air pressure and precipitation data, four-component radiation data, multi-layer soil temperature and humidity and soil heat flux data, and turbulence data composed of sensible heat flux, latent heat flux and carbon dioxide flux. These data can be widely used in the analysis of the characteristics of meteorological elements on the Tibetan Plaetau, the evaluation of remote sensing products and development of the remote sensing retrieval algorithms, and the evaluation and development of numerical models.
MA Yaoming
The matching data of water and soil resources in the Qinghai Tibet Plateau, the potential evapotranspiration data calculated by Penman formula from the site meteorological data (2008-2016, national meteorological data sharing network), the evapotranspiration under the existing land use according to the influence coefficient of underlying surface, and the rainfall data obtained by interpolation from the site rainfall data in the meteorological data, are used to calculate the evapotranspiration under the existing land use according to the different land types of land use According to the difference, the matching coefficient of water and soil resources is obtained. The difference between the actual rainfall and the water demand under the existing land use conditions reflects the matching of water and soil resources. The larger the value is, the better the matching is. The spatial distribution of the matching of soil and water resources can pave the way for further understanding of the agricultural and animal husbandry resources in the Qinghai Tibet Plateau.
DONG Lingxiao
The land-sea thermal contrast is an important driver for monsoon interannual and interdecadal variability and the monsoon onset. The importance of the thermal contrast between the Tibetan Plateau (TP) and the Indian Ocean (IO) in driving the establishment of Indian Summer Monsoon (ISM) has been recognized. The South Asian Summer Monsoon (SASM) is primarily a tropical summer monsoon. As a direct dynamic response to the diabatic heating, the difference between upper and lower-layer winds can be closely linked to the strength of the heat source. The upper-layer thermal contrast is more important for the SASM (Sun et al., 2010; Sun and Ding，2011; Dai et al., 2013). Thermal contrast between the TP and the IO at the mid-upper troposphere is closely related to the onset and the variability of ISM. Considering that the temperature above the TP and IO are the two centers which are most sensitive to the change of ISM, a thermal contrast index (TCI) is proposed based on 500-200hPa air temperature: TCI = Nor[T(25°N-38°N, 65°E-95°E) - T(5°S-8°N, 65°E-95°E)] Where Nor represents standardization and T is 500-200hPa air temperature. The TCI is larger, and the ISM is stronger. The TCI can capture the interannual and interdecadal variability of ISM well. The cooperative thermal effect between TP and IO may contributes more to the ISM than the separately temperature of TP or IO. In addition, from the view of climate mean state, the pentad-by-pentad increment of TCI has a 15-pentad lead when the correlation coefficient between it and the ISM index reaches the maximum. And the correlation coefficient between the pentad-by-pentad increment of TCI and the ISM index is significant when the pentad-by-pentad increment of TCI has a 3-pentad lead. The result indicates the advantage of the TCI for prediction of the ISM. Meanwhile, the averaged pentad-by-pentad increment of TCI for the first 25 (TCI25) pentads may be a predictor of the early or late onset of the ISM. The ISM onset will be earlier when the TCI25 is larger.
LI Zhangqun, XIAO Ziniu, ZHAO Liang
This data set is the data set of climatic factors in the Qinghai Tibet Plateau from 1990 to 2015, which records the spatial distribution change of annual rainfall every five years in the past 25 years. The data is in TIF grid format, with spatial resolution of 1km and annual rainfall unit of 0.1mm. The data comes from the daily observation data of meteorological stations on the Qinghai Tibet Plateau, which is generated by time aggregation calculation and spatial interpolation processing. As an important climate factor, the data set can be used to study the interannual rainfall change and climate change on the Qinghai Tibet Plateau. As the climate background of the ecological environment change on the Qinghai Tibet Plateau, it can provide data support for the study of the interactive stress between urbanization and ecological environment Bracing.
DU Yunyan, YI Jiawei
This dataset is the spatial distribution map of the marshes in the source area of the Yellow River near the Zaling Lake-Eling Lake, covering an area of about 21,000 square kilometers. The data set is classified by the Landsat 8 image through an expert decision tree and corrected by manual visual interpretation. The spatial resolution of the image is 30m, using the WGS 1984 UTM projected coordinate system, and the data format is grid format. The image is divided into five types of land, the land type 1 is “water body”, the land type 2 is “high-cover vegetation”, the land type 3 is “naked land”, and the land type 4 is “low-cover vegetation”, and the land type 5 is For "marsh", low-coverage vegetation and high-coverage vegetation are distinguished by vegetation coverage. The threshold is 0.1 to 0.4 for low-cover vegetation and 0.4 to 1 for high-cover vegetation.
YANG Kun
The strong spatial and temporal changes of precipitation often make it impossible to accurately know the spatial distribution and intensity changes of precipitation during the precipitation observation of conventional foundation stations. Satellite microwave remote sensing can overcome this limitation and achieve global scale precipitation and cloud observation. Compared with infrared/visible light, which can only reflect cloud thickness and cloud height, microwave can penetrate the cloud, and also use the interaction between precipitation and cloud particles in the cloud and microwave to detect the cloud and rain more directly. This data use the surface precipitation, obtained by the DPR double wave band precipitation radar carried by GPM, as the true value, soil temperature/humidity of NDVI, DEM and ERA5 as reference data. And the multi-band passive brightness temperature data of GMI is used to invert the instantaneous precipitation intensity during the warm season (May-September) in Tibetan Plateau, then the result is re-sampled to the spatial resolution of 0.1°and accumulated them to a day.
XU Shiguang
1)Data content (including elements and meanings): surface meteorological observation data product of TP in 1979-2016 2)Data source and processing method: In .tif format, can be opened and analysed in arcgis. 3)Data quality description: daily 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.
ZHU Liping,
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