The dataset of landuse types in Qilian Mountains National Park in 1985 is a vector dataset based on the remote sensing monitoring dataset of the current landuse situation in China by CAS, which is obtained through cropping and splicing operations. The data production production is vector data generated by manual visual interpretation using Landsat TM/ETM remote sensing images as the main data source. 3 datasets for 2000-2020 are raster datasets with 30m resolution based on GlobeLand30 global 30m ground cover data, obtained through mask extraction and other operations. The land use types of all datasets include 10 primary types of cropland, forest, shrubland, grassland, wetland, water, tundra, impervious surface, bareland, glacier, and permanent snow. The data products can detect most of the land cover changes caused by human activities, which is very important in practical applications. This data can be used to analyze the historical land use types in the Qilian Mountains region and to analyze the changes of land use types in the Qilian Mountains region in combination with the current landuse type data.
NIAN Yanyun
This data is the debris flow risk assessment data obtained from the analysis and Research on the debris flow disaster in the China Pakistan Economic Corridor, and the data source is the risk and vulnerability analysis results obtained from this study; The research method is based on the risk expression given by the United Nations Department of Humanitarian Affairs (1992): risk = hazard × Vulnerability, risk analysis of debris flow disaster in the study area.. The purpose of this data is to assess the risk of debris flow disaster in the China Pakistan Economic Corridor, understand the relationship between the intensity of major debris flow risk, and provide scientific guidance for the decision-making of local government departments in disaster prevention and mitigation and urban governance.
SU Fenghuan
This data is the debris flow risk assessment data obtained from the analysis and Research on the debris flow disaster in the China Pakistan Economic Corridor, and the data source is the risk and vulnerability analysis results obtained from this study; The research method is based on the risk expression given by the United Nations Department of Humanitarian Affairs (1992): risk = hazard × Vulnerability, risk analysis of debris flow disaster in the study area.. The purpose of this data is to assess the risk of debris flow disaster in the China Pakistan Economic Corridor, understand the relationship between the intensity of major debris flow risk, and provide scientific guidance for the decision-making of local government departments in disaster prevention and mitigation and urban governance.
SU Fenghuan
This data is the debris flow risk assessment data, which is obtained from the analysis and research of the debris flow disaster in the China Pakistan Economic Corridor. The sample data of debris flow is the detailed data of debris flow disaster through remote sensing interpretation and on-site verification. A risk assessment system is established to evaluate the debris flow risk in the study area by using the information method, and then the risk area is divided by using the natural breakpoint method. This data can be used to assess the risk of major debris flow disasters, understand the relationship between the risk degree of major debris flow, and provide scientific guidance for the decision-making of local government departments in disaster prevention and mitigation and urban governance.
SU Fenghuan
Net Primary Productivity (NPP) refers to the total amount of organic matter produced by photosynthesis in green plants per unit time and area. As the basis of water cycle, nutrient cycle and biodiversity change in terrestrial ecosystems, NPP is an important ecological indicator for estimating earth support capacity and evaluating sustainable development of terrestrial ecosystems. This data set includes the monthly synthesis of 30m*30m surface LAI products in Qilian mountain area in 2021. Max value composition (MVC) method was used to synthesize monthly NPP products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.
WU Junjun , LI Yi, ZHONG Bo
Leaf Area Index (LAI) is defined as half of the total Leaf Area within the unit projected surface Area, and is one of the core parameters used to describe vegetation. LAI controls many biological and physical processes of vegetation, such as photosynthesis, respiration, transpiration, carbon cycle and precipitation interception, and meanwhile provides quantitative information for the initial energy exchange on the surface of vegetation canopy. LAI is a very important parameter to study the structure and function of vegetation ecosystem. This data set includes the monthly synthesis of 30m LAI products in Qilian mountain area in 2021. Max value composition (MVC) method was used to synthesize monthly LAI products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.
WU Junjun , LI Yi, ZHONG Bo
Different forms of precipitation (snow, sleet, and rain) have divergent effects on the Earth’s surface water and energy fluxes. Therefore, discriminating between these forms is of significant importance, especially under a changing climate. We applied a state-of-the-art parameterization scheme with wet-bulb temperature, relative humidity, surface air pressure, and elevation as inputs, as well as observational gridded datasets with a maximum spatial resolution of 0.25◦, to generate a gridded dataset of different forms of daily precipitation (snow, sleet, and rain) and their temperature threshold across mainland China from 1961-2016. The annual snow, sleet, and rain amount were further calculated. The dataset may benefit various research communities, such as cryosphere science, hydrology, ecology, and climate change.
SU Bo , ZHAO Hongyu
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
The Second Tibetan Plateau Scientific Expedition and Research Task V Theme III "Conservation and Sustainable Utilization of Plateau Microbial Diversity" (2019QZKK0503) carried out more than 30 field scientific expeditions in the first and second years. Footprints cover most of the Tibetan Plateau, including the investigation of glaciers (such as Qiangyong Glacier, Tanggula Glacier, Everest East Rongbu glacier, Jiemayangzong Glacier, Palung 4 Glacier, etc.), lakes, soils, fungi, lichens, animals in Southeast Tibet, Qiangtang Plateau, Cocosili and Himalayan region. The dataset contains 6,471 photos and videos, including habitat photos, working photos, and scientific images collected during the first and second years of fieldwork.
LIU Yongqin
This dataset contains the LAI measurements from the Daman superstation in the middle reaches of the Heihe integrated observatory network from July 22 to September 5 in 2021. The site (100.376° E, 38.853°N) was located in the maize surface, near Zhangye city in Gansu Province. The elevation is 1556 m. There are 3 observation samples, each of which is about 30m×30m in size, and the latitude and longitude are (100.374°E, 38.855°N), (100.371° E, 38.854°N), (100.369°E, 38.854°N). Four sub-canopy nodes and one above-canopy node are arranged in each sample. The data is obtained from LAINet measurements; the four-steps are performed to obtain LAI: the raw data is light quantum (level 0); the daily LAI can be obtained using the software LAInet (level 1); further the invalid and null values are screened and using the 5 days moving averaged method to obtain the processed LAI (level 2); for the multi LAINet nodes observation, the averaged LAI of the nodes area is the final LAI (level 3). The released data are the post processed LAI products and stored using *.xls format. For more information, please refer to Liu et al. (2018) (for sites information), Qu et al. (2014) for data processing) in the Citation section.
LIU Shaomin, CHE Tao, Qu Yonghua, XU Ziwei, TAN Junlei
The dataset contains the phenological camera observation data of the Sidaoqiao Superstation in the downstream of Heihe integrated observatory network from May 2 to December 26, 2021. The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 1280×720. For the calculation of the greenness index and phenology, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) needs to be calculated according to the region of interest, then the invalid value filling and filtering smoothing are performed, and finally the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, Peak, growth season end, etc. For coverage, first, select images with less intense illumination, then divide the image into vegetation and soil, calculate the proportion of vegetation pixels in each image in the calculation area. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, and the filtered result is the final time series coverage. This data set includes relative greenness index (Gcc). Please refer to Liu et al. (2018) for sites information in the Citation section.
LIU Shaomin, Qu Yonghua, CHE Tao, XU Ziwei, REN Zhiguo
The dataset contains the phenological camera observation data of the Daman Superstation in the midstream of Heihe integrated observatory network from January 1 to December 31, 2021. The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 1280×720. For the calculation of the greenness index and phenology, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) needs to be calculated according to the region of interest, then the invalid value filling and filtering smoothing are performed, and finally the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, Peak, growth season end, etc. For coverage, first, select images with less intense illumination, then divide the image into vegetation and soil, calculate the proportion of vegetation pixels in each image in the calculation area. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, and the filtered result is the final time series coverage. This data set includes relative greenness index (Gcc). Please refer to Liu et al. (2018) for sites information in the Citation section.
LIU Shaomin, Qu Yonghua, CHE Tao, XU Ziwei, TAN Junlei, REN Zhiguo
The dataset contains the phenological camera observation data of the Arou Superstation in the midstream of Heihe integrated observatory network from January 1 to December 31, 2021. The instrument was developed and data processed by Beijing Normal University. The phenomenon camera integrates data acquisition and data transmission functions. The camera captures data by look-downward with a resolution of 1280×720. For the calculation of the greenness index and phenology, the relative greenness index (GCC, Green Chromatic Coordinate, calculated by GCC=G/(R+G+B)) needs to be calculated according to the region of interest, then the invalid value filling and filtering smoothing are performed, and finally the key phenological parameters are determined according to the growth curve fitting, such as the growth season start date, Peak, growth season end, etc. For coverage, first, select images with less intense illumination, then divide the image into vegetation and soil, calculate the proportion of vegetation pixels in each image in the calculation area. After the time series data is extracted, the original coverage data is smoothed and filtered according to the time window specified by the user, and the filtered result is the final time series coverage. This data set includes relative greenness index (Gcc). Please refer to Liu et al. (2018) for sites information in the Citation section.
LIU Shaomin, Qu Yonghua, CHE Tao, XU Ziwei, ZHANG Yang
Forest is an important terrestrial ecosystem, accounting for about one-third of the total land area. It plays an important role in regulating climate, providing habitat for species, and maintaining global ecosystem balance. The dynamic change of the tree-canopy cover will affect the structure, composition, and function of the forest ecosystem. Landsat data were used to derive the 30-m tree-canopy cover dataset based on the machine learning method. The dataset of the rate of tree-canopy cover change in the Eastern Himalayas from 1990 to 2020 was generated using the annual tree-canopy cover data. The results show that the average tree-canopy cover in this region had increased from 40.67% (1990) to 43.43% (2020), an increase of 2.76%, indicating that the forests in the Eastern Himalayas improved in the past few decades.
WANG Chunling , WANG Jianbang , HE Zhuoyu , FENG Min
This data is the annual average runoff data from 1495 to 2018 of Khorog Hydrometric Station of gunte River, a tributary of Amu Darya River, reconstructed based on tree ring data. The data obtained from the tree ring hydrology research carried out by the Urumqi desert Meteorology Institute of the China Meteorological Administration and the Institute of water issues, hydropower and ecology of the National Academy of Sciences of Tajikistan can be used for scientific research such as water resources assessment and water conservancy projects in mountainous areas of Central Asia.
SHANG Huaming
Data content: groundwater temperature data of Nukus irrigation area from January 2021 to December 2021, unit: 0.1 ℃. Data source and processing method: this data is collected from the automatic groundwater monitoring station in Nukus irrigation area. Data quality description: this data is site data with a time resolution of 3 hours. Results and prospects of data application: combined with other meteorological and hydrological parameters, hydrogeological conditions, especially the recharge, runoff and discharge conditions of groundwater, can be further identified and studied to master the dynamic law of groundwater, so as to provide a scientific basis for the evaluation of groundwater resources, scientific management and the research and Prevention of environmental geological problems.
LIU Tie
Normalized Difference Vegetation Index (NDVI) is the sum of the reflectance values of the NIR band and the red band by the Difference ratio of the reflectance values of the NIR band and the red band. Vegetation index synthesis refers to the selection of the best representative of vegetation index within the appropriate synthesis cycle, and the synthesis of a vegetation index grid image with minimal influence on spatial resolution, atmospheric conditions, cloud conditions, observation geometry, and geometric accuracy and so on. This data set includes the monthly synthesis of 30m*30m surface vegetation index products in Qilian mountain area in 2021. Max value composition (MVC) method was used to synthesize monthly NDVI products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.
WU Junjun , LI Yi, ZHONG Bo
This data is obtained through observation at Namucuo multi cycle comprehensive observation and research station of Chinese Academy of Sciences (2019) and Tibetan southeast alpine environment comprehensive observation and research station of Chinese Academy of Sciences (2021), including the earth atmosphere exchange flux or vertical gradient of species such as O3, NOx, HONO, H2O and HCHO. The time range is from April 28, 2019 to July 10, 2019 (Namuco station) and from May 2, 2021 to May 13, 2021 (Southeast Tibet station). The data consists of five documents. Documents 1-4 are the flux data and H2O vertical gradient, HONO vertical gradient and NO2 vertical gradient observed at Namuco station in 2019. Document 5 is the flux data observed at Southeast Tibet station in 2021. During the monitoring period, data was missing due to instrument status problems. This data has broad application prospects and can serve graduate students and scientists with backgrounds such as atmospheric science, climatology, and ecology.
YE Chunxiang
Forest change (including forest loss and gain) is a complex ecological process influenced by natural and human activities, and has important impacts on global material cycles and energy flows. Based on long-term tree-canopy cover (TCC) data, the Bi-temporal class-probabilities model was used to detect forest changes, and a dataset of forest change of the Natural Forest Conversion Program area in northeast China from 1986 to 2018 was obtained (spatial resolution 30 meters with a temporal resolution of 1 year). The method of stratified random sampling was used to select 1000 points in the reserve and visual interpretation was carried out to evaluate the accuracy of forest change. The results show that the accuracy of forest loss (producer's accuracy = 85.21%; user's accuracy = 84.26%) and forest restoration (producer's accuracy = 87.74%; user's accuracy = 88.31%) are both high, which can effectively reflect the forest change status of the protected area.
WANG Jianbang , HE Zhuoyu , WANG Chunling , FENG Min, PANG Yong, YU Tao , LI Xin
The data set is a three-dimensional lithospheric stress field model in the Sichuan-Yunnan region, which is constrained by GPS velocity field and focal mechanism solution. A 3D finite element model of regional lithospheric deformation is constructed by using the lithospheric structure fracture information in Sichuan-Yunnan region. The velocity boundary constraints of the model are given by integrating the regional GPS velocity published in the existing researches and the latest observation. At the same time, the stress field of the model is constrained by the focal mechanism solution of regional small and medium earthquakes and mantle convection. A comprehensive simulation model of current crustal deformation and stress field in Sichuan-Yunnan region is constructed. The model can be used for further study on valuable scientific issues such as the mechanism of the large earthquakes preparation, tectonic evolution of the lithosphere in Sichuan-Yunnan region and the eastward extrusion of the Tibetan Plateau.
XIONG Xiong
The data set is the S-wave radial anisotropic model in Sichuan-Yunnan region obtained by applying ambient noise tomography. First, the seismic waveform data is applied from National Earthquake Data Center and IRIS, and collected from deployed seismic stations. Using the collected seismic waveform data, we intercept waveform per each day and remove the mean and trend and filter the waveform. We invert the S-wave radial anisotropic model in Sichuan-Yunnan region by applying the ambient noise tomography. The model can be used for further study on valuable scientific issues such as the mechanism of the large earthquakes preparation, tectonic evolution of the lithosphere in Sichuan-Yunnan region and the eastward extrusion of the Tibetan Plateau.
GAO Yuan
The data set is the three-dimensional S-wave velocity and azimuthal anisotropic model in Sanjiang region obtained by applying ambient noise tomography. First, the seismic waveform data is applied from National Earthquake Data Center and collected from deployed seismic stations. Using the collected seismic waveform data, we intercept waveform per each day and remove the mean and trend and filter the waveform. We invert the three-dimensional S-wave velocity and azimuthal anisotropic model in Sanjiang region by applying the ambient noise tomography. The model can be used for further study on valuable scientific issues such as the mechanism of the large earthquakes preparation, tectonic evolution of the lithosphere in Sichuan-Yunnan region and the eastward extrusion of the Tibetan Plateau.
GAO Yuan
The data set is the three-dimensional S-wave velocity and azimuthal anisotropic model in Sichuan-Yunnan region obtained by applying ambient noise tomography. First, the seismic waveform data is applied from National Earthquake Data Center and collected from deployed seismic stations. Using the collected seismic waveform data, we intercept waveform per each day and remove the mean and trend and filter the waveform. We invert the three-dimensional S-wave velocity and azimuthal anisotropic model in Sichuan-Yunnan region by applying the ambient noise tomography. The model can be used for further study on valuable scientific issues such as the mechanism of the large earthquakes preparation, tectonic evolution of the lithosphere in Sichuan-Yunnan region and the eastward extrusion of the Tibetan Plateau.
GAO Yuan
The data set is the uppermost mantle Pn anisotropic model in Sichuan-Yunnan region obtained by applying Pn-wave tomography. First, the seismic waveform data is applied from National Earthquake Data Center and collected from deployed seismic stations. Using the collected seismic waveform data, we intercept Pn waveform as seismic events and remove the mean and trend and filter the waveform. We invert the uppermost mantle Pn anisotropic model in Sichuan-Yunnan region by applying the Pn-wave tomography. The model can be used for further study on valuable scientific issues such as the mechanism of the large earthquakes preparation, tectonic evolution of the lithosphere in Sichuan-Yunnan region and the eastward extrusion of the Tibetan Plateau.
GAO Yuan
The data set is the lithospheric anisotropic model in Sichuan-Yunnan region obtained by applying XKS splitting method. First, the seismic waveform data is applied from National Earthquake Data Center and collected from deployed seismic stations. Using the collected seismic waveform data, we intercept XKS waveform as seismic events and remove the mean and trend and filter the waveform. We invert the lithospheric anisotropic model in Sichuan-Yunnan region by applying the XKS splitting method. The model can be used for further study on valuable scientific issues such as the mechanism of the large earthquakes preparation, tectonic evolution of the lithosphere in Sichuan-Yunnan region and the eastward extrusion of the Tibetan Plateau.
GAO Yuan
The data set is the crustal anisotropic model in Sichuan-Yunnan region obtained by applying Pms receiver functions method. First, the seismic waveform data is applied from National Earthquake Data Center and collected from deployed seismic stations. Using the collected seismic waveform data, we intercept waveform as seismic events and remove the mean and trend and filter the waveform. We invert the crustal anisotropic model in Sichuan-Yunnan region by applying the Pms receiver functions method. The model can be used for further study on valuable scientific issues such as the mechanism of the large earthquakes preparation, tectonic evolution of the lithosphere in Sichuan-Yunnan region and the eastward extrusion of the Tibetan Plateau.
GAO Yuan
The data set is the upper crustal anisotropic model in Sichuan-Yunnan region obtained by applying S-wave splitting method. First, the seismic waveform data is applied from National Earthquake Data Center and collected from deployed seismic stations. Using the collected seismic waveform data, we intercept waveform as seismic events and remove the mean and trend and filter the waveform. We invert the upper crustal anisotropic model in Sichuan-Yunnan region by applying the S-wave splitting method. The model can be used for further study on valuable scientific issues such as the mechanism of the large earthquakes preparation, tectonic evolution of the lithosphere in Sichuan-Yunnan region and the eastward extrusion of the Tibetan Plateau.
GAO Yuan
This data set is the information of a linear seismic array in Daliangshan area in Western Sichuan. The observation time is from december2018 to October 2020. The array is near the NE-SW trend. This array reaches the Sichuan basin to Yibin area to the east, and reaches the Yanyuan basin in Daliangshan area to the west. Each station uses Trillium posthole/horizon 120 broadband seismometer and Centaur data collector. A total of 40 seismic stations are deployed, with an average station spacing of only 10km. This array is used to collect and record high-quality seismic waveforms. Instrument maintenance and data acquisition are carried out every three months.
AI Yinshuang
The data set is the dispersion curves results of seismic stations in Sichuan-Yunnan region obtained by using ambient noise and teleseismic surface waveforms. First, the seismic waveform data is collected from seismic stations deployed in the Sichuan-Yunnan region. Using the collected seismic waveform data, we intercept waveform of each day from each station. After removing the mean and trend and filtering, we invert the dispersion curves of seismic stations in Sichuan-Yunnan region by using the ambient noise and teleseismic surface waveforms based on time-frequency analysis. The model can be used for further study on valuable scientific issues such as the mechanism of the large earthquakes preparation, tectonic evolution of the lithosphere in Sichuan-Yunnan region and the eastward extrusion of the Tibetan Plateau.
AI Yinshuang
The data set is the subsurface interface model in Sichuan-Yunnan region obtained by applying the ambient noise, teleseismic surface wave and body wave joint inversion. First, the seismic waveform data is applied from National Earthquake Data Center. Using the collected seismic waveform data, we remove the mean and trend and filter the waveform. We invert the subsurface interface model in Sichuan-Yunnan region by applying the ambient noise, teleseismic surface wave and body wave joint inversion. The model can be used for further study on valuable scientific issues such as the mechanism of the large earthquakes preparation, tectonic evolution of the lithosphere in Sichuan-Yunnan region and the eastward extrusion of the Tibetan Plateau.
AI Yinshuang
The data set is the three-dimensional S-wave velocity model in Sichuan-Yunnan region obtained by applying the ambient noise tomography. First, the seismic waveform data is applied from National Earthquake Data Center. Using the collected seismic waveform data, we intercept waveform of each day from each station. After removing the mean and trend and filtering, we invert the three-dimensional S-wave attenuation model in Sichuan-Yunnan region by applying the ambient noise tomography. The model can be used for further study on valuable scientific issues such as the mechanism of the large earthquakes preparation, tectonic evolution of the lithosphere in Sichuan-Yunnan region and the eastward extrusion of the Tibetan Plateau.
AI Yinshuang
The data set is the three-dimensional lithospheric velocity model in Sichuan-Yunnan region obtained by applying the full-waveform adjoint tomography. First, the seismic waveform data is applied from National Earthquake Data Center. Using the collected seismic waveform data, we intercept the seismic phase data with high signal-to-noise ratio according to the seismic events. After removing the mean and trend and filtering, the data are used to obtain the three-dimensional lithospheric velocity model in Sichuan-Yunnan region by applying the waveform adjoint tomography. The model can be used for further study on valuable scientific issues such as the mechanism of the preparation of large earthquakes and tectonic evolution of the lithosphere in Sichuan-Yunnan region and the eastward extrusion of the Tibetan Plateau.
YANG Dinghui
The data set is the three-dimensional crustal velocity model in Sichuan-Yunnan region obtained by applying the full-waveform adjoint tomography. First, the seismic waveform data is applied from National Earthquake Data Center. Using the collected seismic waveform data, we intercept the seismic phase data with high signal-to-noise ratio according to the seismic events, and extract the amplitude information after removing the mean and trend and filtering. Finally, the amplitude data are used to obtain the three-dimensional crustal velocity model in Sichuan-Yunnan region by applying the waveform adjoint tomography. The model can be used for further study on valuable scientific issues such as the mechanism of the preparation of large earthquakes and tectonic evolution of the lithosphere in Sichuan-Yunnan region and the eastward extrusion of the Tibetan Plateau.
YANG Dinghui
The data set is the three-dimensional upper mantle S-wave Q model in Sichuan-Yunnan region obtained by applying the full-waveform adjoint tomography. First, the seismic waveform data is applied from National Earthquake Data Center. Using the collected seismic waveform data, we intercept the S-wave seismic phase data with high signal-to-noise ratio according to the seismic events, and extract the S-wave amplitude information after removing the mean and trend and filtering. Finally, the S-wave amplitude data are used to obtain the three-dimensional S-wave attenuation model in Sichuan-Yunnan region by applying the waveform adjoint tomography. The model can be used for further study on valuable scientific issues such as the tectonic evolution of the lithosphere in Sichuan-Yunnan region and the eastward extrusion of the Tibetan Plateau.
YANG Dinghui
The data set is the three-dimensional lithospheric S-wave Q-value model data in the surrounding areas of Sichuan and Yunnan obtained by using the full waveform based adjoint attenuation imaging method. First, apply to the data backup center of the national seismological network for obtaining the seismic waveform data. Using the collected seismic waveform data, intercept the S-wave seismic phase data with high signal-to-noise ratio according to the seismic events, and extract the S-wave amplitude information after de averaging, de trending, waveform pinching and filtering. Finally, the S-wave amplitude data are inverted by using the waveform accompanying attenuation imaging method to obtain the three-dimensional S-wave attenuation model in Sichuan and Yunnan. The model data set can be used to further study important scientific issues such as the tectonic evolution of the lithosphere in Sichuan Yunnan region and the extension of the Qinghai Tibet Plateau.
YANG Dinghui
This data set includes major and trace elements and zircon U-Pb isotope data of Mesozoic sedimentary rocks in Baoshan block, Tengchong, Yunnan Province. The sampling time is 2018, and the area is near lameng Town, Baoshan District, Tengchong, Yunnan. The rock samples include 8 sedimentary rock samples. This data provides key information for understanding the evolution of the middle Tethys structure between Tengchong and Baoshan, and limits the closing time of the middle Tethys ocean to the late Jurassic, which is of great significance for discussing the evolution process of the Tethys structure. The whole rock major and trace elements of rock samples were tested by fluorescence spectrometer (XRF) and plasma mass spectrometer (ICP-MS), and zircon U-Pb was dated by laser ablation plasma mass spectrometer (LA-ICP-MS). The testing units include Institute of Geology and Geophysics, Chinese Academy of Sciences and Institute of Qinghai Tibet Plateau. The related articles of this data set have been published in the Journal of Asian Earth Sciences, and the data results are true and reliable.
ZHANG Jiuyuan
This data set consists of multi-scale and high-resolution seismic wave velocity, attenuation, anisotropy, interface and stress field model of the crust, lithosphere and upper mantle beneath the Sichuan-Yunnan area. The velocity and attenuation models are mainly obtained by applying waveform adjoint tomography, double difference tomography and ambient noise tomography methods. The anisotropic models are mainly obtained by applying shear wave splitting, receiver functions and ambient noise methods. The interface structure is mainly obtained by receiver functions. The stress field model is mainly restrained by GPS velocity field and focal mechanism. Some of the used seismic waveform are from published data, and some are obtained from deployed seismic stations. The model data set can be used for further study on valuable scientific issues such as the mechanism of the occurrence of large earthquakes and the tectonic evolution of the lithosphere beneath the Chuandian Block, and the dynamic mechanism of the eastward extrusion of the Tibetan Plateau.
PEI Shunping
The data from the Digital Mountain Map of China depicts the spatial pattern and complex morphological characteristics of mountains in China from a macro scale, including the mountains’ spatial distribution, classification, morphological elements and area ratio. It is a set of basic data that can be used for mountain zoning, mountain genetic classification and resource environment correlation analysis. Mountains carry great natural resource supply, provide ecological service and regulation functions, and play an important part in eco-civilization construction and socioeconomic development in China. Lately，Prof. Li Ainong of the Institute of Mountain Hazards and Environment, CAS, developed this data set based on the spatial definition of mountains, an a topography adaptive slide window method for the relief amplitude. The data include: (1) Spatial distribution of mountains in China; (2) Mountain classification; (3) Main mountain ranges (with range alignment, relief grade and ridge morphology); (4)Main mountain peaks; (5)Mountain proportion table of the provinces/autonomous regions/municipalities of China; (6) Contour zoning data; (7) General situation of mountain formation; (8)Mountain division and zoning data; (9) List of main mountain peaks. The spatial resolution of the original DEM source is about 90m. And the boundaries of mountains have been revised with multisource remote sensing data, which has good spatial consistency with the relief shading map. The cartographic generalization accuracy of mountain ranges and relevant features is 1:1 000 000. Mountain features in this data set have higher spatial resolution and pertinence, which are available for the zonality of mountain environment and mountain hazards, and the spatial analysis for ecological, production and living spaces in mountain areas, surpporting macro decision-making on mountain areas' development in China. p
NAN Xi , LI Ainong , DENG Wei
Data files are in 7Z compressed package format, which can be decompressed and opened by 7-zip software. There are three files in total, namely file 1, text version of grassland Degradation classification on The Qinghai-Tibet Plateau, file type is Word, and file 2, named As Map, with seven maps in total. The type of the image is PNG, and the name of the image is the trend rate of average NDVI change in the growing season of grass, grassland, meadow, grassland, alpine vegetation, desert and swamp on the Tibetan Plateau from 2010 to 2019. File 3. The folder named as data is filled with pictures. There are 7 kinds of pictures with the same names as above.
ZHOU Huakun
Vegetation primary productivity (Net Primary Production, NPP) dataset, source data from MODIS product (MOD17A3H), after data format conversion, projection, resampling and other preprocessing. The existing format is TIFF format, the projection is Krasovsky_1940_Albers projection, the unit is kg C/m2/year, and the spatial range is the entire Qinghai-Tibet Plateau. The spatial resolution of the data is 500 meters, the temporal resolution is every 5 years, and the time range is from 2001 to 2020. The NPP of the Qinghai-Tibet Plateau showed a trend of increasing gradually from northwest to southeast.
ZHU Juntao
Land cover refers to the mulch formed by the current natural and human influences on the earth's surface. It is the natural state of the earth's surface, such as forests, grasslands, farmland, soil, glaciers, lakes, swamps and wetlands, and roads. The Land Cover (LC) dataset is original from MODIS products and preprocessed by format conversion, projection and resampling. The existing format is TIFF and projection is Krasovsky_1940_Albers. The data set has a spatial resolution of 1000 meters and provides one image per year during the period from 2002 to 2020. Land cover products were classified into 17 categories defined by the International Geosphere Biosphere Programme (IGBP), including 11 categories of natural vegetation, 3 categories of land use and Mosaic, and 3 categories of non-planting land.
ZHU Juntao
The Normalized Difference Vegetation Index (LST) dataset is original from MODIS products and preprocessed by format conversion, projection and resampling. The existing format is TIFF and projection is Krasovsky_1940_Albers. The data set has a spatial resolution of 1000 meters and provides one image per year during the period from 2001 to 2020. NDVI products are calculated by reflectance of red and near-infrared bands, which can be used to detect vegetation growth state and vegetation coverage. NDVI is ranged from -1 to 1, and the negative value means the land is covered by snow, water, etc. By contrast, positive value means vegetation coverage, and the coverage increases with the increase of NDVI.
ZHU Juntao
This dataset contains daily 0.05°×0.05° land surface soil moisture products in Qilian Mountain Area in 2021. The dataset was produced by utilizing the optimized wavelet-coupled-RF downscaling model (RF-OWCM) to downscale the SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture (SMAP L3, V8). The auxiliary datasets participating in the downscaling model include the MUltiscale Satellite remotE Sensing (MUSES) LAI/FVC product, the daily 1-km all-weather land surface temperature dataset for the Chinese landmass and its surrounding areas (TRIMS LST-TP;) and Lat/Lon information.
CHAI Linna, ZHU Zhongli, LIU Shaomin
This data set is a geochemical data set of granitoids in hengduan Mountain area. The total amount and components of rare earth elements in the kangdian paleocontinent, Ailaoshan-Jinshajiang, Lincang-Zuo Gong and Luxi-Tengchong granite belts in Hengduan Mountain area are studied. The ree abundance, variation and distribution patterns of various rocks are discussed, and the genetic types of granites are preliminarily discussed. Isotope geochemistry study, using strontium and neodymium isotope system tracing method, it is helpful to understand the genesis and material source of granitoids. For this reason, the isotopic compositions of strontium and neodymium in the granitoids of the four rock belts, including diorite, granodiorite, porphyritic biotite monzonite, potassic feldspar granite, alkali feldspar granite, hornblende biotite monzonite porphyry and alkali feldspar syenite, have been studied. It is helpful to study the geochemical related aspects of granites.
ZHANG Yuquan , XIE Yingwen
This data set contains the meteorological data of Pengbo irrigation area in Tibet from 2019 to 2022, including rainfall, temperature and relative humidity data, as well as the measured soil moisture and soil temperature data of highland barley, oat and grassland. The data interval is recorded in hours, and the measured time is from 2019 to 2022. The data of soil temperature and soil moisture are relatively detailed, which can reflect the change law of soil moisture and temperature at different time scales of time, day, month, season and year, and can also better meet the calibration and verification requirements of farmland water and heat transport model. The data set also includes crop evapotranspiration data and leakage data, which is helpful to analyze the water consumption of crops in the whole growth period and the water consumption and leakage at different growth stages in the alpine region of Tibet, and plays an important role in clarifying the water balance of different farmland systems. The meteorological, soil moisture, soil temperature, transpiration and leakage data of Pengbo irrigation area in Tibet provided by this data set are helpful to reveal the water transformation process at the farmland scale and irrigation area scale, and fully understand the water and heat transfer process and crop growth state of SPAC system in the high cold region of Tibet.
TANG Pengcheng
This dataset contains the glacier outlines in Qilian Mountain Area in 2015. The dataset was produced based on classical band ratio criterion and manual editing. Chinese GF series images collected in 2018 were used as basic data for glacier extraction. Sentinel-2 images, Google images and Map World images were employed as reference data for manual adjusting. The dataset was stored in SHP format and attached with the attributions of coordinates, glacier ID and glacier area. Consisting of 1 season, the dataset has a spatial resolution of 2 meters. The accuracy is about 1 pixel (±2 meter). The dataset directly reflects the glacier distribution within the Qilian Mountain in 2018, and can be used for quantitative estimation of glacier mass balance and the quantitative assessment of glacier change’s impact on basin runoff.
Li Jia Li Jia LI Jia LI Jia
This dataset contains the glacier outlines in Qilian Mountain Area in 2019. The dataset was produced based on classical band ratio criterion and manual editing. Chinese GF series images collected in 2019 were used as basic data for glacier extraction. Sentinel-2 images, Google images and Map World images were employed as reference data for manual adjusting. The dataset was stored in SHP format and attached with the attributions of coordinates, glacier ID and glacier area. Consisting of 1 season, the dataset has a spatial resolution of 2 meters. The accuracy is about 1 pixel (±2 meter). The dataset directly reflects the glacier distribution within the Qilian Mountain in 2019, and can be used for quantitative estimation of glacier mass balance and the quantitative assessment of glacier change’s impact on basin runoff.
Li Jia Li Jia LI Jia LI Jia
This dataset contains the glacier outlines in Qilian Mountain Area in 2020. The dataset was produced based on classical band ratio criterion and manual editing. Chinese GF series images collected in 2020 were used as basic data for glacier extraction. Sentinel-2 images, Google images and Map World images were employed as reference data for manual adjusting. The dataset was stored in SHP format and attached with the attributions of coordinates, glacier ID and glacier area. Consisting of 1 season, the dataset has a spatial resolution of 2 meters. The accuracy is about 1 pixel (±2 meter). The dataset directly reflects the glacier distribution within the Qilian Mountain in 2020, and can be used for quantitative estimation of glacier mass balance and the quantitative assessment of glacier change’s impact on basin runoff.
Li Jia Li Jia LI Jia LI Jia
Through the joint inversion of seismic waveforms and InSAR coseismic displacement data, our study revealed the spatiotemporal and spatial source rupture processprocesses of the two strong earthquakes that occurred in struck the eastern Tibetan Plateau atin May 2021. The results show that the Yangbi earthquake, which occurred in along the southeastern margin of the TibetTibetan Plateau, was a Mw6.1 event with characterized by unilateral right-dextral strike-slip rupture and 8s an 8 s duration. The In addition, the Maduo earthquake, which occurred in the interior of the Tibetan Plateau, was a Mw7.5 event with characterized by left-sinistral lateral-strike- slip extendedextending along both sides of the earthquake seismogenic fault and 36sa 36 s duration. The rupture properties of these two strong earthquakes reflect the deformation characteristics of different parts of the eastern Tibetan Plateau,. and also These events also caused the increase of the Coulomb stress of the surrounding active faults to increase, so we should pay attention to the risk potential of future earthquakes should be evaluated.
WANG Weimin
This dataset contains the ground surface water (including liquid water, glacier and perennial snow) distribution in Qilian Mountain Area in 2021. The dataset was produced based on classical Normalized Difference Water Index (NDWI) extraction criterion and manual editing. Landsat images collected in 2021 were used as basic data for water index extraction. Sentinel-2 images and Google images were employed as reference data for adjusting the extraction threshold. The dataset was stored in SHP format and attached with the attributions of coordinates and water area. Consisting of 1 season, the dataset has a temporal resolution of 1 year and a spatial resolution of 30 meters. The accuracy is about 1 pixel (±30 meter). The dataset directly reflects the distribution of water bodies within the Qilian Mountain in 2021, and can be used for quantitative estimation of water resource.
Li Jia Li Jia LI Jia LI Jia
Based on the "second Qinghai Tibet Plateau comprehensive scientific investigation" and "China's soil series investigation and compilation of China's soil series" "The obtained soil survey profile data, using predictive Digital Soil Mapping paradigm, using geographic information and remote sensing technology for fine description and spatial analysis of the soil forming environment, developed adaptive depth function fitting methods, and integrated advanced ensemble machine learning methods to generate a series of soil attributes (soil organic carbon, pH value, total nitrogen, total phosphorus, total potassium, cation exchange capacity, gravel content (>2mm) in the Qinghai Tibet plateau region." , sand, silt, clay, soil texture type, unit weight, soil thickness, etc.) and quantify the spatial distribution of uncertainty. Compared with the existing soil maps, it better represents the spatial variation characteristics of soil properties in the Qinghai Tibet Plateau. The data set can provide soil information support for the study of soil, ecology, hydrology, environment, climate, biology, etc. in the Qinghai Tibet Plateau.
LIU Feng, ZHANG Ganlin
This dataset contains the glacier outlines in Qilian Mountain Area in 2021. The dataset was produced based on classical band ratio criterion and manual editing. Chinese GF series images collected in 2021 were used as basic data for glacier extraction. Sentinel-2 images, Google images and Map World images were employed as reference data for manual adjusting. The dataset was stored in SHP format and attached with the attributions of coordinates, glacier ID and glacier area. Consisting of 1 season, the dataset has a spatial resolution of 2 meters. The accuracy is about 1 pixel (±2 meter). The dataset directly reflects the glacier distribution within the Qilian Mountain in 2021, and can be used for quantitative estimation of glacier mass balance and the quantitative assessment of glacier change’s impact on basin runoff.
Li Jia Li Jia LI Jia LI Jia
This data set is the original observation data of magnetotelluric method (MT) collected by the project team in Yangyi geothermal field, Dangxiong County, Tibet. The data format is EDI and contains 36 files. The data set contains 3 MT profiles, with the distance between survey lines of about 1km and the distance between survey points of about 500m. The field data acquisition equipment adopts the new SEP ground electromagnetic detection system developed by the Chinese Academy of Sciences. At each MT measuring point, the two horizontal components ex (north-south direction) and ey (east-west direction) of the electric field are measured with a non polarized electrode, and the three components HX (north-south direction), hy (east-west direction) and Hz (plumb bob direction) of the magnetic field are measured with a magnetic sensor. The observation time of each measuring point exceeds 10 hours, and the effective frequency range is 320 hz~0.001 Hz. Through the preprocessing and inversion of the data set, the electrical structure in the depth of 10km in Yangyi geothermal field can be obtained, which provides a basis for the location and scale of deep heat sources, heat control and heat conduction structures in the investigation area.
CHEN Weiying
Carbon, nitrogen, phosphorus, sulfur and potassium are important basic life elements of ecosystem. It plays an important role in revealing the impact of its regional variation and spatial pattern on human activities and the sustainable development of ecosystem in the future. The Qinghai Tibet Plateau has unique alpine vegetation types and rich vertical zone landforms and surface cover types. The biogeographic pattern of surface elements (carbon, nitrogen, phosphorus, sulfur, potassium) is an important manifestation of the coupling of carbon, nitrogen and water cycle processes and related mechanisms of alpine ecosystems. This dataset focuses on the distribution pattern and spatial variation of surface materials (plant leaf branch stem root and litter) in the complex ecosystem of the southeast edge of the Qinghai Tibet Plateau and Hengduan Mountain area, in order to provide data support for regional model simulation and ecological management.
LI Mingxu
This data set is the original observation data of magnetotelluric method (MT) collected by the project team in Yangbajing Geothermal field, Dangxiong County, Tibet. The data format is EDI and contains 53 files. The data set contains 4 MT profiles, with the distance between survey lines of about 1km and the distance between survey points of about 500m. The field data acquisition equipment adopts the new SEP ground electromagnetic detection system developed by the Chinese Academy of Sciences. At each MT measuring point, the two horizontal components ex (north-south direction) and ey (east-west direction) of the electric field are measured with a non polarized electrode, and the three components HX (north-south direction), hy (east-west direction) and Hz (plumb bob direction) of the magnetic field are measured with a magnetic sensor. The observation time of each measuring point exceeds 10 hours, and the effective frequency range is 320 hz~0.001 Hz. Through the preprocessing and inversion of the data set, the electrical structure in the depth of 10km in Yangbajing Geothermal field can be obtained, which provides a basis for the location and scale of deep heat sources, heat control and heat conduction structures in the investigation area.
CHEN Weiying
Vulnerability of disaster bearing body is the degree of damage that human social and economic activities may suffer under the disturbance or pressure of natural disasters under a certain social and economic background, that is, the nature that disaster bearing body is vulnerable to damage and loss in the face of natural disasters. Based on the actual scientific research and expert guidance, this data constructs the vulnerability assessment indicators of disaster bearing bodies from the three aspects of exposure, sensitivity and adaptability, and uses the revised serv vulnerability model to calculate the Himalayan surrounding areas (domestic part) and the Asian water tower area. In order to systematically analyze the vulnerability of disaster bearing bodies in the study area, this data selects indicators from six aspects: population, economy, traffic lines, ecological environment, livestock and buildings, and constructs an indicator system of 6 first-class indicators, 18 second-class indicators and 29 third-class indicators. After the obtained vulnerability assessment results of population, economy, traffic lines, ecological environment, livestock and buildings are normalized, the vulnerability assessment maps of Himalayan surrounding areas (domestic part) and Asian water tower area are obtained by vector superposition.
ZHOU Qiang, CHEN Yingming , LIU Fenggui, CHEN Ruishan , CHEN Qiong, XIA Xingsheng , NIU Baicheng , DUAN Yufang
To explore inorganic hydrochemical characteristics of the upper Yarlung Zangbo River, water samples were collected from the main stream and different tributaries in this region in August 2020. The water was collected with 100mL polyethylene (PE) plastic bottle, and the basic physical and chemical parameters such as pH value (±0.2) and dissolved oxygen (±1%) of the sampling site were measured by multi -parameter water quality monitor (YSI-EX02,USA).,and HCO3- concentration was titrated with 0.025mol/L HCl.The concentrations of Na+, K+, Ca2+, Mg2+, SO42-, NO3- and Cl- ions were analyzed and determined by ion chromatograph (Shenhan CIC-D160, China) in the laboratory. Using Gibbs model, correlation analysis and principal component analysis method, analyzed the one main ion concentration changes, chemical composition characteristics, analytical, and the ion source was designed to reveal inorganic water chemical characteristics of The Tibet plateau glacier melt water runoff, and for plateau typical river water and changing trend forecast provides the basis.
NIU Fengxia
This dataset includes data recorded by the Heihe integrated observatory network obtained from a Cosmic-ray Soil Moisture Observing System of soil moisture of Sidaoqiao Superstation from January 1 to December 31, 2021. The site (101.1374° E, 42.0012° N) was located in the Ejina Banner in Inner Mongolia Autonomous Region. The elevation is 873 m. The bottom of the probe was 0.5 m above the ground; the sampling interval was 1 hour. The raw COSMOS data include the following: battery (Batt, V), temperature (T, C), relative humidity (RH, %), air pressure (P, hPa), fast neutron counts (N1C, counts per hour), thermal neutron counts (N2C, counts per hour), sample time of fast neutrons (N1ET, s), and sample time of thermal neutrons (N2ET, s). The distributed data include the following variables: Date, Time, P, N1C, N1C_cor (corrected fast neutron counts) and VWC (volume soil moisture, %), which were processed as follows: 1) Data were removed and replaced by -6999 when (a) the battery voltage was less than 11.8 V, (b) the relative humidity was greater than 80% inside the probe box, (c) the counting data were not of one-hour duration and (d) neutron count differed from the previous value by more than 20%; 2) An air pressure correction was applied to the quality-controlled raw data according to the equation contained in the equipment manual; 3) After the quality control and corrections were applied, soil moisture was calculated using the equation in Zreda et al. (2012), where N0 is the neutron counts above dry soil and the other variables are fitted constants that define the shape of the calibration function. Here, the parameter N0 was calibrated using the in situ observed soil moisture by SoilNET within the footprint; 4) Based on the calibrated N0 and corrected N1C, the hourly soil moisture was computed using the equation from the equipment manual. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Zhu et al. (2015) for data processing) in the Citation section.
LIU Shaomin, CHE Tao, ZHU Zhongli, XU Ziwei, REN Zhiguo, TAN Junlei, ZHANG Yang
This dataset includes data recorded by the Heihe integrated observatory network obtained from a Cosmic-ray Soil Moisture Observing System of soil moisture of Daman Superstation from January 1 to December 31, 2021. The site (100.372° E, 38.856° N) was located on a cropland (maize surface) in the Daman irrigation, which is near Zhangye city, Gansu Province. The elevation is 1556 m. The bottom of the probe was 0.5 m above the ground; the sampling interval was 1 hour. The raw COSMOS data include the following: battery (Batt, V), temperature (T, ℃), relative humidity (RH, %), air pressure (P, hPa), fast neutron counts (N1C, counts per hour), thermal neutron counts (N2C, counts per hour), sample time of fast neutrons (N1ET, s), and sample time of thermal neutrons (N2ET, s). The distributed data include the following variables: Date, Time, P, N1C, N1C_cor (corrected fast neutron counts) and VWC (volume soil moisture, %), which were processed as follows: 1) Data were removed and replaced by -6999 when (a) the battery voltage was less than 11.8 V, (b) the relative humidity was greater than 80% inside the probe box, (c) the counting data were not of one-hour duration and (d) neutron count differed from the previous value by more than 20%; 2) An air pressure correction was applied to the quality-controlled raw data according to the equation contained in the equipment manual; 3) After the quality control and corrections were applied, soil moisture was calculated using the equation in Zreda et al. (2012), where N0 is the neutron counts above dry soil and the other variables are fitted constants that define the shape of the calibration function. Here, the parameter N0 was calibrated using the in situ observed soil moisture by SoilNET within the footprint; 4) Based on the calibrated N0 and corrected N1C, the hourly soil moisture was computed using the equation from the equipment manual. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Zhu et al. (2015) for data processing) in the Citation section.
LIU Shaomin, CHE Tao, ZHU Zhongli, XU Ziwei, REN Zhiguo, TAN Junlei, ZHANG Yang
This dataset includes data recorded by the Heihe integrated observatory network obtained from a Cosmic-ray Soil Moisture Observing System of soil moisture of Arou Superstation from January 1 to December 31, 2021. The site (100.4643° E, 38.0473° N) was located in the Caodaban Village, near Qilian County in Qinghai Province. The elevation is 3033 m. The bottom of the probe was 0.5 m above the ground; the sampling interval was 1 hour. The raw COSMOS data include the following: battery (Batt, V), temperature (T, C), relative humidity (RH, %), air pressure (P, hPa), fast neutron counts (N1C, counts per hour), thermal neutron counts (N2C, counts per hour), sample time of fast neutrons (N1ET, s), and sample time of thermal neutrons (N2ET, s). The distributed data include the following variables: Date, Time, P, N1C, N1C_cor (corrected fast neutron counts) and VWC (volume soil moisture, %), which were processed as follows: 1) Data were removed and replaced by -6999 when (a) the battery voltage was less than 11.8 V, (b) the relative humidity was greater than 80% inside the probe box, (c) the counting data were not of one-hour duration and (d) neutron count differed from the previous value by more than 20%; 2) An air pressure correction was applied to the quality-controlled raw data according to the equation contained in the equipment manual; 3) After the quality control and corrections were applied, soil moisture was calculated using the equation in Zreda et al. (2012), where N0 is the neutron counts above dry soil and the other variables are fitted constants that define the shape of the calibration function. Here, the parameter N0 was calibrated using the in situ observed soil moisture by SoilNET within the footprint; 4) Based on the calibrated N0 and corrected N1C, the hourly soil moisture was computed using the equation from the equipment manual. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Zhu et al. (2015) for data processing) in the Citation section.
LIU Shaomin, CHE Tao, ZHU Zhongli, XU Ziwei, ZHANG Yang, TAN Junlei, REN Zhiguo
Fractional Vegetation Coverage (FVC) is defined as the proportion of the vertical projection area of Vegetation canopy or leaf surface to the total Vegetation area, which is an important indicator to measure the status of Vegetation on the surface. In this dataset, vegetation coverage is an evaluation index reflecting vegetation coverage. 0% means that there is no vegetation in the surface pixel, that is, bare land. The higher the value, the greater the vegetation coverage in the region. This data set includes the monthly synthesis of 30m*30m surface vegetation index products in Qilian mountain area in 2021. Max value composition (MVC) method was used to synthesize monthly FVC products on the surface using the reflectivity data of Landsat 8 and sentinel 2 channels from Red and NIR channels.
WU Junjun , LI Yi, ZHONG Bo
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
This dataset provides the monitoring data of runoff, precipitation and temperature of the Duodigou Runoff Experimental Station located in the northern suburbs of Lhasa city. Among the dataset, there are two runoff monitoring stations, which provide discharge data from June to December 2019, with a data step of 10 minutes. There are five precipitation monitoring stations, which provide precipitation data from 2018 to 2021, with a data step of 1 day. There are eight air temperature monitoring stations, which provide air temperature data from 2018 to 2021 in 30 minute steps. The discharge, the precipitation and the temperature data are the measured values. The dataset can provide data support for the study of hydrological and meteorological processes in the Tibet Plateau.
LIU Jintao
The dataset includes the measured soil thickness data at 148 points in the Yarlung Zangbo River Basin, as well as the physical properties and hydraulic characteristics (such as particle size, saturated water content, organic matter content, saturated hydraulic conductivity, etc.) of soil samples at 40 points. The sampling points are distributed from Zhongba County in the upper reaches of the Yarlung Zangbo River basin to Nyingchi city in the lower reaches. The soil thickness data is obtained through the excavation profile measurement, and other soil data are obtained from the collected ring knife samples according to the standardized experimental process, so the data accuracy is high. The soil data of the Yarlung Zangbo River basin provided by this dataset can provide a reference for large-scale soil mapping on the Qinghai Tibet Plateau and improve the prediction accuracy of relevant studies.
LIU Jintao
From September 3 to September 9, 2020, groundwater and surface water were collected in the upper reaches of Nujiang River Basin (i.e. Naqu basin in Nujiang River source area), and the samples were immediately put into 100 ml high density polyethylene (HDPE) bottles. 18O and D are analyzed and tested by liquid water isotope analyzer (picarro l2140-i, USA), and the stable isotope ratio is expressed by the thousand difference relative to Vienna "standard average seawater" (VSMOW). δ 18O and δ The analysis error of D is ± 0.1 ‰ and ± 1 ‰ respectively. It provides basic data support for subsequent analysis of groundwater source analysis in Naqu basin.
LIU Yaping , CHEN Zhenghao
In order to explore the inorganic hydrochemical characteristics of Naqu basin, river water and groundwater were collected in Naqu basin in September 2020 and September 2021. Collect river water and groundwater with 550ml plastic bottles on site. The main anions and anions (ca2+, na+, mg2+, k+, so42- and cl-) are measured with an ion chromatograph (metrohm ecoic, Switzerland), with a measurement accuracy of 1 μ G/l. Bicarbonate (hco3-) is titrated with acid-base indicator and determined with 50ml acid burette. The purpose is to reveal the inorganic hydrochemical characteristics of water bodies in Naqu basin and provide data support for the analysis of groundwater recharge sources in Naqu basin.
LIU Yaping , CHEN Zhenghao
This dataset provides global soil texture data optimized by remote sensing estimation of wilting coefficient, with a spatial resolution of 0.25 degree. The dataset incorporates remote sensing-based (e.g., SMAP satellite) estimation of soil wilting point and uses the SCE-UA algorithm to optimize two prevalently used soil texture datasets (i.e., GSDE (Shangguan et al. 2014) and HWSD (Fischer et al., 2008)). Comparison results with in-situ observations (44 stations in North America) show that, the soil moisture and evaporative fraction simulation from the Noah-MP land surface model by using the optimized soil texture have been significantly improved.
HE Qing , LU Hui, ZHOU Jianhong , YANG Kun, YANG Kun, 阳坤, YANG Kun, SHI Jiancheng
This dataset includes the glacier elevation change data in the High Mountain Asia (HMA) region from 2018 to 2020 derived from Ice, Cloud and land Elevation Satellite (ICESat-2) data. The glacial elevation changes in the High Mountain Asia region were calculated using ICESat-2 data (2018-2020) and SRTM DEM data in 2000, taking into account the inhomogeneity of glacier changes and area distribution at different elevations and slopes (weighted average of glacier area of elevation and slope bins in 1°×1° grid ). The dataset can provide the annual change information of glacier elevation in the High Mountain Asia region from 2018 to 2020 relative to 2000. These data can be used for studies of climate change in the High Mountain Asia.
SHEN Cong , JIA Li
This dataset includes the glacier elevation change data in the High Mountain Asia (HMA) region from 2003 to 2008 derived from Ice, Cloud and land Elevation Satellite (ICESat-1) data. The glacial elevation changes in the High Mountain Asia region were calculated using ICESat-1 data (2003-2008) and SRTM DEM data in 2000, taking into account the inhomogeneity of glacier changes and area distribution at different elevations and slopes (weighted average of glacier area of elevation and slope bins in 1°×1° grid ). The dataset can provide the annual change information of glacier elevation in the High Mountain Asia region from 2003 to 2008 relative to 2000. These data can be used for studies of climate change in the High Mountain Asia.
SHEN Cong , JIA Li
This dataset contains the flux measurements from the large aperture scintillometer (LAS) at Sidaoqiao Superstation in the Heihe integrated observatory network from January 1 to December 31 in 2021. There were BLS900 and BLS450 at Sidaoqiao Superstation. The north towers were set up with these instruments’ receivers and the south towers were transmitters. The site (north: 101.137° E, 42.008° N; south: 101.131° E, 41.987 N) was located in Ejinaqi, Inner Mongolia. The underlying surfaces between the two towers were tamarisk, populus, bare land and farmland. The elevation is 873 m. The effective height of the LAS was 25.5 m, and the path length was 2350 m. The data were sampled 1 minute. The raw data acquired at 1 min intervals were processed and quality controlled. The data were subsequently averaged over 30 min periods, in which sensible heat flux was iteratively calculated by combining Cn2 with meteorological data according to the Monin-Obukhov similarity theory. The main quality control steps were as follows: (1) The data were rejected when Cn2 exceeded the saturated criterion (BLS900&BLS450：Cn2>7.25E-14). (2) The data were rejected when the demodulation signal was small (BLS900&BLS450：Average X Intensity<1000). (3) The data were rejected when collected during precipitation. (4) The data were rejected if collected at night when weak turbulence occurred (u* was less than 0.1 m/s). In the iteration process, the universal functions of Thiermann and Grassl (1992) were selected for BLS900 and BLS450. Detailed can refer to Liu et al. (2011, 2013). Due to instrument adjustment and inadequate power supply, the date of missing data for the large aperture scintillator is: 2021.03.15-2021.03.18；2021.9.12-2021.9.18. Several instructions were included with the released data. (1) The las data are firstly from BLS900, followed by BLS450, and finally the final missing data was marked with-6999. (2) The dataset contained the following variables: Date/time (yyyy/m/d h:mm), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H, W/m^2). In this dataset, a time of 0:30 corresponds to the average data for the period between 0:00 and 0:30, and the data were stored in *.xlsx format. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) (for data processing) in the Citation section.
LIU Shaomin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei, ZHANG Yang
This dataset contains the flux measurements from the large aperture scintillometer (LAS) at Daman Superstation in the Heihe integrated observatory network from January 1 to December 31 in 2021. There were two types of LASs at Daman Superstation: BLS900 and RR-RSS460, produced by Germany. The north tower was set up with the BLS900 receiver and the RR-RSS460 transmitter, and the south tower was equipped with the BLS900 transmitter and the RR-RSS460 receiver. The site (north: 100.379° E, 38.861° N; south: 100.369° E, 38.847° N) was located in Daman irrigation district, which is near Zhangye, Gansu Province. The underlying surfaces between the two towers were corn, orchard, and greenhouse. The elevation is 1556 m. The effective height of the LASs was 24.1 m, and the path length was 1854 m. The data were sampled 1 minute at both BLS900 and RR-RSS460. The raw data acquired at 1 min intervals were processed and quality controlled. The data were subsequently averaged over 30 min periods, in which sensible heat flux was iteratively calculated by combining Cn2 with meteorological data according to the Monin-Obukhov similarity theory. The main quality control steps were as follows: (1) The data were rejected when Cn2 exceeded the saturated criterion (BLS900：Cn2>7.25E-14，RR-RSS460：Cn2>7.84 E-14). (2) The data were rejected when the demodulation signal was small (BLS900：Average X Intensity<1000；RR-RSS460：Demod>-20mv). (3) The data were rejected when collected during precipitation. (4) The data were rejected if collected at night when weak turbulence occurred (u* was less than 0.1 m/s). In the iteration process, the universal functions of Thiermann and Grassl (1992) and Andreas (1988) were selected for BLS900 and RR-RSS460, respectively. Detailed can refer to Liu et al. (2011, 2013). Due to instrument adjustment and inadequate power supply, the date of missing data for the large aperture scintillator is: 2021.05.15-2021.06.10. Several instructions were included with the released data. (1) The data were primarily obtained from BLS900 measurements, and missing flux measurements from the BLS900 instrument were substituted with measurements from the RR-RSS460 instrument. The missing data were denoted by -6999. (2) The dataset contained the following variables: Date/time (yyyy/m/d h:mm), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H, W/m^2). In this dataset, a time of 0:30 corresponds to the average data for the period between 0:00 and 0:30, and the data were stored in *.xlsx format. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) (for data processing) in the Citation section.
LIU Shaomin, CHE Tao, XU Ziwei, REN Zhiguo, TAN Junlei, ZHANG Yang
This dataset contains the flux measurements from the large aperture scintillometer (LAS) at Arou Superstation in the Heihe integrated observatory network from January 1 to December 31 in 2021. There were two types of LASs at Arou Superstation: BLS900 and RR-RSS460, produced by Germany and China, respectively. The north tower was set up with the RR-RSS460 receiver and the BLS900 transmitter, and the south tower was equipped with the RR-RSS460 transmitter and the BLS900 receiver. The site (north: 100.471° E, 38.057° N; south: 100.457° E, 38.038° N) was located in Caodaban village of A’rou town in Qilian county, Qinghai Province. The underlying surface between the two towers was alpine meadow. The elevation is 3033 m. The effective height of the LASs was 13.0 m, and the path length was 2390 m. The data were sampled 1 minute at both BLS900 and RR-RSS460. The raw data acquired at 1 min intervals were processed and quality controlled. The data were subsequently averaged over 30 min periods, in which sensible heat flux was iteratively calculated by combining Cn2 with meteorological data according to the Monin-Obukhov similarity theory. The main quality control steps were as follows: (1) The data were rejected when Cn2 exceeded the saturated criterion (BLS900: Cn2>7.25E-14, RR-RSS460: Cn2>7.84E-14). (2) The data were rejected when the demodulation signal was small (BLS900: Average X Intensity<1000; RR-RSS460: Demod>-20mv). (3) The data were rejected when collected during precipitation. (4) The data were rejected if collected at night when weak turbulence occurred (u* was less than 0.1 m/s). In the iteration process, the universal functions of Thiermann and Grassl (1992) and Andreas (1988) were selected for BLS900 and RR-RSS460, respectively. Detailed can refer to Liu et al. (2011, 2013). Several instructions were included with the released data. (1) The data were primarily obtained from BLS900 measurements, and missing flux measurements from the BLS900 instrument were substituted with measurements from the RR-RSS460 instrument. The missing data were denoted by -6999. Due to the problems of storing and wireless transmission. (2) The dataset contained the following variables: Date/time (yyyy/m/d h:mm), the structural parameter of the air refractive index (Cn2, m-2/3), and the sensible heat flux (H, W/m^2). In this dataset, a time of 0:30 corresponds to the average data for the period between 0:00 and 0:30, and the data were stored in *.xlsx format. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) (for data processing) in the Citation section.
LIU Shaomin, CHE Tao, XU Ziwei, ZHANG Yang, TAN Junlei, REN Zhiguo
The evapotranspiration (ET) is an important variable connecting land energy balance, water cycle and carbon cycle. Accurate monitoring and estimations of ET are essential not only for water resources management but also for simulating regional, global climate, and hydrological cycles. Remote sensing technology is an effective method to monitor ET. At present, a variety of ET remote sensing products have been produced and released. However, in the process of validation, there is a problem of spatial scale mismatch between ET remote sensing estimation value and station observation value, especially on heterogeneous surface. Therefore, it is very important to obtain the ground truth ET values at the satellite pixel scale by upscaling method on heterogeneous surface. In this study, using the station observation data and multi-source remote sensing information, the ET observed at a single ground station is upscaled to the satellite pixel scale, and the ground truth ET values at the satellite pixel scale in Heihe River Basin is obtained. Based on the ET data observed by the eddy covariance (EC) at 15 stations (3 superstations and 12 ordinary stations) in the Heihe integrated observatory network, combined with the fused high-resolution remote sensing data (surface temperature, vegetation index, net radiation, etc.) and atmospheric reanalysis data, the upscaling is carried out to obtain the ground truth ET at the satellite pixel scale. The distribution diagram is shown in Figure 1. Specifically, firstly, the spatial heterogeneity of the spatial heterogeneity of the land surface hydrothermal conditions was evaluated; Secondly, nine upscaling methods (the integrated Priestley-Taylor equation method, the Penman-Monteith equation combined with EnKF method, the Penman-Monteith equation combined with SCE_UA method, EC observation value, artificial neural network, Bayesian linear regression, deep belief network, Gaussian process regression, and random fores and directly taking the EC observation value as the ground truth ET) were compared and analyzed through direct validation and cross-validation; Finally, a comprehensive method (directly using the EC observation value on the homogeneous underlying surface; using the Gaussian process regression method for upscaling on the moderately heterogeneous underlying surface and highly heterogeneous underlying surface) was optimized to obtain the groud truth ET at the satellite pixel scale at 15 typical underlying surfaces in Heihe River Basin (2010-2016, spatial resolution of 1km). The results showed that the ground truth ET at the satellite pixel scale is relatively reliable. Compared with the pixel scale reference value (LAS observation value), the MAPE of the ground turth ET at the satellite pixel scale at the three superstations are 1.57%, 3.23% and 4.59% respectively, which can meet the needs of the validation of ET remote sensing products. For all site information and data processing, please refer to Liu et al. (2018), and for upscaling methods, please refer to Li et al. (2021).
LIU Shaomin, LI Xiang , XU Ziwei
1) The data included the thickness, coordinates and elevation of Xiaodongkemadi glacier and was measured from July 26 to 28, 2021; 2) The data was measured by the ground penetrating radar with working frequency of 100MHz developed by China Institute of Water Resources and Hydropower Research. The thickness of the glacier was obtained through the processing and analysis of the radar echo image. The dielectric constant of the ice was 3.2, and the coordinates and elevation of the measuring points were measured by the RTK system; 3) The data can be used to study the changes of glacier thickness, mass balance , runoff and so on.
FU Hui
In this study，a vegetation classification system for the vegetation types in the Qinghai-Tibet Plateau was designed. The integrated classification method，taken into account of multi-source vegetation classification / land cover classification products， was used to produce the actual vegetation map. This integrated classification method followed the principle of data consistency，and the resultant vegetation map was superior over other vegetation maps in terms of reflection of current situation， classification system， and classification accuracy. This vegetation map is timely and could better reflect current vegetation distribution than earlier ones. This vegetation map could be conducive to fully extract vegetation information from multi-source data products with high reliability and consistency. Compared with previous data products，the overall accuracy （78.09%，kappa coefficient is 0.75） of this new vegetation map was found to increase by 18.84%-37.17%，especially for grassland and shrub.
ZHANG Hui, ZHAO Cenliang, ZHU Wenquan
Hourly spatially complete land surface temperature (LST) products have a wide range of applications in many fields such as freeze-thaw state monitoring and summer high temperature heat wave monitoring. Although the LST retrieved from thermal infrared (TIR) remote sensing observations has high accuracy, it is spatially incomplete due to the influence of cloud, which heavily limits the application of LST. LST simulated by land surface models (LSM) is with high temporal resolution and spatiotemporal continuity, while the spatial resolution is relatively coarse and the accuracy is poor. Therefore, fusing the remote sensing retrieved LST and the model simulated LST is an effective way to obtain seamless hourly LST. The authors proposed a fusion method to generate 0.02° hourly seamless LST over East Asia and produced the corresponding data set. This dataset is the 0.02 ° hourly seamless LST dataset over East Asia (2016-2021). Firstly, the iTES algorithm is employed to retrieve the Himawari-8/AHI LST. Secondly, the CLDAS LST is corrected to eliminate its system deviation. Finally, the multi-scale Kalman filter is employed to fuse Himawari-8/AHI LST and the bias-corrected CLDAS LST to generate 0.02 ° hourly seamless LST. The in situ verification results show that the root mean square error (RMSE) of the seamless LST is about 3k. The temporal resolution and spatial resolution of this dataset are 1 hour and 0.02°, respectively. The time period is 2016-2021 over (0-60°N, 80°E-140°E).
CHENG Jie, DONG Shengyue, SHI Jiancheng
This data includes the maximum 24h precipitation values of all sub watershed units along the Sichuan Tibet railway and its surrounding areas. According to the original data of the hourly observation data of China ground meteorological station, the frequency of the annual maximum 24h precipitation sequence in the assessment area is calculated. The data are accumulated according to the hourly rainfall process of each grid point from 1979 to 2018 to obtain the maximum 24h precipitation sequence year by year, fit the precipitation frequency curve of the assessment area, obtain the maximum 24h precipitation value once in ten years, and use GIS to make statistics to the sub watershed assessment unit. It can be used in the fields of weather and climate monitoring, climate change research, model test and hydrological forecast along and around the Sichuan Tibet railway.
WANG Zhonggen
This dataset organizes and collects the measured and surveyed maximum 24h precipitation point data along the Sichuan-Tibet Railway and its surrounding areas. Contains watershed KID, station, province, X coordinate, Y coordinate, rain, date and other field data. A total of 43 records. The data comes from the atlas of rainstorm statistical parameters in China (2006 Edition). The measured and investigated maximum 24h rainfall point data of China's rainstorm statistical parameter Atlas (2006 Edition) are manually digitized along the Sichuan Tibet railway and the surrounding areas. The accuracy of the original data is 1 ° × 1 ° grid data, which is suitable for the comprehensive study of design rainstorm nationwide.
WANG Zhonggen
This data is obtained through observation at Namucuo multi cycle comprehensive observation and research station of Chinese Academy of Sciences (2019) and Tibetan southeast alpine environment comprehensive observation and research station of Chinese Academy of Sciences (2021), including the earth atmosphere exchange flux or vertical gradient of species such as O3, NOx, HONO, H2O and HCHO. The time range is from April 28, 2019 to July 10, 2019 (Namuco station) and from May 2, 2021 to May 13, 2021 (Southeast Tibet station). The data consists of five documents. Documents 1-4 are the flux data and H2O vertical gradient, HONO vertical gradient and NO2 vertical gradient observed at Namuco station in 2019. Document 5 is the flux data observed at Southeast Tibet station in 2021. During the monitoring period, data was missing due to instrument status problems. This data has broad application prospects and can serve graduate students and scientists with backgrounds such as atmospheric science, climatology, and ecology.
YE Chunxiang
This dataset includes data recorded by the Heihe integrated observatory network obtained from the automatic weather station (AWS) at the observation system of Zhangye wetland station from January 1 to December 31, 2021. The site (100.4464° E, 38.9751° N) was located on a wetland (reed surface) in Zhangye National Wetland Park, Gansu Province. The elevation is 1460 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (HMP45AC; 5 and 10 m, north), wind speed profile (03002; 5 and 10 m, north), wind direction profile (03002; 10 m, north), air pressure (CS100; 2 m), rain gauge (TE525M; 10 m), four-component radiometer (CNR1; 6 m, south), two infrared temperature sensors (SI-111; 6 m, south, vertically downward), soil heat flux (HFP01; 3 duplicates, -0.06 m), soil temperature profile (109ss-L; 0, -0.02, -0.04, -0.1, -0.2 and -0.4 m), and four photosynthetically active radiation (PQS-1; two above the plants, 6 m, south, one vertically downward and one vertically upward; two below the plants, 0.25 m, south, one vertically downward and one vertically upward). The observations included the following: air temperature and humidity (Ta_5 m and Ta_10 m; RH_5 m and RH_10 m) (℃ and %, respectively), wind speed (Ws_5 m and Ws_10 m) (m/s), wind direction (WD_10 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT_1 and IRT_2) (℃), soil heat flux (Gs_1, Gs_2, and Gs_3) (W/m^2), soil temperature (Ts_0 cm, Ts_2 cm, Ts_4 cm, Ts_10 cm, and Ts_20 cm) (℃), on the plants photosynthetically active radiation of upward and downward (PAR_U_up and PAR_U_down) (μmol/ (s m^-2)), and below the plants photosynthetically active radiation of upward and downward (PAR_D_up and PAR_D_down) (μmol/ (s m^-2)). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The missing data were denoted by -6999. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2021-6-10 10:30. (6) Finally, the naming convention was AWS+ site no. Moreover, suspicious data were marked in red. For more information, please refer to Liu et al. (2018) (for sites information), Liu et al. (2011) for data processing) in the Citation section.
LIU Shaomin,