The data include soil organic matter data of Tibetan Plateau , with a spatial resolution of 1km*1km and a time coverage of 1979-1985.The data source is the soil carbon content generated from the second soil census data.Soil organic matter mainly comes from plants, animals and microbial residues, among which higher plants are the main sources.The organisms that first appeared in the parent material of primitive soils were microorganisms.With the evolution of organisms and the development of soil forming process, animal and plant residues and their secretions become the basic sources of soil organic matter.The data is of great significance for analyzing the ecological environment of Tibetan Plateau
Photosynthesis is a key process linking carbon and water cycles, and satellite-retrieved solar-induced chlorophyll fluorescence (SIF) can be a valuable proxy for photosynthesis. The TROPOspheric Monitoring Instrument (TROPOMI) on the Copernicus Sentinel-5P mission enables significant improvements in providing high spatial and temporal resolution SIF observations, but the short temporal coverage of the data records has limited its applications in long-term studies. We use machine learning to reconstruct TROPOMI SIF (RTSIF) over the 2001–2020 period in clear-sky conditions with high spatio-temporal resolutions (0.05°, 8-day). Our machine learning model achieves high accuracies on the training and testing datasets (R^2 = 0.907, regression slope = 1.001). The RTSIF dataset is validated against TROPOMI SIF and tower-based SIF, and compared with other satellite-derived SIF (GOME-2 SIF and OCO-2 SIF). Comparing RTSIF with Gross Primary Production (GPP) illustrates the potential of RTSIF for estimating gross carbon fluxes. We anticipate that this new dataset will be valuable in assessing long-term terrestrial photosynthesis and constraining the global carbon budget and associated water fluxes.
CHEN Xingan , HUANG Yuefei , NIE Chong , ZHANG Shuo , WANG Guangqian , CHEN Shiliu , CHEN Zhichao
Normalized Difference Vegetation Index (NDVI) has been widely used for monitoring vegetation. This dataset employed all available Landsat 5/7/8 data on the Qinghai-Tibetan Plateau (QTP) (> 100,000 scenes), and reconstructed high spatiotemporal NDVI time-series data (30-m and 8-d) during 2000-2020 on the TP (QTP-NDVI30) by using the MODIS-Landsat fusion algorithm (gap filling and Savitzky–Golay filtering；GF-SG). For the details of GF-SG, please refer to Chen et al. (2021). This dataset has been evaluated carefully. The quantitative assessments show that the reconstructed NDVI images have an average MAE value of 0.02, correlation coefficient of 0.96, and SSIM value of 0.94. We compared the reconstructed images in some typical areas with the PlanetScope 3-m images and found that the spatial details were well preserved by QTP-NDVI30. The geographic coordinate system of this dataset is GCS_WGS_84. The spatial range covers the vegetation area of the QTP, which is defined as the areas with average NDVI during July- September larger than 0.15.
CAO Ruyin , XU Zichao , CHEN Yang , SHEN Miaogen , CHEN Jin
Project based on Landsat_ Through manual interpretation and machine learning algorithm, tm30m remote sensing data has completed the extraction of spatial pattern distribution information of six types of ecosystems in Qilian Mountains from 1990 to 2015, including forest, farmland, grassland, wetland, settlement city and desert. This set of data can be used to study the evolution law of regional ecosystem macro pattern, ecosystem service function evaluation, major ecological restoration project planning and effect evaluation. The evolution of ecosystem macro pattern is a macro response to the evolution of natural processes driven by climate socio-economic coupling. It is also a direct reflection of land use and land cover changes. It is also an important data basis for the evaluation of the effectiveness of regional sustainable development. The research can provide data basis for the evaluation of green development index in Qilian mountain area.
The dataset contains microbial amplicon sequencing data from a total of 269 ice samples collected from 15 glaciers on the Tibetan Plateau from November 2016 to August 2020, including 24K Glacier (24K), Dongkemadi Glacier (DKMD), Dunde Glacier (DD), Jiemayangzong Glacier (JMYZ), Kuoqionggangri Glacier (KQGR), Laigu Glacier (LG), Palung 4 Glacier (PL4), Qiangtang 1 Glacier (QT), Qiangyong Glacier (QY), Quma Glacier (QM), Tanggula Glacier (TGL), Xiagangjiang Glacier (XGJ), Yala Glacier (YA), Zepugou Glacier (ZPG), ZhufengDongrongbu Glacier (ZF). The sampling areas ranged in latitude and longitude from 28.020°N to 38.100°N and 86.28°E to 95.651°E. The 16s rRNA gene was amplified by polymerase chain reaction (PCR) using 515F/907R (or 515F/806R) primers and sequenced with the Illumina Hiseq2500 sequencing platform to obtain raw data. The selected primer sequences were "515F_GTGYCAGCMGCCGCGGTAA; 907R_CCGTCAATTCMTTTRAGTTT" "515F_GTGCCAGCMGCCGCGG; 806R_ GGACTACHVGGGTWTCTAAT". The uploaded data include: sample number, sample description, sampling time, latitude and longitude coordinates, sample type, sequencing target, sequencing fragment, sequencing primer, sequencing platform, data format and other basic information. The sequencing data are stored in sequence file data format forward *.1.fq.gz and reverse *.2.fq.gz compressed files.
Soil freezing depth (SFD) is necessary to evaluate the balance of water resources, surface energy exchange and biogeochemical cycle change in frozen soil area. It is an important indicator of climate change in the cryosphere and is very important to seasonal frozen soil and permafrost. This data is based on Stefan equation, using the daily temperature prediction data and E-factor data of canems2 (rcp45 and rcp85), gfdl-esm2m (rcp26, rcp45, rcp60 and rcp85), hadgem2-es (rcp26, rcp45 and rcp85), ipsl-cm5a-lr (rcp26, rcp45, rcp60 and rcp85), miroc5 (rcp26, rcp45, rcp60 and rcp85) and noresm1-m (rcp26, rcp45, rcp60 and rcp85), The data set of annual average soil freezing depth in the Qinghai Tibet Plateau with a spatial resolution of 0.25 degrees from 2007 to 2065 was obtained.
PAN Xiaoduo, LI Hu
This database includes the occurrence records of birds in Qinghai-Tibet Plateau produced during the fieldtrip in December 2020 to January 2021. The geographical area mainly covers the middle-down stream of the Yarlung Zangbo River and eastern coast of Namtso lake, covering mang vallies, villiages and wetlands of Lhasa, Linzhi, Shannan, Rikaze. The information of each record is composed of species name, coordinates, date of field observation and observers.
The data set product contains the aboveground biomass and vegetation coverage data products of the Qinghai-Tibet Plateau every five years from 1990 to 2020 (1990, 1995, 2000, 2005, 2010, 2015 and 2020).The aboveground biomass of the Qinghai-Tibet Plateau is the remote sensing inversion product of above-ground biomass inversion models based on different land cover types including grassland, forest, etc. Vegetation coverage data of the Qinghai-Tibet Plateau is inversed using remote sensing by the dimidiate pixel model. Among them, the aboveground biomass and vegetation coverage data from 2000 to 2020 were estimated based on MODIS data, the spatial resolution was 250 m; the aboveground biomass and vegetation coverage data of 1990 and 1995 were estimated based on NOAA AVHRR data, the spatial resolution after resampling process is 250 m. This dataset can provide basic data for revealing the temporal and spatial pattern of land cover areas and quality on the Qinghai-Tibet Plateau and supporting the assessment of ecosystems, ecological assets and ecological security.
Monthly data of 7cm soil moisture in the surface layer of China. The time range includes the historical period 1850-2014 and the future period 2015-2100 (the future period includes four different shared socio-economic paths: ssp1-2.6, ssp2-4.5, ssp3-7.0 and ssp5-8.5). The spatial resolution is 0.25 °. This data is based on the deep learning method, taking the 7cm surface soil moisture data of era5 land as a reference, and integrating the surface soil moisture data of 25 scaled down cmip6 models. In the context of climate change, data can be used for drought and vegetation correlation analysis.
The data is the land cover data of the Qinghai Tibet Plateau, with a spatial resolution of 300 meters and a temporal resolution of years. The data includes three periods of 1995, 2005 and 2015. The data is in grid format (TIFF), using the 2000 national geodetic coordinate system, and can be opened using software tools such as ArcGIS and envi. The original data comes from the European Copernicus climate change service data center. With reference to the "land cover classification system" developed by the food and Agriculture Organization of the United Nations, the global land cover types are divided into 22 categories. Because of its high accuracy, consistency and annual update, this data has been widely used in the fields of land use and human activity change monitoring worldwide. Based on the original data, this data is obtained in ArcGIS through clipping, projection, accuracy verification, and quality audit by a second person. The data quality is reliable.
This data set includes daily values of temperature, pressure, relative humidity, wind speed, wind direction, precipitation, radiation, water vapour pressure and other elements obtained from the Integrated Observation and Research Station of the Westerly Environment in Muztagh Ata from 18 May 2003 to 31 December 2016. The data are obtained by an automatic meteorological station (Vaisala) that recorded one measurement every 30 minutes. The data set was processed as a continuous time series after the original data were quality controlled. This data set satisfies the accuracy requirements of the meteorological observations of the National Weather Service and the World Meteorological Organization (WMO), and the systematic errors caused by the tracking data and sensor failure have been eliminated. The data set has mainly been applied in the fields of glaciology, climatology, environmental change research, cold zone hydrological process research and frozen soil science. Furthermore, this data set is mainly used by professionals engaged in scientific research and training in atmospheric physics, atmospheric environment, climate, glaciers, frozen soil and other disciplines.
WANG Yuanwei, XU Baiqing
As an important part of the global carbon pool, Arctic permafrost is one of the most sensitive regions to global climate change. The rate of warming in the Arctic is twice the global average, causing rapid changes in Arctic permafrost. The NDVI change data set of different types of permafrost regions in the Northern Hemisphere from 1982 to 2015 has a temporal resolution of every five years, covers the entire Arctic Rim countries, and a spatial resolution of 8km. Based on multi-source remote sensing, simulation, statistics and measured data, GIS method and ecological method are used to quantify the regulation and service function of permafrost in the northern hemisphere to the ecosystem, and all the data are subject to quality control.
The original data of the three pole permafrost range are generated by GCM model simulation, and the original data are from http://www.cryosphere.csdb.cn/portal/metadata/5abef388-3f3f-4802-b3de-f4d233cb333b 。 This data set contains the prediction of future scenarios under different representative concentration paths (RCPs) in the next 2046-2065 years, including rcp2.6 scenario, rcp4.5 scenario and rcp8.5 scenario. The original data content is the spatial range of permafrost and seasonal frozen soil in the Qinghai Tibet Plateau. The data format is netcdf4 format, with a spatial resolution of 0.5 ° and a temporal resolution of years. Through data format conversion, spatial interpolation and other post-processing operations, this research work generates the permafrost range data in netcdf4 format, with a spatial resolution of 0.1 °, a time resolution of years, and a time range of 2046-2065. Permafrost is represented by 1, and seasonal permafrost is represented by 0.
The original thickness data of the active layer of the three pole permafrost are generated by GCM model simulation, and the original data are from http://www.cryosphere.csdb.cn/portal/metadata/5abef388-3f3f-4802-b3de-f4d233cb333b 。 This data set contains the prediction of future scenarios under different representative concentration paths (RCPs) in the next 2046-2065 years, including rcp2.6 scenario, rcp4.5 scenario and rcp8.5 scenario. The content of the original data is the thickness of the active layer in the permafrost area of the Qinghai Tibet Plateau. The data format is netcdf4, with a spatial resolution of 0.5 ° and a temporal resolution of years. Through data format conversion, spatial interpolation and other post-processing operations, the active layer thickness in permafrost area in netcdf4 format is generated, with a spatial resolution of 0.1 °, a time resolution of years, a time range of 2046-2065, and the unit is cm.
The thickness of the active layer of the three pole permafrost combines two sets of data products. The main reference data is the annual value of the active layer thickness from 1990 to 2015 generated by GCM model simulation. The data format of this data set is netcdf4 format, with a spatial resolution of 0.5 ° and a temporal resolution of years. The reference correction data set is the average value of active layer thickness from 2000 to 2015 simulated by statistical and machine learning (ML) methods. The data format is GeoTIFF format, the spatial resolution is 0.1 °, and the data unit is m. Through post-processing operations such as data format conversion, spatial interpolation, data correction, etc., this research work generates the permafrost active layer thickness data in netcdf4 format, with a spatial resolution of 0.1 °, a temporal resolution of years, a time range of 1990-2015, and a data unit of CM.
The original data of carbon flux in the three pole permafrost region are generated by GCM model simulation, and the original data are from http://www.cryosphere.csdb.cn/portal/metadata/5abef388-3f3f-4802-b3de-f4d233cb333b 。 This data set contains the prediction of future scenarios under different representative concentration paths (RCPs) in the next 2046-2065 years, including rcp2.6 scenario, rcp4.5 scenario and rcp8.5 scenario. The original data include parameters representing carbon flux such as NPP and GPP in the permafrost region of the Qinghai Tibet Plateau. The data format is netcdf4 format, with a spatial resolution of 0.5 ° and a temporal resolution of years. Through data format conversion, spatial interpolation and other post-processing operations, the NPP and GPP data in permafrost region in netcdf4 format are generated. The spatial resolution is 0.1 °, the time resolution is years, the time range is 2046-2065, and the data unit is gc/m2yr.
Based on the non survey method, referring to the provincial input-output table and county-level statistical data of the Qilian Mountain region, the project compiled the input-output table of the Qilian Mountain Region in 2017. This table provides a data basis for analyzing the production and consumption of regional economy and the virtual water resources contained in its products or services. The input-output table uses the input-output tables of Qinghai Province, Inner Mongolia Autonomous Region and Gansu Province in 2017, analyzes the industrial production, residents' consumption and interregional trade information of districts and counties included in the Qilian Mountains, and constructs the input-output table of the Qilian Mountains. The input-output table is the characterization of the regional macroeconomic structure and the level of regional products or services.
A dataset of spatio-temporal change of physical and virtual water in Qilian Mountains: Using the single-region input-output method, and the 2012 input-output table of Qilian Mountains, we developed a physical water-virtual water conversion model and explored the virtual water among different departments in Qilian Mountains in 2012. The law of water flow provides a theoretical basis for the optimal allocation of water resources in the natural-society complex system for the research on the optimal allocation of "mountains, waters, forests, fields, lakes, grass and sand" in the Qilian Mountains. It has been verified that this dataset has achieved the balance between the physical water consumption and the total virtual water consumption of various departments in the Qilian Mountains in 2012, indicating that the data is reliable. This data can provide a basis for the optimal allocation of water resources in the Qilian Mountains.
Paleozoic carbonate sequences are well developed along the road from the Leiwuqi County to the Jiangda County, Changdu, eastern Tibet. Preliminary Devonian-Carboniferous biostratigraphy studies based on macro-fauna (e.g. brachiopods and corals) have been conducted by previous researchers, but high-resolution subdivision and correlation is still lacking in study area. For example, Upper Devonian Zhuogedong Formation and Lower Carboniferous Wuqingna Formation exists near Tuoba and Wuqingna village. Abundant conodonts, rock and geochemical samples from the Nuoma section in Tuoba, Karuo District, Changdu have been collected, which was assigned to the Devonian-Carboniferous boundary interval by geological survey. Our studies would provide precise biostratigraphic correlation in this area and have important significance for redefinition of the DCB GSSP. This dataset includes the stratigraphic column and outcrop photo of the Nuoma section in Changdu (GPS coordinates: 97°49’53.06’’ E, 31°27’2.94’’ N). According to the conodont data, the Devonian-Carboniferous boundary is tentatively placed within the interval between 141 m to 188 m in this section.
The Ediacaran to early Cambrian representing the transition of Cryptozoic to Phanerozoic is one of the most important transitional periods in the earth system evolution and a hot period for the study of the origin and evolution of metazoan. Focusing on this scientific question, massive interdisciplinary studies including palaeontology, stratigraphy, geochemistry, geophysics etc. have been taken in many regions which significantly improve our understandings of this period. In the Himalaya zone, the correlative strata only have been reported and studies in a few regions in the Sub Indian Continent. The North Pakistan locating the western Himalaya is one of the adjoining areas of Tibet Plateau. For the lack of basic stratigraphic and palaeontological studies, it’s hard to confirm the exact age of the Neoproterozoic to early Cambrian strata assigned by the previous studies. Thus, for the establishment of the chronological framework in western Himalaya, it’s necessary to do more detailed investigations and sample collections to sort out the sedimentary sequence, biostratigraphy and chemostratigraphy of this interval in North Pakistan. During the expeditions in the Hazara Basin, we detailedly observed the lithostratigraphy and systematically collected samples for petrological, palaeontological and geochemical studies at Sikhar Mountain, Tarnawai Village, Salhad Village, Abbottabad Height, Sobangali, Neelor Village and Pindkhan Khel sections. The result of this preliminary investigation confirmed that the Hazara Basin deposited a relatively successive Ediacaran to early Cambrian strata.