The Second Tibetan Plateau Scientific Expedition (STEP) program

Brief Introduction: Second Tibetan Plateau Scientific Expedition Program

Number of Datasets: 308

  • Annual glacier mass balance data on Tibetan Plateau (2020-2021)

    Annual glacier mass balance data on Tibetan Plateau (2020-2021)

    Glacial mass balance is one of the most important glaciological parameters to characterize the accumulation and ablation of glaciers. Glacier mass balance is the link between climate and glacier change, and it is the direct reflection of glacier to the regional climate. Climate change leads to the corresponding changes in the material budget of glaciers, which in turn can lead to changes in the movement characteristics and thermal conditions of glaciers, and then lead to changes in the location, area and ice storage of glaciers. The monitoring method is to set a fixed mark flower pole on the glacier surface and regularly monitor the distance between the glacier surface and the top of the flower pole to calculate the amount of ice and snow melting; In the accumulation area, the snow pits or boreholes are excavated regularly to measure the snow density, analyze the characteristics of snow granular snow additional ice layer, and calculate the snow accumulation; Then, the single point monitoring results are drawn on the large-scale glacier topographic map, and the instantaneous, seasonal (such as winter and summer) and annual mass balance components of the whole glacier are calculated according to the net equilibrium contour method or contour zoning method. The data set is the annual mass balance data of different representative glaciers in the Qinghai Tibet Plateau and Tianshan Mountains, in millimeter water equivalent.

    2022-05-06 306 6

  • Net primary productivity data set of the Tibetan Plateau (1980-2018)

    Net primary productivity data set of the Tibetan Plateau (1980-2018)

    The data set is based on the NPP simulated by 16 dynamic global vegetation models (TRENDY v8) under S2 Scenario (CO2+Climate) and represents the net primary productivity of the ecosystem. Data was derived from Le Quéré et al. (2019). The range of source data is global, and the Qinghai Tibet plateau region is selected in this data set. Original data is interpolated into 0.5*0.5 degree by the nearest neighbor method in space, and the original monthly scale is maintained in time. The data set is the standard model output data, which is often used to evaluate the temporal and spatial patterns of gross primary productivity, and compared with other remote sensing observations, flux observations and other data.

    2022-04-19 1162 69

  • 1km resolution wind energy resource distribution data of Qinghai Tibet Plateau (1979-2008)

    1km resolution wind energy resource distribution data of Qinghai Tibet Plateau (1979-2008)

    The 1km resolution wind energy resource data of Qinghai Tibet Plateau is developed by using the wind energy resource numerical simulation assessment system of China Meteorological Administration (weras / CMA), which includes typical terrain classification module, mesoscale model WRF and Calmet dynamic diagnosis model. Firstly, the typical days are randomly selected from the historical weather types for hourly wind speed simulation, and then the climate average distribution of wind energy resources is obtained according to the statistical analysis of the frequency of weather types. The data set includes wind speed and wind power density over the Qinghai Tibet Plateau. The data accuracy of wind speed is 0.01m/s, the data accuracy of wind power density is 0.01w/m2, and the vertical height of data is 100m. The data have been checked and corrected by the observation data of meteorological stations, and are mainly used for detailed investigation of wind energy resources and macro site selection of wind farms. This data is the output data of the national wind energy resources detailed survey and evaluation project from 2008 to 2012 (the project cost is 290 million yuan), and then becomes the basic data of wind energy resources related research. The Ministry of finance has no plan to invest in extending this data set in the near future.

    2022-04-19 1355 37

  • Soil carbon storage data of grassland in Qinghai Tibet Plateau (2009)

    Soil carbon storage data of grassland in Qinghai Tibet Plateau (2009)

    1) Data content It includes the observation year, latitude and longitude, altitude, ecosystem type and soil layer (soc0-100 (kgcm-2); 0-100 represents soil layer), underground biomass content. 2) Data sources This part of the data is obtained from the literature, specific literature sources refer to the documentation. 3) Data quality description The data cover a wide range, including comprehensive indicators, showing the content of soil organic carbon under different soil layers, with high integrity and accuracy, which can meet the estimation of soil carbon storage of grassland in Qinghai Tibet Plateau. 4) Data application achievements and Prospects It provides basic data for predicting the carbon source sink effect of soil and realizing the sustainable development of ecosystem carbon in the future.

    2022-04-19 1094 227

  • Modern observational hydrological data set of North and South of the Tibeten plateau (2012-2014)

    Modern observational hydrological data set of North and South of the Tibeten plateau (2012-2014)

    The data set is the seasonal hydrological observation data of the Yellow River from the hydrological station of the Qinghai Tibet Plateau. There are two hydrological stations: 1. Longmen hydrological station in the middle reaches of the Yellow River, which is the weekly hydrological data in 2013, including water temperature (T), runoff (QW), physical erosion rate (per) and pH. 2. Tangnaihai hydrological station of the Yellow River is monthly data from July 2012 to June 2014, including runoff (QW), sediment (salt), pH and EC. The data set was commissioned to be observed by the staff of the hydrological station of the Yellow River Water Conservancy Commission to provide basic hydrological data for the study of hydrology, hydrochemistry and hydrosphere cycle under the background of Qinghai Tibet Plateau uplift.

    2022-04-19 958 186

  • A dataset of above-ground biomass of forests on the Qinghai-Tibet Plateau (2015)

    A dataset of above-ground biomass of forests on the Qinghai-Tibet Plateau (2015)

    Aboveground biomass (AGB) is an important indicator for measuring ecosystem productivity.This dataset provides the forest aboveground biomass with a resolution of 30m in the Qinghai-Tibet Plateau from 1970s-2022. The biomass data is estimated using Landsat series data, based on actual ground data and some literature data, tree height data, and forest types including coniferous forest, broad-leaved forest and mixed forest.Through data disclosure and free download services, it provides basic data support for related research on the dynamic changes of forest ecosystems on the Qinghai-Tibet Plateau, and also provides a scientific basis for sustainable forest management in this region.

    2022-04-19 628 44

  • Satellite remote sensing precipitation reanalysis dataset over the Qinghai-Tibet Plateau (1998-2018)

    Satellite remote sensing precipitation reanalysis dataset over the Qinghai-Tibet Plateau (1998-2018)

    This data is precipitation data, which is the monthly precipitation product of tropical rainfall measurement mission TRMM 3b43. It integrates the main area of the Qinghai Tibet Plateau (25 ~ 40 ° n; 25 ~ 40 ° n); The precipitation data of 332 meteorological stations are from the National Meteorological Information Center of China Meteorological Administration. The reanalysis data set is obtained by the station 3 ° interpolation optimization variational correction method. For the monthly sample data from January 1998 to December 2018, the spatial coverage is 25 ~ 40 ° n; 73 ~ 105 ° e, the spatial resolution is 1 ° * 1 °.

    2022-04-19 2324 0

  • Carbon storage data of grassland vegetation in Qinghai Tibet Plateau (1980-1995, 2005-2006)

    Carbon storage data of grassland vegetation in Qinghai Tibet Plateau (1980-1995, 2005-2006)

    1) Data content It includes the observation year, longitude and latitude, ecosystem type, annual rainfall, drought index, annual net primary productivity, aboveground biomass, underground biomass and other data. 2) Data sources One part is from literature (1980-1995), the other part is from field sampling (2005-2006). 3) Data quality description The data has a long observation year, a large time span, a wide coverage, and many indicators, which has high integrity and accuracy, and can meet the estimation of grassland carbon storage in the Qinghai Tibet Plateau. 4) Data application achievements and Prospects It provides basic data for predicting the carbon source sink effect and realizing the sustainable development of ecosystem carbon in the future.

    2022-04-19 804 200

  • Dataset of measured aboveground plant biomass and remote sensing net primary productivity in desert sites on theTibet Plateau (2000-2020)

    Dataset of measured aboveground plant biomass and remote sensing net primary productivity in desert sites on theTibet Plateau (2000-2020)

    A total of 52 sample sites were selected in the desert belts of Qinghai and Tibet for field sampling of aboveground biomass of vegetation during the vegetation growing season in 2019 and 2020. At the same time, the longitude, latitude and altitude of the experimental site were recorded using handheld GPS devices. The field setting method of the quadrate is as follows: select a section with uniform vegetation. When the vegetation is relatively abundant, the quadrate is set as a 10 m x10 m square plot, and when the vegetation is relatively sparse, the quadrate is set as a 30 m x30 m square plot or a 30 m x90 m rectangular plot. 3-5 small sample boxes (1m x 1m) were randomly thrown into the set sample plot to determine the specific location of the sample. Collect plant samples by sample harvesting method: register plant species, number of plants of each species and other information in sample area of 1 square meter. All kinds of plants in the quadrate were planted and mowed on the ground, and the collected herbaceous plant samples were placed in archives and marked with species, sample site name and number, collection time and other information. They were brought back to the laboratory and dried to a constant weight in a constant temperature drying oven at 65 ℃. The dry weight of the plant samples was measured. Finally, the aboveground biomass of the vegetation was calculated. In addition, two kinds of remote sensing net primary productivity (NPP) data of the 52 sample points were extracted by the longitude and latitude of the sampling points. (1) Enhanced Vegetation Index (EVI) from 2000 to 2018, and calculated the annual Integrated Enhanced Vegetation Index (IEVI). IEVI was highly correlated with net primary productivity (NPP). Can be used as a proxy indicator of net primary productivity (He et al. 2021, Science of The Total Environment). (2) Percentage of remote sensing net primary productivity (NPP) and its quality control (QC) in 2001-2020, NPP remote sensing data from MOD17A3HGF Version 6 product (https://lpdaac.usgs.gov/products/mod17a3hgfv006/), the net photosynthetic value (the total primary productivity - keep breathing) is calculated. In the sample sites with low vegetation coverage, there may be null value (NA) of remote sensing net primary productivity.

    2022-04-18 1000 0

  • Basic geographic data of Qinghai Tibet Plateau (2015)

    Basic geographic data of Qinghai Tibet Plateau (2015)

    The data set is the basic data of the Qinghai Tibet Plateau in 2015. The original data comes from the National Basic Geographic Information Center, and the data of the Qinghai Tibet plateau region is formed by splicing and clipping the segmented data. The data content includes 1:1 million provincial administrative divisions, 1:1 million roads and 1:250000 water system. The data attributes of administrative divisions include name, code and Pinyin; Road data attributes include: GB, RN, name, rteg and type (basic geographic information classification code, road code, road name, road grade and road type); Water system data attributes include: GB, hydc, name, period (basic geographic information classification code, water system name code, name, season).

    2022-04-18 1585 0

  • Socio-economic data set of Qinghai-Tibet Plateau (1982-2018)

    Socio-economic data set of Qinghai-Tibet Plateau (1982-2018)

    The data set includes county-level demographic data of 252 areas in Qinghai-Tibet Plateau in 1982, 1990, 2000, 2010 and 2018, and GDP data in 1988, 1995, 2000, 2010 and 2015. The demographic data includes registered population, resident population, urban population, rural population, male population, female population and non-agricultural population. GDP data includes total GDP output value and GDP output value of primary, secondary and tertiary industries. The data are helpful to study the impact of human activities on the ecological climate of Qinghai-Tibet Plateau, and to explore the urbanization development, urban and rural population mobility, resident population change, local birth rate and agricultural population change in Qinghai-Tibet Plateau. The data were obtained by contacting the local statistics bureau, relevant statistical yearbooks and annual statistical bulletins of various places during the second scientific investigation of Qinghai-Tibet Plateau.

    2022-04-18 1062 0

  • 1km seamless land surface temperature dataset of China (2002-2020)

    1km seamless land surface temperature dataset of China (2002-2020)

    Kilometer-level spatially complete (seamless) land surface temperature products have a wide range of applications needs in climate change and other fields. Satellite retrieved LST has high reliability. Integrating the LST retrieved from thermal infrared and microwave remote sensing observation is an effective way to obtain the SLT with certain accuracy and spatial integrity. Based on this guiding ideology, the author developed a framework for retrieving 1km and seamless LST over China landmass, and generated the LST data set accordingly (2002-2020) Firstly, a look-up table based empirical retrieval algorithm is developed for retrieving microwave LST from AMSR-E/AMSR2 observations. Then, AMSR-E/AMSR2 LST is downscaled by using geographic weighted regression to obtain 1km LST. Finally, the multi-scale Kalman filter is used to fuse AMSR-E/AMSR2 LST and MODIS LST to generate a 1km seamless LST data set. The ground valuation results show that the root mean square error (RMSE) of the 1km seamless LST is about 3K. In addition, the spatial distribution of the 1km seamless LST is consistent with MODIS LST and CLDAS LST.

    2022-04-18 6915 754

  • Fault distribution data of Sichuan Tibet traffic corridor (2020)

    Fault distribution data of Sichuan Tibet traffic corridor (2020)

    The data coverage area is Sichuan Tibet traffic corridor, which is vector line data. The data defines its active period and names it. The strike, nature, active period and exposure of the fault are described. However, the content is missing, and the secondary fault zone is not named. There are 590 linear elements within the Sichuan Tibet traffic corridor in this data set, but some linear elements are multiple elements of the same fault zone. The active fault zone is often the combination zone of different plates and different blocks. It is a relatively weak zone of the crust, which is easy to induce extremely serious earthquake disasters. It is also a concentrated development zone of geological disasters such as collapse, landslide and debris flow. The judgment of the location and nature of fault zone is of great significance to the risk susceptibility evaluation of geological disasters, and it is the key factor to study geological disasters.

    2022-04-18 753 183

  • 8 km resolution evapotranspiration dataset of the Tibetan Plateau (1990-2015)

    8 km resolution evapotranspiration dataset of the Tibetan Plateau (1990-2015)

    Evapotranspiration over the Qinghai Tibet Plateau is calculated by etwatch, a land surface evapotranspiration remote sensing model based on multi-scale and multi-source data. Etwatch adopts the method of combining the residual term method with P-M formula to calculate evapotranspiration. Firstly, according to the characteristics of the data image, the suitable model is selected to retrieve the evapotranspiration on a sunny day; the remote sensing model is often lack of data because the weather conditions can not obtain a clear image. In order to obtain the daily continuous evapotranspiration, the penman Monteith formula is introduced, and the evapotranspiration results on a sunny day are regarded as the "key frame", and the surface impedance information of the key frame is used as the basis to construct the surface impedance Based on the daily meteorological data, the time series data of evapotranspiration are reconstructed. Through the data fusion model, the high spatial and temporal resolution evapotranspiration data set is constructed by combining the low and medium resolution evapotranspiration temporal variation information with the high resolution evapotranspiration spatial difference information, so as to generate the 8 km resolution evapotranspiration of the Qinghai Tibet Plateau Data sets (1990-2015).

    2022-04-18 2150 274

  • The measured and simulated data set of lake water storage in Qinghai Province (2000-2019)

    The measured and simulated data set of lake water storage in Qinghai Province (2000-2019)

    The data set consists of four sub tables, which are remote sensing monitoring of Lake area from 2000 to 2019, total lake water storage based on underwater 3D simulation model, Lake area volume equation based on underwater 3D simulation model, and key parameters and results of water storage measurement and Simulation of 24 typical lakes in Qinghai Province. The first sub table is the time series Lake area data from 2000 to 2019 from remote sensing image data monitoring. The third sub table stores the area storage capacity equation of the lake based on the underwater three-dimensional simulation model of the lake. The second sub table is the estimation result by combining the time series Lake area data and the area storage capacity equation, Finally, the key parameters and results of water storage measurement and Simulation of 24 typical lakes in Qinghai Province from 2000 to 2019 are obtained, including simulated water depth, maximum water depth, simulated reference water level and corresponding Lake area of each lake, which are stored in the fourth sub table.

    2022-04-18 1075 62

  • Hyperspectral remote sensing data of typical vegetation along Sichuan Tibet Railway (2019)

    Hyperspectral remote sensing data of typical vegetation along Sichuan Tibet Railway (2019)

    This data set is hyperspectral observation data of typical vegetation along Sichuan Tibet Railway in September 2019, using the airborne spectrometer of Dajiang M600 resonon imaging system. Including the hyperspectral data observed in the grassland area of Lhasa in 2019, with its own latitude and longitude. The hyperspectral survey was mainly sunny. Before flight, whiteboard calibration was carried out; when data were collected, there was a target (that is, the standard reflective cloth suitable for the grass), which was used for spectral calibration; there were ground mark points (that is, letters with foam plates), and the longitude and latitude coordinates of each mark were recorded for geometric precise calibration. The DN value recorded by Hyperspectral camera of UAV can be converted into reflectivity by using Spectron Pro software. Hyperspectral data is used to extract spectral characteristics of different vegetation types, vegetation classification, inversion of vegetation coverage and so on.

    2022-04-18 1554 0

  • Distribution data of freezing (thawing) depth in Sichuan Tibet engineering corridor (2001-2100)

    Distribution data of freezing (thawing) depth in Sichuan Tibet engineering corridor (2001-2100)

    Based on gipl1.0 permafrost spatial distribution model, combined with the existing basic data, including climate change, soil types, and vegetation data, the permafrost and seasonal permafrost characteristics of Sichuan Tibet railway are simulated. The data result is 500m spatial resolution grid, including the maximum depth of permafrost and the maximum freezing depth of seasonal permafrost. The results are verified by drilling data. The data date is 2001-20192041-20602081-2100 (20-year average), in which the water body and glacier area are excluded from the calculation range through the mask (null value). The climate data is monthly mean, other data remain unchanged in the process of simulation, and the spatial resolution is 500m. Data sources and "woeldc" lim:https :// www.worldclim.org/ , DEM and vegetation soil: https://data.tpdc.ac.cn/zh-hans/ ”According to the characteristics of different data sources, the authenticity and consistency of the original data are checked and standardized; The permafrost model is used to simulate the permafrost and seasonal frozen soil. The output results are ground temperature and active layer (maximum frozen depth). The simulation results are verified with the borehole ground temperature. Finally, the spatial data set is mapped by ArcGIS. Make digital processing operation standard. In the process of processing, the operators are required to strictly abide by the operation specifications, and the special person is responsible for the quality review. The data integrity, logical consistency, position accuracy, attribute accuracy, edge connection accuracy and current situation are all in line with the requirements of relevant technical regulations and standards formulated by the State Bureau of Surveying and mapping. The data can provide necessary data support for the later research on the freezing (thawing) depth of the corridor of Sichuan Tibet project.

    2022-04-18 717 149

  • FY-4A terrestrial solar radiation product data set of the Qinghai Tibet Plateau (2018-2020)

    FY-4A terrestrial solar radiation product data set of the Qinghai Tibet Plateau (2018-2020)

    Surface solar irradiance (SSI) is one of the products of FY-4A L2 quantitative inversion. It covers a full disk without projection, with a spatial resolution of 4km and a temporal resolution of 15min (there are 40 observation times in the whole day since 20180921, except for the observation of each hour, there is one observation every 3hr before and after the hour), and the spectral range is 0.2µ m~5.0 µ m. The output elements of the product include total irradiance, direct irradiance on horizontal plane and scattered irradiance, the effective measurement ranges between 0-1500 w / m2. The qualitative improvement of FY-4A SSI products in coverage, spatial resolution, time continuity, output elements and other aspects makes it possible to further carry out its fine application in solar energy, agriculture, ecology, transportation and other professional meteorological services. The current research results show that the overall correlation of FY-4A SSI product in China is more than 0.75 compared with ground-based observation, which can be used for solar energy resource assessment in China.

    2022-04-18 2005 0

  • The Daily  kernel-driven BRDF model  coefficients retrieved from  5-days-composited multi-sensory data coupling topograpic effects over the Tibet Plateau (2016)

    The Daily kernel-driven BRDF model coefficients retrieved from 5-days-composited multi-sensory data coupling topograpic effects over the Tibet Plateau (2016)

    This daily land surface kernel-driven BRDF model's coeciffients proudct is with a spatl resolution of 0.02 ° x 0.02 ° over the Tibet Plateau in 2016. Multi-sensory data is used to retrieve the the kernel-driven BRDF model and coupled with topographic effects, and prior knowledge is introduced for quality control inversion. The high-precision BRDF of good spatial-temporal continiuty is retrieved by combining MODIS reflectance data (a polar orbiting satellite) and himawari-8 AHI land surface reflectance (a geostationary satellite ). MODIS lans surface reflectance data and AHI TOA reflectance data are downloaded from the official websites. After registration, atmospheric correction and other processing, the daily resolution BRDF is synthesized with a period of 5 days. Compared with similar products, it has more advantages in capturing rapidly changing surface features, and has better temporal and spatial continuity with the shortest composition period. It can effectively support angular effects correction and the BRDF-releated parameters' retrieval.

    2022-04-18 1672 390

  • Long time series ecological background map of Qinghai Tibet Plateau (1990-2015)

    Long time series ecological background map of Qinghai Tibet Plateau (1990-2015)

    Based on the medium resolution long time series remote sensing image Landsat, the data set obtained six periods of ecosystem type distribution maps of the Qinghai Tibet Plateau in 1990 / 1995 / 2002 / 2005 / 2010 / 2015 through image fusion, remote sensing interpretation and data inversion, and made the original ecological base map of the Qinghai Tibet Plateau in 25 years (1990-2015). According to the area statistics of various ecosystems in the Qinghai Tibet Plateau, the area of woodland and grassland decreased slightly, the area of urban land, rural residential areas and other construction land increased, the area of rivers, lakes and other water bodies increased, and the area of permanent glacier snow decreased from 1990 to 2015. The atlas can be used for the planning, design and management of ecological projects in the Qinghai Tibet Plateau, and can be used as a benchmark for the current situation of the ecosystem, to clarify the temporal and spatial pattern of major ecological projects in the Qinghai Tibet Plateau, and to reveal the change rules and regional differences of the pattern and function of the ecosystem in the Qinghai Tibet Plateau.

    2022-04-18 2696 36