The Second Tibetan Plateau Scientific Expedition (STEP) program

Brief Introduction: Second Tibetan Plateau Scientific Expedition Program

Number of Datasets: 698

  • Thunderstorm cloud characteristic data set in China and surrounding areas (2010-2018)

    Thunderstorm cloud characteristic data set in China and surrounding areas (2010-2018)

    This data set is based on the lightning location data calculation of TBB products, cloud classification (CLC) products and world wide lightning location network (wwlln) in the full disk area detected by fy-2e satellite (fy-2e) from 2010 to 2018 to establish the lightning storm cloud feature data set. The algorithm used for wwlln lightning clustering is DBSCAN algorithm. According to Hutchins et al. (2014), it is required that the number of lightning in each lightning cluster in the thunderstorm cloud is greater than 2 and all fall within the radius of 12 km. The data set includes thunderstorm cloud time and location information, thunderstorm cloud shape (long, short axis, rotation angle, etc.) information represented by fitting ellipse, cloud area representing thunderstorm cloud structure, statistical value of black body temperature (TBB), included flash information, and included strong convection core, lightning cluster information and other data information.

    2022-06-15 398 14

  • Data sets of ground air exchange fluxes and vertical gradients of air pollutants at Namco station (2019) and Southeast Tibet station (2021)

    Data sets of ground air exchange fluxes and vertical gradients of air pollutants at Namco station (2019) and Southeast Tibet station (2021)

    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.

    2022-06-13 2278 18

  • Global Soil Texture Datasets Optimized from Satellite-Observed Wilting Point

    Global Soil Texture Datasets Optimized from Satellite-Observed Wilting Point

    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.

    2022-06-12 782 120

  • 90-meter resolution geological hazard risk map of the Himalayas and Asia Water Tower (2021)

    90-meter resolution geological hazard risk map of the Himalayas and Asia Water Tower (2021)

    This data set collates and collects various geological hazard points, topographic relief, landslide, elevation, land use and other influencing factors, with a resolution of 90m. The above factor layers and sample data are used to obtain the risk grade map with random forest. Data sets / atlas are mainly generated by: raw data (investigation, collection and purchase, etc.), processing data (calculation and simulation). The data source is downloaded from the open source website with an accuracy of 90m. The data is downloaded from the open source website and calculated in spider with their own random forest code. The training set is 80% and the test set is 20%. Open it with a computer that can run ArcGIS.

    2022-06-11 459 0

  • Thickness of Xiaodongkemadi glacier (2021)

    Thickness of Xiaodongkemadi glacier (2021)

    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.

    2022-06-10 637 0

  • Map of flood hazard level of Qinghai-Tibetan Plateau (2021, 250m)

    Map of flood hazard level of Qinghai-Tibetan Plateau (2021, 250m)

    Based on the concept of Height Above Nearest Drainage ( HAND ) derived from the international digital elevation model, the HAND model was used to identify the flooded area, and the spatial distribution of flood hazard level in the flood area of the study area was established. Flood hazard in the study area is divided into 1-5 grades, of which 5 represent very high risk, 4 represent high risk, 3 represent medium risk, 2 represent low risk, 1 represent very low risk.

    2022-06-10 578 0

  • Hengduan Mountain Area (Sichuan-Tibet railway) natural disaster risk and comprehensive risk assessment data set (2020)

    Hengduan Mountain Area (Sichuan-Tibet railway) natural disaster risk and comprehensive risk assessment data set (2020)

    Based on China's daily ground meteorological elements data set, national geographic basic data, demographic data, and 30M resolution DEM data, statistical yearbook data, historical disaster records, and other related data, using multi-methods like PCA, random forests to calculate hazard and vulnerability indicators, based on extreme precipitation,high temperature, flood, snow hazard, collapse and landslide hazards, to build comprehensive disaster risk index, and process them with normalization. Among them, we consider all the above disaster types in Hengduan Mountain area, and flood, snow disaster, collapse and landslide disaster in sichuan-tibet railway. The natural disasters hazard map, vulnerability map and comprehensive risk map of Hengduan Mountains (Sichuan-Tibet Railway) are included in the dataset.

    2022-06-09 2985 108

  • The population, grain output, sown area of grain crops, and livestock (1980-2020)

    The population, grain output, sown area of grain crops, and livestock (1980-2020)

    The population, grain, grain sown area and year-end data sets are extracted from the provincial and prefecture level statistical yearbooks of Qinghai, Tibet, Xinjiang, Gansu, Sichuan and Yunnan for many consecutive years. The missing data are interpolated as follows: 1. To ensure the accuracy of county data, Some counties and cities have been merged in this data (there may be errors in dividing and imputing the data for 20 years according to the proportion, but there will certainly be no problem in the merger, and the county area is small, so it is merged). 2. Xiahe County and cooperative city are merged into Xiahe County (cooperative city was separated from Xiahe County in 1998). 3. Gucheng district and Yulong County are merged into Gucheng district (Lijiang County was divided into Gucheng district and Yulong County in 2003). 4. The inner city district, East City District, West City District The four districts in Chengbei district have been merged into the district directly under the central government of Xining City (because the population of the four districts is given separately or the sum is given, and the total area of the four districts is only 487 square kilometers, they are merged). 5. For some missing data, curve fitting has been carried out in combination with similar years, and R2 is between 0.85-0.99. 6. In order to ensure the accuracy of the data, change maps have been prepared County by county

    2022-06-09 805 0

  • A dataset of rainfall erosivity in the Qinghai-Tibet Plateau (1960-2019)

    A dataset of rainfall erosivity in the Qinghai-Tibet Plateau (1960-2019)

    This dataset is a raster dataset of annual rainfall erosivity on the Qinghai-Tibet Plateau from 1960 to 2019. The rainfall erosivity was calculated using the daily rainfall data of 129 stations in the Qinghai-Tibet Plateau and its surrounding 150km range, of which 74 stations were located inside the Qinghai-Tibet Plateau and 55 stations were located outside. The calculation method is consistent with the algorithm of the first national Water Resources Inventory, using WGS_ 1984 coordinate system and Albers projection (central meridian 105°E, standard parallels 25°N and 47°N), and then Kriging interpolation is carried out year by year to generate grid map with spatial resolution of 250m. Rainfall erosivity is the main dynamic factor of soil erosion, and it is also the basic factor calculated by models such as CSLE and RUSLE. The integrated daily rainfall data of long-time series has high data accuracy, which improves the accuracy of rainfall erosivity estimation, and also helpful to further accurately estimate the amount of soil erosion on the Qinghai Tibet Plateau.

    2022-06-09 1050 0

  • Whole rock major and trace and zircon U-Pb isotope data set of the Mesozoic sedimentary rocks in the Tengchong and Baoshan blocks

    Whole rock major and trace and zircon U-Pb isotope data set of the Mesozoic sedimentary rocks in the Tengchong and Baoshan blocks

    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.

    2022-06-09 538 94

  • High resolution surface morphology of Kuoqionggangri Glacier (2020-2021)

    High resolution surface morphology of Kuoqionggangri Glacier (2020-2021)

    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.

    2022-06-09 860 0

  • Data set of groundwater storage change  in Eastern Tibetan Plateau revealed by baseflow recession analysis (2006-2020)

    Data set of groundwater storage change in Eastern Tibetan Plateau revealed by baseflow recession analysis (2006-2020)

    (1) Introduction: this data set is based on the method proposed by Brutsaert to calculate the change of groundwater storage revealed by baseflow recession analysis in 10 basins in the Eastern Tibetan Plateau. (2) Data source and processing: the runoff data of hydrological stations are from the hydrological yearbook of the people's Republic of China. According to the Brutsaert's method, the baseflow recession analysis is carried out to calculate relevant variables, so as to obtain the change of groundwater storage in each basin. (3) The data set has a time resolution of years. (4) The data set provides a reference for the change of groundwater storage in the Eastern Tibetan Plateau and further improves the level of understanding.

    2022-06-09 68 33

  • Terrestrial water storage anomalies in Tibetan Plateau (2003-2016)

    Terrestrial water storage anomalies in Tibetan Plateau (2003-2016)

    (1) Introduction: this data set is based on convolutional neural network(CNN). The terrestrial water storage anomalies from WGHM, Noah, CLSM, Mosaic and VIC are used to simulate GRACE leakage signal for model training. The trained neural network model is used to correct the leakage signal during the processing of GRACE level-2 spherical harmonic coefficient products. (2) Data source and processing: the data comes from GRACE level-2 spherical harmonic coefficient product. In this method, the data of hydrological model are filtered and truncated, the leakage process of signal is simulated, and the leakage signal is recovered through convolution neural network. After that, the trained neural network model is migrated to the leakage recovery of GRACE spherical harmonic data (this data uses CSR level-2 data). (3) Quality description: the data is 1 × 1 ° grid products, which can better reflect the spatial details of regional quality changes than the officially released mascon products; Compared with the traditional scale factor method, it can make more comprehensive use of a variety of hydrological models for signal restoration.(4) This method avoids the lack of prior knowledge in signal leakage error correction, does not rely on a single hydrological model, can consider the characteristics of spatial relationship with the help of convolution neural network, can accurately reverse the spatial details of terrestrial water storage anomalies, and expand the application space of gravity satellite technology in land water reserves change inversion and hydrological field.

    2022-06-09 100 40

  • Classification of Grassland degradation on the Tibetan Plateau - Documents, maps, data - modification. (2010-2019)

    Classification of Grassland degradation on the Tibetan Plateau - Documents, maps, data - modification. (2010-2019)

    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.

    2022-06-09 837 175

  • Namuco station (2019) and Southeast Tibet station (2021) air pollutant flux and vertical gradient data set

    Namuco station (2019) and Southeast Tibet station (2021) air pollutant flux and vertical gradient data set

    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.

    2022-06-08 969 2

  • Runoff and sediment data from Shigatse runoff plot in 2021

    Runoff and sediment data from Shigatse runoff plot in 2021

    The runoff plot is located in Shigatse, the Tibetan Plateau. The area had a serious soil erosion and large areas of low cover vegetation. Therefore, the runoff plot was constructed to monitor the soil erosion. The runoff plot had a length of 10 m, width of 5 m and a slope of 30°。 The vegetation coverage is low. The fully automatic runoff and sediment instrument was used to measured the runoff and sediment process. The temporal resolution varies with runoff process and had a high resolution when the water level changed rapidly. The measured results can provide the data support for the soil erosion in the Tibetan Plateau.

    2022-06-08 615 0

  • Scientific Expedition Album of different types and thickness of unconsolidated sediments in the Yarlung Tsangpo River Basin (2020)

    Scientific Expedition Album of different types and thickness of unconsolidated sediments in the Yarlung Tsangpo River Basin (2020)

    Focusing on the objective of estimating the total amount of unconsolidated sediments in the Yarlung Tsangpo River Basin (YTRB), we marked a series of Quaternary sections of unconsolidated sediments in the whole basin to measure their thickness. The dataset presents a collection of field photos of unconsolidated sediments obtained in the scientific expedition in YTRB in 2020. Specifically, this dataset comprises of 16 composite first–class sub basins, from upstream to downstream, including Dangque–Laiwu Tsangpo, Resu–Lierong Tsangpo, Chaiqu–Menqu, Xiongqu–Wengbuqu, Jiada Tsangpo, Pengji Tsangpo–Sakya Chongqu, Duoxiong Tsangpo, Shabu–Danapu, Nianchu River, Xiangqu–Wuyuma, Manqu, Nimuma–Lhasa River, Gonggapu–Luoburongqu, Niyang River, Yigong Tsangpo–Palong Tsangpo, and Xiangjiang River Basin. A total of 584 sites of unconsolidated sediments were marked. The atlas displays different types of unconsolidated sediments, such as alluvium, eluvium, diluvium, colluvium, eolian, lacustrine and moraine deposits, showing their spatial distribution in hillsides, foothills, floodplains, terraces, alluvial–diluvial fans and glacier fronts. With a scale of 1m benchmarking, it shows the significant difference in distribution of thickness. Generally, the thickness of the eluvium on the upper part of the hillside is about 0.3–2.5m, and the thickness of the alluvium is difficult to bottom out. The thickness of diluvium in the gentle area of the piedmont with steep slope is usually between 5 and 10 m, while the thickness of the deposit at the piedmont gully mouth is related to the scale of the pluvial fan, which can reach tens of meters thick and only 3 to 4 meters thin. From the upstream to the downstream, the thickness of alluvium varies greatly. The bedrock in the canyon area is exposed, and the thickness is almost 0. However, the thickness of alluvium in the upstream river valley is large and difficult to see the bottom interface; The maximum thickness of measured moraine deposits can reach more than 20 m. Aeolian deposits are common in the middle and upper reaches, with a wide range of thickness, ranging from a few meters to more than 20 meters. The dataset provides a wide variety of in–suit photos and measurements of unconsolidated sediments covering the whole basin, showing their characteristics of spatial distribution and genetic types, which lays a material foundation and prior knowledge for further detailed characterization and investigation of unconsolidated sediments. This work presents data for estimating the total accumulation of solid debris deposited in the YTRB, and provides a basis for assessing the risk of natural disasters related to unconsolidated sediments and formulating scientific preventive measures.

    2022-06-08 529 0

  • Dataset of sedimentary characteristics of unconsolidated sediments in the Yarlung Tsangpo River Basin (2022)

    Dataset of sedimentary characteristics of unconsolidated sediments in the Yarlung Tsangpo River Basin (2022)

    This dataset includes the schematic diagrams and lithologic histograms of the measured sections of typical unconsolidated sediments in Shigatse, Yarlung Tsangpo River Basin, as well as the statistical table of measured sections. The source data comes from a two-month field measurement in Shigatse, Tibet. 16 sections of unconsolidated sediments were measured, and 128 samples were collected, including 89 cosmic nuclide samples and 39 optically stimulated luminescence samples. 16 schematic diagrams and 38 lithologic histograms were shown. The dataset primarily shows the genetic types of typical unconsolidated sediments in the Shigatse area, such as alluvium, eluvium, diluvium, colluvium, and moraine deposits. The exposed range of measured sediment thickness is about 1.6–70 m, the average thickness is about 29 m, and the horizontal distribution is 41–9059 m. The dataset demonstrates the discrete, porous, sandy and weakly cemented structural characteristics of the unconsolidated sediments with high gravel content (80%–95%), and the main gravel diameter distribution is 0.05–0.1m; sorting and roundness of alluvium are good, while the colluvial materials are poor. Fining-upward trends are commonly seen in most sections, and parallel and tabular cross-bedding are occasionally developed. Untangling the sedimentary characteristics of unconsolidated sediments in the Yarlung Tsangpo River Basin is vital to reveal the storage of fluvial solid matter across the basin, and provide important instructions for disaster warning and prevention and control of related features caused by sliding, unloading, and collapse of the ground surface. It is also of great scientific value to reveal the source-sink process and evolution of fluvial and alluvial systems in the Tibet Plateau and its surrounding basins.

    2022-06-08 1067 0

  • A dataset of net primary productivity of vegetation on the Qinghai-Tibet Plateau (2001-2020)

    A dataset of net primary productivity of vegetation on the Qinghai-Tibet Plateau (2001-2020)

    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.

    2022-06-08 3202 170

  • Dataset of Land cover over Tibetan Plateau From 2001 to 2020

    Dataset of Land cover over Tibetan Plateau From 2001 to 2020

    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.

    2022-06-08 1407 0