The Second Tibetan Plateau Scientific Expedition (STEP) program

Brief Introduction: Second Tibetan Plateau Scientific Expedition Program

Number of Datasets: 684

  • 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 1913 72

  • 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 10 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.

    2022-06-09 163 22

  • 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.

    2022-06-09 325 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 43 4

  • 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 46 4

  • 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.

    2022-06-09 237 41

  • 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.

    2022-06-08 236 0

  • 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 14 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.

    2022-06-08 169 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.

    2022-06-08 367 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 1255 44

  • 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.

    2022-06-08 372 0

  • Dataset of Normalized Difference Vegetation Index over Tibetan Plateau From 2001 to 2020

    Dataset of Normalized Difference Vegetation Index over Tibetan Plateau From 2001 to 2020

    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.

    2022-06-08 459 0

  • Multi index error component index data set of remote sensing precipitation product data in Qinghai-Tibet Plateau (1979-2018))

    Multi index error component index data set of remote sensing precipitation product data in Qinghai-Tibet Plateau (1979-2018))

    This data set contains five error components of multi-source remote sensing precipitation data sets such as CMFD, CPC, TRMM, cmorph and GPM, as well as a number of comprehensive indicators. The time range of TRMM data is 1998-2018, and the rest products are 1979-2018. Based on the station accuracy, these indexes obtain the daily and multi station average monthly error components of CMFD, CPC and TRMM by using the error component division method. Several overall accuracy evaluation indexes such as correlation coefficient and root mean square error between TRMM, cmorph and GPM data and CMA site data. The above index set has important reference value for evaluating the temporal and spatial accuracy and applicability of these five precipitation products in the Qinghai Tibet Plateau.

    2022-06-08 56 0

  • High resolution cmip6 forcing data of the Tibetan Plateau (1979-2013, 2015-2100)

    High resolution cmip6 forcing data of the Tibetan Plateau (1979-2013, 2015-2100)

    The cmip6 climate model data within the Qinghai Tibet plateau after downscaling and error correction includes three elements: daily precipitation, daily maximum and minimum surface temperatures, and covers the simulation results of ssp126, ssp245, ssp370 and ssp585 in the historical period (1979-2013) and future (2015-2100) of the climate model. The data is in NetCDF format, with a temporal resolution of 1 day and a horizontal spatial resolution of 0.1 °. It can provide support for the research and assessment of climate change in the Qinghai Tibet Plateau. The data set is based on seven cmip6 climate models such as access-esm1-5, which are publicly available in the world. Anu spline statistical interpolation downscaling is used, Bayesian model average multi model ensemble forecasting is used, and cncdfm (improved quantile chart method) method is used to correct the historical period and future prediction results of the model. The detailed process can be found in the references. The historical observation data are from two high-resolution historical meteorological data sets, CMFD and GPQM. After correction, the mean deviation of precipitation is less than 0.2mm/d, and the mean deviation of temperature is less than 0.97 ℃, which is much better than the original model.

    2022-06-08 52 1

  • Data set of flow-sediment processes in the Yarlung Zangbo River (2021)

    Data set of flow-sediment processes in the Yarlung Zangbo River (2021)

    To further investigate the transport process and temporal-spatial evolution of solid material in the Yarlung Zangbo River basin, the Sitting Bottom Bionic Water and sediment Observation System, which is the first set of good at the strong hydrodynamic condition and can continuously measure flow-sediment processes in real-time, was installed at Yangcun hydrology station by the sedimentary Dynamics observation team of Sichuan University on May 15, 2021. The bionic system was equipped with different types of observation equipment for water and sediment characteristics, which can measure the critical characteristics of water and sediment motion with high time resolution for a long time, continuously and synchronously. This data set contains the continuous data of 1) vertical velocity distribution (ADCP20210515.xlsx), 2) instantaneous velocity and turbulence of a single point near-bed, 3) Suspended sediment concentration measured by super turbidimeter (AOBS20210515.xlsx), 4) water depth, suspended sediment concentration and size distribution measured by Laser granulometer (Lisst20210515.xlsx). The data set with nearly a month recorded synchronous and continuous observation data of water and sediment characters with high temporal resolution per 10 minutes, which successfully observed the coupling change process of water and sediment under the increasing discharge of Yarlung Zangbo River. The simultaneous and continuous observation technology of water and sediment based on the bionic observation system provides technical support and scientific basis for revealing the source to sink process and evolution of Yarlung Zangbo River, bedload transport, flood numerical simulation, flash flood disaster warning and prevention, and major infrastructure construction.

    2022-06-08 154 0

  • 0.02° seamless hourly land surface temperature dataset over East Asia (2016-2021)

    0.02° seamless hourly land surface temperature dataset over East Asia (2016-2021)

    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).

    2022-06-08 417 33

  • Data set of surface grain size distribution along the Nyangqu River in the Yarlung Zangbo River (2021)

    Data set of surface grain size distribution along the Nyangqu River in the Yarlung Zangbo River (2021)

    The riverbed surface of the main channel in Nyangqu river is composed of gravel particles with wide grain size distribution. there are abundant gravel particles on the beach and riverbed. In this investigation, the bed surface grain size distribution of the main channel and tributaries of the Nyangqu river was measured. This data set contains the information of the five sampling locations in five main channels and two locations in tributaries of the Nyangqu River Basin (Table 1) and the bed surface grain size distribution (Table 2). The sampling locations were generally selected near the cross-section with obvious riverbed. It was considered that water flow through these sections in the straight channel for a long. At the same time, because it was a dry season, the bed grain size distribution on the river beach could be considered as the movement of gravel bedload carried by the last flood season. Therefore, it was considered that the bed grain size distribution in the sampling area on the river beach in the dry season was the bedload size distribution in the flood season. The grain size distributions were measured by the automatic identification method of full particle size based on image processing (e.g., Baserain software), with high identification accuracy of sediment particles is high. It is of great value to the scientific research on the evolution of source to sink process,bedlaod transport, and flood numerical simualtion, as well as the basic research on the flash flood prevention and control.

    2022-06-07 196 0

  • XRF multi-element data of gongka Lake

    XRF multi-element data of gongka Lake

    The data include the depth of gonka lake and XRF data. The core samples of gongka Lake were collected in October, 2020. In November, 2020, XRF element determination was carried out on the four cores of gk20b borehole. The relative contents of various elements (10 keV, 1mA, counting time 15s; 30 keV, 2mA, counting time 25s) were obtained at the sampling resolution of 0.1cm. According to the sediment properties and element content results, the overlapping parts of the four cores were compared, and finally 2.045m XRF records were obtained.

    2022-06-06 28 0