The three pole soil microbial post-treatment products in typical years collected the distribution and analysis results of soil samples from the north and south polar regions from 2005 to 2006 and the distribution and analysis results of soil samples from the Qinghai Tibet Plateau in 2015. Through sorting and summarizing, the post-processing data products of soil microorganisms in the three pole region are obtained. The data format is excel, which is convenient for users to view. Among them, the collection time of samples from the north and south polar regions was from December 13, 2005 to December 8, 2006, including 52 samples from three regions in the Arctic (Spitsbergen slijeringa, Spitsbergen vestpynten, and Alexandra fjord Highlands), and 171 samples from five regions in the Antarctic (Mitchell Peninsula, Casey station main power house, Robinson ridge, herring Island, browning Peninsula); The Qinghai Tibet Plateau was collected from July 1 to July 15, 2015, including meadow, grassland and desert ecosystems. There were 18 sampling points in total, and the number of samples at each sampling point was 3-5. The precipitation, air temperature and drought degree of the sampling point are estimated from the meteorological information for reference. The soil surface samples were collected and stored in liquid nitrogen, then transported back to the Sydney Laboratory for extraction by fastprep DNA kit. The extracted DNA samples were amplified with the 16S rRNA gene fragment using 27F (5'-gagttttgatcntggctca-3') and 519r (5'-gtnttacngcgckctg-3'). The amplified fragments were sequenced by 454 method, and the original data were analyzed by mothur software. The sequences with poor sequencing quality were first removed, and then the chimeric sequences were sequenced and removed. After that, the similarity between sequences is calculated. Sequences with a similarity of more than 97% are clustered into one OTU, and OTU representative sequences are defined. The OTU representative sequences were aligned with the Silva database, and were identified to belong to the first level when the reliability was greater than 80%.
YE Aizhong
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.
ZHANG Wenbo
This dataset contains daily 0.05°×0.05° land surface soil moisture products in Qilian Mountain Area in 2021. The dataset was produced by utilizing the optimized wavelet-coupled-RF downscaling model (RF-OWCM) to downscale the SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture (SMAP L3, V8). The auxiliary datasets participating in the downscaling model include the MUltiscale Satellite remotE Sensing (MUSES) LAI/FVC product, the daily 1-km all-weather land surface temperature dataset for the Chinese landmass and its surrounding areas (TRIMS LST-TP;) and Lat/Lon information.
CHAI Linna, ZHU Zhongli, LIU Shaomin
Based on the "second Qinghai Tibet Plateau comprehensive scientific investigation" and "China's soil series investigation and compilation of China's soil series" "The obtained soil survey profile data, using predictive Digital Soil Mapping paradigm, using geographic information and remote sensing technology for fine description and spatial analysis of the soil forming environment, developed adaptive depth function fitting methods, and integrated advanced ensemble machine learning methods to generate a series of soil attributes (soil organic carbon, pH value, total nitrogen, total phosphorus, total potassium, cation exchange capacity, gravel content (>2mm) in the Qinghai Tibet plateau region." , sand, silt, clay, soil texture type, unit weight, soil thickness, etc.) and quantify the spatial distribution of uncertainty. Compared with the existing soil maps, it better represents the spatial variation characteristics of soil properties in the Qinghai Tibet Plateau. The data set can provide soil information support for the study of soil, ecology, hydrology, environment, climate, biology, etc. in the Qinghai Tibet Plateau.
LIU Feng, ZHANG Ganlin
The dataset includes the measured soil thickness data at 148 points in the Yarlung Zangbo River Basin, as well as the physical properties and hydraulic characteristics (such as particle size, saturated water content, organic matter content, saturated hydraulic conductivity, etc.) of soil samples at 40 points. The sampling points are distributed from Zhongba County in the upper reaches of the Yarlung Zangbo River basin to Nyingchi city in the lower reaches. The soil thickness data is obtained through the excavation profile measurement, and other soil data are obtained from the collected ring knife samples according to the standardized experimental process, so the data accuracy is high. The soil data of the Yarlung Zangbo River basin provided by this dataset can provide a reference for large-scale soil mapping on the Qinghai Tibet Plateau and improve the prediction accuracy of relevant studies.
LIU Jintao
1) Soil environmental quality data of typical industrial parks in Huangshui basin of Qinghai Province provide basic support for soil pollution control caused by regional industrial activities; 2) The data source is the soil samples of typical areas in Huangshui River Basin. After collection, the samples are quickly stored in the refrigerator at - 4 ℃ and sent to the laboratory as soon as possible. After pretreatment, the relevant parameters are tested; 3) The process of sample collection and transportation meets the specifications, and the experimental detection process strictly follows the relevant standards. Due to the changes of various factors of soil environment, the results are only aimed at the investigation results; 4) The data can be used to analyze regional soil pollution and heavy metal risk assessment;
WANG Lingqing
The dataset based on synthesized data from 1114 sites across the Tibetan permafrost region which report that paleoclimate is more important than modern climate in shaping current permafrost carbon distribution.A new estimate of modern soil carbon stock to 3m depth on Tibetan permafrost region was derived by machine learning algorithm, including factors such as climate (paleoclimate and modern climate), vegetation, soil (soil thickness and soil physical and chemical properties, etc.) and topography. This dataset shows that ecosystem models clearly underestimated the Tibetan soil carbon stock, due to the absence of paleoclimate effects in the model. Future modelling of soil carbon cycling should include paleoclimate .
DING Jinzhi
This dataset contains daily 0.05°×0.05° land surface soil moisture products in Qilian Mountain Area in 2020. The dataset was produced by utilizing the optimized wavelet-coupled-RF downscaling model (RF-OWCM) to downscale the SMAP L3 Radiometer Global Daily 36 km EASE-Grid Soil Moisture (SMAP L3, V8). The auxiliary datasets participating in the downscaling model include GLASS Albedo, MUSES LAI/FVC, Daily 1-km all-weather land surface temperature dataset for Western China (TRIMS LST-TP; 2000-2021) V2 and Lat/Lon information.
CHAI Linna, ZHU Zhongli, LIU Shaomin
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
Dating data of debris flow and dammed lake sediments in complex mountainous areas from 2019 to 2021. The data collection sites are complex mountainous areas prone to debris flow in the eastern and southern edges of the Qinghai Tibet Plateau. The experimental analysis is mainly completed in the salt lake chemical analysis and testing center of Qinghai Salt Lake Research Institute of Chinese Academy of Sciences and the analysis and testing center of Chengdu Mountain Institute of Chinese Academy of Sciences. The instruments used include RIS ø TL / OSL – Da – 20 automatic luminescence instrument, etc. The age data set of debris flow sediments in typical complex mountainous areas is established, the formation age of debris flow sediments in complex mountainous areas is quantitatively studied, and the ancient debris flow disaster activity history in complex mountainous areas is determined.
HU Guisheng
"Based on diverse processes of land degradation, the permafrost degradation, vegetation degradation, salinization, desertification and soil erosion in 1990 and 2015 were selected as the main factors affected land ecosystem of the Qinghai-Tibet Plateau. The changing trend of land degradation on the Qinghai-Tibet Plateau from 1990 to 2015 was assessed by overlaying the key influencing degradation processes. Land degradation types: 0 - No degradation； 1 - Salinization； 10 - Permafrost degradation； 11 - Salinization and permafrost degradation； 100 - Soil erosion； 101 - Soil erosion and salinization； 110 - Soi erosion and permafrost degradation； 111 - Soi erosion, permafrost degradation and salinization； 1000 - Desertification； 1001 - Desertification and salinization； 1010 - Desertification and permafrost degradation； 1011 - Desertification, permafrost degradation and salinization； 1100 - Desertification and soil erosion； 1101 - Desertification, soil erosion and salinization； 1110 - Desertification, soil erosion and permafrost degradation； 1111 - Desertification, soil erosion, permafrost degradation and salinization； 10000 - Vegetation degradation； 10001 - Vegetation degradation and salinization； 10010 - Vegetation degradation and permafrost degradation； 10011 - Vegetation degradation, permafrost degradation and salinization； 10100 - Vegetation degradation and soil erosion； 10101 - Vegetation degradation, soil erosion and salinization； 10110 - Vegetation degradation, soil erosion and permafrost degradation； 10111 - Vegetation degradation, soil erosion, permafrost degradation and salinization； 11000 - Vegetation degradation and desertification； 11001 - Vegetation degradation, desertification and salinization； 11010 - Vegetation degradation, desertification and permafrost degradation； 11011 - Vegetation degradation, desertification, permafrost degradation and salinization； 11100 - Vegetation degradation, desertification and soil erosion； 11101 - Vegetation degradation, desertification, soil erosion and salinization； 11110 - Vegetation degradation, desertification, soil erosion and permafrost degradation； 11111 - Vegetation degradation, desertification, soil erosion, permafrost degradation and salinization；"
ZHAO Guangju
Rainfall erosivity is one of the important basic data to quantify soil erosion in the Tibet Plateau. High precision rainfall erosivity data is the key to understand the current situation of soil and water loss in theTibet Plateau and formulate soil and water conservation measures. Meanwhile, it can provide a powerful reference for the prevention and control of geological disasters in the Tibet Plateau. Based on the 1-min dense precipitation observations and the grid precipitation product, a new annual rainfall erosivity dataset in Tibet Plateau from 1950 to 2020 is constructed through the steps of correction, reconstruction and validation. This dataset is the rainfall erosivity data set with the highest accuracy and the longest time series in the Tibet Plateau.
CHEN Yueli
From July to August 2019, 147 soil samples of different land use types were collected every 20km，in sections from Xining City to Golmud of Qinghai Province, from Golmud of Qinghai Province to Lhasa of Tibet Autonomous Region, and from Golmud to Xining, far away from human disturbance. Totally, 147 soil samples were collected, including 83 grassland, 48 sandy land, 14 agricultural land and 3 forest land. The data set includes serial number, geographical location of sampling points, land use type, longitude and latitude coordinates, altitude, soil total nitrogen, total phosphorus, total potassium content and soil pH. The data format is Excel table. This data set is obtained by the combination of field sampling and indoor experiments. Soil samples in 0-15 cm layer was collected with a soil drill (8 cm in diameter) in each square by random sampling method, and the soil separated from the root system was screened by using a coarse screen. The total nitrogen, total phosphorus and total potassium was measured from the whole sample, and the 0.15 mm soil sample was used, in which the total nitrogen was measured by semi-automatic Kjeldahl nitrogen determinator, the total phosphorus was measured by spectrophotometer, and the total potassium was measured by flame photometer. The measurement of soil PH value was as: weighed 10g of air dried soil sample screened by 1mm into a 50ml beaker, added carbon dioxide free distilled water to maintain the water-soil ratio of 2.5:1, and measured it in PHSJ-4F laboratory. This data can provide data support and scientific basis for alpine ecosystem restoration.
ZHAO Guangju
From September to October 2019, a field survey was carried out along the 214 National Highway in the Sanjiangyuan District to investigate the geology, landform, climate and vegetation type data, and to collect soil samples along the line, a total of 32 soil samples. Field surveys were carried out in the typical desertified grassland, grazing grassland and plateau pika activity areas in the source area of the Three Rivers from June to July 2020, including 15 soil samples with different degrees of desertification, 9 soil samples with different grazing intensity, and pika activity areas There are 12 soil samples, 36 in total. The two field surveys totaled 68 soil samples. The content of the data set includes serial number, geographic location of each sample point, land use type, latitude and longitude coordinates, altitude, soil total nitrogen, total phosphorus, total potassium content and soil pH. The data format is an Excel table. This data set is obtained by self-determination by combining field sampling and indoor experiment. The total nitrogen is measured by a semi-automatic Kjeldahl nitrogen analyzer, the total phosphorus is measured by a spectrophotometer, the total potassium is measured by a flame photometer, and the pH is measured by a PHSJ-4F laboratory pH meter. This data can provide data support and scientific basis for the restoration of the alpine ecosystem.
ZHAO Guangju
The regional water environment data of typical mineral development projects include the water sample detection data set around the typical mineral development area of the super large gold belt in the Qilian Mountain metallogenic belt in the northeast of Qinghai Tibet Plateau (2019), and the sediment and soil sample detection data set around the typical mineral development area of the super large gold belt in the Qilian Mountain metallogenic belt in the northeast of Qinghai Tibet Plateau (2019). The first row of data is longitude and latitude and element name, the second row is element content unit, and the first column is sample point number. The data acquisition method is the water, sediment and soil samples collected in the relevant watersheds around Zaozigou gold mine, Dashui gold mine and Zhongqu tailings pond in Gannan Tibetan Autonomous Prefecture in August 2019. The water samples are detected and analyzed by ICAP sq inductively coupled plasma mass spectrometer and haiguang optical AFS-2202E atomic fluorescence spectrometer of American Thermal Power company, The soil and sediments are detected and analyzed by ieexrf fluorescence spectrometer, mainly analyzing the contents of major elements such as K, Ca and Na and trace elements such as Cr \ Ni \ Cu \ Zn. The data format is xlsx and the data quality is reliable. It can be used to evaluate the comprehensive effect of water environment in typical mineral development areas of super large gold belt in Qilian Mountain metallogenic belt in the northeast of Qinghai Tibet Plateau.
CHENG Hao
The Tibet-Obs established in 2008 consists of three regional-scale soil moisture (SM) monitoring networks, i.e. the Maqu, Naqu, and Ngari (including Ali and Shiquanhe) networks. This surface SM dataset includes the original 15-min in situ measurements collected at a depth of 5 cm by multiple SM monitoring sites of all the networks, and the spatially upscaled SM records produced for the Maqu and Shiquanhe networks.
ZHANG Pei, ZHENG Donghai, WEN Jun, ZENG Yijian, WANG Xin, WANG Zuoliang, MA Yaoming, SU Zhongbo
This dataset contains daily 0.05°×0.05° land surface soil moisture products in Qilian Mountain Area in 2019. The dataset was produced by utilizing the optimized wavelet-coupled-RF downscaling model (RF-OWCM) to downscale the “AMSR-E and AMSR2 TB-based SMAP Time-Expanded Daily 0.25°×0.25° Land Surface Soil Moisture Dataset in Qilian Mountain Area (SMsmapTE, V1)”. The auxiliary datasets participating in the downscaling model include GLASS Albedo/LAI/FVC, Thermal and Reanalysis Integrating Medium-resolution Spatial-seamless LST – Tibetan Plateau (TRIMS LST-TP) by Ji Zhou and Lat/Lon information.
CHAI Linna, ZHU Zhongli, LIU Shaomin
This data set contains the biological property data of soil samples from several scientific research routes in the Qinghai Tibet Plateau from 2019 to 2021, including the information of the collector, collection time, collection location, longitude and latitude, altitude, vegetation type, sampling depth, phosphatase activity, microbial respiration, nitrogen transformation characteristics, functional gene abundance, fungi, bacteria, protobiotic diversity, etc. The analysis of various soil properties refers to the requirements of "technical specification for soil environmental quality monitoring", and the first-hand data obtained through laboratory analysis. The data quality is controlled by determining blank samples, duplicate samples and standard samples. The data set can be used to evaluate soil quality and function under the influence of climate change and human activities.
ZHANG Limei
Soil profiles in this dataset were surveyed in the western and central Qinghai-Tibet Plateau in July 2019, including Ali, Xigaze and Naqu of the Tibet and Kashgar and Hotan of the Xinjiang. Information on the profile ID, longitude, latitude, soil types was provided. Soil types were referenced according to the Chinese Soil Taxonomy. The Chinese Soil Taxonomy is a hierarchical system, in which 6 categories were defined: Order, Suborder, Group, Subgroup, Family and Series. The sampling location was recorded by a handheld GPS receiver. Especially, these soil types were initially determined based on the diagnostic horizons and diagnostic properties identified in field. Due to the effect of epidemic, physicochemical properties of some soil samples have not been achieved and thus some soil types need to be updated in the following months.
Terrestrial actual evapotranspiration (ETa) is an important component of terrestrial ecosystems because it links the hydrological, energy, and carbon cycles. However, accurately monitoring and understanding the spatial and temporal variability of ETa over the Tibetan Plateau (TP) remains very difficult. Here, the multiyear (2000-2018) monthly ETa on the TP was estimated using the MOD16-STM model supported by datasets of soil properties, meteorological conditions, and remote sensing. The estimated ETa correlates very well with measurements from 9 flux towers, with low root mean square errors (average RMSE = 13.48 mm/month) and mean bias (average MB = 2.85 mm/month), and strong correlation coefficients (R = 0.88) and the index of agreement values (IOA = 0.92). The spatially averaged ETa of the entire TP and the eastern TP (Lon > 90°E) increased significantly, at rates of 1.34 mm/year (p < 0.05) and 2.84 mm/year (p < 0.05) from 2000 to 2018, while no pronounced trend was detected on the western TP (Lon < 90°E). The spatial distribution of ETa and its components were heterogeneous, decreasing from the southeastern to northwestern TP. ETa showed a significantly increasing trend in the eastern TP, and a significant decreasing trend throughout the year in the southwestern TP, particularly in winter and spring. Soil evaporation (Es) accounted for more than 84% of ETa and the spatial distribution of temporal trends was similar to that of ETa over the TP. The amplitudes and rates of variations in ETa were greatest in spring and summer. The multi-year averaged annual terrestrial ETa (over an area of 2444.18×103 km2) was 376.91±13.13 mm/year, equivalent to a volume of 976.52±35.7 km3/year. The average annual evapotranspirated water volume over the whole TP (including all plateau lakes, with an area of 2539.49×103 km2) was about 1028.22±37.8 km3/year. This new estimated ETa dataset is useful for investigating the hydrological impacts of land cover change and will help with better management of watershed water resources across the TP.
MA Yaoming, CHEN Xuelong,
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