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.
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.
This data includes the soil carbon and nitrogen content at 0-10cm, 10-20cm and 20-30cm soil depths of 52 sample points in the west of Qinghai Tibet Plateau. The soil samples were obtained by the research team through soil drilling from 2019 to 2020. After the soil was screened with 2mm aperture, it was air dried and fine roots were removed, and then measured by carbon and nitrogen analyzer in the laboratory. This data can provide a theoretical basis for the study of soil carbon and nitrogen processes at different depths in the western Qinghai Tibet Plateau under the scenario of global climate change in the future, and provide data support for the model to simulate the process of soil carbon and nitrogen cycle, which is conducive to a deeper understanding of the process of soil carbon and nitrogen cycle in the western Qinghai Tibet Plateau.
This dataset is a high-frequency observation data of soil temperature and humidity in the active layer of seasonal frozen soil observed in the alpine meadow of Qianhuli Small watershed of Qinghai Lake, with a time resolution of half an hour. The data set can provide data support for the rate-dependent soil hydrothermal model and dynamic characterization of soil active layer.
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 .
The near surface atmospheric forcing and surface state dataset of the Tibetan Plateau was yielded by WRF model, time range: 2000-2010, space range: 25-40 °N, 75-105 °E, time resolution: hourly, space resolution: 10 km, grid number: 150 * 300. There are 33 variables in total, including 11 near surface atmospheric variables: temperature at 2m height on the ground, specific humidity at 2m height on the ground, surface pressure, latitudinal component of 10m wind field on the ground, longitudinal component of 10m wind field on the ground, proportion of solid precipitation, cumulative cumulus convective precipitation, cumulative grid precipitation, downward shortwave radiation flux at the surface, downward length at the surface Wave radiation flux, cumulative potential evaporation. There are 19 surface state variables: soil temperature in each layer, soil moisture in each layer, liquid water content in each layer, heat flux of snow phase change, soil bottom temperature, surface runoff, underground runoff, vegetation proportion, surface heat flux, snow water equivalent, actual snow thickness, snow density, water in the canopy, surface temperature, albedo, background albedo, lower boundary Soil temperature, upward heat flux (sensible heat flux) at the surface and upward water flux (sensible heat flux) at the surface. There are three other variables: longitude, latitude and planetary boundary layer height.
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.
The data set contains soil physicochemical properties of ten scientific expedition routes in Qinghai-Tibet Plateu during 2019-2021, including sample colletor, sampling time, sampling location, longitude and latitude, altitude, vegetation type, sampling depth, soil water content, pH, organic matter content, total carbon content, total nitrogen content, total phosphorus content, inorganic nitrogen content, heavy metal elements content, and etc. The physicochemical properties were measured in the laboratory with quality control, including measuring blanks, replicates and standard samples.The data set can be used for evaluating soil quality and function under the influence of climate change and human activities.
Soil is mineral particles of different sizes formed by weathering of rocks. Soil not only provides nutrients and water for crops, but also has a transforming effect on various nutrients. In addition, the soil also has a self-cleaning function, which can improve organic matter content, soil temperature and humidity, pH value, anion and cation. The soil pollution causes several environmental problems: industrial sewage, acid rain, exhaust emissions, accumulations, agricultural pollution. After the land is polluted, the contaminated tops with high concentration of heavy metals are easily entered under the action of wind and water. Other secondary ecological and environmental problems such as air pollution, surface water pollution, groundwater pollution and ecosystem degradation in the atmosphere and water.he data set comes from the World Soil Database (Harmonized World Soil Database version 1.1) (HWSD) UN Food and Agriculture (FAO) and the Vienna International Institute for Applied Systems Research Institute (IIASA) constructed, which provides data model input parameters for the modeler, At the same time, it provides a basis for research on ecological agriculture, food security and climate change.
Field surveys and soil sample collection were carried out in river and lake source of the Tibet Autonomous Region, and a total of 150 soil samples were collected, from August to September 2020. The data set includes serial number, plot number, latitude and longitude, altitude, soil moisture content, bulk density, organic matter, total nitrogen, total phosphorus, total potassium, pH and mechanical composition (sand, silt and clay content). The data format is an Excel table . The determination of various soil properties refers to the requirements of the "Technical Specifications for Soil Environmental Quality Monitoring" and is obtained through field sampling and indoor testing. Soil bulk density was measured in 5–10 cm and 15–20 cm soil layers, respectively. The mechanical composition is divided into sand (2–0.02 mm), silt (0.02–0.002 mm) and clay (< 0.002 mm) according to the International System of Classification. The soil is removed from impurities such as gravel and roots and crushed. The determination of soil organic matter, total nitrogen, total phosphorus and total potassium is a complete sample. The pH was determined by potentiometric method, and the water-soil ratio was 2.5:1. The collection of soil samples refers to the soil sample collection specifications, and the indoor analysis and testing refers to standard analysis methods. The data quality is controlled by measuring duplicate samples and standard samples. This data can provide data supporting for comprehensive assessment of the environmental effects of typical land use changes.