Soil organic carbon in semiarid alpine regions: the spatial distribution, stock estimation, and environmental controls View Full Text


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Article Info

DATE

2019-03-21

AUTHORS

Meng Zhu, Qi Feng, Mengxu Zhang, Wei Liu, Ravinesh C. Deo, Chengqi Zhang, Linshan Yang

ABSTRACT

Soil organic carbon (SOC) in alpine regions is characterized by a strong local heterogeneity, which may contribute to relatively large uncertainties in regional SOC stock estimation. However, the patterns, stock, and environmental controls of SOC in semiarid alpine regions are still less understood. Therefore, the purpose of this study is to comprehensively quantify the stock and controls of SOC in semiarid alpine regions. Soils from 138 study sites across a typical semiarid alpine basin (1755–5051 m, ~1 × 104 km2) are sampled at 0–10, 10–20, 20–40, and 40–60 cm. SOC content, bulk density, soil texture, and soil pH are determined. Both a classical statistical model (i.e., a multiple linear regression, MLR) and a machine learning technique (i.e., a random forest, RF) are applied to estimate the SOC stock at a basin scale. The study further quantifies the environmental controls of SOC based on a general linear model (GLM) coupled with the structural equation modeling (SEM). SOC density varies significantly with topographic factors, with the highest values occurring at an elevation zone of ~3400 m. The results show that the SOC is more accurately estimated by the RF compared to the MLR model, with a total stock of 219.33 Tg C and an average density of 21.25 kg C m−2 at 0–60 cm across the study basin. The GLM approach reveals that the topography is seen to explain about 58.11% of the total variation in SOC density at 0–10 cm, of which the largest two proportions are attributable to the elevation (44.32%) and the aspect factor (11.25%). The SEM approach further indicates that, of the climatic, vegetative, and edaphic factors examined, the mean annual temperature, which is mainly shaped by topography, exerts the most significant control on SOC, mainly through its direct effect, and also, through indirect effect as delivered by vegetation type. The results of this study highlight the presence of high stocks of organic carbon in soils of semiarid alpine regions, indicating a fundamental role played by topography in affecting the overall SOC, which is mainly attained through its effects on the mean annual temperature. More... »

PAGES

1-15

References to SciGraph publications

  • 2016-06. Stability of soil organic carbon and potential carbon sequestration at eroding and deposition sites in JOURNAL OF SOILS AND SEDIMENTS
  • 2015-04. Climate change and the permafrost carbon feedback in NATURE
  • 2018-09. Design and evaluation of SVR, MARS and M5Tree models for 1, 2 and 3-day lead time forecasting of river flow data in a semiarid mountainous catchment in STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
  • 2001-10. Random Forests in MACHINE LEARNING
  • 2019-04. Effects of topography on soil organic carbon stocks in grasslands of a semiarid alpine region, northwestern China in JOURNAL OF SOILS AND SEDIMENTS
  • 2011-03. Digital mapping of soil organic matter stocks using Random Forest modeling in a semi-arid steppe ecosystem in PLANT AND SOIL
  • 2012-02. Soil organic carbon pools in particle-size fractions as affected by slope gradient and land use change in hilly regions, western Iran in JOURNAL OF MOUNTAIN SCIENCE
  • 2016-07. Organic matter losses in German Alps forest soils since the 1970s most likely caused by warming in NATURE GEOSCIENCE
  • 2013-02. Land use and climate change impacts on soil organic carbon stocks in semi-arid Spain in JOURNAL OF SOILS AND SEDIMENTS
  • 2015-05. Elevation-dependent warming in mountain regions of the world in NATURE CLIMATE CHANGE
  • 2016-10-05. Determinants of carbon release from the active layer and permafrost deposits on the Tibetan Plateau in NATURE COMMUNICATIONS
  • 2017-06-27. A multiscale dataset for understanding complex eco-hydrological processes in a heterogeneous oasis system in SCIENTIFIC DATA
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    URI

    http://scigraph.springernature.com/pub.10.1007/s11368-019-02295-6

    DOI

    http://dx.doi.org/10.1007/s11368-019-02295-6

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112918893


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