Journal of Soils and Sediments

, Volume 19, Issue 10, pp 3427–3441 | Cite as

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

  • Meng Zhu
  • Qi FengEmail author
  • Mengxu Zhang
  • Wei Liu
  • Ravinesh C. Deo
  • Chengqi Zhang
  • Linshan Yang
Soils, Sec 1 • Soil Organic Matter Dynamics and Nutrient Cycling • Research Article



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.

Materials and methods

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).

Results and discussion

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.


Random forest Semiarid alpine regions Soil organic carbon Structural equation modeling Topography 


Funding information

This work was supported by the National Key R&D Program of China (No. 2017YFC0404305), the National Natural Science Fund of China (No. 41771252, 41801015), the Major Program of the Natural Science Foundation of Gansu province, China (No. 18JR4RA002), the Grants from the Key Project of the Chinese Academy of Sciences (No. QYZDJ-SSW-DQC031), the International Science and Technology Cooperation Project of Gansu province (No. 17YF1WA168), and the Foundation for Excellent Youth Scholars of NIEER, CAS (51Y851D61).


  1. Ajami M, Heidari A, Khormali F, Gorji M, Ayoubi S (2016) Environmental factors controlling soil organic carbon storage in loess soils of a subhumid region, northern Iran. Geoderma 281:1–10CrossRefGoogle Scholar
  2. Akpa SIC, Odeh IOA, Bishop TFA, Hartemink AE, Amapu IY (2016) Total soil organic carbon and carbon sequestration potential in Nigeria. Geoderma 271:202–215CrossRefGoogle Scholar
  3. Albaladejo J, Ortiz R, Garcia-Franco N, Ruiz Navarro A, Almagro M, Garcia Pintado J, Martinez-Mena M (2013) Land use and climate change impacts on soil organic carbon stocks in semi-arid Spain. J Soils Sediments 13:265–277CrossRefGoogle Scholar
  4. Ali M, Deo RC, Downs NJ, Maraseni T (2018) An ensemble-ANFIS based uncertainty assessment model for forecasting multi-scalar standardized precipitation index. Atmos Res 207:155–180CrossRefGoogle Scholar
  5. Bangroo SA, Najar GR, Rasool A (2017) Effect of altitude and aspect on soil organic carbon and nitrogen stocks in the Himalayan Mawer Forest range. Catena 158:63–68CrossRefGoogle Scholar
  6. Batjes NH (1996) Total carbon and nitrogen in the soils of the world. Eur J Soil Sci 47:151–163CrossRefGoogle Scholar
  7. Breiman L (2001) Random forests. Mach Learn 45:5–32CrossRefGoogle Scholar
  8. Chen LH, Qu YG, Chen HS, Li FX (1992) Water and land resources and their rational development and utilization in the Hexi region. Science Press, Beijing (in Chinese) Google Scholar
  9. Chen LF, He ZB, Du J, Yang JJ, Zhu X (2016a) Patterns and environmental controls of soil organic carbon and total nitrogen in alpine ecosystems of northwestern China. Catena 137:37–43CrossRefGoogle Scholar
  10. Chen LY, Liang JY, Qin SQ, Liu L, Fang K, Xu YP, Ding JZ, Li F, Luo YQ, Yang YH (2016b) Determinants of carbon release from the active layer and permafrost deposits on the Tibetan plateau. Nat Commun 7:13046CrossRefGoogle Scholar
  11. de Brogniez D, Ballabio C, Stevens A, Jones RJA, Montanarella L, van Wesemael B (2015) A map of the topsoil organic carbon content of Europe generated by a generalized additive model. Eur J Soil Sci 66:121–134CrossRefGoogle Scholar
  12. Development Core Team R (2018) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, AustriaGoogle Scholar
  13. Ding JZ, Li F, Yang GB, Chen LY, Zhang BB, Liu L, Fang K, Qin SQ, Chen YL, Peng YF, Ji CJ, He HL, Smith P, Yang YH (2016) The permafrost carbon inventory on the Tibetan plateau: a new evaluation using deep sediment cores. Glob Chang Biol 22:2688–2701CrossRefGoogle Scholar
  14. Doetterl S, Stevens A, van Oost K, Quine TA, van Wesemael B (2013) Spatially-explicit regional-scale prediction of soil organic carbon stocks in cropland using environmental variables and mixed model approaches. Geoderma 204:31–42CrossRefGoogle Scholar
  15. Feng Q, Endo KN, Cheng GD (2002) Soil carbon in desertified land in relation to site characteristics. Geoderma 106:21–43CrossRefGoogle Scholar
  16. Fernández-Romero ML, Lozano-García B, Parras-Alcántara L (2014) Topography and land use change effects on the soil organic carbon stock of forest soils in Mediterranean natural areas. Agric Ecosyst Environ 195:1–9CrossRefGoogle Scholar
  17. Ghorbani MA, Deo RC, Kashani MH, Shahabi M, Ghorbani S (2019) Artificial intelligence-based fast and efficient hybrid approach for spatial modelling of soil electrical conductivity. Soil Till Res 186:152–164CrossRefGoogle Scholar
  18. Grimm R, Behrens T, Maerker M, Elsenbeer H (2008) Soil organic carbon concentrations and stocks on Barro Colorado Island-digital soil mapping using random forests analysis. Geoderma 146:102–113CrossRefGoogle Scholar
  19. Heung B, Ho HC, Zhang J, Knudby A, Bulmer CE, Schmidt MG (2016) An overview and comparison of machine-learning techniques for classification purposes in digital soil mapping. Geoderma 265:62–77CrossRefGoogle Scholar
  20. IUSS Working group WRB (2015) World Reference Base for Soil Resources 2014, update 2015 International soil classification system for naming soils and creating legends for soil maps. World Soil Resources Reports No. 106. FAO. Rome. Accessed 19 Feb 2019
  21. Karchegani PM, Ayoubi S, Mosaddeghi MR, Honarjoo N (2012) Soil organic carbon pools in particle-size fractions as affected by slope gradient and land use change in hilly regions, western Iran. J Mt Sci 9:87–95CrossRefGoogle Scholar
  22. Kouadio L, Deo RC, Byrareddy V, Adamowski JF, Mushtaq S, Nguyen VP (2018) Artificial intelligence approach for the prediction of Robusta coffee yield using soil fertility properties. Comput Electron Agr 155:324–338CrossRefGoogle Scholar
  23. Li X, Liu SM, Xiao Q, Ma MG, Jin R, Che T, Wang WZ, Hu XL, Xu ZW, Wen JG, Wang LX (2017) A multiscale dataset for understanding complex eco-hydrological processes in a heterogeneous oasis system. Sci Data 4:170083CrossRefGoogle Scholar
  24. Li YQ, Wang XY, Niu YY, Lian J, Luo YQ, Chen YP, Gong XW, Yang H, Yu PD (2018) Spatial distribution of soil organic carbon in the ecologically fragile Horqin grassland of northeastern China. Geoderma 325:102–109CrossRefGoogle Scholar
  25. Lin LI (1989) A concordance correlation coefficient to evaluate reproducibility. Biometrics 45:255–268CrossRefGoogle Scholar
  26. Liu ZP, Shao MA, Wang YQ (2011) Effect of environmental factors on regional soil organic carbon stocks across the Loess Plateau region, China. Agric Ecosyst Environ 142:184–194CrossRefGoogle Scholar
  27. Lozano-García B, Parras-Alcantára L (2014) Variation in soil organic carbon and nitrogen stocks along a toposequence in a traditional Mediteranean olive grove. Land Degrad Dev 25:297–304CrossRefGoogle Scholar
  28. Lozano-García B, Parras-Alcántara L, Brevik EC (2016) Impact of topographic aspect and vegetation (native and reforested areas) on soil organic carbon and nitrogen budgets in Mediterranean natural areas. Sci Total Environ 544:963–970CrossRefGoogle Scholar
  29. Ma WM, Li ZW, Ding KY, Huang B, Nie XD, Lu YM, Xiao HB, Zeng GM (2016) Stability of soil organic carbon and potential carbon sequestration at eroding and deposition sites. J Soils Sediments 16:1705–1717CrossRefGoogle Scholar
  30. McCune B, Keon D (2002) Equations for potential annual direct incident radiation and heat load. J Veg Sci 13:603–606CrossRefGoogle Scholar
  31. Meersmans J, De Ridder F, Canters F, De Baets S, Van Molle M (2008) A multiple regression approach to assess the spatial distribution of soil organic carbon (SOC) at the regional scale (Flanders, Belgium). Geoderma 143:1–13CrossRefGoogle Scholar
  32. Melillo JM, Steudler PA, Aber JD, Newkirk K, Lux H, Bowles FP, Catricala C, Magill A, Ahrens T, Morrisseau S (2002) Soil warming and carbon-cycle feedbacks to the climate system. Science 298:2173–2176CrossRefGoogle Scholar
  33. Nelson DW, Sommers LE (1982) Total carbon, organic carbon and organic matter. In: Page AL, Miller RH, Keeney D (eds) Methods of soil analysis, part2. Chemical and microbiological properties agronomy monograph, vol 9. ASA and SSSA, Madison, pp 539–579Google Scholar
  34. Parras-Alcantára L, Lozano-García B, Galán-Espejo A (2015) Soil organic carbon along an altitudinal gradient in the Despenaperros Natural Park, southern Spain. Solid Earth 6:125–134CrossRefGoogle Scholar
  35. Pepin N et al (2015) Elevation-dependent warming in mountain regions of the world. Nat Clim Chang 5:424–430CrossRefGoogle Scholar
  36. Prasad R, Deo RC, Li Y, Maraseni T (2018a) Ensemble committee-based data intelligent approach for generating soil moisture forecasts with multivariate hydro-meteorological predictors. Soil Till Res 181:63–81CrossRefGoogle Scholar
  37. Prasad R, Deo RC, Li Y, Maraseni T (2018b) Soil moisture forecasting by a hybrid machine learning technique: ELM integrated with ensemble empirical mode decomposition. Geoderma 330:136–161CrossRefGoogle Scholar
  38. Prietzel J, Christophel D (2014) Organic carbon stocks in forest soils of the German Alps. Geoderma 221:28–39CrossRefGoogle Scholar
  39. Prietzel J, Zimmermann L, Schubert A, Christophel D (2016) Organic matter losses in German Alps forest soils since the 1970s most likely caused by warming. Nat Geosci 9:543–550CrossRefGoogle Scholar
  40. Qin YY, Feng Q, Holden NM, Cao JJ (2016) Variation in soil organic carbon by slope aspect in the middle of the Qilian Mountains in the upper Heihe River basin, China. Catena 147:308–314CrossRefGoogle Scholar
  41. Qin Y, Yi SH, Ding YJ, Xu GW, Chen JJ, Wang ZW (2018) Effects of small-scale patchiness of alpine grassland on ecosystem carbon and nitrogen accumulation and estimation in northeastern Qinghai-Tibetan plateau. Geoderma 318:52–63CrossRefGoogle Scholar
  42. Rial M, Martinez Cortizas A, Rodriguez-Lado L (2016) Mapping soil organic carbon content using spectroscopic and environmental data: a case study in acidic soils from NW Spain. Sci Total Environ 539:26–35CrossRefGoogle Scholar
  43. Scharlemann JPW, Tanner EVJ, Hiederer R, Kapos V (2014) Global soil carbon: understanding and managing the largest terrestrial carbon pool. Carbon Manag 5:81–91CrossRefGoogle Scholar
  44. Schuur EAG, McGuire AD, Schaedel C, Grosse G, Harden JW, Hayes DJ, Hugelius G, Koven CD, Kuhry P, Lawrence DM, Natali SM, Olefeldt D, Romanovsky VE, Schaefer K, Turetsky MR, Treat CC, Vonk JE (2015) Climate change and the permafrost carbon feedback. Nature 520:171–179CrossRefGoogle Scholar
  45. Song XD, Brus DJ, Liu F, Li DC, Zhao YG, Yang JL, Zhang GL (2016) Mapping soil organic carbon content by geographically weighted regression: a case study in the Heihe River basin, China. Geoderma 261:11–22CrossRefGoogle Scholar
  46. Stockmann U, Adams MA, Crawford JW, Field DJ, Henakaarchchi N, Jenkins M, Minasny B, McBratney AB, Courcelles VR, Singh K, Wheeler I, Abbott L, Angers DA, Baldock J, Bird M, Brookes PC, Chenu C, Jastrow JD, Lal R, Lehmann J, O’Donnell AG, Parton WJ, Whitehead D, Zimmermann M (2013) The knowns, known unknowns and unknowns of sequestration of soil organic carbon. Agric Ecosyst Environ 164:80–99CrossRefGoogle Scholar
  47. Suuster E, Ritz C, Roostalu H, Kolli R, Astover A (2012) Modelling soil organic carbon concentration of mineral soils in arable land using legacy soil data. Eur J Soil Sci 63:351–359CrossRefGoogle Scholar
  48. USGS (2006) Shuttle Radar Topography Mission, 1 Arc Second scenes. In: Global Land Cover facility-University of Maryland (es), Maryland, USGoogle Scholar
  49. Vågen TG, Winowiecki LA, Abegaz A, Hadgu KM (2013) Landsat-based approaches for mapping of land degradation prevalence and soil functional properties in Ethiopia. Remote Sens Environ 134:266–275CrossRefGoogle Scholar
  50. Were K, Bui DT, Dick OB, Singh BR (2015) A comparative assessment of support vector regression, artificial neural networks, and random forests for predicting and mapping soil organic carbon stocks across an Afromontane landscape. Ecol Indic 52:394–403CrossRefGoogle Scholar
  51. Wiesmeier M, Barthold F, Blank B, Koegel-Knabner I (2011) Digital mapping of soil organic matter stocks using random Forest modeling in a semi-arid steppe ecosystem. Plant Soil 340:7–24CrossRefGoogle Scholar
  52. Wynn JG, Bird MI, Vellen L, Grand-Clement E, Carter J, Berry SL (2006) Continental-scale measurement of the soil organic carbon pool with climatic, edaphic, and biotic controls. Glob Biogeochem Cycles 20:1–12CrossRefGoogle Scholar
  53. Yang YH, Fang JY, Tang YH, Ji CJ, Zheng CY, He JS, Zhu B (2008) Storage, patterns and controls of soil organic carbon in the Tibetan grasslands. Glob Chang Biol 14:1592–1599CrossRefGoogle Scholar
  54. Yang YH, Fang JY, Ma WH, Smith P, Mohammat A, Wang SP, Wang W (2010) Soil carbon stock and its changes in northern China’s grasslands from 1980s to 2000s. Glob Chang Biol 16:3036–3047CrossRefGoogle Scholar
  55. Yang YH, Li P, Ding JZ, Zhao X, Ma WH, Ji CJ, Fang JY (2014) Increased topsoil carbon stock across China’s forests. Glob Chang Biol 20:2687–2696CrossRefGoogle Scholar
  56. Yang RM, Zhang GL, Liu F, Lu YY, Yang F, Yang F, Yang M, Zhao YG, Li DC (2016) Comparison of boosted regression tree and random forest models for mapping topsoil organic carbon concentration in an alpine ecosystem. Ecol Indic 60:870–878CrossRefGoogle Scholar
  57. Yang LS, Feng Q, Yin ZL, Wen XH, Si JH, Li CB, Deo CR (2017) Identifying separate impacts of climate and land use/cover change on hydrological processes in upper stream of Heihe River, Northwest China. Hydrol Process 31:1100–1112CrossRefGoogle Scholar
  58. Yimer F, Ledin S, Abdelkadir A (2006) Soil organic carbon and total nitrogen stocks as affected by topographic aspect and vegetation in the Bale Mountains, Ethiopia. Geoderma 135:335–344CrossRefGoogle Scholar
  59. Yin ZL, Feng Q, Wen XH, Deo RC, Yang LS, Si JH, He ZB (2018) 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. Stoch Environ Res Risk Assess 32:2457–2476CrossRefGoogle Scholar
  60. Zhang X, Li ZW, Tang ZH, Zeng GM, Huang JQ, Guo W, Chen XL, Hirsh A (2013) Effects of water erosion on the redistribution of soil organic carbon in the hilly red soil region of southern China. Geomorphology 197:137–144CrossRefGoogle Scholar
  61. Zhao CY, Nan ZR, Cheng GD (2005) Methods for modelling of temporal and spatial distribution of air temperature at landscape scale in the southern Qilian mountains, China. Ecol Model 189:209–220CrossRefGoogle Scholar
  62. Zhao CY, Nan ZR, Cheng GD, Zhang JH, Feng ZD (2006) GIS-assisted modelling of the spatial distribution of Qinghai spruce (Picea crassifolia) in the Qilian Mountains, northwestern China based on biophysical parameters. Ecol Model 191:487–500CrossRefGoogle Scholar
  63. Zhao BH, Li ZB, Li P, Xu GC, Gao HD, Cheng YT, Chang EH, Yuan SL, Zhang Y, Feng ZH (2017) Spatial distribution of soil organic carbon and its influencing factors under the condition of ecological construction in a hilly-gully watershed of the Loess Plateau, China. Geoderma 296:10–17CrossRefGoogle Scholar
  64. Zhu M, Feng Q, Qin YY, Cao JJ, Li HY, Zhao Y (2017) Soil organic carbon as functions of slope aspects and soil depths in a semiarid alpine region of Northwest China. Catena 152:94–102CrossRefGoogle Scholar
  65. Zhu M, Feng Q, Zhang MX, Liu W, Qin YY, Deo RC, Zhang CQ (2018) Effects of topography on soil organic carbon stocks in grasslands of a semiarid alpine region, northwestern China. J Soils Sediments.
  66. Zhu M, Feng Q, Qin YY, Cao JJ, Zhang MX, Liu W, Deo RC, Zhang CQ, Li RL, Li BF (2019) The role of topography in shaping the spatial patterns of soil organic carbon. Catena 176:296–305CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Meng Zhu
    • 1
    • 2
  • Qi Feng
    • 1
    Email author
  • Mengxu Zhang
    • 2
  • Wei Liu
    • 1
  • Ravinesh C. Deo
    • 3
  • Chengqi Zhang
    • 1
  • Linshan Yang
    • 1
  1. 1.Key Laboratory of Ecohydrology of Inland River BasinNorthwest Institute of Eco-Environment and Resources, Chinese Academy of SciencesLanzhouChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.School of Agricultural Computational and Environmental Sciences, Centre for Sustainable Agricultural Systems and Centre for Applied Climate SciencesUniversity of Southern QueenslandSpringfieldAustralia

Personalised recommendations