Skip to main content
SpringerLink
Log in

Simulating Soil Organic Carbon Turnover with a Layered Model and Improved Moisture and Temperature Impacts

  • Conference paper
  • First Online:
Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making (ISDMCI 2022)

Part of the book series: Lecture Notes on Data Engineering and Communications Technologies ((LNDECT,volume 149))

  • 516 Accesses

Abstract

Decomposition of soil organic carbon (SOC) is an important part of the global carbon cycle, and is connected to multiple ecological processes. A comprehensive understanding of the process and subsequent improvement of soil management practices is an important step in developing sustainable agriculture and other land uses. This can not only increase the quality and productivity of arable lands, but also reduce carbon dioxide emissions to the atmosphere.

Currently developed SOC models are characterized by the multitude of considered factors: biological and physical processes, chemical reactions, influence of weather, crop and soil conditions. The importance of subsoil carbon also has been recognized, and a number of layered SOC models emerged. As decomposition of organic matter depends greatly on the soil physical characteristics such as moisture and temperature, these layered carbon models require corresponding estimates to remain consistent.

In this study, we take the RothPC-1 layered carbon decomposition model, and supply it with a comprehensive physical soil water and heat flow model. The two models are interconnected in a way that the carbon model uses the estimates of soil moisture and temperature from the moisture model, and the physical soil properties are then modified according to the simulated content of organic matter.

The model simulations were conducted for three sites in Ukraine. The experiment covered a period between 2010 and 2020 and involved only data from the open datasets. The results demonstrate the behavior of layered soil carbon decomposition model under different climate conditions.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bashir, O., et al.: Soil organic matter and its impact on soil properties and nutrient status. In: Dar, G.H., Bhat, R.A., Mehmood, M.A., Hakeem, K.R. (eds.) Microbiota and Biofertilizers, Vol 2, pp. 129–159. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-61010-4_7

    Chapter  Google Scholar 

  2. Canty, J., Frischling, B., Frischling, D.: Weatherbase (2022). http://www.weatherbase.com/

  3. Cardinael, R., Guenet, B., Chevallier, T., Dupraz, C., Cozzi, T., Chenu, C.: High organic inputs explain shallow and deep SOC storage in a long-term agroforestry system - combining experimental and modeling approaches. Biogeosciences 15(1), 297–317 (2018). https://doi.org/10.5194/bg-15-297-2018

    Article  Google Scholar 

  4. Chen, W., Shen, H., Huang, C., Li, X.: Improving soil moisture estimation with a dual ensemble Kalman smoother by jointly assimilating AMSR-E brightness temperature and MODIS LST. Remote Sens. 9(3), 273 (2017). https://doi.org/10.3390/rs9030273

  5. Cherlinka, V.: Models of soil fertility as means of estimating soil quality. Geogr. Cassoviensis 10, 131–147 (2016)

    Google Scholar 

  6. Chui, Y., Moshynskyi, V., Martyniuk, P., Stepanchenko, O.: On conjugation conditions in the filtration problems upon existence of semipermeable inclusions. JP J. Heat Mass Transf. 15(3), 609–619 (2018). https://doi.org/10.17654/HM015030609

  7. Coleman, K., Jenkinson, D.: RothC-26.3 - a Model for the turnover of carbon in soil, vol. 38, pp. 237–246 (1996). https://doi.org/10.1007/978-3-642-61094-3_17

  8. Conant, R., et al.: Temperature and soil organic matter decomposition rates - synthesis of current knowledge and a way forward. Glob. Change Biol. 17(11), 3392–3404 (2011). https://doi.org/10.1111/j.1365-2486.2011.02496.x

  9. Del Grosso, S., et al.: Modeling Carbon and Nitrogen Dynamics for Soil Management, pp. 303–332. CRC Press (2001)

    Google Scholar 

  10. Giardina, C., Litton, C., Crow, S., Asner, G.: Warming-related increases in soil CO2 efflux are explained by increased below-ground carbon flux. Nat. Clim. Chang. 4, 822–827 (2014). https://doi.org/10.1038/nclimate2322

    Article  Google Scholar 

  11. Hersbach, H., et al.: Operational global reanalysis: progress, future directions and synergies with NWP. ERA Report 27 (2018). https://doi.org/10.21957/tkic6g3wm, https://www.ecmwf.int/node/18765

  12. Hilinski, T.E.: Implementation of exponential depth distribution of organic carbon in the CENTURY model (2001). https://www2.nrel.colostate.edu/projects/irc/public/Documents/Software/Century5/Reference/html/Century/exp-c-distrib.htm

  13. Ise, T., Moorcroft, P.: The global-scale temperature and moisture dependencies of soil organic carbon decomposition: an analysis using a mechanistic decomposition model. Biogeochemistry 80, 217–231 (2006). https://doi.org/10.1007/s10533-006-9019-5

    Article  Google Scholar 

  14. Jenkinson, D., Coleman, K.: The turnover of organic carbon in subsoils. part 2. modelling carbon turnover. Europ. J. Soil Sci. 59, 400–413 (2008). https://doi.org/10.1111/j.1365-2389.2008.01026.x

  15. Kashtan, V., Hnatushenko, V., Zhir, S.: Information technology analysis of satellite data for land irrigation monitoring : Invited paper. In: 2021 IEEE International Conference on Information and Telecommunication Technologies and Radio Electronics (UkrMiCo), pp. 1–4 (2021). https://doi.org/10.1109/UkrMiCo52950.2021.9716592

  16. Kerr, D., Ochsner, T.: Soil organic carbon more strongly related to soil moisture than soil temperature in temperate grasslands. Soil Sci. Soc. Am. J. 84(2), 587–596 (2020). https://doi.org/10.1002/saj2.20018

    Article  Google Scholar 

  17. Kirschbaum, M.: The temperature dependence of soil organic matter decomposition, and the effect of global warming on soil organic C storage. Soil Biol. Biochem. 27(6), 753–760 (1995). https://doi.org/10.1016/0038-0717(94)00242-S

    Article  Google Scholar 

  18. Kozhushko, O., Boiko, M., Kovbasa, M., Martyniuk, P., Stepanchenko, O., Uvarov, M.: Evaluating predictions of the soil moisture model with data assimilation by the triple collocation method. Compu. Sci. Appl. Math. 2, 25–35 (2022). https://doi.org/10.2413-6549-2021-2-03

  19. Kozhushko, O., Boiko, M., Kovbasa, M., Martyniuk, P., Stepanchenko, O., Uvarov, M.: Field scale computer modeling of soil moisture with dynamic nudging assimilation algorithm. Math. Model. Comput. 9(2), 203–216 (2022). https://doi.org/10.23939/mmc2022.02.203

  20. Krinner, G., et al.: A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Global Biogeochem. Cycles 19(1), GB1015 (2005). https://doi.org/10.1029/2003GB002199

  21. Kroes, J., Roelsma, J.: ANIMO 3.5; user’s guide for the ANIMO version 3.5 nutrient leaching model. Wageningen, SC-DLO, 1998. Techn. Doc. 46, 98 pp. (1998)

    Google Scholar 

  22. Langergraber, G., Rousseau, D., Garcia, J., Mena, J.: CWM1: a general model to describe biokinetic processes in subsurface flow constructed wetlands. Water Sci. Technol. 59(9), 1687–1697 (2009). https://doi.org/10.2166/wst.2009.131

    Article  Google Scholar 

  23. Lei, J., Guo, X., Zeng, Y., Zhou, J., Gao, Q., Yang, Y.: Temporal changes in global soil respiration since 1987. Nat. Commun. 12, 403 (1987). https://doi.org/10.1038/s41467-020-20616-z

    Article  Google Scholar 

  24. Li, C., Farahbakhshazad, N., Jaynes, D., Dinnes, D., Salas, W., McLaughlin, D.: Modeling nitrate leaching with a biogeochemical model modified based on observations in a row-crop field in Iowa. Ecol. Model. 196, 116–130 (2006). https://doi.org/10.1016/j.ecolmodel.2006.02.007

    Article  Google Scholar 

  25. Luo, Z., Luo, Y., Wang, G., Xia, J., Peng, C.: Warming-induced global soil carbon loss attenuated by downward carbon movement. Glob. Change Biol. 26(12), 7242–7254 (2020). https://doi.org/10.1111/gcb.15370

    Article  Google Scholar 

  26. Parton, W., Ojima, D., Cole, C., Schimel, D.: A general model for soil organic matter dynamics: sensitivity to litter chemistry, texture and management, pp. 147–167. John Wiley & Sons, Ltd. (1994). https://doi.org/10.2136/sssaspecpub39.c9

  27. Parton, W., Scurlock, J., Ojima, D., Gilmanov, T., Scholes, R., Schimel, D., Kirchner, T., Menaut, J.C., Seastedt, T., Moya, G., Kamnalrut, A., Kinyamario, J.: Observations and modeling of biomass and soil organic matter dynamics for the grassland biome worldwide. Global Biogeochem. Cycles 7(4), 785–809 (1993). https://doi.org/10.1029/93GB02042

    Article  Google Scholar 

  28. Peralta, G., Di Paolo, L., Omuto, C., Viatkin, K., Luotto, I., Yigini, Y.: Global soil organic carbon sequestration potential map technical manual (2020). https://fao-gsp.github.io/GSOCseq/index.html

  29. Poggio, L., et. al.: Soilgrids 2.0 : producing soil information for the globe with quantified spatial uncertainty. Soil 7(1), 217–240 (2021). https://doi.org/10.5194/soil-7-217-2021

  30. USDA NRCS (Natural Resources Conservation Service): Interpreting the soil conditioning index: a tool for measuring soil organic matter trends, no. 16 (2003). https://www.nrcs.usda.gov/Internet/FSE_DOCUMENTS/nrcs142p2_053273.pdf

  31. Shelia, V., Simunek, J., Boote, K., Hoogenbooom, G.: Coupling DSSAT and HYDRUS-1D for simulations of soil water dynamics in the soil-plant-atmosphere system. J. Hydrol. Hydromech. 66(2), 232–245 (2018). https://doi.org/10.1515/johh-2017-0055

    Article  Google Scholar 

  32. Shibu, M., Leffelaar, P., Van Keulen, H., Aggarwal, P.: Quantitative description of soil organic matter dynamics - a review of approaches with reference to rice-based cropping systems. Geoderma 137(1), 1–18 (2006). https://doi.org/10.1016/j.geoderma.2006.08.008

    Article  Google Scholar 

  33. Shibu, M., Leffelaar, P., van Keulen, H., Aggarwal, P.: LINTUL3, a simulation model for nitrogen-limited situations: application to rice. Eur. J. Agron. 32(4), 255–271 (2010). https://doi.org/10.1016/j.eja.2010.01.003

    Article  Google Scholar 

  34. Stepanchenko, O., Shostak, L., Kozhushko, O., Moshynskyi, V., Martyniuk, P.: Modelling soil organic carbon turnover with assimilation of satellite soil moisture data. In: Modeling, Control and Information Technologies: Proceedings of International Scientific and Practical Conference, pp. 97–99, no. 5 (2021). https://doi.org/10.31713/MCIT.2021.31

  35. Taghizadeh-Toosi, A., Christensen, B., Hutchings, N., Vejlin, J., Katterer, T., Glendining, M., Olesen, J.: C-TOOL: a simple model for simulating whole-profile carbon storage in temperate agricultural soils. Ecol. Model. 292, 11–25 (2014). https://doi.org/10.1016/j.ecolmodel.2014.08.016

    Article  Google Scholar 

  36. Thea, C.: Lametsy – large meteorological system (2022). https://lametsy.pp.ua

  37. Wójcik, W., Osypenko, V., Osypenko, V., Lytvynenko, V., Askarova, N., Zhassandykyzy, M.: Hydroecological investigations of water objects located on urban areas. Environmental Engineering V. In: Proceedings of the 5th National Congress of Environmental Engineering, pp. 155–160 (2017)

    Google Scholar 

  38. Xie, E., Zhang, X., Lu, F., Peng, Y., Chen, J., Zhao, Y.: Integration of a process-based model into the digital soil mapping improves the space-time soil organic carbon modelling in intensively human-impacted area. Geoderma 409, 115599 (2021). https://doi.org/10.1016/j.geoderma.2021.115599

  39. Yang, J., Yang, J., Dou, S., Hoogenboom, G.: Simulating the effect of long-term fertilization on maize yield and soil C/N dynamics in northeastern China using DSSAT and CENTURY-based soil model. Nutr. Cycl. Agroecosyst. 95, 287–303 (2013). https://doi.org/10.1007/s10705-013-9563-z

    Article  Google Scholar 

  40. Yoder, D., et al.: Soil health: meaning, measurement, and value through a critical zone lens. J. Soil Water Conserv. 77(1), 88–99 (2022). https://doi.org/10.2489/jswc.2022.00042

    Article  Google Scholar 

  41. Zhang, X., Xie, Z., Ma, Z., Barron-Gafford, G., Scott, R., Niu, G.Y.: A microbial-explicit soil organic carbon decomposition model (MESDM): development and testing at a semiarid grassland site. J. Adv. Model. Earth Syst. 14(1), e2021MS002485 (2022). https://doi.org/10.1029/2021MS002485

  42. Zhou, J., Cheng, G., Wang, G.: Numerical modeling of wheat irrigation using coupled HYDRUS and WOFOST models. Soil Sci. Soc. Am. J. 76, 648–662 (2012). https://doi.org/10.2136/sssaj2010.0467

    Article  Google Scholar 

  43. Zhou, X., Xu, X., Zhou, G., Luo, Y.: Temperature sensitivity of soil organic carbon decomposition increased with mean carbon residence time: field incubation and data assimilation. Glob. Change Biol. 24(2), 810–822 (2018). https://doi.org/10.1111/gcb.13994

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. National University of Water and Environmental Engineering, Rivne, Ukraine

    Olha Stepanchenko, Liubov Shostak, Viktor Moshynskyi, Olena Kozhushko & Petro Martyniuk

Authors
  1. Olha Stepanchenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liubov Shostak
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Viktor Moshynskyi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Olena Kozhushko
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Petro Martyniuk
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Olena Kozhushko .

Editor information

Editors and Affiliations

  1. Jan Evangelista Purkyně University in Ústi nad Labem, Ústi nad Labem, Czech Republic

    Sergii Babichev

  2. Kherson National Technical University, Kherson, Ukraine

    Volodymyr Lytvynenko

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Stepanchenko, O., Shostak, L., Moshynskyi, V., Kozhushko, O., Martyniuk, P. (2023). Simulating Soil Organic Carbon Turnover with a Layered Model and Improved Moisture and Temperature Impacts. In: Babichev, S., Lytvynenko, V. (eds) Lecture Notes in Data Engineering, Computational Intelligence, and Decision Making. ISDMCI 2022. Lecture Notes on Data Engineering and Communications Technologies, vol 149. Springer, Cham. https://doi.org/10.1007/978-3-031-16203-9_5

Download citation

Publish with us

Policies and ethics

Search

Navigation