Preprints
https://doi.org/10.5194/egusphere-2024-2205
https://doi.org/10.5194/egusphere-2024-2205
23 Jul 2024
 | 23 Jul 2024

A microbially-driven and depth-explicit soil organic carbon model constrained by carbon isotopes to reduce equifinality

Marijn Van de Broek, Gerard Govers, Marion Schrumpf, and Johan Six

Abstract. Over the past years, microbially-driven models have been developed to improve simulations of soil organic carbon (SOC), and have been put forward as an improvement to assess of the fate of SOC stocks under environmental change. While these models do include a better mechanistic representation of SOC cycling in comparison to cascading reservoir-based approaches, the complexity of these models implies that data on SOC stocks are insufficient to constrain the additional model parameters. In this study, we constructed a novel depth-explicit SOC model (SOILcarb) that incorporates multiple processes influencing the δ13C and Δ14C values of SOC and assessed if including data on the δ13C and Δ14C value of SOC during parameter reduces model equifinality, the phenomenon that multiple parameter combinations lead to a similar model output. To do so, we used SOILcarb to simulate depth profiles of total SOC and its δ13C and Δ14C values. The results show that when the model is calibrated based on only SOC stock data , the residence time of subsoil organic carbon (OC) is not simulated correctly, thus effectively making the model of limited use to predict SOC stocks driven by, for example, environmental changes. Including data on δ13C in the calibration process reduced model equifinality only marginally. In contrast, including data on Δ14C in the calibration process resulted in simulations of the residence time of subsoil OC consistent with measurements, while reducing equifinality only for model parameters related to the residence time of OC associated with soil minerals. Multiple model parameters could not be constrained even when data on both δ13C and Δ14C were included. Our results show that equifinality is an important phenomenon to consider when developing novel SOC models, or when applying established ones. Reducing uncertainty caused by this mechanism is necessary to increase confidence in predictions of the soil carbon – climate feedback in a world subject to environmental change.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Marijn Van de Broek, Gerard Govers, Marion Schrumpf, and Johan Six

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2024-2205', Anonymous Referee #1, 10 Aug 2024
    • AC1: 'Reply on RC1', Marijn Van de Broek, 25 Sep 2024
  • RC2: 'Comment on egusphere-2024-2205', Anonymous Referee #2, 15 Aug 2024
    • AC2: 'Reply on RC2', Marijn Van de Broek, 25 Sep 2024
  • RC3: 'Comment on egusphere-2024-2205', Anonymous Referee #3, 23 Aug 2024
    • AC3: 'Reply on RC3', Marijn Van de Broek, 25 Sep 2024
Marijn Van de Broek, Gerard Govers, Marion Schrumpf, and Johan Six
Marijn Van de Broek, Gerard Govers, Marion Schrumpf, and Johan Six

Viewed

Total article views: 795 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
279 98 418 795 20 9 12
  • HTML: 279
  • PDF: 98
  • XML: 418
  • Total: 795
  • Supplement: 20
  • BibTeX: 9
  • EndNote: 12
Views and downloads (calculated since 23 Jul 2024)
Cumulative views and downloads (calculated since 23 Jul 2024)

Viewed (geographical distribution)

Total article views: 768 (including HTML, PDF, and XML) Thereof 768 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 18 Dec 2024
Download
Short summary
Soil organic carbon models are used to predict how soils affect the concentration of CO2 in the atmosphere. We show that equifinality – the phenomenon that different parameter values lead to correct overall model outputs, albeit with a different model behaviour – is an important source of model uncertainty. Our results imply that adding more complexity to soil organic carbon models is unlikely to lead to better predictions, as long as more data to constrain model parameters are not available.