Evaluation and optimisation of the soil carbon turnover routine in the MONICA model (version 3.3.1)

• Published in: Geoscientific Model Development Vol. 17; no. 3; pp. 1349 - 1385
• Main Authors: Aiteew, Konstantin, Rouhiainen, Jarno, Nendel, Claas, Dechow, René
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 15.02.2024; Copernicus Publications
• Subjects: Abiotic factors; Agricultural ecosystems; Agricultural production; Algorithms; Analysis; Bayesian analysis; Carbon; Carbon content; Clay soils; Climate change; Ecosystem components; Environmental management; Mathematical models; Moisture content; Nitrogen; Organic carbon; Parameterization; Plant growth; Probability theory; Simulation models; Soil; Soil temperature; Soil water; Soils; Statistical inference; Stocks; Germany
• ISSN: 1991-9603; 1991-959X; 1991-962X; 1991-9603; 1991-962X
• DOI: 10.5194/gmd-17-1349-2024

Simulation models are tools commonly used to predict changes in soil carbon stocks. Prior validation is essential, however, for determining the reliability and applicability of model results. In this study, the process-based biogeochemical model MONICA (Model of Nitrogen and Carbon dynamics on Agro-ecosystems) was evaluated with respect to soil organic carbon (SOC), using long-term monitoring data from 46 German agricultural sites. A revision and parameterisation of equations, encompassing crop- and fertiliser-specific C contents and the abiotic factors of soil temperature, soil water and clay content, were undertaken and included in the model. The modified version was also used for a Morris elementary effects screening method, which confirmed the importance of environmental and management factors to the model's performance. The model was then calibrated by means of Bayesian inference, using the Metropolis–Hastings algorithm. The performance of the MONICA model was compared with that of five established carbon turnover models (CCB, CENTURY, C-TOOL, ICBM and RothC). The original MONICA model systematically overestimated SOC decomposition rates and produced on average a ∼17 % greater mean absolute error (MAE) than the other models. The modification and calibration significantly improved its performance, reducing the MAE by ∼30 %. Consequently, MONICA outperformed CENTURY, CCB and C-TOOL, and produced results comparable with ICBM and RothC. Use of the modified model allowed mostly adequate reproduction of site-specific SOC stocks, while the availability of a nitrogen, plant growth and water submodel enhanced its applicability when compared with models that only describe carbon dynamics.