Beyond behavioural models: equifinality and overparameterisation undermine confidence in predictions by soil organic matter models

Abstract. The complexity of soil organic matter models is often not supported by sufficient data for parameter optimisation, resulting in the calibration of more parameters than can be reliably optimized with the available data. This results in equifinality, the phenomenon that multiple parameter sets generate behavioural models, i.e., similarly well-performing models that cannot be ruled out. As such trade-offs between model complexity and data availability are often overlooked for soil organic matter models, the aim of this study is to assess how equifinality affects the variability of predictions made by behavioural soil organic matter models. The results show that the number of identifiable parameters, those that do not compensate for one another, increases with the number of calibration constraints, but remained limited to five even under the most data-rich conditions. Furthermore, the size of particulate organic matter (POM) and mineral-associated organic matter (MAOM) can only be accurately simulated when data on these pool sizes are used, while the turnover rate of MAOM is reliably simulated only when Δ¹⁴C data for MAOM are provided. Regardless of the type of mathematical equations used (e.g., absolute vs. relative Michaelis-Menten kinetics), or the number of optimised parameters, the tested models were able to correctly reproduce the measurements in steady state. However, different model structures led to divergent predictions upon a doubling of organic matter inputs, while the variation in the response of the behavioural models was up to eight times larger for overparameterised models compared to models for which only identifiable parameters were optimised. Our results emphasise the necessity of optimising only identifiable model parameters to avoid hidden uncertainty in model predictions.