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

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 optimised with the available data. This leads to 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 for the model used in this study, the number of identifiable parameters, those that do not compensate for one another, increases with the number of calibration constraints. However, this number remained limited to five even under the most data-rich scenario considered, including the C and N content of particulate organic matter (POM) and mineral-associated organic matter (MAOM) and their 14 C signatures. Furthermore, the size of POM and MAOM could only be accurately simulated when data on these pool sizes were used to optimise parameters, while the turnover rate of MAOM was reliably simulated only when Δ 14 C data for MAOM were used. Regardless of the type of mathematical equations (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. These results emphasise the necessity of optimising only identifiable model parameters to avoid hidden uncertainty in model predictions.