Me4soc: a multi-model ensemble interface for soil organic carbon predictions

Model predictions are paramount to understanding climate and land management effects on soil organic carbon (SOC) stocks and greenhouse gas (GHG) emissions in forests. However, SOC models remain highly uncertain, and multi-model ensembles can be used to evaluate the level of uncertainty of the predictions due to model choice. One major barrier to the use of multiple models is data availability and the time-scale consistency across models.

In this work, we present me4soc, a Multi-model Ensemble interface For Soil Organic Carbon predictions. This open-source software offers a complete environment to launch six SOC models widely used by the soil community to predict the dynamics of SOC stocks and GHG fluxes (CO₂, CH₄, and N₂O) in forests. It allows users to explore the effect of nature-based climate solutions over multiple decades under climate and land-use changes. The models can be run with either user-provided observational data or data automatically extracted from large-scale open-source datasets for the European region. Available earth system model predictions are used to simulate climate and land-use change scenarios. The tool has been developed in Shiny, a R-based package for simple web application developments.

The obtained results showed the ability of me4soc to simulate the temporal dynamics of SOC stocks and GHG emissions at site-scale under different climate, land-use, and land management change scenarios. Employing multiple models based on different mathematical structures offers a unique opportunity to estimate the uncertainties in the predictions associated with differences in the model structure.

This tool can be applied by the scientific community, forest managers, and policymakers to acquire scientifically-based information about the effects of forest management and disturbances on SOC stocks and GHG emissions. It is an important step towards the use of state-of-the-art models and large-scale datasets to improve model predictions and assess their uncertainties. The software's systematic validation with observational data and parameter optimization to improve model fit are the key priorities of future work. Further software developments to cover other ecosystems (e.g., croplands and grasslands) and data-less sites outside of Europe are also foreseen.