Equifinality, sloppiness, and emergent structures of mechanistic soil biogeochemical models

Biogeochemical models increasingly consider the microbial control of carbon cycling in soil. The major current challenge is to validate mechanistic descriptions of microbial processes and predicted system responses against experimental observations. We analyzed soil biochemical models of different complexity regarding parameter identifiability using information geometry, i.e. a model is geometrically interpreted as a manifold embedded in data space. The most complex model (PECCAD) was used as a test case to reveal parsimonious process formulations. All models showed sloppiness, i.e. most individual parameter values cannot be inferred from the observed data. We derived a less complex model formulation of PECCAD with effective inferable parameter combinations and identified structural model limitations. The complexity of identified effective models was systematically reduced with decreasing information content of data. Our results suggest that information geometry provides a powerful approach to derive effective descriptions of relevant biogeochemical processes and reduce structural model uncertainty.