Equifinality, Sloppiness, and emergent model structures of mechanistic soil biogeochemical models

This is a Preprint and has not been peer reviewed. The published version of this Preprint is available: https://doi.org/10.1016/j.envsoft.2019.104518. This is version 1 of this Preprint.


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Gianna Marschmann, Holger Pagel, Philipp Kuegler, Thilo Streck


Biogeochemical models increasingly consider the microbial control of car- bon 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 re- veal 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 in- ferable 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.




Biogeochemistry, Earth Sciences, Environmental Sciences, Physical Sciences and Mathematics, Soil Science


Parameter estimation, equifinality, identifiability analysis, soil carbon model, structural model uncertainty


Published: 2019-11-05 06:57


CC BY Attribution 4.0 International

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