A taxonomy-based approach to shed light on the babel of mathematical models for rice simulations.

2016 - Environmental Modelling & Software, 85, 332-341
Confalonieri, R., Bregaglio, S., Adam, M., Ruget, F., Li, T., Hasegawa, T., Yin, X., Zhu, Y., Boote, K., Buis, S., Fumoto, T., Gaydon, D., Lafarge, T., Marcaida, M., Nakagawa, H., Ruane, A.C., Singh, B., Singh, U., Tang, L., Tao, F., Fugice, J., Yoshida, H., Zhang, Z., Wilson, L.T., Baker, J., Yang, Y., Masutomi, Y., Wallach, D., Acutis, M., Bouman, B.


For most biophysical domains, differences in model structures are seldom quantified. Here, we used a
taxonomy-based approach to characterise thirteen rice models. Classification keys and binary attributes
for each key were identified, and models were categorised into five clusters using a binary similarity
measure and the unweighted pair-group method with arithmetic mean. Principal component analysis
was performed on model outputs at four sites. Results indicated that (i) differences in structure often
resulted in similar predictions and (ii) similar structures can lead to large differences in model outputs.
User subjectivity during calibration may have hidden expected relationships between model structure
and behaviour. This explanation, if confirmed, highlights the need for shared protocols to reduce the
degrees of freedom during calibration, and to limit, in turn, the risk that user subjectivity influences
model performance.

Keywords: Model classification, model parameterization, model ensemble, model structure, rice, uncertainty
DOI: 10.1016/j.envsoft.2016.09.007

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