Actually, I would check out some of these research articles on testing multiple models on the same dataset. I just published an article on this subject, and I present two from literature.

Essentially I found that in our circumstance (coastal lagoonal models) that adding more state equations or calibration factors had no correlation to overall fitness. However, the design of the model (the relationships) had the most effect. We originally authored this paper because our original model design was rejected by reviewers as "too simple", then another review committee found it "too complex". However, as long as the model has a good fit to your data and you used common and proven techniques to verify this result complexity shouldn't matter.

My advice is to design your model as you understand the system to be, then validate it. Reference your validation techniques. If this conflicts with previous model designs for the same system then test both and report the scores in your manuscript. If needed reference one of these works, or another on model complexity.

Evan Turner

Turner, E. L., Bruesewitz, D. A., Mooney, R. F., Montagna, P. A., McClelland, J. W., Sadovski, A., & Buskey, E. J. (2014). Comparing performance of five nutrient phytoplankton zooplankton (NPZ) models in coastal lagoons. Ecological Modelling, 277, 13-26.

Ward, B. A., Schartau, M., Oschlies, A., Martin, A. P., Follows, M. J., & Anderson, T. R. (2013). When is a biogeochemical model too complex? Objective model reduction and selection for North Atlantic time-series sites. Progress in Oceanography, 116, 49-65.

Yew Gan, T., Dlamini, E. M., & Biftu, G. F. (1997). Effects of model complexity and structure, data quality, and objective functions on hydrologic modeling. Journal of Hydrology, 192(1), 81-103.