On the effect of scaling conceptual model complexity on stochastic response for water quality modeling

This paper extends previous work comparing the response of water quality models under uncertainty. A new model, the River Water Quality Model no. 1 (RWQM1), is compared to the previous work of two commonly used water quality models. Additionally, the effect of conceptual model scaling within a single modelling framework, as allowed by RWQM1, is explored under uncertainty. Model predictions are examined using against real-world data for the Potomac River with a Generalized Likelihood Uncertainty Estimation used to assess model response surfaces to uncertainty. Generally, it was found that there are tangible model characteristics that are closely tied to model complexity and thresholds for these characteristics were discussed. The novel work has yielded an illustrative example but also a conceptually scaleable water quality modelling tool, alongside defined metrics to assess when scaling is required under uncertainty. The resulting framework holds substantial, unique, promise for a new generation of modelling tools that are capable of addressing classically intractable problems.