Is There A Disconnect Between Mechanistic and Statistical Water Quality Modelers?

In overly simple terms, the surface water quality modeling community can be divided into two camps – mechanistic and statistical. In much of my recent work, I have sought a middle ground that is loyal to process understanding yet yields a measure of uncertainty in predictions. I have argued for years that we should not provide decision makers with predictions of the impact of management actions without an estimate of the confidence we have in those predictions. To me, this point is irrefutable, and it continues to dismay me that EPA and other agencies largely ignore this point in the water quality models they support.

My mechanistic modeling colleagues have tended to stress large elaborate models that are generally motivated by the assumption that models must be sufficiently detailed so the modelers can “get the processes right.” This is a goal that likely will never be achieved. The result is that these elaborate models are overparameterized; this condition, called ‘‘equifinality,’’ is well-documented in the hydrologic sciences, but the concept rarely has been discussed in the water quality modeling literature. Among experienced hydrologic modelers, it is well-recognized that many ‘‘sets’’ of parameter values will fit large simulation models about equally well; unfortunately, this can create problems with the interpretation of sensitivity analyses, since different (equally well-fitting) parameter sets can lead to quite different causal conclusions about the effect of management actions.

I have discussed these points in previous blog posts. Here, I want to comment on the use of water quality models (statistical and mechanistic) for drawing conclusions concerning future trends in water quality over time. Defensible conclusions about water quality trends are made in a statistical hypothesis testing context. A mechanistic water quality model without an error term simply cannot provide a defensible conclusion on trends. Yet in data-poor situations, there appears to be a tendency among some mechanistic modelers to suggest that a mechanistic model forecast can provide a substitute in those situations. Sure, a mechanistic model can yield a deterministic trajectory of expectations associated with management actions, but most mechanistic models cannot provide a confidence interval. The “disconnect” that I am concerned about is that stakeholders and decision makers may accept this deterministic trend forecast while simply failing to request a measure of the reliability of the trend forecast, something they would expect from a statistical analysis. Unfortunately, this perspective is created by those mechanistic modelers who have established this false deterministic modeling environment.