Presentation: An Objective Test of Stochastic Behavior in Riverine Water Quality Models
Fri Sep 19 2008
Post a comment[Transcript here]
Transcript: An Objective Test of Riverine Water Quality Models
Fri Sep 19 2008
Post a comment[Ed: A video version of this presentation is on the way, complete with slides and narration. The transcript here is provided as an alternative.]
Hi, my name is Geoff Parker and this is an [online transcript] of the talk entitled ‘An Objective Test of Riverine Water Quality Models’, given on September 9th at the 2008 IWA World Water Congress in Vienna, Austria.
So, why are we interested in water quality models enough to bother with this? Well, here are two sources that describe the types of cases in which they might be useful, and particularly the problem is one of spatial and temporal changes of constituents in river systems. So what are constituents of concern? DO, Nutrients like N and P, toxics like Atrazine and other pesticides – these are the types of things we’re generally taking about. And the processes we’re interested in are those ones that drive the spatial and temporal changes – and they range from physical processes like sedimentation to chemical processes like redox reactions to biological processes like nutrient uptake. Where are we interested in such things? Well, in a cartoon view of a watershed, we’re talking mostly about what’s going on in the riverine system itself, which we’ll generally model as a 1-D system.
Having covered the why, the what, and the where – now we move on to the crux of the problem, the how – which is also really the when, as we’ll see. So the way we usually see these problems approached is through what we call ‘deterministic’ paradigms. Many different and flavours of coded models exist for these types of problems, but fundamentally they rely on variations of a handful of conceptual models. So we can pose a general formulation of these models, based on 3 major terms, an advection term, a dispersion term, and a conversion-kinetics term. In IOGA terms, the first two are the input and output drivers while the last is the generation component. And for the most part, this is the formulation of models as they have been used since Streeter-Phelps.
The problem, however is that stochastic issues – those dealing with model uncertainty — are really the issue of the day in modeling, either expressly or, at probably even more often, in an implied manner seen in the use, misuse and/or mistrust of model results. So let me give you an example now of what I mean when I talk about deterministic approaches. Here we have a schematic of the major parts of the model construction process.
