Ana Maria Garcia: SPARROW is mathematical representation of nature.
It’s a model.
Most models are that, they are just a representation of dominant features in nature.
Not of every single detail, but of the big features that explain certain phenomenon.
In this case how does water get polluted by nutrients?
That’s the big picture feature that this model is trying to represent.
Anne Hoos: SPARROW is one of many watershed models that describe the sources and transport
of a constituent, in this case we’re talking about nutrients, nitrogen and phosphorous
to rivers and streams and they’re transported on to downstream receiving waters.
SPARROW is an acronym for SPAtially Referenced Regression On Watershed attributes.
So SPARROW is a regression model.
Its approach is to explain the pattern of in-stream loads, of nitrogen and phosphorous
and to explain them as a function of geographic characteristics of the watershed.
Unlike other watershed models, which tend to be driven by rainfall, SPARROW is driven
by the spatial pattern of geographic features.
Ana Maria Garcia: What I’ve been talking about lately is about the two modes that SPARROW
can be employed in, an empirical mode, and a predictor mode.
So SPARROW is a coming together of two ways of looking at information.
We know one way is primarily through deductive reasoning.
We assume certain things about how contaminants transport and accumulate in the landscape
and that’s built into SPARROW through the mass-balance equation, through the way that
we expect landscape features and in-stream features to affect sources.
So that’s the process-based information contained in SPARROW, but you have this other
portion of SPARROW that is empirical.
The Model estimation and the information that you can derive from model estimation allows
you to regionally test information in an empirical way and that definitely sets SPARROW apart.
Anne Hoos: You know nothing is really set in stone before you begin modeling.
You don’t have to make any a priori assumptions and go with them, except for some very basic
structural equations, but as you are developing the model you are trying and then setting
aside based on model findings what the dominant sources are and what the dominant processes
are.
Wouldn’t you see that as strength, that you’re not stuck with any assumptions?
Ana Maria Garcia: Yeah.
Anne Hoos: If for example the model tells you that urban areas, as much as we might
think that they might be a source of phosphorous, we’re really letting the model tell us whether
that’s true or not just based on – and it’s simply based on comparing the spatial
pattern of urban land areas to the spatial pattern of in-stream levels of phosphorous.
Ana Maria Garcia: No, I agree with that.
The model also tells us whether or not – you know the information we derive from the model
could also be a statement about the quality of the input data.
So even though we know that perhaps this variable should really explain – or we really expect
it to explain in-stream load variability, we just don’t have good enough data and
that’s a whole other bit of information.
A lot of people talk about quality of data, what we can gather at a regional or landscape
level is really course, so SPARROW definitely is a tool to highlight where we need better
and more resolute information.
Anne Hoos: Uh-huh.
[End of Audio]
Duration: 6 minutes
1 What is SPARROW?
(5:32 minutes)
1 Filename
Speakers’ Names
_____________________________________________________________________________________
www.gmrtranscription.com