Roland Viger: Hello, my name is Roland Viger.
I'm with the Modeling of Watershed Systems Project.
I'm going to talk to you a little bit about simulating water storage in surface depressions
with PRMS.
This is an optional functionality within the Watershed Model.
Certainly not required and clearly not all locations have a lot of surface-water storage
that you need to worry about.
We decided we needed this kind of functionality because we thought we were doing a very good
job of simulating hydrology in general on a watershed.
But for some reason some of our flows were showing up too early, but not all of our flows.
We realized, in fact, that our runoff from the land surfaces was in some cases ending
up in the stream too quickly.
When looking at more spatial data and the history of the area, we realized there was
a large number of farm ponds and mill ponds and other kinds of structures on the land
surface that runoff would actually end up in, and spend at least a little bit of time
in before ending up in the stream network.
That was our motivation.
Hopefully, if you're using this functionality it's similar to what you're seeing.
Here is an extreme example, where clearly you might have a ton of surface depressions.
Water getting across all of this into a stream could take a long time, and really change
the overall hydrology of our watershed.
Our initial development watershed was nowhere near as heavily pitted as this, but it still
made a big difference.
Terms of the overall importance of surface-water-holding bodies in watershed...This is a very aggravated
view looking across the whole country.
If you look to the graph at the right in particular, we see total area in the country associated
with water bodies of a certain size.
What you can see is that, even fairly early on in the process, towards the left end of
the graph, after a category or two, you've actually got quite a lot of landscape that's
covered by water bodies.
This just drives home that water bodies are really everywhere, even if they're not on
the stream network.
They can add up and have an overall effect, a visible effect on stream-flow response.
This might motivate you to think a little bit more about whether you need to simulate
these explicitly in your watershed with PRMS.
Surface depression is a term that we sort of -- I'm not going to say invented -- but
we have adopted.
There are certainly many synonyms.
You can see a few of them listed down in the middle of the slide.
The idea is that these are depressions in the surface that will hold up the water, and
do so with enough volume that your stream-flow-calibration process should be...understand it that they
exist.
A couple of points at the bottom, there are...
Although you can have an HRU that is really just embodying a lake or is dominated by a
lake -- which is described in other lectures in this series -- these water bodies tend
to be so small that we really don't care to represent them as discreet HRUs individually,
because we'd end up with hundreds or even thousands of extra HRUs.
That's just a bit too much in the views of the modeler.
Even though their explicit spatial representation within the model doesn't exist, they're sort
of part of or characteristic of each HRU.
The model will in fact keep track of the acreage of inundation and the volume in those things.
It will make sure that any precipitation on to the HRU will also include distribution
of that precipitation into the surface depressions and will do things like figure out when it's
going to spill over, or whether the water's going to seep into the ground, and so on.
We have a full suite of physical processes associated with these surface depressions,
even though they are sub-HRU resolution, which is an HRU's sort of basic building block for
PRMS.
Here is a much more detailed view of the various states associated with a surface depression.
These are all simulated for each time step.
You can see they correspond to some of the things we showed on the last slide.
Don't need to retain this.
There is documentation we will talk to you about in the end, which will let you look
at this graphic again and read more about the terms involved.
Surface depressions, coming up with estimates of parameters to describe all the things that
PRMS needs to know about them it takes a little bit of work.
It's not an undue amount of work but we wanted to go over how we have approached coming up
with this formation.
You can see we have really four broad steps and the boundaries between, say, three and
four, they're a little fuzzy.
We'll go through what we're talking about and hopefully it'll be clear.
There's a handful of parameters that we feel relatively confident in setting, based on
spatial analysis in readily available GIS data sets.
There are a number of other ones which relate to flux rates and things like that.
Those are really more set heuristically based on the expertise of the modelers.
This slide, we don't usually describe a method for coming up with.
It's up to you to look at things like hydraulic connectivity or how your soil's glacial till
and things like that, the bedrock and so on.
A very important question, obviously, is, "Where do we get a map of these surface depressions?"
That's our starting point.
What we wanted to just drag you through was a few slides sort of emphasizing how different
estimations of where the water bodies are, and the quantity of those things are, depending
on which data source you use.
On this slide we've got some red speckles in the inset, that you can see maybe a little
more clearly.
Those are sinks derived from a DEM.
Many of those are actually spurious artifacts associated with re-projecting a DEM.
Then when you analyze it in their projected coordinate system, you have a lot of local
spurious pits.
You've probably heard of filling the pits in your DEM, filling the sinks, as almost
just a standard issue DEM massaging process.
That tends to get rid of these by design.
Even if you didn't do that, we find that the resolution of most DEMs...Obviously this might
change with the advent of LIDAR, things like that, or use of that in your modeling application.
We don't generally view the DEM as the best place to get estimates of where water bodies
or surface depressions lie.
In the yellow we're seeing enhanced thematic mapper data, which tends to give you a sense
of where water is resting on the surface and that's looking a lot more realistic.
Obviously we still get the main stream path, which is different than what we're after,
so we would probably throw that out if we use that.
Here's yet another view.
We in the lower 48 or in the Unites States are very fortunate that we have something
called the National Hydrography Dataset, which is a vector-based representation of the nation's
water bodies and streams.
What we're showing here is, we've taken an NHD high resolution, which is a very local-scale
data product, and extracted just what they've designated as water bodies as opposed to rivers
or streams.
We're showing them on the map here.
For instance, at the bottom right you see a very large water body, The Great Swamp.
You could argue whether that's a water body or wetland or what.
We're going to zoom in on the upper area for our sample watershed.
You can see a lot more resolution.
We can also see how we are comparing that with, say, what we got off of the enhanced
thematic mapper in the green.
We tried to highlight where the two datasets were similar in the purple.
You can definitely see there's a lot of red stuff and there's plenty of green stuff.
The degree of agreement between those datasets is kind of low.
It's pretty important to choose the right data source for how you're getting your surface
water bodies.
It can vary quite a lot.
This is just another example of where we might find surface depressions.
We'll move on from there.
One of the most basic parameters we use to describe the presence of water bodies is just
how much of the HRU area is covered by these things.
Obviously, the preceding slides, you could imagine pretty directly how this parameter
would react to this choice of data depiction.
Once you go from this sort of pixelated view that I've been showing you, we come up with
averages per each of our hydrologic response units.
Really we're just showing here, with similar color schemes across all three examples, the
HRU values for this parameter, depending on which data source you use.
On the left we've got the ETM stuff.
In the middle we've got the NHD stuff.
And on the right we've got DEM stuff.
You really do get different values and it pays to think about which one you would like
to use for your application.
A lot of the following parameters are really based on how much of the runoff generated
in the HRU drains to the stream versus draining to the surface depressions.
We tend to need to find the contributing areas to the surface depressions within an HRU.
We have two major depictions of how runoff gets routed.
It's surface runoff to depression storage and surface runoff to depression storage impervious.
The first parameter is about the pervious land surface and obviously the second one
is about the impervious stuff.
In both those cases we're using this watershed analysis associated with each of these depressions
within the HRU.
Then we're going to come up with that number.
The next couple of slides, we're going to illustrate to you how to find any of the contributing
areas.
Takes a little bit of filtering.
It's definitely a judgment call there.
By and large, a lot of the ways in which we get contributing areas includes what we'll
call on-stream water bodies.
We tend to want to exclude those, because all of the HRU will eventually leave through
the associated stream path.
If you maintain surface depressions that are actually on-stream, you're going to find that
a hundred percent of your surface runoff, no matter where you start from, ends up going
into a surface depression of one kind or another.
We think that's not quite accurate, not what we want to say.
We're going to filter on that.
For example, on the far left we see -- if we take our water bodies straight ahead and
we say, "Where's the contributing area?"
-- you can see there's almost no white in that map.
Almost every square foot of this whole basin ends up running into some sort of water body.
What we did in the next, figure B, is we said, "All right.
Well, you need to actually not be touching the stream for your water body to be considered
for this watershed analysis."
We see essentially a drastic reduction.
We feel this is legitimate in that, if you've got a water body that's sitting on a stream
and runoff hits you, you're probably a free-flowing water body.
Any water that comes into you will probably go out of you unfettered.
It's just a matter of distance or travel time as to when it leaves, and not about impoundment.
Growing further from that idea, we said, "All right.
Well, if you're a water body...We're going to exclude at least the portion of your area
that is within a certain distance buffer, in this case 300 meters from a stream."
"If your water body is half in what you might call the riparian zone and half out, we're
going to exclude the half that's in, and then find the contributing areas."
What we see is a relative reduction from B to C in terms of the overall drainage area.
The far right one is the most extreme example of what we just said.
Your water body footprint for each individual water body must be fully beyond 300 meters
from the stream.
You can see how these differences could play through and seriously impact the values you
would assign for SRO to depression storage, impervious
and not impervious.
As you noted, those SRO-to-depression-storage parameters, they care about whether the surface
is impervious or pervious.
Talking about imperviousness a little bit, we tend to get grids from sources like the
Nation Land Cover dataset, for various years.
It's telling us a floating-point number from zero to one, about how impervious a given
pixel or cell is.
We want to...The parameters in PRMS require that you define, "Is it impervious or not?"
In other words it's a binary differentiation.
We need to turn this floating-point number into a binary designation.
What we did was, we looked at the impervious maps.
This is an example in our sample watershed in the Apalachicola-Chattahoochee-Flint.
We've got a little zoom in area at the north end.
Then what we did is, we applied a minimum floating-point-impervious value.
That way, it would allow to maintain an area being called, "impervious."
You can see there's a pretty tremendous reduction there.
The threshold we applied, it was actually very, very low, was five percent or less,
if I remember correctly.
Ultimately, this is a judgement call for you as the modeler making an application.
You need to pick that threshold and you can play around and see how you get fallout in
the red areas -- the impervious areas -- depending on what sort of threshold you set.
We wanted to get rid of areas that were really not very restrictive, as far as infiltration.
We fiddled with our threshold until we saw things like the road network emerge.
We figured roads are nice and impervious, and as long as we're honoring those, we're
happy with what that looks like.
Another way to think about this is we actually -- based on land cover -- said, "What's your
average, mean, min and max type of imperviousness?"
You can see for, for instance, the developed categories, they range from over 90 percent
down to 8 percent or so.
As I mentioned earlier, a threshold of five percent was a little bit lower, but we felt
like it still maintained images, if you will, of certain features like roads and landscape.
You can see the non-developed areas, like forests and so on, have a much, much lower
imperviousness.
We felt like we weren't artificially describing things like forests as impervious with the
threshold we chose.
This is how we made our binary image an effective impervious as opposed to what might be considered
just any sort of imperviousness.
Another view on this, if we did not do this effective imperviousness filter, you can see
the before and after in terms of what the average HRU value would be.
The effective imperviousness is generally lower, which you would expect.
It's an important filter and it will impact your model.
Generally speaking, PRMS is very sensitive to imperviousness of any kind.
What we're showing here is a little synoptic view of the change in performance of PRMS
for the sample watershed, the Apalachicola-Chattahoochee-Flint.
We've been showing you images of, so far, where we have no surface depression on the
y-axis and surface depression on the x-axis.
We are seeing a change that's important and actually shows up in the results.
The usual finishing slide, we have our MoWS home page and this is where you should go
to for information.
If you have questions, you can follow those links and email us through this web-form.
We encourage you to use this instead of trying to email us directly, so we can decide who
the best person to answer your question is and make sure we're all seeing the same questions
from you.
In terms of documentation, there's actually a fair bit of it by now.
Some of the early work was done in collaboration and almost unilaterally by an early user of
ours, Kevin Vining from North Dakota.
There's a couple of pubs that really describe how he put things together and the motivation
for it.
We took that approach and began modernizing it with the 2010 paper you see there.
That gives you probably a lot more background and rationalization as far as the techniques
that you've seen in this particular presentation.
You should always be familiar with the PRMS manual, which is there.
There have been some updates in terms of the parameter names, which are reflected in this
presentation, but might not be in earlier versions.
For instance, the 2010 paper might use slightly old parameter names and things like that.
You should keep your eyes open for that 2015 publication.
It has been approved and is being released through our website right now.
That's all we have.
Thanks very much.