So if we can go ahead and move on to
our next speaker, Dr Zachary Easton, who's assistant
professor and extension specialist, Biological Systems Engineering, He
Did his Master's and PhD in hydrology from Cornell and
has been at Virginia Tech almost not
quite three years. Yeah. Um as research addresses
both native and unmanaged systems considers processes a plot to
large river basin scales and is relatively evenly divided among
field study monitoring, modelling an application of results to
real world problems. So Great, thank you.
Well, it's a pleasure to be here today,
so hopefully I'll stay on track. This is my
third presentation this week, so if I start talking
about coastal eutrophication or agricultural water management, someone can
throw a shoe at me or something like that,
get me back on track. Um I notice is
a lot like a classroom every once in the back
of the class right now. So I'm gonna talk
about some work we've done to develop simple snowmelt routines
um in complex terrain and I'm glad that in the
last presentation you mentioned we have trouble estimating snow depth
. I think it's a big problem and a lot
of a lot of our environments, that's something that
our group has been working on quite a bit and
I like to just acknowledge my, my postdoc dan
Fuca, who has worked on a lot of this
work as well. So just a quick outline,
I'll give some background on the subject some of the
objectives application. This is where we actually do the
modeling. So how do we account for these complex
trains? I'll show how we incorporate this process based
or physically based snow, mountain, snow accumulation routine
which is based on a surface energy budget. And
then we'll have a short application to impact for climate
change, short summary. So first some background.
So what am I talking about when I talk about
terrain or complex trains? So I'm defining train as
the vertical, horizontal or directional aspects of a surface
. And this is just an image here of some
terrain. So I'd also like to point out the
terrain exerts a critical influence on many different processes,
especially environmental processes. Snow, melt, snow accumulation
of apple, transpiration, nutrient cycling, moisture distribution
, so moisture levels, crop growth, your morphology
. These are all things that terrain can really influence
pretty dramatically. So basically my presentation is going to
focus on on snow mountain, assuming snow accumulation,
but you could really apply it to any of these
different environmental processes, so some background. So we're
in Virginia. So we don't really care that much
about snowmelt or snow accumulation. But in many years
the world, um this snow, mountain snow accumulations
are very important processes. And this is an area
or semi arid regions. In addition to areas of
Of high elevation or high latitudes in these areas,
up to 80 of the annual water budget or stream
flow originates from snow packer. Snow mountain. This
is just a plot, it might be a little
bit hard to see, but this is some work
we did in upstate New York, which you might
not think of as a snow dominated region in terms
of snowpack accumulation, but about 65-70 of their water
budget is derived from snow mountain snow accumulation. And
so a lot of my interest is looking at the
impact of land use or climate change on the distribution
of snow and snow accumulation in these environments. This
is just an example of another site where snowmelt is
even more snow accumulation is even more important. This
is data from one of the snow tell which the
federal government runs the snow telemetry sites where they measure
snow depth snow water equivalent, which is the the
accumulated moisture in that snow pack. And right here
, I'm just showing the snow depth and red,
so almost 90 of snow accumulate at this snow tell
site, and that represents almost 30 of water and
for some place like Utah, that can be upwards
of 90 of their annual water budget. So,
pretty important in these environments. So I'm going to
transition a little bit too how different models handle modeling
snow, mountain snow accumulation in these environments. So
many of the watershed. Also, I'm really a
watershed model. I do a lot of modeling,
you know, medium sized watershed scale. So many
of these watershed models use what's called a temperature index
or T. I, to model snow mountain snow
accumulation. So, in this model, it's relatively
simple. Um the the snowmelt and simply a function
of a melt factor, an average temperature and a
base temperature. Now the melt factor and the base
temperature are purely calibration parameters. So you need a
period of measured data to calibrate your model too.
So you can determine what those two parameters are.
So it's relatively simple, but again, it requires
calibration and so I'd argue you can't really apply this
model outside of the range for which it was developed
or calibrated for. You couldn't take this model,
it was developed for snowbird in Utah and apply it
to upstate new york without, you know, going
through the calibration process again. So you can't use
it in areas where there is little data or where
you want to look at and some dynamic process like
climate change or land use change. So at the
other end of the spectrum, there's what we call
these operational energy budget based models. So the government
operates several of these large scale. They generally operate
on a grid scale of of one kilometer or larger
. An example I give here is the snow,
does it snow data assimilation model And it uses an
energy budget. So as opposed to the simple empirical
temperature index type model, the energy budget is purely
physically based. It's based on first principles, so
you could apply it outside of the range for which
it was developed. I argue here that the problem
is that the one kilometre scale and made the systems
that I work in is too large to capture some
of the important hydrological processes that are really interested in
. And I'll give you example of that in a
second. So here's an example. So in this
, in this image, if we were looking at
a one km grid um prediction unit, then most
of this image would be within one grid cell.
And you couldn't sort of tease out the effects of
aspects you can see. There's a pretty significant effect
of which way this slope is facing south facing slopes
tend to accumulate much less snow than north facing slopes
, and there's also a significant elevation effect. You
see that there's very little snow down the lowlands where
most of the snows in the uplands. And one
of the operational models, you would be able to
determine this. So we'd like to go somewhere in
between. We'd like to incorporate the energy, the
physics based model of the, of the energy budget
into the finer scale spatial component of the watershed scale
models. But there's some shortcomings, so like I
said, temperature index are purely sort of empirically based
. So this is an example of that. What
we did is we took one of these models,
We ran 15 individual one year calibrations against measure data
. So we calibrate the model 15 times for 15
individual datasets. And what you see here is the
mean standard deviation coefficient of variation for those temperature index
parameters. And you can see that there's a huge
coefficient of variation. Um you know, all the
coefficients of variation are larger than the means. So
I would ask, is this model really good for
anything? Can you use this to predict anything outside
of the angels calibrated? You couldn't take this model
and even in the next year, say what's,
what it's gonna be able to predict with any sort
of uncertainty. Nice. I say, no,
you can't do that. So again, at the
other end of the spectrum, is this energy budget
, It's a physically based system of equations and this
is just sort of the final equation. Once it's
all derived, I'm not going to go through what
all these things are. But basically the critical components
of that are the snow water equivalent or the change
in the snow water equivalent are a function of all
those different latent heat of fusion, long wage,
long way radiation, sensible heat exchange, etcetera,
etcetera. Now it turns out that most of these
parameters are already, can either be estimated from data
that are in these models or can be taken from
previously published relationships from literature. So things like Julian
day latitude, elevation, mini max temperature. All
things that these these watershed models already in some way
in jest, they don't use them, how we
like them to use them, but they're already embedded
in this model structure. So we can take advantage
of that. Other things like the heat and light
of fusion and the snow pack. Heat storage like
I said, can be taken from easily um derived
relationships from the literature. So I think we can
use this to our advantage and trying to develop a
simple, robust and parsimonious model of snowpack and snowmelt
and some of these complex terrains. So what we
proposed doing is coupling this energy budget with various terrain
metrics and we played with several of them and we
actually came up one with one that we thought worked
pretty well. And this is just sort of an
example of what I talk about when I talk about
a terrain metric. So here's a pretty simple terrain
, it's got some elevation differences, some aspect differences
. And what we'd like to do in this case
is take our energy budget and apply it to these
different grid cells. Each one of these grid cells
would have its own answer when you solve that system
of equations giving you different, you know, snowpack
and snowmelt dynamics across the landscape. So I can
pick when and where in a landscape slowly accumulating or
melting the first. You know, I'd like to
sort of define how most models look at special operations
and this is what I'm talking about. Most models
. I'm talking about the watershed models, How do
they operate spatially? So most of them simply take
a soil layer. So it could be a raster
shape file and a land use layer. And sometimes
there's a slow player in there as well. But
this is a simple case and they combine these two
into what they call hydrologic response unit. So they
simply overlay these two layers and every unique incidents of
soil land use is defined as a as a response
unit or a prediction grid. So in this case
your watershed is defined especially as a combination of soils
and land use. So you can see here that
it becomes incredibly difficult to look at things like aspect
and elevation. Um if you're not explicitly incorporated into
the, into the spatial data set of the model
. So I'm just gonna show conceptually how we try
to get around this problem in some of these models
. An example uses for the swat model saltwater assessment
tool model, but you can do it pretty much
in any any model. So what we do is
we take the DM. We derive the slope from
the D. M. We also derive the aspect
from A. D. M. The flow accumulation
. So the flow accumulation is, the model operates
through every grid cell in the D. M.
And defines how many cells above a given cell drain
through that sell. So it's a measure of landscape
connectivity. Um And we also define elevation bands.
So then we take the slope and the flow accumulation
and we and we develop what's called a topographic index
and it's a relatively simple method to determine landscape connectivity
, contributing areas, areas that might, you know
, in this case generally run off or hold snowpack
, things like that and simply a function of the
slope and the flow accumulation. And then we go
back and we start to add in the land use
and soils with the model requires land use and soils
you can't just throw them out so you put those
back in. We also add an aspect and the
elevation bands and then come up with our with our
prediction good and I'm not sure if you can see
it here or not, but there's considerably more complexity
contained in this prediction grid. And there was in
the first one where was simply soils and land use
. So it contains aspect and the topographic index and
elevation bands and all that information. We need to
start looking at where the landscape snow might accumulate.
And then again we take our energy budget and now
apply it across all of the various grids within that
prediction grid. So you can see that it can
become computational intensive. But I think at this point
we're getting around you know blaming computational capacity as a
reason not to do things spatially. So that's conceptually
how we did it. But how do we actually
do? It's what we did is we built a
python code and arc map that operates through all these
. Basically the D. M. At this point
creates all these um iterations from the D. M
. So I said it only requires a D.
M. It's relatively simple in terms of input data
. This is just a snip of the code.
It's actually about 800 lines of code. So conceptually
it's simple but it takes a lot of geo processing
to get to get it through. And then we
built sort of a toolbox in arc to make it
easier on the end user. So I need input
is your elevation data and what you wanted to calculate
the topographic index, the aspect the elevation man's et
cetera. And you can select and deselect all of
votes. So these are some results. Now this
is comparing the temperature index predictions to the call it
PB PBS process based. That's the physically based energy
budget. And so what we show here again,
I'm showing the same thing I showed a few slides
ago where the temperature next version, we really don't
know what it's telling us in terms of what's going
on this landscape and for the process space, I
have an A. Because like I said, it
doesn't require any calibration. We don't have to go
through and calibrate those parameters to measure data And we
end up getting a pretty good fit. So we're
using as a metric of fit the Nash Sutcliffe efficiency
, which is a measure of the 1-1 fit between
model and predicted responses. And we actually get a
little better fit using the process based models and the
temperature index with no calibration, which was promising.
And again, this just shows it graphically for this
is the 15 year averages for one year Plotted,
plotted for 12 months. And you can see that
both models perform approximately equivalently when you compare it to
observe data. And this is looking at the watershed
outlet. So this is predicting what's going to leave
the water ship. This is important, remember in
a second, because I'll show something interesting. Like
I said, both models predict about the same thing
leaving the watershed. So they both about the same
skill. But if we look at snow, this
is where it gets interesting and I argue that complex
, it actually helps with the calibration issues. So
what this shows here is back to that snow tell
site. Um That's the dash blue line. The
PV is the process based and then the oranges,
the temperature index. When we calibrate the model to
the watershed outlet were calibrated to gauge data. It
turns out that the temperature. Next model predicts no
snow accumulating. So somewhere in that calibration, we
vary those parameters. It doesn't know that there should
be snow in this watershed, right? It says
, well, I got a best fit if I
have no snow in the watershed. So it's compensating
for snow by some other process is groundwater of transpiration
or something like that. When in fact, when
we use the process based model, we get a
decent fit to measure snow. Snow tell is measured
data. The reason is a little bit lower is
the snow tell. Site is about 200 m above
our watershed, so there's obviously an importance of elevation
and snow accumulation. Um but we do tend to
have a pretty good handle on the dynamics of the
snow accumulation of snow melt within this water. This
watershed is Paradise Creek in Idaho, so again heavily
snow dominated, and then this could tie into the
past, the last presentation as well. We're trying
to look at the spatial corroborate, trying to validate
the model spatially. So can we predict across this
landscape with any sort of skill where snow might accumulate
and this this probably highlights what Doctor about Bevilaqua sudden
last presentation. We don't really have good remote sensing
data on snow accumulation. And you can sort of
see that here were able to tease out a decent
relationship. Again, it's not clear so that any
frame shows the land set brightness for a subset of
this watershed, and you can see a little bit
of snow clearly accumulating frame be shows the process based
model predicted snow accumulation. And again, if you
look at the scale the snow water equivalent, but
we don't know what the snow water equivalent is.
The lands that model we do, we are getting
snow accumulation where land set is agreeing that their snow
accumulation where in the T. I. Model there's
essentially no snow accumulation and that sort of validates the
last slide where the model said there was no snow
accumulating in the watershed. So I think especially we
can start to get out where snow might accumulate in
areas where we might want to consider management to protect
snowpack and things like that. And just second last
side. So this is what we did in terms
of application of climate change. So we took the
model and then use some downscaled climate scenarios to look
at, you know, sort of the average change
in what the snow water cooling might look like in
this watershed in the future. So the black line
is the baseline prediction. So that's what we,
our model is predicting essentially now and then. The
other lines that the gray lines are the different ensembles
of the climate change predictions and that the dash line
is the average of the ensembles. It looks like
we're predicting snow accumulation to fall slightly in this watershed
. There's no water going to fall asleep in this
watershed. And I argue that we can really only
do this using the process based model. I don't
think this is something we should or really could attempt
using a temperature and next model. So that was
just sort of a point I'm trying to make with
this with presentation. So some key messages. I
think these process based energy budget models can provide substantial
insights into the various processes occurring in these watersheds and
help us improve predictions and to some extent landscape management
. Whereas the temperature next models really calibrate out snow
accumulation, it doesn't exist in those models. So
I argue to choose the right model for the right
problem. You know, I could see people going
in and just taking a standard temperature. next model
, applying it and inferring something like snow is an
important process, but really the model says is not
and if it doesn't exist, build it, modify
it or create it. And by doing that we
really reduce the calibration needs and build a more parsimonious
model by considering these different complex processes. And I
put this as a caveat. The computational costs of
these complex prediction services can be high. But I
think more and more we're moving into the computational realm
of where this isn't going to be a constraint on
the models were developing and building this is just a
link where we have the software located if anyone's interested
. And then being being a hydrologist, I like
to end with a slide like this. It's just
little hydrological where I'm sorry. Mhm. So if
there's time for questions.