Webinar – Java Newhall Simulation Model (7/2012)

>>– be with you today. And also be able to share some
information about a new version of a rather traditional [Inaudible] climate
simulation model called the Newhall Simulation Model, which we now updated it to run in Java,
so we call it the Java Newhall simulation model. But before, just tell you a
little bit about myself for those who have not met me or I have not met them. I have a master’s degree in [Inaudible]
and morphology from Penn State. I have about 28 years of experience
with [Inaudible] survey and the last 20 of those spent working with the national
[Inaudible] center and also working with simulation models to use the data. And much of my current work
field with [Inaudible] and making our data readily
acceptable to modelers. And in the past I served as data steward
for national products like [Inaudible] and sub orders that were [Inaudible] published
in 1989, major [Inaudible] report series of the United States, and also helped publish
that data twice, in 1994 and also in 2006. There, I told you a little bit about myself,
I want to tell you something that I’m not, and one of those things that I’m not
is an expert in [Inaudible] climate. And I do not wish to suggest that
I am in this presentation today. However, I have learned an awful
lot about weather station records and [Inaudible] model assumptions
during the development of this Java Newhall Simulation Model software. This is prepared in joint cooperation with the
Penn State Center for Environmental Informatics, and I was the manager of the cooperate
ecological studies unit agreement on that. So I will defer to Dr. West and others of
the national [Inaudible] survey center staff, they’re far more experienced in
expertise in [Inaudible] taxonomy than I do, but I’ll give it a shot. This afternoon Dr. West and I hope to share a
little background on the origins of this model. They’ve been with us since
the late 20th century, and how to operate the software
and apply it in a climate study. We will use the [Inaudible] National
Park as a climate study — case study. [ Background sounds ]>>First a few observations about climate. Climate is a major driving force of [Inaudible]
processes and behavior properties in the past and in the present, and [Inaudible] future. Climate’s been treated as
static, [Inaudible] survey. It is a difficult topic to handle, given
various issues with resolution, scale, time, [Inaudible] processes, [Inaudible] climate. Current, [Inaudible] taxonomy
assumes there’s a normal here, and a normal distribution
of climate [Inaudible]. The Java Newhall Simulation Model provides
a systematic and quantitative approach to characterizing [Inaudible] climate
[Inaudible] can provide clues will more variable landscapes and trends through time,
can help recognize rain shadows and declining climate criteria
and ecological [Inaudible]. Climate graphs and various
climate calendars we’ll share with you represent [Inaudible]
processes through the calendar year. Numbers of leaching events, the
intensity of leaching [Inaudible] of clay, periods of drying, and clay deposition. [Inaudible] and climate behavior, that can
provide a description of weather environments, as such, [Inaudible] to be
attached to [Inaudible]. And just to get started here, quick primer or
review of [Inaudible] temperature regime basics. Many of you probably work in a couple
different soil temperature regimes. Like this one up here, left to right, we go from
cold to hot, with the [Inaudible] permafrost, [Inaudible] frigid, mesothermic and hypothermic
in red, on the right-hand side there. We also have iso [Inaudible] of these, and
just for a little history on soil taxonomy, back in 1975 when this model was discussed,
the definitions were a bit different. I think there was a less than five
degree difference than the summer and winter soil temperatures at that time, and
I think that was changed to 6 degrees in 1999. [ Background noise ]>>All right. And here a little review on
[Inaudible] moisture regime. So we have the liquid moisture machine, which is really defined my morphological
characteristics present in [Inaudible] features. So it’s really not dealt with in
this climate simulation model. So I’ll mention that. From top to bottom, we go from
the [Inaudible] climate regime, that [Inaudible] for specific
[Inaudible] in all months of most years, and [Inaudible] moisture control
[Inaudible] we get progressively dryer, to [Inaudible] Mediterranean climate,
into the desert [Inaudible] regime. Where often the moisture
control profile is [Inaudible]. For those who are interested, as you
begin to run the model you’re going to find a few new old terms that
are presented to you by the model. Under the moisture [Inaudible] modifier box
we’ll find a group of proposed subdivisions to those traditional soil
taxonomy moisture regime. For example here in the [Inaudible]
are basically the same. Left-hand side are the proposed
[Inaudible] revisions by doctor [Inaudible] of Cornell University back in 1992 when he
was doing some USAID work funded by the USDA. And on the right-hand side of our
conventional moisture regimes for soil taxonomy. [Inaudible] however, is sub divided into
two, to [Inaudible] that’s three divisions. West [Inaudible] and [Inaudible] each becoming
progressively dryer and very [Inaudible]. Really has three, weak, [Inaudible] and extreme. And just for a little background for you also,
I’ve included some high points on definitions, what these new proposed soil
terms are for reference. The appendixes of the user’s
guide has a detailed explanation of the definitions of these. All right, and now I’m going to
hand this over to Dr. Larry west. He’s going to talk to you a little bit
about some of the Newhall functions, especially those dealing with the way the
moisture is managed through the simulation in the moisture [Inaudible] section.>>Thanks, Sharon. As Sharon said, I guess, John said I’m
the [Inaudible] research and laboratory. I am a [Inaudible] climate
[Inaudible] but probably the only — almost the only person in this
audience that used the basic version of the Newhall model, back
actually in the ’80’s. So I’ve played with it a little bit over time. And my piece of this is yeah, [Inaudible]
shouldn’t be treated as black boxes. So — and so I’ll [Inaudible] overview
of kind of how the model operates. It’s all in the user guide and the
associated articles at the back, [Inaudible] if you’re interested. You can also check to see if I screwed it up. As Sharon said, this model was actually
developed by a person named Dr. Franklin Newhall who was a USDA employee,
it was established in 1972. This is not a new model. It’s also not a sophisticated simulation
of water movement through a sort. It was developed to calculate soil
moisture regimes for soil taxonomy. So there’s not a lot of sophistication to the
mathematics or anything else associated with it. It works, though. As you use this, you need to remember that
the soil is essentially regarded as a sponge, it is a reservoir with fixed capacity. So it is a sponge in a bathtub. The [Inaudible] it’s all based on the available
water holding to pass the soil profile. Default value is 200 millimeters, and
we’ll talk more about that in a minute. That value — the original model, it was fixed. You can change it. Water enters the soil by precipitation,
it’s removed by [Inaudible] the other thing to remember is when the bucket is full
you can’t put any more water in it. When the water holding capacity is satisfied,
if you get additional water added it disappears. It either — the assumption
is it’s run off or leaching, and we’re not talking about
which one it might be. In terms of temperature calculations, the calculations for soil temperature
usually are based on an offset from the mainly annual air temperature. The default is 10 degrees C. You can
vary that if you have additional data that says it should be something different. So the soil in the model is
represented as a matrix of 8 by 8 boxes, an 8 by 8 matrix of boxes, 64 boxes. Each low of that matrix holds 1/8 of the total
available water holding capacity you specify. Each box holds a 64th of that
available water holding capacity. The relationship between available water
holding capacity and depth is something that I don’t think I’ve got my
mind totally wrapped around yet. The basis of available water holding capacity. Each low has from that percent of the
total available water holding capacity. It’s not a depth. It kind of is if you want
to look at it that way. One’s closer to the top, more shallow,
the deeper ones, there is no fixed depth. It’s a variable depth. The soil fills from the top. That would make sense. It empties with slants, as they
call it in the model of [Inaudible]. In other words, it doesn’t empty from
the top, it empties on a diagonal. It’s based on the energy required to move water. So — which is a function of both the matrix
potential or tension and the depth of the box. And I’ll show you some examples,
that’s the [Inaudible] one. So let me back up. I went too far. So you add moisture, adds from the top. If you think of this box, the water
content increases from left to right. Tensions decreases from left to right. Whichever way you want to think of it. You have precipitation, it will fill the
first row before it will fill the second row. And then it will continue in
order until the bucket is full. Now when you deplete soil moisture,
like I said, it’s on slants. And the order of depletion of boxes are one,
two, three, four, five, six, seven, eight, nine, and so forth, where the lower left-hand
corner is the last box emptied. The representation on the right is a
representation of energy that’s based on potential of [Inaudible]
equivalent, that’s [Inaudible]. So you’ll notice the five boxes in the upper
right-hand corner have a potential of 1. So 1 millimeter of [Inaudible]
results in 1 millimeter of water lost. The lower left is a 5, which means it
takes 5 millimeters of PET to get rid of 1 millimeter of water in that box. And in between, it — those number
varies based on some logarithmic version that they don’t specify and I didn’t take the
time to try to figure out what it is, exactly. So water’s in, water’s depleted. That’s kind of the take-home message. The other thing, we have to talk a lot
about the soil moisture control section, I think most of you are familiar,
it’s the depth. If you take 2-and-a-half centimeters
of water and add it to a dry soil, it’s the depth that water
penetrates in 24 hours is the top. You add 8 centimeters, 7-and-a-half
centimeters, 3 inches of water to a dry soil, it’s the depth that that water
will penetrate in 48 hours. It’s represented in the model as
two rows, second and third row of cells, the area of [Inaudible]. So if you want to think about depleting
soil moisture as a model works, we start — this one’s totally full. And if you think about some of
the definitions of soil taxonomy, you can just say it’s moisture, and all
parts of the moisture control section. It’s all wet. You start drying, and you
see it’s drying by slants, and now the moisture control section is dry
in some parts of the moisture control section. If you continue the process
here, we’ve eventually worked in [Inaudible] soil is pretty dry. We’ve only got a little water left in it. We start re-wetting it. Again, from the top. You notice this doesn’t change. But now we’re back to moist, and
my figure got screwed up somewhere. This should be blue all the way across. Sorry about that. [Inaudible] hopefully you get the concept. It varies it, it’s kind of an
interesting way it does it. You should read that and think about it. There’s [Inaudible] available water
holding capacity are if you dealt with laboratory data sheets, soil
water retention difference there. They’re equivalent, kind
of, or they are equivalent. As I said [Inaudible] 200 millimeters. What does that mean? That — so if you say you have 200 — 800 — 200 millimeters of water in a
200 centimeter soil profile, that means that every centimeter
has a 10th of a centimeter of water. If you think about texture and the effect
of texture on water holding capacity, a lot of the times people — we use
sandy loam, they’ll say they have a 10th of a centimeter of water retention. If you talk about, say, 80 millimeters
of available water holding capacity for the profile, you’re still talking
about a 200 centimeter profile. Now it’s 0.04 centimeters per centimeter. In other words. Much less ability to retain water. Maybe that’s something like a sand. So you can get some concepts from texture. That if it’s a shallow soil, 50 centimeters
for hard rock, we use the same 200 millimeters, now it becomes 4/10 of a millimeter, 4/10 of
a centimeter, 4 millimeters per centimeter. There aren’t any textures, [Inaudible]
textures that hold that much water. Organics maybe. But no [Inaudible] textures. You’ve got [Inaudible] 80 millimeters
of available water, it maybe is 0.16, something like a loam or a
[Inaudible] or something like that. Now you’re going to use the model and you want
to think about this, those data are available for all the soils for which
statistics [Inaudible] were collected at the time of sampling. It’s called water retention difference. The data sheet is right out
the side [Inaudible] density. I’ve got two soils here that I took
from the database, one is in Memphis, if you’re not familiar with Memphis,
it’s a fine, loamy thermic I don’t know, mixture or [Inaudible] it’s a [Inaudible]
silty [Inaudible] soil that occurs in western Tennessee and Mississippi. Pardon? Oh, fine silty. Thank you. Wrong one. The other one is [Inaudible]
and I don’t know that soil. I’ve never seen it. It’s a coarse loamy something. I didn’t look at the classification. It’s just a data sheet that I got to
that had some numbers that I like. So if you look at the [Inaudible]
all the way through the two meters, water retention difference varies between
0.22 and 0.66 centimeters per centimeter. The [Inaudible] is more variable in texture and the water retention difference
varies between 0.1 and 0.17. Now remember you can add right to
the meter per centimeter values, you have to multiply by thickness. So if you take that rise in thickness
as water retention difference, you come up with the available water
holding capacity for the horizon. So for the AP, it’s 22 centimeters thick
times the water retention difference, you have to multiply by 10 to get
from centimeters to millimeters. Pay attention to your units,
they’ll kill you every time. So that’s 57 millimeters. You go through that exercise and add it up. And at 10 meters, this has
a water storage capacity, water retention capacity of 480 millimeters. Remember the default is 200. So if you’re working in thick, lush
landscapes, you might want to think about what that default is, what you put in for that value. On the other side of the fence here, if you’re
worried that other soil [Inaudible] to 65, which is pretty close to the default. Now if your soil is shallow, if you only took
it — [Inaudible] 11, 43 centimeters of Memphis, so you have hard rock at 43 centimeters, the available water holding
capacity would be 99 millimeters. So — and the point is you need to
think about the dominant condition and the area you’re trying to
mimic, model the moisture regime. You have a bunch of shallow soils,
you have low water holding capacity. Nice, thick, silty [Inaudible] soils,
you maybe want more [Inaudible]. Because that’s going to make a
difference in the model outcomes. This is a — an annual challenger of
moist-dry conditions in the control section. This is a model output. You’ll see this in the model output. 30 days per month, the top row is
January, the bottom row is December. Give you an idea. This is using climate data
for Lincoln, Nebraska. So if I say it has 80 millimeters
of water holding capacity, you’ll notice that there’s a [Inaudible]
period where the control section is either dry in some parts or dry in all parts, during — actually, this is August and
September, a period of a year it occurs. And it’s still a unit, but on the dry side here. It actually qualifies for the
dry [Inaudible] unit piece. I use 200 millimeters of
available water holding capacity. Same climate data. Everything else is the same. It’s clearly [Inaudible] so it
will vary depending on that. So this is an important thing to think about. Just don’t use the default and expect
to get the right answer in all cases. And I think, Sharon, it’s your turn.>>Okay, thank you, Larry. I think it is my turn. All right, well I’ll going to talk to
you a little bit about the development of the Java version of this kind of
traditional Newhall Simulation Model. And just a few highlights here. Should be [Inaudible] ESU
agreement with Penn State. I think we did that work
principally in 2009-2011. And I also want to acknowledge all of the
NRCS co-operators who helped us review, especially some of the folks from Cornell who
were working at that time in the early ’90’s and 2000’s who provide information
on the study [Inaudible]. It is based on the 1991 [Inaudible] basic
code, it reflects the soil taxonomy rules at that time, and also includes proposed
moisture regime subdivision terms. And if you would like to see a
simulation of what that moisture addition and depletion would actually look like we have
posted a YouTube video on the background page of the Java Newhall Simulation Model web pages,
it’s [Inaudible] I’ll show you later about that. And it really helps you better
understand what the model is doing there. It is a Java application, it is — got a
flex [Inaudible] on it that handles all of the user interface inputs
and output data from the model, and it provides identical results to the 1991
code if you provide the same identical inputs. We did add a summer [Inaudible]
water balance calculation, and also an interactive user interface where
you can actually key in a single year source of data, if you just happen to have that
information from your own [Inaudible] study. It also provides large batch run, and that’s
perhaps its greatest strength at the moment, is [Inaudible] standardized input and out
put parameters, which is a little archaic from the original work, we’ve documented these
in a dictionary you’ll find in the user side. Input is a simple [Inaudible] file or CSE,
and can be created using an Excel input. We provide those too. The out put is stored in an XML format
and it be converted back to a [Inaudible] for easy manipulation and [Inaudible] form. With this update we’ve increased the speed
of the model from about three minutes in a dock box simulator to about
25 milliseconds, so it’s all over, almost, before you click the go button. We took the software and had a
[Inaudible] this year, and we deployed it to a [Inaudible] this month, I
think it started last Friday. And according to my sources I understand
about 1428 installs have occurred. I’m not sure exactly what that means,
but that’s what I’ve been told. Okay, let’s talk about its requirements. This is a monthly step kind of model. Really considered more like a [Inaudible]
model, it’s really pretty appropriate for regional assessment and so forth. it requires fairly complete monthly
precipitation and air temperature for a calendar year or years
from a weather station. And by [Inaudible] complete, we
usually say between 20-25 days of the month should be reporting in to
consider that you’ve got a reasonable estimate of what’s going on in that month for
either air temperature or precipitation. Weather station meta data. We need the name, the station
code, what network it falls within, the location of it, and the
start and the end year. We also record elevation, but that’s
most of the meta data information. Also required, as Larry explained,
available water capacity value computed for the [Inaudible] file, adapted
for the weather station near by. Sometimes we call that value
the [Inaudible] value, valuable water storage within the profile too. We also need what we call the mean annual
soil temperature minus mean air temperature offset value. And this is an important consideration, because this is how the model estimates
the mean annual soil temperature, based off the mean annual air temperature. This — we’ll talk about this in the tutorial,
but we can get this from the scanned data or from the literature of [Inaudible] research. We also include user meta data. And then finally, inputs [Inaudible]
and I can’t emphasize this enough, must all share a common unit — system of units. For example, when we get climate data
sometimes it’s comes to us in [Inaudible] units, non [Inaudible] like in degrees Fahrenheit
or inches, or [Inaudible] or inches of precipitation, and sometimes it comes
to us in metric or [Inaudible] units, like degrees centigrade or millimeters of valuable water capacity or
millimeters of precipitation. And occasionally, it comes in mixed units. That’s why Larry [Inaudible]
be very mindful of the units in our data preparation as well as our analysis. All right, getting started. This is a pretty modest software development
project, so we basically took the old 1991 code and packaged it up and made
it a lot easier to access and use in a modern computing environment. As such, there’s no online
help for things like that. But we have quite a very nice user’s guide and we created a soil web site
devoted to the Newhall model. You’ll find this [Inaudible] web
page and you’ll find some quick links on the soil classification pages right
here on [Inaudible] left-hand side. They [Inaudible] soil climate
model will get you there. So there’s a background piece, that’s where
you can find that little YouTube video. There’s the YouTube site which
I’ll review with you here shortly. There’s also the public software download,
and there’s a bundle that includes everything, [Inaudible] the user’s guide,
all software and so forth for our co-operators or any other public user. We also include pamphlet templates
which you can download here, and some example weather stations, Excel
and [Inaudible] files [Inaudible] through. Also provide the sample [Inaudible]. And we also provide a link to various sources
for the soil climate information sources, which include our [Inaudible] climate
analysis [Inaudible] at the National Water and Climate Center, United States
Historical Climatology Network, and various NOAA regional climate centers,
like the High Plains Regional Climate Center. There’s some excellent information
there, as well as some more of these regional climate centers that we
use in the study we’ll talk about shortly. And with that, we’ll go to the next slide. And also, just a little reminder,
little icon here to the right is what the Java
Newhall model would look like. Those of you who received it on your
desk top through a CCE deployment, the software is already installed
on your computer. If you find you can’t locate it, go to all
programs, you’ll see [Inaudible] Newhall model, something like that, [Inaudible] Newhall. And you will — you can request
it through your [Inaudible] person that you have it manually
installed — requested. A note about data management. Because this is an independent piece of software
it lends itself to people’s files going crazy with the way they manage their data. So I suggest here a little template, an
organization of how to keep your data organized so you can find it yourself the second time
through as well as sharing it with others. You’ll see here I set up a folder for
Java, and add a project name to it. And then I would set up one of these
for each of the projects you wish to do. And then include in it some
of the reference information so you can also [Inaudible]
information like templates. And that will include your Excel
files as well as your PHP file output. This is for your XML and then the [Inaudible]
that you can generate from this [Inaudible]. And also keep the read me text on file which
is a full complement of everything that’s in the original bundle, and
the user sites are right here. All right, right now the — switch gears a
little bit and take you through the user side, which is very well written by a [Inaudible]
from the center of environmental [Inaudible] and at some time going to share the
program — let me [Inaudible] — [ Background sounds ]>>Might be a little bit [Inaudible] here. The newer model when you download the user
site, that is, for the 1.5.1 verse looks like this, this simple little guy. And the first — about a dozen pages — are
devoted to the model and the second half of it is really devoted to the — the content. So I want to review with you too here, just a
little bit — short note about the background, how to run the application, [Inaudible]
on your system, and then a little bit about the intensity or the
[Inaudible] input mode format looks like and the XML output and then
the useful references. So again, just as we reviewed
here a little bit [Inaudible] about Franklin Newhall and his efforts. I believe Dr. Robert Grossman used
to tell me as a [Inaudible] use that to compile all the climate
data that Dr. Newhall needed. So that was a very interesting note there. Running the Newhall application. Let’s take a look. The little icon you see —
[Inaudible] just a moment. Refreshing shot on your screen?>>It started, didn’t it? It’s going.>>Yeah. I think I might just stop
with this next one and I’ll go straight to the demonstration or back to the Power Point. Yeah. I guess I would invite all of you
folks to look at the PDF in detail there, but mainly the important thing to know is that
you can run the model in an interactive mode or you can run it as a batch mode. At this point I think I’m going
to switch over to the — oh okay. I — the model itself — and
we’ll just go from there. [ Background sounds ]>>All right. You should see the Java Newhall
screen interface on one right now. So what we have here is — there’s an input
mode [Inaudible] at the top, and it [Inaudible] and then the second [Inaudible]
have the data and user information. So it’s very simple interface. Get you a little [Inaudible]
but I’d like to go through and just review the results
of the model right now. So chose the single model run,
and I’m going to go to my output. Outlook. And look at the sample data set. The [Inaudible] Pennsylvania from the year 1930. All right, so outcome, I [Inaudible] over
here, you’ll see you’re in a single model run, select the model file, and
we’re looking at the input file. The XML itself will contain both the input
parameters as well as the output parameters. So you can reconsider the model ones you run, make sure you remember what inputs were
considered as well as the results you received. So here we say the station
name, [Inaudible] Pennsylvania. We have the meta data related to elevation,
latitude and longitude, station I.D., the beginning year and the ending year. It also was originally done in English units. All outputs are stored in metric units, however. Down here you can see there’s an air off — airflow off place, and we say the original
model one was 2.2 degrees Fahrenheit. Water holding capacity was 7.9 inches. So now you see the mean monthly precipitation
in inches and the mean monthly air temperature in degrees, in air [Inaudible]
and notice we see gas [Inaudible] by interpretation of the station. Sometimes when we need to run long-term records
you have to help fill in for gas in order to make a month or two useable,
to [Inaudible] a year. Let’s look at the outputs you get, though. There are three kinds of reports that come up. The first one I clicked on was
actually the meta data report. I’d like to start with the first report,
which is called the Climate Calendar Report. Looks a lot like the graphic that
[Inaudible] just a moment ago. Here we see the top, the meta
data information repeated again. It stores a single year, period
is 1930 to 1930, calendar year. And in this case we [Inaudible] presip was 703
millimeters, moisture regime is [Inaudible] which is unusual for that part of the
world, [Inaudible] central Pennsylvania. And the temperature regime is metric, and here
is that unusual term, it’s a [Inaudible] kind of wet — on the wet [Inaudible] side of things. We can take a look here. The output, [Inaudible] air temperature,
mean annual presip, all in metric. And then the model estimate or the monthly
total, potentially is [Inaudible] millimeters as well as the model [Inaudible]
monthly total water balance. The last column shows the
annual use of those as well. Soil temperature calendar shows you when blue
indicates, again, each row is represented by a low and each of the
columns is a day in month. You have 30 days a month and a standard
365 day year with [Inaudible] blue is when the soil temperatures are less than 5
degrees C. The tan is when they’re between 5 and 8 degrees C. And the green areas are where
they’re greater than 8 degrees centigrade. To the right on the soil moisture calendar, dry
is indicated by red, the most dry color is tan, then moist is indicated by green. You can see later in the year it
stopped raining with [Inaudible]. The next report we provide is
something called a [Inaudible] graph. And on the Y axis we have the precipitation
of the [Inaudible] in millimeters, and across the X we have a calendar year
with monthly data points identified, starting in January, ending in December. Again, we have repeated the meta data
information and the classification as a result of the model of [Inaudible] the bottom
there is a small legend, we have a blue line with two little [Inaudible] at the end
indicates the precipitation with a data point. To interact with the model you’ll see that
the precipitation value will actually appear. You can find out, if you
want to just check the model. The same goes for the potential of [Inaudible]
you see that on the curve, this red. You’ll find whatever those signs are as well. Under the curve, the surplus is indicated by
the blue field, and then the pink color is shown by what we call utilization or where
potential [Inaudible] exceeds presip, pumping out more than we have there. Where they overlap together
is the purple area in between. But the utilization is indicated
by the pale orange or red color. And then the last portion — I might
mention, in the lower right-hand corner, these can all be saved out in PDF for each
individual model, [Inaudible] contains the data at the time of the [Inaudible]
ground and so forth. The model [Inaudible] summary here is
some of that meta data information. And it includes user meta data as
well as the station you’re going — and all of the inputs so you
can double-check yourself. This is all [Inaudible] in an XML file. One thing I failed to mention here ,
I’ll mention it now, on the input side, before the user begins to use the
model you are asked to enter — enter your user contact information. So each individual person can
have their personal model run. If [Inaudible] change your mind. E-mails, that’s something I can make an edit to
it, I can submit it, and that will return it, so it will be updated for all future model runs. So that’s what I have on the model
[Inaudible] for the moment right now. We do have a short [Inaudible]
to go to [Inaudible] and I’ll see how far I can go with that. [ Background sounds ]>>All right. This is a little case study we pulled together. My [Inaudible] here are Pete [Inaudible]
who is the soil program coordinator with the National Park Service
in Lakewood, Colorado. And [Inaudible] a research associate
here at [Inaudible] university. And Bill has probably about the most experience
with the Newhall models that I know of. I think Larry has worked with an older version
than we did back in the early ’90’s at Cornell. So let’s go through this quickly. I think we identified about ten steps that
most people should consider when we’re thinking about conducting a soil climate study, especially one where you might
want to apply the Newhall model. The first step is to write a hypothesis about the climate regime study
that you’re going to work on. In our case, the soil climate
regime is [Inaudible] and the temperature regime is [Inaudible]
second step, review the literature. Find out as much as you can about that area. There’s wonderful digital
resources on line, too. Identify all of your data sources and weather
station data that’s available in neighboring — individual years or summary years
within your [Inaudible] study and your neighboring [Inaudible]. In our case, we have studied — we have a
study in [Inaudible] Indiana [Inaudible]. Some of the sources are the
Stand Forest I mentioned before. We used to work [Inaudible] from the
North East Regional Climate Center, and also there’s the US Historical
Climatology Network too. You can prepare that offset
between soil temperature and air temperature from your scanned data. You’re fortunate — Pete
recommended using this national park because there was a SCAN site located there
and we have very good data for that site. We have [Inaudible] make a note. Using one or two year data log
study for our [Inaudible] operation, but they can be a little misleading if
you don’t have enough long term records to figure out what’s going on. Once you’ve assembled your data really need to
go ahead and prepare your input Newhall table. Run the model, examine the results,
and compare with neighboring stations. Analyze your yearly climate probabilities
and generate moisture and temperatures — temperature regimes [Inaudible] for
the study area of [Inaudible] time. Review any anomalies or outlier years for
impacts of natural events such as hurricanes, the greater tropical storms, et cetera. And finally, identify any long-term
trends or patterns in the data. Here is a little summary of the climate
sources we chose, and a timeline. Across the bottom you’ll see starting about 1870
on the left-hand side and goes to the present. [Inaudible] out there, but
the ten year increments. So that’s climate records and
historical climate network usually start about 1895 and go through the present. That’s shown by the red bar there. For our site there was about 117
years of information available. The next [Inaudible] I’d like to
record was the [Inaudible] source, which is like a national —
located at Cornell University. And we’re able to download
monthly information from there. That was 78 years starting in 1934. Much [Inaudible] with that. And then the tiniest — the smallest record
is the Soil Climate Analysis Network, or SCAN network, which has
records from 2004 through 2011. And there’s also across the bottom
you see little [Inaudible] red, [Inaudible] yellow center, and these are little
areas of cell events that we compiled together from the national [Inaudible]
mitigate center’s record. You can see how they come
and go across that timeline. There’s also a [Inaudible] line there between
1955 and 1974, it comes out of an article from the University of Minnesota that talks
about periods of the [Inaudible] climate records and climate things settled
down and be very stable. I want to show you some of our results. Here’s the SCAN information. SCAN [Inaudible] often co-located
with other weather stations and also monitor soil temperature and moisture. So if you can’t find full records
on the SCAN site you might be able to go to neighboring sites to help. Usually, SCAN sites have less than 30
years of records, and the National Weather and Climate Center also provides
additional [Inaudible] and wet tables, like [Inaudible] normal that can be
used to provide input data for Newhall. Historically, the top tables with the 30 year
[Inaudible] value would serve as the input for the Java Newhall Simulation Model. Want to go back just one step here
for the [Inaudible] I failed to show that we have one other — the orange
bar here at the bottom from 1971 to 2000 is a summarized value of 30
years worth of monthly information, from 1971 to 2000 from the northwest
National Weather Service station. And we provided one of our sample data
sets to include the national [Inaudible] at that weather station for
the nation on the web site. Here’s what it looks like for the SCAN site,
in particular the site for [Inaudible]. And you can see what the SCAN site looks like. When downloading SCAN data, it’s
suggested that the settings you use for downloading the data are
highlighted in blue here on the screen. And I think you will get the calendar year for
2011 if you download this, for this one here. Need to chose all sensors, no chart,
full [Inaudible] of information, need to download daily values one year at a time and then summarize monthly
values for the new [Inaudible]. So [Inaudible] report — reported the
quick point [Inaudible] that arrow. You can download and obtain
the information that you need for calculating the available weather
[Inaudible] that Larry described. For our site we used that information and came
up with either 6 inches or 153 millimeters. Again, be careful not to mix English and SI
units when you’re dealing with SCAN data. Next slide is a little sample
of the download here. The [Inaudible] file that you get. And that’s the — averages the
average air temperature in degrees C, and F is the soil temperature
in degrees C at a 20-inch depth, [Inaudible] meters, [Inaudible] units. So be careful of the mixture
between English and metric here. There’s a few other items
in working with SCAN data. Maybe some of you are familiar with
that already, maybe some are not. Be careful with the SCAN — end of
the hydraulic year duplicate record, and [Inaudible] highlighted in orange here. So you need to delete that
when you get going there. And [Inaudible] here. Let’s take a look. Here is the calendar year for 2011,
and we’re looking here at daily air and soil temperatures [Inaudible]. have to take a look at some of this. Take some of that data and
[Inaudible] them by year and depth with the [Inaudible] profile
in centimeters here. Note that the maximum here,
I think the average is shown on the purple curve, is the composite, I guess. 2004 to 2011. And blue and red are cooler
here, from 2008 and 2011. Note that the maximum actually
puts it at 27 meters, which is the bottom of the [Inaudible] there. And also that — the taxonomy
uses the 50 centimeter depth here, and you’ll see the mesothermic threshold here,
15 degrees C, that our temperature values are on the thermic side for most of that time. Here’s an example of a [Inaudible]
compared the mean air temperature and the mean soil temperature. Soil temperature is given in the red
line, higher, air temperature is blue, and here we are in degrees Fahrenheit. The average offset here is about 3.6
degrees Fahrenheit, or about 2 degrees C, and that serves as our [Inaudible] model input. Here’s the summary side where we actually have
[Inaudible] computed in the upper section. So here’s the offset from the years
2004 to 2011, and below we looked at the neighboring SCAN sites
that had reasonable data. No [Inaudible] and we have
offsets that vary for — from about 1.6 degrees C
in [Inaudible] Virginia, to 3.7 degrees C at [Inaudible] in Montana. So there’s — usually very regionally. So there’s no one answer, silver
bullet to an offset value here, as far as regional understanding knowledge. Other sources, regional climate center here. They have lots of good data that
[Inaudible] older records available. Each of the regional groups should
be consulted [Inaudible] your site. Here’s an example of what the Excel files look like for the Java Newhall [Inaudible]
file for that 1930-2000 records. You can see here the first record
is the 1934, highlighted in red. And at the bottom, [Inaudible] record
there, going all the way down to 2011. You can also see here we indicated
[Inaudible] was 6 inches, air temperature, soil temperature offset was 3.6 degrees
[Inaudible] we started with English inputs, and then [Inaudible] English input units. Here’s what it looks like when we can
take a look at the longer term record. Here we’re going from 1934 to 2011. We added the offsets, just mean annual
air temperature, and looked at both of those temperatures through time
by — for each of those years. Across the bottom we have
the decades on the Y axis, we have degrees Fahrenheit
for the mean soil temperature. You can see a lot of the time we’re almost
over the [Inaudible] there, and it does — if you look at the dotted trend line it looks
like we are on an increasing rate there. Also note here, you’ll kind of put in
this little period of [Inaudible] climate from the mid 50’s through early ’70’s. Kind of looks like there’s low amplitude here,
kind of settled down a little bit compared to some of the wilder extremes
that followed before and after. Here we are, on my record SCAN,
the values I use for the inputs, the Java Newhall, and we ran 78 years. So based on the model results,
frequency [Inaudible] moisture and climate regimes were
summarized for this climate record. We did that for each of the 78 years
and then we computed the probability. Look at the number of years, the percent
of frequency during this period of time that the temperature regime shifted from
hypothesized [Inaudible] unit regime. The site is 58% [Inaudible] 23%
dry [Inaudible], 7% wet [Inaudible] and [Inaudible] the site is
also about 14% [Inaudible] and 64% [Inaudible] for the same period. The ten [Inaudible] years
were either [Inaudible] and possibly indicators of [Inaudible]. Here we are back at our timeline again. And if we take a look at events, here’s
a little star burst now is orange, for [Inaudible] for your years. So 1936, ’45, ’52 were all [Inaudible] years. 1953 was [Inaudible]. 1963 was [Inaudible] ’68 as well. 1983 was [Inaudible] and 1999 was [Inaudible]. And then we had two more events
for [Inaudible] in 2007 and ’08. So take a look at what the 1999
[Inaudible] record looked like here. Here is the 1999 record for
the Climate Calendar Report. And we can see here it’s
[Inaudible] very typical year. And you can see here the
number of days of [Inaudible] or moist-dry in the moisture control section. Look at the [Inaudible] graph you can
see an increase here is much lower than we might in precipitate. And then if we compare this with the
average of all of those 78 years together into a long-term record and kind of
average out all that random variation, it will come out to a [Inaudible]
specific unit [Inaudible]. Here we see the web — the precipitation in the blue line is much higher
than you think it would be. And the utilization region is much smaller. And here if we go back to the 1971
to 2000, 30 year normal record, which is the sample data set we gave you, you can see that that one does
[Inaudible] specific unit, and you can see in the moisture
calendar it’s completely moist here. This is what the climate graph looks
like for that same record, 1971 to 2000. So summary and conclusion. The new models adapted for [Inaudible]
modelling and climate regimes with limited data can be used with proven data
sets, I had just a little slide I’ll share with you on that too in a
moment, or with [Inaudible] tables from the national [Inaudible] climate
center or the historical climate network, and also applicable for modelling in national
parks, [Inaudible] wildlife refuge and so forth. You can also use your own
monitoring [Inaudible] information if you were an avid soil climate person
in the field with your own survey. [Inaudible] a couple parameters
for [Inaudible] can be derived. These include the annual water
balance, [Inaudible] water balance — by the way, these are all in the users guide
and detailed in the [Inaudible] growing season, water balance, biological windows at 5
and 8 degrees C and frequency of events, often helping to build [Inaudible]
may have events in time. It handles local, regional
air-soil temperature offsets and [Inaudible] available
water [Inaudible] conditions. Trade off is that it relies on a much
more robust [Inaudible] approach, it’s a thermal versus a [Inaudible]
uses the [Inaudible] method. But it has water use [Inaudible]
applicabilities to the [Inaudible] limited data. And it helps us better understand the
poly climatic character of a [Inaudible] and how climate [Inaudible]
processes are — can behave at times. Now with that I have just one quick other slide
to share with you about how we might begin to take a model that we had shown here just
to run on point data at weather station sites and might begin to be applied
in a [Inaudible] plan. We can — using the [Inaudible]
data gather monthly precipitation and air temperature information
and can [Inaudible] for each [Inaudible] I think
this is 800 by 800 meters, an input record to go into
the [Inaudible] model. We’re working with some of the researchers
at national [Inaudible] at Purdue, and [Inaudible] others at
West Virginia University. And we’ve been able to run the model, that
resolution is about 12 million [Inaudible] across the lower 48 states and
we have some preliminary results. Here we’re looking at the Bowling Green
airport weather station, which has — we’re looking at the [Inaudible] is
water balance, and it’s kind of in the — more yellowy kind of blue range there, that
corresponds with the [Inaudible] water balance that we found at the weather station too. We were using as our input parameter for
valuable water and mapping the average for the state [Inaudible] for
the digital [Inaudible] too. So it’s very broad, regional type of
analysis here we’re talking about. Really good back [Inaudible] to
help us on some of our [Inaudible] or soil data correlation efforts to give us a
good context and setting for that kind of work. This work is still under review and we hope
maybe later this year we will have some of that information to share. And with that, I think we’re done. Thank you for your time.>>All right. And so now we’ll open it up to some questions. And again, that will be by Q and A.
And I received a few questions already, so I’ll start with the ones that I have. Let people enter their questions
in the Q and A tab at the top of the light reading screen,
and we’ll take them off there. So the first question I have is will
Sharon’s presentation be available on the [Inaudible] web site,
[Inaudible] material. So yes, I think that’s a good idea. Of course all of our webinars are archived on our webinars presentations
returning sessions web site. But I think providing a link also on
that J N S M web site is a good idea, so we’ll link in both places. Then there was a question for Sharon. You mentioned that in 1975 the original iso
regime was defined by a 5 degree difference in mean summer and mean winter temps,
and was changed in 1999 to 6 degrees. Do you know why. Why — what was the reason for the
change from 5 degrees to 6 degrees?>>Actually the increment was [Inaudible]
not a good person to answer that question. I was just reporting when we
found all the rule changes.>>Yeah. So [Inaudible] ready to come out,
so you can talk into the microphone here.>>That change happened with the second
edition of Soil Taxonomy in 1999. And it was simply a clarification of
an inconsistency in the first edition. The difference between the iso
temperature regimes was defined as less than 5 degrees C difference between the
mean winter and mean summer temperatures. And in the non iso temperature
regimes were defined as more than 5. Well, that doesn’t work very
well when you actually try to apply temperature in whole numbers. You cannot use temperature in fractional
values, has to be in whole numbers. So the correction was that the
iso regimes are less than 6 and the non iso regimes are 6 or more. So that difference defines, you know, for
instance, the difference between mesic and isomesic has to be less
than 6 to be an isomesic regime. So it was simply a correction
to the first edition. Really didn’t change anything except
to clarify what the actual limit is.>>All right. Next question?>>I’ll make sure the figure gets
updated too, make sure it’s up there.>>Next question comes from the west. How does the model take into account
slope, aspect, and curvature.>>How does it take into account
slope, aspect and curvature? Actually, I don’t think that it does. I think the model assumes kind
of a weather station setting. And when we were working on some
of the proven data, you know, that that proven data has an aspect, an
orographic factor already factored into it. So we worked with it in that
way for large areas. I think there have been others, and I know
that Bill [Inaudible] and myself, [Inaudible] and others actually did some work where we
did terrain regression based on the point data where we got that aspect,
and slope and elevation, [Inaudible] difference with some things as well. But the model itself we’ve done — I think it
does take into account latitude and longitude for identifying proper hemisphere though. The [Inaudible] model does. I don’t know if that’s helping or not.>>My next question is have soil data
collected at NRI sample sites been used for soil climate simulation
modelling and regional assessments?>>Well, that may be a good question for some
of the [Inaudible] and modelers, do we know? Maybe Larry might have a little better
insight into that, I’m not aware.>>The short answer is no.>>I think it’s a good idea, though. Especially if we have actual
[Inaudible] site information or [Inaudible] good estimates
of it at this point.>>Okay. We’ll move onto the next one. How are rock fragments addressed in terms
of [Inaudible] profile in [Inaudible].>>Rock fragments addressed?>>Right.>>Okay, I believe, and Larry
[Inaudible] back me up, the [Inaudible] water retention
difference as well as the AWC that we report calculated the available water
storage are already rock fragment corrected. So those have already been subtracted
from that total in millimeters. Diluted, in effect, for the
rock fragment content.>>And another question — or comment, anyway
— there is another model being developed, Epic, similar to [Inaudible] can both models be used for the same classification
and how would this work?>>That’s a really good question. I think both Larry and I should probably answer. I think that this is an historic
model, pretty much. And it’s used for the basis of a lot of
our early classification assumptions, and it would be great to
have some kind of correlation with more precise [Inaudible] model like Epic.>>I agree with Sharon, [Inaudible]
in the works. I think that model will be available
probably by the end of the summer. Epic’s a lot more input intensive, [Inaudible]
times the water movement simulation, the [Inaudible] it’s also — so for a few
sites you get really good information, but a lot of sites [Inaudible] information.>>All right. And with that I think I need to
bring our webinar to a close. I want to thank you everybody for your time and
attention, and again, this recording will be put on our archive web page on line and
linked to the [Inaudible] SM web site. Thanks all.>>Thank you.

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