Hello, my name is Jeff Simley with the U.S. Geological
Survey. We are going to talk about the National
Hydrography Dataset and The National Map. So what is
The National Map? Well, let us think of it this way.
Let us think of it as a tree and The National Map is
the trunk of the tree; it is kind of the mother board
for a lot of information, a lot of geographic
information. Information such as elevation data,
structures data, transportation information,
orthoimagery, and hydrography. So these different
types of geographic information fit into this platform
that we call The National Map. Kind of like the
branches connected with the trunk of a tree. In
hydrography we have the National Hydrography Dataset
and the Watershed Boundary Dataset components that
make up the hydrography theme of The National Map. In
the National Hydrography Dataset we have things like
lakes and streams; and connected to lakes for example
might be information on the water chemistry of the
lake. Connected with streams we might have information
on fish habitat in the streams. An example might be
say cutthroat trout in a stream. So we need to access
this information and this can be done through
something like a map portal. So a map portal allows
us to enter in this geographic information system and
to access information such as where the cutthroat
trout are in the United States. We do this through The
National Map which adds a component, it has
hydrography and the National Hydrography Dataset. So
you can think of the National Hydrography Dataset very
similar to the GPS you have in your car. In the GPS in
your car, there is a 4 million mile road network that
is connected to that GPS and it allows you to solve
problems. Say for example we want to find out where a
gas station is. We are at a certain location and we
want to find the route to a gas station. This is done
by using this 4 million mile road network. We see for
example here a bunch of streets that make up this
network. The GPS knows where our position is on the
surface of the earth but what we really want to find
out is where we are on the network of roads. Not just
where we are in space but where we are on a network.
And so it snaps to the network and it finds our
network address on the network, so now we know where
we are on the network. The next thing it wants to find
out is where are the gas stations. The gas stations
are also located on the network. Then it tries to
solve problems of how to get from where I am now to
the nearest gas station. There are a couple of
solutions here and it picks the optimal solution and
then plots that solution for us and solves a problem,
it tells us how to get from where we are to a gas
station. The streets that make up this GPS system
really makes all of this possible, and then the
linking of information to those streets and knowing
where I am on those streets is what makes this work.
We do the same thing in hydrography but instead of
using a 4 million mile road network we use a 7.5
million mile stream network. So we can access this
information through a desktop portal such as a GIS
system or something like StreamStats as opposed to the
GPS system that you have on the dashboard of your car.
This is an example of The National Map viewer, another
way of accessing this type of information. Someday we
will be able to put this in the hands of people in the
field on mobile devices but we are not quite there
yet. We are still tied to a desktop application. So
this is basically what the NHD is all about, it is
things like lakes and streams. As you can see in this
map here there is this large lake and a big stream
network around that lake. You can see that there is a
ditch, a diversion tunnel, there is a marsh, there are
stream gages, and dams. It is the kind of information
that you would find on a USGS topographic map for
example. Basic features that make up the surface
water hydrography of the United States. The stream
network is set up into a vast network of stream
connections. It is organized into a hydrologic unit
which you see here. This hydrologic unit is
surrounded by a ridge that more or less kind of
divides the water and has all the water within this
drainage area flowing out in one direction. So most
of the hydrologic units of the United States looks
like this; they have water flowing out in one
direction. That ridge that we see there forms what we
call a hydrologic unit and that hydrologic unit is the
foundation for the Watershed Boundary Dataset. So this
is an example of that unit and the surrounding units
that are drainage areas. This particular unit is
called a Subbasin or a HUC 8 which is the 4th level in
the hierarchy of streams. We can further subdivide
this Subbasin or this hydrologic unit into more
detailed units. And then we can further divide these
units into even more detailed units. So if we look at
the hydrography and the hydrologic units on top of
each other we can see how they are integrated
together. You can see how there are individual stream
networks that drain within these hydrologic units. So
this is an example for the hydrologic units of the
United States at the 4th level of the hierarchy. There
are 2,246 of these over the United States. Here we are
just looking at the conterminous United States, but
the National Hydrography Dataset is one nation-wide
seamless dasaset. So although it is composed of all of
these units put together, it is one continuous
seamless dataset for the country. This is an example
of what the NHD looks like. It is basically streams
and we know some information about these streams. For
example, we know that this is Swan Creek, we know that
some of the streams are perennial, some are
intermittent and some are ephemeral. We also know the
flow direction of all these streams. So this entire
7.5 million mile flow network for the United States,
we know the flow direction, the direction of flow of
the water on all these streams. So for example we can
take a look at a spot, this green square you see there
at that location we can easily identify all the
streams upstream; those are those red lines. We can
also go downstream and trace the pathway downstream
from this green dot. Say for example we had a toxic
spill at this location we could trace the water
downstream and see what it would affect downstream.
One of the problems we have in creating this dataset
is to try and figure out how water goes though lakes.
So if you look at this map here we need to figure out
how water is entering the lake and where it is exiting
the lake. So we use a system of artificial paths that
give us the flow direction of water routing through
the lake so we can get a complete network, even
through all these polygons that make up the lakes of
the United States. One of the things we do is we
identify every stream segment with an identifier
called a ReachCode. This is a 14-digit number. The
first 8-digits tell us the hydrologic unit that we are
in. So in this case here hydrologic unit 14, the first
2-digits is the Upper Colorado River, hydrologic 01 is
the Colorado Headwaters, and then the 00 after that
is not used, and then the 02 is the Blue River. So it
is the Blue River of the Colorado Headwaters of the
Upper Colorado River Region. Every stream segment of
the United States has an identifier like this. So even
if the stream does not have a name we still have a way
of identifying that stream. Something else we do is
we take each one of these identifiers, these
ReachCodes, and we divide it up into an address range
with 0 at the downstream end and 100 at the upstream
end, then we equally divide it up into units between
0 and 100 no matter how short or how long or how
sinuous the stream segment is, it is always a 0 to
100 address range. You can take any point along that
stream and give it a value as to its address range on
the stream. So as an example we have a USGS stream
gage. We know where it is in space, we know the
latitude and longitude of that stream gage. What we
really want to do is to snap it to the network. So
much like the GPS in your car we know where you are
but we really want to know where you are on the
network. So we snap the stream gage to the network and
we get the network address. So in this case here, the
location of that stream gage is on reach
14010002000421 and its measure upstream, its location
upstream, is 19.8392% upstream on that stream segment.
So now we know the address of the stream gage on the
nations rivers network. There is only one spot in the
United States that has this address. It is very
simple. Two identifiers can tell us exactly where
anything is on the network. So for example here is a
stream gage on the network. The stream gage makes the
stream network more intelligent because the stream
gage can tell us how much water is in this river, and
likewise the network can make the stream gage more
intelligent. So we know what is downstream, what is
upstream, we know the name of the stream that we are
on. We know that there is a dam upstream. We know that
the dam is impounding Green Mountain Reservoir so
there is a reservoir upstream of this stream gage. We
know how many acre-feet are in that reservoir. Further
upstream we know that there is another reservoir that
has a diversion that is diverting 300 cubic-feet per
second of water out of the stream system. We know how
many miles of stream are upstream, we know how many
miles of perennial stream, how many miles of
intermittent stream, how many miles of ephemeral
stream. We know the drainage area, we know that there
is a heavy metals tailings pond upstream of this
location. So all of this information on the network
makes the stream gage more intelligent and the stream
gage makes the river more intelligent by telling us
how much water is in the stream. This is an example
of a hydrograph from that stream gage telling us the
stream flow for that river. We also want to talk about
how this data works in an information system. So in
the GPS in your car, for example, we have data such
as your position, where the gas stations are,
where the roads are. We can transform that data into
information that gives us more intelligence, so not
just my position but my position on the network. We
can then further transform that information into
knowledge, so the geospatial processing system that is
on the dashboard of your car takes this information
and process it to give us knowledge such as how to get
to a gas station. Using the National Hydrography
Dataset and other types of data we can do the same
type of thing in a geographic information system. We
can take information on land cover, temperature,
precipitation, elevation data, hydrography data such
as the NHD and process this raw data into information,
and then we can take this information and use a
processing engine such as StreamStats to determine
knowledge such as the discharge of water at an ungaged
site. This is an example of StreamStats, it is a very
advanced systems that is interactively operated over
the internet, that allows you to pick any point along
a river and determine the stream flow at that
location. On the left there you can see that we have
picked a location and that pink shaded area is the
drainage area that drains through the point that we
picked the little red star you see there. Then on the
right we can see information on stream flow. At the
very bottom of those tables at the right you can see
predicted stream flow at that location for the 100 yr
flood interval would be 3,680 cubic feet per second.
And we can do this by using different types of
geographic information combined with hydrologic
modeling. A typical problem that we want to solve is
how do points A, B, and C relate to each other. Point
B is a drinking water intake, point A is an
industrial discharge and point C is a pesticide
application. And on the left there you can see that we
have this model of the NHD/WBD database. We can take
water observation A and realize that it is upstream of
water observation B by the network that is within the
flowline of the NHD dataset. Point C is a pesticide
application; it is not directly on a stream but it is
on a hydrologic unit and we can relate the hydrologic
unit to the flowlines and understand that C is
upstream of B. So we know that A and C affect the
drinking water intake B. That is basically the types
of problems we are trying to solve.