Our basic architecture for this dataset is we have a
geodatabase that consists of the National Hydrography
Dataset and the Watershed Boundary Dataset and when we
display those together it looks like this. The blue
lines you see there are the National Hydrography
Dataset; they are the streams that make up the surface
water network. Then we can see these multicolored
polygons that make up the Watershed Boundary Dataset.
We can see how these stream networks fit neatly into
these hydrologic drainage areas. So in our design of a
dataset me might want to look at more information
besides just the National Hydrography Dataset and the
Watershed Boundary Dataset. We might want to look at
information such as the National Wetlands Inventory
and the 100 year flood plain as might be found in the
“Defirmed”? dataset. These datasets can all work
together in a geodatabase. We have other types of
water information such as point source discharges,
biologic habitat, stream gages, impaired waters,
drinking water intakes, diversion structures, and
dams. Then the NHD Plus dataset that we will talk
about in a minute, a very rich dataset that contains
all kinds of additional information. We can link this
information using pointers, using these network
addresses. For example, look at a point source
discharge; where is that located? Well we know the
address on the network and we can link that to other
types of datasets, to the National Wetlands Inventory,
to the National Hydrography Dataset. All this type of
information can be related to each other using these
network addresses and explicitly identify where things
are in relation to each other. We know what is
upstream, what is downstream of each other, we know
how far away it is. We know lots of information about
the relationships of data. For example here in trying
to identify the relationship between A, B, and C, this
can easily be done simply by looking at a map. We
don’t need a GIS system to do that, we can simply look
at a map and figure this out. But in an example here
we have many thousands of points. This is too
complicated to figure out just by looking at a map. So
we really need a geographic information system and a
data structure such as we find in the National
Hydrography Dataset and the Watershed Boundary Dataset
to understand these types of relationships. So for
example we have a stream gage, this red triangle that
you see in the image. We want to know what water
diversions are upstream of that stream gage that
effect the flow of water through that stream gage. So
we can navigate upstream and identify all the stream
gages. The cyan colored circles you see there, those
are the diversions that are upstream of the stream
gage. Those black dots you see there are also
diversion but they are not upstream of the stream
gage; so using a system such as the NHD it is easy to
figure out the relationship of information to each
other. Traditionally in a GIS, we overlay information
and look at the spatial relationships. This does not
work quite as well in a hydrography system because the
way that surface water is related is through the
network not just through space. So we need these
explicit relationships that we find in the data model
to understand how things relate to each other in the
network and not just through space. So one of the
things we can do with this type of information is to
create a map such as this which shows the surface
water flow through the state of Washington. The width
of the blue lines that you see there tell us how much
water is flowing through the streams on mean annual
flow basis. This is a model that gives us a picture of
where the water is in the state of Washington flowing
through the surface water stream network. This works
by taking information such as the National Hydrography
Dataset, the Watershed Boundary Dataset, and the
National Elevation Dataset, combining this together to
create catchments. So the blue lines you see here are
the stream network and then the multi-colored polygons
you see there are catchments. It is kind of like the
Watershed Boundary Dataset but in a much more detailed
level. You can see that each individual stream segment
has its own polygon, its own drainage area. That
drainage area can then be intersected with other types
of data to get information on that drainage area such
as the precipitation in that area, the temperature,
the elevation, the slope, the land cover, how much of
the land is forest, how much is agriculture, how much
of it is urban area. You can take all this type of
information and model it to get the stream flow
information that you see in a map like this. So using
modeling techniques we can predict how much water will
be in any one of these stream segments and then
represent the amount of water using the line width to
represent that. Something else we can do is look at a
system such as this and account for the diversions of
water. So the blue lines you see there are the natural
flow that you would expect in the surface water system
of Colorado. What we have done is then accounted for
the water that is withdrawn through water diversions.
The red dots you see on this map is water that
transfers water across the Continental Divide. There
are 56 of these and 5 of them are particularly
significant. What we have done is we used the pink
lines to represent water withdrawn by these diversions
so these rivers actually have less water in them. Then
the purple lines represent rivers in which water has
been added to them. So we have taken the blue lines
and then made them wider and turned them into these
purple lines because water has been added through the
Continental Divide diversions. Just an example of some
of the things we can do to improve our understanding
of the water of the United States. How is the NHD and
WBD used? For example at the U.S. Environmental
Protection Agency (EPA), they look at pesticide risk
assessment, they look at where pesticides are applied
on the landscape. How this effects endangered species
for example of flowing downstream; looking at water
samples and looking upstream and seeing where the
pesticides are coming from. One of the things that
they do at the U.S. EPA is they look at water
discharge permits and link these to the NHD, so giving
everyone of these points an address on the network
such as we saw earlier as in the example of the stream
gage. One of the things that can also be done is time
of travel. We can look at a water contaminant entering
the system at a certain point and flowing downstream
to a drinking water intake. If there is a contaminant
in the water we can look at the breakthrough curve
that you see in the upper right-hand corner. This tell
us over a period of time, the y-axis tell us the
concentration in the water, the x-axis looks at a
point on the river and the time interval when that
contaminant plume first reaches a drinking water
intake, when peak concentration arrives and then at
the trailing edge of the plume as the contaminant
passes through, passes by the drinking water intake.
It allows emergency responders to close off the
drinking water intake until the threat has passed.
In California, for example they use the National
Hydrography Dataset to look at water rights. Where
water rights used to be something that was just
manually stored on index cards and maps we now can do
this through a GIS system using a web portal such as
WRMIS system in the state of California to allow
citizens to look at water rights in the state
of California. In Missouri what they have done is look
at fish species. They have take 316 different fish
species and indexed these or referenced these to the
NHD. So we know for example where the Ozark Minnow
live the Wedgespot Shiner the Southern Redbelly Dace
you can see these in red, it shows us which streams in
the state of Missouri these species live in.
Something else we are interested in is
looking at fish barriers. One of the issues in
California is Steelhead Trout migration to spawning
grounds and the barriers in the river systems that
prevent the Steelhead Trout from reaching the spawning
grounds. So we can look at complete fish passage
barriers, partial barriers, and potential barriers and
map those out in the river system. There is also a
system called Sparrow which allows us to look at
nutrient loads in rivers in the United States. In this
case we are looking at nitrogen loads. The red colors
are the highest concentrations of nitrogen. This
system uses an example of the National Hydrography
Dataset, an earlier version of it, and as they
modernize this system they will use the National
Hydrography Dataset for this type of modeling. Some of
the issues that we are concerned about is that we are
trying to keep up with the change in the hydrography
in the US. You can see here the stream, each color of
the stream represents a different 10 year interval of
that stream being mapped. We can see that throughout
time the stream from 1944 to 56 to 72, 81, 92, and
2006, the stream has changed many times and we need to
keep up with that change. Here is an example of the
NHD in blue. When you overlay this on a contemporary
image you can see the stream has changed course since
it was last mapped. We need to keep up with change
like this. So we have different densities, different
scales of the National Hydrography Dataset. This is
what we call the medium resolution NHD. It represents
1:100,000 scale density of streams. We also have a
1:24,000 scale representation of the NHD and many of
our customers have asked for even more detail in the
NHD and we can see this in this example of what we
call the local resolution NHD. One of the things that
is being done is to derive local resolution NHD from
Lidar data. What we have done is we have mapped the
streams from Lidar data in red then overlaid this with
24,000 scale NHD in blue and you can see that the
existing NHD in blue pretty much represents the Lidar
streams to an extent but the red lines that you see
sticking out of the blue lines are an extension of the
network; they are an additional streams that can be
gathered using the Lidar information. Canada and
Mexico are also building dataset such as the National
Hydrography Dataset and the Watershed Boundary
Dataset. This is an example of Canadian data on the
top and US data on the bottom, and for this hydrologic
unit we are combining the data from Canada and the US.
So when you take a look at data over Canada and Mexico
over the border it becomes seamless information and it
allows scientists to study the border regions without
regard to the political boundaries. We are doing the
same thing with Mexico. Here you see Mexican data on
the bottom, US data on the top side of the red line,
combining this together into one seamless hydrologic
unit. So we are adding lots of information to the NHD
to make the NHD more intelligent. The black square you
see there are the dams, the red triangles are stream
gages. Also we are looking at things like water
diversions such as this tunnel that goes under the
Continental Divide connecting the two green dots you
see there. One of the things we are concerned about
too is hydrography over urban areas. So this is
looking at Minneapolis and we can see some hydrography
represented here in the dark and lighter blue colors.
But a lot of the surface hydrography actually flows
underground through culverts and so we can take a look
at the major culverts that drain the water through the
system and add these to the NHD to get a more complete
picture of hydrography over urban areas even though
this hydrography is flowing underground through
culverts. We have the same problem over karst
topography. This is where water is flowing underground
through limestone caves and other types of caves. The
green and blue lines you see are the NHD and WBD
surface water. The red lines that you see there are
underground streams flowing through karst terrain. So
we would like to be able to add these to NHD to get a
better understanding of how the surface flow works.
One of the things that we do is we maintain the
National Hydrography Dataset through a Stewardship
process where we have a number of states who have
joined with the USGS and keep the NHD up to date. We
have 35 states that are in our Stewardship network
right now. The USGS acts as a facilitator for this
process and then the states maintain this data and
keep it up to date. And then other organizations work
through the states such as the U.S. Forest Service,
The Bureau of Land Management work through the states.
Other organizations such as the City of New York would
work through the state of New York. In Arkansas for
example the State Engineers Office or the Department
of Environmental Quality work through the state
Steward to keep the National Hydrography Dataset up to
date. Then we have a process to facilitate this where
we have experts who work in different regions of the
US to help keep the NHD up to date and to answer
questions so you can go to this web map, click on a
state and find out who to contact to maintain the NHD
and keep it up to date. So this is an overview of the
National Hydrography Dataset and the Watershed
Boundary Dataset. One of the things we are also
concerned with is how do we reach our customers and in
this trumpet shaped curve here we have on the top are
expert users and in the middle are knowledgeable users
and at the bottom are more casual users, and one of
the things we want to do with the NHD is make sure we
are addressing our full customer range and not just
providing advanced capabilities but capabilities that
will meet the needs of all users at all levels of
expertise.