1. Hawaii
National Hydrogrpahy Dataset
Stewardship Partnership
Malie Beach-Smith – Principal Steward

2. Overview of
the
National Hydrography Dataset
What it looks like?
How it work?

3. NHD
National Hydrography Dataset

4. collection of points, lines, and polygons
in a digital vector database.
Hanapepe
Bay
Kauai

5. Oceans, lakes, ponds, reservoirs, rivers,
wider streams, swamps and marshes are represented by POLYGONS.
lake/pond
river
reservoir
sea/ocean

6. Coastlines, streams, irrigation ditches, pipelines and artificial paths are
represented by LINES.
artificial
path
pipeline
stream
canal/ditch
coastline

7. Dams, gaging stations, wells, and springs are represented by POINTS.
gaging
stations
dams
springs

8. Note: There are many more feature types represented in the NHD.
Those were just a few of the more prominent ones.

9. seamless dataset covering:
contiguous United States, Alaska, Hawaii, the Virgin Islands, Puerto Rico,
American Samoa, and Guam
Includes: bordering watersheds in Canada and Mexico

10. Data Organized into Hydrologic Units
HYDROLOGIC UNITS
Defined by
headwaters,
bordered by a ridge system,
converging to a “pour point” or “belt flow” “Subregions”

11. Hydrologic Units –
Subregions of the Contiguous United States
22,056 Subregions in the countiguous U.S.

12. Region 20
Ni'ihau Kaua'i
2008 2007
Oahu
2006
Moloka'i
2005
Maui
2004 2002
Lana'i
2003
Kaho'olawe
Hawai'i
2001
8 subregions in Hawaii.
Hawaii’s SUBREGIONS are defined by ISLAND.
Each ISLAND subregion is further divided into drainage units by the WBD.

13. Hydrologic Units –
Watershed Boundary Dataset
Six level hierarchy of
nested watersheds
Hydrologic Unit Code (HUC)
Each level is referred to by the Hydrologic Unit Code or “HUC”

14. Hydrologic Units –
Watershed Boundary Dataset
Hawai'i
(subregion 2001)
HUC_8
This is the HUC 8 for the island of Hawaii (subregion 2001)

15. Hydrologic Units –
Watershed Boundary Dataset
HUC_10
These are the HUC 10s

16. Hydrologic Units –
Watershed Boundary Dataset
HUC_12
These are the HUC 12s

17. Hydrologic Units –
Watershed Boundary Dataset
HUC_12
And these are the HUC 12s uniquely color coded

18. Watershed Boundary Dataset
and the
National Hydrography Dataset
Streams (hydrography dataset)
Sub-watersheds (WBD)
HUC_12
This slide shows the integration of the WBD with the hydrography feature class.

19. Streams - the fundamental core of the NHD
Halawa
Bay
Moloka'i
NHD carries a lot of information about streams. Stream classification is one piece of
information within the data structure. Here, the perennial streams are displayed as dark blue
and the intermittent streams as light blue.

20. Streams - the fundamental core of the NHD
Halawa
Bay
Moloka'i
Flowlines also carry names. These names are collected directly from the Geographic Names
Information System, where all names have been approved by the Board of Geographic Names.

21. Flow Direction –
giving intelligence to the data
Flow direction: Flowlines in the NHD contain information about where the water is flowing.
With directional information we can navigate upstream and downstream.
This allows us to ask questions, and get answers, such as…

22. Navigation –
Creating fundamental knowledge
Kaiaka
Bay
Oahu
What is upstream of a given point?

23. Navigation –
Creating fundamental knowledge
Kaiaka
Bay
Oahu
Because we know which way the water flows, we can navigate upstream…

24. Navigation –
Creating fundamental knowledge
…and capture all the flowlines in the network upstream of a given point.

25. Navigation –
Creating fundamental knowledge
Or you may say…show me everything downstream of a given point…

26. Navigation –
Creating fundamental knowledge
…and the database can navigated downstream through the network.
This ability to navigate upstream or downstream through the network is useful for a variety of
modeling and analysis exercises.

27. Navigation –
Creating fundamental knowledge
Transport downstream
Toxic spill
Sewage spill
For example, you may want to model the downstream transport of a toxic spill. Or, you may
want to know what waters will be affected by a sewage spill.

28. Navigation –
Creating fundamental knowledge
You may want to model fish migration upstream.

29. Navigation –
Creating fundamental knowledge
trace upstream
with barriers
Northern East Maui Coast
And here, you may want to model possible barriers to fish migration upstream. In this
example, the irrigation ditch layer was used as a barrier to fish migration.

30. Linear Referencing – Stream “Reaches”
Another very important part of the NHD is its ability to address information to the dataset.

31. Linear Referencing – Stream “Reaches”
referencing system
Linking information to the dataset gives the data it’s intelligence
The flowlines are more than just blue lines on a map, they are also a referencing system for

32. Linear Referencing – Stream “Reaches”
20060000001050
Reach Malie Street
Street
NHD Road Map
In the NHD, the linear referencing system can be analogous to street addresses on a road map.
The NHD uses “reaches” the way road maps use street names.

33. Linear Referencing – Stream “Reaches”
Each reach has a Similar to the way
20060000001050
unique 14 digit reachcode a zip code describes
Malie Street
that holds information the region of a country,
about the subregion, county,
and the hydrologic unit or town
that flowlines reside in
Reach Street

34. Linear Referencing – Stream “Reaches”
Each reach
uniquely color coded
unique numerical code
Each reach in this slide is uniquely color coded, and each reach has a unique numerical code.

35. Linear Referencing – Stream “Reaches”
region
The first two digits, “2, 0”, tell us what region we are in. “20” indicates Hawaii.

36. Linear Referencing – Stream “Reaches”
subregion
The next two digits, “0,6” identify the subregion . “2006” is the island of Oahu.

37. Linear Referencing – Stream “Reaches”
sub-watershed
The next six digits describe finer divisions of watersheds and sub-watersheds.

38. Linear Referencing – Stream “Reaches”
unique reach
(basis for the address
referencing system)
The last 4 digits describe a unique reach that is the basis for the address referencing system.
This is reach number 2164 of subregion 2006 (Oahu).

39. Linear Referencing – “Measures”
80.00543 2468
20060000001050
Reach StreetMalie Street
Like a house number on a street. A point on a reach has a measure.

40. Linear Referencing – Stream Addresses
Reachcode
20060000000848 100
75
0
25 50
A reach is divided into address ranges called “measures”. A measure works like this…

41. Linear Referencing – Stream Addresses
Reachcode
20060000000848 100
0
The downstream end of a reach is designated as “0”, and the upstream end is designated “100”.

42. Linear Referencing – Stream Addresses
Reachcode
20060000000848 100
75
0
25 50
Every location along the reach has a measure (like a percentage of the total length). It doesn’t
matter how long, or sinuous a reach is, a particular address is anywhere between 0 and 100.

43. Linear Referencing – Data “Events”
Reachcode
20060000000848
data point = “event”
So having an address referencing system, allows us to attach information to the dataset.
Data related to the NHD is referred to as “events”.

44. Linear Referencing – Data “Events”
Reachcode
20060000000848
Water quality sampling site
Biological survey site
Stream gage
etc.
An “event” may be a water quality sampling site, a biological survey site, or a stream gage or
dam.

45. Linear Referencing – Data “Events”
Reachcode
20060000000848
37.52676
(measure)
“Events”have an address within the network using a reachcode and “measure”. This site is at
measure 37.52676 on reach 848 in subregion 2006 (Oahu)

46. Linear Referencing – Data “Events”
Reachcode
20060000000848
data segment = “event”
“Events” can also describe stretches or segments of a flowline.

47. Linear Referencing – Data “Events”
Reachcode
20060000000848
Biological survey segment
Segment of impaired water
Waters with special uses
A “line event” may be a biological survey segment, a segment of impaired water, or waters
with special uses.

48. Linear Referencing – Data “Events”
Reachcode
20060000000848
82.37655
T0_measure
31.49238
FROM_measure
Line “events” are addressed using a “from” measure and a “to” measure.

49. Linear Referencing – Data “Events”
Hanalei
Bay
USGS stream gage
ID: 16103000
“Events” can be layers within the NHD that contain additional information or links to
additional data. This is a USGS gage on the Hanalei River. It is gage number 1610300.

50. Linear Referencing – Data “Events”
Hanalei
Bay
USGS stream gage
ID: 16103000 Latitude 22°10'46.5“
Longitude 159°27'59.0"
We know the stream gage’s position in space.
We know its X Y position in latitude and longitude…

51. Linear Referencing – Data “Events”
Hanalei
Bay
USGS stream gage
ID: 16103000 Latitude 22°10'46.5“
Reachcode: 20070000001000 Longitude 159°27'59.0"
Measure: 98.66498
… but even more importantly, as an event in the NHD, we know where this stream gage is
within the network. We know it’s address. ..

52. Linear Referencing – Data “Events”
Hanalei
Bay
USGS stream gage
ID: 16103000 Latitude 22°10'46.5“
Reachcode: 20070000001000 Longitude 159°27'59.0"
Measure: 98.66498
And having an address on the network allows use to analyze relationships between features.

53. Events – Intelligence about the data
Through the “Events” table, we have a link from this particular stream gage record to the
National Water Information System. (Clicking on this point will take us there.)

54. Events – Intelligence about the data
This is the record of that Hanalei stream gage from March 4th. You can see that the water
flowing though that stream was well above the median daily statistic.

55. Events – Intelligence about the data
By linking an event to the dataset, (in this case we linked a stream gage to a river), we add to
our knowledge about that dataset. We went from knowing we had a river
to knowing something about that river.

56. Events – Intelligence about the data
biological communities
fish habitat
water quality
fish migration barriers
etc.
Events can be added to provide information about all kinds of things. You can add events
about biological communities, fish habitat, water quality, stream flow, and fish barriers, for
example.

57. Events – More than just dots on a map
Data that is truly integrated
The building blocks for knowledge
● Diversions in the headwaters of the Colorado River
(12,500 diversions in this particular dataset)
So events are more than just dots on a map. Events are data that is truly integrated into the
Jeff Simley, USGS
framework of the dataset.

58. Events – More than just dots on a map
Data that is truly integrated
The building blocks for knowledge
● Diversions in the headwaters of the Colorado River
(12,500 diversions in this particular dataset)
The black dots in this slide are diversions in the headwaters of the Colorado Simley,There are
Jeff River. USGS
over 12,500 diversions in this particular dataset.

59. Events – More than just dots on a map
Data that is truly integrated
The building blocks for knowledge
There is so much data, here, it can be a bit overwhelming. But by indexing these diversions to
Jeff Simley, USGS
the dataset, we can use the power of the computer to sort all this information out.

60. Analyzing Information to create knowledge
Jeff Simley, USGS
We can sort out which diversions affect the flow of water through a particular stream gage.
Taking advantage of the upstream - downstream directionality of the dataset we can do these
kinds of calculations.

61. National Water
Information System
Linear Events
The NHD integrates
information from
many resources. In DFIRM Floodplains
the future, the USGS
plans to include National
Wetlands
DFIRM floodplain Inventory
data, the National Watershed
Wetlands Inventory, Boundary
Dataset
and the National
Elevation Dataset. National
Hydrography
Dataset
National
Elevation
Dataset

62. Traditional GIS National Water
Information System
NHD
overlays themes of Data is linked
information, one on Linear Events together within the
top of other data structure.
Relationships between
features are
DFIRM Floodplains Analysis is much
determined by their more powerful.
National
spatial proximity. Wetlands
Inventory
Works well for some Watershed
analysis. Boundary A highly effective
Dataset
solution to
Works for making
National geospatial analysis.
maps. Hydrography
Dataset
The power of the GIS
to sort this information National
Elevation
and understand the Dataset
relationships becomes Well integrated data
strained when you makes this possible.
have a lot of data to
analyze.

63. So, how do we make all this happen? There are a lot of things we need to do to keep the NHD
current: add data to it, add intelligence to it, add new features, new content, things like that.
Jeff Simley
The capabilities of the USGS are rather limited. So what the USGS has done is to leverage
those capabilities with a partnership of people.

64. In building the NHD for the country, the USGS partnered with a lot of different people, States,
and other Federal Agencies…A huge partnership made all this possible. And the USGS needs
to continue these partnerships to help maintain and improve this data. To do this, The USGS
established the Stewardship capability. The partners of the Stewardship join in to help
maintain and improve the National Dataset.

65. Building the National Hydrography Dataset
The National Hydrography Dataset was designed and substantially built by three Federal
partners…⌂The USGS remains the lead agency for: maintaining the NHD framework;
developing NHD software; quality-control of edits; and housing and disseminating the data.

66. Maintaining
the National Hydrography Dataset
Hawaii NHD Stewardship
Partnership
… State Stewardship programs (like Hawaii’s Partnership) take the lead in UPDATING and
MAINTAIING the datasets. Hawaii’s NHD Stewardship Program began in August 2009 as a
partnership between the USGS and three primary State agencies… ⌂ These state agencies took
the lead to establish the Stewardship Program and provide direction and oversight of changes
to the NHD. Since it’s formation, over 10,000 edits have been made to Hawaii’s datasets.

67. GOAL
Provide the State and the Nation with the
most up-to-date and reliable
surface water dataset

68. NHD’s is notable for these Achievements:

69. NHD’s is notable for these Achievements:
1. Developing a standardized data model almost everyone
can agree to.

70. NHD’s is notable for these Achievements:
1. Developing a standardized data model almost everyone
can agree to.
2. Creating a fundamental framework to serve as an
application foundation.

71. NHD’s is notable for these Achievements:
1. Developing a standardized data model almost everyone
can agree to.
2. Creating a fundamental framework to serve as an
application foundation.
3. Developing a robust solution that will advance the
science.

72. NHD’s is notable for these Achievements:
1. Developing a standardized data model almost everyone
can agree to.
2. Creating a fundamental framework to serve as an
application foundation.
3. Developing a robust solution that will advance the
science.
4. Making the solution simple enough to be
implementable.

73. NHD’s is notable for these Achievements:
1. Developing a standardized data model almost everyone
can agree to.
2. Creating a fundamental framework to serve as an
application foundation.
3. Developing a robust solution that will advance the
science.
4. Making the solution simple enough to be
implementable.
5. Creating a national partnership to pool resources.

74. NHD’s is notable for these Achievements:
1. Developing a standardized data model almost everyone
can agree to.
2. Creating a fundamental framework to serve as an
application foundation.
3. Developing a robust solution that will advance the
science.
4. Making the solution simple enough to be
implementable.
5. Creating a national partnership to pool resources.
6. Actually building the national dataset.

75. NHD’s is notable for these Achievements:
1. Developing a standardized data model almost everyone
can agree to.
2. Creating a fundamental framework to serve as an
application foundation.
3. Developing a robust solution that will advance the
science.
4. Making the solution simple enough to be
implementable.
5. Creating a national partnership to pool resources.
6. Actually building the national dataset.
7. Creating a stewardship community and process to
enhance and maintain the data.

76. How to get it. Where to learn more. Who to call to contribute.

80. END

81. The NHD is a collection of points, lines, and polygons in a vector dataset.
Polygons represent lakes, ponds, reservoirs, and oceans,
MakaoKaha'i Point, Kauai
Worldview2 imagery

82. polygons
Lake/pond
Nomilo
Fishpond
reservoir
Sea/Ocean
MakaoKaha'i Point, Kauai

83. as well as rivers, wider streams, swamps and marshes.
' Anahulu River, Oahu
Worldview 2 imagery

84. polygons
swamp/marsh
swamp/
marsh
swamp/
marsh

85. canals and ditches
Kohala Ditch
Hawaii

86. Kohala Ditch, Hawaii
Kohala Ditch, Hawaii
DOQQ natural color imagery
DOQQ Natural Color imagery

87. Kohala Ditch, Hawaii

89. Kohala Ditch
Hawaii

90. springs
Honolulunui Bay
Hanawī Springs
Pali Spring
Big Spring
Hawaii Island
Worldview 2 imagery

91. Honolulunui Bay
Hanawī Springs
Pali Spring
Big Spring
Hawaii Island

92. wells

93. Lanai
Lanai

94. gaging stations
Olowalu stream gage
Maui

95. gaging stations
Maui
Gaging station

96. waterfalls
Kahiwa Falls, Molokai

97. waterfalls
Kahiwa Falls
East Molokai

98. dams
Wahiawa Dam

99. dam
Wahiawā
Reservoir
Oahu

100. coastlines
World Imagery Service

101. Albers_Equal Area_Conic

102. including larger offshore islets
Ford Island
Sand Island
Oahu

103. Lehua State
Seabird Sanctuary
and smaller offshore islets
Ni'ihau

104. Watershed Boundary Dataset
integrated into the NHD
HUC_8
Hawaii Island

105. HUC_10
Hawaii Island

106. HUC_12
Hawaii Island

107. Put it all together…

108. coastline,
Hanapepe
Bay
Kauai

109. watershed boundaries,
Hanapepe
Bay
Kauai

110. rivers,
Hanapepe
Bay
Kauai

111. streams,
Hanapepe
Bay
Kauai

112. irrigation ditches,
Hanapepe
Bay
Kauai

113. lakes, ponds, and reservoirs,
Hanapepe
Bay
Kauai

114. gaging stations,
Hanapepe
Bay
Kauai

115. dams,
Hanapepe
Bay
Kauai

116. and wells.
Hanapepe
Bay
Kauai

118. More than just a collection of shapefiles…
More than just a collection of shapefiles…
Hanalei Bay
TOPOLOGY
Kauai
TOPOLOGY

119. With topology, the flowlines carry information about the relationship between the .
Hanalei Bay