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Development of a National Reservoir
Database of Geographical, Physical,
and Morphological Metrics for
Classification and Discrimination for
Fisheries Habitat Assessment
North American Lake Management Society
November 3 – 5
Oklahoma City, Oklahoma
Kirk Rodgers, University of Arkansas at Little Rock and USGS Arkansas
Water Science Center and Reed Green, USGS Arkansas Water Science
Center
U.S. Department of the Interior
U.S. Geological Survey
Objective
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To develop a database for publically accessible
reservoirs, within the lower 48 states, greater than
250 acres, that will include physical, geographical,
and morphological descriptors for each reservoir
aggregated from existing public databases and
information sources.
The expected outcome will be to deliver a
comprehensive database of publically accessible
reservoirs, within the lower 48 states, greater than
250 acres to include physical, geographical, and
morphological descriptors for each reservoir.
Overview
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Funded (in part) by USFWS – National Fish Habitat
Action Plan, Reservoir Fisheries Habitat Partnership
(RFHP)
Using various data-mining techniques to populate
metrics to develop the RFHP database
Working with ARC-GIS, Microsoft Access and
Excel to link various databases
Mining the databases for QA/QC
Developing new metrics using formulas which use
existing data
Using SARP 14-state reservoirs to determine project
methods feasibility
Reservoir Classification – Databases
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USACE – National Inventory of Dams (NID)
~ 82,000 dams
USGS Geographic Names Information System
(GNIS)
USGS – National Hydrography Dataset (NHD)
EPA – Enhanced Riverreach File (E2RF1)
Databases created by Greg Schwarz (USGS)
and by Kirk Rodgers
USACE – National Inventory of Dams
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Databases downloaded in Access format
Filtered NID by SARP region (~ 82,000 dams)
~ 39,000 dams in the 14 states filtered by
surface area of 250 acres
250 acre query yielded 1,021 dams and
reservoirs in region
16,855 Dams listed as having no surface area
All NID database entries contained Longitude
and Latitude coordinates
NID Dams and Reservoir
SARP Region and NID Dams
SARP States and Dams 250 acres
and above
Reservoirs by State in SARP Prototype
250 acres and over
1.
2.
3.
4.
5.
6.
7.
Alabama – 36
Arkansas – 76
Florida – 104
Georgia – 86
Kentucky – 54
Louisiana – 56
Mississippi – 46
8. Missouri – 16
9. North Carolina – 67
10. Oklahoma – 122
11. South Carolina – 34
12. Tennessee – 40
13. Texas – 243
14. Virginia – 41
Total – 1,021
USGS NHD Database:
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The surface-water component of the National
Map
Comprehensive set of digital spatial data in
the form of ArcGIS geodatabases for each
state
Created in conjunction with the USEPA
NHD Waterbodies
Waterbodies 250 acres and above
EPA Enhanced River Reach File
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EPA River Reach File is a hydrographic
database
Contains rivers and streams of the
continental United States, Alaska and Hawaii
Created to establish hydrologic ordering, to
perform hydrologic navigation for modeling
applications
Provide a unique identifier for each surface
water feature.
http://www.epa.gov/waters/doc/historyrf.pdf
E2RF1 Flowlines
SARP E2RF1 Layer
All 4 Databases
Metrics
 State
 Dam Name / Lake Name
 Other Name
 State ID
 NIDID
 Longitude
 Latitude
 Section, Township and
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Range
County
River
 Nearest City
 Distance from city
 Private Dam
 Purpose for
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impoundment
Year Completed
Dam Length
Dam Height
Structural Height
Hydraulic Height
NID Height
Metrics continued
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Maximum Discharge
(cfs)
Maximum Storage
(acre-feet)
Normal Storage
NID Storage
Surface Area (acres at
normal pool)
Surface Area (sq. feet)
Reservoir Perimeter (ft)
Shoreline Development
Index (unit less)
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Mean Depth and Width
(ft)
Index of Basin
Permanence (unit less)
Development of Volume
(unit less)
Residence Time
Flushing Rate
Maximum Depth (as a
function of hydraulic
height)
Maximum Depth / Mean
depth Ratio
Metrics continued
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Mean Discharge (cfs)
Drainage Area
(Catchment)
Surface Area (sq. mi.)
Catchment / Surface
Area Ratio
Relative Depth
Maximum Effective
Length (to be
determined)
Maximum Effective
Width(to be determined)
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Surface Area / Lake
Volume Ratio
Lake Volume (cu. ft.)
Spillway Type
Spillway Width
Volume of Dam
Number of Locks
Length of Locks
Width of Locks
NHD Common ID
Source Agency
Reservoir Metrics -- Definitions
 Relative Depth, Zr – Zr in % = 50 * Zmax * sqrt(π) * (sqrt(Ao))-1.
The maximum depth as a percentage of mean diameter. For
most lakes, Zr < 2%. Deep lakes with small surface areas
exhibit greater resistance to mixing and usually have Zr > 4%.
 Mean Depth, Zmean – Volume divided by surface area.
 Development of Volume, DV = 3 Zmean÷ Zmax. Measures the
departure of the shape of the lake basin from that of a cone. DV
is greatest in lakes with flat bottoms.
 Shore Line Development, DL –
Shoreline Perimeter / (2* SQRT(3.1416 * Surface Area)
The ratio the shore line length to the circumference of a circle
with an area equal to that of the lake.
Shoreline Development Index
Shoreline Development Index- DL
is high for lakes in flooded river valleys
http://www.unep.or.jp/ietc/publications/short_series/lakereservoirs-2/6.asp
Lakes that approach a circular shape
have DL = 1
http://www.cen.ulaval.ca/pingualuit/index.html
Reservoir Metrics – Definitions
(cont.)
 Index of Basin Permanence, IBP –
Volume divided by shoreline length (IBP = V / SL)
Reflects the littoral effect on basin volume.
 Maximum Length, Lmax – The maximum distance
between any two points on the shoreline (maximum
fetch or effective length, MEL)
 Maximum Width or Breadth, bmax – The maximum
distance between shores perpendicular to the line of
maximum length (maximum effective width, MEW)
 Mean Width, bmean – Equal to the surface area divided
by the maximum length
Index of Basin Permanence
IBP < 0.1, the lake is most likely
dominated by rooted aquatic plants
Lake Baikal has IBP ≈ 10,000 and is not
dominated by rooted aquatic plants
Reservoir Metrics – Definitions
(cont.)
 Mean Depth / Maximum Depth Ratio – as the depth
ratio decreases, potential nutrient recycling from the
sediment surface, productivity and sediment
accretion rate increase
 Catchment / Surface Area Ratio – watershed size
relative to lake area is an important factor in
determining the amount of nutrients are in a lake
 Surface Area / Lake Volume Ratio – important factor
in determining the amount of evaporation occurring
from the lake
Catchment to Surface Area Ratio
Bearskin Lake and
Catchment
Catchment / Surface
Area Ratio
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Catchment Area:
4166.7 Acres
Lake Surface Area:
321.9 Acres
4166.7 Acres/ 321.9
Acres = 12.9 / 1
watershed size relative
to lake area important
factor in determining
the amount of nutrients
are in a lake
Surface Area to Lake Volume Ratio:
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http://learn.genetics.utah.edu/content/gsl/physical_char/
Greater Surface Area to
Volume Ratio indicates
a higher rate of
evaporation from the
lake
Deep lakes with small
surface area exhibit a
higher resistance to
mixing
Dams with no Surface Area:
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16,855 Dams listed as having no surface area
ArcGIS, NHD and NID used to correlate dams
with waterbodies
1.5 mile buffer applied to NHD waterbody
layer of the SARP region
Dams with no surface area clipped to the
buffered layer
Decreased the number of dams to a
manageable size
Buffered Waterbodies and Dams with
no Surface Area prior to clipping:
After Clipping the total number of
Dams decreased:
Reservoirs with no Surface Area
Added to Database:
State
Alabama
Arkansas
Florida
Georgia
Kentucky
Louisiana
Mississippi
Dams no Dams after
Surface Area buffering
1710
466
595
545
144
70
2795
225
64
30
62
0
13
83
Matched
Dams
12
17
29
8
0
5
0
Reservoirs with no Surface Area
Additions cont:
State
Missouri
North Carolina
Oklahoma
South Carolina
Tennessee
Texas
Virginia
Dams no Dams after
surface area buffering
3935
383
695
595
94
2027
356
115
61
23
426
34
129
82
Matched
Dams
31
29
4
30
0
30
12
ArcGIS Model for measuring Fetch
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Created by David Finlayson of
the University of Washington
and the USGS
(Finlayson, 2005) and updated
by Rohweder and Rogala,
2008
Calculates fetch by measuring
9 vectors and taking the
means
Measures 36 vector at 10°
increments
Uses DEM or LULC raster data
Computing and Time intensive
Rohweder and Rogala, 2008
MEL and MEW:
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Maximum Effective Length (MEL) or Fetch –
the length of water over which wind blows
unobstructed
Maximum Effective Width (MEW) – the
maximum width between the shores
perpendicular to the maximum length
Longer the Fetch – results in larger wind
generated waves which in turn can increase
shoreline erosion and sediment resuspension
(Rohweder and Rogala, 2008)
MEL and MEW Model Results:
What next?
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Complete national data set to run
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Relate groupings or clusters with reservoir
impairments
Identify metrics or variables most sensitive in
explaining groupings and determine if related
to impairments
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 Cluster Analysis
 Factorial Analysis
 Principal Components Analysis
 Others?