North American Lake Management Society November 3 – 5 Oklahoma City, Oklahoma

1. 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

2. Objective • 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.

3. Overview • 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

4. Reservoir Classification – Databases • 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

5. USACE – National Inventory of Dams • 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

6. NID Dams and Reservoir

7. SARP Region and NID Dams

8. SARP States and Dams 250 acres and above

9. Alabama – 36 Arkansas – 76 Florida – 104 Georgia – 86 Kentucky – 54 Louisiana – 56 Mississippi – 46 Missouri – 16 North Carolina – 67 Oklahoma – 122 South Carolina – 34 Tennessee – 40 Texas – 243 Virginia – 41 Reservoirs by State in SARP Prototype 250 acres and over Total – 1,021

10. USGS NHD Database: • 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

11. NHD Waterbodies

12. Waterbodies 250 acres and above

13. EPA Enhanced River Reach File • 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

14. E2RF1Flowlines

15. SARP E2RF1 Layer

16. All 4 Databases

18. State Dam Name / Lake Name Other Name State ID NIDID Longitude Latitude Section, Township and Range County River Nearest City Distance from city Private Dam Purpose for impoundment Year Completed Dam Length Dam Height Structural Height Hydraulic Height NID Height Metrics

19. 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) 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

20. 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) 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 Metrics continued

21. 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.

22. Shoreline Development Index Shoreline Development Index- DL is high for lakes in flooded river valleys Lakes that approach a circular shape have DL = 1 http://www.unep.or.jp/ietc/publications/short_series/lakereservoirs-2/6.asp http://www.cen.ulaval.ca/pingualuit/index.html

23. 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

24. 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

25. 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

26. Catchment to Surface Area Ratio Bearskin Lake and Catchment Catchment / Surface Area Ratio 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

27. Surface Area to Lake Volume Ratio: • 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 http://learn.genetics.utah.edu/content/gsl/physical_char/

28. Dams with no Surface Area: • 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

29. Buffered Waterbodies and Dams with no Surface Area prior to clipping:

30. After Clipping the total number of Dams decreased:

32. Reservoirs with no Surface Area Added to Database:

33. Reservoirs with no Surface Area Additions cont:

34. ArcGIS Model for measuring Fetch • 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

35. MEL and MEW: • 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)

36. MEL and MEW Model Results:

37. What next? • Complete national data set to run • Cluster Analysis • Factorial Analysis • Principal Components Analysis • Others? • Relate groupings or clusters with reservoir impairments • Identify metrics or variables most sensitive in explaining groupings and determine if related to impairments