A Reservoir Morphology Database for the Conterminous United States

Abstract

The U.S. Geological Survey, in cooperation with the Reservoir Fisheries Habitat Partnership, combined multiple national databases to create one comprehensive national reservoir database and to calculate new morphological metrics for 3,828 reservoirs. These new metrics include, but are not limited to, shoreline development index, index of basin permanence, development of volume, and other descriptive metrics based on established morphometric formulas. The new database also contains modeled chemical and physical metrics. Because of the nature of the existing databases used to compile the Reservoir Morphology Database and the inherent missing data, some metrics were not populated. One comprehensive database will assist water-resource managers in their understanding of local reservoir morphology and water chemistry characteristics throughout the continental United States.

Introduction

Understanding conditions and trends in the Nation’s lakes and reservoirs requires information on reservoir morphology and water chemistry. Commonly, such information is collected and analyzed at one or a few sites (Hoyer and others, 2004; Jones and others, 2004; Noges, 2009; Weber and others, 2010). Such local studies are useful in better understanding of limnological processes but are of limited value in analyzing conditions and trends at regional or national scales.

Data pertaining to different aspects of reservoir morphology and water chemistry are contained in databases developed by multiple agencies for different purposes. In particular, the National Inventory of Dams (NID) (U.S. Army Corps of Engineers, 1998); the National Hydrography Dataset (NHD) (Simley and Carswell, 2009); the Enhanced River Reach File (ERF1_2) (Nolan and others, 2002); and the SPAtially Referenced Regressions on Watershed (SPARROW) model (Schwarz and others, 2006) collectively comprise datasets, which combined would support national and broad regional analysis of reservoirs in the United States. These datasets include numerous morphological and chemical metrics, which are useful in themselves and moreover can be combined to compute additional attributes currently unavailable in any national scale database. Compiling key morphometric and water-chemistry metrics from multiple data sources into a single database would facilitate large regional- or continental-scale analyses of reservoir health.

In 2010, the U.S. Geological Survey (USGS) began an effort to compile a national database containing physical, modeled chemical, and morphological metrics for reservoirs with surface areas of at least 250 acres. The resulting database, hereafter known as the Reservoir Morphology Database (RMD), can be used by natural resource managers in assessing reservoir characteristics throughout the conterminous United States. This report describes the databases that were used to populate the RMD, selection criteria for the 3,828 reservoirs included in the database, and the computations for new metrics created by combining attributes from multiple input datasets. The RMD is publically available and web accessible from Rodgers (2017).

Databases Used in Construction of the Reservoir Morphology Database

U.S. Army Corps of Engineers National Inventory of Dams

The National Inventory of Dams (NID) (U.S. Army Corps of Engineers, 1998) dataset includes 82,642 dams impounding surface water in the United States. The database contains information describing each dam including the NID identification number (NIDID), longitude and latitude, purpose of the dam (irrigation, hydroelectric power generation, flood control, and so forth), normal and maximum storage, impounded surface area construction date, and public accessibility. In this study, 3,828 dams from the NID were analyzed.

U.S. Geological Survey National Hydrography Dataset

The National Hydrography Dataset (NHD) (Simley and Carswell, 2009; U.S. Geological Survey, 2017) is an ArcGIS geodatabase (Esri, 2011) containing vector-based geographic coordinates for lakes, streams, ponds, reservoirs, and other surface-water features in the United States. Each feature was digitized from USGS 1:24,000 topographic maps. The common identifier is the specific identification number used for streams and reservoirs in the NHD. The NHD preserves the network relations among surface-water bodies and their tributaries, facilitating analysis of watershed-flow routing at multiple scales (Simley and Carswell, 2009).

U.S. Geological Survey Enhanced River Reach File

The Enhanced River Reach File (ERF1_2) (Nolan and others, 2002) is a hydrographic dataset based on the U.S. Environmental Protection Agency River Reach File (Horn and McKay, 1994), which contains the streams of the continental United States and Hawaii. Created to perform hydrologic analysis for modeling applications, the ERF1_2 provides a unique identifier for each surface-water feature (such as a stream) known as a river reach code. The dataset can be downloaded from http://water.usgs.gov/GIS/dsdl/erf1_2.e00.gz.

SPAtially Referenced Regressions on Watershed Attributes Model

The SPAtially Referenced Regressions on Watershed (SPARROW) model is a tool used for the interpretation of water quality based on watershed attributes. This model correlates in-stream water-quality measurements to watershed attributes that are spatially referenced. The SPARROW model includes sources of contamination and terrestrial and aquatic factors that influence contaminant transport (Schwarz and others, 2006). Empirical estimates of the origin and fate of contaminants in rivers and streams are made by SPARROW (Schwarz and others, 2006). The object identifier used by SPARROW is the river reach code found in the ERF1_2.

Methods for Construction of the Reservoir Morphology Database

A list of dams impounding reservoirs with a surface area greater than or equal to 250 acres was obtained from the NID. Filtering the NID by surface area, decreased the number of dams from 82,642 to 4,204. These 4,204 dams were the original reservoir locations in the combined dataset. Examination of the queried dataset found that in many instances more than one dam was listed as impounding a given reservoir. Reservoirs with multiple dams were identified, and all but the main stem dam were removed from the database, yielding a total of 3,828 dams and reservoirs. Run-of-river reservoir systems (such as the McClellan-Kerr Arkansas River Navigation System) were not affected by the removal of wing dikes or saddle dams. The metrics in the original databases and the metric definitions are found in Rodgers (2017).

Longitude and latitude coordinates from the NID were encoded in ArcGIS and displayed in a map document indicating the location of the dam. These dams were used as input for a process in ArcGIS known as clip, which extracts the data points representing the dams that fall within a given region and creates a new dataset of the extracted points.

The NHD dataset was added to the map document created in ArcGIS. To constrain the NHD dataset to reservoirs with a surface area greater than or equal to 250 acres, the area of each NHD polygon representing a reservoir was calculated. The original NHD shapefile was not conducive to the calculation of the reservoir surface area or perimeter because of its geographic coordinate system; therefore, the ArcGIS projections and transformations tool was used. This tool transforms the original geographic vector-based dataset into a projected vector-based dataset, which enabled the calculation of the surface area and perimeter of each reservoir. Reservoirs were ordered by size, and those with surface areas of 250 acres or greater were selected. The selected waterbodies were exported from ArcGIS as a new shapefile.

The NHD waterbodies, NID dams, and conterminous United States shapefiles were added to a new ArcGIS map. When displayed together, a dam coincided spatially with or was close to a waterbody. The association of a dam with a reservoir was accomplished by viewing the attributes table for each shapefile. When dams and reservoirs were matched, the NIDID and the NHD Common ID were selected using the ArcGIS identify tool. The unique IDs were placed in a spreadsheet that became the joining table for the two databases. Upon completion of the join, the new database was exported as a database file and imported into a spreadsheet where additional metrics not included in the original databases were calculated.

Calculation of the new metrics required values from multiple datasets; in many instances, the NID dataset contained reservoirs without a value for surface area. A method was devised to extract the surface area value from the NHD. Each dam was encoded with the longitude and latitude found in the NID and displayed with waterbodies from the NHD. The reservoirs were buffered by 1.5 miles, which extended the area around the entire perimeter of the reservoir so that nearby dams could be identified.

Using the buffered reservoir perimeter, dams from the NID without a surface area value were clipped from the dataset. The attribute table from the extracted dam was examined to determine if the names of the reservoir and dam matched. If names matched, a positive identification could be made. This process reduced the number of dams with no surface area values in the dataset. When dams with no surface area were matched, an additional joining table was created to connect the dams to the correct reservoirs from the NHD. This process was applied to all dams without surface area values in the conterminous United States.

A different method was used to associate dams with waterbodies in Wisconsin and Minnesota. Because of the large number of waterbodies in these States, it was necessary to preprocess the datasets. Preprocessing utilized the ArcGIS near tool. Instead of examining 7 to 8 waterbodies in close proximity to a dam, 2 to 3 waterbodies were examined, which greatly reduced the time needed to manually associate a dam with a waterbody.

The joining of the combined NID–NHD reservoir datasets with the ERF1_2 river reach dataset was accomplished using the ArcGIS intersect tool. The NID–NHD reservoir dataset and the ERF1_2 dataset were added to a map document. By intersecting the ERF1_2 dataset with the combined NID–NHD datasets, the attributes of the ERF1_2 were appended to the NID–NHD dataset.

The joined NID–NHD–ERF1_2 dataset was then connected to the SPARROW database by using the ERF1_2 river reach code, which is used by the SPARROW database as an object identifier. The newly created database containing the compiled data and calculated metrics can be accessed from Rodgers (2017).

Metrics Calculated

The following are metrics that were calculated using the combined databases.

Relative depth (Z_r) (Hutchinson, 1957; Wetzel and Likens, 1991) calculated as

       Z_r = 50 Z_MAX (∏/A₀)^1/2       (1)

where Z_MAX is the maximum reservoir depth (feet) derived from the hydraulic height of the dam, and A₀ is the surface area of the reservoir (acres).

Mean depth (Z_MEAN) is the volume of the lake divided by its surface area (Wetzel and Likens, 1991) and was calculated as

       Z_MEAN = V/A       (2)

where V is the volume of the lake (acre-feet) and A is the surface area (acres).

Development of volume (D_V) is the departure of the shape of a lake basin from that of a cone (Hutchinson, 1957) and was calculated as

       D_V = 3Z_MEAN /Z_MAX       (3)

where Z_MEAN is the mean depth (feet), and Z_MAX is the maximum depth (feet).

Shoreline development index (D_L) is a ratio of the shoreline length to the circumference of a circle with area equal to the lake (Hutchinson, 1957) and was calculated as

       D_L = SL/2(∏/A₀)^1/2       (4)

where SL is shoreline length (feet), and A₀ is surface area of the reservoir (square feet).

Index of basin permanence (IBP) is a reflection of the littoral effect on basin volume (Kerekes, 1977) and was calculated as

       IBP = V/SL       (5)

where V is lake volume in cubic meters, and SL is the shoreline length in kilometers.

Depth ratio is an indication of the potential for nutrient recycling from the lake sediments. As the depth ratio decreases, the productivity, nutrient recycling, and sediment accretion rates begin to increase (Carpenter, 1983). Depth ratio was calculated as

       Depth Ratio = Z_MEAN /Z_MAX       (6)

where Z_MEAN is mean depth (feet), and Z_MAX is maximum depth (feet).

The catchment area to surface area ratio (C) indicates the potential for overland contribution of nutrients and organic matter to a reservoir, which can lower water transparency. A large catchment area to surface area ratio indicates a higher potential for overland contribution (Noges, 2009).

       C = C_a /A       (7)

where C_a is the catchment area (acres), and A is reservoir surface (acres).

The surface area to lake volume ratio (γ) indicates the amount of water that may evaporate from a reservoir. Large surface areas to volume ratios indicate a greater potential for evaporation (McJannett and others, 2008) and was calculated as

       γ = A/V       (8)

where A is measured in square meters, and V is measured in cubic meters.

Summary

The creation of a comprehensive Reservoir Morphology Database by compiling data from the National Inventory of Dams, the National Hydrography Dataset, the Enhanced River Reach File, and the SPAtially Referenced Regressions on Watershed attributes is useful because additional morphological metrics not originally included in each of the databases can be calculated. The Reservoir Morphology Database contains 3,828 dams and reservoirs within the conterminous United States with surface area greater than or equal to 250 acres. Calculation of additional metrics in the new database may provide a better understanding of these dams and reservoirs and of the watersheds in which they are located.

Selected References

Carpenter, S. R., 1983, Lake geometry—Implications for production and sediment accretion rates: Journal of Theoretical Biology, v. 105, p. 273–286. [Also available at http://www.sciencedirect.com/science/article/pii/S0022519383800083.]

Esri, 2011, ArcGIS Desktop—Release 10: Redlands, California, Environmental Systems Research Institute.

Horn, C.R., Hanson, S.A., and McKay, L.D., 1994, History of the U.S. EPA's river reach file—A National Hydrographic database available for ARC/INFO applications: U.S. Environmental Protection Agency, 12 p. [Also available at http://www.horizon-systems.com/NHDPlusData/NHDPlusV21/Documentation/History/History_of_the_USEPA_River_Reach_Files.pdf.]

Hoyer, M.V., Brown, C.D., Canfield, D.E., Jr., 2004, Relations between water chemistry and water quality as defined by lake users in Florida: Lake and Reservoir, Management, v. 20, no. 3 p. 240–248.

Hutchinson, G.E. 1957, A treatise on limnology, volume 1, geography, physics and chemistry: New York, John Wiley and Sons, Inc., 1,015 p.

Jones, J.R., Knowlton, M.F., Obrecht, D.V., and Cook, E.A., 2004, Importance of landscape variables and morphology on nutrients in Missouri reservoirs: Canadian Journal of Fisheries Aquatic Sciences, v. 61, p. 1503–1512.

Kerekes, Joseph, 1977, The index of lake basin permanence: Internal Review of Hydrology, v. 62, no. 2, p. 291–293. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/iroh.1977.3510620207/epdf.]

McJannet, David, Cook, Freeman, and Burn, Steward, 2008, Evaporation reduction by manipulation of surface area to volume ratios—Overview, analysis and effectiveness: Urban Water Security Research Alliance, Technical Report No. 8, 28 p. [Also available at http://www.urbanwateralliance.org.au/publications/UWSRA-tr8.pdf.]

Nõges, Tina, 2009, Relationships between morphometry, geographic location and water quality parameters of European lakes: Hydrobiologia, v. 633, p. 33–43. [Also available at https://link.springer.com/article/10.1007/s10750-009-9874-x.]

Nolan, J.V., Brakebill, J.W., Alexander, R.B., and Schwarz, G.E., 2002, ERF1_2-enhanced river reach file 2.0: U.S. Geological Survey Open-File Report, 2002–40, accessed May 17, 2011, at http://water.usgs.gov/GIS/metadata/usgswrd/XML/erf1_2.xml.

Rodgers, K.D., 2017, A reservoir morphology database for the conterminous United States: U.S. Geological Survey data release at https://www.sciencebase.gov/catalog/item/587fb41ae4b085de6c11f38b.

Schwarz, G.E., Hoos, A.B., Alexander, R.B., Smith, R.A., 2006, The SPARROW surface water-quality model—theory, application and user documentation: U.S. Geological Survey Techniques and Methods book 6, chap. B3, 248 p. and CD–ROM. [Also available at https://pubs.er.usgs.gov/publication/tm6B3.]

Simley, J.D., Carswell, W.J., Jr., 2009, The National Map—Hydrography: U.S. Geological Survey Fact Sheet 2009–3054, 4 p., accessed August 25, 2010, at https://pubs.er.usgs.gov/publication/fs20093054.

U.S. Army Corps of Engineers, 1998, National inventory of dams, accessed June 10, 2010, and October 10, 2015, at http://nid.usace.army.mil/cm_apex/f?p=838:12.

U.S. Geological Survey, 2006, National Water—Quality Assessment Program—Modifications to the status and trends network and assessments of streams and rivers: U.S. Geological Survey Major River Basin Factsheet, accessed June 21, 2010 at http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.184.8359&rep=rep1&type=pdf.

U.S. Geological Survey, 2017, National Hydrography Dataset, accessed August 25, 2010, at https://nhd.usgs.gov/NHD_High_Resolution.html.

Weber, M.J., Brown, M.L., Willis, D.W., 2010, Spatial variability of common carp populations in relation to lake morphology and physicochemical parameter in the upper Midwest United States: Ecology of freshwater fish, v. 19, issue 4, p. 555–565.

Wetzel, R.G., and Likens, G.E., 1991. Limnological analyses (2d ed.): New York, Springer-Verlag, 391 p.