Lake Shoreline in the Contiguous United States
There are millions of lakes, ponds and reservoirs in the United States. Existing datasets are large and unweildy. With this data product, derived from the US NHD (downloaded Jan 2013), we summarize at a high level the distribution of lakes across the US.
The data set examined was the USGS National Hydrography Dataset (retrieved January 2012, http://nhd.usgs.gov). This combines multiple surveys through time, using the resulting high-resolution USGS topographical maps to delineate aquatic boundaries (Simley and Carswell, 2009). While a variety of resolutions are available, only 1 : 24 000 was used in this analysis. The data set covers the area of all 48 U.S. contiguous states, including Washington D.C. Because they are not completely contained within the U.S., we chose to exclude the Laurentian Great Lakes for this analysis.We extracted all lake, reservoir and pond polygons from the GIS data set. Because the data set does not distinguish between artificial or natural lakes and ponds, no distinction was made for this analysis, and all waterbodies hereafter are collectively referred to as lakes. The elevation component of these polygons was removed using ArcGIS (ESRI ArcGIS v10.1; ESRI, Redlands, CA, U.S.A). For simplicity, all island data were excluded from the results presented here, although we discuss the small resulting bias. Duplicate polygons were identified and removed using the permanent identifier field included with the data. All calculations were completed using the MathWorks Mapping Toolbox (v2010b; MathWorks, Natick, MA, U.S.A), which adds geographical information functionality to MATLAB. A single-point location for each lake was defined as the centroid of the lake boundary polygon.To examine perimeter while considering the issue of a fractal lake perimeter, we estimated perimeter using two complexity-insensitive and repeatable techniques. First, a theoretical minimum perimeter (Pmin) was established by calculating the perimeter of a circle with the same area as each lake using: Pmin = 2(pi(A))^(1/2) where A is the total area of the individual lake (Kalff, 2001). This represents the absolute minimum perimeter, on a flat plane, required to encompass a given area (spherical coordinates could reduce Pmin further, though for the size scale of lakes examined here, the difference is negligible). Second, a resolution-specific perimeter was calculated using a simple yardstick method based on Mandelbrot (1979). With this method, a fixed-length line segment was progressively "walked" along the polygon until reaching the start point. This simulated a perimeter estimate at specific and adjustable mapping resolutions. The yardstick length was varied across a range of values (25–1600 m) to examine the sensitivity of the perimeter estimate to measurement resolution. To compare the sensitivity of perimeter estimate with previous studies, the slope of the relationship between logperimeter and log-yardstick length was calculated using least-squares regression, based on Kent and Wong (1982). Lastly, we calculated the maximum-observed perimeter of each polygon (Pobs) using the full-resolution data and summing the lengths of each polygon segment. Shoreline development factor (SDF) was calculated using the equation: SDF = P / (2((A)pi)^1/2) where A is area and P is perimeter. The perimeter measurement technique used in SDF calculations (Pobs or the yardstick method) is indicated where discussed. Calculations of perimeters, area and centroid were fast for any single polygon, but because of the high number of lakes and high polygon resolution, calculations became computationally intensive. This and other geostatistical calculations were accelerated using a computer cluster. HTCondor software (Thain, Tannenbaum and Livny, 2005) was used to distribute this task.The extents of stream and river shorelines were calculated from the National Hydrography Dataset using the same technique as the fractal-naive perimeter (Pobs). Only features classified with a type of stream/river (USGS Feature no. 460) were included. This feature type includes intermittent, ephemeral and perennial streams and rivers, but excludes features such as underground streams and canals. The shoreline length of small streams, represented in the data set by polyline objects, was estimated as the length of the lines doubled to account for both sides of the stream. The shoreline of larger rivers, which are stored in the data set as polygons, was calculated directly as the observed perimeter of the polygons (Pobs) on the WGS84 datum.Maps of lake abundance (number km^-2), area (per cent cover) and shoreline density (m km^-2) were created by dividing the area of the U.S. into equal-area cells (cell size: 50 km^2). Each lake’s attributes were assigned to a cell based on its unique single-point location, and statistics were calculated for lake density and per cent cover in each cell. Where a lake’s area exceeded that of a single cell, the full lake shape was split into overlapping cells based on the amount of overlapping area. Cumulative distributions of lake number, area and perimeter as a function of lake area were used to evaluate general attributes of the entire U.S. lake population.To aid future work in this area, we have released useful derived data sets on the Web. While the data are freely available from the USGS, the National Hydrography Dataset’s large size makes analyses challenging. To encourage additional research in this area, we have released the extracted perimeter data set. It is available at the data repository hosted by the North Temperate Lakes Long-Term Ecological Research website at http://lter.limnology.wisc.edu
<p>This data describes the distribution of lake surface area across the contiguous United States as total m^2 of lake surface area per cell. Winslow, L. A., J. S. Read, P. C. Hanson, and E. H. Stanley. 2013. Lake shoreline in the contiguous United States: quantity, distribution and sensitivity to observation resolution. Freshwater Biology. DOI:10.1111/fwb.12258</p>