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Predicting Maximum Lake Depth from Surrounding Topography
Citation:
HOLLISTER, J. W., W. B. MILSTEAD, AND M. Urrutia. Predicting Maximum Lake Depth from Surrounding Topography. PLOS ONE . Public Library of Science, San Francisco, CA, 6(9):e25764, (2011).
Impact/Purpose:
A component of the US EPA’s Ecosystem Services Research Program is to understand aquatic services provided in Northeastern lakes and ponds. Much of the research centered on these services relies upon modeled estimates of nutrients and other stressors. Lake volume and lake depth are key components of many of these models; however this information is often unavailable for large numbers of lakes. The research described in this manuscript addresses this limitation. We expect our research to have the following three impacts. First, this method allows us to predict maximum lake depth and lake volume for any lake included in the National Hydrography dataset. This allows us, or others using these methods, to expand the scope of our research and model ecosystem services related to Nutrients to all lakes and not just lakes for which field data exist. Second, the estimates resulting for this work will impact other federal modeling efforts and USGS has expressed interest in including the lake volume and lake depth estimates that result from this work in future calibrations of the SPARROW model. Lastly, although not in and of itself reproducible research (RR), this manuscript follows the major tenets of reproducible research as the R-script, detailed results and data to test the script are all included as online supplementary material. Following the tenets of RR, provide a great deal of transparency to our work that can validate out overall approach and lend greater accountability to our work.
Description:
Lake volume aids understanding of the physical and ecological dynamics of lakes, yet is often not readily available. The data needed to calculate lake volume (i.e. bathymetry) are usually only collected on a lake by lake basis and are difficult to obtain across broad regions. To span the gap between studies of individual lakes where detailed data exist and regional studies where access to useful data on lake volume is unavailable, we developed a method to predict maximum lake depth from the slope of the topography surrounding a lake and used these predictions to estimate lake volume. We use the National Elevation Dataset and the National Hydrography Dataset – Plus to estimate the percent slope of surrounding lakes and use this information to predict maximum lake depth and estimate lake volume. We also use field measured maximum lake depths from the US EPA’s National Lakes Assessment to empirically correct our predictions. We test our final predictions with maximum lake depth data from on-line sources. We were able to predict maximum depth for ~28,000 lakes in the Northeastern United States with an average percent difference of 7.1%. In a companion study, we also found that including lake volume estimates based on these depth predictions increases the explanatory power of nitrogen and phosphorus concentration predictions by 8.5% and 19%, respectively. The depth predictions, volume predictions, and the scripts are openly available as supplements to this manuscript.