The importance of lake-specific characteristics for water quality across the continental United States

Authors

Emily K. Read, Cary Institute of Ecosystem StudiesFollow
Vijay P. Patil, University of Alaska, Fairbanks
Samantha K. Oliver, University of Wisconsin
Amy L. Hetherington, Cornell University
Jennifer A. Brentrup, Miami University
Jacob A. Zwart, University of Notre Dame
Kirsten M. Winters, Oregon State University
Jessica R. Corman, University of Nebraska - LincolnFollow
Emily R. Nodine, Florida International University
R. Iestyn Woolway, Lancaster Environment Center
Hilary A. Dugan, University of Wisconsin
Aline Jaimes, University of Delaware
Arianto B. Santoso, University of Waikato
Grace S. Hong, University of Wisconsin
Luke A. Winslow, University of Wisconsin
Paul C. Hanson, University of Wisconsin
Kathleen C. Weathers, Cary Institute of Ecosystem Studies

Document Type

Article

Date of this Version

2015

Citation

Ecological Applications, 25(4), 2015, pp. 943–955

Comments

Copyright 2015 by the Ecological Society of America

Abstract

Lake water quality is affected by local and regional drivers, including lake physical characteristics, hydrology, landscape position, land cover, land use, geology, and climate. Here, we demonstrate the utility of hypothesis testing within the landscape limnology framework using a random forest algorithm on a national-scale, spatially explicit data set, the United States Environmental Protection Agency’s 2007 National Lakes Assessment. For 1026 lakes, we tested the relative importance of water quality drivers across spatial scales, the importance of hydrologic connectivity in mediating water quality drivers, and how the importance of both spatial scale and connectivity differ across response variables for five important in-lake water quality metrics (total phosphorus, total nitrogen, dissolved organic carbon, turbidity, and conductivity). By modeling the effect of water quality predictors at different spatial scales, we found that lake-specific characteristics (e.g., depth, sediment area-tovolume ratio) were important for explaining water quality (54–60% variance explained), and that regionalization schemes were much less effective than lake specific metrics (28–39% variance explained). Basin-scale land use and land cover explained between 45–62% of variance, and forest cover and agricultural land uses were among the most important basin-scale predictors. Water quality drivers did not operate independently; in some cases, hydrologic connectivity (the presence of upstream surface water features) mediated the effect of regional-scale drivers. For example, for water quality in lakes with upstream lakes, regional classification schemes were much less effective predictors than lake-specific variables, in contrast to lakes with no upstream lakes or with no surface inflows. At the scale of the continental United States, conductivity was explained by drivers operating at larger spatial scales than for other water quality responses. The current regulatory practice of using regionalization schemes to guide water quality criteria could be improved by consideration of lake-specific characteristics, which were the most important predictors of water quality at the scale of the continental United States. The spatial extent and high quality of contextual data available for this analysis makes this work an unprecedented application of landscape limnology theory to water quality data. Further, the demonstrated importance of lake morphology over other controls on water quality is relevant to both aquatic scientists and managers.