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

Abstract

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.

Integrated, Regional‐Scale Hydrologic Modeling of Inland Lakes

“…; Read et al. ). Estimating the connectedness of lakes to their landscape for a region can help researchers and managers know which lakes are most vulnerable to changes in landscape characteristics such as conversion to agriculture.…”

Networked lake science: how the Global Lake Ecological Observatory Network (GLEON) works to understand, predict, and communicate lake ecosystem response to global change

“…An analysis of nearly 6 million lakes within the contiguous United States has quantified the length of shoreline of inland lakes at ~1.8 million km (~50 times the perimeter of the Laurentian Great Lakes; Winslow et al 2014) and informed conditions in which smaller lakes might contribute more to continental scale processing than larger lakes, such as carbon mass accumulation rates in sediments (Winslow et al 2015). Further, GLEON students, as part of an NSF-funded GLEON Fellowship training program, have analyzed and synthesized data from the US Environmental Protection Agency's National Lake Assessment to understand controls over lake water quality at the continental scale and found that lake-specific characteristics, such as depth, sediment, and area:volume ratio explained much of the variance (54-60%), whereas regional factors were much less important (28-39% of variance explained; Read et al 2015). Finally, in the time domain, scientists in Florida linked water transparency to 30-year oscillations in the Atlantic Multidecadal Oscillation (Gaiser et al 2009), and scientists who developed their collaboration through GLEON characterized long-term variability in phytoplankton seasonal succession in a north temperate lake (Carey et al 2016).…”

Improving Predictive Models of In‐Stream Phosphorus Concentration Based on Nationally‐Available Spatial Data Coverages

“…While readily available spatial data coverages empower users across all levels of research and governance, they must be used with some caution in specific applications. In studies of lakes throughout the U.S., for example, lake‐specific variables are known to produce significantly improved predictive models of water quality and trophic state than models based on nationally‐available spatial covariates alone (Read et al ., ; Hollister et al ., ). Thus, while national databases enable prediction of response variables in lakes lacking in situ data, these predictions can be greatly improved with additional data not currently available nationally.…”