Lake water quality is affected by local and regional drivers, including lakephysical characteristics, hydrology, landscape position, land cover, land use, geology, andclimate. Here, we demonstrate the utility of hypothesis testing within the landscape limnologyframework using a random forest algorithm on a national-scale, spatially explicit data set, theUnited States Environmental Protection Agency’s 2007 National Lakes Assessment. For 1026lakes, we tested the relative importance of water quality drivers across spatial scales, theimportance of hydrologic connectivity in mediating water quality drivers, and how theimportance of both spatial scale and connectivity differ across response variables for fiveimportant in-lake water quality metrics (total phosphorus, total nitrogen, dissolved organiccarbon, turbidity, and conductivity). By modeling the effect of water quality predictors atdifferent spatial scales, we found that lake-specific characteristics (e.g., depth, sediment area-to-volume ratio) were important for explaining water quality (54–60%variance explained), andthat regionalization schemes were much less effective than lake specific metrics (28–39%varianceexplained). Basin-scale land use and land cover explained between 45–62%of variance, andforest 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 (thepresence of upstream surface water features) mediated the effect of regional-scale drivers. Forexample, for water quality in lakes with upstream lakes, regional classification schemes weremuch less effective predictors than lake-specific variables, in contrast to lakes with no upstreamlakes or with no surface inflows. At the scale of the continental United States, conductivity wasexplained by drivers operating at larger spatial scales than for other water quality responses. Thecurrent regulatory practice of using regionalization schemes to guide water quality criteria couldbe improved by consideration of lake-specific characteristics, which were the most importantpredictors of water quality at the scale of the continental United States. The spatial extent andhigh quality of contextual data available for this analysis makes this work an unprecedentedapplication of landscape limnology theory to water quality data. Further, the demonstratedimportance of lake morphology over other controls on water quality is relevant to both aquaticscientists and managers