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Abstract Whole lake residence time has been associated with various water quality parameters, including harmful algal blooms. Despite observations of spatial variability in commonly measured lake water quality parameters, little attention is given to the spatial variability of residence time in lakes. In this paper we use water age as a surrogate for residence time and we examine its spatial and temporal distribution in 10 bays of varying size in Lake George, New York (USA). Using a validated hydrodynamic model against observations of water temperature and water currents, and using simulated water age, we show that the average residence time in most of the bays is less than 3 days. Timeseries of bay‐average water age shows that it can sharply decrease within 1 day due to a strong wind event. The average spatial distribution is shown to be non‐uniform, with only a small section of the bottom layer of the bays having a substantially greater age, which may be more than 1 week in certain bays. Snapshots of water age transects indicate that strong wind events substantially change the vertical distribution of water age in some bays, even to the extent of inverting the distribution. The substantial decreases of water age in the bays were associated with the shallowing and deepening of the thermocline. Our results highlight how variations in water residence times within lakes could introduce substantial variation in water quality attributes. Whole lake residence times may serve as a poor proxy to understand the dynamics of water masses, especially in large and morphologically complex waterbodies. 

Abstract Atmospheric warming heats lakes, but the causes of variation among basins are poorly understood. Here, multi-decadal profiles of water temperatures, trophic state, and local climate from 345 temperate lakes are combined with data on lake geomorphology and watershed characteristics to identify controls of the relative rates of temperature change in water (WT) and air (AT) during summer. We show that differences in local climate (AT, wind speed, humidity, irradiance), land cover (forest, urban, agriculture), geomorphology (elevation, area/depth ratio), and water transparency explain >30% of the difference in rate of lake heating compared to that of the atmosphere. Importantly, the rate of lake heating slows as air warms (P < 0.001). Clear, cold, and deep lakes, especially at high elevation and in undisturbed catchments, are particularly responsive to changes in atmospheric temperature. We suggest that rates of surface water warming may decline relative to the atmosphere in a warmer future, particularly in sites already experiencing terrestrial development or eutrophication. 

Abstract Identifying the scaling rules describing ecological patterns across time and space is a central challenge in ecology. Taylor's law of fluctuation scaling, which states that the variance of a population's size or density is proportional to a positive power of the mean size or density, has been widely observed in population dynamics and characterizes variability in multiple scientific domains. However, it is unclear if this phenomenon accurately describes ecological patterns across many orders of magnitude in time, and therefore links otherwise disparate observations. Here, we use water clarity observations from 10,531 days of high‐frequency measurements in 35 globally distributed lakes, and lower‐frequency measurements over multiple decades from 6342 lakes to test this unknown. We focus on water clarity as an integrative ecological characteristic that responds to both biotic and abiotic drivers. We provide the first documentation that variations in ecological measurements across diverse sites and temporal scales exhibit variance patterns consistent with Taylor's law, and that model coefficients increase in a predictable yet non‐linear manner with decreasing observation frequency. This discovery effectively links high‐frequency sensor network observations with long‐term historical monitoring records, thereby affording new opportunities to understand and predict ecological dynamics on time scales from days to decades. 

Cold-water species in temperate lakes face two simultaneous climate-driven ecosystem changes: warming and browning of their waters. Browning refers to reduced transparency arising from increased dissolved organic carbon (DOC), which absorbs solar energy near the surface. It is unclear whether the net effect is mitigation or amplification of climate warming impacts on suitable oxythermal habitat (<20 °C, >5 mgO/L) for cold-loving species because browning expands the vertical distribution of both cool water and oxygen depletion. We analyzed long-term trends and high-frequency sensor data from browning lakes in New York’s Adirondack region to assess the contemporary status of summertime habitat for lacustrine brook trout. Across two decades, surface temperatures increased twice as fast and bottom dissolved oxygen declined >180% faster than average trends for temperate lakes. We identify four lake categories based on oxythermal habitat metrics: constrained, squeezed, overheated, and buffered. In most of our study lakes, trout face either seasonal loss (7 of 15) or dramatic restriction (12 to 21% of the water column; 5 of 15) of suitable habitat. These sobering statistics reflect rapid upward expansion of oxygen depletion in lakes with moderate or high DOC relative to compression of heat penetration. Only in very clear lakes has browning potentially mitigated climate warming. Applying our findings to extensive survey data suggests that decades of browning have reduced oxythermal refugia in most Adirondack lakes. We conclude that joint warming and browning may preclude self-sustaining cold-water fisheries in many temperate lakes; hence, oxythermal categorization is essential to guide triage strategies and management interventions. 

Abstract Depth regulates many attributes of aquatic ecosystems, but relatively few lakes are measured, and existing datasets are biased toward large lakes. To address this, we used a large dataset of maximum (Z_max;n = 16,831) and mean (Z_mean;n = 5,881) depth observations to create new depth models, focusing on lakes < 1,000 ha. We then used the models to characterize patterns in lake basin shape and volume. We included terrain metrics, water temperature and reflectance, polygon attributes, and other predictors in a random forest model. Our final models generally outperformed existing models (Z_max; root mean square error [RMSE] = 8.0 m andZ_mean; RMSE = 3.0 m). Our models show that lake depth followed a Pareto distribution, with 2.8 orders of magnitude fewer lakes for an order of magnitude increase in depth. In addition, despite orders of magnitude variation in surface area, most size classes had a modal maximum depth of ~ 5 m. Concave (bowl‐shaped) lake basins represented 79% of all lakes, but lakes were more convex (funnel‐shaped) as surface area increased. Across the conterminous United States, 9.8% of all lake water was within the top meter of the water column, and 48% in the top 10 m. Excluding the Laurentian Great Lakes, we estimate the total volume in the conterminous United States is 1,057–1,294 km³, depending on whetherZ_maxorZ_meanwas modeled. Lake volume also exhibited substantial geographic variation, with high volumes in the upper Midwest, Northeast, and Florida and low volumes in the southwestern United States. 

Understanding controls on primary productivity is essential for describing ecosystems and their responses to environmental change. Lake primary production is strongly controlled by inputs of nutrients and colored dissolved organic matter. While past studies have developed mathematical models of this nutrient-color paradigm, broad empirical tests of these models are scarce. We compiled data from 58 diverse and globally distributed and mostly temperate lakes to test such a model and improve understanding and prediction of the controls on lake primary production. These lakes varied widely in size (0.02-2300 km2), pelagic gross primary production (20-8000 mg C m-2 d-1), and other characteristics. The data package includes high-frequency dissolved oxygen, water temperature, wind speed, and solar radiation data as well as daily estimates of GPP and ER derived from those data. In addition, the data package includes median in-lake and stream concentrations of dissolved organic carbon and total phosphorus for a subset of 18 of those lakes.