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ABSTRACT The water quality of drinking water reservoirs is critical for human and ecosystem health. In this study, we examined the drivers of three metals, aluminum (Al), barium (Ba), and copper (Cu), across two drinking water reservoirs in southwestern Virginia, USA, over 4 years. One reservoir has a hypolimnetic oxygenation system; the other does not. We used time series modeling and multivariate analysis of water column chemistry, suspended sediment, inflow, and precipitation data to assess the relative roles of hydrologic and geochemical drivers of metal behaviors in the two reservoirs. Results suggest that Al concentrations were primarily influenced by high-flow events, consistent with the mobilization of clays from physical weathering. In contrast, Ba showed stronger sensitivity to geochemical drivers, specifically redox conditions. Drivers of Cu behavior were obscured by low Cu concentrations. For all metals, patterns varied among years. Our findings highlight the importance of long-term monitoring and integrated approaches to evaluate the drivers of metal dynamics in reservoir ecosystems and inform strategies for maintaining safe drinking water supplies. 

ABSTRACT Ecosystem states are often influenced by both concurrent and antecedent environmental drivers. However, the relative importance of antecedent conditions varies within and among ecosystems. Here, we analysed long‐term depth‐profile data from 382 temperate lakes across 10 countries to assess how differential changes in spring versus summer air temperature mediate summer water quality. We found that summer bottom‐water conditions were more associated with spring air temperatures, while surface‐water conditions were more associated with summer air temperatures. The relative influence of spring versus summer air temperature was mediated by lake morphometry, stratification and latitude. Across these lakes, summer air temperatures have increased more rapidly than spring air temperatures, potentially contributing to a growing thermal difference between surface and bottom waters (median = +0.5°C/decade). Consequently, our results demonstrate that predicting the ecological impacts of climate change may require considering spatial differences in ecological memory within ecosystems. 

Abstract As ecology becomes a more predictive discipline, identifying the intrinsic predictability, or stochasticity, of ecosystem variables across space and time is needed to help guide the development of ecological models and forecasts. For example, if an ecological time series has high intrinsic predictability, then a high‐performing model should presumably be able to replicate its dynamics. Conversely, if an ecological variable has low intrinsic predictability, then no model—regardless of its performance—will be able to replicate its dynamics. However, despite the proliferation of ecological models and forecasts, the intrinsic predictability of ecological variables remains largely unknown. To bridge this gap, we analyzed a >4‐year time series of high‐frequency sensor data collected from replicate freshwater ecosystems to determine how intrinsic predictability (quantified as permutation entropy) differs among ecological variables, seasons, and ecosystems. We observed greater differences in predictability among ecological variables and days of year than between ecosystems. Although intrinsic predictability was generally low for all variables, it was still significantly higher than white noise, indicating complex yet predictable dynamics. We observed the highest predictability for physical ecosystem variables (e.g., water temperature) and the lowest predictability for biological variables (e.g., phytoplankton biomass), with chemical variables (e.g., dissolved oxygen) intermediate. We observed substantial seasonal differences in predictability among variables: surface water temperature and dissolved organic matter exhibited their highest levels of predictability in autumn, whereas surface chlorophyll and bottom‐layer dissolved oxygen and temperature exhibited highest predictability in summer. Periods of anoxia (low oxygen) were associated with the highest levels of predictability in dissolved oxygen over the time series. Altogether, our analysis highlights how intrinsic predictability data can both guide ecological model development and improve our understanding of how ecological predictability varies across space and time. 

Abstract Seasonality in environmental conditions plays a fundamental role in shaping lake ecosystems. However, patterns of seasonality vary worldwide, and these patterns are shifting over time amid global change. Thus, it is increasingly important to evaluate how seasons and seasonality are represented in lake ecosystem research. Here, we used a literature review and global data analysis to synthesize approaches for conceptualizing seasons and seasonality in lakes. We found that a wide range of criteria are used to delineate discrete seasons in published literature, including fixed dates (e.g., months, solstice/equinox), environmental thresholds (e.g., temperature and precipitation cutoffs), and lake‐specific indicators (e.g., ice cover, plankton phenology). Analyzing data from lakes worldwide, we found that using different criteria to define the same season resulted in divergent interpretations of ecosystem states. Based on our synthesis, we offer recommendations for how to incorporate seasonality into lake research and communications amid global change. 

Abstract While there is a diversity of approaches for modeling phytoplankton blooms, their accuracy in predicting the onset and manifestation of a bloom is still lagging behind what is needed to support effective management. We outline a framework that integrates trait theory and ecosystem modeling to improve bloom prediction. This framework builds on the concept that the phenology of blooms is determined by the dynamic interaction between the environment and traits within the phytoplankton community. Phytoplankton groups exhibit a collection of traits that govern the interplay of processes that ultimately control the phases of bloom initiation, maintenance, and collapse. An example of process‐trait mapping is used to demonstrate a more consistent approach to bloom model parameterization that allows better alignment with models and laboratory‐ and ecosystem‐scale datasets. Further approaches linking statistical‐mechanistic models to trait parameter databases are discussed as a way to help optimize models to better simulate bloom phenology and allow them to support a wider range of management needs. 

Abstract Phytoplankton blooms create harmful toxins, scums, and taste and odor compounds and thus pose a major risk to drinking water safety. Climate and land use change are increasing the frequency and severity of blooms, motivating the development of new approaches for preemptive, rather than reactive, water management. While several real-time phytoplankton forecasts have been developed to date, none are both automated and quantify uncertainty in their predictions, which is critical for manager use. In response to this need, we outline a framework for developing the first automated, real-time lake phytoplankton forecasting system that quantifies uncertainty, thereby enabling managers to adapt operations and mitigate blooms. Implementation of this system calls for new, integrated ecosystem and statistical models; automated cyberinfrastructure; effective decision support tools; and training for forecasters and decision makers. We provide a research agenda for the creation of this system, as well as recommendations for developing real-time phytoplankton forecasts to support management. 

Abstract Near‐term ecological forecasting can be used to improve operational resource management in freshwater ecosystems. Here, we developed a framework that uses water temperature forecasting as a tool to predict the migrations of Atlantic salmon (Salmo salar) and European eel (Anguilla anguilla) between freshwater and the sea. We used historical observations of lake water temperature and fish migrations from an internationally important long‐term monitoring site (the Burrishoole catchment, Ireland) to generate daily probabilistic predictions (0%–100%) of when relatively large numbers of fish migrate. For this, we produced daily lake water temperature forecasts that extended up to 34 days into the future using Forecasting Lake and Reservoir Ecosystems (FLARE), an open‐source ensemble‐based forecasting system. We used this system to forecast lake water temperature conditions associated with percentile‐based fish migrations. Two metrics, P66 and P95, were used to indicate days with migrations in excess of 66% and 95%, respectively, of the historical daily fish counts. The results were first validated against water temperature observations, with an overall root mean squared error (RMSE) of 0.97°C. Our forecasts outperformed two other possible water temperature forecasting approaches, using site climatology (1.36°C) and site persistence (1.19°C). The predictions for fish migrations performed better for the P66 metric than for the more extreme P95 metric based on the continuous ranked probability score (CRPS), and the best results were obtained for the salmon downstream migration. This forecasting approach with quantified uncertainty levels has the potential to assist decision making, especially in the face of increased risks for these species. We conclude by discussing the scalability of the framework to other settings as a tool aimed at supporting management practices in real time. 

Abstract Despite the growing use of Aquatic Ecosystem Models for lake modeling, there is currently no widely applicable framework for their configuration, calibration, and evaluation. Calibration is generally based on direct data comparison of observed versus modeled state variables using standard statistical techniques, however, this approach may not give a complete picture of the model's ability to capture system‐scale behavior that is not easily perceivable in observations, but which may be important for resource management. The aim of this study is to compare the performance of “naïve” calibration and a “system‐inspired” calibration, an approach that augments the standard state‐based calibration with a range of system‐inspired metrics (e.g., thermocline depth, metalimnetic oxygen minima), to increase the coherence between the simulated and natural ecosystems. A coupled physical‐biogeochemical model was applied to a focal site to simulate two key state‐variables: water temperature and dissolved oxygen. The model was calibrated according to the new system‐inspired modeling convention, using formal calibration techniques. There was an improvement in the simulation using parameters optimized on the additional metrics, which helped to reduce uncertainty predicting aspects of the system relevant to reservoir management, such as the occurrence of the metalimnetic oxygen minima. Extending the use of system‐inspired metrics when calibrating models has the potential to improve model fidelity for capturing more complex ecosystem dynamics. 

Abstract Temperate reservoirs and lakes worldwide are experiencing decreases in ice cover, which will likely alter the net balance of gross primary production (GPP) and respiration (R) in these ecosystems. However, most metabolism studies to date have focused on summer dynamics, thereby excluding winter dynamics from annual metabolism budgets. To address this gap, we analyzed 6 years of year‐round high‐frequency dissolved oxygen data to estimate daily rates of net ecosystem production (NEP), GPP, and R in a eutrophic, dimictic reservoir that has intermittent ice cover. Over 6 years, the reservoir exhibited slight heterotrophy during both summer and winter. We found winter and summer metabolism rates to be similar: summer NEP had a median rate of −0.06 mg O₂L⁻¹ day⁻¹(range: −15.86 to 3.20 mg O₂L⁻¹ day⁻¹), while median winter NEP was −0.02 mg O₂L⁻¹ day⁻¹(range: −8.19 to 0.53 mg O₂L⁻¹ day⁻¹). Despite large differences in the duration of ice cover among years, there were minimal differences in NEP among winters. Overall, the inclusion of winter data had a limited effect on annual metabolism estimates in a eutrophic reservoir, likely due to short winter periods in this reservoir (ice durations 0–35 days), relative to higher‐latitude lakes. Our work reveals a smaller difference between winter and summer NEP than in lakes with continuous ice cover. Ultimately, our work underscores the importance of studying full‐year metabolism dynamics in a range of aquatic ecosystems to help anticipate the effects of declining ice cover across lakes worldwide. 

Abstract: Discrete depth profiles of water temperature, dissolved oxygen, oxidation-reduction potential, conductivity, specific conductance, and pH were collected with multiple handheld water quality probes and discrete depth profiles of photosynthetically active radiation (PAR) were collected with a LI-COR underwater light meter from 2013 to 2025 in five drinking water reservoirs in southwestern Virginia, USA. These reservoirs are: Beaverdam Reservoir (Vinton, Virginia), Carvins Cove Reservoir (Roanoke, Virginia), Falling Creek Reservoir (Vinton, Virginia), Gatewood Reservoir (Pulaski, Virginia), and Spring Hollow Reservoir (Salem, Virginia). Beaverdam, Carvins Cove, Falling Creek, and Spring Hollow Reservoirs are owned and operated by the Western Virginia Water Authority as primary or secondary drinking water sources for Roanoke, Virginia, and Gatewood Reservoir is a drinking water source for the Town of Pulaski, Virginia. All discrete depth profiles were collected on approximately 1-meter intervals. The data package consists of two datasets: 1) Secchi depth data; and 2) discrete depth profiles of multiple water quality variables measured by handheld sensors. The Secchi data and discrete depth profiles were measured at the deepest site of each reservoir adjacent to the dam, as well as other in-reservoir sites. Handheld sensor measurements were also collected at a gauged weir on the primary inflow tributary, other inflows, and outflows at Falling Creek Reservoir; inflows and outflows at Beaverdam Reservoir; and inflows at Carvins Cove Reservoir. In 2021, YSI handheld data were also collected from a littoral site in Beaverdam Reservoir. In 2025, YSI handheld data were collected monthly from June to October from nine littoral sites around the perimeter of Falling Creek Reservoir. From 2024 - 2025, additional within-reservoir depth profiles were collected in Carvins Cove Reservoir and multiple sites. Data were collected approximately fortnightly in the spring months (March - May), weekly in the summer and early autumn (June - September), and monthly in the late autumn and winter (October - February) in Falling Creek and Beaverdam Reservoirs; data coverage in the other three reservoirs varies among years. Note there were some YSI depth profiles and Secchi observations that were measured at night during overnight sampling. All of these observations have the correct date time associated with them. Maintenance log and quality assurance/quality control analysis scripts accompany this data package.