Near-term, iterative ecological forecasts with quantified uncertainty have great potential for improving lake and reservoir management. For example, if managers received a forecast indicating a high likelihood of impending impairment, they could make decisions today to prevent or mitigate poor water quality in the future. Increasing the number of automated, realtime freshwater forecasts used for management requires integrating interdisciplinary expertise to develop a framework that seamlessly links data, models, and cyberinfrastructure, as well as collaborations with managers to ensure that forecasts are embedded into decision-making workflows. The goal of this study is to advance the implementation of near-term, iterative ecological forecasts for freshwater management. We first provide an overview of FLARE (Forecasting Lake And Reservoir Ecosystems), a forecasting framework we developed and applied to a drinking water reservoir to assist water quality management, as a potential opensource option for interested users. We used FLARE to develop scenario forecasts simulating different water quality interventions to inform manager decision-making. Second, we share lessons learned from our experience developing and running FLARE over 2 years to inform other forecasting projects. We specifically focus on how to develop, implement, and maintain a forecasting system used for active management. Our goal is to break down the barriers to forecasting for freshwater researchers, with the aim of improving lake and reservoir management globally.

Near‐term iterative forecasting is a powerful tool for ecological decision support and has the potential to transform our understanding of ecological predictability. However, to this point, there has been no cross‐ecosystem analysis of near‐term ecological forecasts, making it difficult to synthesize diverse research efforts and prioritize future developments for this emerging field. In this study, we analyzed 178 near‐term (≤10‐yr forecast horizon) ecological forecasting papers to understand the development and current state of near‐term ecological forecasting literature and to compare forecast accuracy across scales and variables. Our results indicated that near‐term ecological forecasting is widespread and growing: forecasts have been produced for sites on all seven continents and the rate of forecast publication is increasing over time. As forecast production has accelerated, some best practices have been proposed and application of these best practices is increasing. In particular, data publication, forecast archiving, and workflow automation have all increased significantly over time. However, adoption of proposed best practices remains low overall: for example, despite the fact that uncertainty is often cited as an essential component of an ecological forecast, only 45% of papers included uncertainty in their forecast outputs. As the use of these proposed best practices increases, near‐term ecological forecasting has the potential to make significant contributions to our understanding of forecastability across scales and variables. In this study, we found that forecastability (defined here as realized forecast accuracy) decreased in predictable patterns over 1–7 d forecast horizons. Variables that were closely related (i.e., chlorophyll and phytoplankton) displayed very similar trends in forecastability, while more distantly related variables (i.e., pollen and evapotranspiration) exhibited significantly different patterns. Increasing use of proposed best practices in ecological forecasting will allow us to examine the forecastability of additional variables and timescales in the future, providing a robust analysis of the fundamental predictability of ecological variables.

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The US National Ecological Observatory Network's (NEON's) standardized monitoring program provides an unprecedented opportunity for comparing the predictability of ecosystems. To harness the power of NEON data for examining environmental predictability, we scaled a near-term, iterative, water temperature forecasting system to all six NEON lakes in the conterminous US. We generated 1-day-ahead to 35-days-ahead forecasts using a process-based hydrodynamic model that was updated with observations as they became available. Among lakes, forecasts were more accurate than a null model up to 35-days-ahead, with an aggregated 1-day-ahead root-mean square error (RMSE) of 0.61°C and a 35-days-ahead RMSE of 2.17°C. Water temperature forecast accuracy was positively associated with lake depth and water clarity, and negatively associated with fetch and catchment size. The results of our analysis suggest that lake characteristics interact with weather to control the predictability of thermal structure. Our work provides some of the first probabilistic forecasts of NEON sites and a framework for examining continental-scale predictability.

Organic carbon (OC) mineralization in freshwaters is dependent on oxygen availability near the sediments, which controls whether OC inputs will be buried or respired. However, oxygen dynamics in waterbodies are changing globally due to land use and climate, and the consequences of variable oxygen conditions for OC burial are unknown. We manipulated hypolimnetic oxygen availability in a whole-reservoir experiment and used a mass balance OC model to quantify rates of OC burial. Throughout summer stratification, we observed that OC burial rates were tightly coupled to sediment oxygen concentrations: oxic conditions promoted the mineralization of "legacy" OC that had accumulated over years of sedimentation, resulting in negative OC burial. Moreover, our study demonstrates that fluctuating oxygen conditions can switch ecosystemscale OC burial in a reservoir between positive and negative rates. Consequently, changing oxygen availability in freshwaters globally will likely have large implications for the role of these ecosystems as OC sinks.*Correspondence: Cayelan@vt.edu Author Contribution Statement: CCC and PCH developed the idea for this study. CCC led the whole-ecosystem experiment with the assistance of JPD and RPM, and PCH developed the OC model. CCC, JPD, and RPM analyzed field data. CCC led the interpretation of the data and writing of the manuscript; all co-authors contributed to writing. Scientific Significance StatementOxygen concentrations are changing in lakes and reservoirs globally, which may have substantial consequences for the balance of organic carbon (OC) burial and respiration in these ecosystems. However, the vast majority of previous studies on the control of oxygen on OC cycling have been conducted in the laboratory or in mesocosms, so the ecosystem-level effects of changing oxygen dynamics at the daily or weekly scale on OC burial and respiration are unknown. A wholereservoir oxygenation experiment and mass balance model demonstrate that short-term variability in oxygen availability at the sediment-water interface can have major effects on OC fate, resulting in ecosystem-scale OC burial alternating between positive and negative rates.