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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 Globally, lake surface water temperatures have warmed rapidly relative to air temperatures, but changes in deepwater temperatures and vertical thermal structure are still largely unknown. We have compiled the most comprehensive data set to date of long-term (1970–2009) summertime vertical temperature profiles in lakes across the world to examine trends and drivers of whole-lake vertical thermal structure. We found significant increases in surface water temperatures across lakes at an average rate of + 0.37 °C decade⁻¹, comparable to changes reported previously for other lakes, and similarly consistent trends of increasing water column stability (+ 0.08 kg m⁻³decade⁻¹). In contrast, however, deepwater temperature trends showed little change on average (+ 0.06 °C decade⁻¹), but had high variability across lakes, with trends in individual lakes ranging from − 0.68 °C decade⁻¹to + 0.65 °C decade⁻¹. The variability in deepwater temperature trends was not explained by trends in either surface water temperatures or thermal stability within lakes, and only 8.4% was explained by lake thermal region or local lake characteristics in a random forest analysis. These findings suggest that external drivers beyond our tested lake characteristics are important in explaining long-term trends in thermal structure, such as local to regional climate patterns or additional external anthropogenic influences.