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Severe deterioration of water quality in lakes, characterized by overabundance of algae and declining dissolved oxygen in the deep lake (DO B ), was one of the ecological crises of the 20th century. Even with large reductions in phosphorus loading, termed “reoligotrophication,” DO B and chlorophyll (CHL) have often not returned to their expected pre–20th-century levels. Concurrently, management of lake health has been confounded by possible consequences of climate change, particularly since the effects of climate are not neatly separable from the effects of eutrophication. Here, using Lake Geneva as an iconic example, we demonstrate a complementary alternative to parametric models for understanding and managing lake systems. This involves establishing an empirically-driven baseline that uses supervised machine learning to capture the changing interdependencies among biogeochemical variables and then combining the empirical model with a more conventional equation-based model of lake physics to predict DO B over decadal time-scales. The hybrid model not only leads to substantially better forecasts, but also to a more actionable description of the emergent rates and processes (biogeochemical, ecological, etc.) that drive water quality. Notably, the hybrid model suggests that the impact of a moderate 3°C air temperature increase on water quality would be on the same order as the eutrophication of the previous century. The study provides a template and a practical path forward to cope with shifts in ecology to manage environmental systems for non-analogue futures. 

Abstract The ice‐cover period in lakes is increasingly recognized for its distinct combination of physical and biological phenomena and ecological relevance. Knowledge gaps exist where research areas of hydrodynamics, biogeochemistry and biology intersect. For example, density‐driven circulation under ice coincides with an expansion of the anoxic zone, but abiotic and biotic controls on oxygen depletion have not been disentangled, and while heterotrophic microorganisms and migrating phytoplankton often thrive at the oxycline, the extent to which physical processes induce fluxes of heat and substrates that support under‐ice food webs is uncertain. Similarly, increased irradiance in spring can promote growth of motile phytoplankton or, if radiatively driven convection occurs, more nutritious diatoms, but links between functional trait selection, trophic transfer to zooplankton and fish, and the prevalence of microbial versus classical food webs in seasonally ice‐covered lakes remain unclear. Under‐ice processes cascade into and from the ice‐free season, and are relevant to annual cycling of energy and carbon through aquatic food webs. Understanding the coupling between state transitions and the reorganization of trophic hierarchies is essential for predicting complex ecosystem responses to climate change. In this interdisciplinary review we describe existing knowledge of physical processes in lakes in winter and the parallel developments in under‐ice biogeochemistry and ecology. We then illustrate interactions between these processes, identify extant knowledge gaps and present (novel) methods to address outstanding questions.