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Abstract Limnological understanding of the role snow plays in under‐ice thermal dynamics is mainly based on studies of clear‐water lakes. Very little is known about the role snow plays in the thermal dynamics of dystrophic lakes. We conducted a whole lake experiment on a small, 8 m deep dystrophic bog lake in northern Wisconsin, where we removed all snowfall over two consecutive winters. Due to weather variability, only 1 year had predominantly black ice. Under these conditions, the lake rapidly cooled in early and mid‐winter, compared to snow covered conditions that insulated the lake from heat loss. The lake also rapidly gained heat in late winter resulting in isothermal conditions well in advance of ice‐off. These results show how water clarity modulates the influence of snow on under‐ice thermal dynamics, which is relevant to futures with snow droughts.

Abstract Hybrid Knowledge‐Guided Machine Learning (KGML) models, which are deep learning models that utilize scientific theory and process‐based model simulations, have shown improved performance over their process‐based counterparts for the simulation of water temperature and hydrodynamics. We highlight the modular compositional learning (MCL) methodology as a novel design choice for the development of hybrid KGML models in which the model is decomposed into modular sub‐components that can be process‐based models and/or deep learning models. We develop a hybrid MCL model that integrates a deep learning model into a modularized, process‐based model. To achieve this, we first train individual deep learning models with the output of the process‐based models. In a second step, we fine‐tune one deep learning model with observed field data. In this study, we replaced process‐based calculations of vertical diffusive transport with deep learning. Finally, this fine‐tuned deep learning model is integrated into the process‐based model, creating the hybrid MCL model with improved overall projections for water temperature dynamics compared to the original process‐based model. We further compare the performance of the hybrid MCL model with the process‐based model and two alternative deep learning models and highlight how the hybrid MCL model has the best performance for projecting water temperature, Schmidt stability, buoyancy frequency, and depths of different isotherms. Modular compositional learning can be applied to existing modularized, process‐based model structures to make the projections more robust and improve model performance by letting deep learning estimate uncertain process calculations.

Since the 1960s, a deep groundwater system in Wright Valley, Antarctica, has been the hypothesized source of brines to hypersaline Don Juan Pond and Lake Vanda, both of which are rich in calcium and chloride. Modeling studies do not support other possible mechanisms, such as evaporative processes, that could have led to the current suite of ions present in both waterbodies. In 2011 and 2018, an airborne electromagnetic survey was flown over Wright Valley to map subsurface resistivity (down to 600 m) in exploration of liquid water. The surveys revealed widespread unfrozen brine in the subsurface near Lake Vanda, Don Juan Pond, and the North Fork of Wright Valley. While our geophysical survey can neither confirm nor deny deep groundwater connectivity between Lake Vanda and Don Juan Pond, it does point to the potential for deep valley-wide brine, likely within the Ferrar Dolerite formation.

Previous studies of the lakes of the McMurdo Dry Valleys have attempted to constrain lake level history, and results suggest the lakes have undergone hundreds of meters of lake level change within the last 20 000 years. Past studies have utilized the interpretation of geologic deposits, lake chemistry, and ice sheet history to deduce lake level history; however a substantial amount of disagreement remains between the findings, indicating a need for further investigation using new techniques. This study utilizes a regional airborne resistivity survey to provide novel insight into the paleohydrology of the region. Mean resistivity maps revealed an extensive brine beneath the Lake Fryxell basin, which is interpreted as a legacy groundwater signal from higher lake levels in the past. Resistivity data suggest that active permafrost formation has been ongoing since the onset of lake drainage and that as recently as 1500–4000 years BP, lake levels were over 60 m higher than present. This coincides with a warmer-than-modern paleoclimate throughout the Holocene inferred by the nearby Taylor Dome ice core record. Our results indicate Mid to Late Holocene lake level high stands, which runs counter to previous research finding a colder and drier era with little hydrologic activity throughout the last 5000 years.

The concentration of oxygen is fundamental to lake water quality and ecosystem functioning through its control over habitat availability for organisms, redox reactions, and recycling of organic material. In many eutrophic lakes, oxygen depletion in the bottom layer (hypolimnion) occurs annually during summer stratification. The temporal and spatial extent of summer hypolimnetic anoxia is determined by interactions between the lake and its external drivers (e.g., catchment characteristics, nutrient loads, meteorology) as well as internal feedback mechanisms (e.g., organic matter recycling, phytoplankton blooms). How these drivers interact to control the evolution of lake anoxia over decadal timescales will determine, in part, the future lake water quality. In this study, we used a vertical one-dimensional hydrodynamic–ecological model (GLM-AED2) coupled with a calibrated hydrological catchment model (PIHM-Lake) to simulate the thermal and water quality dynamics of the eutrophic Lake Mendota (USA) over a 37 year period. The calibration and validation of the lake model consisted of a global sensitivity evaluation as well as the application of an optimization algorithm to improve the fit between observed and simulated data. We calculated stability indices (Schmidt stability, Birgean work, stored internal heat), identified spring mixing and summer stratification periods, and quantified the energy required for stratification and mixing. To qualify which external and internal factors were most important in driving the interannual variation in summer anoxia, we applied a random-forest classifier and multiple linear regressions to modeled ecosystem variables (e.g., stratification onset and offset, ice duration, gross primary production). Lake Mendota exhibited prolonged hypolimnetic anoxia each summer, lasting between 50–60 d. The summer heat budget, the timing of thermal stratification, and the gross primary production in the epilimnion prior to summer stratification were the most important predictors of the spatial and temporal extent of summer anoxia periods in Lake Mendota. Interannual variability in anoxia was largely driven by physical factors: earlier onset of thermal stratification in combination with a higher vertical stability strongly affected the duration and spatial extent of summer anoxia. A measured step change upward in summer anoxia in 2010 was unexplained by the GLM-AED2 model. Although the cause remains unknown, possible factors include invasion by the predacious zooplankton Bythotrephes longimanus. As the heat budget depended primarily on external meteorological conditions, the spatial and temporal extent of summer anoxia in Lake Mendota is likely to increase in the near future as a result of projected climate change in the region.

Lake Vida, located in Victoria Valley, is one of the largest lakes in the McMurdo dry valleys and is known to contain hypersaline liquid brine sealed below 16 m of freshwater ice. For the first time, Lake Vida was drilled to a depth of 27 m. Below 21 m the ice is marked by well-sorted sand layers up to 20 cm thick within a matrix of salty ice. From ice chemistry, isotopic composition of δ18O and δ2H, and ground penetrating radar profiles, we conclude that the entire 27 m of ice formed from surface runoff and the sediment layers represent the accumulation of surface deposits. Radiocarbon and optically stimulated luminescence dating limit the maximum age of the lower ice to 6300 14C yr BP. As the ice cover ablated downwards during periods of low surface inflow, progressive accumulation of sediment layers insulated and preserved the ice and brine beneath, analogous to the processes that preserve shallow ground ice. The repetition of these sediment layers reveals hydrologic variability in Victoria Valley during the mid- to late Holocene. Lake Vida is an exemplar site for understanding the preservation of subsurface brine, ice, and sediment in a cold desert environment.