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Abstract As a key water quality parameter, dissolved oxygen (DO) concentration, and particularly changes in bottom water DO is fundamental for understanding the biogeochemical processes in lake ecosystems. Based on two machine learning (ML) models, Gradient Boost Regressor (GBR) and long‐short‐term‐memory (LSTM) network, this study developed three ML model approaches: direct GBR; direct LSTM; and a 2‐step mixed ML model workflow combining both GBR and LSTM. They were used to simulate multi‐year surface and bottom DO concentrations in five lakes. All approaches were trained with readily available environmental data as predictors. Indices of lake thermal structure and mixing provided by a one‐dimensional (1‐D) hydrodynamic model were also included as predictors in the ML models. The advantages of each ML approach were not consistent for all the tested lakes, but the best one of them was defined that can estimate DO concentration with coefficient of determination (R²) up to 0.6–0.7 in each lake. All three approaches have normalized mean absolute error (NMAE) under 0.15. In a polymictic lake, the 2‐step mixed model workflow showed better representation of bottom DO concentrations, with a highest true positive rate (TPR) of hypolimnetic hypoxia detection of over 90%, while the other workflows resulted in, TPRs are around 50%. In most of the tested lakes, the predicted surface DO concentrations and variables indicating stratified conditions (i.e., Wedderburn number and the temperature difference between surface and bottom water) are essential for simulating bottom DO. The ML approaches showed promising results and could be used to support short‐ and long‐term water management plans. 

Abstract For over a century, ecologists have used the concept of trophic state (TS) to characterize an aquatic ecosystem's biological productivity. However, multiple TS classification schemes, each relying on a variety of measurable parameters as proxies for productivity, have emerged to meet use‐specific needs. Frequently, chlorophyll a, phosphorus, and Secchi depth are used to classify TS based on autotrophic production, whereas phosphorus, dissolved organic carbon, and true color are used to classify TS based on both autotrophic and heterotrophic production. Both classification approaches aim to characterize an ecosystem's function broadly, but with varying degrees of autotrophic and heterotrophic processes considered in those characterizations. Moreover, differing classification schemes can create inconsistent interpretations of ecosystem integrity. For example, the US Clean Water Act focuses exclusively on algal threats to water quality, framed in terms of eutrophication in response to nutrient loading. This usage lacks information about non‐algal threats to water quality, such as dystrophication in response to dissolved organic carbon loading. Consequently, the TS classification schemes used to identify eutrophication and dystrophication may refer to ecosystems similarly (e.g., oligotrophic and eutrophic), yet these categories are derived from different proxies. These inconsistencies in TS classification schemes may be compounded when interdisciplinary projects employ varied TS frameworks. Even with these shortcomings, TS can still be used to distill information on complex aquatic ecosystem function into a set of generalizable expectations. The usefulness of distilling complex information into a TS index is substantial such that usage inconsistencies should be explicitly addressed and resolved. To emphasize the consequences of diverging TS classification schemes, we present three case studies for which an improved understanding of the TS concept advances freshwater research, management efforts, and interdisciplinary collaboration. To increase clarity in TS, the aquatic sciences could benefit from including information about the proxy variables, ecosystem type, as well as the spatiotemporal domains used to classify TS. As the field of aquatic sciences expands and climatic irregularity increases, we highlight the importance of re‐evaluating fundamental concepts, such as TS, to ensure their compatibility with evolving science. 

Abstract Lake surfaces are warming worldwide, raising concerns about lake organism responses to thermal habitat changes. Species may cope with temperature increases by shifting their seasonality or their depth to track suitable thermal habitats, but these responses may be constrained by ecological interactions, life histories or limiting resources. Here we use 32 million temperature measurements from 139 lakes to quantify thermal habitat change (percentage of non-overlap) and assess how this change is exacerbated by potential habitat constraints. Long-term temperature change resulted in an average 6.2% non-overlap between thermal habitats in baseline (1978–1995) and recent (1996–2013) time periods, with non-overlap increasing to 19.4% on average when habitats were restricted by season and depth. Tropical lakes exhibited substantially higher thermal non-overlap compared with lakes at other latitudes. Lakes with high thermal habitat change coincided with those having numerous endemic species, suggesting that conservation actions should consider thermal habitat change to preserve lake biodiversity. 

Abstract. Empirical evidence demonstrates that lakes and reservoirs are warming acrossthe globe. Consequently, there is an increased need to project futurechanges in lake thermal structure and resulting changes in lakebiogeochemistry in order to plan for the likely impacts. Previous studies ofthe impacts of climate change on lakes have often relied on a single modelforced with limited scenario-driven projections of future climate for arelatively small number of lakes. As a result, our understanding of theeffects of climate change on lakes is fragmentary, based on scatteredstudies using different data sources and modelling protocols, and mainlyfocused on individual lakes or lake regions. This has precludedidentification of the main impacts of climate change on lakes at global andregional scales and has likely contributed to the lack of lake water qualityconsiderations in policy-relevant documents, such as the Assessment Reportsof the Intergovernmental Panel on Climate Change (IPCC). Here, we describe asimulation protocol developed by the Lake Sector of the Inter-SectoralImpact Model Intercomparison Project (ISIMIP) for simulating climate changeimpacts on lakes using an ensemble of lake models and climate changescenarios for ISIMIP phases 2 and 3. The protocol prescribes lakesimulations driven by climate forcing from gridded observations anddifferent Earth system models under various representative greenhouse gasconcentration pathways (RCPs), all consistently bias-corrected on a0.5∘ × 0.5∘ global grid. In ISIMIP phase 2, 11 lakemodels were forced with these data to project the thermal structure of 62well-studied lakes where data were available for calibration underhistorical conditions, and using uncalibrated models for 17 500 lakesdefined for all global grid cells containing lakes. In ISIMIP phase 3, thisapproach was expanded to consider more lakes, more models, and moreprocesses. The ISIMIP Lake Sector is the largest international effort toproject future water temperature, thermal structure, and ice phenology oflakes at local and global scales and paves the way for future simulations ofthe impacts of climate change on water quality and biogeochemistry in lakes. 

Noetzli, J., Christiansen, H.H, Guglielmin, M., Hrbáček, F., Hu, G., Isaksen, K., Magnin, F., Pogliotti, P., Smith, S. L., Zhao, L. and Streletskiy, D. A. 2024. Permafrost temperature and active layer thickness. In: State of the Climate in 2023. Bulletin of the American Meteorological Society, 105 (8), S43–S44, https://doi.org/10.1175/BAMS-D-24-0116.1