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Lake thermal dynamics have been considerably impacted by climate change, with potential adverse effects on aquatic ecosystems. To better understand the potential impacts of future climate change on lake thermal dynamics and related processes, the use of mathematical models is essential. In this study, we provide a comprehensive review of lake water temperature modeling. We begin by discussing the physical concepts that regulate thermal dynamics in lakes, which serve as a primer for the description of process‐based models. We then provide an overview of different sources of observational water temperature data, including in situ monitoring and satellite Earth observations, used in the field of lake water temperature modeling. We classify and review the various lake water temperature models available, and then discuss model performance, including commonly used performance metrics and optimization methods. Finally, we analyze emerging modeling approaches, including forecasting, digital twins, combining process‐based modeling with deep learning, evaluating structural model differences through ensemble modeling, adapted water management, and coupling of climate and lake models. This review is aimed at a diverse group of professionals working in the fields of limnology and hydrology, including ecologists, biologists, physicists, engineers, and remote sensing researchers from the private and public sectors who are interested in understanding lake water temperature modeling and its potential applications. Plain Language Summary: Lake thermal dynamics are fundamental in controlling mixing processes and have significant implications for biological and geochemical processes. Consequently, the impacts of climate change on these dynamics can have severe consequences for the health of lakes and their aquatic ecosystems. In this context, mathematical models are essential for understanding the potential effects of future climate change on lake thermal dynamics and related processes. This manuscript offers a comprehensive review of lake water temperature modeling. It covers the fundamental physical concepts that govern thermal dynamics in lakes and provides an overview of various sources of observational water temperature data, including in situ monitoring and satellite data used in these models. The study evaluates different types of lake water temperature models, including statistical, process‐based, and hybrid models. It explores emerging modeling approaches such as forecasting, digital twins, combining process‐based modeling with deep learning, ensemble modeling, and climate‐lake models coupling. Model performance is also discussed, highlighting suggested evaluation metrics and providing a comprehensive analysis of the state‐of‐the‐art optimization methods to assess model accuracy. This review targets researchers in limnology, hydrology, ecology, biology, physics, engineering, and remote sensing from the private and public sectors interested in lake water temperature modeling and its applications. Key Points: Lake thermal dynamics are central in shaping mixing processes and the health of aquatic ecosystems, and climate change alters these dynamicsMathematical models are essential to understand past and project future climate change impacts on lake thermal dynamicsThis study reviews lake water temperature modeling, covering concepts, data sources, and model evaluation for applications across disciplines [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: Lake models based on physical processes are powerful tools for investigating how lakes and reservoirs respond to local weather and for projecting lake responses to long‐term climate change. Historically, physical processes are the basis for designing these models. Due to an abundance of long‐term and high‐frequency data, deep learning models are used more frequently, although they do not reflect our domain expertise about hydrodynamics and heat transport. Recently, the modeling community has been focusing on merging models based on physical processes with deep learning. We are highlighting a novel methodology, modular compositional learning (MCL), that merges different modeling types in a modularized framework. Our resulting hybrid model outperformed the original model based on physical processes as well as alternative deep learning models regarding the simulation of various lake variables related to water temperature, and showed physically valid results. We are further showing various ways on how MCL can improve future lake model development and applications. Key Points: Deep learning models were pretrained on process‐based lake water temperature model output and fine‐tuned on observed high‐frequency dataFine‐tuned deep learning model was integrated into process‐based model creating the hybrid modelHybrid model outperformed process‐based model and two alternative deep learning models in projecting hydrodynamic lake characteristics [ABSTRACT FROM AUTHOR]

Lake water clarity, phytoplankton biomass, and hypolimnetic oxygen concentration are metrics of water quality that are highly degraded in eutrophic systems. Eutrophication is linked to legacy nutrients stored in catchment soils and in lake sediments. Long lags in water quality improvement under scenarios of nutrient load reduction to lakes indicate an apparent ecosystem memory tied to the interactions between water biogeochemistry and lake sediment nutrients. To investigate how nutrient legacies and ecosystem memory control lake water quality dynamics, we coupled nutrient cycling and lake metabolism in a model to recreate long‐term water quality of a eutrophic lake (Lake Mendota, Wisconsin, USA). We modeled long‐term recovery of water quality under scenarios of nutrient load reduction and found that the rates and patterns of water quality improvement depended on changes in phosphorus (P) and organic carbon storage in the water column and sediments. Through scenarios of water quality improvement, we showed that water quality variables have distinct phases of change determined by the turnover rates of storage pools—an initial and rapid water quality improvement due to water column flushing, followed by a much longer and slower improvement as sediment P pools were slowly reduced. Water clarity, phytoplankton biomass, and hypolimnetic dissolved oxygen differed in their time responses. Water clarity and algal biomass improved within years of nutrient reductions, but hypolimnetic oxygen took decades to improve. Even with reduced catchment loading, recovery of Lake Mendota to a mesotrophic state may require decades due to nutrient legacies and long ecosystem memory. Plain Language Summary: Lake water quality, as measured by algae concentration near the lake surface, the clarity of the water, and the availability of dissolved oxygen, is greatly reduced in lakes with nutrient pollution from phosphorus. In Lake Mendota, Wisconsin, phosphorus applied to the surrounding landscape for more than a century has accumulated in catchment soils and in the lake water column and sediments (i.e., "legacy phosphorus"), leading to poor water quality. To investigate how water quality in Lake Mendota might respond to nutrient reduction, we used computer models to simulate the elimination of phosphorus inputs from the catchment and track water quality change. Phosphorus in the lake water column initially decreased quickly, due to water column flushing, but then decreased very slowly due to release of legacy phosphorus from lake sediments. Water quality recovery lagged that of phosphorus, indicating an inherent "ecosystem memory" for past phosphorus levels. Ecosystem memory was due to biological activity that remained elevated, even when phosphorus was declining in the water column. When nutrient inputs to the lake were eliminated in the model, recovery of algae concentrations and water clarity to pristine conditions required decades, and a return to a fully oxygenated condition required a century. Key Points: Legacy phosphorus in lake sediments controls long term lake water quality response to nutrient remediationCoupled cycles of nutrients, physics, and metabolism explain ecosystem memory of lake phosphorus, water clarity, and oxygen habitatImprovement in lake water quality to pristine levels will require decades of commitment to nutrient load reductions [ABSTRACT FROM AUTHOR]

Lake trophic state is a key ecosystem property that integrates a lake's physical, chemical, and biological processes. Despite the importance of trophic state as a gauge of lake water quality, standardized and machine-readable observations are uncommon. Remote sensing presents an opportunity to detect and analyze lake trophic state with reproducible, robust methods across time and space. We used Landsat surface reflectance data to create the first compendium of annual lake trophic state for 55,662 lakes of at least 10 ha in area throughout the contiguous United States from 1984 through 2020. The dataset was constructed with FAIR data principles (Findable, Accessible, Interoperable, and Reproducible) in mind, where data are publicly available, relational keys from parent datasets are retained, and all data wrangling and modeling routines are scripted for future reuse. Together, this resource offers critical data to address basic and applied research questions about lake water quality at a suite of spatial and temporal scales., (© 2024. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.)

The increase in global air temperatures as well as variability in rainfall shifts due to climate change has been affecting the dynamics of water level fluctuations and thermal regimes in lakes and reservoirs. It is expected that at the end of this decade, such impacts will be even more noticeable and may harm the inland waters use. However, little is known about the possible consequences of climate change in multipurpose subtropical reservoirs. Using data generated by a regionalized climate model (RCM) as input to a simple hydrological model and a one-dimensional vertical hydrodynamic model, we forecast potential changes in the Itupararanga reservoir, São Paulo, Brazil, in an exemplary time period (2028-2030) in the next decade. Two Representative Concentration Pathway (RCP) scenarios were considered: an optimistic one corresponding to a CO 2 increase of about 650 ppm (RCP 4.5) and a pessimistic scenario where CO 2 exceeds 1000 ppm in 2100 (RCP 8.5). We found a significant reduction in the reservoir water level for both scenarios of 35% compared to current conditions. The surface water temperature is expected to increase (+0.6 °C); on the other hand, there would be a cooling of the hypolimnion (RCP 4.5 =-0.3 °C; RCP 8.5 = -1.2 °C). Another consequence is an increase of the duration of stratification periods that would start earlier in the dry period (between July and August), as well as the intensification of the stability of the water column (+43% compared to current conditions) and a deepening of the thermocline. The hydrodynamic modeling results suggest that the water level drop may threaten the reservoir multiple uses, in particular drinking water supply and power generation. Furthermore, the heating of surface water layers and increase of the number of stratified days and thermal stability can have negative impacts on water quality., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

One of the most important physical characteristics driving lifecycle events in lakes is stratification. Already subtle variations in the timing of stratification onset and break-up (phenology) are known to have major ecological effects, mainly by determining the availability of light, nutrients, carbon and oxygen to organisms. Despite its ecological importance, historic and future global changes in stratification phenology are unknown. Here, we used a lake-climate model ensemble and long-term observational data, to investigate changes in lake stratification phenology across the Northern Hemisphere from 1901 to 2099. Under the high-greenhouse-gas-emission scenario, stratification will begin 22.0 ± 7.0 days earlier and end 11.3 ± 4.7 days later by the end of this century. It is very likely that this 33.3 ± 11.7 day prolongation in stratification will accelerate lake deoxygenation with subsequent effects on nutrient mineralization and phosphorus release from lake sediments. Further misalignment of lifecycle events, with possible irreversible changes for lake ecosystems, is also likely.

Tube-dwelling macrozoobenthos can affect lake ecosystems in myriad ways, including changes in nutrient fluxes across the sediment-water interface. The pumping activity of chironomid larvae reinforces the transport of solutes between sediment and water. The transport of oxygen into the area surrounding the burrows generates oxidized compounds such as iron(oxy)hydroxides, which results in an additional phosphorus (P) sorption capacity similar to that of oxidized sediment surfaces. In the present study, the effect of the oxidized burrow walls of Chironomus plumosus on P binding capacity and P binding forms was tested in the laboratory using sediments with differing iron contents and varying numbers of chironomid larvae. In an additional long-term experiment, lake sediment naturally rich in iron was incubated under oxic conditions for 165 days, followed by a 3.5-year anoxic period. These experiments showed that: (1) Under oxic conditions the cumulative P uptake by sediments was dependent on larval densities. (2) The P that accumulated both at the sediment-water interface and in the oxidized burrow walls was mainly present as reductive soluble P (iron-bound P). Surprisingly, the amount of P released during the anoxic period in the long-term experiment was independent of the amount of P previously taken up during the oxic period since a portion of P was permanently retained in the sediment. The increase in alkaline soluble metal-bound P (NaOH-SRP) in formerly colonized sediments is a strong indication that the excessive P fixation by reductive soluble iron triggers the subsequent formation of stable iron phosphate minerals such as vivianite. Our study shows that P fixation that is induced by chironomid larvae is not always a completely reversible phenomenon, even after the emergence of the larvae and the re-establishment of anoxic conditions in the sediment., (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

Urban surface waters face several stressors associated with industry and urban water management. Over much of the past century, the wastewater treatment in Berlin, Germany, relied on inefficient sewage farms, which resulted in severe eutrophication and sediment contamination in the recipient surface waterbodies. A prominent example is Lake Tegel, where a multitude of management measures were applied in the last decades for the purpose of ecosystem restoration. In this study, we analyzed sediment cores of three lakes with X-ray fluorescence spectroscopy: Lake Tegel, Lake Großer Wannsee, which is environmentally similar but has a different management history, and Lake Userin, which serves as a reference located in a nature protection area. Multivariate statistical methods (principal component analysis, k-means clustering, and self-organizing maps) were used to assess the sediment quality and to reconstruct the management history of Lake Tegel. Principal component analysis established two main gradients of sediment composition: heavy metals and lithogenic elements. The impact of the management measures was visualized in the lake sediment composition changing from high abundance of heavy metals and reducing redox conditions to less-impacted sediments in recent layers. The clustering techniques suggested heterogeneity among sites within Lake Tegel that probably reflect urban water management measures. The abundance of heavy metals in recent lake sediments of Lake Tegel is similar to a lake with low urban impact and is lower than in Lake Großer Wannsee suggesting that the management measures were successful in the reduction of heavy metals, which are still a threat for surface waters worldwide.

The ultrastructure of the vegetative cell of the centric diatom Anaulus creticus Drebes & Schulz is described. Exhibiting the organic wall component (diatotepum) separated from the siliceous parts of the cell wall this species belongs to the diatom group with so-called split-walls [17]. A most interesting feature of the Anaulus cell is the horn region and its internal structure. As there is no silicified sieve-plate at the horn top but just a big hole sealed only by a thin diatotepum with a hexagonal meshwork, this wall structure is termed a "reduced ocellus". The inner part of the horn region is largely occupied by a conspicuous protoplasmic plug of proteinaceous nature. The possible function of this peculiar structure is discussed as well as new ideas about the role of the labiate process are provided.