Combined Earth observations reveal the sequence of conditions leading to a large algal bloom in Lake Geneva.

Freshwater algae exhibit complex dynamics, particularly in meso-oligotrophic lakes with sudden and dramatic increases in algal biomass following long periods of low background concentration. While the fundamental prerequisites for algal blooms, namely light and nutrient availability, are well-known, their specific causation involves an intricate chain of conditions. Here we examine a recent massive Uroglena bloom in Lake Geneva (Switzerland/France). We show that a certain sequence of meteorological conditions triggered this specific algal bloom event: heavy rainfall promoting excessive organic matter and nutrients loading, followed by wind-induced coastal upwelling, and a prolonged period of warm, calm weather. The combination of satellite remote sensing, in-situ measurements, ad-hoc biogeochemical analyses, and three-dimensional modeling proved invaluable in unraveling the complex dynamics of algal blooms highlighting the substantial role of littoral-pelagic connectivities in large low-nutrient lakes. These findings underscore the advantages of state-of-the-art multidisciplinary approaches for an improved understanding of dynamic systems as a whole.

Lake surface cooling drives littoral-pelagic exchange of dissolved gases.

The extent of littoral influence on lake gas dynamics remains debated in the aquatic science community due to the lack of direct quantification of lateral gas transport. The prevalent assumption of diffusive horizontal transport in gas budgets fails to explain anomalies observed in pelagic gas concentrations. Here, we demonstrate through high-frequency measurements in a eutrophic lake that daily convective horizontal circulation generates littoral-pelagic advective gas fluxes one order of magnitude larger than typical horizontal fluxes used in gas budgets. These lateral fluxes are sufficient to redistribute gases at the basin-scale and generate concentration anomalies reported in other lakes. Our observations also contrast the hypothesis of pure, nocturnal littoral-to-pelagic exchange by showing that convective circulation transports gases such as oxygen and methane toward both the pelagic and littoral zones during the daytime. This study challenges the traditional pelagic-centered models of aquatic systems by showing that convective circulation represents a fundamental lateral transport mechanism to be integrated into gas budgets.

Enhancing interpretability and generalizability of deep learning-based emulator in three-dimensional lake hydrodynamics using Koopman operator and transfer learning: Demonstrated on the example of lake Zurich.

Three-dimensional lake hydrodynamic model is a powerful tool widely used to assess hydrological condition changes of lake. However, its computational cost becomes problematic when forecasting the state of large lakes or using high-resolution simulation in small-to-medium size lakes. One possible solution is to employ a data-driven emulator, such as a deep learning (DL) based emulator, to replace the original model for fast computing. However, existing DL-based emulators are often black-box and data-dependent models, causing poor interpretability and generalizability in practical applications. In this study, a data-driven emulator is established using deep neural network (DNN) to replace the original model for fast computing of three-dimensional lake hydrodynamics. Then, the Koopman operator and transfer learning (TL) are employed to enhance the interpretability and generalizability of the emulator. Finally, the generalizability of DL-based emulators is comprehensively analyzed through linear regression and correlation analysis. These methods are tested against an existing hydrodynamic model of Lake Zurich (Switzerland) whose data was provided by an open-source web-based platform called Meteolakes/Alplakes. According to the results, (1) The DLEDMD offers better interpretability than DNN because its Koopman operator reveals the linear structure behind the hydrodynamics; (2) The generalization of the DL-based emulators in three-dimensional lake hydrodynamics are influenced by the similarity between the training and testing data; (3) TL effectively improves the generalizability of the DL-based emulators.

Motile bacteria leverage bioconvection for eco-physiological benefits in a natural aquatic environment.

Introduction: Bioconvection, a phenomenon characterized by the collective upward swimming of motile microorganisms, has mainly been investigated within controlled laboratory settings, leaving a knowledge gap regarding its ecological implications in natural aquatic environments. This study aims to address this question by investigating the influence of bioconvection on the eco-physiology of the anoxygenic phototrophic sulfur bacteria community of meromictic Lake Cadagno. Methods: Here we comprehensively explore its effects by comparing the physicochemical profiles of the water column and the physiological traits of the main populations of the bacterial layer (BL). The search for eco-physiological effects of bioconvection involved a comparative analysis between two time points during the warm season, one featuring bioconvection (July) and the other without it (September). Results: A prominent distinction in the physicochemical profiles of the water column centers on light availability, which is significantly higher in July. This minimum threshold of light intensity is essential for sustaining the physiological CO2 fixation activity of Chromatium okenii, the microorganism responsible for bioconvection. Furthermore, the turbulence generated by bioconvection redistributes sulfides to the upper region of the BL and displaces other microorganisms from their optimal ecological niches. Conclusion: The findings underscore the influence of bioconvection on the physiology of C. okenii and demonstrate its functional role in improving its metabolic advantage over coexisting phototrophic sulfur bacteria. However, additional research is necessary to confirm these results and to unravel the multiscale processes activated by C. okenii's motility mechanisms.

Long-term ice phenology records spanning up to 578 years for 78 lakes around the Northern Hemisphere.

In recent decades, lakes have experienced unprecedented ice loss with widespread ramifications for winter ecological processes. The rapid loss of ice, resurgence of winter biology, and proliferation of remote sensing technologies, presents a unique opportunity to integrate disciplines to further understand the broad spatial and temporal patterns in ice loss and its consequences. Here, we summarize ice phenology records for 78 lakes in 12 countries across North America, Europe, and Asia to permit the inclusion and harmonization of in situ ice phenology observations in future interdisciplinary studies. These ice records represent some of the longest climate observations directly collected by people. We highlight the importance of applying the same definition of ice-on and ice-off within a lake across the time-series, regardless of how the ice is observed, to broaden our understanding of ice loss across vast spatial and temporal scales.

A hybrid empirical and parametric approach for managing ecosystem complexity: Water quality in Lake Geneva under nonstationary futures.

Severe deterioration of water quality in lakes, characterized by overabundance of algae and declining dissolved oxygen in the deep lake (DOB), was one of the ecological crises of the 20th century. Even with large reductions in phosphorus loading, termed "reoligotrophication," DOB 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 DOB 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.

Chromium Cycling in Redox-Stratified Basins Challenges δ53Cr Paleoredox Proxy Applications.

Chromium stable isotope composition (δ53Cr) is a promising tracer for redox conditions throughout Earth's history; however, the geochemical controls of δ53Cr have not been assessed in modern redox-stratified basins. We present new chromium (Cr) concentration and δ53Cr data in dissolved, sinking particulate, and sediment samples from the redox-stratified Lake Cadagno (Switzerland), a modern Proterozoic ocean analog. These data demonstrate isotope fractionation during incomplete (non-quantitative) reduction and removal of Cr above the chemocline, driving isotopically light Cr accumulation in euxinic deep waters. Sediment authigenic Cr is isotopically distinct from overlying waters but comparable to average continental crust. New and published data from other redox-stratified basins show analogous patterns. This challenges assumptions from δ53Cr paleoredox applications that quantitative Cr reduction and removal limits isotope fractionation. Instead, fractionation from non-quantitative Cr removal leads to sedimentary records offset from overlying waters and not reflecting high δ53Cr from oxidative continental weathering.

The value of human data annotation for machine learning based anomaly detection in environmental systems.

Anomaly detection is the process of identifying unexpected data samples in datasets. Automated anomaly detection is either performed using supervised machine learning models, which require a labelled dataset for their calibration, or unsupervised models, which do not require labels. While academic research has produced a vast array of tools and machine learning models for automated anomaly detection, the research community focused on environmental systems still lacks a comparative analysis that is simultaneously comprehensive, objective, and systematic. This knowledge gap is addressed for the first time in this study, where 15 different supervised and unsupervised anomaly detection models are evaluated on 5 different environmental datasets from engineered and natural aquatic systems. To this end, anomaly detection performance, labelling efforts, as well as the impact of model and algorithm tuning are taken into account. As a result, our analysis reveals the relative strengths and weaknesses of the different approaches in an objective manner without bias for any particular paradigm in machine learning. Most importantly, our results show that expert-based data annotation is extremely valuable for anomaly detection based on machine learning.

The Imprint of Primary Production on High-Frequency Profiles of Lake Optical Properties.

Water inherent optical properties (IOPs) contain integrative information on the optical constituents of surface waters. In lakes, IOP measurements have not been traditionally collected. This study describes how high-frequency IOP profiles can be used to document short-term physical and biogeochemical processes that ultimately influence the long-term trajectory of lake ecosystems. Between October 2018 and May 2020, we collected 1373 high-resolution hyperspectral IOP profiles in the uppermost 50 m of the large mesotrophic Lake Geneva (Switzerland-France), using an autonomous profiler. A data set of this size and content does not exist for any other lake. Results showed seasonal variations in the IOPs, following the expected dynamic of phytoplankton. We found systematic diel patterns in the IOPs. Phases of these diel cycles were consistent year-round, and amplitudes correlated to the diurnal variations of dissolved oxygen, clarifying the link between IOPs and phytoplankton metabolism. Diel amplitudes were largest in spring and summer under low wind condition. Wind-driven changes in thermal stratification impacted the dynamic of the IOPs, illustrating the potential of high-frequency profiles of water optical properties to increase our understanding of carbon cycling in lake ecosystems.

Integrating Inland and Coastal Water Quality Data for Actionable Knowledge.

Water quality measures for inland and coastal waters are available as discrete samples from professional and volunteer water quality monitoring programs and higher-frequency, near-continuous data from automated in situ sensors. Water quality parameters also are estimated from model outputs and remote sensing. The integration of these data, via data assimilation, can result in a more holistic characterization of these highly dynamic ecosystems, and consequently improve water resource management. It is becoming common to see combinations of these data applied to answer relevant scientific questions. Yet, methods for scaling water quality data across regions and beyond, to provide actionable knowledge for stakeholders, have emerged only recently, particularly with the availability of satellite data now providing global coverage at high spatial resolution. In this paper, data sources and existing data integration frameworks are reviewed to give an overview of the present status and identify the gaps in existing frameworks. We propose an integration framework to provide information to user communities through the the Group on Earth Observations (GEO) AquaWatch Initiative. This aims to develop and build the global capacity and utility of water quality data, products, and information to support equitable and inclusive access for water resource management, policy and decision making.

Water-level fluctuation enhances sediment and trace metal mobility in lake littoral.

Water-level fluctuation (WLF) is a widespread management action in lakes and reservoirs whose impacts on contaminant fate have seldom been investigated. We used near shore hourly measurements (n = 2122) of turbidity (contaminant proxy) and water velocity (sediment resuspension proxy) to track high-frequency contaminant dynamics during a 0.6 m change in water level observed in autumn 2017 in a large French lake. Simultaneously, discrete trace metal measurements highlighted that trapped sediment was more contaminated and finer than surficial sediment supporting that suspended particles (measured by turbidity) were a preferential medium for contaminant mobility. General additive models involving tensor products revealed the enhancement of wind-speed and river discharge effects on turbidity with water draw down. The decrease of the explained deviances by the models over time-lags indicated short time-scale response of turbidity to external forcing. Three of the four major turbid events occurred at the lowest water-level and were concomitant of sediment resuspension as well as precipitation events and/or river flood suggesting a complex interplay among in-lake and watershed processes at controlling sediment mobility during the WLF. These results shed in light that WLF can affect lake littoral hydrodynamic cascading up to the enhancement of contaminant mobility. Sediment resuspension may be an overlooked feature of WLF increasing contamination risk and exposure for littoral organisms with widespread ecological consequences due to the large number of water-level regulated ecosystems.

Meteolakes: An operational online three-dimensional forecasting platform for lake hydrodynamics.

Environmental management depends on high-quality monitoring and its meaningful interpretation. The combination of local weather dynamics, regional anthropogenic stresses and global environmental changes make the evaluation of monitoring information in dynamic freshwater systems a challenging task. While the lake ecosystems gather many complex biogeochemical interactions, they remain constrained by the same physical environment of mixing and transport. It is therefore crucial to obtain high-quality physical system insight. Three-dimensional hydrodynamic models are perfectly suited for providing such information. However, these models are complex to implement, and their use is often limited to modellers. Here, we aim to provide model output via a user-friendly platform to a broad audience ranging from scientists to public and governmental stakeholders. We present a unified approach merging the apparently diverse interests through meteolakes.ch, an online platform openly disseminating lake observations and three-dimensional numerical simulations in near real-time with short-term forecasts and data assimilation. Meteolakes is scalable to a broad range of devices, modular and distributed, hence allowing its expansion to other regions and hardware infrastructures. Since 2016, the platform has continuously provided timely synoptic lake information to more than 250,000 users. This web-based system was built not only to provide guidance to scientists in the design and analysis of field experiments and to foster interdisciplinary lake studies, but also to assist governmental agencies and professionals in the long-term policy and planning of water resources management. Finally, our system aimed at promoting awareness and understanding of the complexity of lakes and providing information to the public through user-friendly interfaces. This article details the design and operation of such a platform and its products. Applications are demonstrated by examples of a recent upwelling and a storm event. Both cases illustrate how Meteolakes help scientists in their quest for process understanding as well as water professionals and civil society in providing specific warnings.

Storm impacts on alpine lakes: Antecedent weather conditions matter more than the event intensity.

Extreme weather events may be just as important as gradual trends for the long-term trajectories of ecosystems. For alpine lakes, which are exposed to both exacerbated atmospheric warming and intense episodic weather events, future conditions might not be appropriately forecast by only climate change trends, i.e. warming, if extreme events have the potential to deflect their thermal and metabolic states from their seasonal ranges. We used high-frequency monitoring data over three open-water seasons with a one-dimensional hydrodynamic model of the high-altitude Lake Muzelle (France) to show that rainstorms or windstorms, notwithstanding their intensity, did not trigger long-lasting consequences to the lake characteristics when light penetration into the lake was not modified. In contrast, storms associated with high turbidity input from the watershed ("turbid storms") strongly modified the lacustrine hydrodynamics and metabolism for the rest of the open-water season through reduced light penetration. The long-lasting effects of turbid storms were related to the inputs and in-lake persistence of very light glacial suspensoids from the watershed. The occurrence of the observed turbid storms was not related to the wind or rain intensities during the events. Instead, the turbid storms occurred after dry and atypically warm spells, i.e. meteorological conditions expected to be more frequent in this alpine region in the upcoming decades. Consequently, storm events, notwithstanding their intensity, are expected to strongly imprint the future ecological status of alpine lakes under climate warming.

Homogenization of lake cyanobacterial communities over a century of climate change and eutrophication.

Human impacts on biodiversity are well recognized, but uncertainties remain regarding patterns of diversity change at different spatial and temporal scales. Changes in microbial assemblages are, in particular, not well understood, partly due to the lack of community composition data over relevant scales of space and time. Here, we investigate biodiversity patterns in cyanobacterial assemblages over one century of eutrophication and climate change by sequencing DNA preserved in the sediments of ten European peri-Alpine lakes. We found species losses and gains at the lake scale, while species richness increased at the regional scale over approximately the past 100 years. Our data show a clear signal for beta diversity loss, with the composition and phylogenetic structure of assemblages becoming more similar across sites in the most recent decades, as have the general environmental conditions in and around the lakes. We attribute patterns of change in community composition to raised temperatures affecting the strength of the thermal stratification and, as a consequence, nutrient fluctuations, which favoured cyanobacterial taxa able to regulate buoyancy. Our results reinforce previous reports of human-induced homogenization of natural communities and reveal how potentially toxic and bloom-forming cyanobacteria have widened their geographic distribution in the European temperate region.

The influence of bottom boundary layer hydrodynamics on sediment focusing in a contaminated bay.

Understanding the dynamics and fate of particle bound contaminants is important for mitigating potential environmental, economic and health impacts linked to their presence. Vidy Bay, Lake Geneva (Switzerland), is contaminated due to the outfall and overflow from the wastewater treatment plant of the City of Lausanne. This study was designed to investigate the fate of particle-bound contaminants with the goal of providing a more complete picture of contaminant pathways within the bay and their potential spread to the main basin. This goal was achieved by investigating the sediment transport dynamics, using sediment traps and radionuclide tracers, and ascertaining how local bottom-boundary hydrodynamic conditions (temperature, turbidity, current velocity and direction) influence these dynamics. Results of the study indicated that sedimentation rates and lateral advections increased vertically with proximity to the lakebed and laterally with proximity to shore, indicating the presence of sediment focusing in the bay. Hydrodynamic measurements showed the persistent influence of a gyre within the bay, extending down to the lake bed, while just outside of the bay circulation was influenced by the seasonal patterns of the main basin. Calculated mean displacement distances in the bay indicated that suspended particles can travel ∼3 km per month, which is 1.7 times the width of the Vidy Bay gyre. This results in a residence time of approximately 21 days for suspended particles, which is much greater than previously modelled results. The calculated mobility Shield parameter never exceeded the threshold shear stress needed for resuspension in deeper parts of the bay. In such, increased lateral advections to the bay are not likely due to local resuspension but rather external particle sources, such as main basin or shallow, littoral resuspensions. These external sources coupled with an increased residence time and decreased current velocity within the bay are the precipitating factors in sediment focusing. While the spread of contaminants from the bay may occur through the transport of fine suspended sediments in shallower zones of the bay (<60 m) by longshore littoral currents, results suggest that particle-bound contaminants are likely to remain within the bay.

Application of remote sensing for the optimization of in-situ sampling for monitoring of phytoplankton abundance in a large lake.

Directives and legislations worldwide aim at representatively and continuously monitoring the ecological status of surface waters. In many countries, chlorophyll-a concentrations (CHL) are used as an indicator of phytoplankton abundance and the trophic level of lakes or reservoirs. In-situ measurements of water quality parameters, however, are time-consuming, costly and of unknown but naturally limited spatial representativeness. In addition, the variety of the involved lab and field measurement methods and instruments complicates comparability and reproducibility. Taking Lake Geneva as an example, 1234 satellite images from the MERIS sensor on the Envisat satellite from 2002 to 2012 are used to quantify the spatial and temporal variations of CHL concentrations. Based on histograms of spring, summer and autumn CHL estimates, the spatial representativeness of two existing in-situ measurement locations is analysed. Appropriate sampling frequencies to capture CHL peaks are examined by means of statistical resampling. The approaches proposed allow determining optimal in-situ sampling locations and frequencies. Their generic nature allows for adaptation to other lakes, especially to establish new survey programmes where no previous records are available.