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Long-term effects of climate change on lakes globally will include a substantial modification in the thermal regime and the oxygen solubility of lakes, resulting in the alteration of ecosystem processes, habitats, and concentrations of critical substances. Recent efforts have led to the development of long-term model projections of climate change effects on lake thermal regimes and oxygen solubility. However, such projections are hardly ever confronted with observations extending over multiple decades. Furthermore, global-scale forcing parameters in lake models present several limitations, such as the need of significant downscaling. In this study, the effects of climate change on thermal regime and oxygen solubility were analyzed in the four largest French peri-alpine lakes over 1850–2100. We tested several one-dimensional (1D) lake models' robustness for long-term variations based on up to 63 years of limnological data collected by the French Observatory of LAkes (OLA). Here, we evaluate the possibility of forcing mechanistic models by following the long-term evolution of shortwave radiation and air temperature while providing realistic seasonal trends for the other variables for which local-scale downscaling often lacks accuracy. Based on this approach, MyLake, forced by air temperatures and shortwave radiations, predicted accurately the variations in the lake thermal regime over the last 4 to 6 decades, with RMSE < 1.95 ∘C. Over the previous 3 decades, water temperatures have increased by 0.46 ∘C per decade (±0.02 ∘C) in the epilimnion and 0.33 ∘C per decade (±0.06 ∘C) in the hypolimnion. Concomitantly and due to thermal change, O2 solubility has decreased by −0.104 mg L−1 per decade (±0.005 mg L−1) and −0.096 mg L−1 per decade (±0.011 mg L−1) in the epilimnion and hypolimnion, respectively. Based on the shared socio-economic pathway SSP370 of the Intergovernmental Panel on Climate Change (IPCC), peri-alpine lakes could face an increase of 3.80 ∘C (±0.20 ∘C) in the next 70 years, accompanied by a decline of 1.0 mg L−1 (±0.1 mg L−1) of O2 solubility. Together, these results highlight a critical alteration in lake thermal and oxygen conditions in the coming decades, and a need for a better integration of long-term lake observatories data and lake models to anticipate climate effects on lake thermal regimes and habitats.

International audience; This dataset complement a previously published dataset [1] and corresponds to the physico-chemical parameters data series produced during the MESOLAC experimental project [2]. The presented dataset is composed of: 1. In situ profiles (0–3m) of temperature, conductivity, pH, dissolved oxygen (concentration and saturation). 2. In situ measurements of light spectral UV/VIS/IR irradiance (300–950 nm wavelength range) taken at 0, 0.25, 0.5, 1, 1.5, 2 and 2.5m. 3. Laboratory chemical analysis of samples collected at 0 and 2 m (conductivity, pH, total alkalinity, NH4, NO2, NO3, total and particulate nitrogen (Ntot, Npart), PO4, total and particulate phosphorus (Ptot, Ppart), total, organic particulate and total particulate carbon (Ctot, Cpart-org, Cpart-tot), Cl, SO4, SiO2. 4. Laboratory analysis of pigments extracted from samples collected at 0 and 2 m (Chla, Chlc, carotenoids, phaeopigments).The experimental design is the same as in Tran-Khac et al [1]. Briefly, it consisted of nine pelagic mesocosms (about 3000 L, 3m depth) deployed in July 2019 in Lake Geneva near the shore of Thonon les Bains (France) aiming to simulate predicted climate scenarios (i.e. extreme events) and assess the response of planktonic communities, ecosystem functioning and resilience.During the experiment, physical parameters were measured twice a week. At the same time, samples were collected at 0 and 2m of depth for subsequent chemical laboratory analyses. These data are presented in the dataset file, ordered by sampling event (numbered from S1 to S8), treatment (Control-C, High-H and Medium-M) and replicates (1 to 3). For each sampling point the measured parameters are listed in columns, missing data and values below the detection limit are marked as NA (not available).This data set aims to contribute to the understanding of the effect of environmental forcing on lake physico-chemical characteristics (such as temperature, oxygen and nutrient concentration) under simulated intense weather events. To a broader extent, the presented data can be used for a wide variety of applications, including monitoring of a large peri-alpine lake functioning under environmental stress and being included in further meta-analysis to generalise the effect of climate change on large lakes. The two complementary dataset differ in the acquired data and methods, temporal and spatial resolution. They complete each other in terms of physico-chemical characterization of the experimental treatments and together can allow comparison of the two different monitoring strategies (continuous vs punctual) during in situ experimental manipulations.

International audience; This dataset corresponds to a data series produced from automated data loggers during the MESOLAC experimental project. Nine pelagic mesocosms (about 3000 L, 3 m depth) were deployed in July 2019 in Lake Geneva near the shore of Thonon les Bains (France), simulating predicted climate scenarios (i.e. intense weather events) by applying a combination of forcing. The design consisted of three treatments each replicated three times: a control treatment (named C - no treatment applied) and two different treatments simulating different intensities of weather events. The high intensity treatment (named H) aimed to reproduce short and intense weather events such as violent storms. It consisted of a short-term stress applied during the first week, with high pulse of dissolved organic carbon (5x increased concentration, i.e. total DOC similar to 6 mg L-1), transmitted light reduced to 15% and water column manual mixing. The medium intensity treatment (named M) simulated less intense and more prolonged exposures such as during flood events. It was maintained during the 4 weeks of the experiment and consisted of 1.5x increased concentration of dissolved organic carbon (i.e. total DOC similar to 2 mg L-1), 70% transmitted light and water column manual mixing. Automated data loggers were placed for the entire period of the experiment in the mesocosms and in the lake for comparison with natural conditions. Temperature, conductivity, dissolved oxygen and CO2 were monitored every 15 min at different depths (0.15, 0.25, 1 and 2 m). This data set aims to contribute our understanding of the effect of environmental forcing on lake ecosystem processes (such as production, respiration and CO2 exchange) under simulated intense weather events and the ability of the planktonic community to recover after perturbation. To a broader extent, the presented data can be used for a wide variety of applications, including monitoring of lake community functioning during a period of high productivity on a large peri-alpine lake and being included in further meta-analysis aiming at generalising the effect of climate change on large lakes.

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.

A reliable and representative monitoring of phytoplankton abundance, especially cyanobacteria, is mandatory for choosing the best management or restoration strategy and for assessing its effectiveness in worldwide eutrophic lakes and reservoirs. In vivo and in situ fluorometry is expected to be faster than the traditional methods of phytoplankton counting and chlorophyll- a (chl a ) extraction and quantification. Contrary to water bodies in temperate climate, the technique still needs investigation in tropical freshwaters. We assessed the performance of a spectrofluorometer, the FluoroProbe (FP) in Lake Pampulha (Minas Gerais, Brazil), a water body where cyanobacteria are the dominant group most of the time. FP chl a concentrations were in average almost three times lower than spectrophotometric chl a concentrations and underestimation was larger when Chroococales were the dominant species. The linear response of the probe was limited to 100 μg chl a L −1 . The correlation between FP and spectrophotometric measurements was significant only for FP measurements below 100 μg chl a L −1 ( r = 0.84 , p 0.001 , n = 25 ). The correlations between FP measurements and biovolumes of phytoplankton ( r = 0.64 , p = 0.001 , n = 23 ) and cyanobacteria ( r = 0.60, p = 0.002 , n = 23 ) were significant but weaker than reported in temperate water bodies. Oscillatoriales fingerprint was the most distant from FP default fingerprint for cyanobacteria, presenting a lower relative fluorescence at 610 nm. Applying an average experimental fingerprint obtained from four dominant cyanobacteria species in Lake Pampulha allowed us to improve FP measurements compared to spectrophotometric data, especially when a single species, Planktothrix isothrix , was highly dominant. Even so, high phytoplankton biomass in Lake Pampulha led to high optical density, which disturbs FP measurements. Thus, in highly eutrophic water bodies, additional laboratory analysis is necessary to validate in situ spectrofluorometer measurements, more than in oligotrophic and mesotrophic environments.

RESUMO No processo de urbanização, o aumento na proporção de superfícies impermeabilizadas e as mudanças no uso do solo são responsáveis por maiores volumes e velocidades do escoamento superficial, refletindo em uma maior capacidade de arraste e em um maior aporte de nutrientes nos corpos d'água receptores. O objetivo deste trabalho foi implementar uma ferramenta matemática capaz de reproduzir o impacto de mudanças na bacia hidrográfica sobre a dinâmica do fitoplâncton em um lago urbano. Neste artigo são apresentados o desenvolvimento e os resultados de um modelo integrado constituído de um modelo hidrológico, para simular vazões escoadas em uma bacia hidrográfica, e de um modelo hidrodinâmico e ecológico, para simular a biomassa fitoplanctônica em um corpo d'água urbano. A Lagoa da Pampulha (Belo Horizonte, Minas Gerais), escolhida como estudo de caso, foi intensamente monitorada, assim como sua bacia de drenagem, entre outubro de 2011 e junho de 2013. Os dados obtidos foram utilizados para calibrar e validar ambos os modelos. Os resultados obtidos com o modelo hidrológico mostraram-se coerentes com as medidas realizadas em campo (o coeficiente de Nash variou entre 0,70 e 0,88). O modelo da lagoa representou corretamente a evolução da comunidade fitoplanctônica (erro médio absoluto normalizado: 0,25-0,42 e o coeficiente de Pearson: 0,82-0,89; p

International audience; Urban lakes mitigate the negative impacts on the hydrological cycle and improve the quality of life in cities. Worldwide, the concern increases for the protection and management of urban water bodies. Since the physical-chemical and biological conditions of a small aquatic ecosystem can vary rapidly over time, traditional low frequency measurement approaches (weekly or monthly sampling) limits the knowledge and the transfer of research outcomes to management decision-making. In this context, this paper presents an automatic monitoring system including a full-scale experimental site and a data transfer platform for high-frequency observations (every 5 min) in a small and shallow urban lake (Lake Champs-sur-Marne, Paris, France, 10.3 ha). Lake stratification and mixing periods can be clearly observed, these periods are compared with the dynamic patterns of chlorophyll-a, phycocyanin, dissolved oxygen and pH. The results indicate that the phytoplankton growth corresponds with dissolved oxygen cycles. However, thermal stratification cannot totally explain the entire dynamic patterns of different physical-chemical and ecological variables. Besides, the cyanobacteria is one of the dominating groups of phytoplankton blooms during the lake stratification periods (8 August–29 September 2016). During the cooling mixed period (29 September–19 October 2016), the high concentration of chlorophyll-a is mainly caused by the other phytoplankton species, such as diatoms. Perspectives are discussed in order to apply this observation system for real-time management of water bodies and lakes.

International audience; Retention ponds, which are part of the stormwater drainage system, have specific hydrodynamics. The residence time of the water, higher than in the conduits, favours the sedimentation of particles. During spring and summer, mixing and thermal stratification alternate. A high phytoplankton biomass can develop and produce organic matter which can interact with micropollutants. That is why it is essential to understand the impact of organic matter locally produced on trace metals. For this purpose, we studied the speciation of nickel, copper, zinc and lead in the waters of a retention pond located in derivation of a channelled river in a Paris suburb (France). Three fractions of trace metals (total, dissolved and labile fractions) were sampled during dry weather and analyzed. During dry weather, few labile metals, potentially bioavailable, were present in the pond (less than 50% of the total concentration), less than in the river (40-70%). The chlorophyll a concentration was higher in the pond (90 to 140 mu g/L) than in the river (30 mu g/L): autochthonous dissolved organic matter seems to favour the complexation of dissolved metals. The reduction of nickel and copper trace metal bioavailability in the retention pond compared to the river which feeds the pond, which needs to be confirmed with more measurements, shows the interest to have urban storm waters transit through retention ponds to reduce their toxicity before their release in the natural waters.; Eléments du réseau d'assainissement pluvial, les bassins de rétention en eau ont un fonctionnement hydrodynamique spécifique, proche de celui d'un étang. Le temps de séjour de l'eau y est plus élevé que dans les conduites, favorisant la sédimentation des particules. Pendant le printemps et l'été, des phases de mélange et de stratification thermique alternent. Une biomasse élevée de phytoplancton peut alors se développer et la matière organique qu'elle produit peut interagir avec les micropolluants. Ainsi, pour mieux comprendre le cycle biogéochimique des éléments traces métalliques (ETM), il paraît important de connaître l'effet de la matière organique produite dans les bassins de rétention sur les ETM.Pour cela, nous avons étudié la spéciation du nickel, du cuivre, du zinc et du plomb dans l'eau dans un bassin de rétention en eau de Seine-Saint-Denis situé en dérivation d'une rivière canalisée qui l'alimente lors des fortes pluies. Les concentrations de temps sec en ETM totaux, dissous et labiles ont été déterminées. Les paramètres globaux ont également été mesurés. Par temps sec, peu de métaux labiles, potentiellement biodisponibles, sont présents dans le bassin (moins de 50 % de la concentration totale), moins que dans la rivière (autour de 70 %). Par ailleurs, la concentration en chlorophylle est plus élevée dans le bassin (90 à 140 µg/L) que dans la rivière (30 µg/L) : la matière organique dissoute autochtone semble favoriser la complexation des métaux dissous dans le bassin. La réduction de la biodisponibilité du nickel et du cuivre dans le bassin de rétention par rapport à la rivière qui l'alimente, qui devra être confirmée sur la base d'un plus grand nombre de mesures, montre l'intérêt de faire passer les eaux pluviales urbaines dans des bassins de rétention en eau pour réduire leur toxicité avant rejet dans le milieu récepteur.

International audience; Water temperature and the alternation of stratification and mixing count amongst key drivers of lake ecology. Reliable long time-series of water temperature are rare. Here, we investigated how a numerical model can fill the gaps in heterogeneous time-series and make it possible to identify a significant trend in the lake thermal regime. We computed the mean water temperature and the Schmidt stability, an indicator of the stratification strength, in a deep and well-stratified lake (Lake Bourget, France), between 1976 and 2008. We first used temperature measurements and then a one-dimensional vertical model, which we describe here. The model performs as well as the best existing models. During the 1976-2008 periods, whereas no statistically significant trend came out of the measurements, we found in the simulation results an increase of 0.12 °C per decade in the water temperature and of 5.9 days per decade in the stratification duration. This shows that the temperature history of a lake can be reconstructed and a reliable long-term trend computed when weather data and a calibrated temperature model are available. Our results also suggest that different local climatic influences cause a slower warming in this lake of the south-western Alps than in other sub-alpine lakes.

Rapport de Master Sciences et Génie de l'Environnement option "Systèmes Aquatiques et Gestion de l'Eau" (50 pages); Lake Bourget, located in the Alps region, is the largest natural French lake. It constitutes an important reservoir of drinking water for thousands of inhabitants. In the 60's, the first occurrence of eutrophication has been observed in Lake Bourget. In order to tackle the eutrophication, many restoration programs have been implemented since the 1970s. The massive reduction in nutrient loads (phosphorus and nitrate) has improved the water quality over the past two decades. In 2008, according to OECD trophic categories, Lake Bourget is classified as mesotrophic. However, blooms of the filamentous, phycoerythrin-rich, non-nitrogen fixing and hepatoxic cyanobacterium Planktothrix rubescens have occurred since 1996. The first objective of this work is to use the one dimensional vertical (1DV) thermal and biogeochemical model to simulate the lake temperature during 3 decades (1976-2008) in order to study the pluriannual water temperature evolution. The second objective involves studying the phytoplankton dynamics, in particular Planktothrix rubescens which dominates Lake Bourget phytoplankton for the last tens years despite the restoration efforts. This study on phytoplankton dynamics is focussed on two years 2003 and 2008 where environmental conditions are representative of the variability of Planktothrix rubescens' development.