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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.

Lakes are essential ecosystems that provide a large number of ecosystem services whose quality is strongly impacted by human pressures. Optimal uses of lakes require adapted management practices which in turn rely on physico-chemical and biological monitoring. Long-term ecological monitoring provides large sets of environmental data. When such data are available, they have to be associated to metadata and to be stored properly to be accessible and useable by the scientific community. We present a data informatics system accessible to anyone who requests it. Maintained online since 2014 (https://si-ola.inrae.fr), it is originated from the Observatory on LAkes (OLA). It contains long-term data from 4 peri-alpine lakes (Lakes Aiguebelette, Annecy, Bourget, Geneva/Léman) and 24 high-altitude lakes of the northern French Alps. We describe the generated long-term data series, the data type, the methodologies and quality control procedures, and the information system where data are made accessible. Data use is allowed under the condition of providing reference to the original source. We show here how such a platform clearly enhances data sharing and scientific collaboration. Various studies referring to these data are regularly published in peer-reviewed journals; providing in fine a better understanding of lakes’ ecosystems functioning under local and global pressures.

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.

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; 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.