Alexis Groleau

Impact of internal waves on the spatial distribution of Planktothrix rubescens (cyanobacteria) in an alpine lake

The vertical and horizontal distribution of the cyanobacterium, Planktothrix rubescens, was studied in a deep alpine lake (Lac du Bourget) in a 2-year monitoring program with 11 sampling points, and a 24-h survey at one sampling station. This species is known to proliferate in the metalimnic layer of numerous deep mesotrophic lakes in temperate areas, and also to produce hepatotoxins. When looking at the distribution of P. rubescens at the scale of the entire lake, we found large variations (up to 10 m) in the depth of the biomass peak in the water column. These variations were closely correlated to isotherm displacements. We also found significant variations in the distribution of the cyanobacterial biomass in the northern and southern parts of the lake. We used a physical modeling approach to demonstrate that two internal wave modes can explain these variations. Internal waves are generated by wind events, but can still be detected several days after the end of these events. Finally, our 24-h survey at one sampling point demonstrated that the V1H1 sinusoidal motion could evolve into nonlinear fronts. All these findings show that internal waves have a major impact on the distribution of P. rubescens proliferating in the metalimnic layer of a deep lake, and that this process could influence the growth of this species by a direct impact on light availability.

Estimation of the water quality of a large urbanized river as defined by the European WFD : what is the optimal sampling frequency?

Assessment of the quality of freshwater bodies is essential to determine the impact of human activities on water resources. The water quality status is estimated by comparing indicators with standard thresholds. Indicators are usually statistical criteria that are calculated on discrete measurements of water quality variables. If the time step of the measured time series is not sufficient to fully capture the variable’s variability, the deduced indicator may not reflect the system’s functioning. The goal of the present work is to assess, through a hydro-biogeochemical modeling approach, the optimal sampling frequency for an accurate estimation of 6 water quality indicators defined by the European Water Framework Directive (WFD) in a large human-impacted river, which receives large urban effluents (the Seine River across the Paris urban area). The optimal frequency depends on the sampling location and on the monitored variable. For fast varying compounds that originate from urban effluents, such as PO43−

Carbon fate in a large temperate human‐impacted river system: Focus on benthic dynamics

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Biogeochemical modelling of anaerobic vs. aerobic methane oxidation in a meromictic crater lake (Lake Pavin, France)

, NH4+

High-frequency monitoring of phytoplankton dynamics within the European water framework directive: application to metalimnetic cyanobacteria

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Tracing calcite precipitation with specific conductance in a hard water alpine lake (Lake Bourget)

and NO2−

Quantifying phytoplankton communities using spectral fluorescence: the effects of species composition and physiological state

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Maintaining meromixis in Lake Pavin (Auvergne, France): The key role of a sublacustrine spring

, a sampling time step of one week or less is necessary. To be able to reflect the highly transient character of bloom events, chl a concentrations also require a short monitoring time step. On the contrary, for variables that exert high seasonal variability, as NO3−

Estimating ecosystem metabolism from continuous multi-sensor measurements in the Seine River

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Biogeochemical Dynamics of Molybdenum in a Crater Lake: Seasonal Impact and Long-Term Removal

and O 2, monthly sampling can be sufficient for an accurate estimation of WFD indicators in locations far enough from major effluents. Integrative water quality variables, such as O 2, can be highly sensitive to hydrological conditions. It would therefore be relevant to assess the quality of water bodies at a seasonal scale rather than at annual or pluri-annual scales. This study points out the possibility to develop smarter monitoring systems by coupling both time adaptative automated monitoring networks and modeling tools used as spatio-temporal interpolators.