Physico-chemical dataset from an in situ mesocosm experiment simulating extreme climate events in Lake Geneva...

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.