Assessing temporal variability of lake turbidity and trophic state of European lakes using open data repositories.

Water turbidity is one of the more important water quality parameters that is strictly linked with the productivity of the lake and is commonly used as an indicator of the trophic state. However, limited field data availability across wide geographic gradients may hinder the conduction of large scale longitudinal studies. In this study, time series of lake turbidity and trophic state index (TSI) between 2002 and 2012 were obtained from the Copernicus Lake Water products to create a large longitudinal dataset of lake variables for 22 European lakes. The dataset was combined with estimates of nutrient concentrations and surface water temperature obtained from the Hydrological Predictions for the Environment (HYPE) and ERA5-Land data repositories, that were used as environmental predictors. Hence, the validity of the lake water quality parameters was tested by a) exploring their spatial and temporal variability and b) identifying associations with the environmental predictors. For this purpose, seasonal Mann-Kendall tests were applied to find significant inter-annual trends of turbidity and TSI for each lake, and generalized additive models (GAMs) were employed to identify the main parameters that shape their temporal dynamics. Although we did not find significant inter-annual changes, our findings highlighted the strong influence of seasonality and surface water temperature in defining the temporal variability patterns in most of the lakes. In addition, the importance of nutrients varied among lakes as several lakes exhibited narrow nutrient gradients reflecting relatively stable nutrient conditions during the examined period. Other lake intrinsic factors, such as local climate and biotic interactions, are important drivers of shaping turbidity and nutrient dynamics. This study highlighted the usefulness of combining lake data from large repositories in conducting large scale spatial studies as a valuable asset for future lake research and management purposes.

Trends of lake temperature, mixing depth and ice cover thickness of European lakes during the last four decades.

Lakes are particularly vulnerable ecosystems to global warming. Surface temperature of most lakes in the world has significantly increased. Here, we analysed time-series of water temperature, mixing-depth, and ice depth of 51 European lakes over the last four decades. We used data of surface temperature, total layer water temperature, mix-layer temperature, mix-layer depth, and ice cover depth obtained from the ERA5-Land reanalysis dataset. Our main objectives were a) to identify significant changes of the examined variables that have occurred from 1981 to 2019 and b) to assess the variability of changes in relation with geographical and lake morphological gradients. To this end, time series analysis was conducted using generalized additive models (GAMs). In addition, we quantified the magnitude of change by estimating the Sen's slopes for each variable and then we examined the variability of these slopes to geographical and lake morphological parameters using GAMs. Our results confirmed that water temperature parameters (surface, total-layer and mix-layer temperature) have significantly increased for all lakes during the last four decades. We also found significant changes of the mixing depth for 14 lakes. In addition, the lake ice depth has significantly decreased in all fifteen lakes of the subarctic climate region. Finally, we showed that the Sen's slopes depend on the geographic coordinates and the elevation of the lakes, whereas lake morphometry (e.g. depth) has a smaller effect on the magnitude of changes. These findings hint that lake ecosystems of Europe have substantially changed over the last forty years and urge the need to take precautionary measures to prevent future implications for the freshwater biota.

Delineating the relative contribution of climate related variables to chlorophyll-a and phytoplankton biomass in lakes using the ERA5-Land climate reanalysis data.

Understanding the climatic drivers of eutrophication is critical for lake management under the prism of the global change. Yet the complex interplay between climatic variables and lake processes makes prediction of phytoplankton biomass a rather difficult task. Quantifying the relative influence of climate-related variables on the regulation of phytoplankton biomass requires modelling approaches that use extensive field measurements paired with accurate meteorological observations. In this study we used climate and lake related variables obtained from the ERA5-Land reanalysis dataset combined with a large dataset of in-situ measurements of chlorophyll-a and phytoplankton biomass from 50 water bodies to develop models of phytoplankton related responses as functions of the climate reanalysis data. We used chlorophyll-a and phytoplankton biomass as response metrics of phytoplankton growth and we employed two different modelling techniques, boosted regression trees (BRT) and generalized additive models for location scale and shape (GAMLSS). According to our results, the fitted models had a relatively high explanatory power and predictive performance. Boosted regression trees had a high pseudo R2 with the type of the lake, the total layer temperature, and the mix-layer depth being the three predictors with the higher relative influence. The best GAMLSS model retained mix-layer depth, mix-layer temperature, total layer temperature, total runoff and 10-m wind speed as significant predictors (p<0.001). Regarding the phytoplankton biomass both modelling approaches had less explanatory power than those for chlorophyll-a. Concerning the predictive performance of the models both the BRT and GAMLSS models for chlorophyll-a outperformed those for phytoplankton biomass. Overall, we consider these findings promising for future limnological studies as they bring forth new perspectives in modelling ecosystem responses to a wide range of climate and lake variables. As a concluding remark, climate reanalysis can be an extremely useful asset for lake research and management.