A multi-site, year-round turbulence microstructure atlas for the deep perialpine Lake Garda.

A multi-site, year-round dataset comprising a total of 606 high-resolution turbulence microstructure profiles of shear and temperature gradient in the upper 100 m depth is made available for Lake Garda (Italy). Concurrent meteorological data were measured from the fieldwork boat at the location of the turbulence measurements. During the fieldwork campaign (March 2017-June 2018), four different sites were sampled on a monthly basis, following a standardized protocol in terms of time-of-day and locations of the measurements. Additional monitoring activity included a 24-h campaign and sampling at other sites. Turbulence quantities were estimated, quality-checked, and merged with water quality and meteorological data to produce a unique turbulence atlas for a lake. The dataset is open to a wide range of possible applications, including research on the variability of turbulent mixing across seasons and sites (demersal vs pelagic zones) and driven by different factors (lake-valley breezes vs buoyancy-driven convection), validation of hydrodynamic lake models, as well as technical studies on the use of shear and temperature microstructure sensors.

Changing nutrient cycling in Lake Baikal, the world's oldest lake.

Lake Baikal, lying in a rift zone in southeastern Siberia, is the world's oldest, deepest, and most voluminous lake that began to form over 30 million years ago. Cited as the "most outstanding example of a freshwater ecosystem" and designated a World Heritage Site in 1996 due to its high level of endemicity, the lake and its ecosystem have become increasingly threatened by both climate change and anthropogenic disturbance. Here, we present a record of nutrient cycling in the lake, derived from the silicon isotope composition of diatoms, which dominate aquatic primary productivity. Using historical records from the region, we assess the extent to which natural and anthropogenic factors have altered biogeochemical cycling in the lake over the last 2,000 y. We show that rates of nutrient supply from deep waters to the photic zone have dramatically increased since the mid-19th century in response to changing wind dynamics, reduced ice cover, and their associated impact on limnological processes in the lake. With stressors linked to untreated sewage and catchment development also now impacting the near-shore region of Lake Baikal, the resilience of the lake's highly endemic ecosystem to ongoing and future disturbance is increasingly uncertain.

Involving citizens in hydrodynamic research: A combined local knowledge - numerical experiment on Lake Garda, Italy.

Local knowledge on surface currents and transport patterns in Lake Garda is acquired through interviews among wind-surfers, sailors, fishermen, ferry boat drivers, firefighters nautical rescue team, and officers from the environmental protection agency. Data are collected by means of individual interviews and focus groups, analyzed for internal consistency and summarized in qualitative maps. Three-dimensional numerical simulations are performed using a one-way coupled atmospheric-hydrodynamic model and the results are compared with the observations of the interviewees. Through this combined effort, currents that were not evident to the scientific community, but are well-known to sailors and surfers, can now be recognized and physically understood, like the 'Corif' that flows along the eastern shore in summertime between late morning and afternoon, when wind blows from the south. The transport patterns are also identified, like the predominant east-to-west surface transport experienced by fishermen under storm events and floods, that is confirmed for northerly wind, and the west-to-east transport for southerly wind. Moreover, the trajectory of a drifting capsized boat is reproduced by the model and the dynamics of the accident (location and timing) are reconstructed in collaboration with the firefighters nautical rescue team of Trento and based on information from local newspapers and witnesses. This exercise demonstrates that the joint effort of the scientific community and local experts can produce advances in the understanding of large-scale hydrodynamic processes in lakes.

Importance of planetary rotation for ventilation processes in deep elongated lakes: Evidence from Lake Garda (Italy).

Ventilation mechanisms in deep lakes are crucial for their ecosystem functioning. In this paper we show the relevance of planetary rotation in affecting ventilation processes in relatively narrow, elongated deep lakes. Through a recent field campaign in Lake Garda (Italy), we provide explicit observational evidence for the development of lake-wide wind-driven secondary flows influenced by the Coriolis force in a narrow lake. The interpretation of these observations is supported by results from numerical simulations with a three-dimensional model of the lake. The results add an additional element, often neglected in narrow lakes, to be carefully considered when assessing the response of lakes to external forcing and climate change.

Modeling daily water temperature for rivers: comparison between adaptive neuro-fuzzy inference systems and artificial neural networks models.

River water temperature is a key control of many physical and bio-chemical processes in river systems, which theoretically depends on multiple factors. Here, four different machine learning models, including multilayer perceptron neural network models (MLPNN), adaptive neuro-fuzzy inference systems (ANFIS) with fuzzy c-mean clustering algorithm (ANFIS_FC), ANFIS with grid partition method (ANFIS_GP), and ANFIS with subtractive clustering method (ANFIS_SC), were implemented to simulate daily river water temperature, using air temperature (Ta), river flow discharge (Q), and the components of the Gregorian calendar (CGC) as predictors. The proposed models were tested in various river systems characterized by different hydrological conditions. Results showed that including the three inputs as predictors (Ta, Q, and the CGC) yielded the best accuracy among all the developed models. In particular, model performance improved considerably compared to the case where only Ta is used as predictor, which is the typical approach of most of previous machine learning applications. Additionally, it was found that Q played a relevant role mainly in snow-fed and regulated rivers with higher-altitude hydropower reservoirs, while it improved to a lower extent model performance in lowland rivers. In the validation phase, the MLPNN model was generally the one providing the highest performances, although in some river stations ANFIS_FC and ANFIS_GP were slightly more accurate. Overall, the results indicated that the machine learning models developed in this study can be effectively used for river water temperature simulation.