Water temperature in lakes is governed by a complex heat budget, where the estimation of the single fluxes requires the use of several hydro-meteorological variables that are not generally available. In order to address this issue, we developed Air2Water, a simple physically based model to relate the temperature of the lake superficial layer (epilimnion) to air temperature only. The model has the form of an ordinary differential equation that accounts for the overall heat exchanges with the atmosphere and the deeper layer of the lake (hypolimnion) by means of simplified relationships, which contain a few parameters (from four to eight in the different proposed formulations) to be calibrated with the combined use of air and water temperature measurements. The calibration of the parameters in a given case study allows for one to estimate, in a synthetic way, the influence of the main processes controlling the lake thermal dynamics, and to recognize the atmospheric temperature as the main factor driving the evolution of the system. In fact, under certain hypotheses the air temperature variation implicitly contains proper information about the other major processes involved, and hence in our approach is considered as the only input variable of the model. In particular, the model is suitable to be applied over long timescales (from monthly to interannual), and can be easily used to predict the response of a lake to climate change, since projected air temperatures are usually available by large-scale global circulation models. In this paper, the model is applied to Lake Superior (USA–Canada) considering a 27 yr record of measurements, among which 18 yr are used for calibration and the remaining 9 yr for model validation. The calibration of the model is obtained by using the generalized likelihood uncertainty estimation (GLUE) methodology, which also allows for a sensitivity analysis of the parameters. The results show remarkable agreement with measurements over the entire data period. The use of air temperature reconstructed by satellite imagery is also discussed.

The role of riparian vegetation in shaping river morphology is widely recognized. The interaction between vegetation growth and riverbed evolution is characterized by complex nonlinear feedbacks, which hinder direct estimates of the role of key elements on the morphological evolutionary trajectories of gravel bed rivers. Adopting a simple theoretical framework, we develop a numerical model which couples hydromorphodynamics with biomass dynamics. We perform a sensitivity analysis considering several parameters as flood intensity, type of vegetation, and groundwater level. We find that the inclusion of vegetation determines a threshold behavior, identifying two possible equilibrium configurations: unvegetated versus vegetated bars. Stable vegetation patterns can establish only under specific conditions, which depend on the different environmental and species‐related characteristics. From a management point of view, model results show that relatively small changes in water availability or species composition may determine a sudden shift between dynamic unvegetated conditions to more stable, vegetated rivers.