Abstract
Abstract. 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.
Highlights
Water temperature is crucial for lakes physical, chemical and biological dynamics: temperature is the primary driver of the vertical stratification, and directly affects vertical exchanges of mass, energy and momentum within the water column
In this work we perform sensitivity analysis by using generalized likelihood uncertainty estimation (GLUE), a methodology proposed by Beven and Binley (1992) that requires the identification of a validity range for each parameter, a strategy for sampling the parameter space and a likelihood measure to be used in order to rank the different parameter sets
We show that our modeling framework is able to reproduce the observed water temperature data with limited information on external meteorological forcing over long timescales, ranging from monthly to interannual
Summary
Water temperature is crucial for lakes physical, chemical and biological dynamics: temperature is the primary driver of the vertical stratification, and directly affects vertical exchanges of mass, energy and momentum within the water column. Water temperature plays a key role in influencing the aquatic ecosystem of lakes, which usually adapts to a specific range of physical and environmental conditions. In the light of these considerations, it is evident that any significant modification to current environmental conditions may influence the limnic system, with direct impacts on the composition and richness of its ecosystem (MacKay et al, 2009). There are several reasons to look for a reliable tool to have information about the dependence of water temperature on the various factors influencing the heat balance of the lake compartments
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