Temperature of the surface layer of temperate lakes is reconstructed by means of a simplified model on the basis of air temperature alone. The comparison between calculated and observed data shows a remarkable agreement (Nash-Sutcliffe efficiency indices always larger than 0.87, mean absolute errors of approximately 1uC) for all 14 lakes investigated (Mara, Sparkling, Superior, Michigan, Huron, Erie, Ontario, Biel, Zurich, Constance, Garda, Neusiedl, Balaton, and Baikal, in west-to-east order), which present a wide range of morphological and hydrological characteristics. Differently from a pure heat flux balance approach, where the different fluxes are determined on the basis of independent relationships, the input data directly inform parameters of a simple model that, in turn, provides meaningful information about the properties of the real system. The dependence of the model parameters on the main morphological indicators is presented, which allows for a quantitative description of the strong influence of the mean depth of the lake on the thermal inertia and the hysteresis pattern between air and lake surface temperatures.The temperature of the surface layer is a crucial factor for the hydrodynamics and ecology of lakes. Water temperature changes may have important direct and indirect ecological effects via their influence on the lifehistory processes of organisms (metabolism, growth, reproduction) and the properties of the habitats (Winder and Sommer 2012). Temperature variations affect foodweb structures and the availability of nutrients for the biological systems in a lake. The effects of temperature on physical and chemical characteristics of lakes may also modify the distribution of individual taxa from the microbiological to the top predator scale (Eggermont and Heiri 2012;Wojtal-Frankiewicz 2012;De Senerpont Domis et al. 2013).Surface temperature is the result of heat fluxes at the lake surface (short-wave solar radiation, long-wave radiation from and to the lake, sensible and latent heat exchange) and at the boundaries (inflows and outflows, groundwater exchange, precipitation, etc.), and of heat transport due to mixing within the lake. All these fluxes depend on several variables (solar radiation, air temperature, wind speed and direction, cloudiness, relative humidity, etc.), some of which may be difficult or expensive to measure reliably and with sufficient precision. Moreover, some of the variables can change significantly over the lake surface and in time (e.g., wind), and reconstructing their spatial variation can be a particularly hard task. Predicting water temperature is nonetheless a desired goal and models of different types and of different complexity have been proposed, ranging from simple regression models (Livingstone and Lotter 1998;Sharma et al. 2008) to more complex process-based numerical one-dimensional Perroud et al. 2009;Thiery et al. 2014) and three-dimensional models (Wahl and Peeters 2014).In this work, we aim at obtaining results that are accurate enough to reliably p...

During the last several decades, the Great Lakes region has been experiencing a significant rise in temperatures, with the extraordinary summer warming that affected Lake Superior in 1998 as an example of the marked response of the lake to increasingly warmer atmospheric conditions. In this work, we combine the analysis of this exceptional event with some synthetic scenarios, to achieve a deeper understanding of the main processes driving the thermal dynamics of surface water temperature in Lake Superior. The analysis is performed by means of the lumped model air2water, which simulates lake surface temperature as a function of air temperature alone. The model provides information about the seasonal stratification dynamics, suggesting that unusual warming events can result from two factors: anomalously high summer air temperatures, and increased strength of stratification resulting from a warm spring. The relative contribution of the two factors is quantified using the model by means of synthetic scenarios, which provide a simple but effective description of the positive feedback between the thermal behavior and the stratification dynamics of the lake.

Abstract:River water temperature is a key physical variable controlling several chemical, biological and ecological processes. Its reliable prediction is a main issue in many environmental applications, which however is hampered by data scarcity, when using datademanding deterministic models, and modelling limitations, when using simpler statistical models. In this work we test a suite of models belonging to air2stream family, which are characterized by a hybrid formulation that combines a physical derivation of the key equation with a stochastic calibration of parameters. The air2stream models rely solely on air temperature and streamflow, and are of similar complexity as standard statistical models. The performances of the different versions of air2stream in predicting river water temperature are compared with those of the most common statistical models typically used in the literature. To this aim, a dataset of 38 Swiss rivers is used, which includes rivers classified into four different categories according to their hydrological characteristics: low-land natural rivers, lake outlets, snow-fed rivers and regulated rivers. The results of the analysis provide practical indications regarding the type of model that is most suitable to simulate river water temperature across different time scales (from daily to seasonal) and for different hydrological regimes. A model intercomparison exercise suggests that the family of air2stream hybrid models generally outperforms statistical models, while cross-validation conducted over a 30-year period indicates that they can be suitably adopted for long-term analyses.

Abstract. The European Alps stretch over a range of climate zones which affect the spatial distribution of snow. Previous analyses of station observations of snow were confined to regional analyses. Here, we present an Alpine-wide analysis of snow depth from six Alpine countries – Austria, France, Germany, Italy, Slovenia, and Switzerland – including altogether more than 2000 stations of which more than 800 were used for the trend assessment. Using a principal component analysis and k-means clustering, we identified five main modes of variability and five regions which match the climatic forcing zones: north and high Alpine, north-east, north-west, south-east, and south and high Alpine. Linear trends of monthly mean snow depth between 1971 and 2019 showed decreases in snow depth for most stations from November to May. The average trend among all stations for seasonal (November to May) mean snow depth was −8.4 % per decade, for seasonal maximum snow depth −5.6 % per decade, and for seasonal snow cover duration −5.6 % per decade. Stronger and more significant trends were observed for periods and elevations where the transition from snow to snow-free occurs, which is consistent with an enhanced albedo feedback. Additionally, regional trends differed substantially at the same elevation, which challenges the notion of generalizing results from one region to another or to the whole Alps. This study presents an analysis of station snow depth series with the most comprehensive spatial coverage in the European Alps to date.

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.