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Rivers are increasingly recognised as active players in the global carbon cycle. They are able to transport, transform, and exchange organic matter, and can emit considerable fluxes of greenhouse gases (e.g., CO2) into the atmosphere, with a magnitude comparable to the global carbon input to the oceans. However, the quantification of these processes is still affected by considerable uncertainties, driven by an incomplete understanding of the interplay between physical, geochemical, and biological parameters, and by a lack of spatially and temporally resolved high-quality data. For instance, and despite a potentially strong impact on kilometres of rivers worldwide, the effects of hydropeaking on riverine CO2 emissions have been almost completely neglected until recently (Calamita et al., Unaccounted CO2 leaks downstream of a large tropical hydroelectric reservoir, PNAS 2020). As a contribution to filling this knowledge gap, we present the results of a field-measurement campaign performed in a single-thread Alpine river (River Noce, Italy) during multiple hydropeaking events. Data of water-dissolved CO2, water temperature, and flow discharge, were collected sub-hourly both downstream and upstream of the outlets of a hydropower plant, revealing a complex pattern of variation in time at both locations. Water released from the hydropower plant during hydropeaking had oversaturated CO2 concentrations relative to the atmosphere, in close agreement with water samples collected in the hypolimnion of the upstream reservoir. Higher flow rates during hydropeaking events were associated with higher rates of gas exchange through the water-air interface. Higher exchange rates and higher CO2 concentrations in water during hydropeaking events enhanced CO2 fluxes, as confirmed by measurements with a floating CO2 flux chamber. Meanwhile, the CO2 concentration upstream of the outlets displayed strong diel fluctuations around the atmospheric equilibrium concentration, which were likely driven by primary production within the residual flow during the day. It is shown that the residual flow can have a previously unacknowledged added value as a CO2 sink during the day, fueled by its biological activity. Hydropower releases bypassed the residual flow and discharged hypolimnetic water oversaturated with CO2 at high flow rates during hydropeaking, offsetting CO2 concentration and fluxes downstream of the outlets and increasing emissions on average. These results highlight the ubiquity of hydropeaking impacts also with respect to greenhouse gas emissions. They illustrate the complexity of the riverine carbon cycle and demonstrate the importance of temporally and spatially-resolved data for the accurate assessment of the riverine carbon balance.

Empirical evidence demonstrates that lakes and reservoirs are warming across the globe. Consequently, there is an increased need to project future changes in lake thermal structure and resulting changes in lake biogeochemistry in order to plan for the likely impacts. Previous studies of the impacts of climate change on lakes have often relied on a single model forced with limited scenario-driven projections of future climate for a relatively small number of lakes. As a result, our understanding of the effects of climate change on lakes is fragmentary, based on scattered studies using different data sources and modelling protocols, and mainly focused on individual lakes or lake regions. This has precluded identification of the main impacts of climate change on lakes at global and regional scales and has likely contributed to the lack of lake water quality considerations in policy-relevant documents, such as the Assessment Reports of the Intergovernmental Panel on Climate Change (IPCC). Here, we describe a simulation protocol developed by the Lake Sector of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) for simulating climate change impacts on lakes using an ensemble of lake models and climate change scenarios. The protocol prescribes lake simulations driven by climate forcing from gridded observations and different Earth system models under various Representative Greenhouse Gas Concentration Pathways, all consistently bias-corrected on a 0.5° × 0.5° global grid. In ISIMIP phase 2, 11 lake models were forced with these data to project the thermal structure of 62 well-studied lakes where data were available for calibration under historical conditions, and for nearly 17,500 lakes using uncalibrated models and forcing data from the global grid where lakes are present. In ISIMIP phase 3, this approach was expanded to consider more lakes, more models, and more processes. The ISIMIP Lake Sector is the largest international effort to project future water temperature, thermal structure, and ice phenology of lakes at local and global scales and paves the way for future simulations of the impacts of climate change on water quality and biogeochemistry in lakes.

Lake surface water temperature (LSWT) is sensitive to climate change. Previous studies have found that LSWT warming is occurring on a global scale and is expected to continue in the future. Recently, new global LSWT data products have been generated using satellite remote sensing, which provides an inimitable opportunity to study the LSWT response to global warming. Based on the satellite observations, we found that the warming rate of global lakes is uneven, with apparent regional differences. Indeed, comparing the LSWT warming in different climate zones (from arid to humid), the lakes in drylands experienced more significant warming (0.28 °C decade−1) than those in semi-humid and humid regions (0.19 °C decade−1) during previous decades (1995–2016). By further quantifying the impact factors, it showed that the LSWT warming is attributed to air temperature (74.4%), evaporation (4.1%), wind (9.9%), cloudiness (4.3%), net shortwave (3.1%), and net longwave (4.0%) over the lake surface. Air temperature is the main driving force for the warming of most global lakes, so the first estimate quantification of future LSWT trends can be determined from air temperature projections. By the end of the 21st century, the summer air temperature would warm up to 1.0 °C (SSP1-2.6) and 6.3 °C (SSP5-8.5) over lakes, with a more significant warming trend over the dryland lakes. Combined with their higher warming sensitivity, the excess summer LSWT warming in drylands is expected to continue, which is of great significance because of their high relevance in these water-limited regions.

Lake surface water temperatures (LSWTs) are sensitive to climate change, but previous studies have typically focused on temperatures from only the last few decades. Thus, while there is good appreciation of LSWT warming in recent decades, our understanding of longer-term temperature change is comparatively limited. In this study, we use a mechanistically based open-source model (air2water), driven by air temperature from a state-of-the-art global atmospheric reanalysis (ERA-20C) and calibrated with satellite-derived LSWT observations (ARC-Lake v3), to investigate the long-term change in LSWT worldwide. The predictive ability of the model is tested across 606 lakes, with 91% of the lakes showing a daily root mean square error smaller than 1.5 °C. Model performance was better at mid-latitudes and decreased towards the equator. The results illustrated highly variable mean annual LSWT trends during the twentieth century and across climatic regions. Substantial warming is evident after ~ 1980 and the most responsive lakes to climate change are located in the temperate regions.

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., Measurement(s) temperature of water • conductivity of water • pressure of water • velocity shear • turbidity • concentration of chlorophyll a in water • turbulence related quantities (TKE and temperature variance dissipation rates, diapycnal diffusivity) • Secchi depth • temperature of air • atmospheric pressure • atmospheric wind speed • humidity • solar radiation Technology Type(s) turbulence microstructure profiler • Secchi disk • weather station Sample Characteristic - Environment lake Sample Characteristic - Location Italy • Lago di Garda Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.14564430

Evaluating a three-dimensional lake model requires large datasets of many variables, including velocity fields, that are seldom available. Here we discuss how to assess the performance of a model at multiple scales (in time and space) with data from standard monitoring systems, i.e., mostly limited to water temperature. The modeling chain consists of a lake hydrodynamic model (Delft3D-Flow) forced by an atmospheric model (WRF, Weather Research and Forecasting). The two models are tested on the case study of Lake Garda (Italy), where a comprehensive dataset of atmospheric and water temperature observations is available. Results show that a consistent picture of the inherent dynamics can be reproduced from a heterogeneous set of water temperature data, by distilling information across diverse spatial and temporal scales. The choice of the performance metrics and their limitations are discussed, with a focus on the procedures adopted to manage and homogenize data, visualize results and identify sources of error.

Evaluating a three-dimensional lake model requires large datasets of many variables, including velocity fields, that are seldom available. Here we discuss how to assess the performance of a model at multiple scales (in time and space) with data from standard monitoring systems, i.e., mostly limited to water temperature. The modeling chain consists of a lake hydrodynamic model (Delft3D-Flow) forced by an atmospheric model (WRF, Weather Research and Forecasting). The two models are tested on the case study of Lake Garda (Italy), where a comprehensive dataset of atmospheric and water temperature observations is available. Results show that a consistent picture of the inherent dynamics can be reproduced from a heterogeneous set of water temperature data, by distilling information across diverse spatial and temporal scales. The choice of the performance metrics and their limitations are discussed, with a focus on the procedures adopted to manage and homogenize data, visualize results and identify sources of error.

Between October 27th and 30th, 2018 the Eastern Alps were hit by a severe storm named Vaia. The storm brought strong wind and heavy rainfall, both culminated on Monday 29th. In Italy, the windstorm caused the loss of 8 million m3 of standing trees (Motta et al., 2018), and the rainfall triggered floods and related geomorphic processes, such as bank erosion, sediment/vegetation transport, and debris flows. In the Province of Trento (275 mm of rainfall on average in 3 days measured by 97 ground stations, corresponding to a median 48-h return period of 115 years), more than 300 events of such type were reported during those days, counting only the most important (Piccolroaz and Michelini, 2018). One of the most violent was the extreme debris flow of the Rotiano Creek, which caused the collapse of check dams and struck the village of Dimaro. The extraordinary magnitude of the debris flow occurred along the Rotiano Creek in October 2018 put the attention on how paroxysmal scenarios, due to concurrent severe meteo-hydrological factors and possible failure of existing hydraulic defence works, should be considered in debris-flow hazard evaluations. Due to the ongoing tendency towards more frequent extreme rainfall events and considering the progressive aging of the existing control works, converging towards an effective definition of residual hazard scenarios is getting more and more important and, at the same time, challenging.

A decrease in hypolimnetic dissolved oxygen (DO) is a commonly seen effect of climate change. However, in oligotrophic Lake Tovel (Italy), a deep mountain lake, annual mean DO (% saturation) has increased from near anoxia to >20% in the bottom layer (35–39 m). We analyzed long‐term patterns of DO (1937–2019) using different methods (correlation and trend analysis, identification of extreme events) to link DO to drivers and indices of mixing. While spring mixing remained temporally limited, later ice‐in (5.1 days decade−1) and the positive relationship between ice‐in and DO the following year evidenced autumn mixing as the main driver for hypolimnetic DO increase. Extreme meteorological events also replenished hypolimnetic DO. Using DO and conductivity (1995–2019), we identified 14 deep mixing events with hypolimnetic DO > 40%. Density‐based indices (Schmidt stability, relative thermal resistance, Lake Number, and Wedderburn Number) only partially captured these events that were related to snowmelt, flooding, and cold spells during spring and autumn, with a carryover effect sometimes lasting >1 year. Recently, annual mean DO in the upper layer decreased beyond temperature‐dependent solubility. This decrease was not comprehensively confirmed by statistical tests but was possibly linked to atmospheric stilling. We suggest that Lake Tovel's shift from meromixis to dimixis was driven by climate warming (i.e., increasing air temperature 0.6°C decade−1) that delayed ice‐in and increased autumn mixing. Our work underlines the vulnerability of mountain lakes and their different response to climate change with respect to more studied lowland lakes.

Lake Baikal is the oldest, deepest, and most voluminous freshwater lake on Earth. Despite its enormous depth, episodically (almost twice a year) large amounts of surface, cold, and oxygenated water sink until the bottom of the lake due to thermobaric instability, with consequent effects on the ecology of the whole lake. A minimal one-dimensional model is used to investigate how changes in the main external forcing (i.e., wind and lake surface temperature) may affect this deep ventilation mechanism. The effect of climate change is evaluated considering the IPCC RCP8.5 and some idealized scenarios and is quantified by (i) estimating the mean annual downwelling volume and temperature and (ii) analyzing vertical temperature and dissolved oxygen profiles. The results suggest that the strongest impact is produced by alterations of wind forcing, while deep ventilation is resistant to rising lake surface temperature. In fact, the seasons when deep ventilation can occur can be shifted in time by lake warming, but not dramatically modified in their duration. Overall, the results show that Lake Baikal is sensible to climate change, to an extent that the ecosystem and water quality of this unique lacustrine system may undergo profound disturbances.

Assessing the accuracy of gridded climate data sets is highly relevant to climate change impact studies, since evaluation, bias correction, and statistical downscaling of climate models commonly use these products as reference. Among all impact studies those addressing hydrological fluxes are the most affected by errors and biases plaguing these data. This paper introduces a framework, coined Hydrological Coherence Test (HyCoT), for assessing the hydrological coherence of gridded data sets with hydrological observations. HyCoT provides a framework for excluding meteorological forcing data sets not complying with observations, as function of the particular goal at hand. The proposed methodology allows falsifying the hypothesis that a given data set is coherent with hydrological observations on the basis of the performance of hydrological modeling measured by a metric selected by the modeler. HyCoT is demonstrated in the Adige catchment (southeastern Alps, Italy) for streamflow analysis, using a distributed hydrological model. The comparison covers the period 1989–2008 and includes five gridded daily meteorological data sets: E-OBS, MSWEP, MESAN, APGD, and ADIGE. The analysis highlights that APGD and ADIGE, the data sets with highest effective resolution, display similar spatiotemporal precipitation patterns and produce the largest hydrological efficiency indices. Lower performances are observed for E-OBS, MESAN, and MSWEP, especially in small catchments. HyCoT reveals deficiencies in the representation of spatiotemporal patterns of gridded climate data sets, which cannot be corrected by simply rescaling the meteorological forcing fields, as often done in bias correction of climate model outputs. We recommend this framework to assess the hydrological coherence of gridded data sets to be used in large-scale hydroclimatic studies.

Can we predict long-term trends of lake surface temperature based on air temperature alone? We explore this question by analyzing the performance of a hybrid model (air2water) as a predictive tool for defining scenarios of lake surface temperature in the framework of climate change studies. Employing Lake Tahoe (U.S.A.) as a case study, we apply the model using different air temperature datasets (in situ measurements, gridded observations, and downscaled General Circulation Models). Through a data-driven calibration of the model parameters based on surface water temperature records, we show that air2water provides good performance (root mean square error ∼ 0.5°C, on a monthly scale) regardless of the input dataset. The model is able to accurately capture the historical long-term trend and interannual fluctuations over decades (from 1969 to present), using only 7 yr of monthly measurements of surface water temperature for calibration. Additionally, when used to predict future surface water temperature of the lake, air2water produces the same projections irrespective of the air temperature dataset used to drive the model. This is certainly desirable, but not immediately expected when using a relatively simple model. Overall, the results suggest the high potential and robustness of air2water as a predictive tool for climate change assessment. Lake surface temperature warming of up to 1.1°C (RCP 4.5) and 2.9°C (RCP 8.5) was simulated at the end of the 21st century during summer months in Lake Tahoe. Such a scenario, if realized, would lead to serious consequences on lake water chemistry, primary productivity, plankton community structure, and nutrient cycling.

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., Proceedings of the National Academy of Sciences of the United States of America, 117 (44), ISSN:0027-8424, ISSN:1091-6490

The selective trapping and erosion of fine particles that occur in a gravel bed river have important consequences for its stream ecology, water quality, and overall sediment budgeting. This is particularly relevant in water bodies that experience periodic alternation between sediment supply-limited conditions and high sediment loads, such as downstream from a dam. While experimental efforts have been spent to investigate fine sediment erosion and transport in gravel bed rivers, a comprehensive overview of the leading processes is hampered by the difficulties in performing flow field measurements below the gravel crest level. In this work, a new two-dimensional, semi-implicit numerical scheme for the solution of the Navier-Stokes equations in the presence of deposited and erodible sediment is presented, and tested against analytical solutions and performing numerical tests. The scheme is mass-conservative, computationally efficient, and allows for a fine discretization of the computational domain. Overall, this makes the model suitable to appreciate small-scales phenomena such as inter-grain circulation cells, thus offering a valid alternative to evaluate the shear stress distribution, on which erosion and transport processes depend, compared to traditional experimental approaches. In this work, we present proof-of-concept of the proposed model, while future research will focus on its extension to a three-dimensional and parallelized version, and on its application to real case studies.

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 data-demanding 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. Copyright © 2016 John Wiley & Sons, Ltd.

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.

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.

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.

In the last decades significant technological advances together with improved modeling capabilities fostered a rapid development of geophysical monitoring techniques in support of hydrological modeling. Geophysical monitoring offers the attractive possibility to acquire spatially distributed information on state variables. These provide complementary information about the functioning of the hydrological system to that provided by standard hydrological measurements, which are either intrinsically local or the result of a complex spatial averaging process. Soil water content is an example of state variable, which is relatively simple to measure pointwise (locally) but with a vanishing constraining effect on catchment-scale modeling, while streamflow data, the typical hydrological measurement, offer limited possibility to disentangle the controlling processes. The objective of this work is to analyze the advantages offered by coupling traditional hydrological data with unconventional geophysical information in inverse modeling of hydrological systems. In particular, we explored how the use of time-lapse, spatially distributed microgravity measurements may improve the conceptual model identification of a topographically complex Alpine catchment (the Vermigliana catchment, South-Eastern Alps, Italy). The inclusion of microgravity data resulted in a better constraint of the inversion procedure and an improved capability to identify limitations of concurring conceptual models to a level that would be impossible relying only on streamflow data. This allowed for a better identification of model parameters and a more reliable description of the controlling hydrological processes, with a significant reduction of uncertainty in water storage dynamics with respect to the case when only streamflow data are used.

[1] The phenomenon of deep water renewal in the South Basin of Lake Baikal is investigated by means of a simplified one-dimensional model. The downwelling process, whereby large volumes of superficial, cold, and oxygenated water periodically sink to the lake bottom ( >1400m) due to thermobaric instability, is simulated by means of three main submodules: a reaction-diffusion equation for temperature and other tracers, and two Lagrangian algorithms, the first for the vertical stabilization of unstable density regions (including thermobaric effects) and the second handling the downwelling mechanism. A self-consistent procedure for the dynamical reconstruction of the diapycnal diffusivity profile is included to account for the effect of the variability of external conditions. The model has been developed aimed at providing a detailed description of deep-ventilation and a quantification of its consequences at the basin scale; the core algorithms have been designed suitably to perform long-term simulations (hundreds of years) and to deal with a limited amount of information about boundary conditions, which are expressed in terms of wind forcing and surface water temperature. The main parameters have been calibrated using measured profiles of temperature and chlorofluorocarbons (CFC-12) concentration over a 40 year historical period. A long-term simulation (one millennium), in which the current meteorological conditions have been kept statistically unchanged, has been used to determine the asymptotic dynamics. The results are consistent with previous measurements and estimates, suggesting that the model is suitable to qualitatively and quantitatively simulate deep water renewal in deep, temperate lakes, capturing the relative contribution and interaction of the different processes involved.

First International Scientific Workshop on GARDEN (Lake Garda Environmental system); Trento, Italy, 2 February 2017

We present HYPERstream, an innovative streamflow routing scheme based on the width function instantaneous unit hydrograph (WFIUH) theory, which is specifically designed to facilitate coupling with weather forecasting and climate models. The proposed routing scheme preserves geomorphological dispersion of the river network when dealing with horizontal hydrological fluxes, irrespective of the computational grid size inherited from the overlaying climate model providing the meteorological forcing. This is achieved by simulating routing within the river network through suitable transfer functions obtained by applying the WFIUH theory to the desired level of detail. The underlying principle is similar to the block-effective dispersion employed in groundwater hydrology, with the transfer functions used to represent the effect on streamflow of morphological heterogeneity at scales smaller than the computational grid. Transfer functions are constructed for each grid cell with respect to the nodes of the network where streamflow is simulated, by taking advantage of the detailed morphological information contained in the digital elevation model (DEM) of the zone of interest. These characteristics make HYPERstream well suited for multi-scale applications, ranging from catchment up to continental scale, and to investigate extreme events (e.g., floods) that require an accurate description of routing through the river network. The routing scheme enjoys parsimony in the adopted parametrization and computational efficiency, leading to a dramatic reduction of the computational effort with respect to full-gridded models at comparable level of accuracy. HYPERstream is designed with a simple and flexible modular structure that allows for the selection of any rainfall-runoff model to be coupled with the routing scheme and the choice of different hillslope processes to be represented, and it makes the framework particularly suitable to massive parallelization, customization according to the specific user needs and preferences, and continuous development and improvements.

Water temperature controls many biochemical and ecological processes in rivers, and theoretically depends on multiple factors. Here we formulate a model to predict daily averaged river water temperature as a function of air temperature and discharge, with the latter variable being more relevant in some specific cases (e.g., snowmelt-fed rivers, rivers impacted by hydropower production). The model uses a hybrid formulation characterized by a physically based structure associated with a stochastic calibration of the parameters. The interpretation of the parameter values allows for better understanding of river thermal dynamics and the identification of the most relevant factors affecting it. The satisfactory agreement of different versions of the model with measurements in three different rivers (root mean square error smaller than 1oC, at a daily timescale) suggests that the proposed model can represent a useful tool to synthetically describe medium- and long-term behavior, and capture the changes induced by varying external conditions.

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.

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 1 degrees C) 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.

Scientists who study the physics of inland and coastal water bodies met in Trento, Italy in July for the 17th in a series of workshops that seek to expand cooperation with researchers in related fields. The workshops aim to facilitate the dialogue among physical limnologists, modelers, and colleagues from other disciplines, such as biologists, chemists, and engineers. This year's workshop was attended by 47 participants from 17 different countries. ©2014 The Authors.

Large-scale hydrological models are demanding both in term of memory allocation and CPU time, particularly when assessment of modeling uncertainty is required. High Performance Computing offers the opportunity to reach resolutions not achievable with standard serial coding. However, the advantages may be offset by poor scalability of the model due to components that have to be executed in series, such as to simulate the presence of hydraulic infrastructures. Driven by this motivation, we developed HYPERstreamHS, a model that adopts a holistic approach to simulate hydrological processes in large river basins with streamflow altered by hydraulic infrastructures. The model adopts a dual-layer parallelization strategy, where the paralleled version of the hydrological kernel is the first-layer, with the second layer taking care of inverse modeling. The results show that the processors should be carefully organized and grouped in order to achieve the best overall performance and suggests that this subdivision is problem specific.

Lake surface water temperature (LSWT) responds rapidly to changes in climatic variables. This response is heterogeneous in space and its spatial distribution is primarily influenced by lake bathymetry and latitude. Such heterogeneity is not captured by one-dimensional water temperature models, which can accurately predict only the average LSWT. We performed a spatially distributed application of the hybrid physically based/data-driven model air2water to predict the LSWT variability in the 5 Laurentian Great Lakes and to deepen our understanding of the role of local depth and latitude in shaping this heterogeneous response. Daily remotely sensed LSWT data were used to calibrate and validate the model during 1995–2018, and additional simulations considering a synthetic warmer climate scenario in which air temperature was increased by 2 °C were run to assess the inter- and intra-lake differences in LSWT warming rates. The model reproduces the observed spatial distribution of LSWT with an average root mean squared error of 1.2 °C and suggests that, under the warmer scenario, the LSWT of the 5 lakes could increase heterogeneously, with the deepest zones showing the maximum warming rates. Summer stratification lengthening is expected to increase with higher local depth; this behaviour attenuates with increasing latitude, whereas the LSWT warming is essentially dependent on the local depth, irrespective of latitude. We highlight the importance of accounting for LSWT spatial heterogeneity to adequately assess the thermal response of the Great Lakes to a warming climate.

Study region The study region comprises 25 lowland Polish lakes in the northern part of the country. The studied lakes provide domestic, industrial and agricultural water supply, and are major attractions for tourism, thus playing a significant role in the Polish economy. Study focus The expected impact of future climate change on lake surface water temperature (LSWT) was predicted using the air2water model, which relies solely on daily air temperature (AT) as model input. LSWT and AT observations for the period 1987–2016 were used for model calibration and validation. Then, historical (1987–2005) and future (2006–2100) AT time series from nine EURO-CORDEX climate models were used to project future LSWT under emission scenarios RCP4.5 and RCP8.5. New hydrological insights for the region The results showed that air2water can well reproduce daily LWST with root mean square errors lower than 1 °C on average. The warming trends of both AT and LSWT are expected to be lower than those observed in the past decades (after the 1980s), and ice cover and weak stratification conditions are expected to partially buffer the LSWT response to the high AT warming expected in future winters. However, the overall enduring warming will substantially alter future thermal dynamics, leading to a shortening of ice cover and inverse stratification periods, possibly leading to serious consequences for lake water quality and ecosystem health.

Lake Ledro (Trentino – Italy) is located at 652 with a surface of 2.1 km2, a volume of 68∙106 m3 and a maximum depth of 48 m. It is used as a reservoir for hydropower produces and is one of the main touristic resource for the surrounding areas. In the recent years it was affected by intense blooms of the harmful cyanobacterium Planktotrix rubescens. The algal vertical distribution depends on several factors like thermal stratification, light penetration, nutrients availability and the withdrawal of water. As a first step in the analysis of the influence of physical processes on the algal dynamics, we present a study of the driven-wind circulation in lake Ledro by the means of LES-COAST model, taking in account the effect of density stratification and the non-homogeneity of wind distribution on the surface of lake.