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A high temporal (hourly) and high spatial resolution (250-meter) multi-decadal (1991 - 2021) gridded dataset of mean temperature and precipitation is presented for the complex mountain area of Trentino-Alto Adige in the north-eastern region of the Alps in Italy. This dataset was obtained from more than 300 meteorological stations covering the entire region of Trentino-Alto Adige as well as the neighbouring countries and regions of Italy. The observed dataset underwent quality checks and quality control such as gross error limit check, two-sided continuity check, persistence check and others for both meteorological variables however, no gap-filling procedure was undertaken in order to keep the originality of the dataset. Using the processed dataset, kriging interpolation technique was used to generate a gridded hourly dataset of the meteorological metered variables on a high spatial resolution grid of 250m x 250m. The accuracy of the kriging dataset was evaluated by a leave-one-out cross-validation approach, wherein the station in consideration is omitted, to remove self-influence, and the time series is reconstructed using the neighbouring stations. For the entire time period and region, the hourly temperature and precipitation show no bias, with a mean absolute error (MAE) of about 1.2 °C and 0.1 mm, comparable with state-of-art high-resolution datasets. This new dataset provides valuable insight into the spatio-temporal distribution of temperature and precipitation for highly variable topography experiencing distinct climatic conditions for the region of Trentino-Alto Adige and it helps to validate climate models for monitoring climate change at the regional level. Moreover, gridded datasets support the modelling of extreme events and climate variance, which are crucial for a range of climate impact assessments.

In the last years, different providers (e.g. NOAA, MeteoFrance, ECMWF, DWD) produced meteorological gridded global and regional data sets. These model outputs are now reanalysis and operative forecasts distributed as open data, with different spatial and temporal resolutions. The aim of this contribution is the comparison of short-term streamflow forecasting outputs using different meteorological data sets as forcing.The use of the above-predicted weather time series as input for the hydrological models allows the evaluation of the streamflow at the catchment closure. Hourly data sets of temperature and precipitation from the different providers were selected for this contribution and the evaluation of the short-term streamflow forecasting results on three small catchments in the Alps was carried out.A data-driven forecasting procedure with the Support Vector Regression machine learning algorithm as a tool for hydrological modeling was implemented. For each provider, training and testing phase were performed using as forcing the weather model outputs of the same provider, in this way the simulations are consistent. The training phase takes advantage of reanalysis data sets when available, otherwise historical forecasting products are used as an alternative. Instead, the testing period is forced by lags of the temperature, precipitation and streamflow metered data for the past. The number of lag hours is defined in the training stage with a grid search approach. Moreover, the actual temperature and precipitation forecasting data sets cover the prediction lead time. The training was carried on for each day of the training period and the output of each run is the hourly short-term streamflow prediction.The use of multiple data sources as input allows us to emphasize the differences between global and local meteorological forcing. Moreover, the simulation ensembles outputs allow the identification and quantification of uncertainty that lead to a better interpretation of the prediction. These results are promising in hydrological modeling to increase the final accuracy of the streamflow predictions and the decision-making under uncertainty.

Global and regional climate models are constantly improving the quality of their outputs with increasingly fine spatial and temporal resolutions. These products, which comprise, for instance, reanalysis, reforecast and forecast, can be used for several applications, such as boundary conditions for climate simulations, initial conditions for local weather forecasting, and reference datasets for environmental and energy uses. Nevertheless, many authors have pointed out that such climate models are not suitable for direct use in local applications due to the presence of biases between the model results and the metered data. At this aim, several statistical methodologies have been proposed to correct and downscale the climate models outputs and make it available also for local purposes. Therefore, the purpose of this contribution is to analyse the current state-of-the-art statistical bias correction methods on different time aggregation to assess the capabilities of these methods from monthly to hourly temporal scale.This study is carried out on the Trentino- Alto Adige, which is an alpine region in north Italy equipped with several measuring weather stations, around 300. The temperature and precipitation observations have been then used to produce a reference dataset through the geostatistical interpolation method called kriging. Instead, ERA5-Land, the reanalysis of ECMWF, has been adopted for the bias correction analysis. Several methods have been tested comprising of univariate and multivariate method including: linear scaling, variance scaling, local intensity scaling, local power transformation, quantile mapping, quantile delta mapping, and multivariate bias correction methods such as MBCn, MBCp, and MBCr. The time scale investigated are monthly, daily and hourly aggregations.The results show a general decreasing of the performance of all the bias correction methods with the increase in the time-frequency of the weather variables. In particular, the mean absolute error of the corrected daily temperature is 50% larger than the monthly one, and the same 50% increase in error is found between daily and hourly corrected data. The increase in error with decreasing temporal resolution is even more pronounced for the precipitation variable, which is known to be discontinuous with respect to temperature. Multivariate bias correction methods seem to have difficulty maintaining dependencies between variables in the case of high-frequency data.Although the results on the hourly data are not so scarce, it is evident that more depth analysis of temporal high-resolution climate data is needed, including sub-hourly data in the future, and therefore become crucial to develop new methodologies capable of correcting sub-daily bias. In conclusion, with this work, the authors seek to support research in the direction of providing high-frequency weather data for local applications, which are crucial, for example, in hydrological simulations for the assessment of hydrogeological risks and the management of renewable energy in the electricity market.

A well-known approach to seasonal hydrological forecasting involves the use of ensembles as forcing to a hydrological model, based either on historical meteorological data (Ensemble Streamflow Prediction, ESP) or on forecasts produced by one or more dynamic climate models (multi-model approach). This work aims at investigating the role of initial hydrologic conditions (ICs) and seasonal climate forecast skill on the accuracy of seasonal hydrologic predictions in alpine regions, as a function of catchment properties. The Integrated Catchment-scale HYdrological Model (ICHYMOD) is employed, forcing it with historical meteorological data and multi-model ensemble climate predictions produced by the climate forecast systems NCEP CFSv2 and ECMWF SEAS5 (the latter analysis is not included in the present work). The hydro-climatic prediction system is tested on two catchments in the Eastern Italian Alps, that are different in terms of orography and soil/groundwater storage capacity. The diverse catchment properties result in differential parameterization of the subsurface processes in the hydrological model, hence in a different impact of the initial hydrologic conditions on the seasonal runoff predictions.

Funder: Libera Università di Bolzano; Id: http://dx.doi.org/10.13039/501100008815, Short‐term forecasting of water demand is a crucial process for managing efficiently water supply systems. This paper proposes to develop a novel graph convolutional recurrent neural network (GCRNN) to predict time series of water demand related to some water supply systems or district metering areas that belong to the same geographical area. The aim is to build a graph‐based model able to capture the dependence among the different water demand time series both in spatial and in temporal terms. This model is built on a set of different graphs, and its performance is compared to two methods, including a state‐of‐the‐art deep long short‐term memory (LSTM) neural network and a traditional seasonal autoregressive moving average model. Additionally, the forecasting model is tested in a condition when a sensor has a malfunction. The results show the ability of the GCRNN to produce accurate and reliable forecasting, especially when based on graph built while accounting for both time‐series correlation and spatial criteria. The GCRNN consistently outperforms the LSTM during the fault test, showing its ability to generate a robust prediction for days after a sensor malfunction, given the GCRNN's ability to benefit from the other time series of the graph.

Efficient energy production and distribution systems are urgently needed to reduce world climate change. Since modern district heating systems are sustainable energy distribution services that exploit renewable sources and avoid energy waste, in-depth knowledge of thermal energy demand, which is mainly affected by weather conditions, is essential to enhance heat production schedules. We hence propose a mixture copula-based approach to investigate the complex relationship between meteorological variables, such as outdoor temperature and solar radiation, and thermal energy demand in the district heating system of the Italian city Bozen-Bolzano. We analyse data collected from 2014 to 2017, and estimate copulas after removing serial dependence in each time series using autoregressive integrated moving average models. Due to complex relationships, a mixture of an unstructured Student-t and a flipped Clayton copula is deemed the best model, as it allows differentiating the magnitude of dependence in each tail and exhibiting both heavy-tailed and asymmetric dependence. We derive the conditional copula-based probability function of thermal energy demand given meteorological variables, and provide useful insight on the production management phase of local energy utilities.

Data-driven methods are widely adopted to forecast short-term streamflow with lead time up to a few days. Flood risk mitigation, multi-use water management and hydropower plants schedule are the most common fields to use forecasting results. Increasing the accuracy and limiting the uncertainty of the predictions are common needs and also this work would evaluates these aspects combining regional climate models and machine learning techniques. Thus, the research question addressed regards the suitability of the machine learning algorithm fed by the ICON forecasting regional climate model for short lead time streamflow prediction in a small and complex Alpine environment.A data-driven forecasting procedure is used for streamflow forecasting on a lead time of two days in small Alpine catchments of the Alto Adige Province (Italy). Bias correction of the ICON prediction data inputs against the historical data and the machine learning module compose the two steps data-driven methodology that we propose. Historical time series of precipitation and temperature provided by weather stations have been used for training the machine learning algorithms, while the ICON prediction data of precipitation and temperature have been adopted for testing them. The use of historical data has been essential for collecting a reasonable amount of data required for algorithm learning. The methodology performance evaluation is on the meteorological correction and on the hydrological forecasting.This first assessment shows promising results for two-day head streamflow prediction even in the context of small catchments with complex orography. This finding suggests that the merging of robust data-driven methodologies with high-resolution detailed weather prevision inputs can be a consistent breakthrough for reliable hydrological short-term forecasting. In conclusion, the flexibility of machine learning and ensemble climate prediction allows for adequate management of uncertainty along the prediction procedure, which is crucial in hydrological applications.

Reservoir sedimentation constitutes a major issue worldwide and a long-lasting priority for dam managers, especially when hydropower, and hence profit, is involved. Commonly, the problem of excessive sedimentation is attributed to failed prediction of the sediment supply from the upland basin prior to the construction of dams, namely to the underestimation of sediment inflow to the reservoir.The sediment input in the Rio di Pusteria reservoir (South Tyrol, Italy) between two consecutive sediment flushing operations in June 2014 and May 2019, was determined by obtaining the volumetric difference between very high resolution (0.25 m) reservoir bathymetries conducted after the flushing of 2014 and before the flushing of 2019. The sediment yield in the reservoir during the latter period was found to be 453,783 t.To calculate the sediment yield in the reservoir, we have applied a gridded seven-factor Universal Soil Loss Equation (USLE) combined with a Sediment Delivery Ratio (SDR) module in a high resolution (2.5 m) GIS environment, which enabled an accurate representation of the rapidly shifting Alpine topography. An additional factor for coarse fragments was added to the conventional six-factor USLE to account for the non-erodible part of the basin. This is of great importance as the USLE-based models are criticized to produce extreme erosion rates in uplands and mountain areas. The topographic factor, LS, was refined by the use of a fine scale DEM and the slope length factor, L, was adjusted to the Alpine terrain by means of a regulating threshold. The proposed SDR module does not rely on one but on several physiographic, topographic and hydrologic characteristics of the basin. Finally, the rainfall erosivity factor, R, was determined in two different ways, one representing the rainfall climatology of the study area and one the specific rainfall conditions of the study period, hence the application of the model in two distinct configurations.The application of the combined USLE-SDR model resulted in five-year reservoir sedimentation rates of 439,279.2 t and 589,520.5 t, with deviations from the measured sediment yield of 3.3% and -25.5%. Excluding very high altitudes with glaciers and perennial snow, we consider the proposed modeling approach ideal for upper lands and mountainous areas such as the Alps.

Water demand management is essential for water utilities, which have the critical task of supplying drinking water from water sources to end-users through the distribution network. Therefore, the water utilities have to make decisions for the current and future functioning of the water distribution system. In this context, the artificial intelligence approach with data-driven methods can be used to develop powerful tools to improve overall water management. In fact, data-driven methods can model water demands for plenty of tasks and applications such as demand forecasting or anomaly detection. In this work, we propose and discuss a practical application of an artificial neural network to model the urban water demand of a water supply system. The flexibility of the proposed method allows the prediction of water demand on different horizons. Moreover, this developed model can effectively support water utilities on different operational schedules and decision tasks.

The present study has the main purpose to experimentally investigate a turbulent momentum jet issued in a basin affected by rotation and in presence of porous obstructions. The experiments were carried out at the Coriolis Platform at LEGI Grenoble (FR). A large and unique set of velocity data was obtained by means of a Particle Image Velocimetry measurement technique while varying the rotation rate of the tank and the density of the canopy. The main differences in jet behavior in various flow configurations were assessed in terms of mean flow, turbulent kinetic energy and jet spreading. The jet trajectory was also detected. The results prove that obstructions with increasing density and increased rotation rates induce a more rapid abatement of both jet velocity and turbulent kinetic energy. The jet trajectories can be scaled by a characteristic length, which is found to be a function of the jet initial momentum, the rotation rate, and the drag exerted by the obstacles. An empirical expression for the latter is also proposed and validated. Graphic abstract

In the current era, the digitization of geographical data is a transverse need of several engineering sectors, including the hydraulic networks management. Thus, water supply systems' modelling requires adequate tools in both the digitization and the simulation phases. This paper presents the QEPANET plugin, which aims at merging the flexibility of QGIS and the robustness of EPANET hydraulic simulations software. Several editing and graphical tools available with QEPANET are introduced to model new and existing water distribution systems, to read and modify existing text-based EPANET files, to run simulations and visualize results in a geo-referenced framework. In addition, an application is illustrated to underline the novelty and the practical functionality of the presented tool, such as 3D pipe lengths' automatic calculation and the supporting functionalities for the network drawing. The plugin is available via the official QGIS Python Plugins Repository and on the world-wide-web at https://gitlab.com/albertodeluca/qepanet.

This paper examines the complex dependence between peak district heating demand and outdoor temperature. Our aim is to provide the probability law of heat demand given extreme weather conditions, and derive useful implications for the management and production of thermal energy. We propose a copula-based approach and consider the case of the city of Bozen-Bolzano. The analysed data concern daily maxima heat demand observed from January 2014 to November 2017 and the corresponding outdoor temperature. We model the univariate marginal behaviour of the time series of heat demand and temperature with autoregressive integrated moving average models. Next, we investigate the dependence between the residuals’ time series through several copula models. The selected copula exhibits heavy-tailed and symmetric dependence. When taking into account the conditional behaviour of heat demand given extreme climatic events, the latter strongly affects the former, and we find a high probability of thermal energy demand reaching its peak.

The aim of this paper is to propose a new simple and robust numerical flux of the centered type in the context of Arbitrary-Lagrangian–Eulerian (ALE) finite volume schemes. The work relies on the FORCE flux of Toro and Billet and is concerned with the solution of general hyperbolic systems of nonlinear equations involving both conservative and non-conservative terms as well as sources which might become stiff. The proposed approach is formulated in a general way using a path-conservative method and the Roe-type system matrix is computed numerically in order to provide a numerical flux function that can be applied to any given hyperbolic system. Furthermore, one great advantage of the FORCE flux is that no information about the eigenstructure of the system is needed, not even eigenvalues, but only information regarding the geometry of the control volumes are required, which are automatically available in the moving mesh framework. Our method is of the finite volume type, high order accurate in space, thanks to a WENO reconstruction operator, and even in time, due to a fully-discrete ADER one-step discretization. The algorithm applies to moving multidimensional unstructured meshes composed by triangles and tetrahedra. Both accuracy and robustness of the scheme are assessed on a series of test problems for the Euler equations of compressible gas dynamics, for the magnetohydrodynamics equations as well as for the Baer–Nunziato model of compressible multi-phase flows.

A method for human safety assessment on a hydropeaked river reach is proposed and applied to an Alpine river. The human safety analysis during hydropeaking events is of particular interest because most of the Alpine watercourses are affected by hydropower plant energy production that cause rapid and frequent flow alterations (hydropeaking), but at the same time these watercourses are used by the population for recreational purposes. In literature, many studies have focused on the effect of hydropeaking on the biota but a study of the interaction between a hydropeaking wave and human safety does not yet exist. The proposed procedure is characterized by the combination of hydraulic numerical simulations to study the characteristics of the flow field with a human safety analysis and is applied to a case study in north Italy. Human safety can be assessed in two different ways: one is by studying human stability during hydropeaking events and the other is exploring the possibility of a “target person” leaving the reach during hydropeaking waves, adapting proper escape strategies. For the escape strategy Dijkstra's algorithm is used, where the distance between adjacent nodes is defined as the difficulty (penalty) of moving from one node to the other. For this reason, an original set of penalty functions is proposed that takes into account the steepness (slope between two adjacent computational cells), the roughness, and the product between the water depth and flow velocity. The results show that the difficulty in escaping increases with the flow rate. Moreover, the areas where the human safety is very low are mainly located in the central part of the watercourse. The present work proposes a possible investigational tool to evaluate and parameterize the risk for the population during hydropeaking events through quantitative indices.

The evolution of smart water grids leads to new Big Data challenges boosting the development and application of Machine Learning techniques to support efficient and sustainable drinking water management. These powerful techniques rely on hyperparameters making the models’ tuning a tricky and crucial task. We hence propose an insightful analysis of the tuning of Artificial Neural Networks for drinking water demand forecasting. This study focuses on layers and nodes’ hyperparameters fitting of different Neural Network architectures through a grid search method by varying dataset, prediction horizon and set of inputs. In particular, the architectures involved are the Feed Forward Neural Network, the Long Short Term Memory, the Simple Recurrent Neural Network and the Gated Recurrent Unit, while the prediction interval ranges from 1 h to 1 week. To avoid the problem of the Neural Networks tuning stochasticity, we propose the selection of the median model among several repetitions for each hyperparameter’s configurations. The proposed iterative tuning procedure highlights the change of the required number of layers and nodes depending on Neural Network architectures, prediction horizon and dataset. Significant trends and considerations are pointed out to support Neural Network application in drinking water prediction.

The ongoing transformation of the electricity market has reshaped the hydropower production paradigm for storage reservoir systems, with a shift from strategies oriented towards maximizing regional energy production to strategies aimed at the revenue maximization of individual systems. Indeed, hydropower producers bid their energy production scheduling 1 day in advance, attempting to align the operational plan with hours where the expected electricity prices are higher. As a result, the accuracy of 1-day ahead prices forecasts has started to play a key role in the short-term optimization of storage reservoir systems. This paper aims to contribute to the topic by presenting a comparative assessment of revenues provided by short-term optimizations driven by two econometric models. Both models are autoregressive time-adapting hourly forecasting models, which exploit the information provided by past values of electricity prices, with one model, referred to as Autoarimax, additionally considering exogenous variables related to electricity demand and production. The benefit of using the innovative Autoarimax model is exemplified in two selected hydropower systems with different storage capacities. The enhanced accuracy of electricity prices forecasting is not constant across the year due to the large uncertainties characterizing the electricity market. Our results also show that the adoption of Autoarimax leads to larger revenues with respect to the use of a standard model, increases that depend strongly on the hydropower system characteristics. Our results may be beneficial for hydropower companies to enhance the expected revenues from storage hydropower systems, especially those characterized by large storage capacity.

Water leakages are one of the most significant uncertainties affecting water supply system (WSSs) modelling. Due to the dependence between water losses and pressure, the WSSs characterised by high values of pressure in the distribution network are strongly affected by this problem. High-pressure conditions are typical of WSSs in the mountain areas. In this study, a real WSS of the Alpine region is analysed through simultaneous calibration of water losses and pressure-reducing valves with a genetic algorithm. The demand is represented by a uniformly distributed along the network pipes scheme, dealing with the water request separately from the losses.

An experimental nonintrusive methodology is proposed to estimate the spatially averaged erosion rate of fine sediment from a gravel bed. The experiments performed in a laboratory flume show...