Climate data plays a crucial role in water resources management, which is becoming an increasingly relevant asset in all types of hydrological analysis not only for climate change studies but for various horizon forecasting. Though the ever-improving accuracy of climate models' spatial and temporal resolution has surged the validity of their outputs, the products of global and regional climate models need to be corrected to be reliably used for local purposes. Here, we propose a comprehensive analysis of statistical univariate and multivariate, as well as machine learning methods for bias correction, which are compared on different temporal scales, ranging from hourly time steps to monthly aggregations, in an environment of complex Alpine orthography, using ERA5-Land reanalysis data. The results reveal different trends in the performance of the bias correction methods for precipitation and temperature across the various time resolutions.

Using the global climate model outputs without any adjustment may bring errors in water resources and climate change investigations. This study tackles the critical issue of bias correction temperature in ERA5-Land reanalysis for 10 ground stations in northern Italy using nine machine learning (ML) techniques. Among standalone ML models, XGBoost regression emerged as the most effective standalone ML model, outperforming others across 6 out of 10 stations, while random forest regression, Gaussian process regression, and support vector regression obtained the second to fourth places. In contrast, AdaBoost regression (ABR) achieved the least favorable performance. Furthermore, nine ensemble ML models are proposed to correct bias of the reanalysis of temperature data. The results indicated that the K-nearest neighbors-based ensemble model excelled and secured the top rank in 7 out of 10 stations, while the multiple linear regression-based ensemble model achieved the highest precision in 4 out of 10 stations. Furthermore, other ML-based ensemble models displayed satisfactory results. On the other hand, the ABR-based ensemble model exhibited the lowest accuracy among ML-based ensemble models. The findings highlight the potential of ML-based ensemble models in effectively addressing bias correction in climate data.

Drinking water demand modelling and forecasting is a crucial task for sustainable management and planning of water supply systems. Despite many short-term investigations, the medium-term problem needs better exploration, particularly the analysis and assessment of meteorological data for forecasting drinking water demand. This work proposes to analyse the suitability of ERA5-Land reanalysis data as weather input in water demand modelling. A multivariate deep learning model based on the long short-term memory architecture is used in this study over a prediction horizon ranging from seven days to two months. The performance of the model, fed by ground station data and ERA5-Land data, is compared and analysed. Close-to-operative forecasting is then presented using observed data for training and ERA5-Land dataset for testing. The results highlight the reliability of the proposed architecture fed by ERA5-Land data for different time horizons. In particular, the ERA5-Land shows promising performance as input of the multivariate machine learning forecasting model, although some meteorological biases are present, which can be improved, especially in close-to-operative application with bias correction techniques. The proposed study leads to practical implications in the use of regional climate model outputs to support drinking water forecasting for sustainable and efficient management of water distribution systems.

This study investigates the optimization of Pumped Storage Hydropower (PSH) integrated with Floating Photovoltaic (FPV) systems, with a focus on sustainable energy management. A nonlinear programming framework combined with scenario analysis was applied to a real hydropower system in Trentino, Italy. The optimization maximizes revenues through energy arbitrage while accounting for water resource and environmental objectives. Upgrading the traditional hydropower plant to PSH operation increases revenues by 4–8% over two hydrological years. Multi-objective optimization further reveals large gains in water availability, confirming PSH’s dual role as energy storage and water management infrastructure. Different FPV configurations analyzed show a 2–3% increase in photovoltaic energy yield due to the water-cooling effect, while the overall hybrid PSH–FPV integration mainly reduces grid dependency and pumping-related emissions, with near-complete decarbonization achievable under optimized scheduling. Overall, PSH provides the primary economic and operational advantage, while FPV strengthens sustainability, enabling resilient hydro–solar operation and contributing to renewable integration and decarbonization in future energy systems.

This contribution investigates the untapped solar power potential across the lakes within the hydropower reservoirs nestled in the Alto Adige region of Italy. With the region housing more than 500 hydropower plants, amounting to approximately 90% of the regions electricity generation, these hydropower plants bear the burden of electricity generation within the region. Moreover, only 3.6% of the total energy is generated by solar energy. However, these plants boast large surface areas on the reservoirs which remains untapped. As the global energy demand surges and sustainable power sources become imperative, leveraging solar energy atop water reservoirs presents a promising opportunity. The study employs Geographic Information Systems coupled with solar radiation modelling to assess and quantify the solar energy capacity of these reservoirs. The region is characterized by a typical Alpine climatology with warm summers and cold and dry winters, and a complex topography of valleys and peaks. Considering the unique topographical characteristics and climatic conditions of the region, the research evaluates the feasibility and viability of harnessing solar energy over these reservoirs. Preliminary findings underscore the substantial yet underutilized solar power potential in this hydro-rich landscape, opening avenues for further renewable energy generation strategies. The outcome of this study is not only to contribute to enhancing the renewable energy portfolio of the region but also to advocate for innovative and sustainable approaches. This added power potential will be further utilized for reducing the load on hydropower plants during peak load times. Moreover, this can be integrated with pumped hydro storage systems for optimum electricity generation and flexibility of the energy systems.