Abstract
Climate change raises the critical need to understand its impact on water resources, particularly as hydropower's role as a flexible, renewable energy source becomes more vital in planning for the energy system's decarbonization. While hydrological modeling represents an established tool for assessing the future evolution of water resources, a key challenge lies in its reliance on data describing the geometry and operation of hydropower systems interacting with the natural stream network. The Italian Alpine Region (IAR) is home to over 300 large hydropower systems (LHSs), and its hydrological cycle is expected to suffer major alterations due to climate change. However, detailed and reliable hydrological studies in this region face hindrances due to the absence of a consistent, comprehensive, and openly available LHS source.
We present IAR-HP (Italian Alpine Region HydroPower), a comprehensive inventory specifically designed for the inclusion in hydrological modeling of LHSs located in the Italian Alpine Region, to overcome this obstacle. This dataset aims to support modelers in the water–energy nexus by providing crucial information for accurately informing their models. Compiled from various online sources, IAR-HP is openly accessible and reproducible, offering a solution to the scarcity of data hindering effective storage-hydropower-related simulations. The dataset includes detailed information about 338 LHSs, with a collective installed power of 14.3 GW and an average production of 32.1 TW h yr−1; these LHSs contribute 11.8 % of the electricity generated in Italy, corresponding to roughly 80 % of the national hydropower generation. The dataset was validated through a hydropower production modeling exercise, and was able to reconstruct 96.2 % of the observed hydropower production across the Italian Alpine Region. By presenting this dataset, we contribute a practical tool for scientists to reduce the inherent uncertainty of hydrological models, improving their ability to represent large hydropower systems accurately. IAR-HP holds potential for numerous applications to inform decision-making in the dynamic context of climate change. The IAR-HP dataset presented in this paper are available through Zenodo at https://doi.org/10.5281/zenodo.14040971 (Galletti et al., 2024).