Ground impacts of Atmospheric Rivers over Italy: the extreme events of Vaia (2018) and Alex (2020) storms.

Atmospheric Rivers (ARs) are recognised as important drivers of extreme precipitation and flooding in many mid-latitude regions. However, their hydrological impacts over the complex terrain of the Italian peninsula remain relatively underexplored. In this study, we investigate the ground-level impacts associated with two extreme AR-related events affecting Italy: Vaia storm in October 2018 and Alex storm in October 2020. These events impacted different regions, characterised by diverse physiographic settings, thus providing an opportunity to analyse how AR-driven precipitation translates into river response under various orographic and hydrological conditions.

The analysis is based on distributed hydrological simulations performed using the CETEMPS Hydrological Model (CHyM) at high spatial resolution. CHyM control simulations were forced with hourly rainfall and air temperature observations from the Italian Civil Protection network and used as comparison for numerical experiments forced with hourly precipitation fields produced by the MOLOCH convection-permitting model at different spatial resolutions. Additional sensitivity experiments were conducted using meteorological simulations in which the AR contribution was largely weakened, allowing the assessment of the role of AR-related moisture transport in the hydrological response.

Hydrological impacts were evaluated using two stress indices derived from CHyM simulations: the Best Discharge-based Drainage index (BDD), which reflects river discharge conditions along the drainage network, and the CETEMPS Alarm Index (CAI), which highlights rapid runoff responses associated with intense precipitation.

The results highlight the important role of AR-driven precipitation in shaping the hydrological response during both events, also emphasizing the strong modulation exerted by regional orography, basin morphology, and drainage network structure. The comparison between simulations with and without AR forcing further illustrates the relevance of accurately representing AR-related processes in coupled meteorological–hydrological modelling frameworks.

Overall, this study contributes to improving the understanding of the links between AR dynamics and hydrological impacts and supports the development of more reliable approaches for forecasting AR-related hydro-meteorological hazards in complex terrain environments.