Abstract ID: 3.12849 | Accepted as Talk | Talk | TBA | TBA

Among adaptation strategies to reduce water-related risks, seasonal predictions are gaining interest for their potential to provide early warning of extreme seasons.

The PRIN-2022 SPHERE project (Seasonal Prediction of water availability: enHancing watER sEcurity from high mountains to plains) aims to use seasonal forecasts from state-of-the-art Copernicus global seasonal forecast systems and snow-hydrological models to develop a modelling chain for the seasonal predictions of snowpack evolution, river discharge, and indicators of water availability (or deficit) at 1 km resolution and with lead time up to six months.

The modelling chain is demonstrated on the Po river basin (Italy), which contributes 40% of the national GDP. The combination of intense socioeconomic activities, climate change, and high population density results in significant challenges for water resource management, increasing the risk of droughts and floods.

We will present the structure of the modelling chain and its application in generating the baseline run, driven by ERA5 reanalysis meteorological variables downscaled to a 1 km spatial resolution over the study region. Furthermore, we will assess the accuracy of the modelling chain by comparing its outputs to various observational and reanalysis datasets of snow depth, snow water equivalent (SWE), and discharge. Finally, we will showcase preliminary results from an application of the modelling chain, forced by retrospective seasonal forecasts of the Copernicus seasonal prediction systems, focusing on the seasonal prediction of snow depth, SWE, and discharge.