Anagrafe della ricerca

The Mediterranean region is warming faster than the global average, heat waves are projected to intensify and summer rainfall will likely be reduced with increasing water shortages. Also relatively water-rich mountain regions such as the Alps registered an increased frequency of droughts and this tendency is expected to further strengthen in the future, triggering conflicts among different sectors of water use including agriculture, energy production, tourism, industry, household, and biodiversity conservation. The SPHERE project addresses the challenge of enhancing water security in Alpine catchments and surrounding plains, as in the framework of UN SDG Target 6.4 (Water use and scarcity), Horizon Europe and Italian PNRR M2C4. Among adaptation strategies to reduce water-related risks, seasonal predictions have been considered with growing interest for their potential to provide early warning of extreme seasons, so that decision makers can take necessary actions to minimize adverse impacts. However, hydrological seasonal predictions are still in their infancy. Recent studies show skill in seasonal prediction of mountain snow water equivalent but it is unclear if this is reflected in skillful prediction of streamflow and water availability locally and downstream. The first objective of this project is to employ the most advanced dynamical seasonal forecasting systems, hydrological models, downscaling and analysis tools to develop a semi-operational forecast chain that delivers high-resolution spatially distributed seasonal forecasts of hydrological indicators, i.e. snow water equivalent, meltwater runoff and streamflow, to estimate the amount of water that will be available for the ecosystems and socio-economic activities in the season ahead, providing early information if extreme dry/wet conditions are expected in the forthcoming 6 months. The second main objective of the project is to employ seasonal forecasts of meteorological variables and a coupled soil water balance - crop growth model to predict irrigation requirements in agricultural areas in the downstream plains and possible issues of water supply with few months lead time.