High resolution & large scale snow monitoring: new approaches and applications

Understanding snow dynamics is critical in mountain-fed basins, where snowmelt plays a key role in sustaining streamflow and water supply. We present j-snow, a hybrid modeling approach developed by Waterjade, designed to reconstruct long-term daily Snow Water Equivalent (SWE) at high spatial resolution by integrating physical modeling, satellite data assimilation, and in-situ measurements.
Our recent application over the Po River District (1991–2021) demonstrates how j-snow enables the production of a high-quality SWE dataset, validated against ground stations and satellite snow cover maps. This dataset not only offers insights into historical snow trends but also provides a robust foundation for forecasting inflows, supporting flood risk assessment, and informing water resource management under climate change scenarios.
In this seminar, we will overview the j-snow architecture, present key validation results, and discuss how the methodology can be embedded in operational systems.

Bio
Matteo Dall'Amico holds a PhD in environmental engineering with a specialisation in hydrology on mountain environments. He is a professional with more than 15 years of experience in water resources analysis. In 2014, he founded Waterjade, winning numerous awards for innovation in the fields of Water, Energy and Earth observation. He is now helping Utilities to monitor and forecast water resources at the catchment scale.

Also available online at https://unitn.zoom.us/j/84031853563 (Meeting ID: 840 3185 3563, Passcode: 660333).