Multidecadal snow water equivalent reconstruction in Central Italy using the Multiple Snow Data Assimilation System

Reliable estimates of snow water equivalent (SWE) are necessary to understand hydrological variability and snow-related extremes in mountain environments of Central Italy and the Apennines, where snowpacks are generally thin, variable, and still insufficiently observed by conventional monitoring networks. As part of broader efforts to improve how snow processes are represented in complex terrain, this work describes the recent developments using the Multiple Snow Data Assimilation System (MuSA). The primary goal of this work is to generate spatially coherent SWE estimates over Central Italy.

The modelling approach employs a physically based snow model within MuSA, driven by MORE meteorological reanalysis (MOloch-downscaled ERA5 REanalysis), which provides high-resolution atmospheric forcing at ~1.8 km over Italy spanning more than three decades from 1990 onward, enabling consistent multidecadal SWE reconstruction. This extended forcing, available at an hourly scale and with a finer spatial resolution, captures the complex orographic precipitation and temperature gradients that are critical for accurate snowpack simulation in the Apennines.  Snow depth observations from Sentinel-1 (S-1) are assimilated as the primary observational input, leveraging their spatial extent and ability to detect snowpack characteristics in areas with limited ground measurements. Given that S-1 snow depth is only available from around 2015 onward, the main objective of this research is to use an observation-constrained MuSA configuration to extrapolate the SWE estimate back to 1990, producing multidecadal records.

The methodological design, data preparation, and assimilation strategy are described to ensure temporal consistency between the observation-rich and pre-observation period. Specific focus is given to basin-scale implementation, uncertainty estimation, and potential scalability to regional-scale applications. Presently, model validation and analysis of SWE are ongoing. This research establishes a concrete framework for long-term SWE estimation in Central Italy. It provides future studies with the opportunity to assess snow variability and extremes at the regional scale in a changing climate.