Assessment of Snow Water Equivalent Estimates from Reanalysis and Rainfall-Runoff Modeling in Northern Italy

Snow water resources play a crucial role in Mediterranean mountainous regions, serving as natural reservoirs that sustain water supply during dry seasons. The accurate estimation of Snow Water Equivalent (SWE) is fundamental for water resource management, though direct measurements remain sparse in mountainous terrain. Current SWE estimation approaches face distinct challenges: for instance, large-scale reanalysis products derived from land-surface models (such as ERA5-Land) are limited by sparse data assimilation of mountain observations, the coarse scale of the modeling grid (often running in the 10+ km), a poor representation of orographic precipitation, and globally optimized parameterizations that may not suit complex mountain environments. On the other hand, hydrological models are constrained by uncertainty in input data, precipitation-phase determination and simplified snow thermodynamics. These limitations necessitate systematic evaluation across different terrain types to improve mountain snow monitoring.

This study compares SWE estimates across Northern Italy by evaluating large-scale reanalysis products and rainfall-runoff modeling against the high-resolution IT-SNOW dataset (Avanzi et al., 2023). The IT-SNOW reference dataset provides validated SWE estimates across Italy at 500m spatial resolution with comprehensive data assimilation from satellite and in-situ measurements. The evaluation examines regional and global reanalyses at various spatial scales, alongside the SWE simulations obtained at catchment scale with the GR6J rainfall-runoff model (Coron et al., 2017) coupled with the CemaNeige snow routine (Valéry et al., 2014), locally calibrated against the observed streamflow. By analysing over 100 catchments during 2010-2023, we assess the performance of these estimates across diverse topographical and climatic conditions to identify their strengths and limitations.

Our methodology involves a two-scale evaluation approach: at the gridded scale, we compare IT-SNOW with reanalysis products; at the catchment scale, we evaluate the CemaNeige-GR6J rainfall-runoff model simulations of the SWE volumes. Both analyses span seasonal and interannual timescales to assess the variations of SWE estimates.

The findings of this comparative analysis advance our understanding of SWE estimation methods across the Italian mountainous regions by systematically evaluating the strengths and limitations of different estimation approaches. Future research will focus on integrating SWE estimates from IT-SNOW into the rainfall-runoff model calibration phase, aiming to develop more robust hydrological models capable of better representing snow dynamics.

REFERENCES

Avanzi, F., Gabellani, S., Delogu, F., Silvestro, F., Pignone, F., Bruno, G., ... & Ferraris, L. (2023). IT-SNOW: a snow reanalysis for Italy blending modeling, in situ data, and satellite observations (2010–2021), Earth Syst. Sci. Data, 15, 639–660. doi: https://doi.org/10.5194/essd-15-639-2023.

Coron, L., Thirel, G., Delaigue, O., Perrin, C., & Andréassian, V. (2017). The suite of lumped GR hydrological models in an R package. Environmental modelling & software, 94, 166-171. doi: https://doi.org/10.1016/j.envsoft.2017.05.002.

Valéry, A., Andréassian, V., & Perrin, C. (2014). ‘As simple as possible but not simpler’: What is useful in a temperature-based snow-accounting routine? Part 1–Comparison of six snow accounting routines on 380 catchments. Journal of hydrology, 517, 1166-1175. doi: https://doi.org/10.1016/j.jhydrol.2014.04.059.