05 Sep 2022
05 Sep 2022
Status: this preprint is currently under review for the journal ESSD.

IT-SNOW: a snow reanalysis for Italy blending modeling, in-situ data, and satellite observations (2010–2021)

Francesco Avanzi1, Simone Gabellani1, Fabio Delogu1, Francesco Silvestro1, Flavio Pignone1, Giulia Bruno1,2, Luca Pulvirenti1, Giuseppe Squicciarino1, Elisabetta Fiori1, Lauro Rossi1, Silvia Puca3, Alexander Toniazzo3, Pietro Giordano3, Marco Falzacappa3, Sara Ratto4, Hervè Stevenin4, Antonio Cardillo5, Matteo Fioletti6, Orietta Cazzuli6, Edoardo Cremonese7, Umberto Morra di Cella1,7, and Luca Ferraris1,2 Francesco Avanzi et al.
  • 1CIMA Research Foundation, Via Armando Magliotto 2, 17100 Savona, Italy
  • 2DIBRIS University of Genoa, Genova, 16145, Italy
  • 3Italian Civil Protection Department, Rome, Italy
  • 4Regione Autonoma Valle d’Aosta, Centro funzionale regionale, Via Promis 2/a, 11100 Aosta, Italy
  • 5Molise Region, Civil Protection, Regional Functional Center, Campochiaro (Cb) - Italy
  • 6Environmental Protection Agency of Lombardia, Milano, Italy
  • 7Environmental Protection Agency of Aosta Valley, Loc. La Maladière, 48-11020 Saint-Christophe, Italy

Abstract. We present IT-SNOW, a serially complete and multi-year snow reanalysis for Italy (300k+ km2) covering a transitional continental-to-Mediterranean region where snow plays an important, but still poorly constrained societal and ecological role. IT-SNOW provides ∼500-m, daily maps of Snow Water Equivalent (SWE), snow depth, bulk-snow density, and liquid water content for the period 01/09/2010–31/08/2021, with future updates envisaged on a regular basis. As the output of an operational chain employed in real-world civil-protection applications (S3M Italy), IT-SNOW ingests input data from thousands of automatic weather stations, snow-covered-area maps from Sentinel 2, MODIS, and H-SAF products, and maps of snow depth from the spazialization of 350+ on-the-ground snow-depth sensors. Validation using Sentinel-1-based maps of snow depth and a variety of independent, in-situ snow data from three focus regions (Aosta Valley, Lombardia, and Molise) shows little to none mean bias compared to the former, and Root Mean Square Errors on the order of 30 to 60 cm and 90 to 300 mm for in-situ, measured snow depth and Snow Water Equivalent, respectively. Estimates of peak SWE by IT-SNOW are also well correlated with annual streamflow at the closure section of 102 basins across Italy (0.87), with ratios between peak SWE and annual streamflow that are in line with expectations for this mixed rain-snow region (22 % on average). Examples of use allowed us to estimate 13.70 ± 4.9 Gm3 of SWE across the Italian landscape at peak accumulation, which on average occurs on the 4th of March. Nearly 52 % of mean seasonal SWE is accumulated across the Po river basin, followed by the Adige river (23 %), and central Apennines (5 %). IT-SNOW is freely available with the following DOI: (Avanzi et al., 2022b) and can contribute to better constraining the role of snow for seasonal to annual water resources – a crucial endevor in a warming and drier climate.

Francesco Avanzi et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-248', Anonymous Referee #1, 04 Oct 2022
    • AC1: 'Reply on RC1', Francesco Avanzi, 04 Nov 2022
  • RC2: 'Comment on essd-2022-248', Anonymous Referee #2, 12 Oct 2022
    • AC2: 'Reply on RC2', Francesco Avanzi, 04 Nov 2022
  • RC3: 'Comment on essd-2022-248', Anonymous Referee #3, 28 Oct 2022
    • AC3: 'Reply on RC3', Francesco Avanzi, 04 Nov 2022

Francesco Avanzi et al.

Data sets

IT-SNOW: a snow reanalysis for Italy blending modeling, in-situ data, and satellite observations Avanzi, Francesco; Gabellani, Simone; Delogu, Fabio; Silvestro, Francesco; Pignone, Flavio; Bruno, Giulia; Pulvirenti, Luca; Squicciarino, Giuseppe; Fiori, Elisabetta; Rossi, Lauro; Puca, Silvia; Toniazzo, Alexander; Giordano, Pietro; Falzacappa, Marco; Ratto, Sara; Stevenin, Hervè; Cardillo, Antonio; Fioletti, Matteo; Cazzuli, Orietta; Cremomese, Edoardo; Morra di Cella, Umberto; Ferraris, Luca

Model code and software

The S3M Model v 5.1.0 Francesco Avanzi, Simone Gabellani, Fabio Delogu, Francesco Silvestro

Francesco Avanzi et al.


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Short summary
The snow cover has profound implications for worldwide water supply and security, but knowledge of its amount and distribution across the landscape is still elusive. We present IT-SNOW, a reanalysis comprising daily maps of snow amount and distribution across Italy for 11 snow seasons from September 2010 to August 2021. The reanalysis was validated using satellite images and snow measurements, and will provide highly-needed data to manage snow water resources in a warming climate.