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

Author:

Avanzi FrancescoORCID,Gabellani Simone,Delogu Fabio,Silvestro Francesco,Pignone Flavio,Bruno Giulia,Pulvirenti Luca,Squicciarino Giuseppe,Fiori ElisabettaORCID,Rossi LauroORCID,Puca Silvia,Toniazzo Alexander,Giordano Pietro,Falzacappa Marco,Ratto Sara,Stevenin Hervè,Cardillo Antonio,Fioletti Matteo,Cazzuli Orietta,Cremonese EdoardoORCID,Morra di Cella Umberto,Ferraris Luca

Abstract

Abstract. We present IT-SNOW, a serially complete and multi-year snow reanalysis for Italy (∼ 301 × 103 km2) – 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 initial period 1 September 2010–31 August 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 (Moderate Resolution Imaging Spectroradiometer), and H SAF products, as well as maps of snow depth from the spatialization of over 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, Lombardy, and Molise) show little to no mean bias compared to the former, and root mean square errors are of the typical order of 30–60 cm and 90–300 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 water volume in snow and annual streamflow that are in line with expectations for this mixed rain–snow region (22 % on average and 12 % median). Examples of use allowed us to estimate 13.70 ± 4.9 Gm3 of water volume stored in snow across the Italian landscape at peak accumulation, which on average occurs on 4 March ± 10 d. Nearly 52 % of the 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 at https://doi.org/10.5281/zenodo.7034956 (Avanzi et al., 2022b) and can contribute to better constraining the role of snow for seasonal to annual water resources – a crucial endeavor in a warming and drier climate.

Publisher

Copernicus GmbH

Subject

General Earth and Planetary Sciences

Reference91 articles.

1. Alfieri, L., Avanzi, F., Delogu, F., Gabellani, S., Bruno, G., Campo, L., Libertino, A., Massari, C., Tarpanelli, A., Rains, D., Miralles, D. G., Quast, R., Vreugdenhil, M., Wu, H., and Brocca, L.: High-resolution satellite products improve hydrological modeling in northern Italy, Hydrol. Earth Syst. Sci., 26, 3921–3939, https://doi.org/10.5194/hess-26-3921-2022, 2022. a

2. Apicella, L., Puca, S., Lagasio, M., Meroni, A., Milelli, M., Vela, N., Garbero, V., Ferraris, L., and Parodi, A.: The predictive capacity of the high resolution weather research and forecasting model: a year-long verification over Italy, Bulletin of Atmospheric Science and Technology, 2, 1–14, 2021. a

3. Avanzi, F., Bianchi, A., Cina, A., De Michele, C., Maschio, P., Pagliari, D., Passoni, D., Pinto, L., Piras, M., and Rossi, L.: Centimetric Accuracy in Snow Depth Using Unmanned Aerial System Photogrammetry and a MultiStation, Remote Sens.-Basel, 10, 765, https://doi.org/10.3390/rs10050765, 2018. a

4. Avanzi, F., Maurer, T., Glaser, S. D., Bales, R. C., and Conklin, M. H.: Information content of spatially distributed ground-based measurements for hydrologic-parameter calibration in mixed rain-snow mountain headwaters, J. Hydrol., 582, 124478, https://doi.org/10.1016/j.jhydrol.2019.124478, 2020. a, b

5. Avanzi, F., Ercolani, G., Gabellani, S., Cremonese, E., Pogliotti, P., Filippa, G., Morra di Cella, U., Ratto, S., Stevenin, H., Cauduro, M., and Juglair, S.: Learning about precipitation lapse rates from snow course data improves water balance modeling, Hydrol. Earth Syst. Sci., 25, 2109–2131, https://doi.org/10.5194/hess-25-2109-2021, 2021a. a, b, c, d, e, f, g

Cited by 7 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3