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IT-SNOW: a snow reanalysis for Italy blending modeling, in-situ data, and satellite observations (2010–2021)
Earth System Science Data ( IF 11.2 ) Pub Date : 2022-09-05 , DOI: 10.5194/essd-2022-248
Francesco Avanzi , Simone Gabellani , Fabio Delogu , Francesco Silvestro , Flavio Pignone , Giulia Bruno , Luca Pulvirenti , Giuseppe Squicciarino , Elisabetta Fiori , Lauro Rossi , Silvia Puca , Alexander Toniazzo , Pietro Giordano , Marco Falzacappa , Sara Ratto , Hervè Stevenin , Antonio Cardillo , Matteo Fioletti , Orietta Cazzuli , Edoardo Cremonese , Umberto Morra di Cella , Luca Ferraris

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: 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 endevor in a warming and drier climate.

中文翻译:

IT-SNOW:意大利混合模型、现场数据和卫星观测的雪再分析(2010-2021)

摘要。我们介绍了 IT-SNOW,这是一项针对意大利(300k+ km 2)的连续完整的多年积雪再分析) 涵盖了从大陆到地中海的过渡地区,在该地区雪发挥着重要的作用,但社会和生态作用仍然很有限。IT-SNOW 提供约 500 米的雪水当量 (SWE)、雪深、积雪密度和液态水含量的每日地图,时间为 2010 年 1 月 9 日至 2021 年 8 月 31 日,预计未来会更新定期。作为实际民用保护应用程序(S3M 意大利)中使用的操作链的输出,IT-SNOW 接收来自数千个自动气象站的输入数据、来自 Sentinel 2、MODIS 和 H-SAF 的积雪区域地图产品,以及来自 350 多个地面雪深传感器的空间化雪深地图。使用基于 Sentinel-1 的雪深地图和来自三个重点区域(奥斯塔谷、伦巴第、和 Molise) 与前者相比几乎没有平均偏差,原位测量的雪深和雪水当量的均方根误差分别为 30 到 60 厘米和 90 到 300 毫米。IT-SNOW 对 SWE 峰值的估计也与意大利 102 个流域关闭段的年流量(0.87)有很好的相关性,峰值 SWE 与年流量之间的比率与这个雨雪混合地区的预期一致(平均 22%)。使用示例使我们能够估计 13.70 ± 4.9 Gm IT-SNOW 对 SWE 峰值的估计也与意大利 102 个流域关闭段的年流量(0.87)有很好的相关性,峰值 SWE 与年流量之间的比率与这个雨雪混合地区的预期一致(平均 22%)。使用示例使我们能够估计 13.70 ± 4.9 Gm IT-SNOW 对 SWE 峰值的估计也与意大利 102 个流域关闭段的年流量(0.87)有很好的相关性,峰值 SWE 与年流量之间的比率与这个雨雪混合地区的预期一致(平均 22%)。使用示例使我们能够估计 13.70 ± 4.9 Gm3次SWE 在整个意大利景观中达到峰值积累,平均发生在 3 月 4。近 52% 的平均季节性 SWE 累积在波河流域,其次是阿迪杰河 (23%) 和亚平宁中部 (5%)。IT-SNOW 可通过以下 DOI 免费获得:https://doi.org/10.5281/zenodo.7034956(Avanzi 等人,2022b),有助于更好地限制雪对季节性至年度水资源的作用——a气候变暖和干燥的关键努力。
更新日期:2022-09-06
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