30-years (1991-2021) Snow Water Equivalent Dataset in the Po River District, Italy.

IF 6.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2025-03-04 DOI:10.1038/s41597-025-04633-5
Matteo Dall'Amico, Stefano Tasin, Federico Di Paolo, Marco Brian, Paolo Leoni, Francesco Tornatore, Giuseppe Formetta, John Mohd Wani, Riccardo Rigon, Gaia Roati
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Abstract

This paper presents a long-term snow water equivalent dataset in the Po River District, Italy, spanning from 1991 to 2021 at daily time step and 500 m spatial resolution partially covering the mountain ranges of Alps and Apennines. The data has been generated using a hybrid modelling approach integrating the hydrological modelling conducted with the physically-based GEOtop model, preprocessing of the meteorological data, and assimilation of in-situ snow measurements and Earth Observation snow products to enhance the quality of the model estimates. A rigorous quality assessment of the dataset has been performed at different control points selected based on reliability, quality, and territorial distribution. The point validation between simulated and observed snow depth across control points shows the accuracy of the dataset in simulating the normal and relatively high snow conditions, respectively. Additionally, satellite snow cover maps have been compared with simulated snow depth maps, as a function of elevation and aspect. 2D Validation shows accurate values over time and space, expressed in terms of snowline along the cardinal directions.

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意大利波河地区 30 年(1991-2021 年)雪水当量数据集。
本文介绍了意大利Po河地区1991 - 2021年逐日时间步长、500 m空间分辨率的长期雪水当量数据集,该数据集部分覆盖阿尔卑斯山脉和亚平宁山脉。数据是通过混合建模方法生成的,该方法将水文建模与基于物理的GEOtop模型、气象数据的预处理、现场雪测量和地球观测雪产品的同化结合起来,以提高模型估计的质量。根据可靠性、质量和地域分布,在不同的控制点对数据集进行了严格的质量评估。模拟雪深和观测雪深在控制点上的点验证分别显示了数据集在模拟正常和相对高雪条件方面的准确性。此外,将卫星积雪图与模拟雪深图进行了比较,并将其作为高程和坡向的函数。2D验证显示了随时间和空间变化的准确值,以沿基本方向的雪线表示。
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来源期刊
Scientific Data
Scientific Data Social Sciences-Education
CiteScore
11.20
自引率
4.10%
发文量
689
审稿时长
16 weeks
期刊介绍: Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.
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