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<p>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 <Emphasis Type="BoldItalic">in-situ</Emphasis> 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月4日

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  • Matteo Dall’Amico,
  • Stefano Tasin,
  • Federico Di Paolo,
  • Marco Brian,
  • Paolo Leoni,
  • Francesco Tornatore,
  • Giuseppe Formetta,
  • John Mohd Wani,
  • Riccardo Rigon,
  • Gaia Roati

摘要

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.