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<p>Accurate snow depth estimation is critical for effective disaster management, particularly in regions like South Korea where snowfall can have significant impacts. This study introduces a high-resolution (1&#xa0;km–1&#xa0;h) model for estimating snow depth across South Korea from 2010 to 2018, using radar-based precipitation data. Additionally, the model adjusts the temperature and relative humidity data to match the resolution of the precipitation data in order to address the high spatial variability of precipitation. By optimizing a single set of parameters for the entire study area, the model simplifies implementation and enhances applicability. The model demonstrated high accuracy, with RMSE values of 4.9&#xa0;cm and 3.5&#xa0;cm during the calibration and validation periods, respectively. The lower RMSE during the validation period is primarily due to the generally smaller snow depth during this period compared to the calibration period. Spatial analysis revealed that over 40% of the winter period was snow-covered in the northeastern and southwestern mountainous regions, and high-altitude areas of Jeju Island. The model effectively captured the progression of snow cover, showing maximum snow depth in January and persistent snow cover in high-elevation areas through March. This study highlights the significant influence of elevation and topography on snow depth distribution, especially in areas above 1500&#xa0;m. It also shows that integrating radar-based precipitation data significantly enhances the accuracy of snow depth estimations compared to using ground-based observations alone.</p>

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High-resolution snow depth modeling in South Korea using radar-based precipitation data

  • 期刊论文

  • 发表日期：

    2025年1月29日

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  • Soohyun Kim,
  • Jeongha Park,
  • Gunhui Chung,
  • Dongkyun Kim

摘要

Accurate snow depth estimation is critical for effective disaster management, particularly in regions like South Korea where snowfall can have significant impacts. This study introduces a high-resolution (1 km–1 h) model for estimating snow depth across South Korea from 2010 to 2018, using radar-based precipitation data. Additionally, the model adjusts the temperature and relative humidity data to match the resolution of the precipitation data in order to address the high spatial variability of precipitation. By optimizing a single set of parameters for the entire study area, the model simplifies implementation and enhances applicability. The model demonstrated high accuracy, with RMSE values of 4.9 cm and 3.5 cm during the calibration and validation periods, respectively. The lower RMSE during the validation period is primarily due to the generally smaller snow depth during this period compared to the calibration period. Spatial analysis revealed that over 40% of the winter period was snow-covered in the northeastern and southwestern mountainous regions, and high-altitude areas of Jeju Island. The model effectively captured the progression of snow cover, showing maximum snow depth in January and persistent snow cover in high-elevation areas through March. This study highlights the significant influence of elevation and topography on snow depth distribution, especially in areas above 1500 m. It also shows that integrating radar-based precipitation data significantly enhances the accuracy of snow depth estimations compared to using ground-based observations alone.