Articles | Volume 17, issue 12
https://doi.org/10.5194/tc-17-5317-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-5317-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Dec 2023
Research article |
| 15 Dec 2023
| 15 Dec 2023
A random forest approach to quality-checking automatic snow-depth sensor measurements
Giulia Blandini
CORRESPONDING AUTHOR
Department of Hydrology and Hydraulics, CIMA Research Foundation, Savona, Italy
Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy
Francesco Avanzi
Department of Hydrology and Hydraulics, CIMA Research Foundation, Savona, Italy
Simone Gabellani
Department of Hydrology and Hydraulics, CIMA Research Foundation, Savona, Italy
Denise Ponziani
Department of Hydrology and Hydraulics, CIMA Research Foundation, Savona, Italy
Hervé Stevenin
Centro Funzionale Valle D’Aosta, Aosta, Italy
Sara Ratto
Centro Funzionale Valle D’Aosta, Aosta, Italy
Luca Ferraris
Department of Hydrology and Hydraulics, CIMA Research Foundation, Savona, Italy
Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy
Alberto Viglione
Department of Environment, Land and Infrastructure Engineering, Politecnico di Torino, Turin, Italy
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We estimate the contribution of extreme precipitation, antecedent soil moisture and snowmelt to changes in small and large floods across Europe.
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Short summary
Automatic snow depth data are a valuable source of information for hydrologists, but they also tend to be noisy. To maximize the value of these measurements for real-world applications, we developed an automatic procedure to differentiate snow cover from grass or bare ground data, as well as to detect random errors. This procedure can enhance snow data quality, thus providing more reliable data for snow models.
Automatic snow depth data are a valuable source of information for hydrologists, but they also...
