Articles | Volume 17, issue 12
https://doi.org/10.5194/tc-17-5317-2023
https://doi.org/10.5194/tc-17-5317-2023
Research article
 | 
15 Dec 2023
Research article |  | 15 Dec 2023

A random forest approach to quality-checking automatic snow-depth sensor measurements

Giulia Blandini, Francesco Avanzi, Simone Gabellani, Denise Ponziani, Hervé Stevenin, Sara Ratto, Luca Ferraris, and Alberto Viglione

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Cited articles

Avanzi, F., De Michele, C., Ghezzi, A., Jommi, C., and Pepe, M.: A processing–modeling routine to use SNOTEL hourly data in snowpack dynamic models, Adv. Water Resour., 73, 16–29, 2014. a, b, c, d, e, f
Avanzi, F., Johnson, R. C., Oroza, C. A., Hirashima, H., Maurer, T., and Yamaguchi, S.: Insights into preferential flow snowpack runoff using random forest, Water Resour. Res., 55, 10727–10746, 2019. a
Avanzi, F., Zheng, Z., Coogan, A., Rice, R., Akella, R., and Conklin, M. H.: Gap-filling snow-depth time-series with Kalman filtering-smoothing and expectation maximization: Proof of concept using spatially dense wireless-sensor-network data, Cold Reg. Sci. Technol., 175, 103066, https://doi.org/10.1016/j.coldregions.2020.103066, 2020. a, b, c
Avanzi, F., Ercolani, G., Gabellani, S., Cremonese, E., Pogliotti, P., Filippa, G., Morra di Cella, U., Ratto, S., Stevenin, H., Cauduro, M., and Juglair, S.: Learning about precipitation lapse rates from snow course data improves water balance modeling, Hydrol. Earth Syst. Sci., 25, 2109–2131, https://doi.org/10.5194/hess-25-2109-2021, 2021. a, b, c
Avanzi, F., Gabellani, S., Delogu, F., Silvestro, F., Pignone, F., Bruno, G., Pulvirenti, L., Squicciarino, G., Fiori, E., Rossi, L., Puca, S., Toniazzo, A., Giordano, P., Falzacappa, M., Ratto, S., Stevenin, H., Cardillo, A., Fioletti, M., Cazzuli, O., Cremonese, E., Morra di Cella, U., and Ferraris, L.: IT-SNOW: a snow reanalysis for Italy blending modeling, in situ data, and satellite observations (2010–2021), Earth Syst. Sci. Data, 15, 639–660, https://doi.org/10.5194/essd-15-639-2023, 2023. a, b, c, d, e
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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.
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