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Measurements of snow depth can vary dramatically over small distances, and as with any other meteorological variable, snow depth time series are affected by inhomogeneities or break points. Such inhomogeneities can arise due to e.g.; changes of instrumentation, changes to station location and observer practices, or changes in the local environment such as urbanisation or plant growth.In order to analyse and monitor variation in snow depth time series accurately, homogenised snow data series are required. In deriving such homogenised series, it is essential to understand the characteristics and impacts of inhomogeneities. Having applied some pre-selection criteria to identify candidate series, time series homogenization for 184 Swiss snow depth series was performed using ACMANT, Climatol, and HOMER, three state-of-the-art break detection algorithms. For the 91 year base period of 1931-2021, we investigated which method and set-up worked best for detecting breaks in this network of Swiss snow data series. The approach identified valid break points in 25% of the series, with HOMER identifying more valid breaks than either ACMANT or Climatol. By evaluating the network using multiple methods, there is more confidence that the results can be applied to snow time series with insufficient metadata or no immediately nearby reference stations in order to include them in future homogenisation efforts. |
