Trend assessment for daily snow depths with changepoint considerations

J. Lee, R. Lund, J. Woody, Y. Xu

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

This paper develops methods to estimate a long-term trend in a daily snow depth record. The methods use a storage equation model for the daily snow depths that allows for seasonality, support set features (snow depths cannot be negative), correlation, and mean level shift changepoint features. Changepoints can occur in snow processes whenever observing stations move or station instrumentation is changed; they are critical features to consider when estimating a long-term trend. A likelihood objective function is developed for the storage model and is used to estimate model parameters. Genetic algorithms are used to optimize a minimum descriptive length model selection criterion that estimates the changepoint numbers and locations. The methods are applied in the analysis of a daily series recorded near Warm Lake, Idaho, from 1948 to 2009.

Original languageEnglish (US)
Article numbere2580
JournalEnvironmetrics
Volume31
Issue number1
DOIs
StatePublished - Feb 1 2020
Externally publishedYes

Keywords

  • changepoints
  • genetic algorithm
  • MDL
  • storage model
  • trends

ASJC Scopus subject areas

  • Statistics and Probability
  • Ecological Modeling

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