10.6084/m9.figshare.8104928.v1
Jaechoul Lee
Jaechoul
Lee
Robert Lund
Robert
Lund
Jonathan Woody
Jonathan
Woody
Yang Xu
Yang
Xu
Dataset for: Trend Assessment for Daily Snow Depths with Changepoint Considerations
Wiley
2019
storage model
changepoints
genetic algorithm
MDL
trends
Environmental Science
2019-08-01 06:53:42
Dataset
https://wiley.figshare.com/articles/dataset/Dataset_for_Trend_Assessment_for_Daily_Snow_Depths_with_Changepoint_Considerations/8104928
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 1948-2009.