Online Program

Return to main conference page
Friday, May 31
Practice and Applications
Science and the Environment
Fri, May 31, 5:20 PM - 6:25 PM
Grand Ballroom J

Trend Assessment for Daily Snow Depths with Changepoints Considerations (306218)

*Jaechoul Lee, Boise State University 
Robert Lund, Clemson University 
Jonathan Woody, Mississippi State University 
Yang Xu, Mississippi State University 

Keywords: Changepoints, Genetic algorithm, MDL, Storage model, Trends

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 the 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.