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Activity Number: 168 - SPEED: Environmental Statistics Methods and Applications, Part 1
Type: Contributed
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract #306843 Presentation
Title: Trend Assessment for Daily Snow Depths with Changepoints Considerations
Author(s): Jaechoul Lee* and Robert Lund and Jonathan Woody and Yang Xu
Companies: Boise State University and Clemson University and Mississippi State University and 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.

Authors who are presenting talks have a * after their name.

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