A storage model approach to the assessment of snow depth trends

Woody, Jonathan; Lund, Robert; Grundstein, Andrew; Mote, Thomas L.

doi:10.1029/2009wr007996

Cited by 10 publications

(21 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Given these limitations, it is critical to focus on the findings where spatially coherent groups of stations exhibit similar trends with time rather than the absolute number of stations with positive or negative trends. The focus on spatial coherence of trends is also important because trends at individual stations may be affected by factors such as 1 7 7 station moves that are unrelated to changes in snow climatology (Kunkel et al, 2009;Woody et al, 2009).…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Trends in Average Snow Depth Across the Western United States

Grundstein¹,

Mote²

2010

Physical Geography

Self Cite

View full text Add to dashboard Cite

A network of cooperative observation stations was used to examine trends in average snow depth across the western United States. This station network includes a greater number of low-to moderate-elevation stations, where the snow is particularly sensitive to temperature changes, than the snow course and SNOTEL datasets used in other studies. Results indicate a widespread decrease in snow depth across the region; 70% of stations with statistically significant trends (p ≤ 0.5 or p ≤ 0.10) exhibit negative trends. Elevation is a key factor in explaining the spatial pattern of snow depth trends across the region. The fraction of stations with significant negative trends is greatest (80%) at the lower-elevation stations (<1000 m) and is somewhat less (62%) for stations between 2000 and 3000 m. Pacific climate indices, including the Pacific Decadal Oscillation (PDO) and North Pacific Index (NPI), are shown to have important but spatially varying influences upon average seasonal snow depths.

show abstract

Section: Resultsmentioning

confidence: 99%

“…One problem with the use of cooperative observation stations is that minor station moves may affect the magnitude of observed trends (Kunkel et al, 2009;Woody et al, 2009). This problem may be mitigated, however, by focusing on geographic coherence in the trends rather than results from any single station.…”

Section: Methodsmentioning

confidence: 99%

Trends in Average Snow Depth Across the Western United States

Grundstein¹,

Mote²

2010

Physical Geography

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is noted here that the trend estimates will be consistent regardless of the weighting factor [14]. The top panel in Figure 9 shows the estimated daily variances at the Fargo Station.…”

Section: Model Estimationmentioning

confidence: 99%

“…, in Equation (10) is considered. First, following the derivation provided in the appendix of Woody et al (2009), one may compute the one-step-ahead predictions at each location as Klimko and Nelson (1978) [17]. Speci cally, it is shown that the parameter estimate Θ that minimizes S(Θ) is consistent and with asymptotically distributional (weak) convergenceθ…”

Section: Model Estimationmentioning

confidence: 99%

“…In particular, a periodic Markov storage model is tted to a time series of daily estimates of the quantity of frozen soil. Similar stochastic storage type models have been used to model time series of daily snow depths [13,14]. The measured quantity of frozen soil constructed here is di erent than the SFD, in that this model allows the soil to alternate between frozen and thawed, perhaps multiple times, at a xed time, as measurement depth increases.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Application of multivariate storage model to quantify trends in seasonally frozen soil

Woody¹,

Wang²,

Dyer³

2016

Open Geosciences

Self Cite

View full text Add to dashboard Cite

This article presents a study of the ground thermal regime recorded at 11 stations in the North Dakota Agricultural Network. Particular focus is placed on detecting trends in the annual ground freeze process portion of the ground thermal regime's daily temperature signature. A multivariate storage model from queuing theory is t to a quantity of estimated daily depths of frozen soil. Statistical inference on a trend parameter is obtained by minimizing a weighted sum of squares of a sequence of daily one-stepahead predictions. Standard errors for the trend estimates are presented. It is shown that the daily quantity of frozen ground experienced at these 11 sites exhibited a negative trend over the observation period.

show abstract

Trend assessment for daily snow depths with changepoint considerations

et al. 2019

View full text Add to dashboard Cite

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.

show abstract

A storage model approach to the assessment of snow depth trends

Cited by 10 publications

References 20 publications

Trends in Average Snow Depth Across the Western United States

Trends in Average Snow Depth Across the Western United States

Application of multivariate storage model to quantify trends in seasonally frozen soil

Trend assessment for daily snow depths with changepoint considerations

Contact Info

Product

Resources

About