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

Woody, Jonathan; Wang, Yan; Dyer, Jamie

doi:10.1515/geo-2016-0036

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…Some minor quality control is applied to the daily snow depths before trend inference is conducted. Following [46], if the day to day change in snow depths during season ν is more than four standard deviations from the mean daily change for season ν,ê ν , then the data for that day is flagged and considered missing.…”

Section: The Storage Modelmentioning

confidence: 99%

A Statistical Analysis of Daily Snow Depth Trends in North America

Woody

Dyer

et al. 2021

Atmosphere

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Several attempts to assess regional snow depth trends have been previously made. These studies estimate trends by applying various statistical methods to snow depths, new snowfalls, or their climatological proxies such as snow water equivalents. In most of these studies, inhomogeneities (changepoints) were not accounted for in the analysis. Changepoint features can dramatically influence trend inferences from climate time series. The purpose of this paper is to present a detailed statistical methodology to estimate trends of a time series of daily snow depths that account for changepoint features. The methods are illustrated in the analysis of a daily snow depth data set from North America.

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Section: The Storage Modelmentioning

confidence: 99%

A Statistical Analysis of Daily Snow Depth Trends in North America

Woody

Dyer

et al. 2021

Atmosphere

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“…Subtracting these estimates from

{X_{1}, \dots, X_{N}}

produces

{{\overset{X}{true}}_{1}, \dots, {\overset{X}{true}}_{N}}

. The changepoint‐adjusted and detrended hourly series

{{\overset{X}{true}}_{1}, \dots, {\overset{X}{true}}_{N}}

is then transformed to the stationary series

{{\overset{X}{true}}_{1}, \dots, {\overset{X}{true}}_{N}}

by taking

{\overset{X}{true}}_{s} = false({\ddot{X}}_{s} ‐ {\ddot{μ}}_{s} false) / {\overset{σ}{true}}_{s}

, where

{\ddot{μ}}_{s}

and

{\ddot{σ}}_{s}

are hourly mean and standard deviation of the

{\ddot{X}}_{s}

series and are calculated by a similar method to Woody et al. (2016). To elaborate, for each hour ν of the synodic lunar month (approximately 708.734 hours or 29.5 days), we compute the means and standard deviations

{\overset{\overset{X}{true}}{true}}_{ν} = \frac{1}{n_{ν}} {false\sum}_{j = 0}^{⌊ N / T_{L} ⌋} {\overset{X}{true}}_{[j T_{L} + ν]}, {\overset{S}{true}}_{ν} = \sqrt{\frac{1}{n_{ν} ‐ 1} {false\sum}_{j = 0}^{⌊ N / T_{L} ⌋} {({\overset{X}{true}}_{[j T_{L} + ν]} ‐ {\overset{\overset{X}{true}}{true}}_{ν})}^{2}},

where

n_{ν}

is ...…”

Section: Case Study: Fishguard Ukmentioning

confidence: 99%

Long-term Trend Analysis of Extreme Coastal Sea Levels with Changepoint Detection

Lee

2021

Journal of the Royal Statistical Society Series C: Applied Statistics

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Sea level rise can bring disastrous outcomes to people living in coastal regions by increasing flood risk or inducing stronger storm surges. We study long‐term linear trends in monthly maximum sea levels by applying extreme value methods. The monthly maximum sea levels are extracted from multiple tide gauges around the coastal regions of the world over a period of as long as 169 years. Due to instrument changes, location changes, earthquakes, land reclamation, dredging, etc., the sea level data could contain inhomogeneous shifts in their means, which can substantially impact trend estimates if ignored. To rigorously quantify the long‐term linear trends and return levels for the monthly maximum sea level data, we use a genetic algorithm to estimate the number and times of changepoints in the data. As strong periodicity and temporal correlation are pertinent to the data, bootstrap techniques are used to obtain more realistic confidence intervals to the estimated trends and return levels. We find that the consideration of changepoints changed the estimated linear trends of 89 tide gauges (approximately 30% of tide gauges considered) by more than 20cmcentury‐1. Our results are summarized in maps with estimated extreme sea level trends and 50‐year return levels.

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“…Before fitting our model, some rough data quality assurance (QA) checks were performed similar to those in Woody, Wang, and Dyer (). First, the day‐to‐day snowpack change series was crudely estimated via

C_{t}^{*} = X_{t} - X_{t - 1}

for t =1,…, N if X t >0; otherwise, it is assigned as missing.…”

Section: The Warm Lake Idaho Gridmentioning

confidence: 99%

Trend assessment for daily snow depths with changepoint considerations

et al. 2019

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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.

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

Cited by 3 publications

References 15 publications

A Statistical Analysis of Daily Snow Depth Trends in North America

A Statistical Analysis of Daily Snow Depth Trends in North America

Long-term Trend Analysis of Extreme Coastal Sea Levels with Changepoint Detection

Trend assessment for daily snow depths with changepoint considerations

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