The Accumulated Winter Season Severity Index (AWSSI)

Boustead, Barbara E. Mayes; Hilberg, Steven D.; Shulski, Martha; Hubbard, Kenneth G.

doi:10.1175/jamc-d-14-0217.1

Cited by 30 publications

(40 citation statements)

References 58 publications

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“…The pattern found is consistent with the results obtained for Europe, where in the second half of the 1951-2010 period, an increase in the frequency of exceptionally mild winters was observed at the expense of exceptionally cold ones (Twardosz and Kossowska-Cezak 2016). In the last two or three decades, an increase in the frequency of warm winters has also been recorded in the Swiss Alps (Scherrer et al 2004;Marty 2008) and the USA (Mayes Boustead et al 2015).…”

Section: Temporal and Spatial Differences And Trendssupporting

confidence: 90%

“…A comprehensive index that combined, inter alia, air temperature, total snowfall, snow cover depth, and the duration of the winter season has been successfully applied in studies of winter severity and associated trends in the US (Mayes Boustead et al 2015).…”

Section: Source Data and Methodsmentioning

confidence: 99%

“…The issue of winter snowiness and severity is a complex one, and it is insufficient to apply just a single criterion to its assessment (Piasecki 1995;Piotrowicz 2004;Mayes Boustead et al 2015). Hence, several criteria are usually used simultaneously to classify and assess winters (Domonkos and Piotrowicz 1998;Piotrowicz 2006).…”

Section: Introductionmentioning

confidence: 99%

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Winter severity and snowiness and their multiannual variability in the Karkonosze Mountains and Jizera Mountains

Urban

Richterová

Kliegrová

et al. 2017

Theor Appl Climatol

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This paper analyses winter severity and snow conditions in the Karkonosze Mountains and Jizera Mountains and examines their long-term trends. The analysis used modified comprehensive winter snowiness (WSW) and winter severity (WOW) indices as defined by Paczos (1982). An attempt was also made to determine the relationship between the WSW and WOW indices. Measurement data were obtained from eight stations operated by the Institute of Meteorology and Water Management -National Research Institute (IMGW-PIB), from eight stations operated by the Czech Hydrological and Meteorological Institute (CHMI) and also from the Meteorological Observatory of the University of Wrocław (UWr) on Mount Szrenica. Essentially, the study covered the period from 1961 to 2015. In some cases, however, the period analysed was shorter due to the limited availability of data, which was conditioned, inter alia, by the period of operation of the station in question, and its type.Viewed on a macroscale, snow conditions in the Karkonosze Mountains and Jizera Mountains (in similar altitude zones) are clearly more favourable on southern slopes than on northern ones. In the study area, negative trends have been observed with respect to both the WSW and WOW indices-winters have become less snowy and warmer. The correlation between the WOW and WSW indices is positive. At stations with northern macroexposure, WOW and WSW show greater correlation than at ones with southern macroexposure. This relationship is the weakest for stations that are situated in the upper ranges (Mount Śnieżka and Mount Szrenica).

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Section: Temporal and Spatial Differences And Trendssupporting

confidence: 90%

Section: Source Data and Methodsmentioning

confidence: 99%

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Winter severity and snowiness and their multiannual variability in the Karkonosze Mountains and Jizera Mountains

Urban

Richterová

Kliegrová

et al. 2017

Theor Appl Climatol

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“…Specific temperature thresholds allow an absolute comparison between different locations, whereas standard deviations in temperature allow relative comparisons with respect to the specific climate. The accumulated winter season severity index (AWSSI) utilizes absolute maximum and minimum temperature thresholds as part of a quantification of winter severity across the United States (Mayes Boustead et al ., ). This index is very useful for the intended latitudes; however, the absolute thresholds make it less applicable in climates where winter temperatures are consistently above or below these thresholds.…”

Section: Introductionmentioning

confidence: 97%

The characteristics of extreme cold events and cold air outbreaks in the eastern United States

Smith

Sheridan

2018

Intl Journal of Climatology

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Periods of extreme cold impact the mid‐latitudes every winter. Depending on the magnitude and duration of the occurrence, extremely cold periods may be deemed cold air outbreaks (CAOs). A systematic CAO index and ranking system was developed from 20 surface weather stations from 1948 through 2016, based on a set of criteria concerning magnitude, duration, and spatial extent. Standard deviations in temperature were used to identify extreme temperatures relative to the station. A total of 49 CAOs occurred during the 67‐year period, with the majority occurring during mid‐winter. The number of CAOs proved to be largely dependent on the stations latitude and maritime influence. The duration, magnitude, and spatial extent were dependent on the time of the winter season in which the CAO occurred. Furthermore, two prominent clusters of an increased number of CAOs occurred during the 67‐year period, suggesting multi‐decadal circulations may be a factor in CAOs.

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“…Therefore, in years of extreme weather conditions, population estimates may be unreliable unless the factors contributing to fluctuations in counts are included in the occupancy modeling process (Boyce et al ). We suggest that our estimates may be improved by using indices for weather severity (Boustead et al ) or spring green‐up (Hamel et al ) to standardize spotlight counts or as predictor variables in the occupancy portions of zero‐inflated models.…”

Section: Discussionmentioning

confidence: 99%

Using spotlight observations to predict resource selection and abundance for white‐tailed deer

2019

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Spotlight surveys for white‐tailed deer (Odocoileus virginianus) can yield large presence‐only datasets applicable to a variety of resource selection modeling procedures. By understanding how populations distribute according to a given resource for a reference area, density and abundance can be predicted across new areas assuming the relationship between habitat quality (measured by an index of selection) and species distribution are equivalent. Habitat‐based density estimators have been applied to wildlife species and are useful for addressing conservation and management concerns. Although achieving reliable population estimates is a primary goal for spotlighting studies, presence‐only models have yet to be applied to spotlight data for estimating habitat selection and abundance for deer. From 2012 to 2017, we conducted spring spotlight surveys in each of 99 counties in Iowa, USA, and collected spatial locations for 20,149 groups of deer (n = 71,323 individuals). We used a resource selection function (RSF) based on deer locations to predict the relative probability of use for deer at the population level and to estimate statewide abundance. The number of deer observed statewide increased significantly with increasing RSF value for all years and the mean RSF value along survey transects explained 59% of the variability in county‐level deer counts, indicating that a functional response between habitat quality and deer distribution existed at landscape scales. We applied our RSF to a habitat‐based density estimator (extrapolation) and zero‐inflated Poisson (ZIP) and negative binomial (ZINB) count models to predict statewide abundance from spotlight counts. Population estimates for 2012 were variable, indicating that atypical weather conditions may affect spotlight counts and population estimates in some years. For 2013–2017, we predicted a mean population of 439,129 (95% CI ∼ ± 55,926), 440,360 (∼ ± 43,676), and 465,959 (∼ ± 51,242) deer across years for extrapolation, ZIP, and ZINB models, respectively. Estimates from all models were not significantly different than estimates from an existing deer population accounting model in Iowa for 2013 and 2016, and differed by <76,000 deer for all models from 2013–2017. Extrapolation and ZIP models performed similarly and differed by <2,897 deer across all years, whereas ZINB models showed inconsistencies in model convergence and precision of estimates. Our results indicate that presence‐only models are capable of producing reliable and precise estimates of resource selection and abundance for deer at broad landscape scales in Iowa and provide a tool for estimating deer abundance in a spatially explicit manner. © 2019 The Wildlife Society.

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The Accumulated Winter Season Severity Index (AWSSI)

Cited by 30 publications

References 58 publications

Winter severity and snowiness and their multiannual variability in the Karkonosze Mountains and Jizera Mountains

Winter severity and snowiness and their multiannual variability in the Karkonosze Mountains and Jizera Mountains

The characteristics of extreme cold events and cold air outbreaks in the eastern United States

Using spotlight observations to predict resource selection and abundance for white‐tailed deer

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