Detecting and Modeling Landfast Ice in the Alaskan Bering Sea
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Notice bibliographique
Résumé
Seasonal sea ice – ice which freezes in late fall and melts completely the following summer – is a central feature in the ecology, geomorphology, and climatology of the Bering Sea. In this region's coastal zones, sea ice becomes locked into a stationary position against the coastlines to become landfast ice, which influences bioegophysical processes in the region, as well as exchanges of energy and matter among land, ocean, and atmosphere. It provides a platform for human mobility and subsistence activities, habitat for certain marine mammals, regulates terregenous nutrient cycling into ocean environments, and modulates the effect of erosive wind/wave action against coastlines. However, a thorough understanding of how this stationary ice, known as landfast ice, affects biogeophysical processes in the Bering Sea is limited by a lack of data on its areal coverage and seasonal duration. This dissertation establishes a baseline set of observations of landfast ice conditions in the Bering Sea through the creation and analysis of continuous spatial datasets. Chapter 1 focuses on the landfast ice annual cycle in the Eastern Bering Sea, which spans from the western tip of the Seward Peninsula to the southernmost point on the Yukon-Kuskokwim River Deltas. Chapter 1 results in the creation of landfast ice spatial data in these areas ranging from 1996 – 2008. Results show the spatial distribution and seasonal duration of landfast ice vary regionally within our study area, does not generally reach water depths associated with stabilization of the landfast ice cover in other regions of the Arctic, and is shortening in seasonal duration by approximately 9 days. Chapter 2 focuses on the landfast ice annual cycle on St. Lawrence Island, an Alaska Island located in the northern Bering Sea. Chapter 2 results in the creation of landfast ice spatial data in these areas ranging from 1996 – 2019. Results show the spatial distribution of landfast ice to vary regionally on the island, based on the coastlines orientation towards prevailing winds that transport pack ice through the Bering Strait. We also observed a sharp decline in landfast ice cover from 2017-2019, which coincides with record-breaking declines in sea ice coverage for the entire Bearing Sea. We also found coastal morphology and orientation have limited explanatory power when modeling landfast ice widths – the distance between the landfast ice edge and coastline – suggesting the consideration of meteorological variables is needed to improve models. Chapter 3 uses the landfast ice data from Chapter 2 to create an explanatory logistic regression model of landfast ice cover on St. Lawrence Island, using a combination of geographic and meteorological predictor variables. Using these variables, the model was able to predict the location of landfast ice cover with 80-90% accuracy, depending on the region of St. Lawrence Island. The model outputs resulted in very low commission error, with high omission error, which may be improved in future studies with the additional predictor variables. Cumulatively, this dissertation is the most comprehensive analysis of landfast ice cover to date on Alaskan Bering Sea coastlines. Research findings advance scholarly understandings of coastal ice conditions in the Bering Sea, and the geographic as wellas meteorological factors that enable their presence.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle