The Past and Future of Alaskan River Discharge, Temperature, and Ice
Notice bibliographique
Résumé
<p dir="ltr" style="line-height: 1.38; background-color: #ffffff; margin-top: 11pt; margin-bottom: 0pt; padding: 0pt 0pt 11pt 0pt;"><span style="font-size: 10.5pt; font-family: Arial,sans-serif; color: #202122; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">Indigenous communities in Alaska use river systems for subsistence fishing and travel. As climate change rapidly transforms Arctic rivers, the future for these Indigenous people, their fisheries and winter travel corridors are deeply uncertain. This research advances our collective understanding of terrestrial hydrologic change and potential impacts on rivers, fish, and communities in the Arctic. The dissertation facilitates actionable, community-based river discharge, temperature, and ice modeling.</span> <p dir="ltr" style="line-height: 1.38; background-color: #ffffff; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 11pt 0pt;"><span style="font-size: 10.5pt; font-family: Arial,sans-serif; color: #202122; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">Arctic hydrology is experiencing rapid changes including earlier snow melt, permafrost degradation, increasing active layer depth, and reduced river ice, all of which are expected to lead to changes in stream flow regimes. Recently, long-term (>60 years) climate reanalysis and river discharge observation data have become available. We utilize these data to assess long-term changes in discharge and their hydroclimatic drivers. River discharge during the cold season (October - April) increased by 10% per decade. The most widespread discharge increase occurred in April and October. Compared to the historical period, mean April and October air temperature in the recent period have greater correlation with monthly discharge, indicating that the recent increases in discharge are directly related to air temperature changes.</span> <p dir="ltr" style="line-height: 1.38; background-color: #ffffff; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 11pt 0pt;"><span style="font-size: 10.5pt; font-family: Arial,sans-serif; color: #202122; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">Expanding the spatial scale, we conduct high-resolution simulations of river discharge and temperature in Alaska and the Yukon River Basin, covering historic and mid-century periods. The simulations involve a chain of river models, including river routing (mizuRoute) and optimized river temperature (River Basin Model) models, forced by a high-resolution (4 km) regional climate model (Regional Arctic System Model) with an optimized land surface model (Community Terrestrial System Model). The river temperature model is optimized using a iii surrogate-based model optimization method, improving model performance in both seen and unseen river gages. To quantify the impacts of climate change on Alaskan rivers, we employ the pseudo global warming (PGW) method, considering median and high hydroclimate change scenarios derived from the ensemble mean of CMIP6 GCMs under the SSP2-4.5 emissions pathway. The river models indicate mixed discharge changes, with consistently higher river temperatures at mid-century. The projected increases in river temperature and altered discharge will significantly impact Alaskan river ecosystems, with implications for local and Indigenous communities.</span> <p dir="ltr" style="line-height: 1.38; background-color: #ffffff; margin-top: 0pt; margin-bottom: 0pt; padding: 0pt 0pt 11pt 0pt;"><span style="font-size: 10.5pt; font-family: Arial,sans-serif; color: #202122; background-color: transparent; font-weight: 400; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">To explore change in winter river conditions, we developed novel statistical, machine learning, and remote sensing techniques to quantify river ice conditions. The analysis reveals that ice presence can be accurately discerned from Sentinel-1 images and climate data processed through machine learning models, achieving high accuracies across Alaska. Predicting ice breakup using these methods also yielded high accuracy. Analysis of ice thickness estimation methods demonstrated comparable performance, with root mean square error ranging from 18-23 cm for out-of-sample years or locations. However, an ensemble approach significantly reduced the RMSE to 13 cm by combining these methods. Ultimately, employing the ensemble model for ice thickness and the machine learning model for ice phenology, we determined ice phenology and thickness for every major Alaskan river. These methods show promise for widespread application in diverse regions, facilitating environmental monitoring and actionable science for local communities.</span>
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
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) | 0,004 | 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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».