Land-Ocean Interactions in Arctic Coastal Waters: Ocean Colour Remote Sensing and Current Carbon Fluxes to the Arctic Ocean
Notice bibliographique
Résumé
Arctic rivers carry about 40 Tg of organic carbon per year into the Arctic Ocean, enough to change the colour of the surface water over entire shelf seas. Ongoing permafrost thaw mobilizes ancient organic matter in the Arctic Ocean’s watershed and, in particular, organic carbon that was previously preserved in the perennially frozen soils. Whereas the particulate fraction of organic matter is prone to settling and subsequent burial, the dissolved fraction of organic matter (DOM) can be transported over large distances and is quickly integrated and cycled within the aquatic environment. Therefore, monitoring of DOM and its carbon (DOC) in terms of fluxes, quality, transport routes and ultimate fate in the Arctic Ocean, is one of the goals of current polar research. In situ observations in the Arctic are challenging and costly and hold tremendous scientific value. Ocean Colour Remote Sensing (OCRS) is a powerful tool that can complement in situ observations by providing frequent and synoptic estimates of surface water DOM and DOC concentration via the coloured fraction of DOM (CDOM). However, use of OCRS in Arctic organic-rich waters is hampered by uncertainties and needs further evaluation and development. The goal of this thesis is to advance our knowledge of the quantity, origin, seasonal variability and fate of DOM and carbon transported from land to sea in the Arctic. Biogeochemical and bio-optical parameters of water across the fluvial and marine zones in two Arctic regions were collected. These in situ datasets include: 1) Lena River DOM measured at least bi-weekly for one full year, 2) Lena River and Laptev Sea Shelf DOM and optical parameters measured intermittently over 11 years and 3) a suite of water column optical, radiometric, and biogeochemical measurements from spring to fall in the Mackenzie River Delta and on the Beaufort Sea Shelf. These data are a unique and novel resource for testing OCRS atmospheric correction and CDOM retrieval algorithms and for improving satellite-derived DOC estimates across the fluvial-marine transition zone. Frequent monitoring of the Lena River revealed that three source water types determine the strong seasonality of fluvial DOM: 1) melt water, 2) rain water and 3) subsurface water. The improved estimation of annual Lena River DOC flux was 6.79 Tg C, most of which (84%) was transported into the Lena River by melt and rain water. Optical properties of the DOM indicated that, in spring, the Lena River dominantly transports young carbon originating from degrading vegetation from land surfaces. With rising air temperatures in summer and fall, optical properties indicated an increasing fraction of older DOM originating from deeper soil horizons and thawing permafrost deposits. Salinity and DOM were strongly correlated (r²>0.8) in both shelf regions, indicating a dominant terrigenous source of DOM and a conservative mixing of DOM-rich river water with DOM-poor water from the Arctic Ocean. Both in situ and space-borne observations of surface waters revealed a strong seasonal variability of river plume propagation and DOC distribution on both shelves. The evaluation of several OCRS algorithms with in situ data showed that the OLCI (Ocean and Land Colour Instrument) neural network swarm (ONNS) algorithm performed best for the retrieval of CDOM in the Lena – Laptev Sea region (r²=0.72, mean percentage error=58.4%), whereas the semi-analytical algorithm “gsmA” performed best in the Mackenzie – Beaufort Sea region (r²=0.52, mean percentage error=24.1%). Furthermore, the Polymer atmospheric correction algorithm resulted in better match-up correlations than either the WFR or the C2RCC atmospheric corrections. For both regions, new DOC – CDOM models, based on the in situ observations, expand the applicability of OCRS to monitor DOC in surface waters to the entire fluvial-marine transition zone and improve the accuracy of DOC retrieval. Overall, the studies of this thesis demonstrated the capability of OCRS to monitor the propagation and distribution of DOM on Arctic shelves on large spatial and temporal scales. In the future, high frequency sampling in combination with OCRS of major Arctic rivers have the potential to improve quantification of DOC export into the Arctic Ocean and reduce current uncertainties due to the lack of data. Long-term OCRS time series merged from multiple satellites can help in identifying trends of land-sea carbon fluxes and their impact on the global carbon cycle and climate in a rapidly changing Arctic.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 source (Gemma direct ou Codex distillé), 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 ».