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dc.contributor.authorGarcia Larez, Edmundo David
dc.date.accessioned2023-12-05T14:49:46Z
dc.date.available2023-12-05T14:49:46Z
dc.date.issued2023-12-01
dc.identifier.urihttp://hdl.handle.net/10222/83180
dc.descriptionFirst characterization of the frequency and effects of deep lateral intrusion events in Bedford Basin (BB) using a combination of observational data from the Bedford Basin Monitoring Program (BBMP) and two 20-year ROMS-based model simulations of BB.en_US
dc.description.abstractThe renewal of deep water in Bedford Basin (BB) is vital for its ventilation. The main ventilation mechanisms are winter convention and lateral intrusion events. However, a characterization of intrusion events has not yet been undertaken. Due to global warming, it is expected that the reliability of winter convection events as a ventilation mechanism will decrease. Therefore, a better understanding of the effects of intrusion events is imperative since they may assume greater importance for the ventilation of BB in the future. The main objective of this thesis is to characterize the frequency and effects of intrusion events into BB using both observational data from the Bedford Basin Monitoring Program (BBMP) and two 20-year multi-nested model simulations of BB (HRM2 and HRM23). Where HRM2 is a one-way nesting simulation (low spatial resolution) and HRM23 is a two-way nesting simulation (high spatial resolution). Based on the BBMP dataset, these events cause sharp decreases in nitrate and sharp increases in temperature, salinity, and oxygen. They can occur from spring to fall. However, the majority and the most impactful intrusions occur later in the year. Fall intrusions cause larger disturbances than summer/spring intrusions in the variables mentioned previously and have a larger impact on the stratification of the water column. Overall, HRM23 outperforms HRM2 since it more closely resembles the observed trends in the BBMP dataset. The reliability of weekly and daily data acquisition for identifying intrusion events was evaluated and compared using HRM23. The results indicate that weekly sampling is sufficient for intrusion identification, but for characterizing the effect of these intrusions daily data would be preferrable. The intrusions were also categorized based on their extent along the Basin. This highlighted the presence of intrusions that do not make it to the bottom of BB and therefore are not recorded by the BBMP sampling. In addition, the speed at which these intrusions approach the Basin increased from January to December. Future work should be focused on a more quantitative study of the effects of mixing regimes observed after and during intrusion events, and on performing a similar study with the biological component of the model.en_US
dc.language.isoenen_US
dc.subjectIntrusion eventsen_US
dc.subjectROMSen_US
dc.subjectBedford Basinen_US
dc.subjectBBMPen_US
dc.subjectOceanographyen_US
dc.subjectMarine Modellingen_US
dc.subjectestuaryen_US
dc.titleUsing observations and model simulations to characterize the effects of lateral intrusion events in Bedford Basinen_US
dc.typeThesisen_US
dc.date.defence2023-11-17
dc.contributor.departmentDepartment of Oceanographyen_US
dc.contributor.degreeMaster of Scienceen_US
dc.contributor.external-examinern/aen_US
dc.contributor.thesis-readerChristopher Algaren_US
dc.contributor.thesis-readerWill Burten_US
dc.contributor.thesis-readerJinyu Shengen_US
dc.contributor.thesis-readerRuth Musgraveen_US
dc.contributor.thesis-supervisorKatja Fennelen_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.copyright-releaseNot Applicableen_US
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