Event Title
Simulating DO Depletion with the PSABC Model
Streaming Media
Presentation Abstract
Based on results from the Salish Sea Model, the Washington State Department of Ecology has determined there is a reasonable potential that anthropogenic nutrients entering Puget Sound are reducing dissolved oxygen below the State’s standard. Similar to the Salish Sea Model, the Puget Sound Aquatic Biogeochemical Cycling Model estimates ocean circulation, biological growth, and the cycling of nutrients; it can be used to estimate how dissolved oxygen is affected by nutrient inputs. By simulating the 2006 calendar year, the model is compared with both observational data and the Salish Sea Model. Sensitivity of model predictions to some key parameters is explored. The assessment of this model identified parameters lacking in consistent long-term monitoring.
Session Title
Poster Session 1: Applied Research & Climate Change
Conference Track
SSE14: Posters
Conference Name
Salish Sea Ecosystem Conference (2022 : Online)
Document Type
Event
SSEC Identifier
SSE-posters-153
Start Date
26-4-2022 4:00 PM
End Date
26-4-2022 4:30 PM
Rights
Copying of this document in whole or in part is allowable only for scholarly purposes. It is understood, however, that any copying or publication of this document for commercial purposes, or for financial gain, shall not be allowed without the author's written permission.
Type
Text
Language
English
Simulating DO Depletion with the PSABC Model
Based on results from the Salish Sea Model, the Washington State Department of Ecology has determined there is a reasonable potential that anthropogenic nutrients entering Puget Sound are reducing dissolved oxygen below the State’s standard. Similar to the Salish Sea Model, the Puget Sound Aquatic Biogeochemical Cycling Model estimates ocean circulation, biological growth, and the cycling of nutrients; it can be used to estimate how dissolved oxygen is affected by nutrient inputs. By simulating the 2006 calendar year, the model is compared with both observational data and the Salish Sea Model. Sensitivity of model predictions to some key parameters is explored. The assessment of this model identified parameters lacking in consistent long-term monitoring.
