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- W2078629761 abstract "The Stockton Deep Water Ship Channel, a stretch of the tidal San Joaquin River, is frequently subject to low dissolved oxygen conditions and annually violates regional water quality objectives. Underlying mechanisms are examined here using the long-term water quality data, and the efficacy of possible solutions using time-series regression models. Hypoxia is most common during June-September, immediately downstream of where the river enters the Ship Channel. At the annual scale, ammonium loading from the Regional Wastewater Control Facility has the largest identifiable effect on year-to-year variability. The longer-term upward trend in ammonium loads, which have been increasing over 10% per year, also corresponds to a longer-term downward trend in dissolved oxygen during summer. At the monthly scale, river flow, loading of wastewater ammonium and river phytoplankton, Ship Channel temperature, and Ship Channel phytoplankton are all significant in determining hypoxia. Over the recent historical range (1983–2003), wastewater ammonium and river phytoplankton have played a similar role in the monthly variability of the dissolved oxygen deficit, but river discharge has the strongest effect. Model scenarios imply that control of either river phytoplankton or wastewater ammonium load alone would be insufficient to eliminate hypoxia. Both must be strongly reduced, or reduction of one must be combined with increases in net discharge to the Ship Channel. Model scenarios imply that preventing discharge down Old River with a barrier markedly reduces hypoxia in the Ship Channel. With the Old River barrier in place, unimpaired or full natural flow at Vernalis would have led to about the same frequency of hypoxia that has occurred with actual flows since the early 1980s." @default.
- W2078629761 created "2016-06-24" @default.
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- W2078629761 date "2005-09-02" @default.
- W2078629761 modified "2023-09-27" @default.
- W2078629761 title "Low Dissolved Oxygen in an Estuarine Channel (San Joaquin River, California): Mechanisms and Models Based on Long-Term Time Series" @default.
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- W2078629761 doi "https://doi.org/10.15447/sfews.2005v3iss2art2" @default.
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