understanding salinity variability in the columbia river estuary
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Understanding Salinity Variability in the Columbia River Estuary. Sierra & Julia. Observation ● Prediction ● Analysis ● Collaboration. Frontline Mentor: Pat Welle Senior Mentor: Dr. Antonio Baptista. Center for Coastal Margin Observation and Prediction. - PowerPoint PPT PresentationTRANSCRIPT
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1
Understanding Salinity Variability in the
Columbia River EstuarySierra & Julia
Observation ● Prediction ● Analysis ● Collaboration
Frontline Mentor: Pat Welle
Senior Mentor: Dr. Antonio Baptista
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2Center for Coastal Margin Observation and Prediction
• Collaboration of scientists aiming to improve the understanding of the Columbia River Estuary and Coastal Margins on a molecular and systematic scale
• National Science Foundation Center• Partnership with OHSU, University of
Washington, Oregon State University
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3Columbia River Estuary
• Border of Oregon and Washington• Columbia River spills into the Pacific Ocean
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4Columbia River Estuary
• Second largest estuary in United States• Columbia River flowing into the Pacific Ocean• Transition zone• Mixing between fresh and salt water• Influence of tides • 70% of fresh water from the Columbia River
goes through Bonneville Dam
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5Saturn Observation Network
• Science and Technology University Research Network
• Combination of endurance stations and mobile sensors– Stations, drifters, gliders
• Includes numerical representation of Columbia River– DB11, DB14, DB16, DB22
• Stations and models encompass estuary, plume and shelf
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6Model
• Set of mathematical equations that represent physical processes and properties applied over a chosen space. The space is broken down into multiple segments that form a grid. Salinity values are determined for each piece of the grid
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7Station Map
Washington
OregonPacific Ocean
Sandi
Am169
Cbnc03
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8Lower Sand Island light (sandi)
• Endurance Station• Saturn Observation Network• CT at 7.9 meters• Salinity and temperature
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9Astoria-Megler Bridge South Channel (am169)
• Endurance Station• Saturn Observation Network• CT at 14.3 meters• Salinity and temperature
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10Cathlamet Bay North Channel (cbnc03)
• Endurance Station• Saturn Observation Network• CT at 6.5 meters• Salinity and temperature
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11Our Project
• Comparing simulated data versus observed data to understand salinity variability in the Columbia River Estuary and what causes the differences between what the model predicts and what the data shows.
Sa
linity
(p
su)
April 30th- May 13th 2009
AM169 Week 18-19
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12Forces in the Estuary
• Tides– Mixing of salt and fresh water
and also effects the salt water intrusion upstream of the mouth
• River discharge– Salt water intrusion
• Wind– Upwelling and Downwelling
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13Tides• Tide Cycle:
– 12.4 hours between high and low tide
• Spring tides– Occur during full and new moons– Low salt water intrusion
• Neap Tides– Occur during quarter moons– High salt water intrusion
Sa
linity
(p
su)
April 16th- April 20th 2009
Week 16-17 Tides
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14River Discharge• Majority of fresh water in the estuary flows through
Bonneville Dam, 140 miles east of estuary• Fresh water not flowing through Bonneville, comes
from Willamette River, other forms of precipitation, tributaries
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15Coastal Upwelling
• Wind blows from north along the coast in a southern direction
• Usually occurs during summer months• Upwelling causes more salt water intrusion during
summer months SurfaceWater Movement
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16
Wind
Surface water sinks
Coastal Downwelling
• Wind blows from south along the coast in a northern direction
• Usually occurs during winter months• Downwelling causes less salt water intrusion during
the winter months
North
South
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17Procedure: MATLAB
• MATLAB– Data analysis tool, similar
to Excel– Graphing– Statistical analysis– Commands– Workspaces
1. Import data from database into MATLAB using pgAdmin or PuTTY
2. Remove bad data(clear NaNs)
3. Interpolate the observed to the model data
4. Graph data
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18PuTTY & pgAdmin
• Programs to access data from database through systems of queries and commands
• Data is imported into MATLAB for use
pgAdmin PuTTY
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19MATLAB
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20MATLAB
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21Smoothing Data
• Takes data points and uses a moving average function to smooth them over a specified period of time– Usually over a day or week
Sa
linity
(p
su)
July 16th- August 13th 2009
Smoothed Data
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22Time Series Project• Creating plot configurations which include:
– A comparison between modeled and observed salinity at stations Sand Island, Astoria-Megler Bridge, and Cathlamet Bay
– Discharge – Tides– Wind velocity
• From west to east• 2 weeks• 4 weeks• Annual
= Stations we focused on
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232 Weeks
• Objective: To view short term trends between tides, discharge, wind direction and salinity values
• Graphs of sandi, am169, cbnc03
• Graphs of tides, discharge and wind velocity
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242 week page
san
di
dis
cha
rge
win
d
Tid
ecb
nc3
am
16
9
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252 week: Conclusions Sandi• SandI
– Minimum simulated values are less than observed values by 2-5psu
– Maximum simulated values 0-3psu less than observed values
– Most accurate of the three stations
Sa
linity
(p
su)
September 3rd- September 16th 2009
Week 36-37 sandi
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262 week Conclusions am169• Am169
– Simulated values show similar trends as observed values but incorrect values
– Simulated values are more accurate during the transitions from spring to neap tides, and are less accurate during transitions from neap to spring tides
• Pattern nonexistent during periods of high discharge– Simulated values are most accurate during periods of low
discharge with spring tides
Spring Neap
Sa
linity
(p
su)
September 3rd- September 16th 2009
Week 36-37 am169
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272 week: Conclusions cbnc03• Cbnc03
– Salinity values are greater during the transition from neap to spring tides and decrease during the transition from spring to neap tides
• Occurs only during low river discharge
– February 5th- End of March; simulated values indicate increased salinity when observed values indicate little or no salinitySpring Neap
Sa
linity
(p
su)
September 3rd- September 16th 2009
Week 36-37 cbnc3
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284-Week
• Objective: To view seasonal patterns for 2009 during periods of high and low discharge
• Graphs of Sandi, am169– Smoothed to 1 week,
original data• Graphs of tides and
discharge– Smoothed to 1 week
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294-Week pagesa
nd
id
isch
arg
eT
ide
am
16
9
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304-Week Conclusions: Low River Discharge
• Difference between simulated and observed values is close to 0 psu during spring to neap transitions
• At am169, difference between simulated and observed values are up to 12 psu transitioning from neap to spring tides
• At Sandi, difference between simulated and observed values are up to 7 psu transitioning from neap to spring tides
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314-Week Conclusions: High River Discharge• During highest discharge: difference between
simulated and observed values is consistent at ≈5 psu (Am169) or ≈2 psu (Sandi)
• Once discharge begins to drop transitional differences emerge
Sandi
Sa
linity
(p
su)
Cu
bic
m
ete
rs/s
eco
nd
May 21st- June 17th 2009
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32Annual
• Objective: To view long term trends between tides, discharge, wind direction and salinity values
• Graphs of Sandi and am169– Smoothed to 28 days
• Graphs of tides, discharge and wind velocity– Smoothed to 28 days
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33Annual Page
san
di
dis
cha
rge
win
d
Tid
e
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34Annual Pages: Conclusions • Sandi
– Constant difference between simulated and observed values of 3-5 psu during the year
– Salinity values for both simulated and observed drop when river discharge increase, and rise as river discharge drops
• am169– Observed values show a monthly fluctuation that
is not apparent in the simulated values– Salinity values for both simulated and observed
drop when river discharge increase, and rise as discharge drops
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35Pros and Cons of Time Windows
• 2 weeks– Pros: see the small patterns and easy viewing– Cons: no long term trends
• 4 weeks– Pros: can see some long term trends, effects of
discharge are more visible.– Cons: too crowded, only seasonal
• Annual– Pros: see long term trends– Cons: no short term trends, no slight fluctuations
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36Future Research
• Look at multiple years to find trends in simulated data versus observed data
• Repeat data analysis after changes to the model have been implemented
• El Niño and La Niña influence in past years • Create plot configurations for temperature (2
weeks, 6 months, annual)• Create plot configurations using 6 month
segments instead of 4 week segments
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37Future Recommendations
• Create plot configurations for velocity data to see if similar trends to the salinity data exist
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38Future Recommendations
• Statistical analysis on simulated data and observed data
Weeks 5- 52
Am169 index of agreement
0-1
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39Acknowledgments
• Pat Welle• Dr.Antonio Baptista• Dr. Grant Law• Dr. Charles Seaton• Karen Wegner• Bonnie Gibbs• Elizabeth Woody• National Science Foundation• Saturday Academy
– Apprenticeship in Science and Engineering(ASE)
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40Thank you!
Any Questions?