1

Understanding Salinity Variability in the

Columbia River EstuarySierra & Julia

Observation ● Prediction ● Analysis ● Collaboration

Frontline Mentor: Pat Welle

Senior Mentor: Dr. Antonio Baptista

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

3Columbia River Estuary

• Border of Oregon and Washington• Columbia River spills into the Pacific Ocean

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

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

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

7Station Map

Washington

OregonPacific Ocean

Sandi

Am169

Cbnc03

8Lower Sand Island light (sandi)

• Endurance Station• Saturn Observation Network• CT at 7.9 meters• Salinity and temperature

9Astoria-Megler Bridge South Channel (am169)

• Endurance Station• Saturn Observation Network• CT at 14.3 meters• Salinity and temperature

10Cathlamet Bay North Channel (cbnc03)

• Endurance Station• Saturn Observation Network• CT at 6.5 meters• Salinity and temperature

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

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

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

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

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

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

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

18PuTTY & pgAdmin

• Programs to access data from database through systems of queries and commands

• Data is imported into MATLAB for use

pgAdmin PuTTY

19MATLAB

20MATLAB

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

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

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

242 week page

san

di

dis

cha

rge

win

d

Tid

ecb

nc3

am

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9

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

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

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

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

294-Week pagesa

nd

id

isch

arg

eT

ide

am

16

9

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

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

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

33Annual Page

san

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Tid

e

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

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

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

37Future Recommendations

• Create plot configurations for velocity data to see if similar trends to the salinity data exist

38Future Recommendations

• Statistical analysis on simulated data and observed data

Weeks 5- 52

Am169 index of agreement

0-1

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)

40Thank you!

Any Questions?