# Presentation: 942730

## Combining Long-term And High Frequency Water Quality Data To Understand Ecological Processes In Estuaries

- Jane Caffrey
- Center for Environmental Diagnostics and Bioremediation
- University of West Florida

## Acknowledgements

- Data
- Thomas Chapin, USGS and Hans Jannasch, MBARI
- Scott Phipps, Weeks Bay NERR and John Haskins, Elkhorn Slough NERR
- Funding - CICEET and NOAA NERR

- J.M. Caffrey, UWF

## Outline of talk

- Calculation of metabolic rates (primary production, respiration and net ecosystem metabolism) from DO data
- Data sondes deployed at NERR
- Salinity, temperature, dissolved oxygen, turbidity, pH
- Understanding short term variability in estuarine processes
- Deployment of *in-situ* NO3- analyzers (developed by Ken Johnson, MBARI)
- Linking physical, chemical and biological processes

- J.M. Caffrey, UWF

## National Estuarine Research Reserve System

- J.M. Caffrey, UWF

## Background

- Dissolved oxygen data collected every half hour between 1995-2001.
- Uses diurnal changes in water column oxygen concentrations to estimate primary production, respiration and net ecosystem metabolism
- Developed by H.T. Odum in 1950s
- Describes the trophic status of the water body
- Autotrophic:  P > R
- Heterotrophic:  R > P

- J.M. Caffrey, UWF

## Dissolved Oxygen

- Diurnal changes in DO result from photosynthesis and respiration
- Gross production= NAP + respiration
- Net Ecosystem Metabolism (NEM) = NAP - respiration

- J.M. Caffrey, UWF

**Notes:**
Using diurnal oxygen curves to calculate metabolic rates – gross primary production, net apparent production, respiration and net ecosystem metabolism.
Photosynthesis during day leads to increases in DO concentrations.  

Calculate gross by adding back in the respiration that occurred during daylight hours.  Also have to include atmospheric exchange across the air-water interface.

## Assumptions

- Respiration rate is constant in light and dark
- System is well mixed vertically
- No advection of water masses with different DO concentrations is occurring – or biology dominates over physics

- J.M. Caffrey, UWF

## Primary ProductionWeeks Bay

- J.M. Caffrey, UWF

**Notes:**
Weeks bay results from 1996-2004
Strong seasonal pattern probably related to temperature and increasing daylength.  But also significant day to day variability.

## Temperature effectsNorth Inlet-Winyah Bay, SC - Oyster Landing

- r = 0.71

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- Temperature °C

- Total respiration gO2/m3/d

- Temperature versus metabolic rate correlations
-  Gross production – 23 sites
-  Total Respiration – 26 sites
-  Net ecosystem metabolism – 19 sites

- J.M. Caffrey, UWF

## Salinity effectsElkhorn Slough, CA – Azevedo Pond

- Salinity versus metabolic rate correlations
-  Gross Production – 16 sites
-  Total Respiration –12 sites
-  Net ecosystem metabolism – 13 sites

- J.M. Caffrey, UWF

## Net ecosystem by habitat

- -8

- -7

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- 0

- 1

- CBV Goodwin Island

- PAD Bay View

- WQB Central Basin

- APA East Bay

- GRB Great Bay

- GRB Squamscott River

- NAR Potters Cove

- NAR T-wharf

- WKB Fish River

- WKB Weeks Bay

- JOB Station 9

- JOB Station 10

- RKB Blackwater River

- RKB Upper Henderson

- CBM Jug Bay

- CBM Patuxent Park

- HUD Tivoli South

- CBV Taskinas Creek

- ACE Big Basin

- ACE St Pierre

- ELK South Marsh

- NIW Oyster Landing

- NIW Thousand Acre

- ELK Azevedo Pond

- PAD Joe Leary Slough

- HUD Sawkill

- g O2 m-2 d-1

- SAV                open water               mangrove                marsh  creeks             upland

- J.M. Caffrey, UWF

## Conclusions

- Water quality monitoring data is useful for estimating metabolic rates
- within site variability
- temperature
- salinity
- nutrient concentration
- chlorophyll concentration
- Among site variability
- habitat (organic matter loading)
- nutrient loading
- residence time

- J.M. Caffrey, UWF

## Understanding Temporal Patterns

- Continuous measurements give greater temporal resolution than discrete measurements

- J.M. Caffrey, UWF

**Notes:**
Salinity record from Weeks Bay 2001.  Red indicates salinity values at the date the in-situ NO3- analyzer was deployed.  Black line is complete YSI dataset.

Obviously, discrete monthly (or even weekly) sample can miss events.

## Relating Runoff to Estuarine Processes

- Rainfall in the Weeks Bay watershed leads to reduced salinity at the head of the estuary

- J.M. Caffrey, UWF

**Notes:**
Same salinity record with precipitation record.  Weeks Bay (at Fish River) salinity is strongly driven by precipitation events in the watershed.  High rainfall can lead to extended periods of low salinity.  

## In-situ nutrient analysis

- J.M. Caffrey, UWF

## Seasonal patterns in rainfall, temperature, salinity and nitrate concentrations in Elkhorn Slough, CA

- J.M. Caffrey, UWF

## Winter rains lead to extended periods of high NO3- concentrations in Elkhorn Slough, CA 

- J.M. Caffrey, UWF

**Notes:**
We observe a similar pattern in Elkhorn Slough.  Extend periods of high nitrate following rain events.

## Relating Runoff to Nutrient Loading

- High NO3- concentrations associated with runoff events in Weeks Bay, AL during winter rains

- 0

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- 2/14

- 2/28

- NO

- 3

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-  concentration, μM

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- Salinity

- ,

- Rainfall, mm

- J.M. Caffrey, UWF

**Notes:**
Taking the first 2 months of previous record and looking at winter runoff events in more detail.  See an increase in NO3- concentrations associated with the freshwater runoff.  Extended periods of high NO3- following the larger rain events.

## Seasonal differences in NO3- concentrations following runoff events

- J.M. Caffrey, UWF

**Notes:**
While we see increased NO3- with lower salinity.  There is a seasonal difference.  In the summer, have a drawdown of NO3- compared to winter.  Probably a result of biological uptake.

## What factors contribute to variability?

- Harmonic regression analysis
- choose periods of interest: tidal 12.5h, diurnal 24h, and lunar 29.5d
- Fit data to linear regression
- Run full models with all periods and reduced models to look at contributions of different components

- J.M. Caffrey, UWF

**Notes:**
Statistical approaches can be useful to see what forces are driving variability in the record.  What factors are most important.  This approach has been used for a long time by physical oceanographers (e.g. using water level data and known astronomical periods to predict tides at specific locations).  Apply this to NO3- data.

## Elkhorn Slough

- Lunar signal most important during winter, capturing runoff events.
- Spring-neap forcing of deep Monterey Bay water into Slough (Chapin et al. 2004)
- Diurnal signal dominates during summer when biological processes dominate.

- J.M. Caffrey, UWF

**Notes:**
Regression analysis could explain between 40 and 80% of variability.
Lunar signal was very important – in addition to capturing spring –neap tidal cycling, it also includes other longer period events such as frontal passages that can occur over 8-10 day periods.

Diurnal signal suggests that biological activity dominates in summer

## Weeks Bay

- Lunar and diurnal signals also important in Weeks Bay.
- Not surprising that tidal signal is weak because tides are
- diurnal rather than semidiurnal.

- J.M. Caffrey, UWF

**Notes:**
Similar results in Weeks Bay – models explain 40-75% of variability in NO3 signal.  Lunar is important – may also include weather activity
Tidal unimportant.

## NO3- inputs enhance gross production in Weeks Bay

- J.M. Caffrey, UWF

**Notes:**
How does production respond to these rainfall events?  Rain, high NO3- in runoff, then a peak in gross production several days later.

## And Elkhorn Slough

- J.M. Caffrey, UWF

**Notes:**
See a similar pattern in Elkhorn Slough.  There we have spring tides bringing in NO3- rich bottom water from the Monterey Bay Canyon.  That may be leading to these peaks in production.  

## Conclusions and Challenges

*In situ* instruments allow you to examine short term temporal variations, e.g. runoff events
- Water quality monitoring data (DO) can be used to estimate metabolic rates.
- How to link these time series together to examine how events at different time scales affect ecological processes

- J.M. Caffrey, UWF

## Nitrogen Loading

- N

- M

- i

- I

- e

- E

- c

- C

- B

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- 2

-  = 0.30

- -7

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- Nitrogen loading mmol m-2 d-1

- Net ecosystem metabolism,

- g O2 m-2 d-1

- J.M. Caffrey, UWF