Lake transparency: a window into
decadal variations in dissolved
organic carbon concentrations in
Lakes of Acadia National Park, Maine
Collin Roesler
Department of Earth and Oceanographic Science, Bowdoin College
Charles Culbertson
New England Water Science Center, USGS, Augusta
Acknowledgements
• William Gawley
• Tom Huntington
• Reviewers
• Patricia Gilbert and
Todd Kana
Motivation
Secchi Depth
Outline
• Motivation
• Secchi Depth
• Building the Model
• Results
• Future Efforts
Model
Results
Future
Motivation
Secchi Depth
Model
Results
Future
Motivation
• Lake properties provide a spatially and temporally
integrated record of
• watershed characteristics (landscape coverage, land use,
hydrologic processes)
• Anthropogenic activities
• development/recovery
• Local pollution of land and water
• Remote pollution such as acid rain
• Which vary on a variety of temporal scales
• seasonal cycles
• episodic events
• climate forcing
Motivation
Secchi Depth
Model
Results
Future
Motivation
• Observations suggest that dissolved organic matter
(DOM) is increasing in surface waters (brownification)
• Threatening water supplies
• Increasing carbon flux to the atmosphere
• Increasing organic carbon flux to the oceans
• Overarching Questions
• How will a warming climates change the mobilization of
organic carbon from soils to aquatic systems?
• How has the reduction in sulfur emissions (e.g. acid rain)
changed the mobilization of organic carbon from soils?
http://domqua.no/tag/brownification/
Motivation
Secchi Depth
Model
Results
Future
Acid Rain and DOC in NE Watersheds
• 1990-2010
• Dry years
• + SO42- anomaly
• - DOC anomaly
• Wet years
• - SO42- anomaly
• + DOC anomaly
• Deviations related to
%wetland coverage
Twenty year record of lake DOC is too short for climate analysis
Motivation
Secchi Depth
Model
Results
Future
We do have a much longer records
of Secchi Depth in this region
Can Secchi Depth provide a useful
long term proxy for biogeochemical
properties in MDI lakes?
Motivation
Secchi Depth
Model
Results
What is Secchi Depth?
• A measure of water transparency
• Low tech
• Independent of operator
• 150 yr (Angelo Secchi, 1865)
http://www.paddling.net/
http://earthobservatory.nasa.gov/Features/WaterQuality/water_quality2.php
Future
Motivation
Secchi Depth
Model
Results
Future
Secchi Depth Observations
• In the open ocean phytoplankton are the major
drivers of variability in Secchi Depth
• Boyce et al (2010) present a global analysis of a
century of Secchi Depth Observations to investigate
the trends in ocean primary productivity
http://www.obs-vlfr.fr/Boussole/html/images/images.php
Motivation
Secchi Depth
Model
Results
Future
Secchi Depth Observations
• In the open ocean phytoplankton are the major
drivers of variability in Secchi Depth
• Maine Lakes are brown due to high concentrations
of dissolved organic matter.
• Is DOM the major driver of lake Secchi Depth variations?
• Is there a robust optical proxy between brownness and
DOC?
Motivation
Secchi Depth
Model
Results
Future
Secchi Depth: Lake to Lake Variability
• Range 0.77 to 13 m
• Coefficient of Variation 3 to 25%
Motivation
Secchi Depth
Model
Results
Future
Secchi Depth: Seasonal Patterns
• Relatively little seasonality
• Lake to lake variations much larger
Lake code
Motivation
Secchi Depth
Model
Results
Future
Secchi Depth: Interannual Variability
• Cyclic pattern
• ~30 years
• Range of annual
means is comparable
to seasonal range
• Some lakes exhibit no
interannual variations
Motivation
Secchi Depth
Model
Results
Future
What drives variations in Secchi Depth?
• Light decreases
exponentially with depth
according to:
E(z)= E(0) exp(-kz)
Where k is the attenuation
coefficient (m-1)
• The Secchi depth, Zs,
occurs from 11-22% light
level
• Range kZs = 0.17 to 0.22
• So we need k
Motivation
Secchi Depth
Model
Results
The attenuation coefficient, k (m-1)
• Absorption and scattering
• Light that travels at larger
angles travels a longer
distance per depth
• Described mathematically
𝑘=𝑎 𝜇
• Where 𝜇 is the cosine of
the average angle
• Dominated by solar angle
• Range 𝝁 = 0.7 to 0.9
• So we need 𝑎
Future
Motivation
Secchi Depth
Model
Results
Future
What constituents dominate absorption?
• Possibilities
• Expected Patterns
• Water
• Constant
• Phytoplankton
• Strong seasonal cycle
• Other particles
• Scatter rather than absorb light
• Dissolved organic matter
(DOM)
• varies with watershed
cover/use/hydrology
√
Motivation
Secchi Depth
Model
Results
Future
Colored Dissolved Organic Matter absorption (CDOM)
• Strongly absorbs in UV, decays exponentially to red
• Described analytically as
𝑎𝐶𝐷𝑂𝑀 𝜆 = 𝑎 𝜆𝑟𝑒𝑓 ∗ exp(−𝑆𝐶𝐷𝑂𝑀 ∗ (𝜆 − 𝜆𝑟𝑒𝑓 ))
• Range SCDOM 0.013 to 0.018
• So we need 𝑎(𝜆𝑟𝑒𝑓)
Motivation
Secchi Depth
Model
Results
Future
Colored Dissolved Organic Matter absorption (CDOM)
aCDOM(254) (m-1)
• The CDOM absorption in the UV (254 nm) is
significantly related to DOC in Maine Rivers
• SUVA = aCDOM(254)/DOC (m-1 (mg/l) -1)
• Range SUVA 2.5 to 7.1
Motivation
Secchi Depth
Model
Results
Future
Model relating Secchi depth to DOC is
• 𝑍𝑆 = Ψ ∗ 𝜇/(𝑆𝑈𝑉𝐴 ∗ 𝐷𝑂𝐶 ∗ 𝑒 −𝑆𝐶𝐷𝑂𝑀
500−254
)
• Where
Ψ describes the light level at the Secchi depth
𝜇 describes the incident solar angle below the interface
𝑆𝑈𝑉𝐴 is the ratio of the UV absorption to [DOC]
𝑒 −𝑆𝐶𝐷𝑂𝑀
500−254
translates CDOM absorption from UV to visible
Motivation
Secchi Depth
Model
Results
Future
Using mean parameter values
• Observations
• 𝑍𝑆 = Ψ ∗ 𝜇/(𝑆𝑈𝑉𝐴 ∗ 𝐷𝑂𝐶 ∗ 𝑒 −𝑆𝐶𝐷𝑂𝑀
• Where
Ψ = 2.0 (14%)
𝜇 = 0.8
𝑆𝑈𝑉𝐴 = 2.85
𝑆𝐶𝐷𝑂𝑀 = 0.145
• Model fit
500−254
)
Motivation
Secchi Depth
Model
Results
Future
Now invert the equation to solve for
𝐷𝑂𝐶 , using same parameter values
• 𝐷𝑂𝐶 = Ψ ∗ 𝜇 /(𝑍𝑆 ∗ 𝑆𝑈𝑉𝐴 ∗ 𝑒 −𝑆𝐶𝐷𝑂𝑀 500−254 )
• So we can use the historical observations of 𝑍𝑆 to
estimate 𝐷𝑂𝐶
Motivation
Secchi Depth
Model
Results
Future
Implications
• Moving the “DOC” record back to the 1970s
provides capability for examining longer term
anthropogenic and climate-scale forces
• Relating DOC to Secchi Depth provides capability
for detecting DOC from Satellite (e.g. LandSat)
Thank you
http://frenchhillpond.org/Acadia/Long%20Pond.htm