Bio-optical Variability of Surface Waters in
the Northeastern Gulf of Mexico
by
Bisman Nababan
A dissertation submitted in partial fulfillment
of the requirements for the degree of
Doctor of Philosophy
Department of Marine Science
College of Marine Science
University of South Florida
Major Professor: Frank E. Muller-Karger, Ph.D.
Kendall L. Carder, Ph.D.
Robert H. Weisberg, Ph.D.
Chuanmin Hu, Ph.D.
Douglas C. Biggs, Ph.D.
Date of Approval:
November 15, 2004
Keywords: chlorophyll-a, color dissolved organic matter, Mississippi River plume,
phytoplankton absorption, detritus absorption, chlorophyll-specific absorption, salinity,
SeaWiFS, cyclonic and anticyclonic eddies, packaging effect, pigment composition
© Copyright 2004, Bisman Nababan
Acknowledgements
I wish to express my deepest gratitude to my major advisor Dr. Frank E. MullerKarger, who provided me support, guidance, insight, encouragement and a wonderful
opportunity for the successful completion of this study.
I also want to express my appreciation to the advisory committee members, Drs.
Chuanmin Hu, Kendall L. Carder, Robert H. Weisberg and Douglas C. Biggs for their
scholarly advise, critical reviews and stimulating ideas as well as their patience for the
completion of this study. Special thanks go Dr. Chuanmin Hu for a tremendous help in
developing my programming skill and exchange ideas for this study.
This research was supported by U.S. Department of Interior, Minerals Management
Service grant (1335-01-97-CA-30857) awarded to Dr. Frank Muller-Karger. This
research was also supported by Texas A&M University, Northeastern Gulf of Mexico
Chemical Oceanography and Hydrography Study (NEGOM-COH) program chaired by
Dr. Worth D. Nowlin, Jr. and Dr. Douglas C. Biggs as one of Co-PIs. Special thanks to
them for allowing us to participate in 8 NEGOM-COH cruises for collecting in situ biooptical data. Special thanks also go to all who participated in nine NEGOM-COH
cruises, both ship’s crew and scientific teams.
I am also grateful and thankful to Dr. Douglas C. Biggs and his colleague for
sharing in situ chlorophyll, salinity, nutrients, and pigments data; Dr. Ou wang for
ii
sharing his ADCP data; Dr. Bob Leben (University of Colordao) for sharing his Sea
Surface Height data; and Doug Myhre, Brock Murch and Judd Taylor who processing
satellite data at the College of Marine Science, USF.
I would also like to acknowledge the College of Marine Science at USF that
provided an excellent academic environment which stimulated my interests in
oceanography. I am particularly grateful to all staff members of the Institute of Marine
Remote Sensing and many individuals in the Optical Oceanography Laboratory
(especially Jennifer Cannizzaro and David English) who provided me direct and indirect
help for the completion of this study. Finally, I thank my family members for their
support and encouragement in all times.
iii
TABLE OF CONTENTS
List of Tables ..................................................................................................................... vi
List of Figures ................................................................................................................... vii
List of Appendices ............................................................................................................ xii
Abstract ............................................................................................................................ xiii
INTRODUCTION ...............................................................................................................1
CHAPTER I: THE IMPACT OF RIVERS AND OCEANIC PROCESSES ON
CHLOROPHYLL-A AND CDOM VARIABILITY OF SURFACE
WATERS OF THE NORTHEASTERN GULF OF MEXICO..………….8
Abstract ........................................................................................................9
1.1. Introduction .......................................................................................10
1.2. Methodology .....................................................................................16
1.2.1. Study Area ............................................................................16
1.2.2. In-situ Data Collection, Processing and Analysis .................16
1.2.3. Wind Field and River Discharge...........................................20
1.2.4. Satellite Data Collection, Processing and Analysis ..............21
1.2.5. Statistic Analysis ...................................................................23
1.3. Result and Discussion .......................................................................24
1.3.1. Spatial and Temporal Distribution of in situ Chlorophyll-a
and CDOM ............................................................................24
1.3.2. Comparison between in situ and SeaWiFS chlorophyll-a
concentration .........................................................................36
1.3.3. Spatial and Temporal Distribution of Satellite
Retrieved Chlorophyll-a .......................................................43
1.3.4. Driving Forces Affecting Chlorophyll-a Concentration
Variability .............................................................................49
1.3.5. CDOM versus Salinity ..........................................................55
1.3.6. CDOM versus Chlorophyll ...................................................61
1.3.7. Estimating Salinity from CDOM ..........................................65
1.4. Conclusions .......................................................................................69
iv
CHAPTER II:
VARIABILITY IN THE PARTICULATE ABSORPTION
COEFFICIENTS OF THE NORTHEASTERN GULF OF MEXICO
SURFACE WATERS .............................................................................73
Abstract ...................................................................................................74
2.1. Introduction ....................................................................................75
2.2. Methodology ..................................................................................79
2.2.1. Sample Collection ..............................................................79
2.2.2. Absorption Measurements .................................................80
2.3. Result and Discussion ....................................................................83
2.3.1. Total Particulate, Detritus ad Phytoplankton Absorption
Coefficients ........................................................................83
2.3.2. Variations in Spectral Shape of the Phytoplankton
Absorption Coefficients .....................................................90
2.3.3. Phytoplankton Absorption Coefficient vs. Chlorophyll-a
Concentration ...................................................................106
2.3.4. Variation in Chlorophyll-Specific Absorption
Coefficient........................................................................109
2.4. Conclusions ..................................................................................115
SUMMARY .....................................................................................................................118
REFERENCES ................................................................................................................124
APPENDICES .................................................................................................................144
v
LIST OF TABLES
Table 1.1. Cruise identifiers, dates, and seasons...............................................................17
Table 1.2. Starting and ending period (in week number of 52) of eastward wind
component measured at south of Mississippi delta (BURL1) and east of
Mississippi delta (42040) in relation to sea surface height and the
Mississippi plume period. Sea surface height was extracted from the
location of 87.5W and 28.2N ........................................................................48
Table 1.3. Regression coefficient of determination (r2), intercept, and slope, with
respective standard error (se), between salinity and ag443 (S=a + b*ag443),
and mean percentage error (MPE) for salinity prediction based on ag443
data within 95% prediction interval of each region in different cruises
(seasons) ...........................................................................................................59
Table 1.4. Adjusted coefficient of determination of ag443 as function of salinity
2
(adj R sal
) and ag443 as function salinity and chlorophyll-a concentration
2
(adj R sal
 chl ) .......................................................................................................63
Table 1.5. Mean percentage error of observed vs. predicted salinity employing
seasonal global NEGOM salinity-ag443 relationships to predict salinities
in other seasons ................................................................................................67
Table 2.1. Mean values of a *ph (440) for all seven NEGOM cruises data corresponding
with the three major size cell markers of phytoplankton ...............................111
vi
LIST OF FIGURES
Figure 1.1.
Study area: the NEGOM region, encompassing the area between
27.3 - 30.7 N and 82.6 - 89.6W. The map shows the 10, 20, 100,
200, 500, and 1000 m bathymetric contours. The line connecting closed
triangles shows a typical cruise track and stations, with cross-shelf
transect lines numbered L1-L11. Numbered open squares show regions
at which time series of chlorophyll-a concentration, CDOM and salinity
were analyzed. Inset: the Gulf of Mexico the area of the study ..................17
Figure 1.2.
Spatial and temporal distribution of near-surface chlorophyll-a
concentration in the NEGOM region based on in situ observation data.
Thin white lines show the cruise track where continuous chlorophyll
fluorescence observations were collected. See Table 1.1 for cruise
identification .................................................................................................25
Figure 1.3.
Near-surface ag443 (m-1) distribution in the NEGOM. Thin white lines
show the cruise track where continuous CDOM fluorescence
observations were collected. See Table 1.1 for cruise identification ...........26
Figure 1.4.
Near-surface salinity distribution in the NEGOM. Thin white lines
show the cruise track where continuous salinity observations were
collected. See Table 1.1 for cruise identification .........................................28
Figure 1.5.
Sea surface height (SSH) dynamic and ADCP-generated (10-14 m-deep)
currents in the NEGOM. Note: There are no ADCP data from cruise of
fall 1998 east of 87W because of mechanical damage to ADCP
equipment. See Table 1.1 for cruise identification……………………….29
Figure 1.6. Average surface wind field in the NEGOM during the period of 9 cruise
observations (data obtained from Texas A&M University). Diamonds
and their identification indicate stations where wind speed and direction
were collected. Several offshore NDBC stations used to generate wind
gridded average were not shown in the picture because they were
located south of 27N ...................................................................................32
vii
Figure 1.7.
Mean and standard deviation of near-surface chlorophyll-a concentration
(top panel), salinity (middle panel) and ag443 (m-1) (bottom panel) values
for each subregion and for different time periods sampled. Note: There
were no ag443 data for fall 1997 and spring 1998. See Table 1.1 for
cruise identification .......................................................................................33
Figure 1.8.
Fresh water discharge of six major rivers in the NEGOM region during
the period of nine cruise observations. Y-axis is daily average of river
discharge * 10^8 (m3/d) ................................................................................34
Figure 1.9.
Comparison between in situ and SeaWiFS estimates (OC4v4) of
chlorophyll-a concentration (0.0<[chlorophyll-a]<50 mg m-3) along
ship track lines for nine NEGOM cruises. In situ ag443 values were also
plotted for the comparison. MRE1 is negative MRE and MRE2 is
positive MRE (see section 1.2.5 for the formula) .........................................38
Figure 1.10. Similar to Figure 1.9 but for SeaWiFS derived chlorophyll-a
concentration <1.0 mg m-3 ...........................................................................39
Figure 1.11. Comparison between in situ and SeaWiFS estimates using MODIS
algorithm (Carder et al., 1999) of chlorophyll-a concentration
(0.0<[chlorophyll-a]<50 mg m-3) along ship track lines for nine NEGOM
cruises. In situ ag443 values were also plotted for the comparison.
MRE1 is negative MRE and MRE2 is positive MRE (see section 1.2.5
for the formula) .............................................................................................41
Figure 1.12. Similar to Figure 1.11 but for SeaWiFS estimates using MODIS
algorithm of chlorophyll-a concentration <1.0 mg m-3 ...............................42
Figure 1.13. Spatial and temporal distribution of near-surface chlorophyll-a
concentration in the NEGOM region based on SeaWiFS data during
the nine separate NEGOM cruise periods. White and purple contours
are positive and negative total sea surface height, respectively....................46
Figure 1.14. Initial and subsequent position of the Mississippi River plume during
summer of 1998, 1999, and 2000. White and purple contour lines
indicate positive and negative sea surface height. White arrow indicates
the extent of the plume and the surface flows in baroclinic balance
following along isopleths of sea surface height ............................................47
Figure 1.15. Time series of near-surface monthly mean chlorophyll-a concentration
based on satellite SeaWiFS data employing MODIS algorithm (Carder et
al., 1999) for ten selected regions .................................................................51
viii
Figure 1.16. Lagged cross-correlation analyses for weekly mean river discharge and
upwelling index vs. chlorophyll-a concentration, with lags measured in
weeks. Satellite chlorophyll-a concentration was derived from SeaWiFS
data employing MODIS algorithm (Carder et al., 1999)..............................52
Figure 1.17. Scatter plots of salinity vs ag443 for each region for summer seasons
(cruises). See Table 1 for cruise identification. Blue line is the global
NEGOM regression line for fall 1998 (Fa-98), yielding the lowest error of
observed vs. predicted salinities for spring and winter seasons within 8
selected regions and global NEGOM region. Note: the x and y axes are in
different scales ..............................................................................................56
Figure 1.18. Scatter plots of salinity vs ag443 for each region for spring seasons (cruises).
See Table 1 for cruise identification. Blue line is the global NEGOM
regression line for fall 1998 (Fa-98), yielding the lowest error of observed
vs. predicted salinities for spring and winter seasons within 8 selected
regions and global NEGOM region. Note: the x and y axes are in different
scales .............................................................................................................57
Figure 1.19. Scatter plots of salinity vs ag443 for each region for fall seasons (cruises).
See Table 1 for cruise identification. Blue line is the global NEGOM
regression line for fall 1998 (Fa-98), yielding the lowest error of observed
vs. predicted salinities for spring and winter seasons within 8 selected
regions and global NEGOM region. Note: the x and y axes are in different
scales .............................................................................................................58
Figure 1.20. Example of the error analysis of salinity predictions based on ag443
data. Mean percentage error (MPE) equals to average of absolute values
of 95% predicted interval values minus regression line values divided by
regression line value multiplied by 100%. MPE for (A)=0.86% and
(B)=8.17% (see also Table2). Note axis scales are different for better
display purpose .............................................................................................68
Figure 1.21. Some example of observed and predicted global NEGOM salinity
employing global NEGOM salinity-ag443 relationship of other season.
MPE equals to average of absolute values of estimate salinity minus
observed salinity divided by observed salinity multiplied by 100%.
These two cases represent the best and the worst scenarios .........................68
Figure 2.1.
Near-surface total particulate absorption spectra in the NEGOM between
1998 and 2000. See Table 1.1 for cruise identification. Note different scales
on y-axis........................................................................................................85
ix
Figure 2.2.
Near-surface detritus absorption spectra in the NEGOM between 1998
and 2000. See Table 1.1 for cruise identification. Note different
scales on y-axis .............................................................................................86
Figure 2.3.
Near-surface phytoplankton absorption spectra in the NEGOM between
1998 and 2000. See Table 1.1 for cruise identification. Note different scales
on y-axis........................................................................................................87
Figure 2.4.
Proportion of detrital to total particulate absorption coefficient at
440 nm, as a function of chlorophyll-a concentration for each cruise.
See Table 1.1 for cruise identification ..........................................................88
Figure 2.5.
Time series of mean ag(440), ad(440), aph(440) and chlorophyll-a
concentration of each cruise. Region with salinity >34.5 (Top Panel)
and region with salinity <34.5 (Lower Panel). Right y-axis is for
chlorophyll-a concentration. See Table 1.1 for detail cruise
identification .................................................................................................89
Figure 2.6.
Phytoplankton absorption spectra normalized to 440 nm from seven
NEGOM cruises. Individual normalized spectra are shown in black and
the average normalized spectra in blue. Panel H shows the average
normalized spectra for spring, summer and fall seasons. ‘N’ followed
by a number identifies each cruise (Table 1.1) .............................................91
Figure 2.7.
Near-surface distribution of fucoxanthin concentration normalized with
chlorophyll-a concentration in the NEGOM. White dots show location of
water sample collections. See Table 1.1 for cruise identification ................92
Figure 2.8.
Near-surface distribution of zeaxanthin concentration normalized with
chlorophyll-a concentration in the NEGOM. White dots show location of
water sample collections. See Table 1.1 for cruise identification ................93
Figure 2.9.
Near-surface distribution of the aph545/440 in the NEGOM. White dots
show location of water sample collections. See Table 1.1 for cruise
identification .................................................................................................95
Figure 2.10. Near-surface distribution of the aph673/440 in the NEGOM. White dots
show location of water sample collections. See Table 1.1 for cruise
identification .................................................................................................96
Figure 2.11. Near-surface distribution of the of mean aph545/440+aph625/440+
aph673/440 in the NEGOM. White dots show location of water sample
collections. See Table 1.1 for cruise identification .......................................97
x
Figure 2.12. Spatial distribution of biomass proportion of cell size marker for
microphytoplankton. See Table 1.1 for cruise identification......................102
Figure 2.13. Spatial distribution of biomass proportion of cell size marker for
nanophytoplankton. See Table 1.1 for cruise identification .......................103
Figure 2.14. Spatial distribution of biomass proportion of cell size marker for
pycophytoplankton. See Table 1.1 for cruise identification .......................104
Figure 2.15. Relationship between the average of aph545/440+aph625/440+aph673/440
and microphytoplankton biomass proportion for each cruises. See
Table 1.1 for detail of cruise identification .................................................105
Figure 2.16. Relationship between phytoplankton absorption coefficient at 440 nm
(aph(440)) and chlorophyll-a concentration for each cruises. Note the
scales on both x and y-axis are different between panels. See Table 1.1
for detail of cruise identification .................................................................107
Figure 2.17. Relationship between phytoplankton absorption coefficient at 673 nm
(aph(673)) and chlorophyll-a concentration for each cruises. Note the
scales on both x and y-axis are different between panels. See Table 1.1
for detail of cruise identification .................................................................108
Figure 2.18. Near-surface chlorophyll-specific absorption spectra measured on the
NEGOM region during summer, spring and fall seasons between 1998
and 2000. ‘N’ followed by a number indicates cruise identification (see
Table 1.1 for cruises identification). ‘aph’ indicates phytoplankton
absorption coefficient. Note the different scales on y-axis ........................112
Figure 2.19. Near-surface distribution of the chlorophyll-specific absorption
coefficient at 440 nm in the NEGOM. White dots show location of
water sample collections. See Table 1.1 for cruise identification...............113
Figure 2.20. Scatter plots of a *ph (440) and biomass proportion per algal group for all
seven NEGOM cruises................................................................................114
xi
LIST OF APPENDICES
Appendix 1. Method to Estimate Coastal Upwelling Index…………………………...145
Appendix 2. Weekly mean time series of wind, upwelling index, river discharge,
and chlorophyll-a concentration off Mississippi region. A negative
value for upwelling index indicates an upwelling .....................................148
Appendix 3. Same as Appendix 2 but off Mobile region ...............................................149
Appendix 4. Same as Appendix 2 but off Escambia region ...........................................150
Appendix 5. Same as Appendix 2 but off Choctawhatchee region ................................151
Appendix 6. Same as Appendix 2 but off Apalachicola region ......................................152
Appendix 7. Same as Appendix 2 but off Suwannee region ..........................................153
xii
Bio-optical Variability of Surface Waters in
the Northeastern Gulf of Mexico
Bisman Nababan
ABSTRACT
Using satellite data and extensive in situ hydrography and bio-optical data, biooptical variability of surface waters in Northeastern Gulf of Mexico (NEGOM) was
examined. In situ observations showed that relatively high chlorophyll-a concentration
(chl>1 mg m-3) and high colored dissolved organic mater (ag443>0.1 m-1) were generally
observed over the inner shelf (water depth <50m), particularly near river mouths, and in
spatially coherent Mississippi plumes that extended offshore and to the southeast of the
Mississippi delta during the three consecutive summer seasons of 1998, 1999, and 2000.
River discharge was the major influencing factor on chlorophyll-a concentration
variability in the inner shelf. Strong interannual variability in in situ chlorophyll-a
concentration was observed during winter to spring in 1998 compared during winter to
spring in 1999 and 2000 off Escambia, Choctawhatchee, Apalachicola, Suwannee and
Tampa Bay regions. This was related to higher fresh water discharge during the 19971998 El Niño-Southern Oscillation event. The Mississippi plume lasting over 14 weeks
between May and September and extended up to >500 km east of the Mississippi delta
and to the Florida Keys appeared to be facilitated by eastward winds and anticyclonic
eddies located just to southeast of the Mississippi delta.
xiii
In general, ag443 covaried linearly and inversely with salinity in inner shelf during
spring and fall cruises, indicating conservative mixing. The global NEGOM salinity-ag443
relationship of fall 1998, i.e., Salinity=36.59-29.86*ag443 (n=8771, r2=0.86;
0.01<ag443<0.52, 16<S<36) may be useful to estimate salinity from satellite ocean color
data for spring and fall seasons (<3% errors; corresponding to <1.03 psu). While river
discharge was an important source for colored dissolved organic matter (CDOM),
phytoplankton blooms during spring and summer seasons were also significant sources
for CDOM formation in NEGOM. During fall season, however, phytoplankton was also
a significant source for CDOM formation offshore.
In general, CDOM absorption coefficient at 440 nm exceeded phytoplankton
absorption coefficient (aph(440)) up to a factor of 3. Using biomass proportion of
phytoplankton taxonomic groups based on pigments index, variability in biomass
proportion of microphytoplankton (BPmicro) can explain up to 76% variability in the
average of aph(545)/aph(440), aph(625)/aph(440), and aph(673)/aph(440). Chlorophyllspecific absorption coefficient, a *ph (440), varied by a factor of 7 (0.02-0.15 m2 mg-1).
Particle size (packaging effects) and pigment composition played important roles in
determining a *ph (440) variability.
xiv
INTRODUCTION
The Northeastern Gulf of Mexico (NEGOM), defined as the region north of 27°N
between the Mississippi River Delta and the Florida coast, is one of great national
importance because of its oil, gas, fisheries, and recreational resources. This area includes
pristine water quality and important shelf, and coastal ecosystems resources such as
wetlands and coastal ocean bottoms. These areas have great economic importance for
tourism and fisheries industries.
The NEGOM region receives more fresh water and riverine sediments than any
other North American shelf from the Mississippi, Mobile, Apalachicola and Suwannee
Rivers. This leads to strong seasonal buoyancy variation on the shelf, and to high
nutrient, chlorophyll, and high dissolved organic carbon concentrations (Guo et al., 1995;
Gilbes et al., 1996; Turner and Rabalais, 1999; Pennock et al., 1999; Del Castillo et al.,
2000). This region also experiences the most intense upwelling within the Gulf of
Mexico (Gilbes et al., 1996; Muller-Karger, et al., 1991; Muller-Karger, 2000; He and
Weisberg, 2003), and sometimes is affected by the Loop Current or by cold and warm
rings shed by the large western Boundary Current (Vukovich et al., 1979; Huh et al.,
1981; Vukovich and Hamilton, 1989; Muller-Karger, et al., 2001; Weisberg and He,
2003). However, the mechanisms leading to upwelling in the NEGOM, and the spatial
and temporal scales of the process, are still poorly understood.
1
The diversity of oceanographic processes in the NEGOM region leads to variability
in the distribution of chlorophyll-a, colored dissolved organic carbon, salinity, and biooptical properties. Previous studies indicated that NEGOM ecosystem varies from
eutrophic in coastal water to oligotrophic in the deeper ocean (Livingston et al., 1975;
McPherson et al., 1990; Muller-Karger et al., 1991; Gilbes et al., 1996, 2002; Lohrenz et
al., 1990, 1999; Muller-Karger, 2000). However, occasional high level of chlorophyll
concentration (plume) in the deeper ocean (offshore) specifically during late spring and
summer had also been documented (Huh et al., 1981; Ortner et al., 1995; Gilbert et al.,
1996; Gilbes et al., 1996, 2002; Wiseman and Sturges, 1999; Jochens et al., 1999, 2002;
Muller-Karger et al., 2000; Muller-Karger, 2000; Belabbassi, 2001; Bigss and Ressler,
2001; Del Castillo et al., 2001; He and Weisberg, 2002; Weisberg and He, 2003; Morey
et al., 2003; Hu et al., 2003; Qian et al., 2003). High levels of chlorophyll concentration
in coastal region and offshore have been attributed to the input of new nutrients from the
river discharge and upwelling processes (Riley, 1937; Thomas and Simmons, 1960; Sklar
and Turner, 1981; Muller-Karger et al., 1991; Walsh et al, 1989; Ortner et al., 1995;
Gilbert et al., 1996; Gilbes et al, 1996, 2002; Wiseman and Sturges, 1999; Jochens et al.,
1999, 2002; Pennock et al., 1999; Muller-Karger et al., 2000; Muller-Karger, 2000;
Belabbassi, 2001; Bigss and Ressler, 2001; Del Castillo et al., 2001; Salisbury et al,
2001; He and Weisberg, 2002; Weisberg and He, 2003; Morey et al., 2003; Hu et al.,
2003; Qian et al., 2003). However, synoptic patterns of variability of chlorophyll-a and
the variability in factors that drive this are still unclear.
2
Knowledge of the spatial and temporal distribution of colored dissolved organic
matter (CDOM) is very important since CDOM strongly absorbs light in the biologically
damaging ultraviolet (UV) and blue regions, can cause degradation in satellite-derived
chlorophyll concentration, can help to understand biogeochemical processes, and can be
used to trace transport and mixing processes in estuaries and coastal regions. Several
studies often found an inverse linear relationship between CDOM and surface salinity
indicating conservative mixing (Vodacek et al., 1997; Ferrari and Dowel, 1998; Carder et
al., 1989, 1999; Del Castillo et al., 2000, 2001; Hu et al, 2003). However, the non-linear
relationship was also observed indicating non-conservative mixing arising from in situ
production or loss of CDOM (Carder et al., 1993; Blough et al., 1993; Nelson and
Guarda, 1995; Vodacek et al., 1997; Klinkhammer et al., 2000; Benner and Opsahl,
2001; Blough and Del Vecchio, 2002). Previous studies have also proposed to use
CDOM to estimate surface salinity from high-resolution ocean color sensors (Carder et
al., 1993; D’Sa et al., 2002; Hu et al., 2003). To achieve this goal, one requirement is
that there exists a predictable relationship between CDOM and salinity (Carder et al.,
1993; D’Sa et al., 2002; Hu et al., 2003). Several studies on CDOM characteristics and
distribution in the NEGOM region have been conducted (Carder et al, 1989, 1993; Del
Castlillo et al., 2000, 2001; Stoval-Leonard, 2003). However, these studies mostly
focused on single cruises and not on understanding of the spatial and temporal
distribution of CDOM, or on factors affecting this distribution. Further little information
has been available in the literature regarding the relationship between CDOM and salinity
and factors affecting the relationship in this region.
3
Light absorption by particulate matters affects satellite-derived pigment biomass,
primary production and mixed layer heating (Yentsch and Phinney, 1989; Bricaud and
Stramski, 1990; Nelson et al., 1993; Carder et al., 1995; Cleveland, 1995; Sosik and
Mitchell, 1995; Lee et al., 1996; Suzuki et al., 1998). For these reasons and to improve
remote sensing capabilities, information on the magnitude, range and sources of
variability in the absorption coefficient of particulate matters is very important. Given the
traditional difficulty of estimating phytoplankton pigment concentration in coastal waters,
information from these areas may be of particular interest for the refinement of Case-II
algorithms.
In general, the operational ocean color algorithms have provided a single
phytoplankton pigment index (chlorophyll-a) as a general indicator of algal biomass.
However, it is also well known that accessory pigments are strongly absorbing light in
the blue and blue-green region, and hence influence spectral variations of ocean
reflectance. Some of these pigments are considered as taxonomic markers (e.g.,
fucoxanthin for diatoms, phycoerythrin and zeaxanthin for cyanobacteria, divinyl-chl-a
for prochlorophytes, etc.), and therefore provide information on the presence and
abundance of these groups (Gieskes and Kraay, 1983; Guillard et al., 1985; Wright and
Jeffrey, 1987; Kimor et al., 1987; Gieskes et al, 1988; Chilsom et al., 1988; Hooks et al.,
1988; Bjornland and Liaaen-Jensen, 1989; Goericke and Repeta, 1992).
The chlorophyll-specific absorption coefficients (the absorption coefficient of
phytoplankton at particular wavelength normalized to its chlorophyll-a concentration)
can vary as a consequence of differences in the composition and concentration of
4
pigments as well as pigment packaging, and cell size structure of phytoplankton
populations (Duysens, 1956; Sathyendranath et al., 1987; Hoepffner and Sythyendaranath
et al., 1991, 1993; Bissett et al., 1997; Carder et al., 1991; 1999; 2004; Nelson et al.,
1993). Previous studies conducted in West Florida Shelf documented that chlorophyllspecific absorption coefficient at 440 nm varies directly with submicron chlorophyll
fraction, with itself varies inversely with total chlorophyll concentration or phytoplankton
size (Carder et al., 1986). In the same region, Bisset et al. (1997) and Lohrenz et al.
(2003) reported that pigment packaging and pigment composition affected variations in
chlorophyll-specific absorption coefficients. These studies, however, only dealt with
small region in NEGOM with single cruise and not on the understanding of the spatial
and temporal distribution of particulate matters and chlorophyll-specific absorption
coefficients, or on factors affecting this distribution. Further, little information is
available in the literature regarding spatial and temporal variations in particulate matters
and chlorophyll-specific absorption coefficients in the NEGOM region.
Earth-observing satellites (SeaWiFS, AVHRR, and TOPEX/POSEIDON) can
provide valuable information on the distribution of river water and sediments on the
continental shelf as well as on the circulation affecting the interaction of river plumes
with oceanic water. Using satellite data and extensive in situ hydrographic, biological,
and optical data collected in collaboration with Texas A&M University NEGOMChemical Oceanography and Hydrography study supported by the Minerals Management
Service (MMS) of the U. S. Department of the Interior, the variability of bio-optical
5
properties of surface water in the Northeastern Gulf of Mexico was examined, with two
major goals:
To determine the temporal and spatial variability of bio-optical properties;
To evaluate forcing factor mechanisms for such variations.
Specifically, my overall objectives are:
(1) To determine inter-annual variability of chlorophyll-a concentrations;
(2) To examine the spatial and temporal variability of CDOM and to further determine
whether surface salinity in the NEGOM can be estimated from CDOM;
(3) To examine the spatial and temporal variability of particulate matter and chlorophyllspecific absorption coefficients, and to determine whether the phytoplankton species
or groups can be estimated from phytoplankton absorption coefficients (aph());
(4) To describe the effect of river discharge, short-lived warm slope eddies and cold
slope eddies, winds and surface current changes on the temporal and spatial
variability of chlorophyll-a concentration, colored dissolved organic matter (CDOM),
and absorption coefficient of particulate materials of the NEGOM surface waters.
The dissertation is developed into two separate chapters and a summary of the
dissertation. Chapters are entitled as follows:
Chapter I: “The impact of rivers and oceanic processes on chlorophyll-a and CDOM
variability of surface water of the Northeastern Gulf of Mexico”.
Chapter II: “Variability in the particulate absorption coefficients of the Northeastern Gulf
of Mexico surface waters”.
6
A list of the cited literature and seven appendices regarding time series of wind,
coastal upwelling index, river discharge, and satellite-derived chlorophyll-a concentration
then follow to conclude this dissertation.
7
CHAPTER I:
THE IMPACT OF RIVERS AND OCEANIC PROCESSES ON
CHLOROPHYLL-A AND COLORED DISSOLVED ORGANIC MATTER
VARIABILITY IN THE SURFACE WATERS OF THE NORTHEASTERN
GULF OF MEXICO
8
Abstract
In situ data revealed that relatively high chlorophyll-a concentrations (>1 mg m-3) and
high CDOM absorptions (ag443>0.1 m-1) were generally observed over the inner shelf
particularly near the major river mouths, and in a spatially coherent Mississippi plume
extending offshore and to the southeast during the three summer seasons (1998, 1999,
and 2000). The Mississippi plume extended over 500 km from the river delta and to
Florida Keys for periods lasting over 14 weeks between May and September. The plume
was forced offshore in part by eastward winds and the presence of anticyclonic eddies
near the slope off the Mississippi delta. Nutrients provided by river discharge appeared
to be the major factor influencing chlorophyll-a variability in nearshore regions. Strong
interannual variability (by factor 4) in chlorophyll-a concentrations was observed during
winter to spring in 1998 compared with winter to spring in 1999 and 2000 off Escambia,
Choctawhatchee, Apalachicola, Suwannee and Tampa Bay regions. This was related to
high fresh water discharge during the 1997-1998 El Nino event. In general, ag443
covaried linearly and inversely with salinity, specifically during spring and fall seasons.
During summer seasons, however, the non-conservative mixing behaviors were also
observed. The global NEGOM salinity-ag443 relationship of fall 1998, i.e.,
Salinity=36.59-29.86*ag443 (n=8771, r2=0.86; 0.01<ag443<0.52, 16<S<36) may be useful
to estimate salinity from satellite ocean color data for spring and fall seasons (<3% errors;
corresponding to <1.03 psu). Phytoplankton blooms during spring and summer seasons
were significant sources for CDOM formation in NEGOM. In the central NEGOM,
phytoplankton was also a significant source for CDOM formation during fall season.
9
1.1. INTRODUCTION
The Northeastern Gulf of Mexico (NEGOM) is an area that experiences significant
variability due to a variety of oceanographic processes acting on shelf and coastal waters.
Particularly, upwelling, cold- and warm-core rings, river discharge, and winds, force
significant biogeochemical variability within the NEGOM. Several studies have focused
on the effect of river outflow on chemistry and biology of estuarine zones within the
NEGOM (Livingstone et al., 1975; Lohrenz et al., 1990; McPherson et al., 1990).
However, synoptic patterns of variability of chlorophyll-a and colored dissolved organic
matter (CDOM) and the factors that drive their variability were still unclear.
The Gulf of Mexico is frequently considered to be an oligotrophic system (Ortner et
al., 1984; Biggs, 1992). However, satellite data and in situ observations (Huh et al.,
1981; Ortner et al., 1995; Gilbert et al., 1996; Gilbes et al., 1996, 2002; Wiseman and
Sturges, 1999; Jochens et al., 1999, 2002; Muller-Karger et al., 1991, 2000; MullerKarger, 2000; Belabbassi, 2001; Bigss and Ressler, 2001; Del Castillo et al., 2001; He
and Weisberg, 2002; Weisberg and He, 2003; Morey et al., 2003; Hu et al., 2003; Qian et
al., 2003) have shown that the Gulf of Mexico does experience intermediate to high
phytoplankton concentrations both over the shelf and in certain offshore areas, and that
some of these patterns are seasonal. Some changes in nearshore areas are related to winddriven coastal upwelling (Murray, 1972; Chuang et al., 1982; Schroeder et al., 1987; Li
and Weisberg, 1999a, 1999b; Yang and Weisberg, 1999; Yang et al., 1999; Muller–
10
Karger, 2000) and river plumes (Walker, 1996; Pennock et al., 1999). Over the shelf,
variation in chlorophyll-a is affected by seasonal convective mixing, upwelling
associated with eddies (Biggs and Muller-Karger, 1994; Wiseman and Sturges, 1999),
and the offshore dispersal of riverine outflow.
Using Coastal Zone Color Scanner (CZCS) images, Muller-Karger et al. (1991)
found that to a first order variability in pigment concentration seaward of the shelf was
synchronous throughout the Gulf, with the highest values from December to February
and the lowest from May to July. Using CZCS images, Gilbes et al. (1996) observed that
an episodic plume with high pigment concentration occurs along the West Florida Shelf
each the spring and persists 1-6 weeks in a pattern that extends >250 km southward over
the middle shelf. During the summer, low pigment concentration was generally observed
along the outer shelf and continental slope of the West Florida Shelf (Gilbes et al., 1996).
The Northeastern Gulf of Mexico (NEGOM, Figure 1.1) receives significant fresh
water input from the Mississippi, Mobile, Escambia, Choctawhatchee, Apalachicola, and
Suwannee Rivers. River discharge varies seasonally, with maximum typically during
spring and minimum during summer (Gilbes et al., 1996). The discharge leads to strong
seasonal buoyancy variation on the shelf, and to high nutrient, chlorophyll, and dissolved
organic carbon concentrations nearshore (Guo et al., 1995; Gilbes et al., 1996; Pennock
et al., 1999; Turner and Rabalais, 1999; Del Castillo et al., 2000). The Mississippi River
provides more than half of all of the fresh water input into the Gulf of Mexico (Deegan et
al., 1986) and is the dominant source of terrestrial nutrient input (Twilley et al., 1999).
Indeed, Walsh et al. (1989) suggested that the Mississippi plume area is the most
11
productive within the Gulf of Mexico. Walsh (1983) also mentioned that secondary
production due to sinking herbivores and ungrazed prey specifically from Gulf of
Menhaden fishery activities was an important factor in increasing productivity in the Gulf
of Mexico. Chlorophyll-a concentrations in the Mississippi River plume are highly
variable in space and time, ranging between 1.1 and 14.4 mg/m3 in the outer plume
during four cruises in 1990-1992 (Redalje et al., 1994), but as high as 40-80 mg/m3 in
near-coastal waters during the late spring in 1993 (Nelson and Dortch, 1996). Del
Castillo et al. (2000) concludes that the CDOM in the West Florida Shelf is directly
related to river input, and that high phytoplankton concentrations did not automatically
result in the production of significant amounts of CDOM.
While buoyancy and wind forcing control the circulation of shelf waters (Weisberg
et al., 1996, 2000; Li and Weisberg, 1999a, 1999b; Yang et al., 1999; Yang and
Weisberg, 1999; Hsueh and Golubev, 2002; He and Weisberg, 2002; Weisberg and He,
2003), it is also affected by northward intrusions of the Loop Current and associated
eddies (Vukovich, 1988; Huh et al., 1981; Muller-Karger, 2000; Nowlin et al., 2000,
Muller-Karger et al., 2001; Jochens et al., 2002; He and Weisberg, 2002; Weisberg and
He, 2003). The Loop Current rarely penetrates north of 28 °N in the eastern Gulf of
Mexico (Vukovich et al., 1979); therefore, its direct influence in the NEGOM region is
considered occasional (Muller-Karger et al., 2001).
Knowledge of the spatial distribution and temporal changes of CDOM is important
since CDOM strongly absorbs light in the biologically damaging ultraviolet (UV) and
blue regions hence protecting phytoplankton and other biota and reduce light available
12
for phytoplankton photo-synthesis (Arrigo and Brown, 1996; Blough and Green, 1995),
can cause degradation in satellite-derived chlorophyll concentration (Morel and Prieur,
1977; Hochman et al., 1995, 1994; Vodacek et al., 1994; Carder et al., 1989), can help to
understand biogeochemical processes, and can be used to trace transport and mixing
processes in estuaries and coastal regions. Specifically, photolysis of CDOM is a
pathway of carbon cycling in the ocean (Moran and Zepp, 1997).
Colored dissolved organic matter (CDOM) originates from the degradation of
organic material in terrestrial and aquatic ecosystems (Kirk, 1994). In coastal waters,
most CDOM typically derives from land drainage and river runoff. Away from
continental margins the effect of rivers declines, and CDOM is presumably related to
primary productivity as a by-product of algal cell degradation (Siegel and Michaels,
1996; Carder et al., 1989, 1991 and 1993; Yentsch and Reichert, 1962; Fogg and Boalch,
1958) and zooplankton grazing (Momzikoff et al., 1994). In upwelling areas, an increase
in nutrient availability may lead to an increase in chlorophyll concentration exceeding
two orders of magnitude. While historical observations have suggested that there may be
little change in CDOM concentration in these areas affected by upwelling (Bricaud et al.,
1981) there is evidence that, in some regions at least, upwelling source water itself may
be colored by higher background CDOM concentrations (Carder et al., 1991; Coble et
al., 1998; Siegel et al., 2002).
Since CDOM and pollutants transported in river plumes are often diluted in a
manner consistent with that of salinity, CDOM has been proposed as a proxy to estimate
surface salinity from high-resolution (~<1km) ocean color sensors (e.g., Carder et al.,
13
1993; D’Sa et al., 2002; Hu et al., 2003). Surface salinity is a conservative property
(assuming balanced precipitation/evaporation), and therefore it can be used to trace
transport and mixing processes in estuaries and coastal areas. Salinity is also frequently
used as an index of the health of coastal habitats (e.g., Montague and Ley, 1993). It
would be of great utility to establish a methodology to assess synoptic salinity
distribution rapidly and repeatedly. Unfortunately, planned space-based microwave
sensors provide only coarse spatial resolution (~80 km) and limited accuracies for
instantaneous salinity estimates (~0.4-0.6 psu; Lagerloef et al., 1995). It would be ideal to
complement such capabilities with higher resolution information. The potential use of
CDOM as a salinity proxy is based on the assumptions that 1) CDOM can be estimated
accurately from satellites and 2) there exists a predictable relationship between CDOM
and salinity.
Several studies have documented the relationship between CDOM and surface
salinity in coastal regions (Hu et al., 2003; Benner and Opsahl, 2001; Klinkhammer et
al., 2000; Ferrari and Dowell, 1998; Vodacek et al., 1997; Carder et al., 1999). Although
an inverse linear relationship is often found because of conservative mixing (Hu et al.,
2003; Ferrari and Dowell, 1998; Carder et al., 1999), non-conservative relationships have
also been reported (Blough et al., 1993; Carder et al., 1993; Benner and Opsahl, 2001).
Studies on CDOM characteristics and distribution in the NEGOM region have also been
conducted (Del Castillo et al., 2001; Del Castillo et al., 2000; Carder et al., 1989). Yet,
these focused on single cruises and not on the understanding of the spatial and temporal
distribution of CDOM, or on factors regulating the distribution and variability. Further,
14
little information has been available in the literature regarding the relationship between
CDOM and salinity, and factors affecting the relationship in this region.
Using field observations of hydrographic and biological data from nine cruises
within the NEGOM region conducted between 1997 and 2000 and satellite data from
Sea-viewing Wide-Field-of-View Sensor (SeaWiFS), and TOPEX/ POSEIDON, I
examined the spatial and temporal variability of chlorophyll-a and CDOM in the
NEGOM region and assessed factors regulating their distribution and variability. The
specific objectives of this section of the dissertation are: (1) to determine the spatial and
temporal variability of chlorophyll-a and CDOM, and to identify the factors affecting the
variability; (2) to determine the extent, duration and forcing factors of offshore river
plumes in the NEGOM during the three consecutive years of 1998, 1999, and 2000; (3) to
examine the relationship between surface salinity and CDOM and determine whether
surface salinity in the NEGOM can be estimated from CDOM, specifically because this is
relevant to the possibility of using ocean color to assess the distribution of salinity in
areas affected by rivers; (4) to evaluate several bio-optical algorithms for estimation of
chlorophyll-a concentration and CDOM.
15
1.2. METHODOLOGY
1.2.1. Study Area
The study was conducted in the region extending from the Mississippi River Delta
to the West Florida Shelf off Tampa Bay. For this study the NEGOM is defined inshore
by the 10-m isobath and offshore by the 1000-m isobath. Six major river-impacted
regions, namely the Mississippi (R1), Mobile (R2), Escambia (R3), Choctawhatchee
(R4), Apalachicola (R5), and Suwannee (R6), plus one coastal region near Tampa Bay
(R7), and three offshore regions (R8A, R8B, and R8C) were chosen to characterize the
NEGOM (Figure 1.1).
1.2.2. In situ Data Collection, Processing and Analysis
Nine two-week cruises were conducted in three different seasons between 1997 and
2000 onboard the Texas A&M University (TAMU) R/V Gyre (Table 1.1). Each of the
cruises surveyed eleven cross-margin transects from the 10-m to the 1000-m isobath
(Figure 1.1).
About 100 stations were occupied for conductivity-temperature-depth (CTD) casts
on each cruise. During a CTD cast, water samples from twelve 10-liter niskin bottles
were collected and analyzed by TAMU for oxygen, salinity, nutrients, DOC, and
pigment analysis. Salinity was analyzed using an Autosal Laboratory Salinometer as
described in Fofonoff and Millard, Jr. (1983). Nitrate and Nitrite were analyzed using an
16
Figure 1.1. Study area: the NEGOM region, encompassing the area between 27.3 - 30.7
N and 82.6 - 89.6W. The map shows the 10, 20, 100, 200, 500, and 1000 m bathymetric
contours. The line connecting closed triangles shows a typical cruise track and stations,
with cross-shelf transect lines numbered L1-L11. Numbered open squares show regions
at which time series of chlorophyll-a concentration, CDOM and salinity were analyzed.
Inset: the Gulf of Mexico the area of the study.
Table 1.1. Cruise identifiers, dates, and seasons.
Cruise no.
1
2
3
4
5
6
7
8
9
Start date
16 November 1997
4 May 1998
25 July 1998
13 November 1998
15 May 1999
15 August 1999
13 November 1999
15 April 2000
28 July 2000
End date
Cruise ID
26 November 1997 Fa-97
15 May 1998
Sp-98
6 August 1998
Su-98
24 November 1998 Fa-98
28 May 1999
Sp-99
28 August 1999
Su-99
23 November 1999 Fa-99
26 April 2000
Sp-00
8 August 2000
Su-00
17
Cruise season
fall-1997
spring-1998
summer-1998
fall-1998
spring-1999
summer-1999
fall-1999
spring-2000
summer-2000
AutoAnalyzer technique as described in Atlas et al. (1971). Both salinity and nutrients
were analyzed by TAMU aboard ship.
In addition to the discrete stations, continuous (along track) surface data were
collected by TAMU every 2 minutes using a flow-through system for chlorophyll-a
fluorescence, temperature (SST), and salinity (SSS). Flow-through data were collected
using a stream pumped at a rate of 10 liters/minute from a hull depth of 3-m into the main
laboratory through a debubbler and mixing chamber of 10-liter volume. The residence
time of water in the mixing chamber was about one minute. Using garden hoses
connected by adjustable ball valves to a “Y” splitter valve leading off the debubbler, the
flow of the water was reduced from 10 liters/minute to 1 liter/minute. The 1 liter/minute
flow was directed to conductivity and temperature sensor (Sea-BirdTM) and to a Turner
DesignsTM model-10 Fluorometers for chlorophyll-a and CDOM fluorescences.
Chlorophyll-a fluorescence was measured with 440 nm (blue) excitation and 683 (red)
nm emission filters. CDOM fluorescence was measured with 330 nm (ultraviolet)
excitation and 450 nm (blue) emission filters. CDOM fluorescence data were recorded
by USF.
Water samples from the fluorometer out-flow hose were collected by TAMU at
about 100 locations to calibrate the fluorescence data with concurrent chlorophyll-a
concentration. Glass fiber filters (GF/F) were used to filter water samples and pigments
were extracted using acetone. Samples were extracted in 90% acetone immediately after
water collection and then analyzed at sea using a calibrated Fluorometer.
18
At selected stations, water samples from the flow-through outflow were filtered
using 0.2 μm Millipore filters into amber-colored glass bottles precombusted at 500C for
24 h. Samples were stored in a freezer for later analysis of the CDOM (Gelbstoff)
absorption coefficient (ag). The water absorption spectra were analyzed using a Hitachi
U-3000 Double Beam Spectrophotometer (10-cm pathlength). Absorption coefficients
were determined using the equation: ag()=(A()*2.303)/L where, A() is the absorbance
spectra and L is the cuvette length (m). The ag spectra were also smoothed to eliminate
instrument noise and corrected for residual scattering by subtracting residual absorption
at 700 nm from the entire spectra.
The flow-through CDOM fluorescence data were converted to ag443 (i.e., ag at 443
nm) using 30 discrete samples collected during each cruise and following the method of
Hu et al., (2003). This ensured coverage of various water types and a broad dynamic
range.
Continuous subsurface (10-14 m-deep) currents were measured using a shipboard
153 kHz Acoustic Doppler Current Profiler (ADCP). To produce circulation maps, the
ADCP data were gridded by TAMU graduate student Ou Wang, employing 40 km as the
smoothing radius (Daley, 1991). Features with scales less than 40 km are therefore likely
smoothed out, but this allowed filtering out noise features and keeping the basic largescale patterns. Details of these measurements and data processing can be found in
Jochens et al. (2002) and Wang et al. (2002). To see the pattern of subsurface currents,
the ADCP gridded currents was overlaid with the sea surface height (SSH) field rendered
at the middle date of each of the nine cruises.
19
1.2.3. Wind Field and River Discharge
Hourly wind speed and direction were obtained from NDBC buoys
(http://www.ndbc.noaa.gov) of BURL1 (28.90N, 89.40W) for the Mississippi region,
DPIA1 (30.25N, 88.07W) for Mobile, Escambia, and Choctawhatchee regions, CSBF1
(29.67N, 85.36W) for Apalachicola region, and CDRF1 (29.14N, 83.03W) for
Suwannee region. Hourly wind speed and direction were averaged to produce daily and
weekly mean wind speed and direction employing a true vector average. In this scheme,
the magnitude of the vector was represented by the wind speed observation and the
direction observations were used for the orientation. The vectors were then broken down
into their u and v components. All u and v components are then averaged separately.
The resulting average speed and direction were calculated from the Pythagorean Theorem
and “arctan(u/v)”, respectively.
Surface wind field data for NEGOM region of each of nine cruises were obtained
from Texas A&M University. The product was produced by gridded averaging of
surface wind field data from several NDBC buoys station in the NEGOM region for the
same cruises period of time.
River discharge data were compiled from six major rivers entering into NEGOM
region i.e., The Mississippi, Mobile, Escambia, Choctawhatchee, Apalachicola and
Suwanne Rivers for the period of October 1997 to December 2000. The data were
obtained from the U.S. Geological Survey Water Data Report. The Mississippi,
Escambia, Choctawhatchee, Apalachicola, and Suwannee discharge was recorded
respectively at Tarbert Landing (30.96N, 91.66W), near Century (30.96N, 87.23W),
20
near Bruce (30.45N, 85.89W), Sumatra (29.95N, 85.02W), and Branford (29.96N,
82.93W). The Alabama River discharge recorded at Millers Ferry (32.1N, 87.40W)
was used to estimate Mobile River discharge since discharge data from Tombigbee River
were not available for the study period of time. Both Alabama and Tombigbee Rivers
join to form the Mobile River. Weekly mean time series were built from these rivers
discharge data.
1.2.4. Satellite Data Collection, Processing and Analysis
The satellite data used in this study are chlorophyll-a concentration from SeaWiFS
ocean color measurements, and sea surface height from TOPEX/ERS observations. The
SeaWiFS data were collected using a high resolution picture transmitter (HRPT) antenna
located at University of South Florida (USF), St. Petersburg, Florida, USA.
SeaWiFS data were processed using the atmospheric correction algorithms
described by Gordon and Wang (1994), and Chlorophyll-a concentration fields were
derived using the OC4v4 bio-optical algorithm described by O'Reilly et al. (2000). I am
aware that there are unresolved issues that lead to severe errors in estimates of
chlorophyll-a using SeaWiFS data in coastal zones, particularly in Case II waters (Morel
and Prieur, 1977) where suspended sediments and CDOM significantly affect the ocean
color signal.
An algorithm (Carder et al., 1999) proposed for MODIS was also used to estimate
both the chlorophyll-a concentration and CDOM absorption from the remote sensing
reflectance spectra. This algorithm uses the 412 nm band to distinguish CDOM from
21
chlorophyll-a pigment because of the strong absorption of CDOM at 412 nm. For
validation purposes, I compared in situ chlorophyll-a concentration to chlorophyll-a
concentration derived from satellite data using the OC4v4 and Carder et al. (1999)
algorithms. I also compared in situ absorption coefficient of CDOM at 440 nm (ag(440))
to ag(440) derived from satellite using Carder et al. (1999) algorithm.
For time series and statistical analyses, SeaWiFS (October 1997 - December 2000)
images were averaged (arithmetic average) into a time series of weekly means.
Sea surface height (SSH) fields were obtained from the University of Colorado
(courtesy of Dr. Robert Leben). This was a blended product of TOPEX/ POSEIDON and
ERS-2 satellite altimeter data. SSH fields were produced by temporal and spatial
smoothing using decorrelation scales of 12 days and 100 km. Therefore, features may
appear weaker than they actually were, and smaller-scale features may not be
represented. To estimate the total dynamic topography, the residual mean in the SSH was
removed before adding a model mean to produce the synthetic height estimate. The
resulting time series of SSH fields were interpolated to obtain one SSH field per day.
More details about data processing and analysis can be found in Leben et al. (2002). For
comparison with ADCP gridded currents, the SSH field at the middle date of each of the
seven cruises was selected. For weekly time series, each middle date of a week range
(e.g., a week range of 1-7, middle date of the week is 4) for sea surface height was
overlaid over the weekly mean composite of SeaWiFS.
To determine the difference between data collected in situ and data estimated by
satellite, the mean relative error (MRE) was estimated. MRE is defined as follows:
22
N
MRE = [∑((satellitei – in situi)/in situi)]/N,
i=1
(1.1)
where i is the index for all valid data points and N is total number of valid points. In
order to avoid misinterpretation, data were grouped into two categories i.e., satellitei< in
situi (MRE1)and satellitei> in situi (MRE2). Otherwise large positive errors could be
neutralized by any large negative errors, producing small, inaccurate mean errors.
1.2.5. Statistic Analysis
Programs based on Interactive Data Language (IDL) software by Research System,
Inc. were developed to process and analyze data as well as for descriptive statistical
analysis. A one-way analysis of variance (ANOVA) using Statistix8 Software by
Analytical Software was used to test the significant different on chlorophyll-a
concentration, ag443 concentration, salinity and aquatic optical properties among regions
and seasons. Statistix8 Software was also used for multivariate regression analysis to test
the significant impact of salinity and chlorophyll-a concentration variability on CDOM
absorption variability.
23
1.3. RESULT AND DISCUSSION
1.3.1. Spatial and Temporal Distribution of In Situ Chlorophyll-a and CDOM
The spatial and temporal near-surface distribution of in situ chlorophyll-a
concentration and CDOM absorption coefficients at 443-nm (ag443) for the different
sampling periods are shown in Figures 1.2 and 1.3, respectively. Relatively high
chlorophyll-a concentrations (>1 mg m-3) and ag443 (>0.1 m-1) were generally found only
inshore, particularly near the major river mouths, or offshore in the Mississippi River
plume during summer sampling periods (Su-98, Su-99, and Su-00).
The spatial and temporal distribution of near-surface salinity generally followed
those of chlorophyll-a concentration and ag443 (Figure 1.4). Low salinity (<34) was
generally observed inshore particularly near the major river mouths, but it was also found
in offshore waters during summer cruises (Su-98, Su-99, and Su-00).
The results clearly show the influence of freshwater discharge on the distribution of
chlorophyll-a concentration, CDOM, and salinity and the importance of the Mississippi
plume as a source of nutrients and terrigenous CDOM to offshore waters of the Gulf of
Mexico. To facilitate understanding of the transport of the high chlorophyll- a
concentration, high CDOM, and low salinity Mississippi River water to the southeast
along the West Florida Shelf during summers, I examined the ship-mounted ADCP data
and the SSH field product. Satellite altimeter data are usually considered of limited value
over continental shelves because of reflectance of radar diffractive side-lobes from
24
Figure 1.2. Spatial and temporal distributions of near-surface chlorophyll-a
concentration in the NEGOM region based on in situ data. Thin white lines show the
cruise track where continuous chlorophyll fluorescence observations were collected. See
Table 1.1 for cruise identification.
25
Figure 1.3. Near-surface ag443 (m-1) distributions in the NEGOM. Thin white lines show
the cruise track where continuous CDOM fluorescence observations were collected.
Note: There are no CDOM data for fall 1997 and spring 1998 cruises. See Table 1.1 for
cruise identification.
26
adjacent land areas, a poor knowledge of the geoid, and because of short time and space
scales that may not be resolved by the sampling frequency of present-day altimeters.
Figure 1.5 suggested the presence of two distinct surface circulation regimes in the
NEGOM, namely one to the west and one to the east of 85W (Cape San Blas). To the
west of 85W, there was a significant difference in SSH fields from season to season. To
the east of 85W, a region of low (negative) sea surface height was almost always found
near the coast. Golubev and Hsueh (submitted) also found that the currents on the shelf to
the west and to the east of 85W of the NEGOM had different origins. From drifter path
data, they concluded that the monthly mean surface currents on the shelf east of 85W
were well correlated with sea surface height anomalies in Florida Strait, reflecting an
offshore pressure forcing that drives a barotropic flow on the west Florida shelf on a
nearly annual basis. In contrast, monthly mean surface currents on the shelf west of 85W
were correlated with zonal wind component, indicating that alongshore winds drive
alongshore shelf currents (see also Muller-Karger, 2000). Schroeder et al. (1987) and
Chuang et al. (1982) also reported that the nearshore circulation off eastern Mississippi
and Mobile was strongly wind dependent, in spite of the potential for westward
geostrophic flow generated by coastal fresh water input.
The SSH field maps constructed for each summer cruise indicated that either small
or large anticyclonic (warm) eddies were present near the slope off the Mississippi delta.
SSH ranged from +11 cm for a mesoscale (~100 km diameter) eddy in summer 2000 (Su00) to +35 cm for a large offshore eddy (centered around 88.5W and 26.5N) in summer
1999 (Su-99; eddy largely outside the area shown in Figure 1.5). These anticyclonic
27
Figure 1.4. Near-surface salinity distributions in the NEGOM. Thin white lines show
the cruise track where continuous salinity observations were collected. See Table 1.1 for
cruise identification.
28
Figure 1.5. Sea surface height (SSH) dynamic and ADCP-generated (10-14 m-deep)
currents in the NEGOM. Note: There are no ADCP data from cruise of fall 1998 east of
87W because of mechanical damage to ADCP equipment. See Table 1.1 for cruise
identification.
29
eddies entrained and transported low salinity with high nutrients, high CDOM surface
water from the delta area offshore (see also Qian et al, 2003; Jochens et al., 2002;
Belabbassi, 2001). The ADCP currents at 10-14 m depth documented northeastward and
eastward (Su-00), or eastward and southeastward (Su-98, Su-99) flow along the northern
edge of the anticyclonic eddies, with speeds of up to 0.6 m s-1. These vectors help explain
the chlorophyll-a concentration, ag443 and salinity patterns observed. Based on a
numerical circulation model, He and Weisberg (2002) and Weisberg and He (2003) also
found a strong southeastward current at mid-shelf of NEGOM by the baroclinic response
to combined wind and buoyancy forcing that leads to annually occurring cold and low
salinity tongues. Eastward dispersal of the Mississippi plume was also recorded during
summer of 1993 (Walker et al., 1994), summer of 1997 (Biggs et al, 2000), and in many
other instances (see Del Castillo et al., 2000 and 2001; Gilbes et al., 1996 and 2002;
Muller-Karger, 2000; Muller-Karger et al., 1991).
Some cyclonic features appeared along the coast west of 85º W during summer
(Figure 1.5). It is unclear whether these features are mesoscale cyclonic eddies or
whether they are simply local areas of lower than average sea surface height or artifacts
in the altimeter data.
In fall 1997 and 1998 (Fa-97, Fa-98), SSH of +8 cm and +13 cm was seen off the
Mississippi Delta (Figure 1.5). Although sub-surface currents indicated eastward flow,
there was no indication of eastward dispersal of the Mississippi River water. This
evidence can be seen from the relatively low chlorophyll-a concentration, high salinity
and low CDOM near the slope off the Mississippi delta (Figure 1.2, 1.3, 1.4). Relatively
30
high westward (easterly) wind speed component during fall season (at speeds up to ~10.5
m s-1) may have helped keep the Mississippi water inshore and to the west of the delta
(Figure 1.6, Jochens et al. 2002).
Figure 1.7 shows the in situ mean values and standard deviation of chlorophyll-a
concentration, salinity, and ag443 for each region (as defined in Figure 1.1) and for each
season (cruise). In general, there is no consistent seasonal pattern of chlorophyll-a
concentration off Mississippi, Mobile, Escambia, Choctawhatchee, Apalachicola, and
Suwannee regions (Figure 1.7, top panel). However, linear and inverse relationships
between chlorophyll-a concentration and salinity (r2 up to 0.80 for Suwannee region) for
the above regions were observed, indicating that the variability of chlorophyll-a
concentration in these regions seemed to relate to the variability of fresh water discharge
of each major rivers (Figure 1.8). Based on weekly river discharge and SeaWiFS
chlorophyll-a concentration, positive correlation between fresh water discharge and
chlorophyll-a concentration was also found in the above regions as further discussed in
Section 1.3.3. While fresh water discharge of Mobile and Escambia Rivers was relatively
low during summer 1999 compared to spring 1999 (Figure 1.8), the higher chlorophyll-a
concentration observed during summer 1999 than during spring 1999 along the Mobile
and Escambia regions (Figure 1.7) may be attributed to the eastward dispersal of the
Mississippi River fresh water that contains high nutrients (see Figure 1.2) and due to
eastward (westerly) upwelling-favorable winds (Appendix 2, 3).
For the Apalachicola region, the chlorophyll-a concentrations during fall seasons
were higher than those of spring and summer (Figure 1.7, R5, top panel). However,
31
Figure 1.6. Average surface wind field in the NEGOM during the period of 9 cruise
observations (data obtained from Texas A&M University). Diamonds and their
identification indicate stations where wind speed and direction were collected. Several
offshore NDBC stations used to generate wind gridded average were not shown in the
picture because they were located south of 27N.
32
Chlorophyll Conc. (mg/m^3)
8.00
Fa-97
Sp-98
Su-98
Fa-98
Sp-99
Su-99
Fa-99
Sp-00
Su-00
7.00
6.00
5.00
4.00
3.00
2.00
1.00
0.00
R1
R2
R3
R4
R5
R6
R7
R8A
R8B
R8C
Region
Fa-97
Sp-00
38.0
Sp-98
Su-00
Su-98
Fa-98
Sp-99
Su-99
Fa-99
Salinity
36.0
34.0
32.0
30.0
28.0
26.0
R1
R2
R3
R4
R5
R6
R7
R8A
R8B
R8C
R8A
R8B
R8C
Region
ag443 (1/m)
0.34
0.32
0.30
0.28
Fa-97
Sp-98
Su-98
Fa-98
Sp-99
Su-99
Fa-99
Sp-00
Su-00
0.26
0.24
0.22
0.20
0.18
0.16
0.14
0.12
0.10
0.08
0.06
0.04
0.02
0.00
R1
R2
R3
R4
R5
R6
R7
Region
Figure 1.7. Mean and standard deviation of near-surface chlorophyll-a concentration (top
panel), salinity (middle panel) and ag443 (m-1) (bottom panel) values for each subregion
and for different time periods sampled. Note: There were no ag443 data for fall 1997 and
spring 1998. See Table 1.1 for cruise identification.
33
Figure 1.8. Fresh water discharge of six major rivers in the NEGOM region during the
period of nine cruise observations. Y-axis is daily average of river discharge * 10^8
(m3/d).
34
the discharge of the Apalachicola River during fall was less than in spring (Figure 1.8).
If the river discharge was considered as the only nutrient source for this region, the
opposite pattern for chlorophyll-a concentration should be observed i.e., higher
chlorophyll-a concentration during spring than that of fall. Therefore, there should be
other sources of nutrients for this fall bloom. Strong upwelling-favorable wind observed
several weeks prior to fall cruises (Appendix 5) provided nutrients from deeper water
(Walsh et al. 2003, Weisberg and He, 2003). Cold frontal systems and stronger winds
during fall and winter may also increase the vertical mixing of the photic layer, bringing
nutrients from the deeper water.
For the central offshore NEGOM region (R8A, R8B, and R8C), a seasonal pattern of
chlorophyll-a concentration was observed with maxima occurring during summer
seasons and minima during spring seasons (Figure 1.7, top panel). The opposite pattern
for salinity was consistently observed in these regions during summer seasons (Figure
1.8, middle panel). Using salinity as an independent variable, variability in chlorophylla concentration can be explained up to 93% (R8C), indicating fresh water with high
nutrients from the Mississippi River play a major role for the phytoplankton bloom in
these regions during the three consecutive summer seasons.
Mean ag443 ranged from 0.011+0.001 in the offshore NEGOM (R8C) during spring
2000 (Sp-00) to 0.201+0.065 off the Mississippi (R1) during summer 2000 (Su-00)
(Figure 1.7, lower panel). While specific locations showed seasonality in ag443 within the
NEGOM, there was no consistent (synchronized) seasonal pattern for the entire NEGOM.
Seasonality was strongest off Mobile (R2), Tampa Bay (R7), and in the central offshore
35
NEGOM (R8). Off Mobile (R2), the maxima were found during spring (Sp-99, Sp-00)
and the minima during summer and fall (Fa-98, Su-99). Off Tampa Bay (R7), maxima
occurred in winter and minima during spring. In offshore NEGOM (R8A, R8B, R8C),
maxima occurred in summer and minima during spring. If all locations are considered, a
one-way analysis of variance (ANOVA) showed that there are significant differences in
the mean of chlorophyll-a concentration and ag443 values among seasons for each location
(P<0.01). The mean chlorophyll-a concentration and ag443 values are also different
(P<0.01) among locations within each season (cruise). ag443 had a positive relationship
with chlorophyll-a concentration and a negative relationship with salinity (Figure 1.7).
More detail of the relationship between ag443 and salinity and chlorophyll-a will be
discussed in section 1.3.4 and 1.3.5.
1.3.2. Comparison between in situ and SeaWiFS chlorophyll-a concentration
To improve the spatial coverage and reduce noise, SeaWiFS images were averaged
over the time period of each of the nine cruises. Chlorophyll-a concentrations estimated
from these SeaWiFS images were compared with in situ chlorophyll-a concentration.
Figure 1.9 shows the in vivo chlorophyll-a concentration and ag443 estimated from the in
situ flow-through measurements compared with the SeaWiFS (OC4v4) retrievals along
the ship tracks of all nine NEGOM cruises. The correlation between in situ and satellite
chlorophyll-a measurements, and the mean relative errors (MRE, see section 1.25 for the
formula) are shown in the Figure 1.9.
36
From Figure 1.9, it showed that most data points (~75%) indicated that SeaWiFS
overestimated in situ chlorophyll-a concentration with positive MRE from 84.24% (Fa99) to 283.40% (Su-98). This indicated that OC4v4 algorithm generally overestimated in
situ measurements in average of up to 300%. The higher positive MRE values were
found where in situ chlorophyll-a concentration and CDOM were also high specifically
during summer seasons where relatively high CDOM values were observed offshore.
Most of these data points were also found along the coast and offshore during summer
seasons, indicating suspended particulate materials, bottom reflectance and CDOM may
interfere chlorophyll-a concentration derived from SeaWiFS data. Meanwhile, negative
MRE values ranged from -8.46% (Sp-00) to -38.60% (Sp-99) and these values were
generally within the SeaWiFS Project Office objectives to within +35% over the range of
0.05-50.0 mg m-3 (Hooker et al., 1992).
To see the performance of the OC4v4 algorithm on the low chlorophyll-a
concentrations (case-1 waters) and to eliminate errors due to suspended materials, CDOM
and bottom reflectance, SeaWiFS chlorophyll-a concentration data between 0.01 mg m-3
and 1.0 mg m-3 were considered for comparison with in situ data. The correlation
between in situ and satellite measurements, and the mean relative errors (MRE) are
shown in the Figure 1.10.
The satellite estimates generally agreed well with the in situ
data for fall (Fa-97, Fa-98, Fa-99), when the Mississippi plume influence was minimal.
The MRE for these three cruises was generally within +35% (Hooker et al., 1992).
Meanwhile, for spring and summer, SeaWiFS overestimated in situ chlorophyll-a
37
Su-00
Sp-00
Fa-99
Su-99
Sp-99
Fa-98
Su-98
Sp-98
Fa-97
Figure 1.9. Comparison between in situ and SeaWiFS estimates (OC4v4) of chlorophylla concentration (0.0<[chlorophyll-a]<50 mg m-3) along ship track lines for nine NEGOM
cruises. In situ ag443 values were also plotted for the comparison. MRE1 is negative MRE
and MRE2 is positive MRE (see section 1.2.5 for the formula).
38
Su-00
Sp-00
Fa-99
Su-99
Sp-99
Fa-98
Su-98
Sp-98
Fa-97
Figure 1.10. Similar to Figure 1.9 but for SeaWiFS derived chlorophyll-a concentration
<1.0 mg m-3.
39
concentration. The positive MRE for spring and summer cruises ranged from 60.07%
(Sp-00) to 146.80% (Sp-98) (Figure 1.10).
Some factors that may lead to a divergence between satellite and in situ
measurements are (Hu et al. 2003) the time difference between satellite and in situ
measurements, which is further masked by the average of SeaWiFS data over the cruise
period. It is clear, however, that the SeaWiFS chlorophyll algorithm was affected by
colored dissolved organic matter (CDOM) absorption of light. It seemed that the higher
CDOM concentration resulted in higher error between in situ and satellite derived
chlorophyll-a concentration. These evidences were observed during summer 1998, 1999,
2000 and spring 1999 and 2000. The largest error (MRE=146.80%) between in situ and
satellite derived chlorophyll-a concentration was observed during summer 1998 where
largest CDOM concentration and distribution in offshore NEGOM were observed (see
also Figure 1.3). Further, the atmospheric correction used over turbid coastal water may
have led to additional errors in estimating chlorophyll-a concentration (Hu et al., 2000).
The MODIS algorithm proposed by Carder et al. (1999) to estimate chlorophyll-a
concentration as well as CDOM absorption from satellite data was also used and results
compared with in situ data (Figure 1.11, 1.12). Even though the positive MRE is still
above +35%, there was better agreement between in situ and MODIS chlorophyll
estimates vs. OC4v4 chlorophyll estimates, particularly for spring and summer cruises.
For SeaWiFS derived chlorophyll-a concentration ranged from >0.0 mg m-3 to <50 mg
m-3, the positive MRE values ranged from 48.58% (Sp-00) to 121.30% (Su-98) (Figure
40
Su-00
Sp-00
Fa-99
Su-99
Sp-99
Fa-98
Su-98
Sp-98
Fa-97
Figure 1.11. Comparison between in situ and SeaWiFS estimates using MODIS
algorithm (Carder et al., 1999) of chlorophyll-a concentration (0.0<[chlorophyll-a]<50
mg m-3) along ship track lines for nine NEGOM cruises. In situ ag443 values were also
plotted for the comparison. MRE1 is negative MRE and MRE2 is positive MRE (see
section 1.2.5 for the formula).
41
Su-00
Sp-00
Fa-99
Su-99
Sp-99
Fa-98
Su-98
Sp-98
Fa-97
Figure 1.12. Similar to Figure 1.11 but for SeaWiFS estimates using MODIS algorithm
of chlorophyll-a concentration <1.0 mg m-3.
42
1.11). These errors were reduced up to almost three times from the errors produced by
OC4v4 algorithm.
For the lower range of SeaWiFS derived chlorophyll-a concentration (<1.0 mg m-3),
significant improvements were observed. The positive MRE values using MODIS
algorithm during fall seasons were generally within +35%, which agreed with the
SeaWiFS mission specification. Meanwhile, during summer and spring the positive
MRE values ranged from 41.82% (Sp-00) to 99.44% (Su-98) (Figure 1.12). These values
were still above 35%, however, significant improvement from OC4v4 algorithm was
found. Similar to the OC4v4 algorithm problem, the MODIS algorithm also produced
relatively larger errors when relatively high CDOM was found. Clearly, more research is
needed to properly separate effects such of those from CDOM in the algorithms.
1.3.3. Spatial and Temporal Distribution of Satellite Retrieved Chlorophyll-a
SeaWiFS satellite data revealed relatively high chlorophyll-a concentration (>1 mg
m-3) inshore, particularly near the major river mouths, or offshore in the Mississippi
River plume during summer cruises (Su-98, Su-99, Su-00) (Figure 1.13). Based on error
analysis between in situ and satellite retrieved chlorophyll-a concentration, as discussed
in previous section, OC4v4 algorithm produce errors up to about 300% in coastal region
or in region with high CDOM concentration. Employing MODIS algorithm (Carder et
al., 1999) these errors were significantly reduced to about 120%. Therefore, for monthly
and weekly time series analysis, satellite retrieved chlorophyll-a employing MODIS
algorithm was used. It is also important to note that in the region of high CDOM
43
concentration (ag443>0.25 m-1), MODIS algorithm tends to “mask out” satellite data
(outstanding SeaWiFS data become zero or invalid). Therefore, to study the extent and
duration of offshore dispersal of the Mississippi plume in the NEGOM region, weekly
mean SeaWiFS images employing OC4v4 were generated and superimposed with sea
surface high field.
Based on weekly mean SeaWiFS images, a plume of elevated chlorophyll-a
concentration was seen extending east-southeastward (ESE) from the Mississippi River
delta each summer in 1998, 1999, and 2000, respectively. The plume normally formed
toward the end of May and lasted until about the first week of September. In 1998, a
spatially coherent plume, with highest concentrations up-plume and lowest
concentrations down-plume, up to 330 km to the ESE, was first observed toward the end
week of June from the Mississippi Delta. Apparent chlorophyll-a concentration toward
the distal end of the plume was about 1 mg m-3. The plume reached a maximum eastward
extent (about 550 km to the ESE of the Mississippi Delta) in late July (Figure 1.14). The
plume became smaller afterward and was effectively absent by mid August.
In 1999, a spatially coherent plume of ~280 km was observed in the offshore
NEGOM region the last week of May, extending ESE from the Mississippi Delta. The
plume reached its maximum extent in late July (~550 km ESE; Figure 1.14). This plume
lasted about 14 weeks, through the first week of September.
In 2000, the Mississippi plume was first observed in early July, extending some 220
km ESE off the Mississippi Delta. The plume reached its maximum extent in late early
44
August (about 580 km ESE from the Mississippi Delta; Figure 1.14). The plume lasted
about 9 weeks and disappeared during the first week of September.
The offshore dispersal plume observed in summers of three consecutive years
(1998, 1999, 2000) appeared to be related to wind direction and speed, to the Mississippi
River discharge, and to sea surface height dynamic off the Mississippi delta. Weekly
average wind data measured south and east of the Mississippi delta (BURL1, 28.9N,
89.4W, and NDBC 42040, 29.2N, 88.2W) recorded eastward (westerly) wind from the
end of April to the end of September, with average speeds of 0.5 to 6.10 m s-1. It seemed
that a spatially coherent plume (south or southeastward Mississippi plume) developed
when the advent of eastward (westerly) wind concomitant with the migration of a warm
core ring (anticyclonic, high sea surface height) into the vicinity of the Mississippi delta
(Table 1.2, see also Figure 1.14). The SSH field maps constructed with weekly SeaWiFS
images indicated that either small or large anticyclonic (warm) eddies were present near
the slope off the Mississippi delta during summer seasons. SSH ranged from +7 cm for a
mesoscale (~100 km diameter) eddy in summer 2000 (Jul 7-13, 2000) concomitant with
the initiation of the plume to +20 cm for a large offshore eddy in summer 1998 (Jul 2228, 1998) with the maximum offshore dispersal plume (Figure 1.14).
45
Su-00
Sp-00
Fa-99
Su-99
Sp-99
Fa-98
Su-98
Sp-98
Fa-97
Figure 1.13. Spatial and temporal distribution of near-surface chlorophyll-a
concentration in the NEGOM region based on SeaWiFS data during the nine separate
NEGOM cruise periods. White and purple contours are positive and negative total sea
surface height, respectively.
46
Jul 7 - 13, 00
Jul 28-Aug 3, 00
May 27-Jun 2, 99
Jul 15 - 21, 99
Jun 24 - 31, 98
Jul 22 - 28, 98
Figure 1.14. Initial and maximum position of the Mississippi River plume during summer
of 1998, 1999, and 2000. White and purple contour lines indicate positive and negative
sea surface height. White arrow indicates the extent of the plume and the surface flows
in baroclinic balance following along isopleths of sea surface height.
47
Table 1.2. Starting and ending period (in week number of 52) of eastward wind
component measured at south of Mississippi delta (BURL1) and east of Mississippi delta
(42040) in relation to sea surface height and the Mississippi plume period. Sea surface
height (SSH) was extracted from the location of 87.5W and 28.2N.
Eastward wind component
Initiation and maximum extent of
BURL1
42040
the Mississippi plume
-----------------------------------------------------------------------------------------------------start end start end
start
maximum end
1998 (wk # of 52) 18
SSH (cm)
-10
30
+20
18
-10
30
+20
26
+8
30
+20
33
+16
1999 (wk # of 52) 20
SSH (cm)
-12
34
+4
21
-12
29
-15
22
-10
29
+10
36
-10
2000 (wk # of 52) 21
SSH (cm)
-7
34
+5
21
-7
35
+7
28
+4
31
+6
36
+6
48
1.3.4. Driving Forces Affecting Chlorophyll-a Concentration Variability
Figure 1.15 shows the monthly mean values of chlorophyll-a concentration derived
from SeaWiFS data employing MODIS algorithm (Carder et al., 1999) for each region
(as defined in Figure 1.1) and for each season (cruise). Based on SeaWiFS data, mean
chlorophyll-a concentration ranged from 0.1+0.0 mg m-3 in the central NEGOM (R8C) in
June 2000 to 23.2+11.2 mg m-3 off the Mississippi (R1) in April 1999. While specific
regions showed seasonality in chlorophyll-a concentration, there was no consistent
(synchronized) seasonal pattern for the entire NEGOM.
Figure 1.15 also shows strong interannual variability of chlorophyll-a concentration
in most regions, particularly during winter and spring. The year 1998 was different from
1999 and 2000 and showed much higher chlorophyll-a concentrations higher by a factor
of 4 off Escambia (R3), Choctawhatchee (R4), Apalachicola (R5), Suwannee (R6) and
Tampa Bay (R7) regions, particularly between January and May. The anomalous
chlorophyll-a concentration during winter and spring of 1998 appeared to be related to
the 1997-1998 El Nino event with anomalously high river discharge. The discharges of
Mobile, Escambia, Choctawhatchee, Apalachicola, and Suwannee Rivers during spring
1998 were up to 5 times the discharge of spring 1999 or 2000 (see also Figure 1.8).
Indeed, Schmidt et al. (2000) reported that Florida experienced excessive rain in winter
and spring 1998 due to the 1997-1998 El Nino event. In late spring and summer 1998,
significant upwelling was also observed in these regions (Muller-Karger, 2000; He and
Weisberg, 2002; Weisberg, and He, 2003).
49
Considering only non-El Nino years (i.e., 1999 and 2000), a seasonal pattern in
chlorophyll-a concentration was found off the Mississippi (R1), Central NEGOM (R8),
Apalachicola (R5), Suwannee (R6) and Tampa Bay (R7) regions. Off the Mississippi
and central NEGOM regions, the maxima occurred in summer and minima in fall. Off
Apalachicola, the maximum occurred at the end of fall and early winter (December,
December, January) and minima in spring (March, May). Off the Suwannee (R6) region,
the maxima occurred during summer months (August, September) and the minima in
winter months (December, February). Off Tampa Bay (R7) region, the maxima occurred
in fall (October) and the minima in spring (March, May) (Figure 1.15). These patterns
were also consistent with in situ observations of chlorophyll-a concentration (see Figure
1.8).
To study the effect of river discharge on chlorophyll-a concentration in the
NEGOM region, cross-correlation analyses of weekly satellite chlorophyll-a
concentration vs. river discharge were conducted. River discharge has a strong influence
on chlorophyll-a concentration variability off Mississippi, Mobile, Escambia,
Choctawhatchee, Apalachicola, and Suwannee regions. This evidence can be seen from
cross-correlation coefficients i.e., Mississippi (r=0.50, lag=5 weeks), Mobile (r=0.70,
lag=1 week), Escambia (r=0.50, lag=1 week), Choctawhatchee (r=0.55, lag=9 week),
Apalachicola (r=0.55, lag=0 week), and Suwannee (r=0.65, lag=1 week) (Figure 1.16).
These results demonstrated that changes in the river discharge preceded changes in
chlorophyll-a concentration by a few days to several weeks for the above regions.
50
Figure 1.15. Time series of near-surface monthly mean chlorophyll-a concentration
based on satellite SeaWiFS data employing MODIS algorithm (Carder et al., 1999) for
ten selected regions.
51
Figure 1.16. Lagged cross-correlation analyses for weekly mean river discharge vs.
chlorophyll-a concentration, with lags measured in weeks. Satellite chlorophyll-a
concentration was derived from SeaWiFS data employing MODIS algorithm (Carder et
al., 1999).
52
Off Mobile, Escambia, and the Choctawhatchee regions, upwelling-favorable winds
were observed intermittently between early spring and late summer (Appendix 2, 3, and
4). Muller-Karger (2000) reported strong intermittent upwelling in May-June 1998 in
nearshore areas, generally within 15 km of the coast along the Florida Panhandle.
Upwelling-favorable winds also occur occasionally during winter. SeaWiFS-derived
chlorophyll-a concentrations appeared to be elevated during these periods of upwellingfavorable winds (Appendices 2, 3, and 4). However, variability in the freshwater
discharge appears to have a stronger influence on the variability of chlorophyll-a
concentration than the upwelling process (Appendices 2, 3, and 4). The strength of the
upwelling signal was also probably masked in part by the large size of the sampling
boxes along the coast (Figure 1.1), since west of Cape San Blas, upwelling was largely
confined to a narrow strip along the coast.
Off the Apalachicola, upwelling-favorable winds were frequent during summer and
fall. Chlorophyll-a concentrations rose during these upwelling periods. The variability in
the freshwater discharge seemed to have an influence on the variability of chlorophyll-a
concentration (r=0.55, lag=0 week) (Figure 1.16). However, upwelling-favorable winds
and strong vertical mixing due to stronger wind may also play roles in the variability of
chlorophyll-a concentration (Appendix 5). As discussed in previous sections, in situ
chlorophyll-a concentrations during fall seasons were relatively higher than during spring
and summer, even though the Apalachicola River discharge during fall was relatively
lower than during spring. Therefore, the phytoplankton blooms observed during fall
seasons were apparently related to upwelling-favorable winds observed during fall and
53
strong vertical mixing due to stronger winds during fall. Satellite derived chlorophyll-a
concentrations also supported the in situ observation in which higher chlorophyll-a
concentrations were higher in fall than during spring and summer in 1999 and 2000 (see
Figure 1.15, R5). A chlorophyll plume that extends from Cape San Blas to the south
parallel to the shelf break and as far south as the Florida Keys has been observed in this
region for many years, each lasting 1-6 weeks during spring (Gilbes et al., 1996). Our
observations confirmed its regular occurrence. Gilbes et al. (1996) speculated that the
plume might be associated with one or a combination of the following processes: (1)
discharge from small, local rivers along the northwest Florida coast; (2) seasonal changes
in steric height differences between the shelf and deep gulf of Mexico waters; (3)
circulation of water associated with the Loop Current and upwelling in the DeSoto
Canyon; and (4) discharge from the Mississippi and Mobile Rivers. Gilbes et al. (2002)
have confirmed that the Apalachicola River has a major role in the formation of offshore
blooms. A cold tongue of water has been detected in AVHRR images in the same area as
this chlorophyll plume during spring 1998. This is independent of the Loop Current
position and is a result of circulation driven by buoyancy and heat-flux forcing (Weisberg
and He, 2003; He and Weisberg, 2002).
Off the Suwannee river, only relatively weak upwelling-favorable winds were
observed between late fall and early summer (Appendix 6). This was also the situation in
1998, when a significant increase in chlorophyll concentration was observed in the
region. From a purely statistical point of view, variability in the Suwannee discharge had
54
stronger influence on the variability of chlorophyll-a concentration (r=0.65, lag=1 week)
than the wind-driven upwelling processes (Appendix 6).
In the central NEGOM (R8A, R8B, R8C) region, the relatively high chlorophyll-a
concentrations observed in summer of three consecutive years (1998, 1999, 2000)
appeared to be related to wind direction and speed, to the Mississippi River discharge,
and to sea surface height. As discussed in previous sections, a spatially coherent
Mississippi plume was observed when eastward (westerly) wind was concomitant with
the presence of an anticyclonic eddy off the Mississippi delta.
1.3.5. CDOM versus Salinity
Figures 1.17, 1.18, and 1.19 show salinity-ag443 scatter plots within each of the
selected sampling regions organized by summer, spring and fall cruises. Statistics are
provided in Table 1.3. In general, ag443 covaried linearly and inversely with salinity.
Using ANOVA, there were also significant differences among slopes of salinity-ag443
regressions, both among regions within each season, and among seasons within each
region (P<0.01), indicating strong regional and seasonal differences in CDOM sources
and composition. For nearshore regions, mostly in spring and winter seasons, high
coefficients of determination (r2>0.70 to r2=0.98) were found, indicating of fairly
conservative mixing behavior (Figure 1.18, 1.19). Some low (r2<0.70) to non-significant
(r2=0.00) coefficients of determination were also found, but mostly during summer, for
example in the Mississippi river region (R1) during summer 1999 (Su-99) when
effectively no high salinity waters were found within this sample region.
55
Figure 1.17. Scatter plots of salinity vs ag443 for each region for summer seasons
(cruises). See Table 1 for cruise identification. Blue line is the global NEGOM
regression line for fall 1998 (Fa-98), yielding the lowest error of observed vs. predicted
salinities for spring and winter seasons within 8 selected regions and global NEGOM
region. Note: the x and y axes are in different scales.
56
Figure 1.18. Scatter plots of salinity vs ag443 for each region for spring seasons (cruises).
See Table 1 for cruise identification. Blue line is the global NEGOM regression line for
fall 1998 (Fa-98), yielding the lowest error of observed vs. predicted salinities for spring
and winter seasons within 8 selected regions and global NEGOM region. Note: the x and
y axes are in different scales.
57
Figure 1.19. Scatter plots of salinity vs ag443 for each region for fall seasons (cruises).
See Table 1 for cruise identification. Blue line is the global NEGOM regression line for
fall 1998 (Fa-98), yielding the lowest error of observed vs. predicted salinities for spring
and winter seasons within 8 selected regions and global NEGOM region. Note: the x and
y axes are in different scales.
58
Table 1.3. Regression coefficient of determination (r2), intercept, and slope, with
respective standard error (se), between salinity and ag443 (S=a +b*ag443), and mean
percentage error (MPE) for salinity prediction based on ag443 data within 95% prediction
interval of each region in different cruises (seasons).
The Mississippi River region (R1)
Alabama River region (R2)
cid
n intercept(se) slope(se) r^2 MPE(%) cid
n intercept(se) slope(se) r^2 MPE(%)
Su-98 178 34.46(0.14) -31.16(0.76) 0.90 4.75 Su-98 143 31.27(0.18) 2.92(2.09) 0.01 2.10
Fa-98 202 36.49(0.09) -34.60(0.39) 0.98 5.71 Fa-98 152 36.32(0.04) -26.07(0.68) 0.90 0.86
Sp-99 176 36.99(0.09) -33.81(1.06) 0.84 8.17 Sp-99 147 32.52(0.17) -18.95(0.84) 0.77 4.30
Su-99 240 30.98(0.21) -17.42(1.44) 0.38 7.67 Su-99 145 31.25(0.24) 18.11(3.26) 0.18 2.81
Fa-99 496 36.49(0.03) -25.87(0.41) 0.88 2.33 Fa-99 221 36.52(0.04) -33.07(0.46) 0.96 1.51
Sp-00 272 36.36(0.03) -36.19(0.57) 0.94 1.41 Sp-00 179 35.44(0.07) -24.08(0.39) 0.96 2.36
Su-00 283 35.66(0.22) -23.30(0.95) 0.69 7.22 Su-00 129 35.88(0.12) -16.46(1.56) 0.46 1.42
Escambia River region (R3)
Choctawhatchee River region (R4)
Su-98 140 36.36(0.14) -38.84(2.07) 0.72 3.55 Su-98 168 38.03(0.14) -87.69(1.47) 0.96 2.28
Fa-98 170 36.65(0.05) -27.44(0.53) 0.94 1.68 Fa-98 237 36.74(0.04) -25.26(0.42) 0.94 1.46
SP-99 152 36.37(0.16) -36.01(1.23) 0.85 3.43 Sp-99 161 37.83(0.22) -54.11(2.25) 0.79 4.82
Su-99 139 35.44(0.25) -20.34(3.87) 0.17 5.81 Su-99 178 35.07(0.12) -17.70(1.91) 0.32 2.62
Fa-99 135 36.84(0.07) -21.40(0.76) 0.86 1.39 Fa-99 155 37.02(0.10) -23.39(1.62) 0.58 1.37
Sp-00 135 36.66(0.13) -34.03(1.32) 0.83 2.01 Sp-00 157 36.98(0.03) -26.76(0.99) 0.83 0.55
Su-00 139 35.07(0.12) -2.61(2.24) 0.01 1.04 Su-00 148 35.94(0.09) -31.45(2.79) 0.46 1.00
Apalachicola River region (R5)
Suwanne River region (R6)
Su-98 158 36.37(0.16) -30.14(3.83) 0.28 2.44 Su-98 190 35.84(0.01) -10.87(0.23) 0.92 0.51
Fa-98 138 35.92(0.04) -11.74(0.29) 0.92 1.01 Fa-98 175 35.42(0.02) -9.34(0.17) 0.94 0.70
Sp-99 140 36.51(0.02) -35.75(0.67) 0.96 0.42 Sp-99 188 36.22(0.02) -21.67(0.34) 0.96 0.70
Su-99 132 34.41(0.46) 7.31(9.47) 0.01 3.17 Su-99 161 36.43(0.06) -17.23(0.71) 0.79 0.83
Fa-99 126 36.59(0.04) -20.16(0.53) 0.92 0.78 Fa-99 123 35.94(0.04) 1.42(0.85) 0.02 0.73
Sp-00 218 37.16(0.03) -32.71(0.62) 0.92 0.66 Sp-00 219 36.89(0.03) -15.99(0.05) 0.94 0.55
Su-00 290 36.56(0.04) -13.63(0.69) 0.58 1.13 Su-00 296 37.38(0.04) -20.41(0.42) 0.88 0.78
Tampa Bay region (R7)
Central of the NEGOM region A (R8A)
Su-98 175 33.704(0.06) 7.00(2.03) 0.06 1.27 Su-98 898 35.28(0.04) -38.52(0.33) 0.94 4.56
Fa-98 183 35.58(0.03) -13.91(0.35) 0.90 0.84 Fa-98 869 36.62(0.01) -36.02(0.56) 0.83 0.63
Sp-99 173 36.27(0.01) -10.81(0.34) 0.84 0.44 Sp-99 1145 36.08(0.01) -43.90(0.22) 0.97 1.22
Su-99 160 36.21(0.02) -3.08(0.45) 0.23 0.53 Su-99 712 32.79(0.11) -27.05(0.93) 0.54 5.83
Fa-99 87 36.81(0.05) -12.13(0.86) 0.70 1.10 Fa-99 923 36.08(0.01) -7.62(0.42) 0.27 0.65
Sp-00 69 36.33(0.01) 2.70(0.17) 0.79 0.09 Sp-00 840 36.80(0.01) -36.02(0.33) 0.93 0.45
Su-00 343 36.69(0.03) 0.04(0.56) 0.00 0.63 Su-00 813 35.04(0.07) -26.08(0.45) 0.80 4.10
Central of the NEGOM region B (R8B)
Central of the NEGOM region C (R8C)
Su-98 1058 33.99(0.04) -38.52(0.33) 0.93 3.57 Su-98 792 33.03(0.05) -36.45(0.62) 0.81 2.11
Fa-98 1159 36.12(0.03) -2.45(1.84) 0.00 0.64 Fa-98 802 36.17(0.05) -0.97(3.21) 0.00 0.70
Sp-99 1057 37.24(0.01) -89.93(0.78) 0.93 0.63 Sp-99 766 36.99(0.01) -72.82(1.04) 0.87 0.44
Su-99 1149 36.46(0.05) -72.59(0.64) 0.92 5.40 Su-99 718 32.53(0.11) -13.96(0.85) 0.27 4.74
Fa-99 752 36.87(0.02) -39.39(1.02) 0.67 0.66 Fa-99 556 35.99(0.01) -3.67(0.56) 0.07 0.29
Sp-00 1007 36.44(0.00)
0.62(0.14) 0.02 0.11 Sp-00 623 36.44(0.00)
2.08(0.24) 0.10 0.14
Su-00 1520 35.82(0.03) -29.41(0.28) 0.87 2.92 Su-00 1407 36.25(0.01) -38.15(0.42) 0.85 1.30
Note: cid=cruise identification (see Table 1.1), n=number of observations
59
The most non-conservative CDOM mixing curves (non-linear plots) found during
most of the summertime cruises in the nearshore (low salinity) regions, suggesting that
destructive (removal) processes (e.g. CDOM photo-degradation, consumption) and in situ
formation (addition) processes (e.g. phytoplankton degradation, CDOM released by
phytoplankton during active growth, excretion by organisms, and microbial
remineralization) may play a role. Nelson et al. (1998) indicated that the microbial
community may be the source of the “new” summertime CDOM in sub-surface waters in
the Sarggasso Sea. The removal processes were apparent as convex (upward) salinityag443 curves during summer 1998 and 1999 in Mississippi region (Figure 1.17). Strong
thermal and salinity stratification during summertime may also enhance rates of CDOM
photolytic decay in near-surface waters (Vodacek et al., 1997). Jolliff et al. (2003) also
reported that photochemical losses of CDOM on the West Florida Shelf were significant
during summer 1998. A non-conservative behavior with net removal of dissolved
organic carbon in the Mississippi River plume was also observed during summer by
Benner et al. (1992). Meanwhile, the in situ CDOM formation (addition) processes were
also observed as concave (upward) salinity-ag443 curves especially during summer in most
of the NEGOM regions (Figure 1.17). I speculate that this in situ CDOM formation was
due to the phytoplankton degradation, zooplankton excretion, and microbial
remineralization. Examination of CDOM with relation to phytoplankton (chlorophyll-a
concentration) is discussed in the next section.
In the two central NEGOM regions (R8B and R8C), low (r2<0.7) to no correlations
(r2=0.0) between salinity and ag443 were observed fall 1998, 1999 and late spring 2000,
60
indicating low influence of freshwater. Low correlations between salinity and ag443 were
also observed during summer 1999 in central NEGOM region A and B (R8A and R8B)
(Table 1.3). In central NEGOM region A (DeSoto Canyon region), however, high
negative correlation coefficients between salinity and ag443 was recorded most of the
seasons, except during summer and fall 1999, indicating that this region was affected by
fresh water discharge most of the seasons. For summer and early spring seasons, high
negative correlations and steep negative slopes were observed in all three central
NEGOM regions (R8A, R8B, and R8C) (Table 1.3, Figure 1.17, and 1.18). The high
correlation was due to the east and southeastward transport of the Mississippi River
plume during summer and late spring periods (Hu et al., 2003; Del Castillo et al., 2001;
Nowlin et al., 2000; Muller-Karger, 2000, and others).
1.3.6. CDOM versus Chlorophyll
River plumes and upwelling are the two significant sources of new nutrients to the
NEGOM. In upwelling areas, chlorophyll concentration and CDOM often covary or
chlorophyll is the dominant bio-optical constituent, indicating a classical Case I water
type (Morel and Prieur, 1977). Kowalczuk (1999) also reported a significant correlation
between CDOM absorption and chlorophyll concentration in offshore waters, suggesting
that in situ formation of CDOM from phytoplankton was also playing an important role.
In contrast, in river plumes (Case II water) there are frequently conditions where CDOM
and chlorophyll are not correlated (Klinkhammer et al., 2000; Hu et al., in press).
61
In situ CDOM formation processes include release or excretion by organisms, lysis
of cells by viruses (Nelson and Siegel, 2002), phytoplankton release during active growth
(Vernet and Whitehead, 1996; Whitehead and Vernet, 2000), microbial remineralization,
(Nelson, et al., 1998; Nelson and Siegel, 2002), phytoplankton degradation (Fogg and
Boalch, 1958; Yentsch and Reichert, 1962; Harvey et al., 1983, Zweifel et al., 1995), the
release of CDOM from sediments (Skoog, et al., 1996), and vertical transport from
“deep” CDOM (Nelson and Siegel, 2002). In this study, I examined the contribution of
phytoplankton to in situ CDOM formation by analyzing the relationship of ag443 as a
function of salinity, and ag443 as a function of salinity and chlorophyll-a concentration
using multivariate regression.
Table 1.4 shows the adjusted coefficient of determination (adj-R2) both for ag443salinity and ag443-salinity and in situ chlorophyll-a concentration relationships. Adjusted
R2 was used in order avoid the bias of R2 due to an additional variable to a regression.
For each additional variable added to a regression equation, R2 increases even when the
new variable has no real predictive capability. Instead, when variables are added to a
regression equation, the adjusted R2 does not increase unless the new variables have
additional predictive capability (Chatterjee and Price, 1991; Weisberg, 1985). Results
show that there was no significant increase in adjusted R2 values between ag443 vs. salinity
and ag443 vs. salinity and chlorophyll-a concentration during fall seasons for most inshore
regions, except in Suwannee and Tampa Bay regions in fall 1999 (Table 1.4). This
suggests that in most coastal regions there were no significant increase in CDOM
associated with increase in chlorophyll. These results also indicated that the main
62
2
Table 1.4. Adjusted coefficient of determination of ag443 as function of salinity (adj R sal
)
2
and ag443 as function salinity and in situ chlorophyll-a concentration (adj R sal
 chl ).
Cruise Mississippi region
Id.
(R1)
2
2
adjR sal
adj R sal
 chl
Su-98 0.90
0.91
Fa-98 0.97
0.97
Sp-99 0.85
0.85
Su-99 0.39
0.80**
Fa-99 0.89
0.89
Sp-00 0.88
0.88
Su-00 0.69
0.77*
Cruise Apalachicola
Id.
region (R5)
2
2
adjR sal adj R salchl
Alabama region
(R2)
2
2
adjR sal
adj R sal
 chl
0.01
0.45**
0.91
0.91
0.78
0.87*
0.15
0.29**
0.96
0.96
0.96
0.96
0.46
0.51*
Suwannee region
(R6)
2
2
adjR sal
adj R salchl
Escambia region
(R3)
2
2
R sal
adj
adjR sal
 chl
0.64
0.87**
0.94
0.95
0.85
0.92*
0.16
0.55**
0.85
0.86
0.83
0.95*
0.00
0.31**
Tampa Bay region
(R7)
2
2
R sal
adj
adjR salchl
Choctawhatchee
region (R4)
2
2
R sal
adj
adjR sal
 chl
0.95
0.95
0.94
0.96
0.78
0.95**
0.32
0.53**
0.57
0.57
0.82
0.97**
0.46
0.46
Central Negom A
(R8A)
2
2
R sal
adj
adjR salchl
Su-98
Fa-98
Sp-99
Su-99
Fa-99
Sp-00
Su-00
Cruise
Id.
0.92
0.96
0.95
0.95
0.95
0.96
0.79
0.89**
0.02
0.38**
0.95
0.95
0.89
0.89
Central Negom C
(R8C)
2
2
adjR sal
adj R salchl
0.81
0.86
-0.00
0.28**
0.87
0.88
0.27
0.54**
0.07
0.47**
0.10
0.43**
0.85
0.88
0.06
0.89
0.85
0.23
0.69
0.78
0.00
0.94
0.83
0.97
0.54
0.26
0.93
0.80
Su-98
Fa-98
Sp-99
Su-99
Fa-99
Sp-00
Su-00
0.28
0.46**
0.92
0.92
0.95
0.95
0.00
0.60**
0.92
0.94
0.93
0.96
0.57
0.89**
Central Negom B
(R8B)
2
2
adjR sal adj R salchl
0.93
0.94
0.00
0.47**
0.67
0.82**
0.92
0.92
0.67
0.82**
0.02
0.13**
0.88
0.88
0.72**
0.93
0.98*
0.66**
0.95**
0.81
0.78**
0.96
0.83
0.98
0.76**
0.29
0.93
0.81
Note: *) Indicates >5% increase after adding chlorophyll-a concentration to the equation.
**) Indicates >10% increase after adding chlorophyll-a concentration to the equation
63
sources of CDOM during fall seasons was river inflow. Del Castillo et al. (2000)
reported a similar result, finding that there was no increase in CDOM abundance
associated with increases in chlorophyll concentration and primary productivity on West
Florida Shelf during March 1995.
In spring and summer seasons, there were significant increases in adjusted R2
values when adding a chlorophyll-a concentration component to the multivariate equation
in most NEGOM regions (Table 1.4). The increase in adjusted R2 after adding the
chlorophyll-a concentration component to the equation ranged from 5% (R2, Su-00) to
78% (R7, Su-00) (Table 1.4). This indicated that phytoplankton blooms during spring
and summer played a significant role in enhancing the CDOM pool. Carder et al. (1989,
1993) reported that phytoplankton degradation was a significant source of CDOM. In
general, adjusted R2 values were larger in summer relative to the previous spring season.
Higher adjusted R2 values during summer may be related to slower growth of
phytoplankton growth diminishes in summer as nutrients become limiting. In the central
NEGOM regions (R8B and R8C), there were significant additions of CDOM associated
with the increase of chlorophyll-a concentration (up to 47%) during fall (Table 1.4).
High salinity (>36) was observed in these regions during fall, indicating limited or no
freshwater influence. The increase in CDOM was therefore most likely produced by
phytoplankton or its degradation.
64
1.3.7. Estimating Salinity from CDOM
Can surface salinity be estimated from CDOM observations? CDOM has been
proposed as a proxy to estimate surface salinity from satellite ocean color sensors
previously (Hu et al., 2003; D'Sa et al., 2002). However, this will depend on whether
CDOM can be estimated accurately from satellite (Carder et al., 1999; Hu et al., 2003)
and whether or not there is a robust relationship between CDOM and salinity.
To answer this question I analyzed the errors for salinities estimated from ag443
data, using empirical relationships between these variables (Table 1.3). I first estimated
salinities at the 95% confidence interval (also called the predicted interval). I then
calculated the mean percentage error (MPE), i.e. the average of the absolute value of the
difference between the estimated salinities at the 95% confidence interval and the
estimated salinities at the regression line, divided by the estimated salinity at the
regression line multiplied by 100% (graphical example presented in Figure 1.20).
The MPE of ag443-derived salinity varied regionally and seasonally, ranging
between 0.09% (R7, Sp-00, correspond to +0.03 psu) and 8.17% (R1, Sp-99, correspond
to +2.29 psu). These results indicate that using CDOM data to estimate salinity of river
plumes results in MPE of less than 9%. In average of all seasons for each region, the
lower MPEs were found off the Suwannee River (0.51%-0.83%, corresponding to +0.18
to +0.29 psu), and the higher MPEs off the Mississippi River (1.41%-8.17%,
corresponding to +0.43-2.29 psu) (Table 1.3). The higher MPE found in the Mississippi
River region may result from variation in the CDOM content from the various tributaries,
65
marshlands, and primary production. Lower MPE variability was found during fall and
higher during summer seasons. It is clear that phytoplankton blooms contribute to
distortion of the ag-salinity relationship as descried in the previous section, but also
widespread patchiness, greater influence from adjacent rivers, primary production, and
photo-bleaching may all contribute to variation in the salinity-ag relationship.
To assess whether a single universal relationship between CDOM and salinity is
applicable within each of eight selected regions and within the whole NEGOM area, we
derived a salinity-ag443 regression (applying all NEGOM combined salinity-ag443 data) for
each season of each year, and attempted to predict salinity in other seasons and in each of
eight selected regions. First, we performed a “global” (whole NEGOM area) error
analysis on observed vs. predicted salinity. For example, the salinity-ag443 regression for
winter 1998 (Fa-98) was used to predict salinities based on ag443 data of spring 1999 (Sp99). Our results showed that mean percentage error of observed vs. predicted salinity for
the global analysis ranged from 0.67% to 10.33% (corresponding to +0.24 to +3.28 psu;
Table 1.5; Figure 1.21). Relatively high global mean percentage errors (up to 10.33%)
were found when winter or spring regression formulas were applied to predict salinities
for summer cruises and vice-versa. This may be due to the local production by
phytoplankton as well as to photobleaching occurred during summer seasons as discussed
in the previous section. Meanwhile, relatively low global mean percentage errors were
obtained when applying global relationships based only on winter and spring data to only
those seasons in other years (Table 1.5). The winter 1998 (Fa-98) relationship i.e.,
Salinity (S)=36.59-29.86*ag443 (n=8771, r2=0.86, 0.01<ag443<0.52, 16<S<36) produced
66
the lowest mean percentage errors for winter and spring seasons (0.82%-2.92%;
corresponding to +0.29-1.01 psu; Table 1.5). Employing the global winter 1998 (Fa-98)
relationship to predict salinities in winter and spring seasons both globally and regionally
produced the lowest mean percentage errors. Therefore, this relationship may be useful
to estimate salinity from satellite ocean color data for non-summer data.
Table 1.5. Mean percentage error of observed vs. predicted salinity employing seasonal
global NEGOM salinity-ag443 relationships to predict salinities in other seasons.
Cruise Number
Mean percentage error (%)
Id
of obs.
R_N3 R_N4 R_N5
R_N6 R_N7
R_N8 R_N9
N3
8899
3.46
8.75
6.01
4.00
10.33
9.59
8.12
N4
8771
6.72
1.08
2.09
4.68
1.26
1.37
1.23
N5
9057
5.06
2.92
1.83
3.13
4.02
3.55
2.67
N6
8733
4.92
7.89
5.74
4.86
9.44
8.56
7.44
N7
7483
6.32
0.82
1.93
4.21
0.67
0.94
1.07
N8
7865
6.51
1.00
2.15
4.40
1.13
0.81
1.43
N9
11817
6.85
2.27
2.36
4.87
3.01
2.68
2.01
Note: R_N3, R_N4, R_N5, R_N6, R_N7, R_N8 and R_N9 refers to regression formula
derived from global NEGOM salinity-ag443 relationship during Su-98, Fa-98, Sp-99, Su99, Fa-99, Sp-00 and Su-00, respectively used to predict salinities in others seasons
(cruises).
67
Figure 1.20. Example of the error analysis of salinity predictions based on ag443 data.
Mean percentage error (MPE) equals to average of absolute values of 95% predicted
interval values minus regression line values divided by regression line value multiplied
by 100%. MPE for (A)=0.86% and (B)=8.17% (see also Table2). Note axis scales are
different for better display purpose.
Figure 1.21. Some example of observed and predicted global NEGOM salinity
employing global NEGOM salinity-ag443 relationship of other season. MPE equals to
average of absolute values of estimate salinity minus observed salinity divided by
observed salinity multiplied by 100%. These two cases represent the best and the worst
scenarios.
68
1.4. Conclusions
Analyses of in situ CDOM and chlorophyll-a concentrations collected within nine
of two-weeks cruises from fall 1997 to summer 2000 showed a relatively high
chlorophyll-a concentration (>1 mg m-3) and CDOM absorption (ag443>0.1 m-1) inshore
particularly near the major river mouths and offshore in the Mississippi plume during
summers of 1998, 1999, and 2000. These patterns were also observed from SeaWiFS
images. Based on weekly winds, SeaWiFS, and altimeter satellite imagery data collected
between October 1997 and December 2000, eastward (westerly) winds, together with the
action of one or more anticyclonic eddies located just to southeast of the Mississippi delta
worked to advect (transport) Mississippi River water offshore in late spring and summer
of each of three consecutive years of 1998, 1999, and 2000. The transport of Mississippi
River water offshore created a spatially coherent high chlorophyll-a concentration, low
salinity, and high CDOM absorption plume offshore NEGOM. The spatially coherent
high chlorophyll-a concentration plume was observed as early as late May and lasted
until the first week of September. The plume extended up to >500 km east of the
Mississippi delta and to the Florida Keys. The plume initiation was facilitated by
eastward winds, while the offshore plume development was facilitated by an anticyclonic
eddy located southeast of the Mississippi delta.
The variability of river discharge appeared to be the dominant forcing factor in the
variability of chlorophyll-a concentration off Mississippi, Mobile, Escambia,
69
Choctawhatchee, Apalachicola and Suwannee regions. Upwelling-favorable winds
observed during late spring and summer may have limited river influence in chlorophylla concentration variability off Mobile, Escambia, and Choctawhatchee regions because
the upwelling process may be largely confined to a narrow strip along the coast.
Meanwhile, off Apalachicola region, upwelling-favorable winds seemed to play an
important role in chlorophyll-a concentration variability during fall seasons.
A strong interannual variability of chlorophyll-a concentration during winter and
spring 1998 compared to winter and spring of 1999 and 2000 was observed off Escambia
(R3), Choctawhatchee (R4), Apalachicola (R5), Suwannee (R6) and Tampa Bay (R7).
Mean chlorophyll-a concentrations during winter and spring 1998 were anomalously
higher by a factor of four than during winter and spring of 1999 and 2000. The high
concentrations related to anomalously high river discharge in connection with 1997-1998
El Nino Southern Oscillation event.
Strong relationships between ag443 and CDOM fluorescence were consistently
observed during each of 7 cruises conducted in the NEGOM region. In general, ag443
covaried linearly and inversely with salinity, particularly below salinities of ~36 psu.
Therefore, salinity-CDOM relationships are sufficiently robust for river plume
identification. For all near-shore regions, high linear coefficients of determination
(r2~0.70 to r2~0.98) between salinity and ag443 were observed during spring and fall
seasons, indicating a generally conservative mixing behavior. In contrast, during summer
seasons low (r2<0.70) to non-significant (r2=0.0) coefficients of determination were
observed between salinity and ag443, indicating non-conservative mixing behavior. In
70
contrast to near-shore regions, outer-shelf regions showed high negative correlations
during summer seasons because of the presence of the Mississippi plume on the outer
shelf.
There was no significant increase of the CDOM pool associated with the increase of
chlorophyll-a concentration in most coastal regions during fall seasons, indicating river
discharge was the main source of CDOM. However, a significant increase of the CDOM
pool associated with the increase of chlorophyll-a concentration in coastal and offshore
regions was observed during spring and summer seasons, indicating phytoplankton
blooms play important roles in the in situ CDOM formation during spring and summer
seasons. The increase of in situ CDOM formation associated with phytoplankton blooms
ranged from 10% to 100%. In central NEGOM, it seemed that phytoplankton was also a
significant source for CDOM formation during fall seasons.
The high inverse correlation between ag443 and surface salinity during winter and
spring seasons in near-shore regions suggested that it might be possible to derive salinity
from satellite ocean color sensors, provided that ag443 is estimated consistently from
ocean color, and the relationship between ag443 and salinity is obtained empirically from
field measurements. The global NEGOM salinity-ag443 relationship of fall 1998 (Fa-98),
i.e., Salinity=36.59-29.86*ag443 (n=8771, r2=0.86; 0.01<ag443<0.52, 16<S<36) produced
the lowest mean percentage error of observed vs. predicted salinity for all other fall and
spring seasons (<3% errors; corresponding to <1.03 psu); therefore, this relationship may
be useful to estimate salinity from satellite ocean color data. However, there was no
robust global relationship that helped explain summer values. This may be affected by
71
the influence of phytoplankton to significantly increase CDOM production during
summer seasons.
For chlorophyll-a concentration ranged from >0.0 mg m-3 to <50 mg m-3,
comparison between in situ and SeaWiFS retrieved chlorophyll-a concentration
employing OC4v4 indicated that satellite estimates generally overestimate in situ data up
to about 300%. For the same range of chlorophyll-a concentration, MODIS algorithm
(Carder et al., 1999) improved significantly to estimate chlorophyll-a concentration for
almost three times better than the OC4v4 estimate specifically within the region of
relatively high of CDOM concentration. For chlorophyll-a concentration <1.0 mg m-3,
both OC4v4 and MODIS algorithms produced errors within the SeaWiFS mission
specification (+35%) during fall seasons. However, during spring and summer seasons,
when high CDOM concentrations were observed, MODIS algorithm improved
significantly compared to OC4v4 algorithm in estimating chlorophyll-a concentration.
The OC4v4 algorithm used the blue band (443 nm channel) to estimate chlorophyll-a
concentration which more subject to the effects of ag443 being interpreted as pigment.
The MODIS algorithm corrects chlorophyll retrievals for CDOM effects.
72
CHAPTER II:
VARIABILITY IN THE PARTICULATE ABSORPTION COEFFICIENTS OF
THE NORTHEASTERN GULF OF MEXICO SURFACE WATERS
73
Abstract
Variability in the light absorption coefficient of particulate materials in near-surface
waters of the Northeastern Gulf of Mexico (NEGOM) was examined during 7 seasonal
cruises in 1998-2000. Generally, the absorption coefficient of phytoplankton at 440 nm
[aph(440)] followed a power law relationship (r2=0.87-0.98) with chlorophyll-a
concentration. For the range of chlorophyll-a concentration of 0.06-12.25 mg m-3, the
chlorophyll-specific absorption coefficient, a *ph (440), varied by a factor of 7 (0.02-0.15
m2 mg-1). In general, lower values of a *ph (440) (<0.06 m2 mg-1) were observed in the
inner shelf and major rivers outflow regions. During summer, lower values of a *ph (440)
were also observed offshore associated with low salinity waters of the Mississippi River
plume. Higher values of a *ph (440) (>0.1 m2 mg-1) were otherwise observed outside the
river plumes in the outer shelf and slope, where lower chlorophyll-a concentration
occurred. Using pigments to derive taxonomic phytoplankton groups, variability in
biomass proportion of microphytoplankton (BPmicro) can explain up to 76% variability in
the average of aph(545)/aph(440), aph(625)/aph(440), and aph(673)/aph(440). The average
value of a *ph (440) corresponding to major BPmicro was also significantly lower than that
of nanophytoplankton and picophytoplantkon groups, suggesting that an increase in cell
size (pigment packaging) resulted in decreasing a *ph (440) values. The relationship
between a *ph (440) and chlorophyll-a concentration was also not linear, indicating
pigment composition also played important role in determining a *ph (440) variability.
74
2.1. INTRODUCTION
Light absorption by phytoplankton and particulate material (detritus) play a
significant role in determining the variability of optical properties of oceanic and coastal
waters (Allali, et al., 1997). This optical variability also affects primary production,
remote sensing of pigment biomass and mixed layer heating (Suzuki et al., 1998; Lee et
al., 1996; Carder et al., 1995; Cleveland, 1995; Sosik and Mitchell, 1995; Nelson et al.,
1993; Bricaud and Stramski, 1990; Yentsch and Phinney, 1989). For these reasons, and a
requirement to improve remote sensing capabilities, information on the magnitude, range
and sources of variability in the absorption coefficient of phytoplankton and other
particulate matter is very important. Given the traditional difficulty of estimating
phytoplankton pigment concentration in coastal waters, information from these areas may
be of particular interest for the refinement of Case-II algorithms.
The phytoplankton absorption coefficient per unit of chlorophyll concentration
(chlorophyll-specific absorption coefficient, a *ph ()) is a key factor when modeling light
propagation within the ocean and ocean color (e.q., Gordon et al., 1988; Morel, 1988,
Carder et al., 1986), carbon fixation by phytoplankton (Kiefer and Mitchell, 1983), the
contribution of phytoplankton to the total absorption coefficient of seawater, as well as
for modeling marine primary production (Ishizaka, 1998; Morel and Andre, 1991; Carder
et al., 1995; Sakshaug et al., 1997).
75
In many studies, the value of a *ph () has been considered to be relatively constant
(Bannister, 1974; Kiefer and Mitchel, 1983; Berthon and Morel, 1992). However, the
variability in a *ph () has been extensively documented for both laboratory cultures (e.g.,
Fujiki and Taguchi, 2002; Ahn et al., 1992; Bidigare et al., 1990; Stramski and Morel,
1990; Berner et al., 1989; Mitchell and Kiefer, 1988a; Bricaud et al., 1988, 1983) and
natural populations (e.g., Lohrenz et al., 2003; Suzuki et al., 1998; Allali et al., 1997;
Sosik and Mitchell, 1995; Nelson et al., 1993; Bricaud et al., 1995; Cleveland, 1995;
Babin et al., 1993; Hoepffner and Sathyendranath, 1992, 1991; Carder et al., 1986, 2004,
1999, 1991; Bricaud and Stramski, 1990; Yentsch and Phinney, 1989; Mitchell and
Kiefer, 1988b). For instance, using a data set that included 815 spectra from different
regions of the world ocean and covering chlorophyll concentrations ranging from 0.02 to
25 mg m-3, a *ph (440) values were observed to decrease from oligotrophic to eutrophic
waters over more than one order magnitude (0.01 to 0.18 m2 mg-1) (Bricaud et al., 1995).
Variability in the magnitude and spectral shape of a *ph () can be attributed to two
factors: (1) packaging effect i.e., pigments packed into stacks of shelf-shaded chloroplasts
are less efficient in absorbing light per unit pigment mass than an optically thin solution
(Kirk, 1994), and (2) pigment composition. An increase in pigment packaging can occur
either as cell size increases or the internal concentration of pigments increases (Lohrenz
et al., 2003; Kirk, 1994; Morel and Bricaud, 1981). Differences in phytoplankton species,
as well as variation within a species grown under different environmental conditions
(e.g., growth irradiance), also causes variability in a *ph () due to pigmentation and
76
packaging effects (Fujiki and Taguchi, 2002; Mitchell and Kiefer, 1988a; Bricaud et al.,
1988; Morel and Bricaud, 1986; Dubinsky et al., 1986; Berner et al., 1989, Stramski and
Morel 1990).
Several investigators have reported an increase in a *ph () with a decrease in
chlorophyll concentration (Carder et al., 1986, 1991; Bricaud et al., 1998, 1995;
Cleveland, 1995; Bricaud and Stramski, 1990). They interpreted this result mainly due to
packaging effect or a general trend of decreasing cell size with decreasing chlorophyll
concentration. However, the above relationship can also be attributed solely to the
increasing contribution of accessory pigments in waters with low chlorophyll
concentration, with no consideration of packaging effect (Ciotti et al., 1999; Sakshaug et
al., 1997; Bricaud et al., 1995; Wozniak and Ostrowska, 1990). Carder et al. (1991) also
suggested that chlorophyll dependency of a *ph () could differ between regions such as
subtropical and temperate regions due to the typical differences in cell size, light, and
nutrient regimes. In contrast, Hoepffner and Sathyendranath (1992) reported that there
was no dependency of a *ph (440) on chlorophyll concentration in the Gulf of Maine and
Georges Bank regions within the range of 0.05-2.5 mg m-3 chlorophyll, although that
could all be considered part of the same region.
The Northeastern Gulf of Mexico (NEGOM) encompasses a wide variety of
ecosystems which are influenced by a combination of nutrient-rich input from rivers and
estuaries, coastal upwelling, vertical mixing (Muller-Karger, 2000; Pennock et al., 1999;
Gilbes et al., 1986), and the Loop Current (Vukovich, 1988; Huh et al., 1981; MullerKarger et al., 2001; Weisberg and He, 2003). These processes exert a strong influence on
77
the phytoplankton abundance and distribution in the NEGOM. To date, a *ph () variability
had not been studied in the NEGOM region.
This study uses field observations from seven two-week cruises in the NEGOM
region (1998 through 2000), to examine the relationships among variability in a *ph (),
riverine influence (salinity), nutrient supply, and pigment concentration. The specific
objectives of this dissertation chapter are to: (1) determine the seasonal and spatial
variation of the chlorophyll-specific absorption coefficient of phytoplankton in the
NEGOM, (2) determine the percentage contribution of phytoplankton and detritus to the
total particulate absorption coefficient, (3) determine whether phytoplankton species or
groups can be estimated from phytoplankton absorption coefficients (aph()), and (4)
understand the relationship between community structure and pigment composition of
surface waters.
78
2.2. METHODOLOGY
2.2.1. Sample Collection
Near-surface (+ 3 m-deep) water samples were collected from the Northeastern
Gulf of Mexico (NEGOM; Figure 1.1, Chapter I) during seven two-week cruises aboard
the Texas A&M University R/V Gyre. Cruises were conducted in spring (April or May;
Sp-99, Sp-00), summer (July/August; Su-98, Su-99, Su-00), and fall (November; Fa-98,
Fa-99) of 1998, 1999, and 2000 (Table 1.1). Each cruise surveyed eleven cross-margin
transects from the 10-m to the 100-m isobath. Water samples for determining the total
particulate, phytoplankton, and detritus absorption coefficients were collected from the
outflow of a flow-through system that pumped at a rate of 10 liters/minute from a hull
depth of about 3-m, which passed water via a 10-liter debubbler and mixing chamber.
Seawater samples (0.5-5.0 L) were collected and filtered immediately aboard ship
through Whatman GF/F filters (25 mm diameter) under low vacuum pressure (<0.5 atm).
The volume of water filtered varied between ~0.05 and 5.0 liters depending on the
concentration of pigmented particles in the sample. Filtering was discontinued once the
filter displayed sufficient color to the naked eye. Each filter pad was folded and placed
into a 2.0 ml sterile Nalgene cryogenic vial, and stored in liquid N2 for analysis. After
arrival from the field, samples were stored in a deep-freezer.
Water samples for pigment analyses were collected from one of twelve 10-L
Niskin bottles operated with a Sea-Bird SBE 911 profiling system used to collect
79
conductivity-temperature-depth (CTD) profiles. Pigment sampling and analyses,
including the calibration of a fluorometer on the flow-through system, were conducted by
Texas A&M University. Detailed description of data collection and analysis methods can
be found in Qian et al. (2003).
2.2.2. Absorption Measurements
The quantitative filter technique (Yentsch, 1962; Kiefer and SooHoo, 1982) was
used to determine absorption spectra due to total particulates (phytoplankton and detritus,
ap()) and detritus (ad()). Prior to analysis, sample and reference filter pads were
allowed to thaw slowly at room temperature for about 5-10 minutes prior to being placed
in a dark petri dish and moistened with a drop of Milli-Q water. The moist sample and
reference filter pads were placed on individual glass plates (diameter=2.4cm) in a
custom-made diffuse transmissometer box. Prior to each scan, the filters were slid one at
a time over a tungsten-halogen light source that shone through a blue long-pass/cut-off
filter and a quartz glass diffuser. Using a custom made, 512-channel spectroradiometer
(~350-850nm), the transmittances of the sample filter (Tsample()) and the reference filter
(Treference()) were measured three times. These measurements were averaged and used to
obtain the optical densities of total particulate matter (ODp()) as shown below.
The sample filter was then soaked with ~40-50ml of hot 100% methanol for 10-15
minutes in the dark to extract phytoplankton pigments (Kishino et al., 1985; Roesler et
al., 1989; Bissett et al., 1997). Transmittances of the extracted filter (Tdetritus()) and the
80
reference filter were once again measured three times, and the optical density of detritus
(ODd()) calculated.
Optical densities were calculated as follows:
T
λ  
OD λ   log10  reference
p
 Tsample λ  


(2.1a)
T
λ  
OD λ   log10  reference
d
 T
λ  
 detritus
(2.1b)
The absorption coefficients of particulate matter (ap()) and detritus (ad()), were
calculated as follows:
a p λ  
a
λ  
d
ln 10  OD p λ   β
l
ln 10  OD λ   β
d
l
(2.2a)
(2.2b)
where “l” is the geometric pathlength equal to the volume of seawater filtered divided by
the effective filtration area of the filter (r2, r=0.0215/2 m), and  is the pathlength
amplification or “ factor” (Butler, 1962). The  factor is an empirical formulation
defined as the ratio of optical to geometric pathlength that corrects for multiple scattering
inside the filter. In this study, an average of two published  factor formulations (Bricaud
and Stramski, 1990; Nelson et al., 1993) was used as follows:
0.5
β  1.0  0.6  ODp λ 
(2.3)
Spectra with ODp(675) less than 0.04 were omitted from this study to minimize artifacts
due to uncertainty in the  factor (Bissett et al., 1998; Mitchell and Kiefer, 1988; Bricaud
81
and Stramski, 1990; Cleveland and Weidemann, 1993; Nelson et al., 1993; Moore et al.,
1995; Lohrenz, 2000). All other spectra were set to zero at 750 nm to correct for residual
scattering caused by non-uniformity in wetness between the sample and reference filters
or for stray light.
Using Eqs. 2a and 2b, the absorption spectra due to phytoplankton pigments,
aph(), was calculated as follows:
a ph ( )  a p ( )  a d ( )
(2.4)
Fluorometric chlorophyll and phaeopigment concentrations were determined on the
filtrate of phytoplankton pigments extracted from the sample filter with hot 100%
methanol using a Turner 10-AU-005 fluorometer according to the methods of HolmHansen and Riemann (1978). Finally aph() was converted to chlorophyll-a specific
absorption coefficient ( a *ph ()) dividing by chlorophyll-a concentration.
The local maximum of the total particulate absorption coefficient spectra near 440,
545, 625 and 673 nm was normally observed in the range of 440+5, 545+5 and 673+5
nm, respectively. Therefore, the magnitude of phytoplankton, detritus, total particulate
and chlorophyll-specific absorption coefficients at these wavelengths (440, 545 and 673
nm) was obtained by averaging spectra from the above range of wavelengths.
82
2.3. RESULTS AND DISCUSSION
2.3.1. Total Particulate, Detritus and Phytoplankton Absorption Coefficients
The near-surface absorption coefficients for total particulate (ap()), detritus (ad()),
and phytoplankton (aph()) in the NEGOM from 1998 to 2000 are presented in Figures
2.1, 2.2 and 2.3. Higher total particulate absorption coefficients at 440 nm were observed
near the coast and particularly near the Mississippi river mouth (as high as 0.85 m-1, with
salinity of 28.22). Lower values were observed offshore (as low as 0.01 m-1, with salinity
of 36.24 psu) (Figure 2.1). Detritus and phytoplankton absorption coefficients at 440 nm
also showed higher values in the coastal region relative to offshore (range of 0.01 to 0.51
m-1 for detritus and 0.01 to 0.61 m-1 for phytoplankton; Figure 2.2 and 2.3). A similar
pattern was observed for all absorption coefficients at 673 nm.
In coastal waters, particularly near river mouths, chlorophyll concentrations were
relatively high, but absorption by detritus in the blue (ad(440)) exceeded that of
phytoplankton absorption (aph(440)). In other words, detritus contributed on average
between 52% and 66% to the total particulate absorption in coastal areas near the river
mouths. In contrast, in offshore waters where chlorophyll concentration was relatively
low, absorption by phytoplankton generally exceeded that by detritus (phytoplankton
contributed on average between 68% and 77% to the total particulate absorption). This
result is contrary to many previous studies that have reported that the proportion of
83
detritus absorption increases offshore (especially in oligotrophic areas) relative to
nearshore, upwelling region (Gordon, 1989; Morel, 1988; Smith and Baker, 1978).
The mean absorption coefficient at 440 nm for total particulate, detritus, and
phytoplankton varied seasonally, with maxima during summer cruises (i.e.,
ap(440)=0.0875 m-1, ad(440)=0.0277 m-1 and aph(440)=0.0605 m-1, respectively) and
minima during spring cruises (i.e., ap(440)=0.0206 m-1, ad(440)=0.0076 m-1 and
aph(440)=0.0130 m-1, respectively).
There was no clear relationship between the detritus absorption coefficient at 440
nm and chlorophyll-a concentration in individual samples (Figure 2.4). At low
chlorophyll-a concentration (<1 mg m-3), ad/ap seems to vary significantly. However, the
average absorption coefficients (ag, aph, and ad) for each cruise for water salinity >34.5
(limit for oceanic water not affected by fresh water) showed a tendency to covary with
average chlorophyll concentration (Figure 2.5, top panel). These results indicate that both
colored dissolved organic matter and chlorophyll-a played a major role in determining
total particulate absorption coefficient in the NEGOM region where there is no recent
fresh water influence. At higher chlorophyll concentrations (>1 mg m-3) and where there
is fresh water influence (salinity <34.5), there was no clear relationship between average
absorption coefficients (ag, aph, and ad) and chlorophyll concentration (Figure 2.5, lower
panel). In both salinity ranges, however, ag(440) exceeded aph(440) with average values
for ag(440)/aph(440) of 3.09+0.88 (for salinity range <34.5) and 1.81+0.58 (for salinity
range >34.5).
84
Figure 2.1. Near-surface total particulate absorption spectra in the NEGOM between
1998 and 2000. See Table 1.1 for cruise identification. Note different scales on y-axis.
85
Figure 2.2. Near-surface detritus absorption spectra in the NEGOM between 1998 and
2000. See Table 1.1 for cruise identification. Note different scales on y-axis.
86
Figure 2.3. Near-surface phytoplankton absorption spectra in the NEGOM between 1998
and 2000. See Table 1.1 for cruise identification. Note different scales on y-axis.
87
Figure 2.4. Proportion of detrital to total particulate absorption coefficient at 440 nm, as a
function of chlorophyll-a concentration for each cruise. See Table 1.1 for cruise
identification.
88
0.20
3.0
2.5
Absorption Coefficients (1/m)
0.15
ag440
aph440
ad440
Chl
0.10
2.0
1.5
1.0
0.5
0.05
0.0
- 0.5
0.00
- 1.0
- 1.5
- 0.05
- 2.0
- 0.10
- 2.5
Su- 98
Fa- 98
Sp- 99
Su- 99
Fa- 99
Sp- 00
Su- 00
Cruise Identification
0.40
5.0
ag440
0.35
Absorption Coefficients (1/m)
4.0
aph440
0.30
ad440
0.25
3.0
Chl
0.20
2.0
0.15
1.0
0.10
0.05
0.0
0.00
- 1.0
- 0.05
- 0.10
- 2.0
Su- 98
Fa- 98
Sp- 99
Su- 99
Fa- 99
Sp- 00
Su- 00
Cruise Identification
Figure 2.5. Time series of mean ag(440), ad(440), aph(440) and chlorophyll-a
concentration of each cruise. Region with salinity >34.5 (Top Panel) and region with
salinity <34.5 (Lower Panel). Right y-axis is for chlorophyll-a concentration. See Table
1.1 for detail cruise identification.
89
2.3.2. Variation in Spectral Shape of the Phytoplankton Absorption Coefficient
Most phytoplankton absorption spectra collected in NEGOM (Figure 2.3) show
peaks at approximately 440 and 673 nm, typical of absorption by chlorophyll-a
(Cleveland, 1995). The spectral shape of the absorption coefficient associated with
phytoplankton pigments was examined by normalizing each of the spectra to its value at
440 nm and average normalized spectra for spring, summer, and fall are presented in
Figure 2.6. There was considerable variation around the mean for each season, which is
attributed to spatial variation along the cruise track.
A secondary absorption peak was observed around 545 nm in inner shelf samples
(water depth <50 m), except during summer cruises when this was also occurred in the
river plume extending over the outer shelf (water depth 50-400 m) and slope (water depth
>400 m).
The 545 nm peak may be due to the present of fucoxanthin and/or phycoerythrobilin pigments. Anderson and Barrett (1986) reported that the fucoxanthinchlorophylla/c-protein complex enhances absorption near 542 nm. Phycoerythrobilin,
found in cyanobacteria, also has an absorption band around 545 nm (Moore et al., 1995;
Morel et al., 1993, Hoepffner and Sathyendranath, 1991). Indeed, fucoxanthin
concentrations (a biomarker for diatoms) were generally high in the inner shelf of the
NEGOM and offshore areas affected by fresh water discharge (Figure 2.7). On the other
hand, zeaxanthin concentrations (a biomarker for cyanobacteria) were observed in
patches over the NEGOM during spring and summer seasons (Figure 2.8). These results
are similar to those Qian et al. (2003), derived based on the same data.
90
Figure 2.6. Phytoplankton absorption spectra normalized to 440 nm from seven NEGOM
cruises. Individual normalized spectra are shown in black and the average normalized
spectra in blue. Panel H shows the average normalized spectra for spring, summer and
fall seasons. See Table 1.1 for cruise identification.
91
Figure 2.7. Near-surface distribution of fucoxanthin concentration normalized with
chlorophyll-a concentration in the NEGOM. White dots show location of water sample
collections. See Table 1.1 for cruise identification.
92
Figure 2.8. Near-surface distribution of zeaxanthin concentration normalized with
chlorophyll-a concentration in the NEGOM. White dots show location of water sample
collections. See Table 1.1 for cruise identification.
93
There was no significant difference between the phytoplankton absorption spectra
of spring (Sp-99, Sp-00) and fall (Fa-98, Fa-99). However, a one-way analysis of
variance (ANOVA) showed that there were significant differences in the mean values of
normalized phytoplankton absorption coefficient at 545 nm and 673 nm between those of
summers (Su-98, Su-99, Su-00) and those of spring and fall (P>0.05). There were no
significant differences between spring and fall. For example, the mean values of
normalized phytoplankton absorption coefficient for 545 nm was higher during summer
(0.209) than during spring (0.122) and fall (0.124) seasons (Figure 2.6, plot H).
Higher mean normalized phytoplankton absorption coefficients at 545 during
summer may be attributed to the offshore dispersal of the Mississippi River plume in the
NEGOM which provides nutrients for growth and development of large-celled diatoms.
Diatoms containing fucoxanthin pigment enhances absorption around 542 nm (Anderson
and Barrett, 1986). Patches of cyanobacteria found during summer may also contributed
to higher normalized aph(545) versus spring and winter seasons. Relatively high mean
chlorophyll-a concentrations during summer versus spring and winter lead to higher
normalized aph(673) in summer than that in spring and winter.
Seasonal and spatial distribution of aph545/aph440, aph673/aph440 and the average are
presented in Figures 2.9, 2.10 and 2.11. Relatively high values of these normalized
phytoplankton absorption coefficients were observed in the inner shelf especially in the
major river mouths and relatively low values offshore, except during summer season
where high values were also observed offshore associated with the Mississippi river
plume. The spatial distribution of aph545/aph440 and aph673/aph440 also seems to
94
Figure 2.9. Near-surface distribution of the aph545/440 in the NEGOM. White dots show
location of water sample collections. See Table 1.1 for cruise identification.
95
Figure 2.10. Near-surface distribution of the aph673/440 in the NEGOM. White dots
show location of water sample collections. See Table 1.1 for cruise identification.
96
Figure 2.11. Near-surface distribution of the of mean aph545/440+aph625/440+
aph673/440 in the NEGOM. White dots show location of water sample collections. See
Table 1.1 for cruise identification.
97
correlate with spatial distributions of chlorophyll-a (see also Figure 1.2). The correlation
between these normalized phytoplankton absorption coefficients seem to be better when
aph545/aph440, aph625/aph440 and aph673/aph440 were averaged (Figure 2.11).
To study the effect of normalized phytoplankton absorption coefficients on
taxonomic phytoplankton groups in the NEGOM, an index was computed based on
pigment composition as per Vidussi et al., (2001). They used pigments to derive cell size
class markers of phototroph groups, such as picophytoplankton (<2 m),
nanophytoplankton (2-20 m), and microphytoplankton (>20 m).
Pigments marker for picophytoplankton (<2 m) used were: Zeaxanthin (biomarker
for cyanobacteria and prochlorophytes; Gieskes et al, 1988; Guillard et al.,
1985; Chisholm et al., 1988), Divinyl-chlorophyll a (biomarker for
prochlorophytes; Goericke and Repeta, 1992; Gieskes and Kraay, 1983;
Chisholm et al., 1988) and Chlorophyll b+Divinyl-chlorophyll b (biomarkers
for green flagellates and prochlorophytes; Partensky et al., 1993; Moore et al.,
1995; Jeffrey, 1976; Simon et al., 1994).
Pigment markers for nanophytoplankton used were: 19’hexanoyloxyfucoxanthin
and 19’butanoyloxy-fucoxanthin (biomarker for chromophytes nanoflagellates;
Bjornland and Liaaen-Jensen, 1989; Hooks et al., 1988; Arpin et al., 1976;
Wright and Jeffrey, 1987; Jeffrey and Vesk, 1977; Andersen et al., 1993;
Bjornland et al., 1989), and Alloxanthin (a biomarker for cryptophytes; Jeffrey
and Vesk, 1977; Gieskes and Kraay, 1983).
98
Pigment markers for microphytoplankton used were: Fucoxanthin (a biomarker for
diatoms; Bjornland and Liaaen-Jensen, 1989; Hooks et al., 1988; Wright and
Jeffrey, 1987; Kimor et al., 1987), and Peridinin (a biomarker for
dinoflagellates; Kimor et al., 1987; Johansen et al., 1974; Jeffrey et al., 1975).
The formula to derive cell size class marker is as follows:
DP (Diagnostic Pigments)=Zea+chl_b+Allo+19’-HF+19’-BF+Fuco+Peri
(2.5)
BPpico = (Zea+chl_b)/DP
(2.6)
BPnano = (Allo+19’-HF+19’-BF)/DP
(2.7)
BPmicro= (Fuco+Peri)/DP
(2.8)
where BPpico=Biomass Proportion of picophytoplankton, BPnano= Biomass Proportion of
nanophytoplankton, BPmicro= Biomass Proportion of microphytoplankton, Zea= [zeaxanthin], chl_b=[chlorophyll_b], Allo=[alloxanthin], 19’-HF=[19’hexanoyloxyfucoxanthin],
19’-BF=[19’butanoyloxyfucoxanthin], Fuco=[fucoxanthin], Peri=[peridinin].
Biomass proportion distribution for microphytoplankton, nanophytoplankton and
picophytoplankton are presented in Figure 2.12, 2.13, and 2.14. Relatively high biomass
proportions of microphytoplankton (diatoms and dinoflagellates) were observed in the
inner shelf particularly in the major river mouths and the relatively low values in
offshore. During summer cruises, relatively high biomass proportions of microphytoplankton were also observed in the offshore region (Figure 2.12) along the Mississippi
River plume. It seems that relatively high biomass proportions of microphytoplankton
were associated with low salinity. This trend is consistent with those shown in Qian et al.
(2003) in which the applied different methods to quantify the abundance and distribution
99
of diatoms in the same region. It appears that the distribution pattern of
microphytoplankton biomass proportion correlates with the distribution of aph545/440
and aph673/440 (see Figure 2.9 and 2.10).
Relatively high values of biomass proportion for nanophytoplankton (chromophytes
nanoflagellates and cryptophytes) were observed particularly offshore and were relatively
low inshore. It seemed that relatively high biomass proportion for nanophytoplankton
associated with low chlorophyll-a concentration. During summer, it appeared that
relatively low nanophytoplankton biomass proportion extended offshore following the
Mississippi River plume (Figure 2.13).
The abundance of pycophytoplankton group (cyanobacteria, prochlorophytes and
green flagellates) was mixed along the NEGOM but relatively high biomass proportions
were only observed in late spring (Sp-99) and summer (Su-98, Su-99, Su-00) over the
outer shelf and slope (Figure 2.14). This pattern was also consistent with the observation
conducted by Qian et al. (2003).
To examine whether algal groups can be estimated from normalized phytoplankton
absorption coefficients, I examined several relationships between the combinations of
several bands of normalized phytoplankton absorption coefficients and taxonomic
phytoplankton groups (BPpico, BPnano, and BPmicro). From several combinations and
trials, the average of aph545/440, aph625/440, aph673/440 produced better relationships
with taxonomic phytoplankton groups. Results also showed that only biomass proportion
for microphytoplankton (BPmicro) has significant relationships with the average of
aph545/440, aph625/440, aph673/440. Positive relationships between the average of
100
aph545/440, aph625/440, aph673/440 and BPmicro were found with coefficient of
determination (r2) from 0.21 (Su-99) to 0.76 (Sp-99) (Figure 2.15). In general, the
relationships between the average of aph545/440, aph625/440, aph673/440 and BPmicro were
lower during summer than during fall and spring seasons (Figure 2.15). The lower
relationships during summer may contribute to the relatively high biomass proportion of
picophytoplankton (BPpico). Picophytoplankton (cyanobacteria) contains
phycoerythrobilin pigment producing peak absorption at around 545 nm (Hoepffner and
Sathyendranath, 1991; Morel et al., 1993; Moore et al., 1995). Microphytoplankotn
(diatoms) containing fucoxanthin pigment also enhanced absorption near 542 nm
(Anderson and Barnet (1986). Therefore, high biomass proportion of these two
phytoplankton groups in summer and late spring may interfere the relationship between
the normalized phytoplankton absorption coefficients and taxonomic phytoplankton
groups. Since phycoerythrobilin pigment is water soluble, it may be possible to reduce
the influence of this pigment to phytoplankton absorption at 545 nm. If the influence of
phycoerythrobilin pigment on phytoplankton absorption at 545 nm can be eliminated,
then the relationship between the average of aph545/440, aph625/440, aph673/440 and
BPmicro may improve, and therefore normalize phytoplankton absorption coefficient may
be used to estimate biomass proportion of microphytoplankton.
101
Figure 2.12. Spatial distribution of biomass proportion of cell size marker for
microphytoplankton. See Table 1.1 for cruise identification.
102
Figure 2.13. Spatial distribution of biomass proportion of cell size marker for
nanophytoplankton. See Table 1.1 for cruise identification.
103
Figure 2.14. Spatial distribution of biomass proportion of cell size marker for
pycophytoplankton. See Table 1.1 for cruise identification.
104
Su-00
Sp-00
Fa-99
Su-99
Sp-99
Fa-98
Su-98
All cruise data
Figure 2.15. Relationship between the average of aph545/440+aph625/440+aph673/440
and microphytoplankton biomass proportion (BPmicro) for each cruises. See Table 1.1 for
detail of cruise identification.
105
2.3.3. Phytoplankton Absorption Coefficient vs. Chlorophyll-a Concentration
I examined the relationship between phytoplankton absorption coefficient (aph())
and chlorophyll-a concentration at 440 and 673 nm (Figure 2.16 and 2.17). The general
trend of aph(440) was to increase with increasing chlorophyll-a concentration. This trend
was similar to observation conducted by Bricaud et al. (1995) and Yentch and Phinney
(1989). The relationship between aph(440) and chlorophyll-a concentration, exhibited a
power law relationship with coefficient of determination (r2) range from 0.87 to 0.98
(Figure 2.16).
The general trend of aph(673) values was also to increase with increasing
chlorophyll-a concentration. However, stronger power law relationship between aph(673)
and chlorophyll-a concentration were generally observed during all seasons (r2=0.970.99, Figure 2.17). Using linear regression, the relationships between aph(673) and
chlorophyll-a concentration were also strong (r2=0.87-0.99). The results also
documented that the exponent values of the power law relationships were very close to
one, indicating the relationship between aph(673) and chlorophyll-a concentration can be
considered linear. The slopes of the regressions also spanned within the small range
(0.013-0.017). Strong relationships between aph(673) and chlorophyll-a concentration for
all cruises indicated that variability in chlorophyll-specific absorption coefficient at 673
nm was small. By combining all data from seven cruises, the relationship between
aph(673) and chlorophyll-a concentration employing linear and power law equations are
as follows:
aph(673) = 0.0153*chla (r2=0.96, n=436)
106
aph(673) = 0.0167*chla0.972 (r2=0.96, n=436)
For chlorophyll-a concentrations < 1.0 mg m-3, the slopes of both aph(440) and
aph(673) were steeper than at chlorophyll-a concentrations > 1.0 mg m-3. I believe that
the non-linearity of these relationships was largely due to the differences in cell size of
phytoplankton species (Sathyendranath et al., 1999; Bricaud et al., 1995; Yentch and
Phinney, 1989).
Figure 2.16. Relationship between phytoplankton absorption coefficient at 440 nm
(aph(440)) and chlorophyll-a concentration for each cruises. Note the scales on both x and
y-axis are different between panels. See Table 1.1 for detail of cruise identification.
107
Figure 2.17. Relationship between phytoplankton absorption coefficient at 673 nm
(aph(673)) and chlorophyll-a concentration for each cruises. Note the scales on both x and
y-axis are different between panels. See Table 1.1 for detail of cruise identification.
108
2.3.4. Variation in Chlorophyll-Specific Absorption Coefficient
The chlorophyll-specific absorption coefficient at 440 nm was found to be highly
variable in the NEGOM region in all cruises (Figure 2.18). a *ph (440) varied by about a
factor of 7 ranging from 0.02 to 0.15 m2 mg-1 for the range of 0.06-12.25 mg m-3
chlorophyll-a concentration over the study period (Figure 2.18). Variability of a *ph (673)
was less pronounced and only varied by a factor 2. For chlorophyll-a concentration
range of 0.06-12.25 mg m-3 over the study period, a *ph (673) ranged from 0.0100 to
0.0248 m2 (mg chl)-1 with the average value of 0.0175 m2 (mg chl)-1.
Near-surface spatial and temporal variability of a *ph (440) for seven cruises is
presented in Figure 2.19. There was a general trend of increasing a *ph (440) with distance
from shore where chlorophyll-a concentration also decreased. During summer cruises,
relatively low a *ph (440) was also observed in the outer shelf and slope of the western
NEGOM (Figure 2.20) and higher a *ph (440) values (~0.15 m2 mg-1) were observed only
in the middle shelf off Florida. Relatively low a *ph (440) were observed along the inner
shelf and specifically near river mouths including the Mississippi, Mobile, Apalachicola
and Suwannee river outflow regions.
The general spatial pattern of a *ph (440) seemed to relate to general spatial pattern
of zeaxanthin (a biomarker for cyanobacteria) (see Figure 2.8) and biomass proportion of
nanophytoplankton (Figure 2.13), indicating increase in a *ph (440) values associated with
increase small-celled phytoplankton. Meanwhile, opposite pattern was observed between
109
a *ph (440) and biomass proportion of microphytoplankton (see Figure 2.12), indicating
decrease in a *ph (440) values associated with increase large-celled phytoplankton. The
a *ph (440) values were also higher in areas of lower chlorophyll-a concentration and vice
versa. The positive relationship between a *ph (440) and small-celled phytoplankton and
the negative relationship between a *ph (440) and large-celled phytoplankton and
chlorophyll-a concentration indicated that particle size (packaging effect) play role in
determining a *ph (440) variability. Because the relationship between a *ph (440) and
chlorophyll-a concentration was not linear, indicating pigment composition also played a
role in determining a *ph (440) variability (Lohrenz et al., 2003; Fujiki and Taguchi, 2002;
Carder et al., 1986, 1999; Kirk, 1994; Stramski and Morel 1990; Berner et al., 1989;
Mitchell and Kiefer, 1988a; Bricaud et al., 1988; Morel and Bricaud, 1986; Dubinsky et
al., 1986; Morel and Bricaud, 1981). The spatial distribution of a *ph (440) also followed
salinity patterns. Low salinity, with a higher nutrient content, may have played a role in
selecting for species with low a *ph (440). However, there was no statistical relationship
between surface nutrient concentrations (NO2+NO3+NH4+Urea) and a *ph (440), likely
because nutrients are consumed as fast as they are supplied.
To examine the effect of taxonomic groups on a *ph (440) in the NEGOM, an index
was computed based on pigment composition as per Vidussi et al., (2001) as discussed in
Section 2.3.2. For each major Biomass Proportion (BP>0.5), the a *ph (440) values were
110
grouped into the three corresponding cell size markers. Scatter plots of a *ph (440) and
Biomass Proportion of cell size markers is presented in Figure 2.20. Although scatter is
high, it seems that biomass proportion of nanophytoplankton and picophytoplankton have
a positive correlation with a *ph (440). While, microphytoplankton showed negative
correlation suggesting higher abundance of microphytoplankton lowered the value of
a *ph (440) (Figure 2.20). The average value of a *ph (440) corresponding to major biomass
proportion of microphytoplankton was also significantly lower than that of
nanophytoplankton and picophytoplantkon groups (Table 2.1). These results strongly
suggest that an increase in cell size resulted in decrease a *ph (440) values or increase in
pigment packaging.
Table 2.1. Mean values of a *ph (440) for all seven NEGOM cruises data corresponding
with the three major size cell markers of phytoplankton (BP>0.5).
ID
Cell size markers
A
B
C
nanophytoplankton
picophytoplankton
microphytoplankton
N
Mean
SD
136
64
29
0.0875
0.0815
0.0484
0.0020
0.0029
0.0044
*Average
Different From ID*
C
C
A,B
values were compared for the differences among cell size markers using the
Least Significant Difference (LSD) Method
111
Figure 2.18. Near-surface chlorophyll-specific absorption spectra measured on the
NEGOM region during summer, spring and fall seasons between 1998 and 2000. See
Table 1.1 for cruises identification. Note the different scales on y-axis.
112
Figure 2.19. Near-surface distribution of the chlorophyll-specific absorption coefficient at
440 nm in the NEGOM. White dots show location of water sample collections. See Table
1.1 for cruise identification.
113
Figure 2.20. Scatter plots of a *ph (440) and biomass proportion per algal group for all
seven NEGOM cruises.
114
2.4. Conclusions
In the coastal NEGOM near river outflows where chlorophyll concentration was
relatively high, light absorption in the blue band (440 nm) by detritus normally exceeded
that of phytoplankton absorption (ad was 54% to 66% of the total particulate absorption
or ap). In contrast, in offshore waters, absorption by phytoplankton contributed more than
that of detritus (aph was 68% to 77% of ap). There was no clear relationship between
detritus absorption coefficient and chlorophyll-a concentration. For water salinity >34.5,
chlorophyll-a and CDOM seemed to play major role in determining total particulate
absorption coefficient. Meanwhile, for water salinity <34.5, no clear correlation between
chlorophyll-a concentration and total particulate absorption coefficient. In both salinity
range, however, ag(440) exceeded aph(440) with average values for ag(440)/aph(440) of
3.09+0.88 (for salinity <34.5) and 1.81+0.58 (for salinity >34.5)
The spatial distributions of aph(545)/aph(440) and aph(673)/aph(440) seem to correlate
with spatial distribution of chlorophyll-a concentration and fucoxanthin pigment
(biomarker for diatoms), indicating that information from normalized phytoplankton
absorption coefficients may be used to estimate taxonomic phytoplankton groups. Up to
76% variability of biomass proportion of microphytoplankton (BPmicro) specifically
during spring and fall seasons can be explained by the variability of the average values of
aph545/440, aph625/440, aph673/440. During summer, however, the relationships between
the average of aph545/440, aph625/440, aph673/440 and BPmicro were relatively lower than
115
during spring and fall. This may be contributed to the presence of different taxonomic
phytoplankton groups i.e. diatoms (microphytoplankton, containing fucoxanthin pigment)
and cyanobacteria (picophytoplantkon, containing phycoerythrobilin pigment) during
summer and late spring in NEGOM. Both fucoxanthin and phycoerythrobilin pigments
enhance absorption around 545 nm. By removing phycoerythrobilin from the solution,
the relationship between the average of the normalized phytoplankton coefficients and
BPmicro may be improved.
In general, relationship between aph(440) and chlorophyll-a concentration,
exhibited a power law relationship with coefficient of determination (r2) range from 0.87
to 0.98. Meanwhile, aph(673) covaried almost linearly with chlorophyll-a concentration
in all seasons (r2=0.94-0.99).
Variability of a *ph (440) was more pronounced within a cruise than among the
seasons. It varied by about a factor of 7, i.e. a *ph (440) ranged from 0.02 to 0.15 m2 mg-1
for the range of 0.06-12.25 mg m-3 chlorophyll-a concentration. In general, lower values
of a *ph (440) were observed in the inner shelf and major rivers outflow regions. Higher
a *ph (440) were observed in outer shelf and slope, associated with high salinity, and lower
chlorophyll-a, except during summer when lower a *ph (440) were also observed in outer
shelf and slope to the west of about 85W in the low salinity Mississippi plume. Mean
values of a *ph (440) in waters dominated by microphytoplankton (diatoms) were
significantly lower than in waters with nanophytoplankton and picophytoplankton
communities, indicating particle size (packaging effect) play roles in determing a *ph (440)
116
variability. The relationship between a *ph (440) and chlorophyll-a concentration was also
not linear, indicating pigment composition also play a role in determining a *ph (440)
variability.
Variability of a *ph (673) was less pronounced and only varied by a factor 2, i.e.
a *ph (673) ranged from 0.0100 to 0.0248 m2 (mg chl)-1 with average value of 0.0175 m2
(mg chl)-1 for the range of 0.06-12.25 mg m-3 chlorophyll-a concentration.
117
SUMMARY
The alternating influence of nutrient inputs through riverine discharge and offshore
river plume dispersal on the surface waters of the northeastern Gulf of Mexico (NEGOM)
was studied by examining weekly winds, river discharge, satellite data (ocean color and
altimetry) and in situ observations including chlorophyll-a concentration, absorption
coefficients of colored dissolved organic matter (CDOM) and particulate materials from
October 1997 to December 2000. Six major river-impacted regions, namely the
Mississippi (R1), Mobile (R2), Escambia (R3), Choctawhatchee (R4), Apalachicola (R5),
Suwannee (R6), plus one coastal region near Tampa Bay (R7), and three offshore regions
(R8A, R8B, and R8C) were used to characterize the NEGOM. Nine two-week cruises
were conducted in three different seasons between 1997 and 2000 onboard the Texas
A&M University R/V Gyre: spring (April or May), summer (July and August), and fall
(November).
Relatively high in situ chlorophyll-a concentrations (>1 mg m-3) and CDOM
absorptions (ag443>0.1 m-1, as high as ag443=0.38 m-1 with salinity=25.77) were observed
inshore particularly near the major river mouths and spatially coherent Mississippi
plumes that extended offshore and to the east-south-east (ESE) of NEGOM during the
three consecutive summer seasons (1998, 1999, and 2000). The patterns of in situ
chlorophyll-a concentration were also confirmed by the SeaWiFS images. The plume
observed as early as late May and lasted until the first week of September can extend up
118
to >500 km east of the Mississippi delta to Florida Keys. Eastward (westerly) wind,
together with the action of one or more anticyclonic eddies located just to southeast of the
Mississippi delta transported Mississippi River water offshore in late spring and summer
of the three consecutive years of 1998, 1999, and 2000.
River discharge appeared to be the dominant forcing factor in determining
chlorophyll-a concentration variability off Mississippi, Mobile, Escambia,
Choctawhatchee, Apalachicola and Suwannee regions. Upwelling favorable winds
observed during late spring and summer along Mobile, Escambia, and Choctawhatchee
regions may have limited its influence on chlorophyll-a concentration variability in these
regions because the upwelling process may be largely confined to a narrow strip along
the coast. Meanwhile, off Apalachicola region, upwelling-favorable wind processes
seemed to play an important role in chlorophyll-a concentration variability during fall
seasons.
A strong interannual variability (up to a factor of four) in in situ chlorophyll-a
concentration was observed during winter to spring in 1998 compared to winter and
spring in 1999 and 2000 off Escambia, Choctawhatchee, Apalachicola, Suwannee and
Tampa Bay regions. Anomalously high (up to a factor of four) of chlorophyll-a
concentration in winter and spring 1998 relative to the year of 1999 and 2000 in these
regions was related to higher fresh water discharge during the 1997-1998 El NiñoSouthern Oscillation event.
The comparison between in situ and satellite derived chlorophyll-a concentrations
showed that the SeaWiFS algorithm OC4v4 overestimated in situ measurements up to
119
about 300% during summer cruises when relatively high CDOM absorptions were found
offshore. Using MODIS algorithm (Carder et al., 1999), however, this errors can be
reduced up to about 120% (about three times better than OC4v4). For chlorophyll-a
concentration <1.0 mg m-3, both OC4v4 and the MODIS algorithm agreed well within the
SeaWiFS project mission (+35% errors) during fall cruises. During spring and summer
seasons when CDOM absorptions were relatively high offshore, MODIS algorithm
performed significantly better than the OC4v4. However, the errors produced by MODIS
algorithm still beyond the SeaWiFS projects mission (+35% errors) indicating CDOM
play major role in resulting errors of satellite measurement, and therefore, more research
is needed to properly separate effects such of those from CDOM in the algorithms.
Strong correlations between the CDOM absorption coefficient at 443 nm (ag443) and
CDOM fluorescence (330 nm excitation and 450 nm emission filters) were consistently
observed (r2>0.86). In general, ag443 covaried linearly and inversely with salinity in
nearshore areas during spring and fall cruises, indicating conservative mixing. Using all
NEGOM data of fall 1998, the salinity-ag443 relationship i.e., Salinity (S)=36.5929.86*ag443 (n=8771, r2=0.86, 0.01<ag443<0.52, 16<S<36) produced the lowest mean
percentage errors of observed vs. predicted salinities (<3% errors, corresponding to <1.04
psu) for all other spring and winter seasons, therefore, this relationship may be useful to
estimate salinity from satellite ocean color data. However, estimates with this
relationship led to unacceptable errors in the summer (>10% errors in salinity estimates),
due to less of a coherent relationship between ag and salinity over the range of salinities
observed, and the significant influence of phytoplankton in CDOM production during
120
summer seasons. Only within the river plumes in outer shelf and slope regions was the
correlation somewhat more robust and similar to that seen nearshore.
In most regions of inshore NEGOM, there was no indication that a local increase of
chlorophyll-a concentration led to increase CDOM during fall seasons. However, during
spring and summer seasons, significant increases in CDOM pool associated with the
increase of chlorophyll-a concentration were observed in coastal and offshore regions,
indicating phytoplankton bloom was a significant source of CDOM pool. In central
NEGOM, it seemed that phytoplankton was also significant sources for CDOM formation
during fall seasons. The increase of in situ CDOM formation related to phytoplankton
blooms ranged from 10% to 100%.
Chlorophyll-a concentration variability seemed to have major influence on the
variability of total particulate absorption coefficients where low or no fresh water impact
in the region (salinity>34.5). Meanwhile, no clear correlation between chlorophyll-a
concentration and of total particulate absorption coefficients where fresh water influence
is significant (salinity<34.5). In general, however, CDOM absorptions were higher than
phytoplankton absorption by a factor of 3.
The spatial distributions of chlorophyll-a concentration and fucoxanthin pigment
(biomarker for diatoms) seemed to correlate with spatial distributions of normalized
phytoplankton absorption coefficients at 545 and 673 nm. Using microphytoplankton
biomass proportion (BPmicro) as an independent variable, we were able to explain as much
as 76% of the variance in the average normalized phytoplankton absorption coefficient of
545, 625, and 673 nm specifically during spring and fall seasons, indicating normalized
121
phytoplankton absorption coefficient can be used to estimate biomass proportion of
microphytoplankton (diatoms). Lower correlation, however, was observed during
summer seasons due to the high abundance of cyanobacteria (containing phycoerythrobilin pigments) and diatoms (containing fucoxanthin pigment). Both these pigments
enhance absorption at around 545 nm.
The absorption coefficient of phytoplankton at 440 nm [aph(440)] followed a power
law relationship (r2=0.87-0.98) with chlorophyll-a concentration. Meanwhile, aph(673)
covaried almost linearly with chlrophyll-a concentration (r2=0.94-0.99). The chlorophyllspecific absorption coefficient at 440 nm, a *ph (440), varied by a factor of 7 (0.02-0.15 m2
mg-1 with chlorophyll-a concentration range of 0.06-12.25 mg m-3). Lower values of
a *ph (440) (<0.06 m2 mg-1) were observed over the inner shelf and near the outflow regions
of major rivers. During summer, lower values of a *ph (440) were also observed in the
outer shelf and slope in the western NEGOM (west of 85W) in low salinity waters of the
Mississippi River plume. Higher values of a *ph (440) (>0.1 m2 mg-1) were otherwise
observed outside the river plumes in the outer shelf and slope, where lower chlorophyll-a
concentration occurred. Particle size (packaging effects) and pigment composition were
major factors in determining a *ph (440) variability. There were no differences in the mean
spectral phytoplankton absorption coefficients between spring and fall seasons, but these
were different from those observed in the summer. The average value of a *ph (440) for
major biomass proportion of microphytoplankton was also significantly lower than that
of nanophytoplankton and picophytoplantkon groups, suggesting that an increase in cell
122
size associated with increase in pigment packaging. Meanwhile, aph(673) covaried almost
linearly with chlorophyll-a concentration in all seasons (r2=0.94-0.99). The variability of
a *ph (673) was very small (0.0100-0.0248 m2 (mg chl)-1 with average value of 0.0175 m2
(mg chl)-1 for the range of 0.06-12.25 mg m-3 chlorophyll-a concentration).
123
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APPENDICES
144
Appendix 1. Method to Estimate Coastal Upwelling Index
Hourly wind speed and direction were obtained from NDBC buoys BURL1
(28.90N, 89.40W) for the Mississippi region, DPIA1 (30.25N, 88.07W) for Mobile,
Escambia, and Choctawhatchee regions, CSBF1 (29.67N, 85.36W) for Apalachicola
region, and CDRF1 (29.14N, 83.03W) for Suwannee region. Hourly wind speed and
direction were averaged to produce daily and weekly mean wind speed and direction
employing a true vector average. In this scheme, the magnitude of the vector was
represented by the wind speed observation and the direction observations were used for
the orientation. The vectors were then broken down into their u and v components. All u
and v components are then averaged separately. The resulting average speed and
direction were calculated from the Pythagorean Theorem and “arctan(u/v)”, respectively.
Surface wind field data for NEGOM region of each of nine cruises were obtained from
Texas A&M University. The product was produced by gridded averaging of surface
wind field data from several NDBC buoys station in the NEGOM region for the same
cruises period of time.
Upwelling is a very important process in marine ecosystems since the upwelled
water has higher concentrations of nutrients such as nitrate, phosphate and silicate than
the original surface water (Huyer, 1983; Traganza et al., 1981; Kosro et al., 1991); such
nutrients are key to sustaining biological production. Upwelling water is also typically
cooler and more saline (Huyer, 1983; Kosro et al., 1991).
Based on Ekman’s (1905) theory and under Ekman’s assumptions of steady state
motion, uniform wind, and infinite homogenous ocean, the mass transport per unit width
145
of ocean surface is directed 90 to the right of the wind direction in the Northern
Hemisphere and is related to the magnitude of wind stress as follows:
y
Mx = ----f
(a1.1)
where Mx (kg m-1 s-1) is the offshore mass transport (Ekman transport) resulting from a
wind stress parallel to local coastline orientation, y (kg m-1 s-2), and f (s-1) is the Coriolis
parameter. To estimate Ekman transport perpendicular to the coastline (Bakun, 1973 and
1975; Mason and Bakun, 1986; Schwing et al., 1996) for the northern coasts of the Gulf
of Mexico, the wind component parallel to the local coastline was estimated (Arfken,
1985) as follows:
un = uo cos  + vo sin 
(a1.2a)
vn = -uo sin  + vo cos 
(a1.2b)
where un and vn are u and v components of the wind after rotation so that un is parallel to
the coast and vn is perpendicular, uo and vo are u and v of original wind components, and
 is the counterclockwise rotation angle with land laying to the right side. Sea surface
wind stress was calculated using the classic square-law formula as follows:
x = aCd w un
(a1.3a)
y = aCd w vn
(a1.3b)
where x is the x component of the wind stress vector, y is the y component, a (kg m-3)
is the air density, Cd (dimensionless) is an empirical drag coefficient, un and vn (m s-1) are
the wind vectors near the surface in the x and y component after rotation, and w is the
wind speed.
146
The drag coefficient (Cd) is itself a function of wind speed. In this study, I used the
approach of Hsu (1986) to estimate the Drag Coefficient as follows:
Cd = [0.4/(14.56 - 2 x ln(wind speed)]2
(a1.4)
The coastal upwelling index (CUI) within 100 meter coastline was estimated from
Ekman Transport (Mx) using the following formula:
100 m coastline
CUIx = Mx -------------------1025 kg/m^3
(m3/s/100 m coastline)
(a1.5)
Negative (offshore Ekman transport) value of the CUIx leads to an upwelling process
whereas positive (onshore Ekman transport) value of the CUIx leads to a downwelling
process.
While the derivations and calculations of the CUIx were based on established theory
and based on numerous assumptions and simplified parameterizations, they are,
nonetheless, a simplified and incomplete representation of the physics that drive coastal
circulation and specifically of Ekman transport. Schwing et al. (1996) concluded that
upwelling was as much due to the combined effect of coastal upwelling (the process that
is the basis of the coastal upwelling index) and Ekman pumping (wind curl, or the spatial
variation in the wind). The Ekman pumping process may be an equally important
contributor to surface Ekman divergence and upwelling (Bakun and Nelson, 1991),
therefore the CUIx presented here may not represent the true amount of upwelling.
147
Appendix 2. Weekly mean time series of wind, upwelling index, river discharge, and
chlorophyll-a concentration off Mississippi region (R1). A negative value for upwelling
index indicates an upwelling.
148
Appendix 3. Same as Appendix 2 but off Mobile region (R2).
149
Appendix 4. Same as Appendix 2 but off Escambia region (R3).
150
Appendix 5. Same as Appendix 2 but off Choctawhatchee region (R4).
151
Appendix 6. Same as Appendix 2 but off Apalachicola region (R5).
152
Appendix 7. Same as Appendix 2 but off Suwannee region (R6).
153