The Oceanography of Intermittent HABs off the Caloosahatchee River, FL:
Satellite Remote Sensing Component – Final Report
Investigators:
Chuanmin Hu and Frank Muller-Karger
(727)5533987, hu@seas.marine.usf.edu; (727)5533335, carib@seas.marine.usf.edu
Objective and Deliverables:
Analyze satellite data as well as on other ancillary data (river flows), while help collect in situ
data; A technical report describing the findings of this analysis will be delivered.
Achievements as of May 2006:
 Data collection:
Field data, including daily river flow rate and bi-monthly measured nutrient (N, P, NH3)
concentration for the Caloosahatchee River (Lock 79) were obtained from the Southwest
Florida Water Management District (SWFWMD). The data were collected by the US
Corp of Engineers;
Satellite data from the SeaWiFS ocean color sensor between 1998 and 2006 were
processed and trimmed to cover the study region (Figure 1);
Satellite data from the MODIS ocean color and temperature sensor between 2003 and
2006 were processed and trimmed to cover the study region;
Satellite data from the AVHRR sensors (NOAA satellite series) between 1993 and 2006
were reprocessed to remove cloud-contaminated pixels (see below);
Help was provided to collect bio-optical data from in situ sensors mounted on MARVIN
 Data analysis:
River data (flow rate and nutrient concentration) were binned to generate a monthly timeseries between 1998-2006 and between 1998-2004, respectively (e.g. Figure 2);
Satellite data were analyzed in a novel way to target the ocean color response to river
flow, and not to wind-induced resuspension events. Specifically, a total of 3675 SeaWiFS
data files (overpasses) between 1998 and 2006 were screened first to exclude those files
when significant resuspension was found near Charlotte Harbor. Water-leaving radiance
data in the red channel (670 nm, Lwn670) were examined, and all files when Lwn670 was
above its monthly median value were discarded. The remaining files were used to
generate monthly composite data for Lwn for the six visible channels (412, 443, 490, 510,
555, 670 nm), for chlorophyll concentration (Chl in mg m-3), and for diffuse attenuation
at 490 nm (K_490, m-1);
Because SeaWiFS Chl was derived using a band-ratio algorithm, in coastal waters it is
interfered by colored dissolved organic matter (CDOM, typically rich in rivers and
coastal runoff) and also by the shallow bottom (< 30 m) if the water is relatively clear.
Therefore, SeaWiFS Chl should be interpreted with caution and it should be treated as an
index to indicate Chl concentration and CDOM abundance, if the shallow bottom effects
are not considered. Chl monthly time-series for the pre-defined six areas (Figure 1) were
extracted and correlated with river data;
MODIS data cover a shorter period (2003-2006). Because of the overlap with SeaWiFS,
the main purpose of using MODIS is to examine its fluorescence data products, which
1
can be used to qualitatively differentiate Chl from CDOM. Thus, MODIS data were only
visually examined and no time-series analysis was performed;
AVHRR data provides the longest time series to examine sea surface temperature (SST).
However, because of the artifacts in the cloud-screening algorithm, residual errors are
frequently found due to cloud contamination. These errors are not problematic when each
individual image is examined visually, but they can cause serious bias during image
composing (e.g., monthly mean) and during time-series analysis. Further, because there is
no onboard GPS, there are frequent geo-referencing errors. These errors, typically in the
order of several to tens of 1-km pixels, are insignificant for large scale, open ocean
studies, but they can cause serious problems for small-size, coastal regions. For the latter
problem, each image has been manually and visually corrected. For the former cloud
contamination problem, a filter has been designed to use multi-year climatologic and 3
days median values as baselines. This filter has been applied to the entire time series (>
45,000 data files) from 1993 to 2006 to “clean” all the data, and the results were used to
compose monthly means.
Oct. 2004
SeaWiFS
Chl
Figure 1. SeaWiFS chlorophyll concentration
(Chl) image for October 2004, overlaid with
bathymetry lines (10, 20, 30, and 50 m) and
outlines of six predefined study regions:
1: < 10 m south of Charlotte Harbor (CH);
2: 10 to 20 m south of CH;
3: 20 to 30 m south of CH;
4: < 10 m west of CH;
5: 10 to 20 m west of CH;
6: 20 to 30 m west of CH.
Note that all turbidity events were discarded
during image compositing. However, due to the
limitation of the bio-optical algorithm and the
interference of CDOM and shallow bottom,
Chl may have some uncertainties and therefore
should be treated as a relative index.
Charlotte
Harbor
5
4
6
1
2
3
2
 Results
Fig. 1 shows the study region, with several bathymetry lines overlaid on a SeaWiFS Chl
image. Six sub-regions are outlined, from which time-series data were extracted and
analyzed. A “reference” region, from offshore waters (> 2000 m deep) in the eastern Gulf of
Mexico at the same latitude of Charlotte Harbor, was also examined to see similarity/contrast
to these nearshore regions.
Nutrients
Flow rate
Fig. 2 shows the river flow as well as nutrient concentrations at Lock 79 on the
Caloosahatchee River. The spring 1998 high flow rate is due to the 1997-1998 El-Nino event.
Except for this episodic high-flow event and the dry year of 2000, the flow data show
seasonal patterns, with lows in the springs and highs in the falls. It appears that from 2002,
the fall high-flow rates are increasing, possibly due to increased rainfall. Nutrient
concentrations do not appear to correlate with river flow rate except for nitrite. Further, there
is little inter-annual variability in nutrient concentrations. Therefore, our time-series analysis
will focus on flow rates.
10000
Nitrite
Phosphate
Ammonia
Nitrate
Flow rate
5000
0
1
0.1
0.01
0.001
0
(1998)
12
(1999)
24
(2000)
36
(2001)
48
(2002)
60
(2003)
72
(2004)
Months since January 1998
Figure 2. Mean flow rate (ft3 s-1) and nutrient concentrations (mg L-1) measured from the
Caloosahatchee River at Lock 79.
Fig. 3 shows the monthly average chlorophyll concentration (Chl, mg m-3) from SeaWiFS for
2004 (after all turbidity events were removed), while Fig. 4 shows the monthly average sea
surface temperature (SST, oC) patterns (1995-2005) for the region. Their seasonal change
patterns appear different, possibly due to different physical, environmental, and biological
forcing. For example, SST in summer is relatively homogeneous in the region, but Chl shows
sharp gradient from offshore to nearshore in the same period. The time-series analysis results
below clearly show the contrast between the two properties.
3
84
Jan. 2004
Feb. 2004
Mar. 2004
Apr. 2004
May. 2004
Jun. 2004
Jul. 2004
Aug. 2004
Sep. 2004
Oct. 2004
Nov. 2004
Dec. 2004
Figure 3. SeaWiFS monthly mean Chl (mg m-3) for 2004.
4
January
February
March
April
May
June
July
August
September
October
November
December
Figure 4. Sea surface temperature (SST, oC) monthly climatology for 1995-2005.
5
10000
10
1000
1
10
100
10000
1000
100
10000
1
1000
0.1
100
10000
10
1000
1
100
10000
10
1000
1
10
100
10000
1
1000
0.1
0
12
(1998)
24
(1999)
36
48
60
72
84
96
(2000) (2001) (2002) (2003) (2004) (2005)
100
Month from January 1998
Figure 5. SeaWiFS Chl (mg m-3) for Area #1 to #6 (from top to bottom; see Fig. 1 for area
designation) near Charlotte Harbor and river flow rate (ft3 s-1) between 1998 and 2006. The green
stars are for the Caloosahatchee River flow.
6
3 -1
10
River flow (ft s )
-3
Chl (mg m )
1
25
1000
15
100
10000
25
1000
15
100
10000
25
1000
15
100
10000
25
1000
15
100
10000
25
1000
15
100
10000
25
1000
15
0
12
(1998)
24
(1999)
36
48
60
72
84
96
(2000) (2001) (2002) (2003) (2004) (2005)
3 -1
River flow (ft s )
10000
o
SST ( C)
35
100
Month from January 1998
Figure 6. Monthly average SST (oC) from AVHRR and MODIS for Area #1 to #6 (from top to bottom; see
Fig. 1 for area designation) near Charlotte Harbor and river flow rate (ft3 s-1) between 1998 and 2006. The
green stars are for the Caloosahatchee River flow.
7
Fig.5 shows that there is some seasonality in the SeaWiFS Chl patterns, with peaks typically in
the fall and troughs in the spring. The seasonality is more apparent in near shore waters (Area1
and Area 4) than in offshore waters (Area 3 and Area 6). Further, the Chl temporal patterns tend
to mirror or follow the river flow patterns. Indeed, a correlation analysis shows that all 6 areas
are significantly correlated with the Caloosahatchee River flow (p < 0.05). A cross-correlation
analysis shows that these areas are 0 to 2 months behind the river flow in their temporal patterns
(Table 1). For example, for Area 1 when Chl data is shifted by one month behind river flow
(Shift_Month = -1), correlation coefficient increases from 0.60 to 0.64, indicating that there is a
one-month lag (relative to river flow) in the Chl temporal changes. For Area 4 and Area 5 (inside
and west of Charlotte Harbor), correlation coefficient is the highest when there is no time lag.
Shift_Month
-3
-2
-1
0
1
Area 1
0.26
0.49
0.64
0.60
0.37
Area 2
0.30
0.47
0.59
0.52
0.34
Area 3
0.44
0.51
0.47
0.27
0.09
Area 4
0.20
0.41
0.62
0.63
0.48
Area 5
0.29
0.47
0.63
0.66
0.50
Area 6
0.48
0.58
0.58
0.50
0.32
Table 1. Correlation coefficient between SeaWiFS Chl time-series and Caloosahatchee River flow
between 1998 and 2006. The column “Shift_Month” indicates the number of months shifted in the
correlation analysis between the two datasets; negative numbers mean that Chl is behind river flow.
Such a close relation does not exist between SST and river flow or between SST and Chl,
suggesting that SST is not driven by river flow and that in general, SST plays an insignificant
role in determine Chl in this region. In fact, SST shows much less inter-annual variability than
Chl, and SST is out of phase with river flow (Fig. 6).
Further comparison between these coastal areas and a deep-water area (> 2000 m bottom depth)
clearly shows the different processes responsible for the observed Chl and SST patterns for the
two regions (coastal and deep waters). Fig. 7 shows that while SST has similar seasonal patterns
between the two regions, in the coastal area SST has a larger range due to its shallow bottom and
high concentration of light-absorbing materials (Chl and CDOM). In contrast, Chl is nearly
completely out of phase between the two regions. As shown above, Chl in the nearshore region is
significantly driven by river flow, which is mainly determined by the summer rainfall.
Consequently, Chl shows the maximum in the fall. In the deep water (> 2000m), however, winter
mixing due to high winds plays a major role, leading to Chl maxima in the winter. Clearly,
coastal and deep-water regions are dominated by different processes.
8
0.1
35
Area #5
> 2000m
25
15
0
12
(1998)
24
(1999)
36
(2000)
48
(2001)
60
(2002)
72
84
96
(2003) (2004) (2005)
Month from January 1998
Figure 7. Comparison between coastal water (Area #5 in Fig. 1) and deep water (bottom depth >
2000m) in their Chl and SST temporal patterns. In the top panel, Chl values for the >2000m water are
shown on the right-hand side of the figure.
 Conclusion
Clearly, Caloosahatchee River has a significant influence on the bio-optical properties in
its immediate adjacent waters to at least 30 m isobath, where 26% to 44% (square of the
correlation coefficient) of the Chl variability can be explained by the river flow. From
Fig. 5, the peaks in the fall of 2000 for Areas #1 to #3 (south of Charlotte Harbor), and
the peaks in the fall of 2001, 2003, 2004, and 2005 for all selected 6 areas were
confirmed red tide (Karenia brevis) events. These latter events were concurrent with the
high river flows, suggesting that the river discharge may directly contribute to the red tide
intensity and duration. However, these results do not rule out other possible nutrient
sources such as those from upwelling, dust, and even from submarine groundwater
discharge which has been found to provide higher nutrient flux than rivers in the US east
coast and also in Tampa Bay. We recently conducted a study to document the importance
of rainfall that leads to excessive river flow and possibly higher than normal groundwater
discharge, which can potentially contribute to the long-lasting, extensive 2005 red tide
over the west Florida shelf (Hu et al., 2006). Clearly, there are more than one source to
supply nutrients, and further research is needed to clarify rivers’ role in initiate and
maintain red tides.
 Publications
Hu, C., F. E. Muller-Karger, and P. W. Swarzenski (2006). Hurricanes, submarine
groundwater discharge, and Florida’s red tides. Geophysical Research Letters. Vol. 33,
L11601, doi:10.1029/2005GL025449.
9
-3
0.4
0.3
0.2
> 2000m
Chl (mg m )
Area #5
o
SST ( C)
-3
Chl (mg m )
(1998)
96
84
72
60
48
36
24
012
35
25
15
10000
1000
100
3River
-1
)Month
s
(1999)
from
flow
(2000) (ft
January
(2001)
(2002)
1998
10
(2003)
(2004)
(2005)
1