Phytoplankton dynamics in shelf waters
around Australia
DECEMBER 2011
PRODUCED BY David Blondeau-Patissier, Arnold Dekker, Thomas Schroeder and Vittorio Brando,
CSIRO Land and Water
FOR the Department of Sustainability, Environment, Water, Population and Communities
ON BEHALF OF the State of the Environment 2011 Committee
Citation
Blondeau-Patissier D, Dekker AG, Schroeder T and Brando VE. Phytoplankton dynamics in shelf
waters around Australia. Report prepared for the Australian Government Department of
Sustainability, Environment, Water, Population and Communities on behalf of the State of the
Environment 2011 Committee. Canberra: DSEWPaC, 2011.
© Commonwealth of Australia 2011.
This work is copyright. Apart from any use as permitted under the Copyright Act 1968, no part may
be reproduced by any process without prior written permission from the Commonwealth. Requests
and enquiries concerning reproduction and rights should be addressed to Department of
Sustainability, Environment, Water, Populations and Communities, Public Affairs, GPO Box 787
Canberra ACT 2601 or email public.affairs@environment.gov.au
Disclaimer
The views and opinions expressed in this publication are those of the authors and do not necessarily
reflect those of the Australian Government or the Minister for Sustainability, Environment, Water,
Population and Communities.
While reasonable efforts have been made to ensure that the contents of this publication are factually
correct, the Commonwealth does not accept responsibility for the accuracy or completeness of the
contents, and shall not be liable for any loss or damage that may be occasioned directly or indirectly
through the use of, or reliance on, the contents of this publication.
Cover image
Series of four images depicting (clockwise from top left) an artist impression of the Envisat-MERIS satellite sensor
(credit: ESA), Trichodesmium sp. surface expressions off the coast of Cairns (aerial photograph, credit: Dr T.
Schroeder, CSIRO), an estimate of chlorophyll concentrations for the oceanic and coastal waters around Australia
(credit: NASA) and phytoplankton cells floating in water (credit: oceanlab, Aberdeen UK)
Australia ■ State of the Environment 2011 Supplementary information
i
Preface
This report was commissioned by the Department of Sustainability, Environment, Water,
Population and Communities to help inform the Australia State of the Environment (SoE) 2011
report. As part of ensuring its scientific credibility, this report has been independently peer
reviewed.
The Minister for Environment is required, under the Environment Protection and Biodiversity
Conservation Act 1999, to table a report in Parliament every five years on the State of the
Environment.
The Australia State of the Environment (SoE) 2011 report is a substantive, hardcopy report
compiled by an independent committee appointed by the Minister for Environment. The report is an
assessment of the current condition of the Australian environment, the pressures on it and the
drivers of those pressures. It details management initiatives in place to address environmental
concerns and the effectiveness of those initiatives.
The main purpose of SoE 2011 is to provide relevant and useful information on environmental
issues to the public and decision-makers, in order to raise awareness and support more informed
environmental management decisions that lead to more sustainable use and effective conservation
of environmental assets.
The 2011 SoE report, commissioned technical reports and other supplementary products are
available online at www.environment.gov.au/soe.
Australia ■ State of the Environment 2011 Supplementary information
ii
CONTENTS
ACKNOWLEDGMENTS .............................................................................................. vi
EXECUTIVE SUMMARY ............................................................................................ vii
1.
INTRODUCTION ................................................................................................. 1
1.1.
1.2.
1.3.
Aims and objectives .................................................................................................. 1
Relevance and contribution of this research to Australia’s state of the environment assessment
.................................................................................................................................. 2
Remote sensing of ocean colour............................................................................... 3
1.3.1.
1.3.2.
1.3.2.1.
1.3.2.2.
1.3.2.3.
2.
DATA AND METHODS ....................................................................................... 6
2.1.
2.2.
3.
The MODIS and MERIS ocean colour sensors ..................................................... 3
Atmospheric correction and ocean colour algorithms ........................................... 5
Atmospheric correction and quality flags .............................................................. 5
MODIS chlorophyll algorithm used for this study .................................................. 5
MERIS surface algal bloom index used for this study ........................................... 6
Data preparation and availability ............................................................................... 6
Trend estimates of the chlorophyll time-series ......................................................... 9
RESULTS AND INTERPRETATIONS ................................................................. 9
3.1.
3.2.
3.3.
3.4.
3.5.
North region: Darwin ............................................................................................... 10
East region: Burdekin, Great Barrier Reef .............................................................. 13
Southeast region: Storm Bay, Tasmania ................................................................ 16
Southwest region: Geographe Bay, Perth .............................................................. 18
Northwest region: South Kimberley, Broome .......................................................... 20
4.
SUMMARY AND KEY FINDINGS ..................................................................... 22
5.
RECOMMENDATIONS AND FUTURE WORK ................................................. 23
APPENDIX ................................................................................................................. 25
GLOSSARY................................................................................................................ 29
REFERENCES ........................................................................................................... 30
Australia ■ State of the Environment 2011 Supplementary information
iii
LIST OF FIGURES
Figure 1 Locations of the five reporting sites (red boxes). The extent of the continental shelf waters
(pink) and the 100-m isobath (running black line) are shown.
2
Figure 2 Artist impression of the various processes that contribute to the signal as measured by a
remote sensor in an optically shallow water (left) where the substrate has a significant effect on the
water leaving radiance at the water surface or an optically deep water (right) where there is no
measurable effect of the substrate on the water leaving radiance.
4
Figure 3 MODIS-Aqua true-colour image (left) showing an increase in phytoplankton near Broad
Sound, Great Barrier Reef, on 4 January 2011. A spectral band stretch (right) helps discern the
increase in chlorophyll-a pigment (green patch delineated by red, dashed line). This bloom is likely
to have resulted from organic matter-rich freshwater outflows containing high concentrations of
nutrients. No chlorophyll algorithm was applied to this image.
5
Figure 4 Aerial photography of a surface Trichodesmium sp. bloom off Cairns on 26th October
2006.
6
Figure 5 Example of the study region of Storm Bay, Tasmania. The green pixels are those of the
continental shelf waters, the red pixels (N= 2676) delineate the study site for this region and the
back pixels are masked (land or outer shelf).
8
Figure 6 Example of the relative frequency distribution of MODIS chlorophyll-derived
concentrations for the region of Tasmania (2003-2010, N=1,737,078 pixels). Inset shows a
chlorophyll map illustrating the variability for a weekly composite of the selected region.
8
Figure 7 Multi-annual (2003-2010) chlorophyll-a median (a) and interquartile range (b) maps for the
reporting region of Darwin. Masked pixels appear in black, land pixels are in grey. This same
legend applies to all the multi-annual maps of chlorophyll presented in this report.
11
Figure 8 Satellite-derived monthly time-series of MODIS-chlorophyll-a (a,b) and MERIS-surface
bloom expressions (c,d) for the region of Darwin. Seasonal cycles (a,c) are based on the 20032011 time-series (b,d). The monthly standard deviation (orange error bars) and the least-square
linear trend (thick blue line) are shown for the chlorophyll time-series (a,b).
11
Figure 9 ESA-MERIS (1-km resolution) image showing a surface bloom East of Cobourg on 25
September 2011. From top to bottom: true colour image (a); Spectral band stretch of NIR bands
showing the patterns of the surface bloom (b); MERIS surface algal bloom index (c). This event
was not captured by the time-series for this study because it occurred after 2010.
12
Figure 10 A bloom of Lyngbya majuscula at Vesteys Beach, Darwin in May–June 2010.
(Photo/credit: Julia Fortune, Aquatic Health Unit, Darwin Harbour).
12
Figure 11 Multi-annual (2003-2010) chlorophyll-a median (a) and interquartile range (b) maps for
the reporting region of the Burdekin.
14
Figure 12 Satellite-derived monthly time-series of MODIS-chlorophyll-a (a,b) and MERIS-surface
bloom expressions (c,d) for the region of Burdekin. Seasonal cycles (a,c) are based on the 20032011 time-series (b,d). The monthly standard deviation (orange error bars) and the least-square
linear trend (thick blue line) are shown for the chlorophyll time-series (a,b).
14
Figure 13 Sequence of MERIS weekly composite of surface algal bloom expressions for the
Burdekin region from 1st July to 22 September 2008. The outer shelf is masked.
15
Australia ■ State of the Environment 2011 Supplementary information
iv
Figure 14 Monthly frequency counts of pixels with positive MERIS MCI for a period spanning 20032010 for the whole Great Barrier Reef. The wet season months are shown between brackets (from
[Blondeau-Patissier, 2011]).
15
Figure 15 Multi-annual (2003-2010) chlorophyll-a median (a) and interquartile range (b) maps for
the reporting region of Storm Bay.
17
Figure 16 Satellite-derived monthly time-series of MODIS-chlorophyll-a (a,b) and MERIS-surface
bloom expressions (c,d) for the region of Storm Bay. Seasonal cycles (a,c) are based on the 20032011 time-series (b,d). The monthly standard deviation (orange error bars) and the least-square
linear trend (thick blue line) are shown for the chlorophyll time-series (a,b).
17
Figure 17 Multi-annual (2003-2010) chlorophyll-a median (a) and interquartile range (b) maps for
the reporting region of Geographe Bay.
19
Figure 18 Satellite-derived monthly time-series of MODIS-chlorophyll-a (a,b) and MERIS-surface
bloom expressions (c,d) for the region of Geographe Bay. Seasonal cycles (a,c) are based on the
2003-2011 time-series (b,d). The monthly standard deviation (orange error bars) and the leastsquare linear trend (thick blue line) are shown for the chlorophyll time-series (a,b).
19
Figure 19 ESA-MERIS (1-km resolution) image showing several surface algal blooms (within the
red circles) off the coast of Perth on 10 March 2009. This event was identified by the time-series
shown for this study. The study region is delineated by the dash lines.
20
Figure 20 Sequence of four weekly composites of median chlorophyll for the reporting region of
Broome and the period of 1st to 31st March 2005.
21
Figure 21 Multi-annual (2003-2010) chlorophyll-a median and interquartile range maps for the
reporting region of Broome.
21
Figure 22 Satellite-derived monthly time-series of MODIS-chlorophyll-a (a,b) and MERIS-surface
bloom expressions (c,d) for the region of Broome. Seasonal cycles (a,c) are based on the 20032011 time-series (b,d). The monthly standard deviation (orange error bars) and the least-square
linear trend (thick blue line) are shown for the chlorophyll time-series (a,b).
22
LIST OF TABLES
Table 1 Geographic location and size of the study sites for the five reporting regions
2
Table 2 MERIS and MODIS sensor characteristics.
4
Table 3 Number of weekly composites available for each regional datacubes (per sensor) for the
period 01/01/2003-31/12/2010 (based on the MODIS and MERIS CLW archive).
7
Table 4 Summary statistics (in mg.m-3) from the five regional MODIS weekly median chlorophyll-a
composite datacubes
9
Table 5 Satellite products used in this study and their proxies
Australia ■ State of the Environment 2011 Supplementary information
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v
ACKNOWLEDGMENTS
This project was funded by the department of Sustainability, Environment, Water, Population and
Communities (SEWPaC), the State of the Environment (SoE). We are thankful to Dr. Trevor Ward
(Greenward Consulting, Marine Ecosystems and Biodiversity, Perth, WA) for guidance and Dr
Richard Stanford (Biotext, Yarralumla, ACT) for the graphic design of the Figure 6 in the Appendix.
We are grateful to Dr. Peter Thompson, Principal Research Scientist at CSIRO Marine Research
(CMAR), Hobart, for reviewing this report.
We acknowledge Mr. Garth Warren and Mr. Jorge Luis Peña Arancibia, CSIRO Land and Water
(CLW), Canberra, for providing support with ArcGIS and Matlab respectively. We are thankful to
Geoscience Australia for providing the bathymetric grid of Australia [Heap et al., 2002].
We acknowledge the European Space Agency (ESA) and the National Aeronautics and Space
Administration (NASA) for providing the satellite images.
Australia ■ State of the Environment 2011 Supplementary information
vi
EXECUTIVE SUMMARY
Phytoplankton forms the bottom of the marine food web and thus affects all the subsequent
trophic levels. The monitoring of phytoplankton activity, such as the timing, location and magnitude
of phytoplankton blooms, provides a sensitive indication of marine ecosystem health. Such
information is critical for coastal resource management, because phytoplankton blooms may
ultimately impact on the environment, the local economy and human health [Babin et al., 2008;
Stumpf, 2009].
The Australian continent is surrounded by three oceans and its marine waters, extending
over 16 million Km2, are amongst the largest in the world [Newton and Boshier, 2001]. The coastal
and shelf waters of Australia are very diverse in their water temperature, sun light exposure and
nutrient concentrations, the three key drivers of phytoplankton blooms. The large size and
variability of the Australian coastal and continental shelf waters make the monitoring of
phytoplankton blooms (and any water quality parameter in general) challenging. Traditional
methods of oceanographic sampling (research vessels, buoys) are costly and can only cover
sparse areas over a discrete period of time. An alternative method is the use of satellite ocean
colour remote sensing, a recognised, cost-effective and suitable technology for such a task
[McClain, 2009; Klemas, 2011]. Ocean colour remote sensing overcomes the spatial and temporal
uncertainties inherent to in situ sampling: earth observation data provides an unparalleled synoptic
view of the surface of the coastal and oceanic waters with almost daily coverage. As a result,
ecological indicators can be derived over large time and spatial scales to detect changes in
ecosystems which can be then associated to natural causes or anthropogenic pressures [Platt and
Sathyendranath, 2008].
This report aims at delivering a comprehensive summary of phytoplankton bloom
occurrences, both in-water and surface expressions, for five coastal sites around Australia based
on earth observation data of up to eight years (2003-2010). Concentrations in chlorophyll-a (CHL),
a widely-used measure of algal biomass, were estimated for each site from the NASA-MODIS
(Aqua) ocean colour sensor. Surface algal bloom expressions, likely composed of different
phytoplankton species than the in-water algal blooms, were mapped by the ESA-MERIS (Envisat)
ocean colour sensor. Time-series of satellite-derived CHL and surface bloom expressions are
presented and discussed for the five reporting regions. Results show that the phytoplankton
biomass varies in its cycle and amplitude depending on the Australian continental shelf region
considered. The key findings were as follows:

Site 1, Darwin: phytoplankton biomass peaks in January and surface phytoplankton
blooms occur in early spring.

Site 2, Burdekin, Great Barrier Reef: phytoplankton biomass peaks in summer and
surface phytoplankton blooms occur in winter/spring.

Site 3, Storm Bay, Tasmania: phytoplankton biomass peaks in spring and surface
phytoplankton blooms occur in early winter.

Site 4, Geographe Bay, Perth: phytoplankton biomass peaks in winter and surface
phytoplankton blooms occur in early autumn.

Site 5, Broome, South Kimberley: phytoplankton biomass peaks in winter and
surface phytoplankton blooms occur in summer.
This report provides a basis for future practical assessments of phytoplankton bloom events
in Australian coastal waters.
Australia ■ State of the Environment 2011 Supplementary information
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Phytoplankton dynamics in shelf waters around Australia
1.
INTRODUCTION
1.1. Aims and objectives
An algal bloom, or phytoplankton bloom, can be described as a rapid and abundant growth
of one (or sometimes more) algal species at a rate that significantly exceeds the rate of cell loss,
leading to an increase in the biomass of the species [Richardson, 1997]. While the most
representative indicator of a phytoplankton bloom is an increase in chlorophyll concentration (see
section 1.3.2.2), some algal bloom types are characterised by surface water expressions that are
better detected by other means (see section 1.3.2.3). In this report, the first type of bloom will be
described as in-water algal blooms and the second type as surface blooms, respectively.
Phytoplankton dynamics are known to fluctuate largely according to season and location [Goés et
al., 2004; Thompson et al., 2009]. The monitoring of algal blooms is a very useful measure of
ecosystem health, and is particularly relevant in the coastal zones. Coastal regions are amongst
the most productive ecosystems on the planet [Gazeau et al., 2004]: despite only representing
10% of the surface area of the global ocean, coastal waters account for 30% of the global primary
production [Wollast, 1998]. Therefore, knowledge of phytoplankton biomass, location and timing of
occurrence has become increasingly important for coastal ecosystems management and
exploitation [Beman et al., 2005; Cembella et al., 2005; Dekker et al., 2011].
There is a concern about the spatial and temporal dynamics of phytoplankton blooms
because of their potential link to climate and ecosystem change [Boyce et al., 2010; Marinov et al.,
2010]. Monitoring algal bloom events within a specific location over a long period of time (i.e.,
several years) allow for the derivation of an ecosystem baseline. This baseline can then be used to
detect, and anticipate [Wong et al., 2009; Roiha et al., 2010], changes in the system [Pauly, 1995].
In this report, satellite ocean colour remote sensing data from the National Aeronautics and Space
Administration (NASA) Moderate Resolution Imaging Spectrometer (MODIS) “Aqua” and the
European Space Agency (ESA) Medium Resolution Imaging Spectrometer (MERIS) sensors are
used to analyse the characteristics of the spatial and temporal variability of chlorophyll-a
concentrations (CHL) and algal bloom surface expressions for five reporting regions of the
Australian continental shelf. Literature on the frequency and distribution of algal blooms in
Australian waters already exists (e.g., [Furnas, 2007; Brodie et al., 2010; Hanson et al., 2010;
Thompson et al., 2010]), but with limited systematic time-series analyses of CHL and other water
quality parameters. This report partly addresses this information gap.
The five reporting regions have been selected to represent examples of Australian coastal
waters that are likely to be subject to anthropogenic influence as they are close to urbanized or
agricultural areas (Figure 1):
1. North region: Darwin
2. East region: Burdekin, Great Barrier Reef
3. South-East region: Storm Bay, Tasmania
4. South-West region: Geographe Bay, Perth
5. North-West region: Broome, South Kimberley
The analysis of these five regions was restricted to waters between the shoreline and the 100-m
isobath (or the edge of the continental shelf) (Table 1; Figure 1). The key objectives of this project
were to identify, for each region and for the reporting period 2003-2010:
(a.) The seasonal cycles and temporal trends of in-water algal blooms from satellitederived chlorophyll,
Australia ■ State of the Environment 2011 Supplementary information
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Phytoplankton dynamics in shelf waters around Australia
(b.) The seasonal cycles and surface coverage of satellite-derived surface bloom
expressions.
Figure 1 Locations of the five reporting sites (red boxes). The extent of the continental shelf waters
(pink) and the 100-m isobath (running black line) are shown.
Table 1 Geographic location and size of the study sites for the five reporting regions
Region
Longitude (E)
Latitude (S)
Length x width (km)
Climate
Burdekin
147.1 – 148.0
43.0 - 43.5
52.5 x 55.6
Tropical
Darwin
129.5 – 131.0
12.0 – 13.0
109.1 x 111.1
Tropical
Broome
121.3 - 122.3
17.3 - 18.3
106.2 x 111.1
Tropical
Perth
115.1 - 115.6
32.5 - 33.6
122.4 x 46.5
Temperate
Tasmania
147.1 – 148.0
43.0 - 43.5
72.7 x 55.7
Temperate
1.2. Relevance and contribution of this research to Australia’s state of
the environment assessment
Estuarine, coastal and oceanic ecosystems are inter-connected and may respond differently
to water quality degradation. Coastal habitats alone account for approximately one third of all
marine biological productivity, and estuarine ecosystems (e.g., Burdekin) are among the most
productive regions on the planet [Burke et al., 2001]. At the same time, those ecosystems are
increasingly exposed to rapidly increasing anthropogenic pressure and extensive exploitation of
marine resources [Creel, 2003]. Australia marine habitats host a wide diversity of animal and plant
species that are of commercial, recreational, scientific and cultural importance [Beeton et al.,
Australia ■ State of the Environment 2011 Supplementary information
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Phytoplankton dynamics in shelf waters around Australia
2006], therefore the monitoring of water quality, and algal blooms in particular, is essential for
preserving these ecosystems.
The objective of this study is to provide an initial assessment of Australian shelf waters
based on an up to 8-year-time-series of satellite-derived proxies of in-water and surface expression
of phytoplankton blooms. Monitoring the spatio-temporal distribution of phytoplankton blooms is
important because they are indicators of water quality [Boyer et al., 2009] and ecosystem health
status [Brodie et al., 2010] and possible climate change [Henson et al., 2010]. Also, phytoplankton
is intrinsically related to fish stocks [Platt et al., 2003] and therefore can have a measurable
economical impacts [Paerl, 2006]. Globally, algal blooms are responsible for almost half of the total
primary production [Field et al., 1998]. This baseline can be used for future comparisons.
1.3. Remote sensing of ocean colour
This section provides a brief background on the theory and limitations of remote sensing, as
well as the specific characteristics of the two sensors used in this study, MODIS and MERIS. A
brief description of the algorithms used for the atmospheric correction of the remotely-sensed
signal and the retrievals of in-water chlorophyll and surface algal blooms is also outlined.
1.3.1. The MODIS and MERIS ocean colour sensors
The term “ocean colour” refers to the remote measurements of the water-leaving radiance
at visible wavelengths by polar orbiting satellite sensors at an altitude of ~800 km (Figure 2; Table
2). Post-processing steps include the atmospheric and geometric correction of this signal. Then
algorithms can be applied to translate this water-leaving reflectance into estimates of water quality
parameters, principally the upper water column concentration of CHL, dissolved organic matter and
suspended sediment concentrations ([Lee et al., 1996]; see section 1.3.2). Ocean colour remote
sensing provides estimates of these water quality parameters from the near-surface, typically for
the upper 22% of the euphotic zone [Kirk, 2011].
The high spectral, temporal and spatial resolution of the MERIS and MODIS satellite
sensors (Table 2) make them suitable instruments for the monitoring of in-water and surfaceexpressed algal blooms in most ocean regions, including the continental shelf waters off Australia.
The spectral band settings of an ocean colour sensor, such as the position and number of spectral
bands, influence the detection of water quality parameters, including algal blooms in coastal waters
(Table 2). Extracting detailed and accurate information from ocean colour imagery is a more
challenging task in coastal waters in comparison to the open ocean [Dekker et al., 2001; Robinson,
2004]: coastal systems are known to be generally shallow and dynamically active, with strong
variations in sediment, organic matter and CHL concentrations depending on space and time
[Blondeau-Patissier et al., 2009]. A minimum of six wavelengths have been shown to be sufficient
to capture most of the spectral information contained in open ocean waters [Sathyendranath et al.,
1994] but for both coastal and oceanic applications, Lee and Carder [2002] reported that 15 bands,
located between 400-800 nm, would be adequate. This requirement more closely corresponds to
the design of MERIS [Bricaud et al., 1999; Doerffer et al., 1999], while MODIS was mainly
designed for open ocean applications. Details on the spectral characteristics of these two sensors
can be found at http://www.mumm.ac.be/OceanColour/Sensors/index.php
The water quality parameters derived from these two sensors can be assimilated into
coupled hydrodynamical and biogeochemical models [Cherukuru et al., 2009] and help improve the
modelling of algal assemblage responses to environmental conditions (circadian rhythms,
adaptation to changing depth and irradiance, and ecological succession), presently poorly
represented in current models [Carr et al., 2006]. The information retrieved from satellite oceancolour remote sensing is strictly limited to the cloud-free portions of the images. Therefore some
Australia ■ State of the Environment 2011 Supplementary information
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Phytoplankton dynamics in shelf waters around Australia
sites, such as Storm Bay in Tasmania (Figure 1), may have a more restricted spatial and temporal
representation due to more frequent cloud cover.
The MODIS and MERIS satellite images used in this report have a nominal pixel size of 1 km
and 1.2 km, respectively. The five sites were considered “optically deep” (Figure 2) and the effect
of possible bottom reflectance in shallow and clear water environments such as the GBR (see
section 3.2) was not quantified in this study.
Table 2 MERIS and MODIS sensor characteristics.
Sensor
(Agency)
Launch
Equator
crossing
Frequency
(days)
Pixel
resolution (m)
Number of
ocean colour
bands
Spectral
coverage
(nm)
MERIS
Feb 2002
10:00
2-3
300†, 1200
12
412 – 900
May 2002
14:30
1
250*,500*,
1000
8
412 - 14500
(ESA)
MODIS-Aqua
(NASA)
* only for specific bands; † not routinely archived by ESA
Figure 2 Artist impression of the various processes that contribute to the signal as measured by a
remote sensor in an optically shallow water (left) where the substrate has a significant effect on the
water leaving radiance at the water surface or an optically deep water (right) where there is no
measurable effect of the substrate on the water leaving radiance.
Australia ■ State of the Environment 2011 Supplementary information
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Phytoplankton dynamics in shelf waters around Australia
1.3.2. Atmospheric correction and ocean colour algorithms
1.3.2.1.
Atmospheric correction and quality flags
The NASA atmospheric correction of Gordon and Wang [1994] (see details in Patt et al.
[2003]) was used prior to the retrieval of chlorophyll concentrations from the MODIS sensor. The
atmospheric correction algorithm, from which MODIS ocean surface reflectances are computed,
was implemented through SeaDAS® 5.1. For further background information on MODIS and the
atmospheric correction, see Chapters 1 and 2 of Vermote and Vermeulen [1999].
The MERIS surface algal bloom index was computed directly from top of the atmosphere
radiance data because the surface bloom events in this study are generally characterized by
radiances that are too high to be handled by atmospherically corrected algorithms (see section
1.3.2.3 for further details). To avoid having those pixels being mostly masked by the Level 2 quality
flags, no atmospheric correction was applied to MERIS data for the estimates of surface bloom
events.
1.3.2.2.
MODIS chlorophyll algorithm used for this study
Chlorophyll is a direct proxy for phytoplankton biomass (Figure 3) and is used in this study
for the detection of in-water algal blooms. The successful use of semi-analytical bio-optical models
over band ratio algorithms to retrieve CHL, as well as other water quality parameters, has been
demonstrated several times [Schalles, 2006; Matthews, 2011; Mouw et al., 2011]. The GarverSiegel-Maritorena (GSM) [Maritorena et al., 2002] semi-analytical model was used in this study to
retrieve CHL concentrations from MODIS data for the five reporting regions. This globally
optimised model is based on a quadratic relationship between the remote sensing reflectance and
the in-water absorption and backscattering coefficients from Gordon et al. [1988]. The GSM model
was originally designed for non-coastal waters and its performance can degrade significantly over
highly absorbing or backscattering waters. This is however the case for most global algorithms.
Global ocean colour algorithms have been widely demonstrated to retrieve CHL in the open ocean
within ±35 % [Moore et al., 2009] but this accuracy lowers considerably in coastal waters. Qin et al.
[2007] compared the performance of seven MODIS global algorithms (including GSM) at retrieving
CHL in complex estuarine waters of the northeast of Australia and implemented in SeaDAS®. The
marine environments tested in Qin et al. [2007] would be typical of the Burdekin (section 3.2),
Fitzroy or Mossman-Daintree. The authors have shown that CHL was overestimated by a minimum
of 75 % for the algorithms tested but that the GSM CHL algorithm performed best. Thus, the GSM
model was chosen for this study. Further description of this model is given in Maritorena and
Siegel [2006].
Figure 3 MODIS-Aqua true-colour image (left) showing an increase in phytoplankton near Broad
Sound, Great Barrier Reef, on 4 January 2011. A spectral band stretch (right) helps discern the
increase in chlorophyll-a pigment (green patch delineated by red, dashed line). This bloom is likely to
have resulted from organic matter-rich freshwater outflows containing high concentrations of
nutrients. No chlorophyll algorithm was applied to this image.
Australia ■ State of the Environment 2011 Supplementary information
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Phytoplankton dynamics in shelf waters around Australia
1.3.2.3.
MERIS surface algal bloom index used for this study
The detection of surface algal blooms were estimated using the MERIS Maximum
Chlorophyll Index (MCI) [Gower et al., 2003]. The MCI is directly computed from three MERIS
bands in the NIR, namely 681, 705, 754 nm. The 705 nm band is typically used as the peak
response. This band is specific to the MERIS sensor and has been identified as being sensitive not
only to high concentrations of CHL (> 30 mg. L-1) [Gower et al., 2005; Dierssen et al., 2006], but
also to the “shifted red edge” produced by floating vegetation such as Sargassum [Gower et al.,
2006] and phytoplankton blooms with strong surface expressions such as Trichodesmium sp.
(Table 6.1 of [Gower and King, 2011]). The MERIS MCI was used in this study to detect and map
the surface bloom expressions of phytoplankton that have a surface signature and that commonly
occur in Australian marine waters, such as Trichodesmium sp. (Figure 4), Lyngbya majuscula
(Figure 10) or Hincksia sordida. The use of the MERIS MCI alone, however, is not sufficient to
confirm the genus.
Figure 4 Aerial photography of a surface Trichodesmium sp. bloom off Cairns on 26th October 2006.
2.
DATA AND METHODS
2.1. Data preparation and availability
The MODIS dataset used in this study spans up to 8 years from 1st January 2003 to 31st
December 2010. MODIS Level 2 quality flags were applied to each image to mask pixels
contaminated by clouds, land, sun glint, bad quality atmospheric correction, suspect radiance,
stray light or navigation failure. A larger amount of pixels was masked during winter (temperate
regions) or wet season months (tropical regions) due to more frequent cloud cover depending on
the latitude (Table 1).
Australia ■ State of the Environment 2011 Supplementary information
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Phytoplankton dynamics in shelf waters around Australia
The global distribution of CHL in the ocean is often considered log-normal [Campbell, 1995],
but this is not always the case [Banse and English, 1994]. For this study, the satellite-derived CHL
data was used in the linear space. Often showing a skewed distribution, the median CHL
concentration (in linear space) was found to be often more representative of the central tendency
of the CHL distribution than the arithmetic mean for most regions (Figure 6). For consistency, the
weekly median was selected as a statistical parameter to represent the variability of CHL for this
study.
Datacubes of weekly median MODIS CHL composites were computed for the five regions
between 2003 and 2010 and subsequently presented on a monthly basis in the time-series.
Following preliminary examination of the CHL temporal and spatial variability of each region from
these datacubes, study sites (or boxes) covering surface areas between 2,923 and 12,123 km2
(Table 1; App.-Figure 6) were positioned at locations that best described the waters of the
continental shelf for the regions considered. All land-affected pixels were masked and thus not
taken into account in the statistics (Figure 5).
Table 3 Number of weekly composites available for each regional datacubes (per sensor) for the
period 01/01/2003-31/12/2010 (based on the MODIS and MERIS CLW archive).
Region
Nweekly MODIS
Nweekly MERIS
Burdekin
343
318
Darwin
358
257*
Broome
332
132*
Perth
369
323
Tasmania
375
329*
* Incomplete dataset
The atmospheric correction (and the associated quality flags) and the presence of clouds can
severely reduce the amount of pixel available in a satellite image. To avoid computing statistics on
any weekly median satellite image composite with too few pixels within a study site, a minimum
threshold of 25 % of valid MODIS CHL pixels within each box for each weekly composite was
used.
The same box sizes and positions were used for the quantification of surface algal blooms
expressions (Figure 4) using the MERIS surface bloom index. MERIS data from the 1st and 2nd
ESA reprocessing were used for this study. Because this product is based on presence/absence of
surface algal bloom expressions, weekly composites were computed with the maximum pixel
value.
The CLW MERIS archive was incomplete and limited to only a few years (e.g., 2007-2010)
for some of the regions (Table 3). The time-series and areas covered by surface bloom
expressions were estimated based on the number of pixels with positive MERIS MCI values within
each study site and weekly composite (Table 3).
Australia ■ State of the Environment 2011 Supplementary information
7
Phytoplankton dynamics in shelf waters around Australia
Land
Outer
Shelf
Figure 5 Example of the study region of Storm Bay, Tasmania. The green pixels are those of the
continental shelf waters, the red pixels (N= 2676) delineate the study site for this region and the back
pixels are masked (land or outer shelf).
Median (0.43 mg. m-3)
Mean (0.59 mg.m-3)
Relative Frequency
Land
Continental
Masked
shelf
0.5
1.5
CHL concentrations
3.5
2.5
(mg.m-3,
bin size= 0.1)
Figure 6 Example of the relative frequency distribution of MODIS chlorophyll-derived concentrations
for the region of Tasmania (2003-2010, N=1,737,078 pixels). Inset shows a chlorophyll map
illustrating the variability for a weekly composite of the selected region.
Australia ■ State of the Environment 2011 Supplementary information
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Phytoplankton dynamics in shelf waters around Australia
2.2. Trend estimates of the chlorophyll time-series
For state of the environment assessments, trends and anomalies in phytoplankton blooms
are key information. Trends and anomalies may indicate natural variability, but may also indicate
change due to human effects on the environment. A least-square linear trend was computed for
each CHL time-series. Least-squares regression necessitates the data to be detrended [Wilks,
2006]. This statistical technique was performed on monthly, standardized satellite-derived CHL
data. The standardization of each month for each year of the time-series was computed as follows:
Standardized month (m) 
m y  m 2003,2010
 2003,2010
where (m) is a month of year (y), and ( m ,  ) are the multiannual mean and standard deviation of
the month (m).
The non-parametric Sen’s estimator [Sen, 1968] was used to compute the value and
significance of the slopes that characterise the trends of the CHL time-series. This method is
distribution free and is not affected by seasonal fluctuations or extreme values [van Belle and
Hughes, 1984]. Because the slope and its significance were not computed from the linear trend
plotted on each graph, this regression line should be interpreted as visually indicative only (i.e.,
positive or negative).
3.
RESULTS AND INTERPRETATIONS
In this section, the results obtained for the two satellite products, namely the MODIS CHL
and MERIS MCI (Table 5), are presented and discussed for each of the reporting regions.
Basic statistics are presented in Table 4 based on the weekly median CHL composites for
each region (e.g., green and red pixels of Figure 5). Because the median CHL value was found to
be a more representative summary of the CHL distribution (section 2.1), the interquartile range is
also provided as a measure of spread, a more robust measure of statistical dispersion than the
standard deviation. The southeast region of Tasmania, Storm Bay, showed the highest multiannual median CHL (0.43 mg.m-3) while the reporting region of Darwin had the most variability
(IQR=0.41 mg.m-3). The reporting region of Perth (Geographe Bay) had the lowest multiannual
CHL median (0.19 mg.m-3) and variability (IQR<0.1 mg.m-3).
Table 4 Summary statistics (in mg.m-3) from the five regional MODIS weekly median chlorophyll-a
composite datacubes
Region
Median
Interquartile Range
Burdekin
0.21
0.16
Darwin
0.40
0.41
Broome
0.23
0.20
Perth
0.19
0.09
Tasmania
0.43
0.36
The time-series and trends discussed in the next sections are based on monthly statistics
extracted from each study site for each region (App. - Figure 6). For this study, the time-series are
based on three metrics of in-water and surface algal blooms (Table 5). Figures showing the
frequency of pixel available for each region are also provided in the Appendix.
Australia ■ State of the Environment 2011 Supplementary information
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Phytoplankton dynamics in shelf waters around Australia
Table 5 Satellite products used in this study and their proxies
Variable
Derived from satellite product
Index for
CHL (monthly)
MODIS GSM CHL
in-water phytoplankton biomass (mean, std. deviation)
N counts (monthly)
MERIS MCI
Counts of the number of surface phytoplankton bloom
expressions (median)
Surface area covered
(monthly)
MERIS MCI
Portion (in %) of the study site covered by surface
phytoplankton blooms
3.1. North region: Darwin
Darwin is the most densely populated area of the Northern Territory with the largest
concentration of commerce and industry. Estuarine and coastal waters, such as those of the
Darwin Harbour, are often complex transition zones between continental and open, deep ocean
waters. The waters of Darwin Harbour, a macrotidal ecosystem, show high CHL concentrations
contrasting with the remaining of the coastline for this region (Figure 7, note that the frequency of
MODIS pixels available for this reporting region was not very high, i.e. <60 %; App. - Figure 1).
CHL “hot spots” (median >1.80 mg.m-3 in Figure 7) are typically located near river mouths, but it is
also likely that the GSM algorithm overestimated chlorophyll concentrations for those turbid waters.
An overall IQR of ~0.80 mg.m-3 characterised the coastal waters of this reporting region.
In-water phytoplankton biomass peaks in January (monthly average >1 mg.m-3) and
progressively declines the rest of the year, before increasing again at the start of spring (Figure 8).
The 2003-2010 CHL time-series for this region displayed the strongest decline in CHL in
comparison to the other sites (-0.029 mg.m-3.year-1, p<0.05).
Based on the (limited number of) MERIS satellite images for this region (Table 3), the annual
cycle of surface phytoplankton bloom expressions peaks between September and October over a
small spatial extent (<8% of the study site; Figure 8). These algal surface expressions (Figure 9)
are likely to be of blooms of cyanobacteria Lyngbya majuscula and Trichodesmium sp. [Drewry et
al., 2010]. Large blooms of L. majuscula can hamper the recreational use of nearby beaches and
lead to beach closure for public safety (Figure 10). Both these cyanobacteria genera can impact
the local economy [Preston et al., 1998; Negri et al., 2004].
Schroeder et al. [2009] analysed the in-water light attenuation for this region using 20022008 MODIS data. The authors reported a distinct seasonal cycle of light attenuation due to high
turbidity, with a sharp gradient between nearshore and offshore waters. This gradient can also be
seen in the multi-annual CHL map (Figure 7), with the offshore waters showing little variability as
compared to the coastal waters. Schroeder et al. [2009] reported an increase of up to 50% in
turbidity for the nearshore coastal waters of this region during the wet season (with reference to the
annual mean) in comparison to the dry season months. This increase in turbidity for the coastal
waters of Darwin during the wet season months is due to sediment and nutrient-rich runoff
following monsoonal rainfall. In contrast, the authors found that a general increase in turbidity is
observed offshore during the dry season months due to trade winds-induced mixing.
Australia ■ State of the Environment 2011 Supplementary information
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Phytoplankton dynamics in shelf waters around Australia
(b)
Satellite-derived Chlorophyll-a (mg.m-3 )
(a)
Interquartile range
Median
Figure 7 Multi-annual (2003-2010) chlorophyll-a median (a) and interquartile range (b) maps for the
reporting region of Darwin. Masked pixels appear in black, land pixels are in grey. This same legend
applies to all the multi-annual maps of chlorophyll presented in this report.
(a)
(b)
(c)
(d)
Figure 8 Satellite-derived monthly time-series of MODIS chlorophyll-a (a,b) and MERIS surface bloom
expressions (c,d) for the region of Darwin. Seasonal cycles (a,c) are based on the 2003-2011 timeseries (b,d). The monthly standard deviation (orange error bars) and the least-square linear trend
(thick blue line) are shown for the chlorophyll time-series (a,b).
Australia ■ State of the Environment 2011 Supplementary information
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Phytoplankton dynamics in shelf waters around Australia
(a)
(b)
+1.0
-1.0
MCI (mW.(m-2.sr-1.nm-1))
(c)
Figure 9 ESA-MERIS (1-km resolution) image showing a surface bloom East of Cobourg on 25
September 2011. From top to bottom: true colour image (a); Spectral band stretch of NIR bands
showing the patterns of the surface bloom (b); MERIS surface algal bloom index (c). This event was
not captured by the time-series for this study because it occurred after 2010.
Figure 10 A bloom of Lyngbya majuscula at Vesteys Beach, Darwin in May–June 2010. (Photo/credit:
Julia Fortune, Aquatic Health Unit, Darwin Harbour).
Australia ■ State of the Environment 2011 Supplementary information
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Phytoplankton dynamics in shelf waters around Australia
3.2. East region: Burdekin, Great Barrier Reef
In the Great Barrier Reef waters, phytoplankton growth is primarily limited by the availability
of nutrients [Furnas, 2003] and may show sudden increases as a response to river flood plumes
that contain large amounts of sediments, nutrients and other dissolved and particulate organic
matter [Devlin and Brodie, 2005]. The Burdekin has one of the largest catchments (130,000 km2) of
the Great Barrier Reef and, together with the Fitzroy, discharges up to 70% of this organic and
inorganic material into the lagoon [Raupach et al., 2006]. The coastal waters off the Burdekin are
characterised by a multi-annual CHL mean of 0.36±0.15 mg.m-3, typical of the Great Barrier Reef
waters [Wooldridge et al., 2006]. There is a nearshore-offshore longitudinal gradient in CHL
decreasing from >2.0 to ~0.2 mg.m-3 with an IRQ of ~1.0 mg.m-3 (Figure 11). In comparison to the
previous reporting region of Darwin (section 3.1), the variability in CHL for the Burdekin is higher
and based on a higher frequency of pixels (80 %; App. - Figure 2). Also, the median and IQR
gradients have similar extent for the coastal waters of this region. Although not shown in this study,
the Burdekin has a pronounced seasonal increase in CHL (up to 50% difference) between the dry
and wet season months (see [Blondeau-Patissier et al., 2011]).
The seasonal cycle of in-water phytoplankton biomass peaks during the early wet season
months, between February and March (Figure 12) but can last until May as a result of large
nutrient inputs following the wet season river discharges. The multiannual linear trend computed
shows a decline in phytoplankton biomass over the years (-0.013 mg.m-3.year-1, p<0.1), although
high phytoplankton biomass occurred during the 2007 and 2008 wet seasons. This biological
response is likely resulting from the high nutrient concentrations available in the system following
the large flood events for those years. A cross-shelf gradient in phytoplankton biomass develops
from January onwards [Blondeau-Patissier, 2011].
Surface algal bloom expressions occur predominantly between June and October covering
large areas (>30% of the study site; Figure 12). Figure 13 shows the development of a surface
bloom event on a MERIS sequence of MCI weekly maximum composites over three months in
2008. A large (> 400 km in length) surface algal bloom (identifiable from positive MCI values) can
be seen offshore parallel to the coast on 1st July 2008. This event is not mapped for the following
fortnight, possibly due to environmental conditions. It occurs once more at a similar location on
22nd July, then seems to get closer to shore and to decrease progressively until the 22nd of
September 2008. It is also possible that this sequence reported on three or four independent
surface bloom events. These surface algal blooms are likely to be of Trichodesmium sp. for this
region (Figure 4; [McKinna et al., 2011]), although blooms of Hincksia sordida have also been
reported for the same periods in the southern Great Barrier Reef [Dekker et al., 2011].
Figure 14 displays the results obtained if the same technique was applied to the entire
Great Barrier Reef. Surface bloom expressions are occurring all year round in the Great Barrier
Reef though an increasing frequency of positive MCI counts occurs mostly in its southern region
(near the Fitzroy estuary mouth) between the months of August and November-December. This
cycle differs from that of CHL (Figure 12), thus confirming that the annual cycle of surface algal
blooms as derived from MCI characterises blooms that do not necessarily coincide with in-water
raised chlorophyll concentrations.
Please refer to Brando et al. [2011a] for further information on the water quality in the Great
Barrier Reef derived from a decade of MODIS data and a regionally parameterized algorithm
[Brando et al., 2011b].
Australia ■ State of the Environment 2011 Supplementary information
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Phytoplankton dynamics in shelf waters around Australia
(b)
Satellite-derived Chlorophyll-a (mg.m-3 )
(a)
Interquartile range
Median
Figure 11 Multi-annual (2003-2010) chlorophyll-a median (a) and interquartile range (b) maps for the
reporting region of the Burdekin.
(a)
(b)
(c)
(d)
Figure 12 Satellite-derived monthly time-series of MODIS chlorophyll-a (a,b) and MERIS surface
bloom expressions (c,d) for the region of Burdekin. Seasonal cycles (a,c) are based on the 2003-2011
time-series (b,d). The monthly standard deviation (orange error bars) and the least-square linear
trend (thick blue line) are shown for the chlorophyll time-series (a,b).
Australia ■ State of the Environment 2011 Supplementary information
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Phytoplankton dynamics in shelf waters around Australia
Figure 13 Sequence of MERIS weekly composite of surface algal bloom expressions for the Burdekin
region from 1st July to 22 September 2008. The outer shelf is masked.
Figure 14 Monthly frequency counts of pixels with positive MERIS MCI for a period spanning 20032010 for the whole Great Barrier Reef. The wet season months are shown between brackets (from
[Blondeau-Patissier, 2011]).
Australia ■ State of the Environment 2011 Supplementary information
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Phytoplankton dynamics in shelf waters around Australia
3.3. Southeast region: Storm Bay, Tasmania
For the south-eastern Tasmanian region of Storm Bay, satellite ocean colour data were
affected by cloudy conditions. Based on a 3-year climatology of MODIS-Terra L2 cloud-top phase,
Morrison et al. [2011] (and references therein) reported that this region was affected by cloud cover
>80% for both summer and winter months. As a result, the frequency of available pixels for the
coastal waters of this region was < 20% (App. - Figure 3). The coastal waters around Tasmania
are also highly dominated by coloured dissolved organic matter, which makes the retrieval of
chlorophyll from remote sensing difficult [Schroeder et al., 2008]. A source of coloured dissolved
organic matter for Storm Bay is the Derwent river estuary.
Within the boundaries of these limitations, the CHL variability was found to be the highest
(median= 0.43±0.36 mg.m-3) of all reporting regions. The onshore-offshore CHL gradient is also
smoother than that of other regions (e.g., Burdekin), revealing a progressive decrease in CHL from
the coast to the open ocean (Figure 15) with a matching deviation magnitude (IQR~1.2 for coastal
waters). The reporting region of Storm Bay is thus very dynamic in terms of biological activity and
further information on its environmental conditions from in situ nutrient and phytoplankton data can
be found in Crawford et al. [2011].
The cycle of phytoplankton biomass for this CHL rich region (Table 4) is typical of temperate
waters in the southern hemisphere. CHL peaks in September-October, a few months after the
winter low (Figure 16). This is the so-called “spring bloom” of temperate waters of the southern
hemisphere. In accordance with the findings of Thompson et al. [2009] who reported a decrease in
growth rate of the spring bloom near Maria Island by ~8% per year, a decline (-0.006 mg.m-3.year1
) in phytoplankton biomass has occurred over the years for the selected site of this study, though
the slope was not found to be statistically significant (p > 0.1). Thompson et al. [2009] also
reported an increase in surface water temperature and a decrease in dissolved silicate
concentrations over the years on the East Australian coast (App. - Figure 7) and specifically at
Maria Island, which might affect the phytoplankton growth rate for this region. The authors
concluded that the main driver of in-water phytoplankton biomass seasonal dynamics at Maria
Island was water column mixing from both water temperature and wind. The water column mixing
would favour a nutrient enrichment of the surface waters approximately three months before the
spring boom, with additional influence from the Eastern Australian Current (EAC) and subantarctic
waters.
Surface phytoplankton bloom expressions occur predominantly in June and were found to
cover up to ~10% of the surface of the study site. These blooms are likely to occur in the shallower
region of the study site during the winter months.
Australia ■ State of the Environment 2011 Supplementary information
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Phytoplankton dynamics in shelf waters around Australia
(b)
Satellite-derived Chlorophyll-a (mg.m-3 )
(a)
Interquartile range
Median
Figure 15 Multi-annual (2003-2010) chlorophyll-a median (a) and interquartile range (b) maps for the
reporting region of Storm Bay.
(a)
(b)
(c)
(d)
Figure 16 Satellite-derived monthly time-series of MODIS chlorophyll-a (a,b) and MERIS surface
bloom expressions (c,d) for the region of Storm Bay. Seasonal cycles (a,c) are based on the 20032011 time-series (b,d). The monthly standard deviation (orange error bars) and the least-square linear
trend (thick blue line) are shown for the chlorophyll time-series (a,b).
Australia ■ State of the Environment 2011 Supplementary information
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Phytoplankton dynamics in shelf waters around Australia
3.4. Southwest region: Geographe Bay, Perth
Thompson et al. [2009; 2010] merged long-term in situ data (phytoplankton pigments,
nutrients, salinity…) with ocean colour remote sensing (NASA-SeaWiFS) to assess changes
occurring over space and time at specific sites located on the East and West Australian coast. The
implication of the Leeuwin, and Capes, currents (see App. - Figure 7) in the high winter productivity
has been widely investigated [Hanson et al., 2005; Thompson et al., 2009; Thompson et al., 2010].
For the site of Rottnest Island, Thompson et al. [2009] (and references therein) reported that the
warm, silicate-rich Leeuwin current reduced water column stratification and was significantly
associated with shelf-scale phytoplankton bloom. The authors however also found that the major
phytoplankton biomass anomaly was in late summer, when the Leeuwin current is relatively weak,
positively correlated with the Southern Oscillation Index. This late summer bloom was not
observed in the present study, but our results were in accordance with Figure 3 of Thompson et al.
[2010] that shows lower chlorophyll concentrations on the West coast compared to the East coast
of Australia.
The coastal waters are along a thin band following the coastline (median ~0.6 mg.m-3). The
majority of nutrients for the phytoplankton of the continental shelf waters off Perth are likely to be
sourced mainly from river runoff and winter storms. Thus the majority of the increase in
phytoplankton biomass tends to be localised close to the coast (Figure 17) and occurs during
winter months (Figure 18a, b). This is clearly seen in the CHL time-series with a seasonal cycle
that usually peaks in June (Figure 18a).
This reporting region was well represented in terms of pixel availability (App. - Figure 4). The
average percentage difference for one week apart satellite-derived median CHL values for the
region of Perth is 25%, the lowest difference for all the regions considered. The multi-annual CHL
median for this region was also the lowest in comparison to the other regions (0.19±0.09 mg m-3).
A typically lower CHL concentration on the West coast is in accordance with the findings of
Thompson et al. [2010]. The authors reported that the difference in phytoplankton biomass (and
composition) between the East and West Australian coasts were due to the seasonality differences
in the two major boundary currents, namely the EAC on the East coast and the Leeuwin current on
the West coast, which influence water column stability and nutrient availability. The West coast is
characterised by a more stable water column. In comparison to the four other sites presented in
this study, Geographe Bay was found to be the only region with a positive increase in CHL over the
years (+0.008 mg.m-3.year-1), though the slope was not found statistically significant (p> 0.1).
Bloom surface expressions were found to predominantly occur in March, sometimes covering
large portions of the study site (>30% of pixels within box) (Figure 18d). A spectrally-band
stretched MERIS true-colour image displayed in Figure 19 shows several surface algal blooms
occurring off the coast of Western Australia in March 2009. These events are typical of those
captured by the time-series of surface expressions for this region (Figure 18c,d) .
Australia ■ State of the Environment 2011 Supplementary information
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Phytoplankton dynamics in shelf waters around Australia
(b)
Satellite-derived Chlorophyll-a (mg.m-3 )
(a)
Interquartile range
Median
Figure 17 Multi-annual (2003-2010) chlorophyll-a median (a) and interquartile range (b) maps for the
reporting region of Geographe Bay.
(a)
(b)
(c)
(d)
Figure 18 Satellite-derived monthly time-series of MODIS chlorophyll-a (a,b) and MERIS surface
bloom expressions (c,d) for the region of Geographe Bay. Seasonal cycles (a,c) are based on the
2003-2011 time-series (b,d). The monthly standard deviation (orange error bars) and the least-square
linear trend (thick blue line) are shown for the chlorophyll time-series (a,b).
Australia ■ State of the Environment 2011 Supplementary information
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Phytoplankton dynamics in shelf waters around Australia
Perth
Glint
Figure 19 ESA-MERIS (1-km resolution) image showing several surface algal blooms (within the red
circles) off the coast of Perth on 10 March 2009. This event was identified by the time-series shown
for this study. The study region is delineated by the dash lines.
3.5. Northwest region: South Kimberley, Broome
The northwest shelf is amongst the most significant marine environments of Australia
[Condie and Andrewartha, 2008] with high biological productivity [Hallegraeff and Jeffrey, 1984;
Tranter and Leech, 1987] influenced by large tides [Holloway, 1983], a monsoonal climate and
cyclones [McKinnon et al., 2003]. These physical processes favourably influence the presence of
nutrients, mostly in the nearshore waters, due to the generally shallow bathymetry (~50 m). The
shallowness of the area and strong mixing can also increase the concentration in suspended solids
and possibly either limit the phytoplankton response (e.g., [Condie et al., 2009]) or create false
satellite-derived CHL of high concentrations (e.g., [Marinelli et al., 2008] – note that global standard
ocean algorithm are used for Marinelli et al., whereas a semi-analytical model is applied in this
study). This environment is comparable to that of the North Shelf, with the reporting region of
Darwin (section 3.1).
For this region, the median CHL concentration was of 0.23±0.20 mg.m-3, based on a high
frequency of pixels (App. - Figure 5) (Table 4). The coastal water band for this region typically has
a value of ~0.80 mg.m-3 with little variability (IQR<0.15 mg.m-3) (Figure 21).
The CHL of the continental shelf waters off Broome rises steadily during the tropical summer
(October to March) (Figure 22). It is during this season that 90% of the rainfall occurs from
cyclones and thunderstorms. The phytoplankton biomass typically peaks in May, likely from this
increased nutrient supply, and then decreases until September. Based on the results presented
here, the continental waters off Broome show a significant decline in CHL (-0.010 mg.m-3.year-1,
p<0.05).
Australia ■ State of the Environment 2011 Supplementary information
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Phytoplankton dynamics in shelf waters around Australia
Figure 20 illustrates an increase in CHL productivity in the nearshore waters between the 1st
and 31st March 2005. Likely due to the high turbidity (and therefore algorithm failure), most of King
Sound is masked out in this sequence. A large in-water algal bloom is developing off the coast
near Mitchell Plateau and offshore further east for the week of the 15 th of March. This algal bloom
recedes towards the end of the month and an enhanced phytoplankton biomass is seen off
Broome.
Satellite-derived Chlorophyll-a (mg.m-3 )
The cycle of surface algal bloom expressions displays two peaks, in March and September.
These algal surface expressions seem to cover a smaller area in size (<5%). However, the limited
occurrence mapped by MCI might be related to the limited MERIS satellite image dataset for this
reporting region (Table 3), as for Darwin (section 3.1).
01 March 2005
08 March 2005
15 March 2005
22 March 2005
Figure 20 Sequence of four weekly composites of median chlorophyll for the reporting region of
Broome and the period of 1st to 31st March 2005.
(b)
Satellite-derived Chlorophyll-a (mg.m-3 )
(a)
Median
Interquartile range
Figure 21 Multi-annual (2003-2010) chlorophyll-a median and interquartile range maps for the
reporting region of Broome.
Australia ■ State of the Environment 2011 Supplementary information
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Phytoplankton dynamics in shelf waters around Australia
(a)
(b)
(c)
(d)
Figure 22 Satellite-derived monthly time-series of MODIS chlorophyll-a (a,b) and MERIS surface
bloom expressions (c,d) for the region of Broome. Seasonal cycles (a,c) are based on the 2003-2011
time-series (b,d). The monthly standard deviation (orange error bars) and the least-square linear
trend (thick blue line) are shown for the chlorophyll time-series (a,b).
4.
SUMMARY AND KEY FINDINGS
Algal blooms are naturally occurring ecological responses to changes in light availability,
temperature, zooplankton grazing and nutrients. Algal blooms are also transient phenomena that
occur at different scales of time and intensity depending on the latitude, the distance from shore
and the season. During the past decade, coastal water conditions of the world’s oceans have
become a significant environmental concern. The ever-increasing anthropogenic pressures on
coastal environments have multiple impacts, some of which are yet to be quantified. Thus the
monitoring of algal blooms is important because it is a key indicator of a coastal ecosystem health.
In this study, we have used MODIS-Aqua GSM CHL and MERIS MCI, proxies for in-water
phytoplankton biomass estimates and algal surface expressions respectively, to report on the
temporal trends in phytoplankton dynamics in shelf waters around Australia. Five reporting coastal
regions located at different latitudes and in three different oceans were chosen for this research.
Australia ■ State of the Environment 2011 Supplementary information
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Phytoplankton dynamics in shelf waters around Australia
Significant differences were found between the reporting regions (see App. - Table 1). The
key findings are summarized below for each region:
5.

North region: Darwin is a tropical site with a complex oceanography. Phytoplankton
biomass peaks in January in high concentrations in comparison to the other regions
and showed the strongest decline over the years. Surface algal blooms peaked
around September over a limited portion (<10% of surface covered) of the study
site.

East region: The Burdekin system is mostly influenced by wet season river flood
events. Phytoplankton biomass peaks towards the end of the wet season (around
March). A decline in phytoplankton biomass was found over the years 2003-2010.
Surface algal blooms, likely of Trichodesmium sp., occur mostly between August
and November and can cover large portions of the study site (30% of surface
covered).

South-East region: Storm Bay is a temperate site with an onshore-offshore gradient
in CHL. Phytoplankton biomass peaks in September and was found to decline over
the years. A small amount of surface algal expression events was found to occur
mostly in June.

South-West region: Geographe Bay is a temperate site which displayed the
smallest variability in CHL of all the regions. Phytoplankton biomass peaks in
winter. This is the only site with an apparent increasing trend in phytoplankton
biomass (though not statistically significant). Surface algal bloom events tend to
occur in early autumn over sometimes large areas (30% of surface covered).

North-West region: South Kimberley-Broome is a tropical site with a complex
oceanography and a shallow bathymetry that influences the availability of nutrients.
Phytoplankton biomass peaks in May following the winter months and was found to
decline over the years (though not statistically significant). Surface algal bloom
events occur both in March and September over small areas (<5% of surface
covered).
RECOMMENDATIONS AND FUTURE WORK
The performance of the MODIS GSM CHL algorithm used for this study is known to be
reasonable for the lagoonal waters of the Great Barrier Reef, including the Burdekin. However this
algorithm has not been tested for the waters of Perth, Broome, Darwin or Tasmania. Thus it is
possible that some CHL retrievals might have been overestimated due to the presence of
suspended sediment or organic matter in those waters. It is therefore recommended to validate
further this CHL algorithm against in situ CHL concentrations, preferably from high-pressure liquid
chromatography (HPLC), sampled in the waters of this study to assess the suitability of the GSM
algorithm.
The new directions currently driving ocean colour remote sensing around the world, and
especially in Australia, include more accurate retrievals of water quality parameters (CHL) in
optically complex coastal waters with regionally-tuned algorithms [Brando et al., 2011b] and
atmospheric corrections [Schroeder et al., 2008] from MODIS. These new methods should be used
in future work and parameterized for each of the water type of this study in order to obtain an
increased reliability in the products derived, which will in turn improve our interpretations of the
results.
MERIS MCI was used as a surface algal bloom index for this study. This algorithm is likely
to have performed well for most regions, though it has so far been validated solely for the waters of
the Great Barrier Reef (Burdekin) [Blondeau-Patissier, 2011]. Thus further validation of this
Australia ■ State of the Environment 2011 Supplementary information
23
Phytoplankton dynamics in shelf waters around Australia
algorithm against field observations is recommended. The CLW MERIS archive used in this study
was composed of MERIS images from the 2nd ESA reprocessing with, at times, limited coverage
over some of the reporting regions. Therefore a fully complete and up-to-date (3rd reprocessing)
MERIS satellite image dataset for the entire Australian continent and for the period 2002-present
should be obtained. CSIRO is in the process of gathering this updated MERIS archive for the
whole Australian continent. An in depth analysis of the occurrence of algal surface expressions for
all sites should be repeated with this updated dataset. An improved mapping of algal bloom
expressions from MERIS for all the regions may then be obtained.
The ability to derive information on the phytoplankton type or size class [Brewin et al.,
2011a; Brewin et al., 2011b] from ocean colour remote sensing measurements is now possible.
Although mostly limited to the open ocean, this technique should be developed over coastal waters
in the near future. This will enhance the information on the marine ecosystems that will be
complementary to the products presented in this report.
For future state of the environment reporting these analyses can be repeated with an ever
increasing time-series of satellite data. Other variables such as suspended sediment and coloured
dissolved organic matter concentration, transparency, turbidity and vertical attenuation of light
could be also be derived nationally from earth observation data in future.
Australia ■ State of the Environment 2011 Supplementary information
24
Phytoplankton dynamics in shelf waters around Australia
Frequency of pixel availability (% )
APPENDIX
Frequency of pixel availability (% )
App. - Figure 1 Availability of valid MODIS pixels (in %) for the period 2003-2010 based on the MODIS
chlorophyll dataset for the reporting region of Darwin.
App. - Figure 2 Availability of valid MODIS pixels (in %) for the period 2003-2010 based on the MODIS
chlorophyll dataset for the reporting region of the Burdekin.
Australia ■ State of the Environment 2011 Supplementary information
25
Frequency of pixel availability (% )
Phytoplankton dynamics in shelf waters around Australia
Frequency of pixel availability (% )
App. - Figure 3 Availability of valid MODIS pixels (in %) for the period 2003-2010 based on the MODIS
chlorophyll dataset for the reporting region of Storm Bay. Note change on scale (70% is the
maximum) for this figure only.
App. - Figure 4 Availability of valid MODIS pixels (in %) for the period 2003-2010 based on the MODIS
chlorophyll dataset for the reporting region of Geographe Bay.
Australia ■ State of the Environment 2011 Supplementary information
26
Frequency of pixel availability (% )
Phytoplankton dynamics in shelf waters around Australia
App. - Figure 5 Availability of valid MODIS pixels (in %) for the period 2003-2010 based on the MODIS
chlorophyll dataset for the reporting region of Broome.
App. - Figure 6 Locations and size of the five reporting sites (Courtesy of Dr Richard Stanford, CSIRO
Forestry Precinct, ACT).
Australia ■ State of the Environment 2011 Supplementary information
27
Phytoplankton dynamics in shelf waters around Australia
App. - Figure 7 Schematic map showing the major surface ocean currents off the Australian and
Tasmanian coasts.
App. - Table 1 Summary of the key results obtained for each reporting region
Region
Size of the study site
(Km2)
In-water
phytoplankton
biomass
Sen’s slopes of
CHL trends
(mg.m-3.year-1)
Surface
phytoplankton
expression
Burdekin
2,923
February-March
-0.013*
June-October
Darwin
12,123
January
-0.029**
September-October
Broome
11,800
May
-0.010**
March, September
Perth
5,694
June
0.008
March-April
Tasmania
4,049
September-October
-0.006
June
* significant at p<0.1 ; ** significant at p<0.05
Australia ■ State of the Environment 2011 Supplementary information
28
Phytoplankton dynamics in shelf waters around Australia
GLOSSARY
CHL
Chlorophyll-a (mg. m-3)
CLW
CSIRO Land and Water
EAC
Eastern Australian Current
ESA
European Space Agency
GSM
Garver-Siegel-Maritorena (ocean colour model)
HPLC
High-Pressure Liquid Chromatography
IQR
InterQuartile Range
MCI
MERIS Maximum Chlorophyll Index
MERIS
Medium Resolution Imaging Spectrometer
MODIS
Moderate Resolution Imaging Spectrometer
NASA
National Aeronautics and Space Administration
NIR
Near-Infra red
SeaWiFS
Sea-viewing Wide Field-of-view Sensor
Australia ■ State of the Environment 2011 Supplementary information
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Phytoplankton dynamics in shelf waters around Australia
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