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UNDERSTANDING SEA SURFACE HEIGHT ANOMALY
(SSHA) VARIABILITY ACROSS POTENTIAL FISHING ZONES
G. Bhakta Thukaram , M.Tech
ABSTRACT
About 7 million people in India are dependent on
fishing activity for their livelihood. As per census by
CMFRI (Central Marine Fisheries Research Institute)
in 2010, there are 1.2 million (12 lac) fishermen
directly involved in marine capture fishery in India.
A reliable and timely short-term forecast on the fish
aggregation zones helps them. Features such as
oceanic fronts, meandering patterns, eddies, rings and
up-welling areas are identified from the satellite
images, transferred to navigational charts and
provided as PFZ advisories by ESSO-INCOIS
(Hyderabad) as a no-cost service on operational basis
since year 2001. Such advisories help the fisherman
to easily find out high productive fishing areas that
reduces the search-time and saves costly fuel. These
remote-sensing products have limitation of data gaps
during cloudy days and observed features are valid
for only one day. In order to prepare PFZ advisory
even on cloudy days and with validity of 4-7 days,
Sea surface Height anomaly (SSHa) is useful in
determining productive areas. However, such data
has become available in relatively recent times only.
Hence, it is important to understand SSH variability
over time and space that lead to productive areas.
This dissertation work aims to understand such
dynamics over the PFZ areas that are demarcated
during year 2007-2014. Expected outcome are
understanding thresholds that help emerge productive
zones in the ocean, their spatio-temporal distribution,
evolution and dynamics as well as inter-seasonal and
inter-annual variability of the same
I. Introduction
Fishing is one of man's oldest activities, and the sea
has always been one of the main sources of human
food. Although about 15% of the total production of
fish and shell fish now come from aquaculture
(Rhodes, 1993; Tacon, 1994), the world continues to
depend mainly on fishing to obtain seafood.
Considering that fishermen have remained ``hunters'',
they have constantly tried to find out how to predict
where marine animals are available and catchable.
This search for the knowledge of the distribution and
behavior of fishes was and continues to be a necessity
for commercial fishermen. The need of exploiting
marine resources with lower effective costs has
created a strong need of saving both fuel and time in
fishing activities. Another important issue related
with the burning of fossil fuels is the resulting
increase of atmospheric carbon dioxide and hence
global warming.
Time spent in seeking fish schools and
potential fishing areas is the main source of fuel
consumption in many fisheries (e.g., purse-seine
fisheries, pole and line fisheries, etc.), so it is of great
importance to predict precisely aggregation zones of
fish in space and time.
Aim
The main aim of this dissertation work is to
understand SSHa variability over PFZ demarcated
during year 2007-2014 for different seasons and parts
of Indian EEZ.
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Objectives
The current project has been carried out with
the following objectives
To generate database of SSHa along PFZ
using historical data (2007-2014).
To understand spatial-temporal variability.
To determine thresholds useful for PFZ
advisory generation
II. FISHING IN INDIAN SCENARIO
India has 8,118 kilometres of marine coastline,
3,827 fishing villages, and 1,914 traditional fish
landing centres. India's fresh water resources consist
of 195,210 kilometres of rivers and canals,
2.9 million hectares of minor and major reservoirs,
2.4 million hectares of ponds and lakes, and about
0.8 million hectares of flood plain wetlands and water
bodies. As of 2010, the marine and freshwater
resources offered a combined sustainable catch
fishing potential of over 4 million metric tonnes of
fish. In addition, India's water and natural resources
offer a tenfold growth potential in aquaculture (farm
fishing) from 2010 harvest levels of 3.9 million
metric tonnes of fish, if India were to adopt fishing
knowledge, regulatory reforms,
and sustainability policies adopted by China over the
last two decades.
Despite rapid growth in total fish production, a
fish farmers’ average annual production in India is
only 2 tonnes per person, compared to 172 tonnes
in Norway, 72 tonnes in Chile, and 6 tonnes per
fisherman in China. Higher productivity, knowledge
transfer for sustainable fishing, continued growth in
fish production with increase in fish exports have the
potential for increasing the living standards of Indian
fishermen. Considering that fishermen have remained
``hunters'', they have constantly tried to find out how
to predict where marine animals are available and
catch-able.. Another important issue related with the
burning of fossil fuels is the resulting increase of
atmospheric carbon dioxide and hence global
warming. Time spent in seeking fish schools and
potential fishing areas is the main source of fuel
consumption in many fisheries so it is of great
importance to predict precisely commercial fishable
aggregations of fish in space and time..
Food Chain
Fish are known to react to changes in their
surrounding environment by migrating to areas where
more favorable conditions exist. Availability of food
is another important factor which effects the
occurrence, abundance and migration of fish.
Figure 1.3 below shows the food chain of the fish in
which Phytoplankton is the first in their food chain.
The phytoplankton concentration (represented as
chlorophyll concentration) is measured with the
ocean color sensors onboard the satellites.
Primary producers
Phytoplankton is an index of Primary productivity
Phytoplankton
Secondary consumers
Primary consumers
Food Chain
Higher trophic levele.g. Fishes
Basic food chain under Sea waters
Physical processes & Biogeochemistry
Productivity of oceans depends on nutrient
availability in sun-lit upper waters known as,
euphotic zone (Behrenfeld and Falkowski, 1997; Platt
and Sathyendranath, 1988). Oceanographic
phenomena such as upwelling help contribute to
much of this requirement, byentraining nutrients
above mixed layer depth and in turn, allowing
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phytoplankton to sustain food-web with the help of
photosynthesis. Stronger the upwelling, deeper the
upper mixed layer of oceanic water column. This
allows colder nutrient rich waters to surface and
resultantly lowering Sea Surface Temperature (SST).
Thus, SST provides handy signature in detecting
upwelling zones with the help of remote-sensing
data. Productive waters may initially attract only
planktivorous fishes but eventually, also to bigger
fishes which prey upon them.
Fish Finding-Remote Sensing Approach
First major rise in annual marine fish
production occurred in 1960s with introduction to
mechanization of fleet (Planning Commission Study.
Ramakrishnan Korakandy, 1994). However, fishery
remained mostly individual affair and till date it has
not taken any significant corporate shape. This had
inhibited the fleet from venturing away from the
shore in many parts of the country till early 1990s
when primary studies started towards locating
resources with the help of satellite. Fishery research
organizations with the help of Indian Space Research
Organization (ISRO) laboratories took up primary
studies with encouraging results (Dwivedi et. al.
2005, ShaileshNayak, et. al., 2003, Solanki, et. al.
2001a, 2001b, 2003, 2005, 2008). Such efforts were
utilizing satellites by US and European countries
such as NOAA, MetOp and MODIS, SeaWiFS for
retrieval of SST and Chlorophyll, respectively.
Monitoring of SST and chlorophyll in space and time
by in situ measurement is time-consuming and
expensive. The link between satellite-derived sea
surface temperature (SST) and chlorophyll with fish
aggregation was established.In India, the efforts of
oceanographers, remote sensing specialists and
fishery scientists resulted in a unique service called
the Potential Fishing Zone (PFZ) advisory.
Remote Sensing
Fish Aggregation PFZ
OCM / MODISNOAA AVHRR /
METOP
Upwelling Boundary
Food
SST
Phytoplankton Nutrients
Ocean Color
Processes
Fish Finding -The Remote Sensing Approach
Fish Finding-Remote Sensing Approach
The PFZ forecast is issued on daily basis by INCOIS,
except during the fishing ban period and on cloudy
days. The validity of such forecasts is one day. This
is the only short-term marine fishery forecast
available in the country for the benefit of small
mechanized / motorized sector fishermen (about
100,000 vessels). The PFZ advisory has matured into
an operational application of satellite remote sensing,
which provides timely and reliable advisories to
fishermen. The effort is part of the Common
Minimum Programme(CMP), lead by the
Government of India.
INCOIS Ground Station
•Chlorophyll, TSM(Oceansat-2)
•SST(NOAA-18/19, MetOp-A/B)
JPL -PO.DAAC
•SST(OSTIA GHRSST)
INCOIS-OSF Lab
•Wind/Current Vectors•MLD, D20•High Wind-wave
INCOIS-PFZ Lab
•Potential Fishing Zones•Bathymetry•Landing Centers•Coastal Districts•EEZ boundaries•Fishing-Restriction Zones
PFZ Maps
Indian Marine Fishery Advisory System
•Landing Center (LC)•Direction (with angle)•Distance from LC•Bathymetry•Position (Lat/Long)
PFZ Text
Inputs & Processing Products Information Dissemination
For
Tun
a-P
FZ
Ad
viso
ries
INCOIS-ChloroGIN
Chlorophyll, Kd490[Daily & 3d roll](MODIS-Aqua)
Direct
Fax
Phone
Web-GIS
Mass-comm.
Web-text
EDBs
DEAL
University projects
SMS/Mobile-Apps
NGOs
IVRS/Helpline
Community Radio
TV/Radio
Newspapers
ENVI, ArcMap,ERDAS Imagine
Indian Marine Fishery Advisory System-INCOIS
Utilizing the remotely sensed data available
from various satellites, ESSO-Indian National Centre
for Ocean Information Services (INCOIS), provides
these advisories to the fishermen on a daily basis with
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specific references to 586 fish landing centers along
the Indian coast. This operational service is rendered
by ESSO-INCOIS throughout the year except during
the periods of Marine Fishing ban imposed by
Government of India and adverse sea state conditions
such as Cyclones, High Waves, Tsunamis, etc
Operational hurdles
Limitation in satellite data availability can
impact a service such as PFZ. This becomes more
important post-ISM (Indian Summer Monsoon)
period – approximately, September and October -
when government imposed fishing ban is over and
fishing season starts. Coincidently, that is the same
time when fishermen expect higher fish-catch, but
receding monsoon often cloud cover limits satellite
data coverage. In this regard, gap free data is much
important. Also as fishermen are to be provided a
realistic forecast, it has to be based on the ocean
property that triggers productivity.
SSHa Vs SST & Ocean Color (Chlorophyll)
products
Altimeter gives the information on Sea
Surface Height (SSH). Ocean being a dynamic
medium, processes result in anomalies in the SSH.
Hence, SSHa can be used as a proxy for detection of
many of the phenomena such as upwelling or eddy.
Such phenomena scale on 10-100km spatially and
from days to weeks temporally. Even though along
track product of sea surface height has very narrow
swath, models/tools have been successfully
developed to optimally interpolate/merge satellite
data with in-situ observations. On the other hand,
ocean color sensor operate in visual range of
spectrum and thus, does not have night view facility.
AVHRR sensors that provide sea surface temperature
does have night view facility. However, both of these
sensor performances get hampered with presence or
cloud. In other words, these sensors do not have see-
through capabilities and for a tropical country such as
India - surrounded by two vast basins and peninsula
that experience two distinct monsoon seasons - these
prove to be a great setback in seamless service
delivery. Moreover, physical processes lead to the
biogeochemical and biological response in the region
- in that order. With sea surface height information,
forecasting can be possible in short-time period as
fishery advisories have need. In this way, from gap-
free and advanced information aspects, anomaly
maps of Sea Surface Height can address both of these
Study Area
Indian Exclusive Economic Zone (EEZ)
The above figure shows the North Indian
Ocean with Arabian Sea and Bay of Bengal. The
dashed lines demarcate INDIA’s Exclusive economic
Zone (EEZ), which covers about 2 million sq.km,
which is roughly 60% of India’s land area. India’s
coastline including islands is about 7000km long.
The living and non living resources in this
,which measures about two-third of the landmass of
the country, are exclusive to India, so also the trading
and transport facilities navigated through this area.
Moreover several million people living along the
coastline are directly influenced by Oceanography of
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the Exclusive Economic Zone (EEZ), various
environmental hazards and related social issues..
INCOIS issues the PFZ Advisories for
entire Indian EEZ region under 12 sectors viz.
Gujarat, Maharashtra, Karnataka & Goa, Kerala,
South Tamilnadu, North Tamilnadu, South Andhra
Pradesh, North Andhra Pradesh, Orissa & West
Bengal, Andaman Islands, Nicobar Islands and
Lakshadweep Islands for Bay of Bengal and Arabian
Seas.
Hence the present study area covers part of
the Northern Indian Ocean with main focus on Bay of
Bengal and Arabian Seas.
The Exclusive Economic Zone(EEZ) if India that
falls within the grid box with coordinates 6.5°S to
23.5°N & 65°E to 93°E, based on these Lat/Lon
values Sea Surface Height variability across Potential
Fishing Zone is studied.
Gujarat --1600 km
Maharashtra --840km
Goa --300 km
Karnataka --400 km
Kerala --580 km
Tamilnadu --1076 km
AndhraPradesh -- 973.7 km
Orissa --560 km
West Bengal --950 km
III. METHODOLOGY
The methodology involved in understanding
Sea Surface Height variability across Potential
Fishing Zones is as follows.
1. Converted polyline shape file of PFZ advisories
into an equidistant point shape file using Hawth Tool
in ArcGIS
2. SSHa data in netCDF format is downloaded from
CCAR. and is processed and stored in image file
format for extracting the values along the PFZ lines.
3. Extracted SSHa data corresponding to the point
shape file using extract value to point tool.
4. The SSH data was studied for the regions spatially
divided as quarters namely North West, North East,
South East, South West, Andaman and Nicobar and
All India.
5. Yearly analysis was done for extracted SSH data
and Frequency of occurrence of SSH data and
Percentage of occurrence of SSH data within the
range of -30 to 30(cm) were found.
Methodology-Flow chart
IV. RESULTS AND DISCUSSION
Sea Surface Height anomaly (SSHa) values
were extracted by overlaying PFZ data on SSHa data
are used for plotting graphs of percent frequency
occurrence of SSHa (in cm) for defined range of
SSHa values. The results are plotted for values across
whole of Indian EEZ as well as for the five quarters
namely North West, North East, South west, South
East and Andaman and Nicobar islands Except A&N,
the quarters were created by partitioning EEZ along
15 ºN (North-South partitioning) and 77.5 ºE (East-
West partitioning). This was done to address and
understand sub-regional oceanography.
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Month-wise SSHa climatology along PFZ within
Indian EEZ
Occurrence of Potential Fishing Zones was found to
be very coherent with the SSHa. For data within
whole Indian EEZ aggregated, more than 90% of the
PFZs were confined within the waters with SSHa
values between ±10 cm (Fig 5.1). This indicates that
biological productivity is strongly driven by
geophysical processes in the ocean. The processes
that may contribute to higher productivity include
mesoscale eddies and geostrophic currents.
Geostrophic currents are known to be having strong
correlation to the peripheral regions of an eddy or
similar circulation and the SSHa in the region span
on the either side of the neutral SSHa (i.e. 0 cm). The
distribution for all months was observed to be a
Gaussian curve across the neutral SSHa. The curve
was found to have some deformation for monsoonal
months. This is due to the river runoff and mixing
induced by monsoonal winds which contributes to the
productivity but does not affect to SSHa.
Month-wise SSHa climatology along PFZ within
North-west quarter of Indian EEZ.
As oceanic process and factors that drive to
higher productivity differ in different regions along
the Indian coastline, it is important to study these
sub-regions separately. For this, aforementioned
quarters were created and data belonging to them
were analyzed separately. Northwest region of Indian
EEZ is having a vast continental shelf adjacent to
Gujarat and Maharashtra coastline. Apart from short
monsoon, it receives riverine inputs in the gulfs,
mainly from major rivers such as Narmada and Tapti.
Coastal upwelling is well studied for this region and
spring bloom is a known phenomena. Upwelling
induced productivity contributed to a minor but
distinct peak in observations towards negative SSHa
for the March-April months. Similar trend was
observed from September-December (post-monsoon)
period as well. Some minor peaks towards positive
SSHa were observed for January month are believed
to be due to coastal productivity
Month-wise SSHa climatology along PFZ within
South West quarter of Indian EEZ
Similar trends were observed for southern
counterpart of the Arabian Sea along the coast of
Karnataka and Kerala and in Lakshadweep Sea.
However, due to witnessing relatively stronger
summer as well as winter monsoons – and resultant
accelerated upwelling as well as river runoff – the
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region shown separate peaks toward negative as well
as positive SSHa, respectively
Month-wise SSHa climatology along PFZ within
South East quarter of Indian EEZ
Whereas, parts of the EEZ off southern
states in the Bay of Bengal, shown different patterns
due to varies dynamics of the monsoon as well as due
to different oceanography at the basin level (Fig 5.4).
The overall base of the distribution was observed to
be relatively wider in compare to their counterpart
from the Arabian Sea. Additionally, independent
peaks were observed for months of August and
November, indicating to the inter-monsoon forcing
driven productivity.
Month-wise SSHa climatology along PFZ within
North Eastern quarter of Indian EEZ
Northeast quarter of the Indian EEZ is one
of the regions influenced most by river runoffs. These
include major rivers of south Asia such as Ganges,
Brahmaputra and Irrawaddy. Even though the later is
on the eastern portion of North Bay of Bengal, the
basin level circulation result in lateral advection of
freshwater contributed by Irrawaddy towards west.
This freshwater pool then expands southward where
mesoscale eddies along the EEZ boundary does not
allow this pool to shift towards the center of the basin
and hence, resultant flow along the coastline. Due to
combination of factors such as river induced
productivity, salinity-driven front generation and
mesoscale eddies, SSHa signature in this part of the
Indian EEZ can be observed as a complex dynamic
across the months. The influence from the Indian
Summer monsoon can be clearly seen during June-
August months. Andaman and Nicobar island group
is at the epicenter of the east equatorial Indian Ocean
dynamics, being influenced by northern branch of
Kelvinwave propagation. This region is also first to
experience southwest monsoon. These factors
together contribute complex scenario for May-
October months whereas for rest of the months SSHa
signature along PFZs were observed to be Gaussian
distribution.
Month-wise SSHa climatology along PFZ within
Andaman & Nicobar quarter of Indian EEZ
As PFZs are generated with the use of SST
fronts with high chlorophyll concentrations, their
dynamics are inherently related with met-ocean
variability, including teleconnection to the processes
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such as ENSO. Thus it is important to understand
what parts of Indian Seas are showing cohesion of
SSHa with dynamics of PFZ regardless to these
teleconnection being enforced. For regions and
months in which oceanic processes such as upwelling
dominate, it is expected that SSHa should show better
correlation to PFZs at intraseasonal to interannual
scales. For regions where meteorological factors such
as rainfall – by mechanisms of generating salinity33
driven fronts or by triggering river runoffs – enhance
productivity and thus, PFZs; it is expected that SSHa
may not show good correlation as these processes do
not influence Sea Surface Height. To study this, La
Niña and El Niño years during the study-period was
focused for further analysis as follows:
Indian summer monsoon that originates in the eastern
equatorial Indian Ocean is long known to have been
influenced by processes in western Pacific Ocean.
Collectively the oscillation in Sea Surface
Temperature in that area (and resultant variability in
sea-level atmospheric pressure) is known as ENSO
(El Niño Southern Oscillation). Over the time,
methodology of calculating magnitude of this
oscillation has evolved and now it is being calculated
as difference of temperature (normalized over the
unit area) within eastern box of Niño 3.4 region to
that of its western counterpart. In this way warmer
waters in the western box will yield negativevalue of
the index, known as Oceanic Niño Index . This is
generally associated with above average Indian
summer monsoon and known as La Niña year. The
opposite will affect the monsoon adversely and
known as El Niño year. Magnitude will indicate how
strong or weak either of these events are. Current
year of 2018 is the year witnessing a strong El Niño
event.
Niño 3.4 region in the pacific currently being used
to determine ONI (Oceanic Niño Index) to
represent El Niño or La Niña conditions, known to
affect weather of India (especially, monsoonal
winds)
As ENSO episodes affect the weather of our
region, it becomes necessary to understand if they
affect to the oceanography and resultant productivity
in our waters. Within the period for which the PFZ
datasets are analyzed, there have been two La Niña
and one El Niño events. Month-wiseSSHadistribution
along PFZs was studied in order to understand
interannual variability for each of the aforementioned
quarters in the Indian EEZ.
Long term annual variability of ONI and time-
period studied (encircled) in this work.
Subset of ONI for which PFZ data was available
and analysed in the present study
Studying the variability at scale from
intraseasonal to interannual (year on year, with
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reference to ENSO teleconnection) can help us
understand if a parameter such as SSHa is robust
enough to become a major input for generation of
fishery advisories. Thus, this needs to be studied at
EEZ quarter scale.
At the intraseasonal scale northwest quarter
shown prominent coherence for most of the months.
However, whenever productivity is influenced by
spring bloom (March-April) or monsoonal mixing
(August-September), this coherence was weaker. At
the interanual scale also same observations were
derived. This indicates that factors such as intensity
of a bloom ormonsoonal mixing that does not affect
SSHa signature may lead to marginally complex
variability in determination of PFZs with SSHa in
this region.
Month-wise variability of SSHa (percent
frequency distribution) along PFZs for years
2011-2018 within the North-West quarter of the
Indian EEZ.
In comparison, southwest quarter (southeast
Arabian Sea) shown better consistency in SSHa
trends along PFZs at the intraseasonal scale.
However, being influenced by the monsoon
significantly, interannual scale variability was higher.
This can be factored by setting up a boundary
conditions in the deterministic model.
Month-wise variability of SSHa (percent
frequency distribution) along PFZs for years
2011-2018 within the South-West quarter of the
Indian EEZ
Among all the quarters of Indian EEZ,
southeast quarter (southwest Bay of Bengal, off
Tamil Nadu and southern Andhra Pradesh) shown
prominent Gaussian distribution of SSHa along PFZ
at both intraseasonal (month-wise) as well as at
interannual (year on year) scale with lower
variability. Whereas its northern counterpart
(northeast quarter, mainly off Odisha and West
Bengal) shown maximum variability across the
months in any year as well as across the year during
study period.
Month-wise variability of SSHa (percent
frequency distribution) along PFZs for years
2007-2014 within the South-East quarter of the
Indian EEZ
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Month-wise variability of SSHa (percent
frequency distribution) along PFZs for years
2011-2018 within the North-East quarter of the
Indian EEZ.
As discussed above, this can be explained
with freshwater influence that generates fronts based
on salinity instead of processes such as upwelling
(which can be resolved in SSHa). Andaman and
Nicobar islands, studied as a separate quarter, shown
overall cohesive Gaussian distribution for November-
April months. For rest of the months (May-October)
the data points were scantly available due to
persistent cloud cover (an inherent limitation in
present PFZ advisory generation).
Month-wise variability of SSHa (percent
frequency distribution) along PFZs for years
2011-2018 within the Andaman &Nicobar quarter
of the Indian EEZ.
V. CONCLUSION
Based on the above study the following conclusions
can be made as follows:
1. Occurrence of Potential Fishing Zones were found
to be very coherent with the SSHa. For data within
whole Indian EEZ aggregated, more than 90% of the
PFZs were confined within the waters with SSHa
values between ±10 cm.
2. The influence from the Indian Summer monsoon
can be clearly seen during June-August months.
Andaman and Nicobar island group is at the epicentre
of the east equatorial Indian Ocean dynamics, being
influenced by northern branch of Kelvin wave
propagation. This region is also first to experience
southwest monsoon. These factors together
contribute complex scenario for May-October months
whereas for rest of the months SSHa signature along
PFZs were observed to be Gaussian distribution.
3. As the data we are getting is a gap-free, it is well
enough to use SSHa data for plotting Potential
Fishing Zones of high Productive regions along EEZ
which are exclusive for India
References:
1. Achari. T.R. and Thankappan 1987.
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2. Annual Report 2011-12 of National Agricultural
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3. Anukul B, Penchan, L, Natinee, S, Ritthirong P,
Sayan, P. and Tetsuo, Y. 2010. Upwelling induced by
meso-scale cyclonic eddies in the Andaman Sea,
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4. Behrenfeld, M. J., and P. G. Falkowski (1997), A
consumer’s guide to phytoplankton primary
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Websites:
1. INCOIS PFZ Portal:
http://www.incois.gov.in/MarineFisheries/PfzAdvisor
y
2. NASA Ocean Color Portal: http://oceancolor.gsfc.nasa.gov 3. GEBCO Project portal: http://www.gebco.net
4. Aviso Altimetry Portal: http://www.aviso.altimetry.fr/en/home.html 5. CCAR (Colorado University) Global Historical 6.Gridded SSH Data Viewer: http://eddy.colorado.edu/ccar/ssh/hist_global_grid_viewer
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