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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.

International Journal of Research

Volume 7, Issue XI, November/2018

ISSN NO:2236-6124

Page No:432

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



ISSN NO:2236-6124

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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

Email

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



ISSN NO:2236-6124

Page No:434

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



ISSN NO:2236-6124

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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.



ISSN NO:2236-6124

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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.


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


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



ISSN NO:2236-6124

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region shown separate peaks toward negative as well

as positive SSHa, respectively


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.


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.


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



ISSN NO:2236-6124

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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



ISSN NO:2236-6124

Page No:439

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.



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.



2007-2014 within the South-East quarter of the

Indian EEZ



ISSN NO:2236-6124

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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).



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.

Maldevelopment of fishery: a case study in Kerala

state, India. Symposium on the Exploitation and

Management of Marine Fishery Resources in South

East Asia held in conjunction withthe twenty second

session of the IPFC, Australia, RARA/Report: 1987 /

10 182 – 195

2. Annual Report 2011-12 of National Agricultural

Innovation Project (NAIP), Indian Council of

Agricultural Research (ICAR) pp. 105.

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,

Coastal Marine Science 34(1): 68-73



ISSN NO:2236-6124

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4. Behrenfeld, M. J., and P. G. Falkowski (1997), A

consumer’s guide to phytoplankton primary

productivity models, Limnol. Oceanogr., 42(7), 1479

– 1491.

5. Choudhury, S.B., Jena, B., Rao, M.V., Rao. K.H.,

Somvanshi, V.S., Gulati, D.K., Sahu, S.K. 2007.

Validation of Integrated potential fishing zone (IPFZ)

forecast using satellite based chlorophyll and sea

surface temeprature along the east coast of India.

International Journal of Remote Sensing, 28(12), pp.

2683-2693.

6. Das, S., Madhu, V. R., Sreejith, P. T. and

Meenakumari, B. (2010) Validation of potential

fishing zones along Saurashtra coast, Gujarat. In:

Coastal Fishery Resources of India: Conservation and

Sustainable Utilization (Meenakumari, B.,

Boopendranath, M. R., Edwin, L., Sankar, T. V.,

Gopal, N. and Ninan, G., Eds), pp 360-369, SOFTI,

Cochin

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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