ASIA/OCEANIA REPORT

Patterning limnological characteristics of the Chilika lagoon(India) using a self-organizing map

Kwang-Seuk Jeong Æ Dong-Kyun Kim Æ Ajit Pattnaik ÆKrupasindhu Bhatta Æ Bishnu Bhandari Æ Gea-Jae Joo

Received: 23 October 2006 / Accepted: 26 October 2007 / Published online: 13 August 2008

� The Japanese Society of Limnology 2008

Abstract In this study, a self-organizing map (SOM) was

utilized to classify habitats in the Chilika lagoon located in

India, the largest lagoon ecosystem in Asia (maximum

length, 64.3 km; mean width, 20.1 km). The lagoon was

marginally eutrophic (nitrate, 0.25 ± 0.22 mg L-1; ortho-

phosphate, 0.26 ± 0.22 mg L-1; n = 1,980, respectively)

for six years (1999–2004), and it used to be warm, shallow,

turbid and predominantly brackish. The SOM model suc-

cessfully identified the changing patterns of limnology in the

lagoon using the monthly limnological dataset from 30 study

sites (July 1999–December 2004). Comparative re-sectoring

evaluation of current monitoring sites was accomplished

based on the outcome of the modeling. The new site

clustering that emerged from the model was similar to

conventional ones, and several sites were reorganized.

Water physicochemistry was affected by freshwater inflow

during monsoon and the new lagoon mouth constructed in

September 2000, which resulted in variations in site char-

acteristics in terms of limnology. The results of this study

may provide information on the limnological patterns in

Chilika lagoon, and they leave room for further study into

functional changes in the lagoon ecology with respect to

changes in climatic factor, freshwater flow and lagoon

morphology.

Keywords Chilika lagoon � Water quality �Habitat clustering � Self-organizing map (SOM) �Limnological patterning

Introduction

Wetlands are unique in terms of the spatial heterogeneity

of their environments, the physical and chemical properties

of their substrates and communities, and the large variation

in functional ecological processes in their active com-

partments (Whitaker and Matvienko 1998). Wetland

ecosystems cover between 7 and 8 million km2 of land

globally, consisting of numerous types such as bogs, fens,

marshes, swamps, riparian wetlands and lagoons (Mitsch

and Gosselink 1993). They are extremely productive

landscape components that sustain a high biodiversity

(Maltby and Turner 1983; Matthews and Fung 1987;

Mitsch et al. 1994). In conjunction with biodiversity

research, there have been numerous studies on the devel-

opment of wetland classification systems, geographical

distributions and their losses, flood control, conservation

and restoration, and structural and functional characteris-

tics of wetlands (Cowardin et al. 1979; Robinson 1986;

Hey and Philippi 1995; Castella and Speight 1996;

DeAngelis et al. 1997; Metzker and Mitsch 1997; Findlay

and Zheng 1999; Ozesmi and Ozesmi 1999; Buzzelli et al.

2000; Wang and Mitsch 2000; Getzner 2002; Zacharias

et al. 2005; Verhoeven et al. 2006; Hallwood 2007).

Lagoon systems, which are easily observed in coastal

areas, have environmental features which result in a high

variability in ecological dynamics (Mitsch and Gosselink

1993). They usually have dynamic limnological variations,

and provide crucial systems for migratory birds and bio-

diversity (Hattori and Mae 2001). Lagoons also support the

K.-S. Jeong � D.-K. Kim � B. Bhandari (&) � G.-J. Joo

Department of Biology, Pusan National University,

Jang-Jeon Dong, Gum-Jeong Gu, Busan 609-735, South Korea

e-mail: bishnu@pusan.ac.kr

A. Pattnaik � K. Bhatta

Chilika Development Authority, Bhubaneswar, India

123

Limnology (2008) 9:231–242

DOI 10.1007/s10201-008-0243-7

basic needs of human society (e.g., fish production, culti-

vation, etc.) and also possess biological functions and

properties (Mohapatra et al. 2007a). These are highly

threatened ecosystems, many of which have relatively

small volumes of water and very restricted connections

with the sea (Panigrahi et al. 2007). Proper management of

the lagoon ecosystem is often critical to the sustainability

of the biodiversity and livelihoods associated with the

lagoon (Choi et al. 1998). Identification of key factors

governing the ecological dynamics of a lagoon are strongly

required to establish the basis for the ecological research

needed to undertake the efficient management and biodi-

versity conservation of lagoon ecosystems. Environmental

patterning from accumulated data is thought to be one of

the solutions to this problem.

Patterning ecosystem dynamics is a fundamental require-

ment when establishing management plans, understanding

ecosystem properties and communicating interdisciplinarity

in marine-coastal as well as freshwater ecosystems (Mumby

and Harborne 1999; Rabeni et al. 2002). Pattern analysis

should be able to represent both the planners’ intention for the

ecosystem (Stevens and Connolly 2004) and ecological inter-

or intra-habitat interactions. The associations between

structures and functions of the ecosystem are also very

complex (see Fielding 1999) and therefore it is necessary to

utilize ecologically suitable methods to discover patterns in

these ecosystems.

The Chilika lagoon, located in Eastern India, is a

wetland of international importance (i.e., Ramsar site; as

designated on 1 October 1981), supporting a large degree

of biodiversity and the livelihoods of 200,000 local

inhabitants. The lagoon has shown environmental and

anthropogenic impacts over the last several decades, such

as environmental degradation, siltation, changes in

salinity gradients, extensive growth of invasive species,

depletion of fishery resources, shifting of the lagoon

inlet, choking of the lagoon-sea channel, etc. (Authority

2002). After this lagoon was included in the Montreux

Record on 16 June 1998 by the Ramsar Bureau due to

changes in its ecological characteristics, many restoration

projects were carried out in the area. In these efforts,

diverse parameters were monitored at numerous moni-

toring stations and the lagoon was entirely managed

along with defined sectors by the Chilika Development

Authority (CDA) (Authority 2002). Also, in the last

decade, various research programs have been conducted

regarding morphological changes, water quality changes

and anthropogenic impacts, fisheries and resource man-

agement, and sediment management in the lagoon

(Nayak et al. 2004; Nayak and Behera 2004; Rath and

Adhikary 2004; Sekhar 2004; Kannan et al. 2005; Rath

and Adhikary 2005; Tripati and Vora 2005; Panda et al.

2006; Mohapatra et al. 2007a; Mohapatra et al. 2007b;

Panigrahi et al. 2007; Sekhar 2007).

Fig. 1 Map of the Chilika lagoon. Numbers indicate the study sites. a Map of India, b the Chilika lagoon and its drainage area, c map of the

Chilika lagoon

232 Limnology (2008) 9:231–242

123

Most studies from the Chilika lagoon were based on

conventional zoning (Fig. 1), and this must be reconsidered

because of continuous changes in the lagoon’s morphology

and limnology (see Pattnaik 2002). Furthermore, efficient

and effective monitoring should be considered in order to

maximize the long-term research at the lagoon. In order to

accomplish this, it is necessary to discover ecological

pattern dynamics in the lagoon. Accumulated data for a

long period will provide necessary information for the

work, and patterns derived from the data will help to

overcome monitoring, research, and management limits

caused by the lack of information.

Recently, highly effective methods of ecological pattern

analysis have been designed, such as artificial neural net-

works (ANNs), genetic algorithms, and fuzzy logic (see

Chon et al. 1996). ANN has been successfully applied to a

variety of ecosystems to develop or analyze ecological

criteria using biological as well as environmental param-

eters (Lek et al. 1996; Jeong et al. 2003; e.g., Dedecker

et al. 2004). There are numerous branched algorithms that

are intended for specific purposes. For example, the feed-

forward network and its relatives are good for developing

predictive models (Joy and Death 2004; Kasabov 2004; Liu

et al. 2004; Ryan et al. 2004; Almasri and Kaluarachchi

2005; Chang et al. 2005; Taskaya-Temizel and Casey

2005; Valdes and Bonham-Carterb 2006; Curry 2007),

while a self-organizing map (SOM) system is an especially

powerful patterning method for understanding complex

ecosystem dynamics (Chon et al. 2001; Park et al. 2003a,

2004, 2006; Joo and Jeong 2005; Grenier et al. 2006; Lee

and Scholz 2006; Lee et al. 2006; Astel et al. 2007).

This study is an attempt to apply the SOM system to the

Chilika lagoon environment. Three major objectives were

established in this study: (1) to detect seasonal variations in

water quality (mainly to investigate the relationship with

monsoon climate), (2) to delineate the lagoon into different

ecological sectors in accordance with current limnological

characteristics, and (3) to explore changes in the salinity

gradient due to the opening of a new mouth to the sea. The

SOM algorithm analyzed a large set of data (for a period of

six years and from 30 study sites) on various limnological

parameters for the Chilika lagoon. The results of this study

will not only encourage the application of SOM models to

complex environmental data but will also provide infor-

mation on the lagoon’s environmental dynamics, which is

necessary for systematic ecological research studies.

Description of the study site

The Chilika lagoon is the largest lagoon along the east

coast of India, situated between latitudes N 19� 280 and N

19� 540, and longitudes E 85� 050 and E 85� 380 (Pattnaik

2002) (Fig. 1). It covers the Puri, Khurda and Ganjam

districts of the State of Orissa and is separated from the

Bay of Bengal by a sand bar whose width varies from

100 m to 1.5 km. A 32 km long narrow outer channel used

to connect the main lagoon to the Bay of Bengal near the

village of Arakhakuda.

The extent of the lagoon varies between 906 and

1,165 km2 during summer and monsoon, respectively. The

lagoon is pear-shaped, with a linear maximum length of

64.3 km and an average width of 20.1 km; the mean widths

during summer and monsoon are 14.08 and 18.10 km,

respectively. The vegetation-free area of the lagoon is

726 km2, while the area covered with macrophytes is

179 km2 (varies seasonally). The lagoon is estuarine, with

a unique assemblage of marine, and brackish and fresh-

water water ecosystems. Over one million migratory

waterfowl, shorebirds and resident birds winter here, and

the lagoon also supports many endangered species and is

also home to a number of endemic species of flora and

fauna. Therefore, it is a biodiversity hot spot and a wetland

of international importance.

The CDA, based on the outcomes of numerical model

studies, took the decision to restore the lagoon. The sea inlet

of the lagoon was located 32 km away from the outer

channel. On September 2000, the CDA carried out a major

hydrological intervention by opening a new mouth (100 m

width, 2.5 m below the lowest lagoon water level) between

sites 27 and 28 (Fig. 1; it is recommended that readers visit

http://www.chilika.com for more information on the new

mouth). After opening the new mouth, the distance between

the lagoon and outlet was reduced to 18 km. This caused a

large change in salinity. A high salinity level could be

observed near the outer channel and the connection between

the lagoon and channel (site 25). However, the opening of

the new mouth facilitated a spread in the salinity to sites 19,

20, 22, and 23 (Pattnaik 2002). The overall salinity also

increased after the construction work. This may lead to

further ecological changes in the Chilika lagoon.

As previously mentioned in the ‘‘Introduction,’’ the lagoon

was included in the Montreux Record in 1998. The Ramsar

Bureau, based on their assessment of initiatives by the CDA

to restore and maintain the ecological integrity of the lagoon,

removed the lagoon from the Montreux Record on 11

November 2002; the Montreux Record is available at the

website (http://www.ramsar.org/key_montreux_record.htm).

Materials and methods

Selection of sampling station

Under the Hydrobiological Monitoring Programme, the

entire lagoon was taken into consideration by the Chilika

Limnology (2008) 9:231–242 233

123

Development Authority (CDA). The Chilika lagoon was

divided into four ecological sectors and the hydrophysico-

chemical parameters were monitored monthly at 30

sampling stations (eight stations in the southern sector,

seven stations in the central sector, ten stations in the

northern sector, and five stations in the outer channel sectors;

see Fig. 1). These stations were fixed using GPS (geographic

positioning system), which had an error of ±10 m.

A series of limnological variables were monitored for

the surface and bottom layers of the lagoon water. Sam-

pling and field study were conducted for a week (the 18th–

25th days) every month. The monitoring was conducted

using a mechanized site-specific boat equipped with safety

measures and coordinated by the CDA. Monthly monitor-

ing was confined to hydrophysicochemical parameters

such as air temperature, water temperature, depth, Secchi

transparency, specific conductivity, pH, and alkalinity,

salinity, dissolved oxygen, nitrate, and orthophosphate.

These variables were monitored for six years from 1999 to

2004.

Data collection on environmental parameters

Air temperature was recorded with an ordinary thermo-

meter with ±0.1% error at all sampling stations. Surface

and bottom water temperatures were also recorded (with a

salinity conductivity temperature meter). The water trans-

parency was measured using a Secchi disc consisting of a

brass plate of 20 cm diameter with four alternate black and

white quadrants painted on the upper surface. Water depth

was recorded using a sounding chain. The water samples

from the top and the bottom of the water column were

collected from each station and taken to the laboratory in

order to measure the total suspended solids (TSS). The

coarse and medium silt particles were sieved by 75- and

212-micron sieves. The finer particles were filtrated with a

vacuum pump filtration unit and Whatman filter paper No.

42. The bottom water was collected by Ruttner sampler and

Niskin samplers from KC Denmark (Silkeborg, Denmark).

Right after collection, some samples were kept separately

in plastic and glass bottles. The samples were preserved in

5% chloroform, 4N sulfuric acid in glass bottles and deep

frozen (Golterman and Clymo 1971).

Water pH was measured using a Grip D field pH meter,

while the specific conductivity of water was measured

using a digital conductivity meter. Total alkalinity was

analyzed titrimetrically with phenophathelin and methyl

orange indicators (Association 1992). The salinity of water

was measured by both the agentometric method of Harvey

(Barnes 1959) and a Systronics SCT meter. In the agen-

tometric method it was titrated against silver nitrate using

potassium chromate as indicator. DO was measured by

modified Winkler’s titrimetric method (Strickland and

Parsons 1972) and electronically by ELICO water analyzer

with a DO probe. Water nitrate was estimated by a spec-

trophotometry method using phenol disulfonic acid and

ammonia as color developer. Orthophosphate was esti-

mated using the ascorbic acid method incorporating a

double beam spectrophotometer.

Self-organizing map modeling with limnological data

A self-organizing map is an algorithm capable of reducing

data dimensionality (Kohonen 1984). A Kohonen network

is a competitive network system where neurons in

Euclidean map space compete with each other. To pattern

as well as cluster the study sites, a two-dimensional

Kohonen network was adopted.

In this study, a Kohonen network was constructed with

artificial neurons (processing elements) connected to input

neurons (Fig. 2). The input for the network were the data

on the limnological variables i, xi identified during the

study period. Every node, j, of the output layer was con-

nected to each node, i, in the input layer. A hexagonal array

of neurons was selected. The weight vector, w(t), repre-

sented the connection between the input and output layers.

As training preceded, each weight value, wij(t), was adap-

tively changed at each iteration t. In the initial stage, w(t)

was randomly and uniformly distributed in the network

Var. iVar. 2 Var. nVar. 1

- - - -- - - -- - - -- - - -- - - -- - - -

Input variables(water quality parameters)

Limnological data

- - - -

Fig. 2 An illustration of the SOM model utilized in this study

234 Limnology (2008) 9:231–242

123

architecture. As input signals entered the network, each

neuron computed the summed distance between the weight

and input.

The neuron with the maximum response among the

given input data was selected as the winning neuron, whose

weight vector had the minimum distance to the input

vector. The winning neuron and its neighborhood can be

learned by changing the weights so as to reduce the dis-

tance between the weight and the input vector. Further

detailed references can be found in (Kohonen 1984, 1998).

SOM training was achieved using a series of limno-

logical data examined at the Chilika lagoon. A total of

1980 data samples (July 1999–December 2004) from 30

study sites were gathered for each variable. There are no

strict rules regarding the number of output neurons that

should be obtained (Park et al. 2007), and so we trained the

SOM with different map sizes in order to choose a suitable

model. A batch learning algorithm was adapted in this

study. After training, the data were clustered according to

the calculated U-matrix, which provides dissimilarity of

data samples on the map plane. The major focus of clus-

tering was how the monitoring stations were organized

into clusters on the basis of (1) different discrete cli-

mate conditions (monsoon and non-monsoon season) and

(2) pre- and post-construction of the new mouth (outlet).

To develop the SOM model, Matlab 6.1 and the SOM

coding solution based on the SOM Toolbox for Matlab

(Alhoniemi et al. 2000) were used.

Results

Limnological characteristics of the Chilika lagoon

Table 1 summarizes the limnological characteristics of the

Chilika lagoon during the study period. Among the vari-

ables, physical (depth, Secchi transparency) and salinity-

related properties (salinity, conductivity, and alkalinity)

had the largest variations. Limited variations for each

parameter were observed between the surface and bottom

layers due to the shallow depth. Relatively large seasonal

variations were observed in the air and water temperatures,

and the other variables retained strong spatial heterogene-

ity. From the water quality changes, it was determined that

the lagoon was slightly eutrophicated and that the water

quality was largely determined by salinity changes.

SOM clustering of the lagoon water physicochemistry

The best SOM model consisted of 30 output neurons

arranged in six rows and five columns, and training results

were satisfactory (quantization error, 0.50; topographic

error, 0.01) (Fig. 3). The U-matrix generated from the

trained model provided four clusters. Map characteristics

could be determined by comparing the changing pattern of

input variables on the map (Fig. 4). Salinity-conductivity

variations were the major cause of clustering (higher

salinity was found for lower neurons), and a gradient of

Table 1 Limnological

characteristics of the Chilika

lagoon during the study period

(n = 1,980 for each variable)

Letters in parentheses shown

after the variable name indicate

whether the variable was

detected at the surface (S) or the

bottom (B)

Variables Mean Standard deviation Standard error Min. Max.

Air temperature (�C) 28.7 2.8 0.1 17 37

Depth (cm) 174.4 96.4 2.1 28 682

Transparency (cm) 76.5 46.9 1.0 5 289

Water temperature (S) (�C) 28.5 2.8 0.1 19 35.5

Water temperature (B) (�C) 28.3 2.7 0.1 20 35

Salinity (S) (ppt) 11.1 9.5 0.2 0 37

Salinity (B) (ppt) 11.5 9.5 0.2 0 37

pH (S) 8.3 0.5 0.01 6.1 10.3

pH (B) 8.3 0.5 0.01 6.2 10.1

Conductivity (S) (m mho cm-1) 20.0 16.5 0.4 0 69.7

Conductivity (B) (m mho cm-1) 20.7 16.6 0.4 0 69.7

Alkalinity (S) (mg L-1) 96.1 28.2 0.6 22 304

Alkalinity (B) (mg L-1) 96.4 27.9 0.6 0 300

DO (S) (mg L-1) 6.9 1.7 0.04 0 15.2

DO (B) (mg L-1) 6.7 1.8 0.04 0 16.36

Nitrate (S) (mg L-1) 0.3 0.2 \0.01 0 1.42

Nitrate (B) (mg L-1) 0.3 0.2 0.01 0 2.115

Orthophosphate (S) (mg L-1) 0.3 0.2 \0.01 0 1.94

Orthophosphate (B) (mg L-1) 0.3 0.2 0.01 0 1.72

TSS (S) (g L-1) 0.5 0.3 \0.01 0.01 2.84

TSS (B) (g L-1) 0.5 0.3 \0.01 0 2.63

Limnology (2008) 9:231–242 235

123

eutrophic status was also observed longitudinally (high

nutrients for the neurons on the left). The water quality

characteristics for the surface and bottom layers had rela-

tively similar patterns of distribution on the map plane.

Most of the clusters had high temperature samples except

for cluster 2, and depth and transparency increased toward

cluster 4. Both pH and dissolved oxygen showed similar

patterns, and tended to increase toward cluster 2. Samples

of high TSSs were found for cluster 4. Thus it can

be summarized that the lagoon water quality was pri-

marily determined by the salinity gradient and nutrient

concentrations.

Clustering of the study sites

The results of site clustering are shown in Figs. 5 and 6.

Seasonal changes in water quality and the influence of the

new mouth opening were clearly reflected in the develop-

ment of site groups by the SOM model. Overall clustering

results for the study sites were similar to conventional zone

development by the CDA (Fig. 5a; see Fig. 1 for the newly

reorganized sites). The only differences between the two

zone developments were changes in the borderline among

the central, northern and outer channel sectors. Sites 14, 15

and 21, which were previously designated part of the

northern sector, were included in cluster 3, and site 25

close to the outer channel sector was excluded from the

central sector, resulting in the expansion of the saline area

of the outer channel sector (cluster 4). From this result, the

southern sector exhibited similar characteristics to the outer

channel sector from a water quality point of view.

Dynamic changes in site clustering were found when

seasonal changes of water quality were considered (Fig. 5).

Most of the sites frequently changed among the clusters in

accordance with climate variations. From winter to summer

(January–June) when dry conditions persisted, a substantial

expansion of the saline area was noted (Fig. 5b, c). The

freshwater area, represented by cluster 1, was recovered in

the southwest monsoon season (July–September), and

occupied a large part of the lagoon during the transition

period (October) (Fig. 5d, e). In the northeast monsoon

period (November–December), freshwater as well as saline

areas retreated, and the center of the lagoon was represented

by cluster 2, identified by its high photosynthetic activity.

The influence of the new mouth on site clustering is

shown in Fig. 6. After the construction of the new mouth,

the lagoon waterbody became more saline. The clustering

of study sites before the new mouth was constructed

(Fig. 6a) was similar to the overall clustering pattern

shown in Fig. 5a. In this case, the moderately saline area

represented by cluster 3 was larger than the overall pattern.

After the opening, however, several sites (sites 22, 24, and

0.15

0.20

0.25

0.30

0.15

0.20

Cluster 1

Cluster 2

Cluster 3

Cluster 4

1 7 13 19 25

2 8 14 20 26

3 9 15 21 27

4 10 16 22 28

5 11 17 23 29

6 12 18 24 30

5 11 6 12 17 18 23 24 29 30 3 9 4 10 16 15 21 22 27 28 1 7 2 13 8 14 19 25 20 260

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

BA

C

Fig. 3 The results of SOM clustering. a Clustering of the cells on the map plane, b U-matrix, c dendrogram showing dissimilarity of the cells in

the map

236 Limnology (2008) 9:231–242

123

25) were included in cluster 4, and the southern sector

became more saline (Fig. 6b).

Discussion

Applicability of the SOM

The SOM has been widely used in ecological science. In

most cases, this algorithm was a powerful tool for dis-

covering patterns of ecological components such as the

community dynamics of freshwater fish (Oberdorff et al.

1999), macroinvertebrates (Chon et al. 1996; Park et al.

2003b, 2006), river phytoplankton and plant community

dynamics (Foody 1999; Joo and Jeong 2005), and bird

population dynamics (Lee et al. 2006). Some studies

revealed that an SOM algorithm was also applicable to the

exploration of the spatiotemporal dynamics of ecological

properties (e.g., Chon et al. 1996). The capacity of the

model to explain ecological dynamics was reported to be

higher than other statistical methods (Gevrey et al. 2003).

The application of the SOM model to the Chilika lagoon

is a good example of this. The applicability of the SOM

model was similar to those from the aforementioned

research works; however, many SOM studies have been

conducted in rivers, lakes, or inland wetland systems (Lee

and Scholz 2006; Park et al. 2006, 2007; Astel et al. 2007).

Examples of SOM modeling in wetland ecosystems are

very few. The SOM model in this study successfully

demonstrated and extracted patterns from the physico-

chemical parameters and can be regarded as an excellent

tool for applying to complex wetland ecosystems.

Related to the issue of the model’s applicability to spa-

tiotemporal dynamics is algorithm selection. Researchers

should carefully select the appropriate SOM algorithm

according to their final application goal: i.e., data classifi-

cation under different discrete environmental conditions or

the discovery of relationships among data from a temporal

point of view. Even though the SOM algorithm is recog-

nized as being a powerful tool for handling complex

ecological datasets, there have been many arguments for

processing spatiotemporal information simultaneously.

Total suspended solid (B)

3.27

3.36

3.45Air temperature

3.25

3.35

3.44Water temperature (S)

3.24

3.34

3.44Water temperature (B)

4.78

5.08

5.38Depth

3.29

3.93

4.57Transparency

0.549

1.79

3.04Salinity (S)

0.588

1.83

3.07Salinity (B)

2.19

2.23

2.27pH (S)

2.18

2.22

2.26pH (B)

0.876

2.25

3.62Conductivity (S)

0.921

2.29

3.65Conductivity (B)

4.25

4.46

4.68Alkalinity (S)

4.25

4.46

4.68Alkalinity (B)

1.9

2.04

2.17Dissolved oxygen (S)

1.87

2.01

2.15Dissolved oxygen (B)

0.125

0.225

0.324Nitrate (S)

0.135

0.24

0.345Nitrate (B)

0.133

0.253

0.372Orthophosphate (S)

0.144

0.273

0.401Orthophosphate (B)

0.222

0.399

0.575Total suspended solid (S)

0.248

0.426

0.604

Fig. 4 The patterning results for water quality parameters on the SOM plane. S and B in the brackets after each variable indicate surface and

bottom layers, respectively

Limnology (2008) 9:231–242 237

123

This algorithm is occasionally limited when strictly focus-

ing on the spatial patterns of variation having partially

removed the temporal variations, which is equivalent to

performing a PCA in a linear context. Over the last decade,

there have been some reports on modifying the SOM

algorithm to process temporal information along with spa-

tial information by preparing multilayered feature maps or

using recurrence in the developed models (Euliano and

Principe 1996; Koskela et al. 1998; Horio and Yamakawa

2004). Their models were able to discover significant time

lags when processing spatial data, resulting in time-delayed

associations of data into clusters. However, the current

study was not primarily focused on the relationship between

monitoring stations in terms of time flow, but rather on

discovering possible associations between the stations over

a discrete timespan, which can be found in other SOM

A B

C D

E F

Cluster 3

Cluster 1

Cluster 4

Summer

Cluster 3

Cluster 1

Cluster 4

Cluster 4

Southwest monsoon

Cluster 1

Cluster 4

Cluster 3

Transition

Cluster 2

Cluster 1

Cluster 4

Cluster 3

Winter

Cluster 1

Cluster 4

Cluster 3

Overall

Cluster 4

Cluster 2

Cluster 1

Cluster 4

Northeast Monsoon

Cluster 3

Fig. 5 Clustering results for the

study sites. a Site clustering for

overall data, b for winter data,

c for summer data, d for

southwest monsoon period data,

e for transition period data, f for

northeast monsoon period data

238 Limnology (2008) 9:231–242

123

applications to ecological data (Chon et al. 1996, 2000,

2001; Park et al. 2003b, 2005, 2007; Joo and Jeong 2005).

Temporal processes interacting with ecological data in

the SOM plane can identify the periodic influence of the

environment on ecological entities, which requires further

improvements in ecological modeling.

Environmental changes in the lagoon

As mentioned earlier, the Chilika lagoon is one of the

largest coastal lagoon systems in Asia. However, scientific

approaches adopted by the CDA to manage the lagoon

were rare compared with other lagoon systems. The sci-

entific report provided by the CDA (2002) is the most

systematic exploration of environmental variations in the

lagoon to date. The results of this study may help to pro-

vide adequate information for lagoon water environment

classification and management planning.

The monsoon is a characteristic climate variation in the

Asian region. Usually a large amount of rainfall occurs

during the southwest monsoon period (Ahrens 2005). The

Rivers Daya, Bhargavi and Nuna connect to the northeast

part of the lagoon (close to site 18; Fig. 1) and small

streams located in the western and southern sector bring

freshwater into the lagoon, mostly during the southwest

monsoon period (Panda et al. 2006). In the other seasons,

the lagoon water level decreases due to a lack of rainfall

(Mohanty and Behera 2002). These rainfall characteristics

caused the changes in water physicochemistry and depth

fluctuation. The expansion of cluster 1 in the southwest

monsoon and transition period can be explained by a

substantial increase in rainfall runoff.

Salinity changes accompanied by water level fluctua-

tions may govern the ecological diversity of the lagoon

system. From the results of SOM modeling, water depth

changed greatly across the seasons, and a usually high

water level was observed at sites included in cluster 4. The

water physicochemistry of the sites located in the middle of

the lagoon and close to the outer channel frequently

changed due to climate variability, which resulted in dif-

ferent seasonal site compositions in the clusters. A reduced

fluctuation in water level and physicochemistry may be an

important problem for the biodiversity of the lagoon.

Mohanty and Behera (2002) found a trend for the surface

area of the lagoon to shrink during the southwest monsoon

period. Similar results obtained by Panigrahy (2002) were

mainly due to the reclamation of the shoreline area for

human requirements. Besides the hydrological aspects, the

decrease in lagoon salinity was also understood to be a

crucial problem (Panigrahy 2002). The natural modifica-

tion of the outer channel by the tidal activity gradually

affected lagoon salinity.

The opening of the new mouth at the outer channel

altered the salinity pattern successfully. The sites with

freshwater characteristics in the central sector were grad-

ually transformed into saline sites, which is also evident

from the SOM modeling results. This mouth can also

increase the rate of fluctuation of the water level by effi-

cient tidal influx from the sea to the lagoon. Because the

Chilika lagoon supports high biodiversity and provides an

important habitat for endangered species such as Irrawaddy

dolphins (Bhatta and Pattnaik 2002; Dutta 2002; Padma

et al. 2002; Panda and Pattnaik 2002; Panigrahy 2002;

Kannan et al. 2005), comprehensive ecological research

into variability and lagoon morphology is needed. The

results of SOM modeling provide a simultaneous com-

parison between water quality changes reflecting climate

seasonality and the changes in lagoon morphology. Mod-

eling information can provide the basis for this research.

Environmental and ecological analysis using diverse data

from lagoon morphology, climatology, hydrology, water

physicochemistry and ecology will be more informative

than just using water quality variations (Joo and Jeong

2005; Park et al. 2005, 2006).

Cluster 4

Cluster 3

Cluster 1

Cluster 4

Before opening the new mouth

Cluster 4

Cluster 3

Cluster 1

Cluster 4

After opening the new mouth

A BFig. 6 The changes in site

clustering with respect to the

new mouth opening. a Before

the mouth opening, b after the

mouth opening

Limnology (2008) 9:231–242 239

123

Suggestion for future research in the lagoon

Long-term ecological research (LTER) is a good way to

obtain a comprehensive understanding of the Chilika

lagoon ecosystem. Pattnaik (2002) and Panigrahy (2002)

listed some important issues for the in-depth investigation

of the lagoon system. To improve the efficiency of moni-

toring and the consistency of data management for the

LTER, it would be reasonable to establish several fixed

monitoring stations out of the 30 study sites. Examining the

vast lagoon system often requires much effort and logistics.

After comprehensive validation of the suggested clustering

of the study sites with ecological datasets such as the

abundance of species, it would be possible to select rep-

resentative sites for LTER. The sampling interval might be

shortened (e.g., monthly to biweekly), and representative

sites could be located near the Wetland Research Centre of

the CDA in the central sector. Finally, the deployment of

automatic water quality monitoring systems at selected

sites could increase data precision and improve monitoring

efficiency.

Conclusion

A limnological database from the Chilika lagoon was

modeled using the SOM, and the model successfully

demonstrated patterns in the lagoon water quality dynam-

ics. The water physicochemistry in the lagoon responded

well to changes in climate. The major hydrological resto-

ration activity, which involved opening a new mouth in the

outer channel of the lagoon, affected the limnology, in

terms of salinity gradient changes, resulting in the reor-

ganization of the study sites. A potential re-sectored system

for classifying the monitoring sites was presented which

was slightly different from the previous zoning pattern.

Acknowledgments This work was supported by the LTER Project

from the Ministry of the Environment in South Korea. The authors

deeply appreciate Dr. Sovan Lek at the Universitie Paul Sabatier,

France, for his courtesy of providing an efficient SOM coding solu-

tion, which was utilized throughout the entire analysis in this study.

We also wish to thank the personnel in the Chilika Development

Authority who were involved in collecting a large set of data from

the Chilika lagoon. Most of this work was conducted during the

appointment of the postdoctoral fellowship of K.S. Jeong (PNU

2006).

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