57

CHAPTER 4

METHODOLOGY

This chapter is to illustrate the research design followed in the

study to fulfill the objectives set forth earlier in the introductory chapter. The

research has devised a combination of qualitative and quantitative

methodology in which primary and secondary sources are thoughtfully tapped

for information and perspectives. First, RS and GIS based methodology to

study LULC of the study area and methodology for procuring processed data

from the global sea level data archive to study the observed sea level changes

is described. Then, climate model based methodology for future projection of

SLR of the study, GIS based methodology for impact and vulnerability

assessment of the study area to SLR, the methodological framework for

adaptation strategies and stakeholders’ engagement to SLR, conceptual

methodological approach for SLR policy emphasizes are explained.

4.1 LAND USE LAND COVER

Coastal areas are highly dynamic and undergoing rapid changes.

The knowledge of LULC changes is very important in understanding (coastal)

natural resources, their utilization, conservation and management

(Arunachalam et al 2011). To identify long-term trends and short-term

variations, such as the impact of rising sea levels and hurricanes on wetlands,

one needs to analyze time series of remotely sensed imagery. With the wide

variety of remote sensing systems available, choosing the proper data source

58

for observing land cover and coastal waters can be challenging (Klemas

2011). The methodology followed to prepare LULC maps of this study area

viz., Vellar-Coleroon estuarine region, from the 1987-2009 and the

subsequent rate of change is given below.

4.1.1 Data Source and Preparation of Study Area Boundary

Satellite images acquired in this study are Landsat 5- TM for the

year 1987 is obtained from publically available United States Geological

Survey (USGS) website www.usgs.gov, where as other satellite images

include IRS 1D-LISS 3 and IRS P6- LISS 4 for the year 2000 and 2009

(Table 4.1) are procured from National Remote Sensing Centre (NRSC),

Department of Space, Government of India and 1:50,000 Toposheet No. 58

M/15 from Survey of India (SOI). Study area boundaries were extracted from

the topographic maps obtained from SOI by manual digitizing methods.

Table 4.1 Data source for LULC classification

S.

NoSensor Satellite Bands

Spatial

Resolution

(m)

Radio

metric

Resolution

(bit)

Acquisition

Date

1 TMLandsat

5

Near IR,

Middle, Far and

Thermal IR

30 8 23/05/1987

2 LISS 3 IRS 1D

Green, Red,

Near IR and

Middle IR

23.5 7 21/05/2000

3 LISS 4 IRS P6Green, Red,

Near IR5.8 7 09/5/2009

59

4.1.2 Image Processing

Image processing is extremely useful in periodic assessment of the

coastal LULC changes (Arunachalam 2011). In the preprocessing phase, the

datasets were cut to include only the area of interest (11o30’14.69 N, 79

o46’

38.14 E; 11o28’44.93 N, 79

o45’11.80 E; 11

o21’51.98 N, 79

o45’14.07 E,

11o21’41.40 N, 79

o 49’ 51.24 E) i.e. Vellar-Coleroon esturaine region in the

East coast of Tamil Nadu, India. Since the digital data do not have the real

earth coordinates, they were geometrically corrected using a reference image

by taking common feature (Kumar et al 2012), by using ERDAS imagine

Version 9 digital image-processing software. Thus, the satellite images were

rectified for geometric errors using 1:50,000 scale survey of India toposheet

as a base, in cartographic projection (UTM Zone 44N, WGS84).

4.1.3 Image Classification

Supervised signature extraction with the maximum likelihood

algorithm was employed to classify the image, because this classification

algorithm produces consistently good results for most habitat types

(Donoghue and Mironnet 2002; Ardil and Wolff 2009). Training site data

were collected by means of on-screen selection of polygonal training data

method (Weng 2002) based on extensive field knowledge. To increase the

size of the sample to be used in the classification accuracy assessment, the

layer with the field checked sites was overlaid on the corrected satellite

images and homogeneous polygons with similar spectral reflectance, when

viewed in several band combinations, were drawn those sites and the layer of

polygons created using this process was later used for checking the accuracy

of the classified map (Ardil and Wolff 2009). After ground truth verification,

LULC maps were prepared by using the visual interpretation method. ERDAS

imagine Version 9 is used for image processing and analysis, and Arc GIS

version 9.3 was used for GIS analysis.

60

4.1.4 Accuracy Assessment

Quantifying and documenting the accuracy of maps and spatial

data are important components of any mapping process (Muller et al 1998).

Descriptive technique of the error matrix was used in this study to calculate

overall classification accuracy using Erdas Imagine Version 9.2. Accuracy

assessment of the 1987, 2000 and 2009 LULC maps of Vellar-Coleroon

estuarine region of Tamil Nadu Coast was performed using reference data

created from visual interpretation of the emerge image data (for classified

image of LULC 1987 and 2000), and ground truth points recorded during the

field survey were used as a reference to validate the classified image of LULC

2009. 75 test pixels from classified images were selected to assess image

classification. The total number of correct pixels in a category (LULC

classified category) is divided by the total number of pixels of the same

category as derived from the reference data. This accuracy measure indicates

the probability of a reference pixel being correctly classified and is a measure

of omission error, which is called as producer accuracy. On the other hand,

the total number of correct pixels in a category is dived by the total number of

pixels that were classified in the same category, and the result is a

commission error which is called as user’s accuracy or reliability, is indicative

of the probability that a pixel classified on the map/image actually represents

the same category on the ground (Story and Congalton 1986; Congalton

1991).

4.1.5 Post Classification Analysis

The classified images were transferred to the GIS facilities to

produce the final LULC maps. Analysis and quantification of LULC included

in the GIS database, and they were tabulated (1987, 2000, and 2009). The

characterization of LULC is made from cumulative measurements of area

(ha-1

) by cover based on the data source available for the study area. Analysis

61

Geometric Correction

Subset Generation

Supervised Classification Preparation of LULCmaps

Accuracy Assessment

Post Classification

Analysis (Rate of change)

Satellite digital data

1987-2000-2009

Preparation of FinalLULC maps

and quantification of LULC differences between different years was included

in this study following the methodology of Diallo et al 2009. Rate of change

(ha-1

) of LULC between the years viz., 1987-2000 and 2000-2009 were

calculated by difference in area covered by each class for two different years.

Similarly, the average rate of change (ha yr-1

) of LULC between the years

viz., 1987-2000 and 2000-2009 was calculated by difference in area covered

by each class for two different years divided by the total number of years.

4.1.6 Hamlets Location Mapping

Hamlets of social communities who depend on these coastal natural

resources are identified based on secondary sources (Selvam et al 2002). The

revenue village boundaries, hamlets geographical locations based on latitude

and longitude values, hamlet settlement boundaries taken by GPS are

superimposed over 2009 LULC map using Arc GIS 9.3. Figure 4.1 illustrates

the outline of overall methodology followed in the preparation of LULC of

the study area.

Figure 4.1 Outline of LULC preparation

62

4.2 OBSERVATION OF PAST SEA LEVEL CHANGES

Tide gauges are one of the primary instruments that are used to

measure changes in sea level (Breaker and Ruzmaikin 2011). Tidal datum is

used as references to measure local sea levels near the tide gauge at which the

measurements were collected. Tidal datum is based on averaged stages of the

tide, such as mean high water and mean low water (NOAA 2010). To obtain

tide gauge data, the global networks of long-term tide gauges provide an

instrumental record of sea level over the past approximately 100 years

(Donoghue 2011). The methodology followed to select tide gauge station and

to obtain processed tide gauge data for the present study is given below.

4.2.1 Selection of Tide Gauge Station

The PSMSL RLR (Permanent Service for Mean Sea Level-Revised

Local Reference) data were the source of all major work on long term analysis

and projection of global sea level from which the assessment reports of IPCC

were prepared. The datasets were also used for studies on sea level trends

around India (Emery and Aubrey 1989; Unnikrishnan 2007). The RLR datum

is arbitrarily taken as approximately 7m below the MSL to avoid negative

values in gauge records (Woodworth 1991; Nandy and Bandyopadhyay

2011). For this study, tide gauge stations located in the Bay of Bengal region

over Tamil Nadu coast are taken into consideration. The tidal data for these

stations were obtained from PSMSL global archive of tide gauge records

http://www.psmsl.org/. The annual MSL RLR data are obtained for each tide

gauge station, and the availability of data set for each station is checked

(PSMSL 2012; Woodworth and Player 2003).

63

Permanent Service for Mean

Sea Level (PSMSL)

Identifying tide gauge stationsfor Tamil Nadu Coast

Obtaining tide gauge data for

selected station

Delineating selected tide gauge

station

CO-OPS- National Oceanic and

Atmospheric Administration

(NOAA)

Linear Trend of Mean Sea

Level (1916-2007)

Products-Sea Level Trend

Chennai Tide Gauge Station

(Station ID 500-091)

4.2.2 Obtaining Station Specific Processed Mean Sea Level Trend

Sea level trend of a specific tide gauge station can be obtained from

Tides and Currents products of Center for Operational Oceanographic

Products and Services (CO-OPS) of National Oceanic and Atmospheric

Administration (NOAA). NOAA uses data from PSMSL, quality control of

data for each specific station is checked and processed statistically. The

following are the processed data viz., Linear trend, Average seasonal cycle,

Inter annual variation, Inter annual variations since 1990 are available for the

end users for each tide gauge station. In this study, based on availability of

tide gauge data of four major tide gauge stations of PSMSL, Chennai station

has been selected and processed MSL trend for Chennai station has been

obtained from http://tidesandcurrents.noaa.gov/index.shtml of CO-OPS,

NOAA for the period of 1916-2007 (NOAA 2012). Linear trend of MSL at

Chennai station alone taken into consideration to understand the MSL trend

and as per the requirement of SimCLIM to simulate future SLR scenarios.

Figure 4.2 illustrates the Outline of overall methodology followed to obtain

station specific processed MSL trend of the study area.

Figure 4.2 Outline for selecting tide gauge station and obtaining

MSL trend

64

4.3 SIMCLIM SEA LEVEL RISE PROJECTION MODEL

SimCLIM is a computer model system for examining the effects of

climate variability and change over time and space. Its "open-framework"

feature allows users to customize the model for their own geographical area

and spatial resolution and to attach impact models. The main objective is to

support decision making and climate proofing in a wide range of situations

where climate and climate change poses risk and uncertainty. Vulnerability

can be assessed both currently and for the future. Adaptation measures can be

tested for present day conditions and under future scenarios of climate change

and variability (ETC CCA 2011), and it can be applied from global to local

scales. The size of geographical area and the spatial resolution are determined

by data availability and computational demands. Tools within SimCLIM can

be used to interpolate to different spatial resolutions (Mc leod et al 2010). The

modeling system can use outputs from individual GCMs or “ensembles” of

GCMs (i.e., averages of multiple GCM runs) and allows users to generate

scenarios of future climate and sea-level changes and to examine sectoral

impacts or to conduct sensitivity analyses. In terms of coastal impacts,

SimCLIM includes a sea level scenario generator which allows the inclusion

of regional and local components of sea level change (Mc Leod et al 2010).

The methodology employed to project SLR of the study area using SimCLIM

under different SRES scenarios is given below.

4.3.1 Sea Level Scenario Generator

For generating scenarios of future climates, SimCLIM generally

employs the commonly used method of “pattern scaling” (Santer et al 1990;

Hulme et al 2000; Carter and La Rovere 2001). It involves the scaling of

“standardized” (or normalized), spatial patterns of climate change from very

complex, computationally demanding 3-D global climate models (GCMs)

with the time dependent (e.g. Year by year) projections of global mean

65

climate change from simpler models. These changes are used to perturb the

present climate (whether time-series data or a spatial climatology) and thereby

create climate scenarios for a year of interest (e.g.2100). The system contains

high, mid and low projections for the six SRES scenarios (A1B, A1FI, A1T,

A2, B1, B2) which are consistent with the values given in IPCC AR4

(Nicholls et al 2011). The SimCLIM user interface (Figure 4. 3) provides the

user with considerable scope for choosing amongst global projections, GCM

patterns, model sensitivity values and future time horizons, and thus for

examining the range of uncertainties involving future GHG emissions and

scientific modeling.

Figure 4.3 User interface of SimCLIM SLR scenario generator

4.3.2 Ensemble Construction

In this study SimCLIM version 2.5.9 is used, which has 13GCMs

for SLR scenario generation study. The lists of 13 GCMs are given in the

Table 4.2. The use of single GCM and single scenario is misleading in

climate-change impact studies. Therefore, there is a need to use multimodel

66

ensembles for prediction (Mujumdar and Ghosh 2008) of SLR incorporating

climate change. In addition, ensembles are being recognized increasingly in

the climate-change adaptation and risk assessment literature as an appropriate

method for managing uncertainty in GCM impacts on climate-change risk

analysis (Nicholls et al 2011). For this purpose, 13GCMs are selected and

grouped as one ensemble as the multi-model ensemble for SLR scenario

generation of the present study area (i.e. Long. 80.5; Lat. 11.5). These GCMs

are arranged hierarchically by SimCLIM based on normalized GCM values

(pattern scaling) and the measure of central tendency as the median value with

respect to given study area (Long/Lat), i.e. For 13 GCM patterns selected, the

one that has the value in the 7th place in terms of the magnitude is chosen as

the median value. The value is defined by the GCM that has value position

decided by

Median Value = (n-1)*50%+1 (4.1)

where n is the number of GCMs selected, in this case it is 13

Median Value = (13-1)*50%+1=7 (4.2)

4.3.3 Sensitivity Analysis

Sensitivity analysis is used to study how the uncertainty in the

output of a model can be apportioned to different sources of uncertainty in the

model input (Saltelli et al 2008). For this purpose local observed sea level

trend (mm yr-1

) of the study area (data from a tide gauge station near to the

study area) is given as an input and sensitivity is analyzed for local sea level

scenarios by SimCLIM for the selected ensemble (group of 13GCMs) of the

given study area.

67

Table 4.2 Description of 13GCM models used in SimCLIM

GCMPlace

Name of theModels

Synonym Organization Country

Normalized GCMValues Patterns(cm/cm) from

SimCLIM1. mpi_echam5 Max Planck Institute

_European Centre (for mediumrange weather forecast)Hamburg 5th generation model

Max Planck Institutefor Meteorology Germany 0.78

2. giss_e_r Goddard Institute for SpaceStudies_Model E_Russel

NASA (NationalAeronautic and SpaceAdministration),Goddard Institute forSpaceStudies

USA 0.91

3. giss_aom Goddard Institute for SpaceStudies-Atmosphere OceanModel

NASA (NationalAeronautic and SpaceAdministration),Goddard Institute forSpaceStudies

USA 0.95

4. gfdl_cm2_1 Geophysical Fluid DynamicLaboratory_Coupled Modelversion 2.1

NOAA (NationalOcean andAtmosphericAdministration),Geophysical FluidDynamics Laboratory

USA 0.97

5. giss_e_h Goddard Institute for SpaceStudies_Model E_Hycom

NASA (NationalAeronautic and SpaceAdministration),Goddard Institute forSpaceStudies

USA 0.97

6. csiro_mk30 Commonwealth Scientific andIndustrial ResearchOrganization_Mark 3.0

CSIRO(CommonwealthScientific andIndustrial ResearchOrganization)

Australia 0.99

7. ukm_hadcm3 United Kingdom Metoffice_Hadley centre CoupledModel, version 3

Hadley Centre forClimatePrediction andResearch,MetOffice.

UK 1.12

8. ncar_ccsm3 National Centre forAtmosphericResearch_Community ClimateSystem Model version 3.0

National Centre forAtmospheric Research USA 1.14

9. miub_echog Meteorological InstituteUniversity of Bonn_ ECHO-G

MeteorologicalInstitute University ofBonn

Germany 1.15

10. cccma_cgcm Canadian Centre for ClimateModeling and Analysis_Coupled Global CirculationModel

Canadian Centre forClimate Modeling andAnalysis

Canada 1.22

11. mri_cgcm23 Meteorological ResearchInstitute_ Coupled GeneralCirculation Model Version 2.3

MeteorologicalResearch Institute Japan 1.23

12. miroc32_hi Model for InterdisciplinaryResearch on Climate, HighResolution Version

Center for ClimateSystem ResearchNational Institute forEnvironmental Studies

Japan 1.25

13. miroc32_me Model for InterdisciplinaryResearch on Climate, MediumResolution Version

Center for ClimateSystem Research,National Institute forEnvironmental Studies

Japan 1.67

68

Similar as the median value, the determination of the change value

is based on the number of GCM selected for ensemble, as well as the

percentile value. In this study, for the total number of 13GCMs both low

percentile (10%) and high percentile (90%) are selected in SimCLIM

ensemble option. For low percentile value, the position is defined by

(13-1)*10%+1= 2.2 (4.3)

The change value for low percentile is calculated as the

combination of the 2nd

and 3rd

place GCM value in terms of their magnitude

in the total GCM. For high percentile value, the position is defined by

(13-1)*90%+1= 11.8 (4.4)

The change value for high percentile is calculated as the

combination of the 11th and 12

th place GCM value in terms of their magnitude

in the total GCM.

4.3.4 Computation of Sea Level Rise Projection

In this study, both (i) Total trend (the total observed, the

undifferentiated trend of observed relative sea level change, which includes

GHG-related effects) is selected for the simulation of SLR projection for the

given study area from the year 1990 (base line) to 2100 years, (ii) Vertical

Land Movement (VLM) component only (trend of relative sea level that

excludes climate change related components, e.g. land subsidence or uplift) is

considered for computation of SLR projection (SimCLIM 2011). For this

purpose, relative sea level change for a specific location needs to consider the

contributions from the components at the global, regional and local scales,

and it is represented as follows (Nicholls et al 2011).

G RM RG VLMRSL SL SL SL SL (4.5)

69

where,

RSL is the change in relative sea level

GSL is the change in global mean sea level

RMSL is the regional variation in sea level from the global mean

due to metero-oceanographic factors

RGSL is the regional variation in sea level due to changes in the

earth’s gravitational field

VLMSL is the change in sea level due to vertical land movement

The global SLR projection is obtained from the SimCLIM SLR

scenario generator main tool bar. It contains tabled year by year output from

Model for the Assessment of Greenhouse Gas Induced Climate Change

(MAGICC), a simple global climate model, as forced by the six key SRES

GHG emission scenarios used by IPCC AR4. For each scenario, low, medium

and high projections are provided for global mean changes in sea level.

Regional and local SLR projection is obtained from “SLR Scenario” of the

SimCLIM SLR scenario generator main tool bar. The latitude and longitude

value for desired location given along with the local observed SLR trend

obtained from PSMSL and NOAA data source (Refer section 4.2). The

constructed ensemble is then selected and SLR projection for different SRES

scenario of the given location is obtained both in tabular as well as in the

graphical format. The results obtained from SimCLIM lend hands for further

analysis to study the impact of projected SLR of the study area. Figure 4.4

illustrates the outline of overall methodology followed to project future SLR

under different SRES scenarios.

70

SLR Scenario

Local Observed SLR

Trend (mm yr-1

)

Area of Interest

(Longitude/Latitude)

GCM/Ensemble

Emission Scenarios-SRES

A1B, A1FI, A1T, A2,

B1, B2

Global Projection

(Low-Mid-High

Sensitivity)

Regional Projection

(Spatial pattern)

Local Projection

(Median Projection,

Low & High

Percentile)

SimCLIM-SLR

Total Trend/VLM

(Climate change and non

climate change

components)

Figure 4.4 Outline of SimCLIM based global, regional and local SLR

projection

71

4.4 GIS BASED SEA LEVEL RISE INUNDATION MODEL

As a tool for managing and analyzing geographic data, GIS has

been used to delineate potentially inundated areas resulting from projected

SLR (Gornitz et al 2001; Titus and Richman 2001; Cooper et al 2005;

Dasgupta et al 2007; Li et al 2009). It offers real case scenarios to

communities, land use, development and land areas that are potentially

vulnerable to flooding based on ground elevation (Boateng 2012a). Elevation

is one of the most important parameters that determine the vulnerability of

coastal lands to inundation from flooding events and SLR (Gesch et al 2009).

In many coastal inundation impacts assessments conducted at various scales,

elevation is a primary variable that is analyzed to determine vulnerability to

adverse effects of rising water levels. These assessments require the use of

topographic maps or DEMs to identify low-lying lands with low or no slopes

that are at risk (Committee on Floodplain Mapping Technologies 2007;

Gesch 2012). In this context, the methodologies adopted in the present study

to identify area of inundation, which are vulnerable and at risk to SLR based

upon the elevation of the study area are discussed below.

4.4.1 Data Source

Arc GIS 9.3 is used to superimpose the inundation zones with the

2009 LULC map of coastal natural resources of the study area along with the

coastal resource dependent revenue villages and hamlets location of the study

area for the projected SLR of 0.25m and 0.5m. For this purpose, DEMs used

for estimating the inundation extent caused by SLR were generated from three

different sources. The first source is ASTER 30m dataset was obtained from

the publically available website of Earth Remote Sensing Data Analysis

Centre (ERSDAC) and SRTM 30m resolution data of the study area were

72

procured from Institute of Remote Sensing (Anna University, Chennai), and

the third source is from real time ground level elevation measurement by

DGPS surveying process.

4.4.2 DGPS Based Real Time Elevation Measurement

The vertical accuracy of the input elevation data in coastal

inundation hazard assessments is a critical parameter that significantly affects

the veracity of the modeling results, it must be described fully according to

standards and accepted best practices (Gesch 2012). The use of DGPS

procedures is what allows the operational application of GPS to obtain

vertical and horizontal positioning required for topographic and bathymetric

surveying, in addition, regular hand held GPS units do not provide the vertical

and horizontal accuracies required for surveying (National Ocean Service

2004). Under ideal conditions, DGPS technology is capable of measuring

vertical change of 1 cm or less (Little et al 2003). For this purpose, Timble R3

Digital Field Book Version 6.0 was used in the present study for the real time

elevation measurement of the study area.

4.4.2.1 Trimble R3 set up

Trimble Digital Field book controls the Trimble R3 GPS system in

the field. It makes performing static, fast static, kinematic, and continuous

kinematic surveys on short to moderate baselines fast, easy, and productive

(Trimble 2012). In this study Trimble R3 Version 6.0 Digital field book is

widely used to take elevation data. Setting up the field book involves two

steps viz., base station set up and rover set up. A reference point with known

coordinates (base station with base receiver antenna) is fixed at MGR Nagar

73

of the study area (Figure 4.5). This location is chosen based on its central

location which covers the entire circumference of the study area, to avoid

disturbances and to ensure security of the instrument. The base station is

assembled in the open space without any obstruction to sky by fixing tripod

stand with tribrach, base antenna and an adapter with reference to a permanent

benchmark. It is essential that no obstructions interfere with satellite signals to

the base station receiver antenna (Little et al 2003). Simultaneously, the rover

is assembled by using the bipod to hold the rover antenna receiver in the

upright position (Figure 4.5).

4.4.2.2 Functionality of Trimble R3

Trimble R3 Digital Field Book Version 6.0 working manual is used

to operate the field book. The base receiver should be turned on and activate

configuration, controller, and set Bluetooth connect to GPS receiver. The

rover receiver should be turned on at the same time the base receiver was

powered up; this allows the rover receiver to collect satellite data. Survey is

then begun to measure points after calibration. GPS acquires coordinate

positions through triangulation by determining the distance between an

antenna receiver and at least four satellites (UNAVCO 2002; Little et al

2003). The principle behind the functionality of base and rover of the DGPS

instrument is explained as a base receiver with a known position tracks four

or more satellites, and a rover receiver placed on a stable target device for a

required length of time. DGPS improves accuracy by reducing systematic

errors (e.g. atmospheric delays, precision of orbits) resulting from GPS signal

propagation delays or inability to discern the precise details of satellite orbit

(Little et al 2003). A receiver at the reference point (base receiver) transmits

74

a) Base station set up

b) Rover set up

Figure 4.5 Trimble R3 digital field book version 6.0 set up and DGPS

surveying process

the error in the measured coordinates to a receiver at the unknown point, and

the results of measurements at the unknown point are corrected by deducting

the error from the results of this measurement (Fujii et al 2001). The survey of

elevation measurements of 2096 DGPS position points were taken for the

75

given study area of 6541ha approximately in the 100m interval for the period

of 60days from 01.03.2011 to 30.04.30211 (Figure 4.5 and Figure 4.6). After

completion of measurements of elevation points on each day, the survey is

completed by selecting “End Survey” option.

Figure 4.6 DGPS survey locations in the study area

4.4.2.3 Post process analysis

Post process analysis is done by using “Trimble Business Centre

Software”. The obtained elevation measurements of the field data are

transferred to this software in the personal computer to process baselines and

sub centimeter results are produced. The errors in the data are identified and

corrected by the software itself. Data reduction, computation, quality

assurance/quality check (QA/QC) and network adjustment are also

performed. The post processed data are then exported to Microsoft Office and

used for mapping purpose by generating DEM to identify areas of inundation

to SLR.

76

4.4.3 Computation of Inundation Zones for Impact and

Vulnerability Assessment of Sea Level Rise

Inundation modeling is most often a simple process in which the

water level along the shoreline on a coastal DEM is raised by selecting a land

elevation above the current water level elevation and then delineating all areas

at or below that elevation, thus placing them into the inundation zone (Gesch

2012). This approach has been commonly referred to as the “bathtub”

method, or the “equilibrium” method (Gallien et al 2011; Gesch 2012) and

such a procedure is essentially a contouring process. In this study a first-order

estimate of potential losses of land to SLR was arrived at by integrating

digital elevation data with the above SLR scenarios using a GIS (Natesan and

Parthasarathy 2010). ERDAS Imagine software is used to generate DEM from

the source data viz., ASTER 30m, SRTM 30m, and DGPS. Contours were

then generated from DEM at contour intervals of 0.25m and 0.5m using Arc

GIS. The generated DEM is validated with original data source. The area of

inundation is identified from DEM using Raster Calculator. Thus, the

inundation zones to different SLR scenarios viz., 0.25m and 0.5m were

derived from DEM. The inundated areas were identified by overlaying

inundation zones with LULC 2009 and Hamlet locations. Further, the

inundated areas are classified as low, medium and high vulnerable

regions/zones based on their elevation measurements. For this purpose Spatial

Analyst tool of Arc GIS has been used. The ranges of vulnerable zones are

fixed based on the classification of the given elevation data using reclassify

tool and it is subjective to this study. Figure 4.7 illustrates the outline of

overall methodology followed for computation of inundation zones to SLR

using GIS.

77

Load data in Arc Map

and convert to point data

Identify area of inundation from

DEM using Raster Calculator in

Arc GIS

Create Shape file using

Arc Map in Arc GIS

Digitize and Calculate area of

inundation to SLR

(0.25m and 0.5m)

Delineate area of inundation in

LULC 2009

Create surface (DEM)

using ERDAS

Source data

DGPS

From DEM create

contour at 0.25m and

0.5m contour interval

using Arc GIS

Create Inundation Maps using

Arc Map in Arc GIS

ASTER 30m,

SRTM 30m

Validate generated

DEMs with original data

source

Figure 4.7 Outline on computation of inundation zone to simulated

SLR using GIS

4.5 SEA LEVEL RISE ADAPTATION ACTION STRATEGY

FRAMEWORK

Climate change creates both risks and opportunities worldwide. By

understanding, planning for and adapting to a changing climate (SLR),

individuals and societies can take advantage of opportunities and reduce risks.

A more realistic approach is needed to use existing methods and strategies of

78

coastal adaptation that inform and meet new challenges of climate change

induced (SLR) vulnerabilities (Cheong 2010). In this study, strategic

responses to SLR and coastal inundation was developed by constructing

coastal adaptation response strategy approach to predicted rise and impact of

SLR at the cadastral level. This approach has identified the appropriate SLR

(both 0.25 and 0.5m) adaptation options at the local level with particular

relevance to the present study area and has been tailored to the local

vulnerabilities, and requirements.

To meet this objective of the adaptation approach of the present

study, response strategy (adaptation options) for coastal system to SLR of the

study area is constructed by following the recommendation of IPCC (1990)

strategies for adaptation to SLR, IPCC(2007e) assessment of adaptation

practices, options, constraints and capacity and, USAID (2009) guide book

for development planners. Ecosystem (coastal natural resources) and

community based (coastal natural resources dependent social communities)

adaptation options are identified based on peer-reviewed literature and need

of the study area’s response to SLR inundation. For this purpose, relevant

publications on the topic related to climate-change adaptation were identified

from ISI web of knowledge by following the methodology of Berrang-Ford et

al (2011). The identified adaptation options are then prioritized together with

participation of key stakeholders of the study area (Refer 4.6.3). Figure 4.8

illustrates the outline of overall methodology followed to construct adaptation

action strategy.

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Figure 4.8 Outline of coastal adaptation framework to SLR

4.6 STAKEHOLDERS’ ENGAGEMENT AND SEA LEVEL RISE

A well-designed environmental message could convince people that

they can reduce the scale of the phenomenon and could link adaptation and

mitigation actions to people’s positive aspirations through providing local

examples of climate-change impacts and illustrated information. Improving

public awareness and developing overall communication strategies make

climate-change science accessible to the average citizen and could reduce

Delineating adaptation options

Planned-Anticipatory adaptation

Ecosystem based adaptation Community based adaptation

Review on coastal adaptation options

(IPCC, Global adaptation dataset)

Tailoring adaptation options based on

impact and vulnerability assessment of the

study area

SLR adaptation action strategy framework

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their vulnerability (UNFCCC 2007). This, in turn, will enhance capacities of

various stakeholders in the community and improve sustainability at the local

level (Khan et al 2012). In this context, the present study also aims to find

ways to communicate SLR to create awareness among different stakeholders

and to engage them in prioritizing identified SLR adaptation options.

4.6.1 Stakeholder Analysis

Stakeholder analysis is frequently used to identify and investigate

any group or individuals who will be or are affected by a change and whether

they are equipped to deal with it. It is a process of systematically gathering

and analysing qualitative information to determine interests that should be

taken into account when developing and implementing a policy or

programme. Studies are often undertaken at the local or regional level, as

these scales can reveal the specific adaptation options among particular actors.

In the context of climate change induced SLR, the key considerations in

stakeholder studies are to produce information on the circumstances,

problems and climate change (SLR) perceptions of stakeholders with the aim

of informing policy processes (Carina and Keskitalo 2004). In this study,

stakeholder analysis was performed following the methodology of

McCracken and Narayan (1998). Stakeholder analysis is best done in the

field, together with a project development team, and with extensive use of

participatory consultation techniques, which includes brainstorming and

group discussions methods to understand the perspectives and concerns of the

different groups involved. It was conducted to (i) identify stakeholders, (ii)

prioritize different stakeholders, (iii) assess stakeholders who may be affected

by a SLR, and (iv) outline the importance of stakeholder participation in the

adaptation process by prioritizing the identified adaptation options and to

build capacity at the community level.

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4.6.2 Participatory Rural Appraisal

Participatory Rural appraisal (PRA) is a bridge between formal

surveys and both structured and unstructured research methods such as in-

depth interviews, focus groups and observation studies. It provides a rapid

method to gather information for planning and formulating community

projects (Lamug 1985) and for eliciting local community participation at the

outset of any development programme. PRA in this study followed the

methodology of IISD (1995) to obtain new information and formulate new

hypotheses on the coastal natural resource dependent community with respect

to SLR. It was performed using methods like; (i) review of secondary sources,

including mapping, (ii) direct observation, foot transects, familiarization and

participation in activities, (iii) interviews with key informants and group

discussion, (iv) diagrammatic representations, and (v) rapid report writing in

the field.

4.6.3 Sea Level Rise Communication and Stakeholder Participation

Research in social and decision science has identified several key

lessons that are especially relevant to communicating climate science. First,

identify climate change induced risks and convey the message to targeted

people; second, understand SLR risks and make people understand the extent

of risks, which can be voluntary, controllable, uncertain, irreversible or

catastrophic (Slovic et al 2000); third, find suitable solutions to face or

overcome the risk and engage them with adaptation activities and fourth to

build capacity at the community level to equip people with skills to cope with

the anticipated risk. In this context, the aim of this framework is to create a

SLR awareness initiative at the community level to and involve them in

adaptation process and to enhance the capacity building at the local grassroots

82

level. It is important to engage in community mobilization and awareness

rising through designing activities that are tailored to local practices and

establish strong relationships with the communities.

Community-based climate change induced SLR communication

and stakeholders’ engagement in this study followed the method of Monroe et

al (2008). The four objectives were to (i) convey information (the method

used to disseminate information are information campaign, brochures and

posters), (ii) build understanding (the method used are exhibits on impact and

vulnerability mapping, focus group interview, guided field trip to predicted

impact and vulnerable zones), (iii) to enable sustainable actions by engaging

stakeholder with adaptation activities For this purpose, the identified

adaptation options are then prioritized by pair wise ranking method,

following the methodology of Pretty (1995) together with the participation of

key stakeholders of the study area (the method involves cross tabulation of

identified adaptation option and scores for each option is given based on

stakeholders opinion, and it is ranked), (iv) finally to build capacity at the

community and to cope with the anticipated skills (the method involves

introducing samples on SLR adaptation education programs, training the

trainers program and cooperative learning workshops). These categories were

used in community-based climate change (SLR) communication and

stakeholder participation; in particular, focusing on SLR and coastal natural

resource dependent communities. The success of the strategies listed depends

on the quality of interaction with the communities. Figure 4.9 illustrates the

outline of methodology followed to communicate SLR and engage

stakeholders in adaptation actions.

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Participatory Rural Appraisal

Stakeholders Analysis

Identification of Stakeholders

Prioritization of Stakeholders

Involve in adaptation process

Convey Information

Build Understanding

Capacity Building

SLR Communication

Figure 4.9 Outline of SLR communication and capacity building

4.7 SEA LEVEL RISE ADAPTATION POLICY STRATEGY

EMPHASIS

The policy making process and the planning systems required for

sustainable adaptive action is very complex due to several limitations imposed

by the significant uncertainties in the projection of SLR, financial

considerations and numerous physical, social, economic, legal and political

factors which, make many countries more vulnerable because they have

inadequate adaptive capacity in financial, planning, social, economic, legal

and in some case, political considerations (Boateng 2008). In order to

recognize a holistic and competent way of integrating SLR adaptation into

planning policies of the present study, the following approaches are employed

(i) the present study reviewed the existing policies in the coastal management,

based on literature review, and (ii) conceptual methodological outline on need

of climate change induced SLR and coastal adaptation policies was

emphasized to meet the requirement of India’s NAPCC. Figure 4.10

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Review on existing national and local

policies

Conceptual SLR adaptation policy strategy

emphasize paradigm

Response Policy Strategies

Highlight the need of SLR adaptation

policies (UNFCCC, IPCC)

illustrates the outline of overall methodology followed to construct adaptation

policy strategy.

Figure 4.10 Outline of SLR adaptation policy strategy emphasize