Upload
others
View
5
Download
0
Embed Size (px)
Citation preview
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.
79
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
80
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.
81
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.
83
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
84
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