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Multi-Hazard Vulnerability Assessment along the Coast of Visakhapatnam,

North-East Coast of India

Conference Paper · October 2017

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 Multi-Hazard Vulnerability Assessment along the Coast of Visakhapatnam, North-East
Coast of India

Vivek G1, Santonu Goswami1, Mahendra R.S.2, S. B. Choudhury1, Mohanty P.C.2, Srinivasa Kumar T.2

1
Ocean Colour and Monitoring Division, Earth and Climate Sciences, National Remote Sensing Centre, Dept. of
Space, Hyderabad – 500037, India.
2
Indian National Centre for Ocean Information services (INCOIS), Hyderabad – 500090, India.

Abstract
Globally coastal areas are vulnerable to disasters such as tropical cyclones where 40 % of the global population
lives within 100 km of coast. In India, about 13 % of the population live within this coastal belt and hence they are
vulnerable to cyclone disasters. The East Indian coast is mostly affected by tropical cyclones originating from
northern and southern Bay of Bengal, where 1-2 tropical cyclones occur every year along the coast. Our study
carried out a Multi-hazard vulnerability assessment for the Visakhapatnam coast using five parameters as inputs i.e.
the probability of Coastal erosion, Coastal slope, Coastal elevation, Sea level change rate and Tsunami arrival
height. Multi-hazard maps were prepared by overlaying the multi-hazards which are affecting the coastal zone. Our
analysis revealed that the study area experienced erosion at 5.8 m/y during 1973-2015. The sea level rise trend for
the study region was about 0.75mm/y for a recorded slope of 0.2° indicating the high vulnerability of the area to
storm surges and sea level rise. The maximum extent of the hazard zone was found to be 20 km from the current
coastline of the Visakhapatnam district with a total area of 150 km2 for the delineated hazard zone.

Keywords Coastal Erosion, Coastal Slope, Coastal Elevation, Tsunami Arrival Height, Hazard Map

1. Introduction

Coastal zones have significantly changed during the 20 th century due to increase of population, urbanization and
other development activities which are taking place near to the coastal area. The coastal area and its inhabitants are
vulnerable to climate change impacts such as sea level rise, storm surge and precipitation. According to IPCC, 2007
report a global sea level rise of 28-61 cm by 2100 is predicted, which would threaten the coastal cities. India has an
extensive coastline of 7500 km and nearly 250 million people lives within a 50 km from coastal belt. Most of the
coastal areas are low lying and vulnerable to oceanographic disasters such as Tsunamis, Storm surge and sea level
rise. Both Bay of Bengal and the Arabian Sea area along the coastline are mostly affected by the tropical cyclone
and related hazards. The eastern coast of India is mostly affected by the tropical cyclones originating from both
southeast Arabian Sea and Northern Bay of Bengal where 1-2 tropical cyclones form every year along the coast. It
has been estimated that a 1m rise in sea level could displace nearly 7 million people from their homes in India
(IPCC, 2001). The Multi Hazard Vulnerability Mapping (MHVM) helps us to understand the risk due to an
occurrence of various natural hazards and it also gives the vulnerability, risk and hazards information on a single
map and referred as composite hazard map. Mitigating the effects of potential disasters and having the appropriate
infrastructure in place for response requires detailed knowledge on the vulnerability of the place to wide range of
environmental hazards (Cutter et al., 2000). A tool was developed by Federal Emergency Management (FEMA) to
address the situation called Multi-Hazard Mapping Initiative (MMI) (FEMA, 1997).

Increasing population and industrial development have been building pressure in the coastal areas for the last four
decades. UNEP report shows that on average worldwide population density in the coastal zone was 77 people per
km2 in 1990 and 87 people per km2 in 2000 and a projected 99 people per km2 in 2010 (UNEP, 2007). In 1990
estimate was made that 200 million people were living in the coastal flood plain and predication was made that
number will increase to 600 million by the year 2100 (Mimura and Nicholls, 1998). Climate change and accelerated
sea level rise are major threats for the coastal population. Vulnerability can be defined as the degree to which a
person, community or a system is likely to experience harm due to an exposure to an external stress (Mahendra et
al., 2011). Vulnerability is a set of condition and processes resulting from physical, social, economic and
environmental factors that increase the susceptibility of a community to the impact of hazards (Mahendra et al.,
2011). The literature survey indicates that significant work was carried out on individual hazards such as storm
surge, seal level change, shoreline change (Dube et al., 2006; Rao et al., 1997, Kumar et al., 2008). Remote sensing
and GIS tool effectively used to carry out the coastal vulnerability (Gorntiz, 1990; Hegde and Reju, 2007; Kumar et
al., 2010). While a lot of work was carried out to assess individual hazard assessment, few studies have taken into
account multiple stressors to assess coastal vulnerability towards disasters. Mahendra et al., 2011 assessed the
coastal multi-hazard vulnerability using geospatial technique for Cuddalore-Villupuram using six parameters which
are shoreline change rate, sea level change rate, coastal slope, tidal range, coastal regional elevation and storm
surge. Therefore, more research needs to be carried out to understand the coastal vulnerability due to multiple
coastal hazards. The damages occur from cyclone during the land fall is due to three factors which are rain, strong
winds, and storm surges. Most of the damages are caused due to inundations which are caused due to heavy rain
from cyclone, flooding of the river deltas. The impact can be minimized by giving early predictions and warnings
of the cyclones and preparing the evacuation plans before any disaster occurs.

In this study we try to assess the multi- hazard vulnerability by using quantitative approach to estimate the spatial
extent of inundations cause by the composite hazards. The parameters used in the current study are shoreline
change rate, sea level change rate, tsunami run–up and topography data to prepare a multi hazard vulnerable map by
overlaying the roads network to establish evacuation route during disasters.

2. Study Area

Figure 1 Study area

The current study area is the coastal zone covering Visakhapatnam district of Andhra Pradesh in the Bay of Bengal
along the north east coast of India (Fig.1). The study area lies between 17°43'43.278"N to 17°47'52.017" N latitude
and 83°14'23.022"E to 83°20'46.786" E longitude. Visakhapatnam is vulnerable to natural disasters such as storm
surge, cyclones, erosion. The two tropical cyclones Hudhud (October 13, 2014) and Phylin (October 11, 2013)
formed on Bay of Bengal has caused devastating impact on the coastal region of Visakhapatnam. As per the census
of India the city has the population of 2.1 million in 2011 and it has been estimated that, it will increase to 3.78
million in 2017. The average elevation along the Visakhapatnam coast is around 30 m. There are 60 tropical
cyclones which affected the Andhra Pradesh coast from year 1971 to 2014 (IMD eAtlas).

3. Data Used

The data sets used for the generation of MHVM for the study area is given in Table 1. The satellite data from the
Landsat Multi Spectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper (ETM) and
Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) sensor for the duration of 1973 to 2015 were
used. The digital terrain model was generated using ASTER data for topographic analysis. Long term monthly
mean tide data were downloaded from the GLOSS network. The slope was generated using Bathymetry data
acquired from General Bathymetry Chart of the Ocean (GEBCO).

Table 1 Details of Data used

Data Resolution (m) Period Source

Landsat 1-5 MSS 60 Feb, 1973

Landsat 4-5 TM 30 Mar, 1988
USGS
Landsat 7 ETM+ 30 Mar, 2003
Landsat 8 OLI/TIRS 30 Feb, 2015

ASTER DEM 30 2014 USGS

Bathymetry 1 arc minute interval grid 2008 Grid GEBCO

Sea Level Rise Change - 1937-2011 GLOSS

Tsunami Run-up - 2004 INCOIS

4. Methodology

The multi-hazard mapping has been carried out using the parameters; i) shoreline change rate, ii) coastal slope, iii)
coastal elevation, iv) sea level change rate, and v) tsunami run-up. The flow chart (Fig. 2) shows the general
methodology that has been adopted. To estimate the sea level trend, the tide gauge data of Visakhapatnam location
were obtained from the GLOSS to observe the long term tidal observations. The digital terrain model (DTM) was
generated using ASTER data. The elevation contour was generated using the DTM by using Golden Surfer
Software v.14 by Golden Software, LLC, Golden, Colorado, USA. The shoreline change rate was estimated using
the Landsat data as indicated in Table 1. Based on the rate of shoreline change the future shoreline position for the
erosion area was estimated using ArcGIS Software. The Tsunami model has been used (Kumar et al., 2010), which
takes the seismic deformation and bathymetry as input to predict the run-up heights and travel times of a tsunami
wave for different parts of the coastline. The maximum water heights (MWH) generated from the tsunami model
were interpolated using krigging interpolation method in ArcGIS Software to identify the extent of water run up
towards the land for the nearby regions of Visakhapatnam. The results obtained from the remote sensing data and
sea level data were combined and overlaid with the road network (digitized from Google Earth) to generate the final
multi-hazard vulnerable map which gives the information about evacuation routes, hazard and safe areas.
 Figure 2 Flow chart depicting the methodology

4.1 Shoreline Change Rate Calculation

Shoreline changes occur due to dynamic coastal processes controlled by wave characteristics, near shore
circulation, sediment characteristics, beach form etc. The Ortho-rectified and georegistered Landsat MSS, TM,
ETM+ and OLI/TIRS data were downloaded from USGS for the dates as shown in Table 1. These data were
projected to Universal Transverse Mercator (UTM) projection system with WGS-84 datum. The Shoreline along
the coast was digitized using the ArcGIS Software. The digitized shoreline for the years 1973, 1988, 2003 and 2015
in vector format was used as input for the Digital Shoreline Analysis (DSAS) extension in ArcGIS (USGS, 2005) to
calculate the rate of shoreline change. Coast with accretion is considered as less vulnerable while the erosion
indicates vulnerability due to loss of private and public properties. The End point Rate (EPR) method was used to
calculate net shoreline change rate (Fig. 3).

Figure 3 Graph depicting erosion and accretion

4.2 Sea Level Trend Calculation

Sea level rise due to climate change can cause significant impact on coastal regions. Tide gauge data from Global
Sea level observing system (GLOSS) was used as primary source of information for sea level trend for the study
area. Long term monthly mean tide gauge data during the period (1937 to 2011) was extracted for the study region
from the Visakhapatnam tide gauge location (Fig. 4). The monthly mean value of sea level was recorded from
Visakhapatnam tide gauge station and plotted with least square method to identify the sea level trend.
 Figure 4 Graph depicting the mean sea level data from Visakhapatnam tide gauge showing the sea level trend

4.3 Coastal Regional Elevation

Figure 5 Coastal elevation map

Regional elevation helps us to indentify the extent of land threatened due to sea level rise and climate change
impacts. Coastal region having low elevation is considered as highly vulnerable towards coastal disasters, while
higher elevation areas are considered as less vulnerable (Fig. 5). Ortho rectified ASTER-DEM data was used to
derive the coastal elevation for the study area. The elevation contour using Golden Software Surfer with one metre
contour interval was generated to identify the high and low elevated areas.

4.4 Coastal Slope

Bathymetric data show the depth from the coast towards the open ocean. General Bathymetric Chart of the Oceans
(GEBCO) data of one-minute grid resolution bathymetric data was used to get the regional slope of the coastal area.
GEBCO data are useful in deriving the costal slope values on both land and in the ocean. Coastal area having
gentle slope refers to as highly vulnerable areas and steep slope refers to as having low vulnerability (Fig. 6).
 Figure 6 Slope Map of the Visakhapatnam

4.5 Tsunami run-up

Figure 7 Graph depicting the Tsunami run-up for nearby location of the Visakhapatnam

Tsunamis results in generation of waves of different periods and heights. The wave’s parameters generated from
Tsunami model depends on earthquake source parameters, bathymetry, beach profile, coastal land topography and
presence of coastal structure. These surges cause flooding of sea water into the land as much as 1 km or even more,
resulting in loss of property and human life. The Tsunami model was used, which takes the seismic deformation
and bathymetry as input, to predict the run-up heights and travel times of a tsunami wave for different parts of the
coastline (Kumar et al., 2010). The maximum water heights (MWH) was identified for the nearby regions of
Visakhapatnam and those values were interpolated using krigging interpolation to identify the extent of water run
up towards the land.

4.6 Generation of Multi-Hazard Vulnerable Map

A MHVM helps in understanding the risk due to the various natural hazards. The main purpose of MHVM is to
represent the vulnerability, risk and hazard information together on a single map. Multi-hazard maps provide the
information of extent of risk involved due to the hazards along with geographical details such as human settlement,
infrastructure , resources that will be affected. The results obtained from the remote sensing data and sea level data
were combined and a composite map was generated as the multi hazard map.

For risk assessment, different levels of risks need to be understood within the demarcated hazard zone. Using the
visual interpretation technique built up area, and roads were extracted from the Google Earth image and
georeferenced so that roads can overly properly on the satellite images. Different types of roads such as major and
minor roads were extracted and names were assigned based on their local names. The risk classes were assigned
based on the land use information’s within the coastal hazard zone. The built up areas were classified as high risk
class, beach and sandy patches near to the coast were identified as moderate risk class while the remaining classes
under hazard area was considered as having low risk.

5. Results and Discussions

Figure 8 Multi-hazard vulnerable map showing the areas under different risk levels and the no risk areas
overlaid with roads, settlements and the evacuation routes.

Shorelines change analysis was carried out for the years 1973, 1988, 2003 and 2015, which revealed that the area
experienced both erosion and accretion. The erosion of up to 5.8 m/y and accretion of up to 10 m/y was recorded
for the study region (Fig. 3). The shoreline analysis was assessed along the open coast excluding the river and creek
mouths as these areas are seeing rapid changes due to development activities such as port development.
Visakhapatnam tide gauge data was used to calculate the sea level change for 70 years during 1940 – 2011 which
indicated a value of 0.75 mm/year (Fig. 4) which is slightly lower than the global average of 1-2 mm/year. The
topographic data from ASTER DEM depicted that large coastal areas were situated under low elevation, and hence
vulnerable to sea level rise and storm surge (Fig. 5). The Coastal slope for the study area ranged between (0 to
0.2°) which indicated a threat for the coastal communities because of inundation due to flooding hazards (Fig. 6).
The Tsunami run – up in this study was used as an additional parameter which provided information about the
maximum water height for the Visakhapatnam and its nearby locations. The maximum water height (MWH) at
Visakhapatnam was found to be 2.8m while for the neighbouring areas highest MWH of 4.1 m was found to be at
Patapolvaram 4.1m and the lowest MWH of 2.0 m was found to be at Koyyam (Fig. 7). The multi-hazard zones
were calculated for the study region using shoreline erosion, sea level rise, coastal regional elevation, coastal slope
and tsunami run-up. The maximum extent of the coastal hazard zone was found to be 20 km from the current
coastline of the Visakhapatnam district with an area of 150 km2 for the delineated hazard zone.

Total population living in the hazard area is 2.1 million urban populations. The population is partially and fully
threatened by the disaster such as storm surge, sea level rise. For preparing the MHVM additional parameters such
as land use/land cover information, road networks, built up areas were taken as reference to identify the social
vulnerability and for preparing the mitigation and rescue efforts. The roads and settlements were overlaid on the
maps to produce a final multi hazard map with risk classes classified as high, medium and low (Fig. 8).

6. Conclusion and Recommendations

The quantitative approach for generating Multi-hazard vulnerable map indicates that the Visakhapatnam district is
under high risk when compared to other districts in the region. The use of remote sensing and GIS used in this
study helps in visualizing the resulting MHVMs and thus adds value by identifying evacuation routes. The maps
presented here can be useful for management action plans such as prioritization of the areas for the evacuation,
planning of evacuation routes, identification of the safe shelters, etc. The use of observed hourly sea level data can
further improves the methodology or accuracy.

7. Acknowledgements
We would like to acknowledge NRSC-NICES for giving their support in completing this work. Our sincere thanks
go to INCOIS for providing the Tsunami Run-up Data and giving necessary support for completing this research
work.

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