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Assessment and management of coastal multi-hazard vulnerability along the Cuddalore–Villupuram, east coast of India using geospatial techniques
Ocean & Coastal Management 54 (2011) 302e311
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Ocean & Coastal Management journal homepage: www.elsevier.com/locate/ocecoaman
Assessment and management of coastal multi-hazard vulnerability along the CuddaloreeVillupuram, east coast of India using geospatial techniques R.S. Mahendra a, *, P.C. Mohanty a, H. Bisoyi a, T. Srinivasa Kumar a, S. Nayak b a b
Indian National Centre for Ocean Information Services (INCOIS), Hyderabad 500 055, India Ministry of Earth Sciences (MoES), New Delhi 110003, India
a r t i c l e i n f o
a b s t r a c t
Article history: Available online 9 January 2011
The current study area is coastal zone of Cuddalore, Pondicherry and Villupuram districts of the Tamil Nadu along the southeast coast of India. This area is experiencing threat from many disasters such as storm, cyclone, flood, tsunami and erosion. This was one of the worst affected area during 2004 Indian Ocean tsunami and during 2008 Nisha cyclone. The multi-hazard vulnerability maps prepared here are a blended and combined overlay of multiple hazards those affecting the coastal zone. The present study aims to develop a methodology for coastal multi-hazard vulnerability assessment. This study was carried out using parameters probability of maximum storm surge height during the return period (mean recurrence interval), future sea level rise, coastal erosion and high resolution coastal topography with the aid of the Remote Sensing and GIS tools. The assessment results were threatening 3.46 million inhabitants from 129 villages covering a coastal area 360 km2 under the multi-hazard zone. In general river systems act as the flooding corridors which carrying larger and longer hinterland inundation. Multihazard Vulnerability maps were further reproduced as risk maps with the land use information. These risk caused due to multi-hazards were assessed up to building levels. The decision-making tools presented here can aid as critical information during a disaster for the evacuation process and to evolve a management strategy. These Multi-hazard vulnerability maps can also be used as a tool in planning a new facility and for insurance purpose. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction Natural disasters have always been a serious threat to the life and property all over the world. Though it is not possible to prevent natural disasters, it is certainly possible to reduce their impact by evolving appropriate preparedness plans and mitigation measures. A guiding principle behind these mitigation plans is the school of thought that the risk reduction plans should be compatible for multiple hazards, rather than being specific to tropical cyclone or tsunami. Such a composite approach not only minimizes the duplication efforts and computational overloads but eliminates the possibility of risk substitution. Mitigating the effects of potential disasters and having the appropriate infrastructure in place for response requires detailed knowledge on the vulnerability of the places to a wide range of environmental hazards (Cutter et al., 2000). A tool developed to address the situation was first
* Corresponding author. Tel.: þ91 4023886038; fax: þ91 4023895001. E-mail addresses: [email protected] (R.S. Mahendra), mohanty@incois. gov.in (P.C. Mohanty), [email protected] (H. Bisoyi), [email protected] (T.S. Kumar), [email protected] (S. Nayak). 0964-5691/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.ocecoaman.2010.12.008
proposed by Federal Emergency Management Agency (FEMA) called Multi-hazard Mapping Initiative (MMI) (FEMA, 1997). In simple words, 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. Generically, vulnerability is a set of conditions and processes resulting from physical, social, economic and environmental factors that increase the susceptibility of a community to the impact of hazards. Vulnerability also encompasses the idea of response and coping, since it is determined by the potential of a community to react and withstand a disaster (Kumpulainen, 2006). The degree to which populations are vulnerable to hazards is not solely dependent on proximity to the potential source of the threat. Social factors such as wealth and housing characteristics can contribute to greater vulnerability on the part of some population subgroups. Vulnerability assessment is an estimate of the degree of loss or damage that could result from a hazardous event of given severity, including damage to structures, personal injuries, and interruption of economic activities and the normal functions of settlements. A Multi-hazard Vulnerability Map (MHVM) incorporates vulnerability in understanding the risk due to a hazard. By facilitating the interpretation of hazard information, it increases the
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likelihood that the information will be used in the decision-making process. The main purpose of MHVM is to represent vulnerability, risk, and hazard information together on a single map. These may include: the different hazard-related information for a study area to convey a composite picture of the natural hazards of varying magnitude, frequency, and area of effect. The Multi-Hazard Map (MHM) is referred to as a composite, synthesized and an overlaid hazard map. This is an excellent tool to create awareness in mitigating multiple hazards (USAID, 1991). The maps produced can be easily read and accessed, which facilitate the administrators and planners to identify risk areas and prioritize their mitigation/ response efforts. The 7500 km long coastline of India is threatened by many natural hazards resulting in the loss of life and property. The hydrologic factors with respect to coastal hazards that are influential are: tropical cyclones, sea level rise, floods, coastal erosion, and storm surge along Indian coast. The east coast of India is affected by the seasonal tropical cyclones in SW monsoon as well as in the return NE monsoon as compared to west coast. The damage from land falling cyclones is mainly due to three factors: rain, strong winds, and storm surges. Storm surges associated with severe tropical cyclones are by far the most damaging (Dube et al., 2009). About 90% of the damage is due to inundation of land by sea water. In addition, flooding of the river deltas occurs from the combined effects of tides and surges from the sea, which penetrate into the rivers, because at the same time, excess water in the rivers due to heavy rains from the cyclone is trying to flow through the rivers into the sea. The impact would be much lower if these hazards could be predicted reasonably well in advance allowing effective warnings and evacuations in the threatened areas. The literature survey indicates that there is significant work on the individual hazards such as storms, cyclones, tsunamis, sea level change and shoreline change. (Dube et al., 2006; Rao et al., 1997; Chittibabu et al., 2002; Indu Jain et al., 2006; Unnikrishnan and Shankar, 2007; Rao, 2005; Rao et al., 2009; Kumar et al., 2008). However, the remote sensing and GIS tools can be effectively used to assess the coastal vulnerability (Gornitz, 1990; Pendleton et al., 2005; Kumar et al., 2010; Hegde and Reju, 2007; Pethick and Crooks, 2000; Thieler, 2000). In particular very few works were carried out regarding general multi-hazard vulnerability mapping within the subcontinent. Federal Emergency Management Agency (FEMA) has started Multihazard Mapping Initiative (MMI) to provide multi-hazard advisory maps (FEMA, 2003). The coastal multi-hazard vulnerability mapping to bring out the composite maps representing the various disasters were not extensively carried out. Very few works on multi-hazard mapping were reported apart from the Americas and Europe (Tate et al., 2010; Habib and Fahmi, 2009). The current study aims at developing the methodology for assessing the multi-hazard vulnerability and gather quantitative estimate on the spatial extent of the inundation caused by composite hazards. The parameters used in the current study are: shoreline change rate, sea level change rate, historical storm surges and the high resolution topography. Further risk maps and evacuation routes are generated by imbibing land use, transport, and structural (a proxy for demographic information) information. 2. Study area The selected study area is part of the Tamil Nadu state in the Bay of Bengal covering the districts of Cuddalore, Villupuram and Pondicherry along the southeast coast of India (Fig. 1). The geographical constraints of the study area are 11180 2000 to 12150 2100 N latitudes and 79 350 3600 to 80 020 3400 E longitude covering a length of 100 km coastline (Fig. 1). The Bay of Bengal is one of the six regions in the world where severe tropical cyclones
303
originate in the months of May, November, and December. This area experienced nearly 60 cyclonic surges and severe cyclonic surges in past century (IMD eAtlas). The storm surges are well known for their destructive potential and impact on human activities due to associated strong winds along the coast and heavy rainfall. An added risk factor is that large parts of the coastal zone are low lying with gentle slope resulting large inundation, thus increasing the vulnerability of the region. This area is one of the worst hit due to 2004 Indian Ocean tsunami (Murthy et al., 2006). 3. Data and methods The data sets used for generation of the MHVM in this study area listed in Table 1. The details of the historical storms were obtained from the National Institute of Oceanography (NIO) database, the storm surges were calculated using tide gauge data (Unnikrishnan et al., 2004). The MHVM has been carried out using the parameters sea level change rate, shoreline change rate, elevation contours and historical storm surge. The flow chart (Fig. 2) shows the general methodology adopted in current study. 3.1. Calculation of sea level change rate Tide gauge dataset from Global Sea-level Observing System (GLOSS) database during the past century was used as the primary source of sea level information. The nearest tide gauge location in the study area, Chennai was selected (inset map in Fig. 1). Sea level change rate was estimated using long-term (1952e2005) monthly mean tide gauze data. Long-term monthly mean tide gauge data recorded for Chennai during the period from 1952 to 2005 was used estimate sea level change rate. The monthly mean values of sea level recorded from Chennai tide gauge were plotted, from which a linear best fit line using least squares method was computed calculate the sea level change rate. 3.2. Calculation of shoreline change rate Ortho-rectified Landsat MSS and TM images covering the study area for the period 1972, 1990 and 2000 were downloaded from the website www.landsat.org. The data were projected to Universal Transverse Mercator (UTM) projection system with WGS-84 datum. The shoreline along Cuddalore coast was digitized using ArcMap 9.2 and ERDAS Imagine software using on-screen point mode digitization technique. Near infrared band that is most suitable for the demarcation of the landewater boundary has been used to extract the shoreline. The digitized shoreline for the years 1972, 1990 and 2000 in the vector format were used as the input to the Digital Shoreline Analysis System (DSAS) to calculate the rate of shoreline change (USGS, 2005). 3.3. Estimation of extreme storm surges and return periods The prediction of the design extreme surge heights were incident at coast needs to be estimated. Probability and statistics were used to estimate future frequencies based on the historic surge records. The annual extreme storm surge heights were extracted for the Chennai station from Unnikrishnan et al. (2004) recorded during the period 1975e1987. Based on historical storm surge a value of 2.06 m was maximum height recorded. Using this as base data the extreme storm level was estimated using Gringorten method for the Chennai tide gauge station was 2.31 m in a return period 100 years. Non-exceedance probability is an index of the likelihood that a specified surge level will not be exceeded an extreme value of the variable under study for the chosen period. For instance the
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Fig. 1. Study area.
probability that the surge level will not exceed 2.06 m is 0.94 as observed from the surge level data for Chennai station (data not included). Observed surge heights during 1975e1987 for the Chennai were used to estimate the probability of non-exceedance in this study. The highest water level during storm surge in each year is extracted and assigned a rank series from smallest to largest m ¼ 1, 2.N. Gringorten method (Eq. (1)) is used to calculate the probability of non-exceedance (p) (Gringorten, 1963).
py
m 0:44 m 0:44 ¼ N þ 1 0:88 N þ 0:12
where, ‘m’ is rank of the variable (surge level) ‘N’ is the total number of observations
(1)
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‘a’ is the slope (estimated 12.492in this study) and ‘u’ is the intercept (estimated 173.06 in this study)
Table 1 List of data used for the current study. Data
Resolution (m)
Period
ALTM Carto DTM Landsat, MSS Landsat, TM Landsat, ETM Quickbird Tide gauge data Historical storm surge
5 10 57 30 30 0.6 e e
2007 2006e2007 1972 1990 2000 2006 1952e2005 1975e1987
e: Not applicable.
Reduced variate is applied for consistent and direct comparisons between different distributions in a probability plot. Reduced variate ‘y’ is linearly related to probability of non-exceedance ‘p’. Reduced variate is estimated using probability of non-exceedance as shown in Eq. (2). y ¼ loge(loge p)
(2)
where, “y” is reduced variate and ‘p’ is probability of non-exceedance. Values of observed annual maximum surge levels versus reduced variate were plotted and the best fit line computed using least squares method is shown in Fig. 3 (left panel). The slope 12.492 and intercept 173.06 obtained were used to calculate maximum surge height for desired return periods (Fig. 3, right panel) using Generalized Extreme Value (GEV) distribution function (Eq. (3)).
1 Maximum surge heightU ¼ uþa loge loge 1 R
(3)
where,
3.4. Generation of elevation contours The high resolution topographic data using Airborne Lidar Terrain Mapping (ALTM) was obtained from National Remote Sensing Centre (NRSC) has been incorporated in the current study. ALTM data extends 2 km into the coastal zone on landward side. Data for the areas beyond 2 km was used from the Digital Terrain Model (DTM) generated using the Cartosat-1 data. These two data sets were merged and the elevation contours were generated using the ArcMap software. The krigging interpolation was used to interpolate the contour lines were smoothed to avoid sharp bends. 3.5. Generation of multi-hazard map The sea level change rate was observed to be equal to 0.085 mm/ y for the area hence after 100 years the sea level will be 8.5 mm. The shoreline after 100 years has been projected based on the rate of the shoreline change. The future areas of erosion were extracted from these results. The maximum storm surge estimated for the 100 years of return period was 2.31 m. This is comparable to previous works (Rao, 1968). The inundation level for the area is 2.32 m including the extreme storm surge level and future sea level. This inundation level considered as 2.5 m for the selected study area. The projected areas under erosion after 100 years and area under 2.5 m elevation were overlaid in a GIS environment. The union of all these information is taken as the multi-hazard zone. The MHVM of the study area was prepared by overlaying the multihazard areas on the base maps. 3.6. Risk assessment for hazard management
‘U’ is the extreme surge height (extreme water level) ‘R’ is the return period in years,
Just an MHVM is not sufficient enough for the coastal managers. They need to understand the different levels of risk within the
Long Term Sea Level Data (Tide gauges)
Remote Sensing Data (Landsat MSS/TM/ETM, QB)
Sea Level Trend
Historical Storm Surge Heights (Hmax)
ALTM, DTM (Cartosat)
Shoreline Change Rate
Sea Level at T (A)
Storm Surge Level at T (B)
Elevation Contours
Future Shoreline after T (D)
Inundation Level (C) = A+B at T
Contour Level of C
Land use, Transport, Buildings
Composite Multi-hazard Line=Union of C & D
Multi-hazard Map
Risk Maps ‘T’ is return period
Fig. 2. The flow chart is depicting the methodology.
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2.40
220 210
2.30
190
Water Level (cm)
Water Level (cm)
200
180
y = 12.492x + 173.06
170
2
R = 0.9301
2.20
2.10
2.00
160 1.90
150
1.80
140 -1
0
1
2
3
5
4
10
20
30
40
50
60
70
80
90
100
Return Periods (year)
Reduced variate (-In(-In(p)))
Fig. 3. Reduced variate (left panel) and extreme water levels during storm surge for different return periods (right panel) calculated using Gringorten method for the Chennai station.
demarcated hazard zone. The coastal land use, buildings and roads were extracted using the Quickbird Image (acquired during 2006) based on the visual interpretation technique. The map of MHVM has been intersected with coastal land use. Then the risk classes were assigned within the coastal hazard zone based on the existing land use/land cover information. Based on the threat posed, the risk areas within the multi-hazard zones were further categorized. The builtup areas were assigned the high risk; beach/sandy patches near the coast were assigned moderate risk and the remaining classes in the hazard areas were kept under low risk. The buildings falling within and outside hazard area were also assessed and overlaid on the risk map. The different types of roads such as major, minor and street were extracted and the named according the local names.
Shorelines extracted for the years 1972, 1990 and 2000 indicate that the area experienced both the erosion (up to 5.5 m/y) and accretion (up to 6 m/y). Shoreline change rate assessed along the open coast, the river/creek mouths were excluded in this study. Because the changes at the river/creek mouth depict dynamic changes especially in the morphology of the river spits, hence the trend will be off the norms. The shoreline projected for the next 100 years for the open coasts indicates that an area of 3.77 km2 is going to be eroded in the 100 years. 4.1. Multi-hazard vulnerability assessment The multi-hazard zones were calculated for the study area using future sea level, maximum estimated storm surge and maximum possible shoreline erosion. From the Fig. 5, maximum extent of the hazard zone is seen as 20 km from the current coastline of the Cuddalore district. The extent of the hazard zone in the vicinity of the rivers was observed to be up to 10 km. The study shows that total area of 360 km2 has been delineated hazard zone with 314 km2, 23 km2 and 23 km2 under Cuddalore, Villupuram and Pondicherry
4. Results and discussions Sea level change calculated using the Chennai tide gauge data recorded for 54 years during 1952e2005 indicates a value of 0.085 mm/y (Fig. 4), which is very much lower than the global average 1e2 mm/y.
7400
y = 0.0071x + 6983.4 R2 = 0.0001
7200 7100 7000 6900 6800
Aug-2003
Aug-2000
Aug-1997
Aug-1994
Aug-1991
Aug-1988
Aug-1985
Aug-1982
Aug-1979
Aug-1976
Aug-1973
Aug-1970
Aug-1967
Aug-1964
Aug-1961
Aug-1958
Aug-1955
6700 Aug-1952
Sea Level (mm)
7300
Date (mmm-yyyy) Fig. 4. Graph depicting the monthly mean sea level data from Chennai tide gauge showing the sea level trend.
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Fig. 5. Map showing the Multi-hazard zones and safe zones pertaining to Cuddalore, Pondicherry and Villupuram.
districts respectively. The safe zones are marked on the map (Fig. 5) as green.1 They are at higher elevation and can be used as temporary safe shelters during a disaster. The southern parts of the Cuddalore district near Chidambaram Town in the vicinity of the Kolladam River indicate larger areas under threat due to multiple hazards, because of lower mean elevation. Murthy et al. (2006) reported extensive damage along the Cuddalore coast during the 2004 Indian Ocean tsunami. With the MHVM, it is now clear that why large area
was inundated during cyclone Nisha in the year 2008 (Alertnet, 2008). In general, the extent of the hazard zones is much more in the vicinity of the river and creeks, with the exception of Chidambaram area. The river systems are corridors for the inundation allowing the flood water to be carried upstream for long distances. This results in flooding along the proximal areas of the rivers.
4.2. Risk assessment and management action plans 1 For interpretation of the references to colour in the text, the reader is referred to the web version of this article.
From our MHVM analysis, there are a total of 129 inhabited villages falling under the multi-hazard zone including nine urban
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areas. Total population living in the identified hazard area is approximately 0.26 million of rural populations and 3.2 million urban populations. This population is partially or fully threatened by the disasters and is under risk thus needing massive relief effort for the evacuation. The additional parameters land use/land cover, roads and buildings were generated for the small area to demonstrate the social vulnerability and thus guide in mitigation and rescue efforts. The identified and classified road network from satellite image can be used as an evacuation route. The hazard zones were further classified into risk classes (see Fig. 7) using the land use/land cover
information (Fig. 6). It can be seen that there are no risk areas outside of the hazard zone. Thus the hazard zones derived using our adopted methodology depicts the maximum extent of damage possible and priorities needed within the identified zone. The roads and the building polygons are overlaid on the risk maps. The roads are labeled according the local name and buildings positioned in the hazard area and safe area were represented in different symbols. The reason for the usage of common street names is that it simplifies management decisions during an event, and common populace can understand the directives and advisories easily. A rigorous census (under a different project sponsored by INCOIS) is
Fig. 6. Map showing the land use classes of the Cuddalore town with roads in the foreground and Quickbird imagery in the background.
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Fig. 7. Risk map showing the areas under different risk levels and the no risk areas overlaid with roads and the evacuation routes. The polygons of the safe and hazard buildings overlaid those are fall in the hazard and safe area.
currently underway to get the details on the building owner, number of people living in the building and their contact details. Once ready, the database of mobile telephone can be used to directly alert specific building or areas. In terms of causeeeffect, most of the hazards affect the coastal areas similarly with flooding/inundation. Consequently, the human population responds to flooding by rushing to higher ground. Immediate casualties during an extreme event are attributed more due to panic and resulting stampedes than the
rising water level. With the availability of evacuation routes from MHVM, it is hoped that the immediate panic related casualties could be reduced. With the usage of MHVM, critical communication infrastructure can be identified for coordinating search-andrescue (S&R) operations. Such a planned S&R activity can further mitigate the impact of the disaster. Integration of building structural aspects in MHVM can further alleviate the problem of identification of safe shelters. MHVM can identify and delineate possible coastal areas that are prone to water-logging and
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subsequent material decay, thus incidences of water-borne diseases can be contained at least spatially. In terms of environmental factors, flooding causes damage to agricultural land affecting the crops, thus the livelihood and the economy of the country. The encroachment of sea water into soil alters the pH value, decreasing the soil quality and affecting the produce. Also in terms of ecological factors, sea water encroachment alters the floral habitats and their environment. All wet-land elements like coral reefs, mangroves and sea-grass are disturbed due to flooding. Typical incidents have been the death of coral reefs off Andaman due to sedimentation (Bahuguna et al., 2008; Saxena et al., 2008) and reef banks of Gulf of Mexico (Lugo-Fernández and Gravois, 2010), caused due to inundation run-offs. Coastal habitats are also affected due to salt-water intrusion and flooding. Some of these risks may have long-term impact on human, environment and economy. The concept of MHVM can further extended by including vulnerability of these habitats to various oceanic hazards. Some of the action plans that can be chalked up based on MHVM are: i. The setback line can be physically demarcated on ground using these multi-hazard maps. ii. The building codes for the new developments and for the modification of existing structures within this setback line has to be updated and implemented. iii. Identify various safe shelters equipped with emergency facilities. iv. Conduct awareness program for the coastal community living in the multi-hazard zone. Provide instructions to community of each area on how to reach their respective safe shelters via identified evacuation routes. v. Set-up the volunteer team from the identified vulnerable local community and train them. vi. Estimate the requirement of the rescue task force and emergency aids in different identified administrative areas during a disaster. vii. Prioritize the evacuation based on the risk maps and population. viii. The drainage system gets chocked during the disasters. Hence this drainage network needs to be maintained and the settlements in the vicinity need to be prohibited. ix. The mangroves and coral reefs in the coastal act as the barriers for the natural disasters, hence these need to be protected. Also these need to be developed (mangrove plantation and coral culture) if the suitable environment available. x. Finally, MHVM can help in planning for the aftermath of an event: rehabilitation, civil disturbances, restoration of the utilities, health problems, etc. 5. Conclusions The application of MHVM approach indicates that Cuddalore district is under high risk when compared to other districts in the region. The remote sensing and GIS techniques used in the current study in compliment the long-term in-situ observations. The use of GIS further helps in visualizing the resulting MHVMs, and adds value by identifying evacuation routes. The use of observed hourly sea level data can further improve the methodology or accuracy. The risk maps presented here are useful to draw management action plans such as prioritization of the areas for the evacuation, planning of evacuation routes, identification of the safe shelters, etc. Furthermore these maps are useful tools in raising awareness among the local community and government authorities which are responsible for disaster management. If a hazard warning were
issued the local people should have answers for: Where should they move? Which road they should take? And which direction they have to move? The MHVM and risk maps produced here helps in answering such questions in a form easily understandable graphical representation. Acknowledgements The authors would like to thank the Global Observatory for Ecosystem Services (GOES), Michigan State University for the Landsat data, Global Sea Level Observing System (GLOSS) for the sea level data and USGS for the making available the Digital Shoreline Analysis Software (DSAS) on their website. Thanks to the Director, INCOIS for the encouragement. Authors are thankful to Mr. Raghavendra, S. Mupparthy and T.V.S. Udaya Bhaskar for their useful suggestions. This is INCOIS contribution number 67. References Alertnet, 2008. ACT alert: Cyclone Nisha e floods in Tamil Nadu, India. http://www. alertnet.org/thenews/fromthefield/222031/122829549032.htm (accessed 23.06.10). Bahuguna, A., Nayak, S., Roy, D., 2008. Impact of the tsunami and earthquake of 26th December 2004 on the vital coastal ecosystems of the Andaman and Nicobar Islands assessed using RESOURCESAT AWiFS data. International Journal of Applied Earth Observation and Geoinformation 10, 229e237. Chittibabu, P., Dube, S.K., Rao, A.D., Sinha, P.C., Murty, T.S., 2002. Numerical simulation of extreme sea levels for the Tamil Nadu (India) and Sri Lanka coasts. Marine Geodesy 25 (3), 235e244. Cutter, S.L., Mitchell, J.T., Scott, M.S., 2000. Revealing the vulnerability of people and places: a case study of Georgetown County, South Carolina. Annals of the Association of American Geographers 90 (4), 713e737. Dube, S.K., Indu Jain, Rao, A.D., Murty, T.S., 2009. Storm surge modelling for the Bay of Bengal and Arabian Sea. Natural Hazards 51, 3e27. Dube, S.K., Indu Jain, Rao, A.D., 2006. Numerical storm surge prediction model for the North Indian Ocean and the South China Sea. Disaster and Development 1, 47e63. FEMA, 1997. Multi-hazard Identification and Risk Assessment. Government Printing Office, Washington. http://www.fema.gov/pdf/fhm/mhira_in.pdf (accessed 08.01.10). FEMA, 2003. Multi-hazard mapping site a big success, FEMA says. http://www. fema.gov/news/newsrelease.fema?id¼3121 (accessed 04.01.10.). Gornitz, V., 1990. Vulnerability of the east coast, U.S.A. to future sea level rise. Journal of Coastal Research 9, 201e237. Gringorten, I.I., 1963. A plotting rule for extreme probability paper. Journal of Geophysical Research 68 (3), 813e814. Habib, S., Fahmi, A., 2009. On integrating of multi-hazard mapping in Indonesia. http://www.gisdevelopment.net/application/natural_hazards/overview/ mma09_habib.htm (accessed 04.01.10.). Hegde, A.V., Reju, V.R., 2007. Development of coastal vulnerability index for Mangalore coast, India. Journal of Coastal Research 23, 1106e1111. Indu Jain, Chittibabu, P., Agnihotri, N., Dube, S.K., Sinha, P.C., Rao, A.D., 2006. Numerical storm surge prediction model for Gujarat coast of India and adjoining Pakistan coast. Natural Hazards 42, 67e73. Kumar, C.S., Murugan, P.A., Krishnamurthy, R.R., Batvari, B.P.D., Ramanamurthy, M.V., Usha, T., Pari, Y., 2008. Inundation mapping e a study based on December 2004 tsunami hazard along Chennai coast, Southeast India. Natural Hazards and Earth System Sciences 8, 617e626. Kumar, T.S., Mahendra, R.S., Nayak, Shailesh, Radhakrishnan, K., Sahu, K.C., 2010. Coastal vulnerability assessment for Orissa State, east coast of India. Journal of Coastal Research 26 (3), 523e534. doi:10.2112/09-1186.1. Kumpulainen, S., 2006. Vulnerability concepts in hazard and risk assessment. Natural and technological hazards and risks affecting the spatial development of European regions. In: Schmidt-Thomé, Philipp (Ed.), Geological Survey of Finland, Special Paper, 42, pp. 65e74. Lugo-Fernández, A., Gravois, M., 2010. Understanding impacts of tropical storms and hurricanes on submerged bank reefs and coral communities in the northwestern Gulf of Mexico. Continental Shelf Research. doi:10.1016/ j.csr.2010.03.014. Murthy, K.S.R., Subrahmanyam, A.S., Murty, G.P.S., Sarma, K.V.L.N.S., Subrahmanyam, V., Rao, K.M., Rani, P.S., Anuradha, A., Adilakshmi, B., Sri Devi, T., 2006. Factors guiding tsunami surge at the NagapattinameCuddalore shelf, Tamil Nadu, east coast of India. Current Science 90 (11), 1535e1538. Pendleton, E.A., Thieler, E.R., Jeffress, S.W., 2005. Coastal Vulnerability Assessment of Golden Gate National Recreation Area to Sea-Level Rise. USGS Open-File Report 2005-1058. Pethick, J.S., Crooks, S., 2000. Development of a coastal vulnerability index: a geomorphological perspective. Environmental Conservation 27, 359e367. Rao, A.D., 2005. Development of long-term hazard planning, management and vulnerability reduction action plan. In: Respect of Cyclones. International
R.S. Mahendra et al. / Ocean & Coastal Management 54 (2011) 302e311 Symposium Disaster Reduction on Coasts, 14e16 November 2005, Monash University, Melbourne, Australia. Rao, N.S.B., 1968. On Some Aspects of Local and Tropical Storms in India. Ph.D. thesis, Univ. of Jadavpur. Rao, V.R., Ramana Murthy, M.V., Bhat, M., Reddy, N.T., 2009. Littoral sediment transport and shoreline changes along Ennore on the southeast coast of India: field observations and numerical modelling. Geomorphology 112, 158e166. Rao, Y.R., Chittibabu, P., Dube, S.K., Rao, A.D., Sinha, P.C., 1997. Storm surge prediction and frequency analyses for Andhra coast of India. Mausam 48, 555e566. Saxena, A., Rajan, R., Choudhury, S.S., 2008. Status of coral reefs in post-tsunami period in Andaman & Nicobar Islands. In: Proceedings of the 11th International Coral Reef Symposium, 7e11 July 2008, Session No. 18, Ft. Lauderdale, Florida. Tate, E., Cutter, S.L., Berry, M., 2010. Integrated multihazard mapping. Environment and Planning B: Planning and Design 37 (4), 646e663. Thieler, E.R., 2000. National Assessment of Coastal Vulnerability to Future Sea-level Rise. USGS Fact Sheet, fs-076-00, US Geological Survey, 2 pp.
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Unnikrishnan, A.S., Shankar, D., 2007. Are sea-level-rise trends along the coasts of the North Indian Ocean consistent with global estimates? Global and Planetary Change 57, 301e307. Unnikrishnan, A.S., Shetye, S.R., Shankar, D., Sundar, D., Michael, G.S., Fernandes, S.E., Telang, M., 2004. Extreme Sea Level Variability along the Coast of India. NIO/TR-6/2004, 1e2, 262 pp. USAID, 1991. Primer on Natural Hazard Management in Integrated Regional Development Planning. Department of Regional Development and Environment Executive Secretariat for Economic and Social Affairs Organization of American States. With Support from the Office of Foreign Disaster Assistance United States Agency for International Development Washington, D.C. (Chapter 6). USGS, 2005. The Digital Shoreline Analysis System (DSAS) Version 3.0, an ArcGIS Extension for Calculating Histrionic Shoreline Change. Open-File Report 20051304. http://woodshole.er.usgs.gov/project-pages/DSAS/version3/ (accessed 27.02.08).
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Ocean & Coastal Management journal homepage: www.elsevier.com/locate/ocecoaman
Assessment and management of coastal multi-hazard vulnerability along the CuddaloreeVillupuram, east coast of India using geospatial techniques R.S. Mahendra a, *, P.C. Mohanty a, H. Bisoyi a, T. Srinivasa Kumar a, S. Nayak b a b
Indian National Centre for Ocean Information Services (INCOIS), Hyderabad 500 055, India Ministry of Earth Sciences (MoES), New Delhi 110003, India
a r t i c l e i n f o
a b s t r a c t
Article history: Available online 9 January 2011
The current study area is coastal zone of Cuddalore, Pondicherry and Villupuram districts of the Tamil Nadu along the southeast coast of India. This area is experiencing threat from many disasters such as storm, cyclone, flood, tsunami and erosion. This was one of the worst affected area during 2004 Indian Ocean tsunami and during 2008 Nisha cyclone. The multi-hazard vulnerability maps prepared here are a blended and combined overlay of multiple hazards those affecting the coastal zone. The present study aims to develop a methodology for coastal multi-hazard vulnerability assessment. This study was carried out using parameters probability of maximum storm surge height during the return period (mean recurrence interval), future sea level rise, coastal erosion and high resolution coastal topography with the aid of the Remote Sensing and GIS tools. The assessment results were threatening 3.46 million inhabitants from 129 villages covering a coastal area 360 km2 under the multi-hazard zone. In general river systems act as the flooding corridors which carrying larger and longer hinterland inundation. Multihazard Vulnerability maps were further reproduced as risk maps with the land use information. These risk caused due to multi-hazards were assessed up to building levels. The decision-making tools presented here can aid as critical information during a disaster for the evacuation process and to evolve a management strategy. These Multi-hazard vulnerability maps can also be used as a tool in planning a new facility and for insurance purpose. Ó 2011 Elsevier Ltd. All rights reserved.
1. Introduction Natural disasters have always been a serious threat to the life and property all over the world. Though it is not possible to prevent natural disasters, it is certainly possible to reduce their impact by evolving appropriate preparedness plans and mitigation measures. A guiding principle behind these mitigation plans is the school of thought that the risk reduction plans should be compatible for multiple hazards, rather than being specific to tropical cyclone or tsunami. Such a composite approach not only minimizes the duplication efforts and computational overloads but eliminates the possibility of risk substitution. Mitigating the effects of potential disasters and having the appropriate infrastructure in place for response requires detailed knowledge on the vulnerability of the places to a wide range of environmental hazards (Cutter et al., 2000). A tool developed to address the situation was first
* Corresponding author. Tel.: þ91 4023886038; fax: þ91 4023895001. E-mail addresses: [email protected] (R.S. Mahendra), mohanty@incois. gov.in (P.C. Mohanty), [email protected] (H. Bisoyi), [email protected] (T.S. Kumar), [email protected] (S. Nayak). 0964-5691/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.ocecoaman.2010.12.008
proposed by Federal Emergency Management Agency (FEMA) called Multi-hazard Mapping Initiative (MMI) (FEMA, 1997). In simple words, 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. Generically, vulnerability is a set of conditions and processes resulting from physical, social, economic and environmental factors that increase the susceptibility of a community to the impact of hazards. Vulnerability also encompasses the idea of response and coping, since it is determined by the potential of a community to react and withstand a disaster (Kumpulainen, 2006). The degree to which populations are vulnerable to hazards is not solely dependent on proximity to the potential source of the threat. Social factors such as wealth and housing characteristics can contribute to greater vulnerability on the part of some population subgroups. Vulnerability assessment is an estimate of the degree of loss or damage that could result from a hazardous event of given severity, including damage to structures, personal injuries, and interruption of economic activities and the normal functions of settlements. A Multi-hazard Vulnerability Map (MHVM) incorporates vulnerability in understanding the risk due to a hazard. By facilitating the interpretation of hazard information, it increases the
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likelihood that the information will be used in the decision-making process. The main purpose of MHVM is to represent vulnerability, risk, and hazard information together on a single map. These may include: the different hazard-related information for a study area to convey a composite picture of the natural hazards of varying magnitude, frequency, and area of effect. The Multi-Hazard Map (MHM) is referred to as a composite, synthesized and an overlaid hazard map. This is an excellent tool to create awareness in mitigating multiple hazards (USAID, 1991). The maps produced can be easily read and accessed, which facilitate the administrators and planners to identify risk areas and prioritize their mitigation/ response efforts. The 7500 km long coastline of India is threatened by many natural hazards resulting in the loss of life and property. The hydrologic factors with respect to coastal hazards that are influential are: tropical cyclones, sea level rise, floods, coastal erosion, and storm surge along Indian coast. The east coast of India is affected by the seasonal tropical cyclones in SW monsoon as well as in the return NE monsoon as compared to west coast. The damage from land falling cyclones is mainly due to three factors: rain, strong winds, and storm surges. Storm surges associated with severe tropical cyclones are by far the most damaging (Dube et al., 2009). About 90% of the damage is due to inundation of land by sea water. In addition, flooding of the river deltas occurs from the combined effects of tides and surges from the sea, which penetrate into the rivers, because at the same time, excess water in the rivers due to heavy rains from the cyclone is trying to flow through the rivers into the sea. The impact would be much lower if these hazards could be predicted reasonably well in advance allowing effective warnings and evacuations in the threatened areas. The literature survey indicates that there is significant work on the individual hazards such as storms, cyclones, tsunamis, sea level change and shoreline change. (Dube et al., 2006; Rao et al., 1997; Chittibabu et al., 2002; Indu Jain et al., 2006; Unnikrishnan and Shankar, 2007; Rao, 2005; Rao et al., 2009; Kumar et al., 2008). However, the remote sensing and GIS tools can be effectively used to assess the coastal vulnerability (Gornitz, 1990; Pendleton et al., 2005; Kumar et al., 2010; Hegde and Reju, 2007; Pethick and Crooks, 2000; Thieler, 2000). In particular very few works were carried out regarding general multi-hazard vulnerability mapping within the subcontinent. Federal Emergency Management Agency (FEMA) has started Multihazard Mapping Initiative (MMI) to provide multi-hazard advisory maps (FEMA, 2003). The coastal multi-hazard vulnerability mapping to bring out the composite maps representing the various disasters were not extensively carried out. Very few works on multi-hazard mapping were reported apart from the Americas and Europe (Tate et al., 2010; Habib and Fahmi, 2009). The current study aims at developing the methodology for assessing the multi-hazard vulnerability and gather quantitative estimate on the spatial extent of the inundation caused by composite hazards. The parameters used in the current study are: shoreline change rate, sea level change rate, historical storm surges and the high resolution topography. Further risk maps and evacuation routes are generated by imbibing land use, transport, and structural (a proxy for demographic information) information. 2. Study area The selected study area is part of the Tamil Nadu state in the Bay of Bengal covering the districts of Cuddalore, Villupuram and Pondicherry along the southeast coast of India (Fig. 1). The geographical constraints of the study area are 11180 2000 to 12150 2100 N latitudes and 79 350 3600 to 80 020 3400 E longitude covering a length of 100 km coastline (Fig. 1). The Bay of Bengal is one of the six regions in the world where severe tropical cyclones
303
originate in the months of May, November, and December. This area experienced nearly 60 cyclonic surges and severe cyclonic surges in past century (IMD eAtlas). The storm surges are well known for their destructive potential and impact on human activities due to associated strong winds along the coast and heavy rainfall. An added risk factor is that large parts of the coastal zone are low lying with gentle slope resulting large inundation, thus increasing the vulnerability of the region. This area is one of the worst hit due to 2004 Indian Ocean tsunami (Murthy et al., 2006). 3. Data and methods The data sets used for generation of the MHVM in this study area listed in Table 1. The details of the historical storms were obtained from the National Institute of Oceanography (NIO) database, the storm surges were calculated using tide gauge data (Unnikrishnan et al., 2004). The MHVM has been carried out using the parameters sea level change rate, shoreline change rate, elevation contours and historical storm surge. The flow chart (Fig. 2) shows the general methodology adopted in current study. 3.1. Calculation of sea level change rate Tide gauge dataset from Global Sea-level Observing System (GLOSS) database during the past century was used as the primary source of sea level information. The nearest tide gauge location in the study area, Chennai was selected (inset map in Fig. 1). Sea level change rate was estimated using long-term (1952e2005) monthly mean tide gauze data. Long-term monthly mean tide gauge data recorded for Chennai during the period from 1952 to 2005 was used estimate sea level change rate. The monthly mean values of sea level recorded from Chennai tide gauge were plotted, from which a linear best fit line using least squares method was computed calculate the sea level change rate. 3.2. Calculation of shoreline change rate Ortho-rectified Landsat MSS and TM images covering the study area for the period 1972, 1990 and 2000 were downloaded from the website www.landsat.org. The data were projected to Universal Transverse Mercator (UTM) projection system with WGS-84 datum. The shoreline along Cuddalore coast was digitized using ArcMap 9.2 and ERDAS Imagine software using on-screen point mode digitization technique. Near infrared band that is most suitable for the demarcation of the landewater boundary has been used to extract the shoreline. The digitized shoreline for the years 1972, 1990 and 2000 in the vector format were used as the input to the Digital Shoreline Analysis System (DSAS) to calculate the rate of shoreline change (USGS, 2005). 3.3. Estimation of extreme storm surges and return periods The prediction of the design extreme surge heights were incident at coast needs to be estimated. Probability and statistics were used to estimate future frequencies based on the historic surge records. The annual extreme storm surge heights were extracted for the Chennai station from Unnikrishnan et al. (2004) recorded during the period 1975e1987. Based on historical storm surge a value of 2.06 m was maximum height recorded. Using this as base data the extreme storm level was estimated using Gringorten method for the Chennai tide gauge station was 2.31 m in a return period 100 years. Non-exceedance probability is an index of the likelihood that a specified surge level will not be exceeded an extreme value of the variable under study for the chosen period. For instance the
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Fig. 1. Study area.
probability that the surge level will not exceed 2.06 m is 0.94 as observed from the surge level data for Chennai station (data not included). Observed surge heights during 1975e1987 for the Chennai were used to estimate the probability of non-exceedance in this study. The highest water level during storm surge in each year is extracted and assigned a rank series from smallest to largest m ¼ 1, 2.N. Gringorten method (Eq. (1)) is used to calculate the probability of non-exceedance (p) (Gringorten, 1963).
py
m 0:44 m 0:44 ¼ N þ 1 0:88 N þ 0:12
where, ‘m’ is rank of the variable (surge level) ‘N’ is the total number of observations
(1)
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‘a’ is the slope (estimated 12.492in this study) and ‘u’ is the intercept (estimated 173.06 in this study)
Table 1 List of data used for the current study. Data
Resolution (m)
Period
ALTM Carto DTM Landsat, MSS Landsat, TM Landsat, ETM Quickbird Tide gauge data Historical storm surge
5 10 57 30 30 0.6 e e
2007 2006e2007 1972 1990 2000 2006 1952e2005 1975e1987
e: Not applicable.
Reduced variate is applied for consistent and direct comparisons between different distributions in a probability plot. Reduced variate ‘y’ is linearly related to probability of non-exceedance ‘p’. Reduced variate is estimated using probability of non-exceedance as shown in Eq. (2). y ¼ loge(loge p)
(2)
where, “y” is reduced variate and ‘p’ is probability of non-exceedance. Values of observed annual maximum surge levels versus reduced variate were plotted and the best fit line computed using least squares method is shown in Fig. 3 (left panel). The slope 12.492 and intercept 173.06 obtained were used to calculate maximum surge height for desired return periods (Fig. 3, right panel) using Generalized Extreme Value (GEV) distribution function (Eq. (3)).
1 Maximum surge heightU ¼ uþa loge loge 1 R
(3)
where,
3.4. Generation of elevation contours The high resolution topographic data using Airborne Lidar Terrain Mapping (ALTM) was obtained from National Remote Sensing Centre (NRSC) has been incorporated in the current study. ALTM data extends 2 km into the coastal zone on landward side. Data for the areas beyond 2 km was used from the Digital Terrain Model (DTM) generated using the Cartosat-1 data. These two data sets were merged and the elevation contours were generated using the ArcMap software. The krigging interpolation was used to interpolate the contour lines were smoothed to avoid sharp bends. 3.5. Generation of multi-hazard map The sea level change rate was observed to be equal to 0.085 mm/ y for the area hence after 100 years the sea level will be 8.5 mm. The shoreline after 100 years has been projected based on the rate of the shoreline change. The future areas of erosion were extracted from these results. The maximum storm surge estimated for the 100 years of return period was 2.31 m. This is comparable to previous works (Rao, 1968). The inundation level for the area is 2.32 m including the extreme storm surge level and future sea level. This inundation level considered as 2.5 m for the selected study area. The projected areas under erosion after 100 years and area under 2.5 m elevation were overlaid in a GIS environment. The union of all these information is taken as the multi-hazard zone. The MHVM of the study area was prepared by overlaying the multihazard areas on the base maps. 3.6. Risk assessment for hazard management
‘U’ is the extreme surge height (extreme water level) ‘R’ is the return period in years,
Just an MHVM is not sufficient enough for the coastal managers. They need to understand the different levels of risk within the
Long Term Sea Level Data (Tide gauges)
Remote Sensing Data (Landsat MSS/TM/ETM, QB)
Sea Level Trend
Historical Storm Surge Heights (Hmax)
ALTM, DTM (Cartosat)
Shoreline Change Rate
Sea Level at T (A)
Storm Surge Level at T (B)
Elevation Contours
Future Shoreline after T (D)
Inundation Level (C) = A+B at T
Contour Level of C
Land use, Transport, Buildings
Composite Multi-hazard Line=Union of C & D
Multi-hazard Map
Risk Maps ‘T’ is return period
Fig. 2. The flow chart is depicting the methodology.
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2.40
220 210
2.30
190
Water Level (cm)
Water Level (cm)
200
180
y = 12.492x + 173.06
170
2
R = 0.9301
2.20
2.10
2.00
160 1.90
150
1.80
140 -1
0
1
2
3
5
4
10
20
30
40
50
60
70
80
90
100
Return Periods (year)
Reduced variate (-In(-In(p)))
Fig. 3. Reduced variate (left panel) and extreme water levels during storm surge for different return periods (right panel) calculated using Gringorten method for the Chennai station.
demarcated hazard zone. The coastal land use, buildings and roads were extracted using the Quickbird Image (acquired during 2006) based on the visual interpretation technique. The map of MHVM has been intersected with coastal land use. Then the risk classes were assigned within the coastal hazard zone based on the existing land use/land cover information. Based on the threat posed, the risk areas within the multi-hazard zones were further categorized. The builtup areas were assigned the high risk; beach/sandy patches near the coast were assigned moderate risk and the remaining classes in the hazard areas were kept under low risk. The buildings falling within and outside hazard area were also assessed and overlaid on the risk map. The different types of roads such as major, minor and street were extracted and the named according the local names.
Shorelines extracted for the years 1972, 1990 and 2000 indicate that the area experienced both the erosion (up to 5.5 m/y) and accretion (up to 6 m/y). Shoreline change rate assessed along the open coast, the river/creek mouths were excluded in this study. Because the changes at the river/creek mouth depict dynamic changes especially in the morphology of the river spits, hence the trend will be off the norms. The shoreline projected for the next 100 years for the open coasts indicates that an area of 3.77 km2 is going to be eroded in the 100 years. 4.1. Multi-hazard vulnerability assessment The multi-hazard zones were calculated for the study area using future sea level, maximum estimated storm surge and maximum possible shoreline erosion. From the Fig. 5, maximum extent of the hazard zone is seen as 20 km from the current coastline of the Cuddalore district. The extent of the hazard zone in the vicinity of the rivers was observed to be up to 10 km. The study shows that total area of 360 km2 has been delineated hazard zone with 314 km2, 23 km2 and 23 km2 under Cuddalore, Villupuram and Pondicherry
4. Results and discussions Sea level change calculated using the Chennai tide gauge data recorded for 54 years during 1952e2005 indicates a value of 0.085 mm/y (Fig. 4), which is very much lower than the global average 1e2 mm/y.
7400
y = 0.0071x + 6983.4 R2 = 0.0001
7200 7100 7000 6900 6800
Aug-2003
Aug-2000
Aug-1997
Aug-1994
Aug-1991
Aug-1988
Aug-1985
Aug-1982
Aug-1979
Aug-1976
Aug-1973
Aug-1970
Aug-1967
Aug-1964
Aug-1961
Aug-1958
Aug-1955
6700 Aug-1952
Sea Level (mm)
7300
Date (mmm-yyyy) Fig. 4. Graph depicting the monthly mean sea level data from Chennai tide gauge showing the sea level trend.
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Fig. 5. Map showing the Multi-hazard zones and safe zones pertaining to Cuddalore, Pondicherry and Villupuram.
districts respectively. The safe zones are marked on the map (Fig. 5) as green.1 They are at higher elevation and can be used as temporary safe shelters during a disaster. The southern parts of the Cuddalore district near Chidambaram Town in the vicinity of the Kolladam River indicate larger areas under threat due to multiple hazards, because of lower mean elevation. Murthy et al. (2006) reported extensive damage along the Cuddalore coast during the 2004 Indian Ocean tsunami. With the MHVM, it is now clear that why large area
was inundated during cyclone Nisha in the year 2008 (Alertnet, 2008). In general, the extent of the hazard zones is much more in the vicinity of the river and creeks, with the exception of Chidambaram area. The river systems are corridors for the inundation allowing the flood water to be carried upstream for long distances. This results in flooding along the proximal areas of the rivers.
4.2. Risk assessment and management action plans 1 For interpretation of the references to colour in the text, the reader is referred to the web version of this article.
From our MHVM analysis, there are a total of 129 inhabited villages falling under the multi-hazard zone including nine urban
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areas. Total population living in the identified hazard area is approximately 0.26 million of rural populations and 3.2 million urban populations. This population is partially or fully threatened by the disasters and is under risk thus needing massive relief effort for the evacuation. The additional parameters land use/land cover, roads and buildings were generated for the small area to demonstrate the social vulnerability and thus guide in mitigation and rescue efforts. The identified and classified road network from satellite image can be used as an evacuation route. The hazard zones were further classified into risk classes (see Fig. 7) using the land use/land cover
information (Fig. 6). It can be seen that there are no risk areas outside of the hazard zone. Thus the hazard zones derived using our adopted methodology depicts the maximum extent of damage possible and priorities needed within the identified zone. The roads and the building polygons are overlaid on the risk maps. The roads are labeled according the local name and buildings positioned in the hazard area and safe area were represented in different symbols. The reason for the usage of common street names is that it simplifies management decisions during an event, and common populace can understand the directives and advisories easily. A rigorous census (under a different project sponsored by INCOIS) is
Fig. 6. Map showing the land use classes of the Cuddalore town with roads in the foreground and Quickbird imagery in the background.
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Fig. 7. Risk map showing the areas under different risk levels and the no risk areas overlaid with roads and the evacuation routes. The polygons of the safe and hazard buildings overlaid those are fall in the hazard and safe area.
currently underway to get the details on the building owner, number of people living in the building and their contact details. Once ready, the database of mobile telephone can be used to directly alert specific building or areas. In terms of causeeeffect, most of the hazards affect the coastal areas similarly with flooding/inundation. Consequently, the human population responds to flooding by rushing to higher ground. Immediate casualties during an extreme event are attributed more due to panic and resulting stampedes than the
rising water level. With the availability of evacuation routes from MHVM, it is hoped that the immediate panic related casualties could be reduced. With the usage of MHVM, critical communication infrastructure can be identified for coordinating search-andrescue (S&R) operations. Such a planned S&R activity can further mitigate the impact of the disaster. Integration of building structural aspects in MHVM can further alleviate the problem of identification of safe shelters. MHVM can identify and delineate possible coastal areas that are prone to water-logging and
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subsequent material decay, thus incidences of water-borne diseases can be contained at least spatially. In terms of environmental factors, flooding causes damage to agricultural land affecting the crops, thus the livelihood and the economy of the country. The encroachment of sea water into soil alters the pH value, decreasing the soil quality and affecting the produce. Also in terms of ecological factors, sea water encroachment alters the floral habitats and their environment. All wet-land elements like coral reefs, mangroves and sea-grass are disturbed due to flooding. Typical incidents have been the death of coral reefs off Andaman due to sedimentation (Bahuguna et al., 2008; Saxena et al., 2008) and reef banks of Gulf of Mexico (Lugo-Fernández and Gravois, 2010), caused due to inundation run-offs. Coastal habitats are also affected due to salt-water intrusion and flooding. Some of these risks may have long-term impact on human, environment and economy. The concept of MHVM can further extended by including vulnerability of these habitats to various oceanic hazards. Some of the action plans that can be chalked up based on MHVM are: i. The setback line can be physically demarcated on ground using these multi-hazard maps. ii. The building codes for the new developments and for the modification of existing structures within this setback line has to be updated and implemented. iii. Identify various safe shelters equipped with emergency facilities. iv. Conduct awareness program for the coastal community living in the multi-hazard zone. Provide instructions to community of each area on how to reach their respective safe shelters via identified evacuation routes. v. Set-up the volunteer team from the identified vulnerable local community and train them. vi. Estimate the requirement of the rescue task force and emergency aids in different identified administrative areas during a disaster. vii. Prioritize the evacuation based on the risk maps and population. viii. The drainage system gets chocked during the disasters. Hence this drainage network needs to be maintained and the settlements in the vicinity need to be prohibited. ix. The mangroves and coral reefs in the coastal act as the barriers for the natural disasters, hence these need to be protected. Also these need to be developed (mangrove plantation and coral culture) if the suitable environment available. x. Finally, MHVM can help in planning for the aftermath of an event: rehabilitation, civil disturbances, restoration of the utilities, health problems, etc. 5. Conclusions The application of MHVM approach indicates that Cuddalore district is under high risk when compared to other districts in the region. The remote sensing and GIS techniques used in the current study in compliment the long-term in-situ observations. The use of GIS further helps in visualizing the resulting MHVMs, and adds value by identifying evacuation routes. The use of observed hourly sea level data can further improve the methodology or accuracy. The risk maps presented here are useful to draw management action plans such as prioritization of the areas for the evacuation, planning of evacuation routes, identification of the safe shelters, etc. Furthermore these maps are useful tools in raising awareness among the local community and government authorities which are responsible for disaster management. If a hazard warning were
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