Nat Hazards DOI 10.1007/s11069-014-1336-8 ORIGINAL PAPER
Assessment of Tsunami Hazard Vulnerability along the coastal environs of Andaman Islands R. Prerna • T. Srinivasa Kumar • R. S. Mahendra • P. C. Mohanty
Received: 10 December 2012 / Accepted: 27 July 2014 Springer Science+Business Media Dordrecht 2014
Abstract The December 2004 Sumatra–Andaman earthquake emphasized consistent and comprehensive assessment of areas that are prone to the hazard of Tsunami. It also focused attention on the hazards that could be posed by large subduction zone earthquakes and the Tsunamis that could be further generated. Due to the extremely high vulnerability in the Andaman Islands in South East India, it is essential for planners to develop a comprehensive a priori information database in order to minimize the impact of these destructive situations. A similar effort has been done in this study wherein the entire Andaman Islands have been assessed to target ‘‘Tsunami Hazard Vulnerable areas’’ in accordance with the maximum wave run-up heights and topography. These areas have been extracted from the total area keeping in mind the run-up wave heights on the very day of the Sumatra– Andaman earthquake, i.e., on December 26, 2004. Also, the topographic variations in the region have been studied to establish a relation between the vulnerability of an area and its topography. The hazard of Tsunami puts at threat, the lives of approximately 314,084 people over an area of 5,833.1 km2 in the Andaman Islands. Out of the total area, 708.8 km2 is the hazardous portion which is 12.1 %. The islands have experienced a total of 386 earthquakes (above 5.0 magnitude) from the time of Sumatra–Andaman Tsunami till the end of 2009. These statistics clearly indicate the need for hazard preparedness and planning in order to minimize impact during unfortunate circumstances. This study thus aims at the preparation of Tsunami Hazard Vulnerability Map for the Andaman Islands
R. Prerna (&) Department of Natural Resources, TERI University, Vasant Kunj, New Delhi, India e-mail:
[email protected] T. Srinivasa Kumar R. S. Mahendra P. C. Mohanty Indian National Center for Ocean Information Services, Hyderabad, Andhra Pradesh, India e-mail:
[email protected] R. S. Mahendra e-mail:
[email protected] P. C. Mohanty e-mail:
[email protected]
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which can be further used by administrative and disaster mitigation organizations as and when required. Keywords Maximum wave run-up heights TUNAMI N2 model Shuttle radar topography mission (SRTM) Sumatra–Andaman Tsunami Land-use/land-cover classification
1 Introduction Tsunamis can be defined as a dramatic by-product of certain types of earthquakes. It is a Japanese term that means ‘‘harbor wave.’’ They are often confused with tidal waves, but in reality they are a result of a sudden vertical offset in the ocean floor caused by earthquakes, submarine landslides and volcanic deformations. A sudden offset changes the elevation of the ocean and initiates a water wave that travels outward from the region of sea-floor disruption. Tsunamis have been noted as naturally occurring devastative events for several centuries now. Numerous incidents have shown in the past that they are mostly catastrophic in nature, and hence, raise the level of preparedness needed against them. Many regions of the earth are ‘‘vulnerable’’ to the threat of Tsunami, and thus, they could grow into ‘‘hazards’’ for which the people and the government must ever be prepared. ‘‘Hazard’’ is the probability of occurrence of a potentially damaging phenomenon, while ‘‘Vulnerability’’ is the degree of loss resulting from the occurrence of the phenomenon (ITC 2006). Vulnerability, broadly defined as the potential for loss, is an essential concept in hazard research and is central to the development of hazard mitigation strategies at the local, national and international level (Cutter 1996). Vulnerability assessments used to determine the potential damage and loss of life from extreme natural events are also important in proposing hazard reduction alternatives where mitigation normally takes the form of structural, i.e., engineered approaches to hazard reduction. The 2004 Sumatra–Andaman Tsunami showed several recordings where the wave heights in certain parts of Sumatra Island went up to 20–30 m (65–100 feet) (Gibbons and Gelfenbaum 2005). The magnitude of the earthquake that triggered this Tsunami was somewhere between 9.1 and 9.3 Mw, the epicenter of which was between mainland Indonesia and Simeuleu Regency, 150 km off the west coast of Sumatra. The fault rupture propagated 1,300–1,600 km northwest for about 10 min along the boundary between the Indo-Australian plate and the Eurasian plate, from northwestern Sumatra to the Nicobar Islands and to the Andaman Islands (Cluff 2007). The scenario after the Tsunami was extremely catastrophic and destructive in several parts of the Indian Ocean. Out of 12 countries that were affected by the Tsunami waves, India experienced the third highest death toll (after Indonesia and Sri Lanka) of 10,744 people dead and 5,640 missing persons (courtesy BBC News 2005). In India, the worst hit regions were Tamil Nadu, Pondicherry, Andhra Pradesh, Kerala and Andaman and Nicobar Islands. The impact along the southeastern coast of India was massive, where waves were seen to completely wipe off villages and devastating cities, resulting in over 9,000 deaths. In the state of Tamil Nadu, Nagapattinam district was the worst hit region accounting for over half of the deaths (5,500) out of the total 8,000 deaths in the state (Cramer 2005). Even though events such as the Sumatra–Andaman earthquake and Tsunami are rare, the very large loss of life and the tremendous material destruction over large geographical
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areas warrant a significant effort toward the mitigation of the Tsunami hazard. It goes without saying that if a similar magnitude earthquake was to occur closer to the Andaman Islands or the Indian Coast for that matter, it would have nothing less than catastrophic effects. In recent years, vulnerability by Tsunami on coastal communities from a variety of sources has become apparent, but it has received much less attention than has been paid to the hazard from strong ground motions. The lack of a consistent framework to evaluate Tsunami hazard has given rise to unnecessarily conservative estimates of the hazard, which can result in an economic barrier to the development of coastal communities and facilities. The need for a comprehensive and consistent methodology to evaluate Tsunami hazard is clear. Therefore, it becomes a prerogative need for planners and developers to derive necessary hazard management and mitigation operations in order to be prepared for the worst (Cummins et al. 2009). The outcomes of this study can be considered as a platform which can be further expanded to any extent by the addition of other parameters such as shore-line change rate, sea-level change rate, variations in coastal slope, variations in tidal range along the coasts, role of bathymetry, i.e., undersea configuration modifying the propagation of Tsunami wave to prepare a composite and comprehensive coastal vulnerability index (CVI) map in order to exhaustively classify the hazard prone areas (Srinivasa Kumar et al. 2010). Such maps are not only informative post-disaster times but, if effectively and timely implemented, they can serve the purpose of minimizing impact and saving human and capital resources. Through this research, the authors wish to study the capability of satellite data and elevation data for the identification of vulnerability zones from the threat of Tsunami waves. This work can be referred by others in the future who wish to blend wave height data and satellite data for observing threat zones. The methodology adopted herein has been designed in such a way that this technique can be applied easily with data of similar or better accuracy to gain useful outputs. The classification schema discussed in the following sections also displays a systematic endeavor.
2 Literature review The Andaman Basin, toward the southeastern part of Bay of Bengal around the Andaman– Nicobar chain of islands, is part of a large geotectonic unit that extends from Indonesian Islands in the south to Myanmar in the north. This basin marks the edge of the Alpine Himalayan belt and happens to be a seismically active region (Mohan et al. 2006). For millions of years, the India tectonic plate has moved in a north/northeast direction, colliding with the Eurasian tectonic plate. The Indian plate’s eastern boundary, along the Andaman and Nicobar Islands and Northern Sumatra, is a diffuse zone of seismicity and deformation, characterized by extensive faulting and numerous large shallow and intermediate earthquakes. The Burma microplate encompasses the northwest portion of the island of Sumatra, as well as the Andaman and Nicobar Islands. Further to the east of the Andaman and Nicobar islands, a divergent boundary separates the Burma plate from the Sunda plate. The Indian plate’s oblique subduction beneath the Burmese microplate has created the Andaman segment of the great Sunda Trench. The Andaman and Nicobar Islands are located within the tectonic sliver near the boundary of the Indian plate and the Burmese microplate (Fig. 1). Similarly, the oblique subduction has created the north–south trending West Andaman fault—another strike-slip fault system in
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the Andaman Sea to the east of the island chain. It is due to these geological aspects that the Andaman–Sumatra belt becomes more vulnerable to experience such disastrous episodes in the future and hence has been chosen as an area for study. Fortunately, even before this destructive event, a significant amount of work was carried out in this field, primarily through deterministic modeling of Tsunami scenario (Thio et al. 2007). Such studies often address worst case scenarios or some type of maximum credible event. In order to put these types of studies on a firm basis, it is necessary to conduct a comprehensive review of Tsunamigenic sources that can affect a certain locality and determine the probabilistic hazard level based on this set of sources. Various studies have been performed in the past related to the impact and vulnerability assessment of Tsunami prone areas. Researchers all over the world have developed different models and mechanisms to monitor the threats and delineate the regions as per the degree of hazard vulnerability. Some of the research works have been discussed hereinafter. Usha et al. (2009) used a numerical model of Tsunami propagation and run-up which was compared with field data collected immediately after the Tsunami using real-time kinematic GPS. Multiple past events of Tsunami in the vicinity of the study area were considered, and a ‘‘worst case scenario’’ approach was followed. Chittibabu and Baskaran (2009) studied the Karaikal coast of India using remote sensing and GIS techniques to show the effect of the hazard and also suggested suitable sites for rehabilitation. Mahendra et al. (2011) performed a study on the vulnerability of Cuddalore coast, East India, wherein multiple were taken into account. Changes in shoreline, sea-level trends, pattern of historical storms and their return periods etc. were brought together, and in this manner, multi hazard vulnerability assessment was done. TUNAMI N2 model is a numerical simulation program with the linear theory in deep sea; with the shallow water theory in shallow sea and with constant grid length in the whole region. This model was used for simulation of propagation and coastal amplification of long waves. The model was originally authored by Dr Shuto and Dr Imamura of the Disaster Control Research Centre in Tohoku University through the Tsunami Inundation Modeling Exchange (TIME) program (Shuto et al. 2006). There are several precedents where the employment of TUNAMI N2 model has been done to address several hazard centered issues. An example is a study to understand the cause for significant time delay between the origin time of the earthquake and the observed arrival times at various locations, attributed to the occurrence of submarine landslides triggered by the quake (Rajendran et al. 2008). Here, the TUNAMI N2 model output was used to predict wave propagation. Ram Mohan and Krishnamurthy (2007) showed the application of TUNAMI N2 model for a part of the Indian Ocean using the 2004 Indian Ocean Tsunami scenario to see the suitability of this model for inundation mapping and vulnerability assessment. For the purpose of doing Tsunami modeling by predicting surges for different earthquake scenarios, this model’s outputs were incorporated in a study by Nayak and Srinivasa Kumar (2008). Bathymetry has been seen to cause variations in the movement of Tsunami waves especially so when they are at close proximity to the shoreline. This important factor was considered in the current study as well, and the behavior of the waves with respect to the bathymetry was studied. Similar study was performed by Satake (1988) on the application of ray tracing of Tsunamis to see the effect of bathymetry on wave propagation.
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Fig. 1 Location of 2004 Indian Ocean Tsunami and surrounding tectonic plates
3 Study area The Andaman Islands are situated in the Bay of Bengal, midway between peninsular India and Myanmar, spreading in the north–south direction. These islands are located between 10300 and 14120 North latitudes and 92200 and 93570 East longitudes (Fig. 2). There are in all 325 islands in Andaman group of islands (courtesy Andaman and Nicobar Administration 2011). These are a north–south trending chain of islands that are extremely susceptible to the various naturally occurring disasters such as cyclones, earthquakes and Tsunamis, which is primarily so due to their geographical location near the Indo–Burma– Sumatra subduction zone.
4 Data and methods 4.1 Data used The specifications of the datasets employed in the study are specified in Table 1.
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Fig. 2 Map of study area
Table 1 Data used for the current study Data
Resolution (m)
Time period
Projection
Tsunami wave Height
5,000
26 December 2004
Geographical (WGS 1984)
SRTM data
90
February 2000
Geographical (WGS 1984)
LANDSAT ETM?
30
07 February 2000
Universal Transverse Mercator (Zone 46N)
4.2 Methods 4.2.1 Calculation of Tsunami run-up Tsunami wave heights for the Sumatra–Andaman Tsunami dated Dec 26, 2004, were computed using the numerical TUNAMI N2 model. The resolution of the said data is approximately 5,000 m or 5 km projected on a Geographic Coordinate System (WGS 1984). The output of TUNAMI-N2 program is not easily interpreted as given in the output files. In order to get the best results, the output files are converted into diagrams or graphs so that the interpretation and the comparison of different data can be achieved easily. The diagrams or the graphs of the output files can be plotted by using the programs Surfer, Grapher and/or Microsoft Office Excel. This model takes into consideration the seismic deformation and bathymetry as input to predict the run-up heights and travel times of a
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Tsunami wave for different parts of the coastline for any given earthquake. These data represent the Tsunami wave heights for a period of 15 h from the initial generation of seismic waves at a time-step of 1 min for each location. Thus, for each point, wave height data are generated at an interval of 1 min for a span of 15 h out of which, the maximum wave heights are chosen in our output, representing the Z-max value. The inputs and outputs are both in ASCII format generated using FORTRAN (a general purpose, procedural programming language especially suited to numeric and scientific computing). GEBCO bathymetric data are an input along with various other earthquake parameters. The grid so created was converted into a GIS compatible raster format. The resultant output showing the wave run-up heights for the entire region is shown in Fig. 3. Validation of the output of TUNAMI N2 model was performed using the observed runup heights of 2004 Indian ocean Tsunami available online at NOAA’s National Geophysical Data Center (NGDC). Unfortunately, observation data from tide-gauges were not available for the study since water column height from DART, i.e., Deep-Ocean Assessment and Reporting of Tsunamis, is provided only after 2006. Murthy et al. (2011) show that TUNAMI N2 model output results when compared with observed run-up heights do prove the competency of using the former for coastal vulnerability-related studies. This model is also used in the operational Tsunami forecast at the Indian Tsunami Early Warning Centre (ITEWC 2011). In the present study also, model output values of five station locations from South Andaman have been compared with observed run-up heights given in Table 2 and Fig. 4. The percentage error in the model output with respect to the observed run-up heights was calculated to be 36.68 % using (1) which may be attributable to the low resolution of input bathymetric and topographic data. % of Error ¼ ½ðmodel Obs:Þ=Obs: 100
ð1Þ
4.2.2 Processing of SRTM topographic data SRTM data of 90 m resolution were applied for the purpose of extracting the Tsunami Inundation areas along the coastal environs of the Andaman Islands. These data show the elevations of the area at a fairly good scale sufficient enough to make reasonable conclusions. SRTM data were availed from srtm.csi.cgiar.org which is an open source domain. These data are available in the form of grids where each cell represents an area of 1 latitudinally and longitudinally. Figure 5 shows a portion of the SRTM data acquired for the study wherein the color tones have been modified in order to make the variations in the elevation more prominent. 4.2.3 Preparation of land-use Landsat ETM? data for the Andaman Islands were downloaded from www.landsat.org (open source domain). This is seven band multi-spectral data with 30 m resolution and is good enough for the purpose of level II land-use/land-cover (LU/LC) classification (Anderson et al. 1976). This classification was essential in the study because demarcation of more sensitive or vulnerable zones within the inundated area would help in better operation of disaster management and mitigation techniques. Three cells (path 134, row 51–53) were mosaicked together using ERDAS Imagine 9.1 (Fig. 6). The basic methodology employed for the purpose of extracting results has been explained in the form of flow chart in Fig. 7.
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Fig. 3 Data representing wave run-up height (m) in raster format derived from FORTRAN program
Table 2 Tabular comparison of the TUNAMI N2 model outputs with the field observations of runup heights at South Andaman
Location
Latitude
Longitude
Observed value
Model output
1
11.49045
92.71112
2.46
2.01
2
11.52792
92.72562
3.98
1.08
3
11.667
92.75
4
3.36
4
11.672
92.739
4.12
3.36
5
11.65525
92.75662
2.82
1.19
4.2.4 Assessment of Tsunami vulnerability To attain the inundated area along the coasts of the Andaman Islands, delineation of those areas had to be done wherein the ground elevations of the land were lesser than the wave
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Fig. 4 Graphical comparison of the Tsunami N2 model outputs with the field observations of runup heights at South Andaman
Run Up Height (m)
Nat Hazards
5
Observation
4
Model
3 2 1 0
1
2 3 4 Locations in South Andaman
5
run-up heights. The flooded areas could be identified by applying a series of tools and logical expressions in the ArcGIS environment. Firstly, the raster format of the wave heights was converted into ‘‘Point’’ form. A fishnet, i.e., a grid of equal sized cells was created so that each cell could contain continuous wave heights by the interpolation of values from within the cell and also the adjoining ones. Secondly, the ‘‘Less than Equal’’ function was applied to extract those areas from the SRTM area which were either equal to or less than the Tsunami wave heights. This way numerous pockets of segmented land were derived showing inundated areas. Finally, with the help of the administrative boundaries of the islands, the inundated land pockets were sorted so that only those parts would be seen which are above mean sea level or 0 m contour line. A segment of the study area representing the area under inundation has been shown in Fig. 8. 4.2.5 Estimation of Tsunami vulnerability based on elevation For the purpose of demarcating high, moderate and low threat zones of Tsunami vulnerability within the inundated area, the elevation data of SRTM were used. A manual classification of the elevation values in ArcMap was sufficient to visualize the regions falling in different categories which can be seen in Fig. 9. Three classes were prepared: a. b. c.
High—sections with elevation of 3 m or below Moderate—sections with elevation of 4–6 m Low—sections with elevation of above 6 m.
The raster file was converted into vector (polygons) for the calculations of areas and quantifying the percentage of each category in the total area. 4.2.6 Estimation of Tsunami vulnerability based on land-use Majority portions of the land area in the Andaman Islands consist of thickly vegetated forest lands that too on high elevations. Most of the land part rises above 100 feet on an average and is covered by thick rainforests. Therefore, only four main categories of LU could be clearly identified which were Vegetation, Wetland/Mangroves, Open Grounds and Agricultural land/Fallow land. Since most of the agricultural lands were lying in the hazard prone area, it was necessary to pick out these parts and place them in the most vulnerable category so as to make the classification more fruitful.
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Fig. 5 SRTM data (color tones modified) denoting height (m) for a part of the study area
For this purpose, unsupervised classification was done on the Landsat ETM? data only for the inundated regions. Similar to the previous classification based on elevation, raster format of classified image was vectorized to quantify areas and proportions. 4.2.7 Cumulative vulnerability assessment The areas that are below 3 m of elevation were containing both vegetated and non-vegetated patches of land, and the degree of vulnerability of both categories was certainly
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Nat Hazards Fig. 6 Mosaicked LANDSAT data used for land-use classification
variable. Hence, a further sub-classification of the areas below 3 m was done to extract the ‘‘most vulnerable’’ areas which were the non-vegetated segments of land also falling in the high threat zone as per elevation. Due to close proximity of these parts to the ocean waters, low level of elevation coupled with no vegetation to hold soil made this zone the most vulnerable as per the findings of the study. Intersection of the high threat zone (as per elevation) with the LU/LC classification was done to demarcate the aforementioned zone successfully. And further on, data were quantified to show the resulting area in square kilometers.
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Fig. 7 Methodology of the study
The three data sources used, namely—Z-max values (5 km), SRTM (90 m) and Landsat ETM? (30 m), were of variable resolutions. Resampling the three datasets to match each other’s spatial resolution was not a necessity in this work because they were not used in conjunction to derive an output. At each step, only a combination of two data sources was used. • Firstly, in the case of delineating inundation zones, from every 5 9 5 km pixel of Z-max data, all 90 9 90 m pixels were identified with values lesser than the Z-max value using Less Than Equal function. • Secondly, for assessing Tsunami vulnerability based on LU/LC in areas below 3 m elevation, all 30 9 30 m pixels within every 90 9 90 m pixel (\3 m elevation) were classified. In this manner, without performing any resampling techniques on the datasets, outputs could still be generated.
5 Results 5.1 Tsunami hazard vulnerable areas The entire land area in the Andaman Islands approximates to 5,833.1 km2 out of which the total inundated area is 708.8 km2 (Fig. 10), which corresponds to 12.1 % recorded as Tsunami hazard zones. A region wise division of inundated area has been denoted in Table 3. In the following assessments, efforts have been made to usefully and logically divide the hazardous regions as per two criteria:
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Fig. 8 Inundated area (red patches) super-imposed on the elevation data
• Elevation • LU/LC classification A combination of these two essential parameters, if applied systematically, could yield extremely valuable results and help in expanding research. It may seem so that there is no
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Fig. 9 Division of the Inundated area into high, moderate and low threat on the basis of elevation
purpose of dividing threat areas as per different criteria, but the usefulness of this classification is that even within threat prone areas, there may be certain patches of land which may be even more vulnerable to the repercussions of Tsunami than others for, e.g., a piece of land which is barren would be more prone to erosion and rapid soil run-off as opposed to another patch of land having some amount of vegetation to hold the soil together.
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Nat Hazards Fig. 10 Total inundated area represented over the LANDSAT data
Also, the chances of higher casualties would be greater in an area that is inhabited with settlements compared with thickly vegetated regions having extremely sparse population. Thus, it is essential to demarcate the regions under threat and derive as much data out of it as possible so that speedy and effective hazard mitigation operations can be applied in the hour of need.
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Nat Hazards Table 3 Total area under threat of inundation in the different parts of Andaman Area
Total area (km2)
Inundated area (km2)
Inundated area (%)
North Andaman
1420.4
185.9
13.0
Middle Andaman
1979.2
238.1
12.0
South Andaman
2433.5
284.8
11.7
Total
5833.1
708.8
12.1
5.2 Elevation-based vulnerability assessment According to land elevation within the inundated area, three levels have been identified which are as follows: • High threat Areas having elevation of 3 m or lesser were placed in the category of high threat as per the calculations of Tsunami run-up heights. The threat of inundation by Tsunami would be most significant in these areas due to their low lying topography. Out of the total area vulnerable to Tsunami wave inundation, 10.7 % of the land was identified in high threat zone. • Moderate threat This zone comprises of the parts of land with elevation varying from 4 to 6 m in height above mean sea level. Therefore, it can be held that these areas would face the threat of inundation only if a Tsunami wave of 4 m or above shall hit. Regions falling in moderate threat zone total up to 7.9 % of the total inundated area. • Low threat Due to the high rising topography of Andaman Islands, most part of the land is safe from the threat of Tsunami waves. Tsunami waves greater than 6 m in height are capable of posing threat to these areas, i.e., 81.4 % of the total inundated area. 5.3 Land-use-based vulnerability assessment Coastal areas in most regions of the world face a greater chance of soil erosion and loss of land cover making them furthermore vulnerable to degradation. Due to the loose soil and poor fertility of these lands, heavy erosion by sea waves can make these areas unfit for the purpose of cultivation. The Indian economy, even in the current times, heavily depends on agriculture. In light of this, it is essential for planners to protect and appropriately manage the fast depleting agricultural lands in India. Since the Andaman Islands comprise mostly of a rural economy, their dependence on agriculture is even more significant as compared to the other parts of the country. Therefore, it is a must for them to protect their already scanty agricultural lands so as to maintain and further develop their resources. The natural topography of Andaman Islands is such that most of the area is undulating with high elevations. Vegetation is so thick in these lands that there is no option for the local dwellers than to cultivate in whatever little flat cultivable areas existing on the islands. Therefore, a further assessment of LU classification is done (Fig. 11) within the Tsunami hazard prone regions to deduce the higher threat areas based on the land-use patterns, the values of which are as follows: Class Class Class Class
I: agricultural land—8.4 % II: open areas—5.1 % III: vegetation—57.1 % IV: wetland areas/mangroves—29.4 %
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Fig. 11 Land-use classification of the inundated area
These four classes can be clubbed together to divide the area into vegetated and nonvegetated area as the probability of erosion and soil run-off would be greater in areas without a significant amount of vegetative cover.
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Thereby, we get two categories: • Vegetated—86.5 % (Class III and IV) • Non-vegetated—13.5 % (Class I and II) 5.4 Cumulative vulnerability assessment Using the aforementioned two parameters (Elevation and LU pattern), the high threat zone as per elevation can be further sub-classified to segregate the area into: 1. 2.
Most vulnerable—1.3 % Vulnerable—98.7 %
The ‘‘most vulnerable zone’’ (as per the combined criteria) is that portion of the inundated land which has elevation of 3 m and below, coupled with an absence of preventive vegetative cover to hold the soil together causing run-off at the time of Tsunami surges. This zone mostly comprises of agricultural areas, fallow lands and Built up areas. Figure 12 shows a segment of the study area divided in these two zones. Table 4 represents the calculated areas under threat of inundation, as per each criterion.
6 Discussions For the purpose of assessing the findings of the study in a better fashion, the Andaman Islands have been divided into their respective administrative sections hereinafter (Fig. 13). The three main groups so attained are as follows: • North Andaman (including Coco Islands, Landfall Island, Narcondam Island, North Reef Island, Madhupur and South Island) • Middle Andaman (including Interview Island, Flat Island, Anderson Island, Smith Island, Adazig, Kadamtala, North Passage Island, Barren Island and Long Island) • South Andaman (including Outram Island, Henry Lawrence Island, John Lawrence Island, Havelock Island, Neill Island, Kyd Island, Port Blair, Defence Island, Rutland Island, Ross Island, North and South Sentinel Island and Little Andaman Island) Three major parameters have been considered in this discussion to understand the impact of Tsunami waves at the individual group of islands. They are elevation, LU/LC and bathymetry. 6.1 Effect of elevation Fortunately for the Andaman Islands, high rising undulating topography acts beneficial for most regions along the coast. Steep cliffs, narrow beaches and thick vegetation protect parts of the eastern edge of the islands which are also less vulnerable due to the direction of the Tsunami wave in the current scenario, i.e., 2004 Indian Ocean Tsunami. The advantage of wave direction along with higher elevations curbs the possible impact of on-shore inundation in the eastern edges of the study area. Nevertheless, the western coast almost throughout is prone due to lesser elevations and also falls right in the way of wave propagation as per the present scenario. Various small islands lying on the eastern side also have to bear the brunt of the Tsunami waves due to
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Fig. 12 Division of the inundated area into most vulnerable and vulnerable zones on the basis of elevation and land-use
their extremely small spatial extent making them easy to be engulfed by wave action, and a few inland extending water openings exist on the eastern side allowing surge water to penetrate further landward.
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Nat Hazards Table 4 Table showing the proportion of area under threat as per different criteria Risk criteria
Properties
Inundated area (km2)
Inundated area (%)
High
\3 m
75.8
10.7
Moderate
4–6 m
57
7.9
Low
More than 6 m
577
81.4
High
Builtup area/agricultural/fallow land (non-vegetated)
95.7
13.5
Low
Vegetated
613.1
86.5
High
\3 m and non-vegetated
9.2
Low
More than 3 m and vegetated
699.58
As per elevation
As per LU/LC
Elevation and LU/LC 1.3 98.7
6.2 Effect of land-use/land-cover Since Tsunami waves of even 10–15 feet are capable of wiping out coastal surface features, the LU in these vulnerable areas has to be carefully planned in order to minimize impact. It may be a helpless situation as the cultivable lands naturally exist on the prone sides of the islands, but efforts must be made to either find alternative areas for cultivation or to protect the existing ones by effective LU planning like polderization, land reclamation and coastal afforestation. The establishment of integrated coastal zone management (as is being done in Bangladesh) (Islam 2006) for coordination, demarcation of land zoning, mangrove afforestation through community participation, better preparedness against disaster and the introduction of modern land management systems can be a few steps toward controlling the effect of natural hazards. 6.3 Effect of bathymetry It has been established after several studies that the bathymetry of any area tends to affect the wave propagation, direction, speed and amplitude in the event of Tsunami. In a work done on the role of bathymetry in Tsunami devastation along the East Coast of India, the authors have established that ‘‘the narrowing of the shelf in the southern part of India and steep gradient in the vicinity of Nagapattinam, Cuddalore and Chennai provided proper conditions for enhancement of tidal wave, thus causing maximum damage at these points. Reefs, bays, entrances to rivers etc., help in modifying the wave as it approaches the shore’’ (Thakur and Pradeep Kumar 2007). The same can be applied for the Andaman Islands as well, because they too lie adjacent to deep-ocean ridges and trenches reaching depths of more than 5,000–6,000 feet within 50–70 km from the coastline on the eastern side of the Islands (Fig. 14). This gives rise to anomalies in gradient of the ocean floor topography that may lead to focusing or defocusing of waves near the shoreline.
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Nat Hazards Fig. 13 Map showing north, middle and south Andaman Islands
6.4 Spatial distribution of the Tsunami vulnerability along the Andaman Coast The following section describes the spatial extents of the Tsunami vulnerability assessed along the different coasts of the Andaman and Nicobar Islands.
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Nat Hazards b Fig. 14 Illustration showing bathymetry around the study area (courtesy—ArcGIS Online Viewer, World Imagery)
6.4.1 North Andaman The total land area in this group is 1,420.4 km2 out of which 185.9 km2 is inundated approximating to 13 %. This part of the study area, as compared to others, has the highest percentage of land under the threat of inundation. Many agricultural fields lie in the inundated area in this part, few lying near the Madhupur village while others are in the upper edges of the island. Also in the south eastern part, backwater inlets exist creating a gateway for sea wave surges to penetrate landward. 6.4.2 Middle Andaman Out of the 1,979.2 km2 area of the Middle Andaman Islands, 238.1 km2 of area is in the hazard prone region making up to 12 %. Mayabunder (in NNE direction) is the second largest tehsil of Andaman Islands home to about 25,000 people (courtesy Amateur Seismic Centre 2011). Greater part of this town is under threat of inundation as parts of this extend into the sea waters as ‘‘headlands’’ making them further more vulnerable. The south eastern margins have backwater streams cutting inward toward the land putting at stake the villages of Bakultala and Rangat which together inhabit around 40, 000 people. Another small village lying in the southern part of middle Andaman Islands is Adazig along the National highway has certain parts under threat. Many land holdings extending further south from this village are vulnerable to Tsunami wave submergence. The western edges of this part have certain areas under threat despite the higher elevations observed. This is due to the effect of bathymetry causing changes in the directivity of Tsunami wave propagation thereby leading to higher wave run-ups. 6.4.3 South Andaman The South Andaman Group comprises of a total of 2,433.5 km2 area where 284.8 km2 is inundated, i.e., 11.7 %.This part is comparatively less vulnerable due to higher elevations compared with other regions and sparse populations. The only exception is Port Blair, capital city of the Andaman and Nicobar Islands, and two mid-sized villages namely Tusnabad and Chouldari. Phoenix Bay, the most densely populated area in the Port Blair city would surely face the wrath of disaster if similar Tsunami situations were to arise. All small islands in this part have most of their land under threat. In Little Andaman Islands, villages of Chetamale and Geinyale lie toward the eastern edge within close proximity to the shoreline making them vulnerable. They are the only two recognizable villages having reasonable amount of settlements and land holdings in Little Andaman. 6.5 Advantages A successful effort has been made into the extraction of ‘‘Tsunami Hazard Vulnerability Mapping’’ for the Andaman Islands using previously mentioned methodology. Near accurate results were deduced from the current study that validated the credibility of the methodology followed. The aim behind this project was to attempt to map those areas which could face the threat of inundation by Tsunami waves under present day conditions.
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The same parameters were used for the study, which were obtained as on the day of the 2004 Indian Ocean Tsunami. This study has led to the recognition of further scope for research and development with advancements in data quality and quantity. It has helped in the evaluation of possible results which can be accurately acquired by following similar methodologies. A worst case scenario approach can be taken wherein the maximum possible impact can be projected and according results be obtained (Usha et al. 2009). It goes without saying that coarse resolution data act as an impediment in any study but here, efforts have been made to prove the validity of the methodology within the constraints of data unavailability. The intention of the current study was to extract the best possible outcomes, both in terms of quality and quantity, and it can be said that this aim has been achieved to a large extent.
7 Limitations There were no primary data sources available for the area. Hence, the study has been carried out based on the available data. The reason to initiate such a study was the high vulnerability of the study area to the malignant influence of Tsunamis, and so, efforts were made to assess the regional vulnerability in the region. The most note-worthy demerit that was faced during the process of this study was the lack of availability of high-resolution data both for on-shore and off-shore regions. Highresolution Tsunami modeling could have been performed giving far more accurate, and reliable results for which extremely precise elevation and bathymetry data were required. Because the Tsunami wave run-up height model was extracted at a resolution of 5 km, precise wave run-up heights along the shoreline could not be generated. A common method of resampling called upsampling in which a mathematical equation is used to achieve the desired number of pixels could have been applied. But, it has been found that this only helps in reducing graininess and not in increasing the quality of the data as such because, each big pixel is basically divided into numerous smaller pixels, all of which hold the same value as the former. Hence, it was considered best to use 5 9 5 km resolution data for delineating the inundation zone. SRTM (90 m) used in the study was not of high resolution to generate a smooth Tsunami Inundation Line all along the coastal environs and only patches of vulnerable areas could be acquired. Contours could not be successfully generated at small intervals on this data making it impossible to delineate continuous threat zones. Classification of the inundated areas as per LU/LC was one of the objectives of this study which also could be performed only up to a certain level. Landsat ETM? data (30 m) were good enough for the purpose of level II classification, but problems like nonrecognition of differences between settlements and agricultural land were encountered, and therefore, precision in quantification of data could not be achieved.
8 Conclusions As per the results of the study conducted, the Andaman Islands have around 12.1 % of their area under threat from the hazard of Tsunami and subsequent inundation. The figure may not seem to be alarming, but what has to be understood is that the dependence of over 300, 000 people inhabiting these islands is on the already limited agricultural lands, which
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again mostly lie in the hazard prone zone. It has been assessed that a mere 14 % of the total geographical area of the islands is available for agriculture and other human activities against an appalling 86 % covered by forests (Senani 2004). So the entire population (excluding the aboriginal population) is at the mercy of these sparse agricultural pockets, and thus, a major need arises to protect them and be well prepared for any environmental hazards. In today’s times, the efficiency of technology has blessed civilizations in protecting others as well as in being prepared for the worst. The only thing that is needed is the timely application, and implementation of the available resources and expansion of our databases for the speedy dissemination of information to those affected. Advances in remote sensing, GIS analysis and numerical modeling have been so significant in the recent past that any heights of useful interpretation can be achieved if applied systematically and logically (Tsunami Warning Centre Reference Guide 2011). The demerits must be weeded out as early as possible, and the limits be expanded. In context with the current study, it is an undisputable fact that with the available resources, reasonable and valuable information has been extracted and also an insight has been made into the vast scope of expansion that exists in such fields for research and development. Various works have been done in the recent past, which have successfully incorporated multiple parameters in order to demarcate maximum risks along with finding solutions to fight them using geo-spatial techniques (Mahendra et al. 2010, 2011). Coastal hazard mitigation programs, evacuation planning, building construction strategies, optimal path routing for essential facilities, rehabilitation center planning, agricultural planners, plantation developers, and so, many others could be benefitted from the creation of such maps and information so that a further loss to humanity could be timely averted in the Andaman Islands. Acknowledgments The authors are thankful to Dr. S. S. C. Shenoi, Dirtector, INCOIS for providing the facility to carry out this work at INCOIS. The authors would like to thank Global Observatory for Ecosystem Services (GOES), Michigan State University for making the Landsat data available and also NOAA’s National Geophysical Data Center (NGDC) for facilitating the Tsunami run-up data. This is INCOIS contribution No. 194.
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