tsunami in sri lanka essay

The Causes and Consequences of the 2004 Tsunami in Sri Lanka Analytical Essay

Introduction.

The stay of mankind on planet earth has often witnessed moments of helplessness when natural disasters have stuck; thus, destroying many lives and disorienting mankind in the process. From our earliest ancestors to the modern man, we have not been safe from the anger of natural disasters.

Ranging from volcanoes, hurricanes, tornadoes, and the destructive tsunamis, we are susceptible to an array of natural disasters that is always waiting to strike. Such was the case when a tsunami hit the coastline of Sri Lanka on 26 th December, 2004.

Without warning, thousands of people were killed by the 2004 tsunami. Such is the common story that is often told when catastrophic disasters like a tsunami strikes. Although we have evolved in science and thus learned many secrets of our world, we have not been able to subdue natural disasters. Since all life is precious, it is our responsibility to use the resources we have, and adopt a behaviour that can help us preserve even a few lives during catastrophic events.

Generally, a tsunami can be defined as a series of wave disturbances which usually originate from a vertical displacement of a water column (Abek 1561). The word “tsunami” has an origin in the Japanese language. Here, “tsunami” can be directly translated as a series of waves that often form at bays (Hassain 51).

Usually, anything with a potential of displacing, or moving a large volume of water can cause a tsunami (Vitarana 84). The most common causes of tsunamis include earthquakes, moving heavenly bodies such as meteorites and asteroids, volcanoes, and landslides (Vitarana 84).

Earthquakes

Most tsunamis originate from earthquakes. Once an earthquake occurs on a sea bed, a large mass of water is displaced upwards. Due to the force of gravity, the displaced volume of water will move downwards to regain its original position (Vitarana 84).

A repetitive cycle where a water column moves up and down is created; hence forming a wave. Usually, a displacement of a water column will occur when part of a sea bed is displaced (UNEP 12). A fault line in the earths crust can especially create a boundary where a vertical displacement of the sea bed can easily occur (Liu 106).

Earthquakes that occur on subduction trenches are the main causes of most tsunamis (Liu 106). The tsunami waves that originate from an earthquake source can then move away from the place where they originate thousands of miles (Moore 143). Knowing the magnitude of an earthquake can especially be useful in determining the scale of tsunamis that have travelled thousands of miles from their source (Hassain 51).

Tsunamis can also originate from landslides. Landslides can occur at the seabed, or at the coast (Vitarana 84). Possible causes of such landslides include the earthquakes, the erosion of sea slopes, and volcanoes (Hanson 67).

The erosion of coastal slopes can occur as a result of rain action, from sea waves, and storms (Hanson 67). Due to a displacement of sea water as a result of displaced debris from landslides, a series of waves that has a potential of causing a tsunami is formed (Van 24).

When a volcano occurs above the surface of the sea, but in proximity to the sea’s surface, a large quantity of rock debris is thrown into the sea. When such debris falls into the sea, it displaces water; hence, creating waves that can cause a tsunami.

However, volcanic eruptions that occur under a sea are more hazardous in forming tsunamis (Hassain 51). Tsunamis can originate from a displacement of water that is caused from a rising slope of a volcano (Van 24).

Besides, gases that are usually released from an erupting volcano can also cause a large water displacement; hence, forming a tsunami in themselves (Hanson 67). Moreover, a volcano can also trigger an earthquake, which can then trigger a tsunami (Damen 106).

Meteorites, Comets and Asteroids

Our earth is constantly in danger of colliding with heavenly bodies like comets, asteroids and meteorites (Hanson 67). While one might think that great damage from such collisions will occur if such bodies hit the earth’s surface in an area that is densely populated (such as a town), a greater catastrophic damage will happen if such bodies strike at sea (Abek 1561).

For example, scientists estimate that if an asteroid that is about six kilometres in size was to fall in the middle of the Atlantic Ocean, a third of the US population would be killed (Damen 106). The gigantic speed and size of a typical asteroid can create huge waves (tsunamis) that can cause a devastating damage to our population (Liu 106).

Although the possibility of us experiencing such a collision (from a heavenly body such as a comet) is almost zero, there is always a possibility, however remote, of such a catastrophe (Abek 1561).

Activities of Man

Any human activity that can result in a displacement of a large volume of sea water can lead to the build-up of a tsunami wave. The carrying out of nuclear tests in deep sea can especially release large amounts of energy which can then displace large volumes of sea water; hence, leading to the development of a tsunami wave.

Characteristics of a Tsunami

As we had seen earlier, a tsunami originates from a displacement of a large volume of water (Van 24). Such a displacement creates a wave that moves up and down. A tsunami is therefore a wave that is very similar to the kind of wave ripples that we usually observe when we throw a stone in a tank of water (Damen 106).

Very often, a tsunami will travel as a series of multiple waves; hence, it is usually called a train of waves (Damen 106). Once it forms, a tsunami can travel thousands of miles in the sea before reaching a coastal area (Liu 106).

For example, a tsunami with an origin in the Atlantic can travel at an incredible speed of over 1000 km/hr to reach the Japanese coastline within 24 hours. Usually, tsunamis travel at very high speeds in deep sea waters (Hassain 51).

However, on reaching shallow waters, the speeds of tsunamis usually reduce gradually (Van 24). Such behaviour results from an energy flux that remains constant during a wave travel (Hanson 67). Since the energy flux of a tsunami is proportional to the speed and amplitude of a tsunami wave, tsunamis at deep waters will travel at high speeds and low amplitudes.

Unlike their counterparts in shallow waters, such tsunamis will usually consist of waves that are small in amplitude and long (in horizontal size) (Abek 1561). Indeed, it is sometimes difficult to physically observe a tsunami wave that is travelling in the deep sea (Moore 115).

However, as a tsunami moves into shallow waters towards a coastal area, its speed decreases while its amplitude increases. Travelling at a speed that is proportional to the square root of gravity constant multiplied by depth, a tsunami that is travelling in water that has a depth of five kilometres can move at a speed of over 800 km/hr.

On hitting a coastline, such a tsunami can travel at amplitude that is within ten to fifty meters. Such tsunamis can be observed as a series of water waves forming a series of rises that alternate with falls at the coastline. Usually, tsunamis can occur for several hours.

Like any other wave, tsunami waves can add to one another to form waves with higher amplitudes, or subtract from one another to form waves of lower (or zero) amplitudes (Moore 116).

The 2004 Tsunami in Sri Lanka

The date of 26 th December, 2004, will be remembered for a long time by many people in Sri Lanka. During this particular date, a tsunami of a large magnitude and scale hit the coastline of Sri Lanka (Liu 117). The 2004 tsunami in Sri Lanka was caused by an earthquake of a high magnitude that occurred in the western coast of Sumatra (Moore 138).

The focal depth of this particular earthquake was about thirty kilometres (Liu 117). Described as the worst earthquake to occur in the history of our planet in the past five decades, the earthquake that caused Sri Lanka’s tsunami measured over 9.0 on the Richter scale (Liu 106).

The above earthquake originated from an interaction of Australian, Sunda, and Burmerse tectonic plates. Here, as much as 30 meters of the sea bed covering a distance of over one thousand kilometres was displaced (Abek 1561).

Thus, the displacement that was caused by the described tsunami resulted in a vertical displacement of a very large volume of water; hence, creating a tsunami (Moore 129). Moreover, the 9.0 earthquake caused a series of about fifteen other earthquakes in the affected region.

As a result of a water displacement (caused by the resulting upward movement of the sea floor) that was caused by the 9.0 earthquake, a series of three tsunami waves was formed (Liu 106). Time duration between these tsunami waves averaged about twenty minutes.

Effects of the 2004 Sri Lanka’s Tsunami

When a tsunami reaches a coastline, it travels with high amplitude (from ten meters to even fifty meters). Thus, water overflows from the coastline towards the inland. Such a moving volume of water travels at a high speed, and with enormous energy that can cause huge destructions.

The 2004 tsunami, which hit the Sri Lankan coastline, affected more than two thirds of the Sri Lankan coastline. More than 20% of the Sri Lankan population was thus affected by the 2004 tsunami. Here, the moving tsunami wave drowned and killed thousands of people.

There were especially many deaths since the country was not expecting a tsunami, and was not therefore prepared for an emergency evacuation. It is reported that due to ignorance, thousands of Sri Lanka’s went to learn of what had happened when the first of the three series of tsunami waves hit their coastline (Liu 119).

Usually, due to interactions with a coastline (often leading to a loss of energy), the first wave of a tsunami is often less devastating than those that follow after it. Thousands of people here were thus caught unaware and killed by the second wave of the 2004 tsunami.

The total number of people who were killed in Sri Lanka by the 2004 tsunami has been estimated to be around 31,000 in total (Liu 119). A larger proportion of those killed by the 2004 Tsunami consisted of women and children.

It is estimated that over 10,000 of those killed here during the tsunami disaster consisted of children (Liu 119). Apart from deaths, about seventeen thousand people were injured by the tsunami tragedy while more than five thousand were reported as missing (Liu 119).

Moreover, the overflowing waters of the 2004 tsunami resulted in a massive destruction of property along the Sri Lankan coastline. As a result of the 2004 tsunami tragedy, more than eighty thousand homes were destroyed; hence, displacing about a million individuals as a result.

So as to cater for the needs of the people who were displaced by the 2004 tsunami in Sri Lanka, about 800 camps for the displaced were formed. Many of the people in these resettlement camps had lost their property and livelihoods as a result of the tsunami tragedy. Many others had to undergo psychological trauma due to the negative ways in which the tsunami had affected their ordinary living.

Apart from houses, many infrastructural facilities were destroyed by the 2004 tsunami; thus affecting transport, communication and also posing a reconstruction challenge. A lot of debris could be seen around all the areas that had been affected by the tsunami.

Besides, the 2004 tsunami affected the ground fresh water table through salinization of fresh water sources. It will take a lot of efforts and time to clean some of these water tables that were affected.

Since the Indian Ocean bed has been proven to contain lesser amounts of titanium as compared to the Pacific Ocean, more studies will be required before we can determine if large amounts of titanium was deposited inland during the 2004 tsunami.

Economic Impacts

The 2004 tsunami affected the economy of Sri Lanka in several negative ways. Many people lost their sources of livelihoods as a result of the 2004 tsunami. Some of these people who had lost their livelihoods had lost their businesses and houses, which had been destroyed by the destructive tsunami waves (Dawson 224).

Moreover, since a large community of the Sri Lankan population depends on fishing, many fishermen became poor as a result of losing their boats to the destructive tsunami waves that had hit their coastline (Dawson 224). A United Nations report on the 2004 tsunami catastrophe estimated that about two hundred thousand people in Sri Lanka were in danger of becoming poorer following the 2004 tsunami tragedy (Dawson 224).

Because a large part of hotels, beaches, among other tourist facilities were destroyed by the tsunami tragedy, the tourism economy of Sri Lanka was affected negatively. With no places were tourists could visit and relax, the number of tourists arrivals in Sri Lanka decreased significantly.

As a result, many people who were directly employed by the tourism sector lost their jobs. Moreover, due to a decrease in tourist arrivals, Sri Lanka was thus losing an important source of foreign exchange for developing her economy.

With a destruction of her infrastructure as a result of the tsunami tragedy, billions of dollars were needed for restructuring. With limited resources, it will be difficult to meet the cost of restructuring affected infrastructure. Meanwhile, as a result of infrastructural destruction, many businesses were affected negatively (Dawson 224).

Many of these businesses depend on telecommunication, electrical power, and transport to carry on with their day to day activities. Most of these businesses are small businesses that contributed positively to the Sri Lankan economy (through employment of people and paying of taxes).

Since most of these businesses have suffered from a displacement of people (hence a loss of market and human resource), and also from a physical damage of their properties and facilities, they have found themselves in a very repelling environment to operate in; thus, limiting their capacity and output to the Sri Lankan economy.

Measures that can be taken to Avoid Future Tsunami Disasters

No matter how much we prepare, we can never be ready for natural disasters. However, a possible measure of precautions and preparedness can help us to avoid, or minimize the destructive effects of natural disasters.

Although there is uncertainty on the effectiveness of using technology to mitigate the effects of natural disasters such as tsunamis, we should always work in the direction of utilizing such technologies if we can save a few lives as a result.

Together with several countries, the United States has developed and placed sensors at specific areas on the seabed of the Pacific Ocean. These pressure sensors have been designed to detect possible tsunami waves (UNEP 18).

Since tsunamis consist of waves that have rising and falling amplitudes, the resulting difference in the depth of water can cause pressure alterations at sea beds. These pressure alterations can thus be detected by pressure sensors at the seabed (Meihde 56).

However, since such a process of measurement is highly complex, it is very difficult for sensors at the sea bed to have accuracies that can detect tsunami waves. Still, the progress that has been made in using sensors to analyse data for a possible tsunami waves is positive (UNEP 12).

Moreover, since tsunamis interact with the coastline in a way that is difficult to predict, it is equally difficult to predict their behaviour. Sometimes, tsunami waves can add together or cancel one another; hence, making it difficult to determine their eventual behaviour (Meihde 56).

Several researchers remain uncertain if the tsunami warning technology in the Pacific Ocean can be replanted in the Indian Ocean. Still, Sri Lanka has been part of an ongoing program that has been working with several other countries to develop a system that can monitor for possible tsunamis before they occur (Liu 106).

Under the tsunami warning system, The Sri Lankan meteorological department will work together with Japanese technologies and the Pacific Tsunami Warning System (PTWC) to possibly help in the detection of future tsunamis (Yadav 107).

Although we do not have a reliable system of detecting tsunamis, the above development is progressive in developing reliable systems that can be useful in detecting and monitoring tsunamis before they hit a coastline (Andrew 23). However, since the Indian Ocean bed is different in structure and complexity to the Pacific Ocean bed, there is a need for scientists to develop a warning system that is unique to the Indian Ocean (Patra 362).

Currently, there has been a debate on whether to allow building of houses a few meters from the coastline (UNEP 21). Here, it will be useful to develop buildings that are protective and safe from tsunamis. Importantly, there is also a need to conserve the Sri Lankan coastline (Meihde 56).

A huge part of the coastline should be planted with mangrove forests (Patra 362). A thick cover of mangrove forests is useful in cutting and breaking tsunami waves before they move further inland (Yadav 107). Conservation of the Sri Lankan coastline will thus be useful in at least reducing possible damages that may occur from future tsunamis (Andrew 22).

Importantly, the political leadership in Sri Lanka has become more aware of damages that can result from natural disasters like tsunami attacks.

With such awareness, the political leadership in Sri Lanka can prepare for emergencies and move with speed to mitigate damages that can arise from similar disasters in the future (Patra 362). Here, it will be useful to develop a quick response system that can help in quick evacuations and treatments during times of natural disasters (Yadav 107).

An important area that needs to be utilised for the purposes of mitigating the effects of future natural disasters is the use of technology. Here, technology can especially be used to help in assessing the damage that could have occurred following the occurrence of a natural disaster (Patra 362).

Importantly, technology can be employed to communicate with the people who are in danger of being hit by a natural disaster (Andrew 22). It is fruitful to relay information to a target populace with instructions on where they can move to stay safe, what they can do, and how they can ask for help during such times (Meihde 56).

As we had seen earlier, a large number of people who died during the 2004 tsunami attack died from the second wave (Yadav 107). Having heard of what had happened, many people moved in ignorance to witness the effects of the first wave before the more deadly second wave hit (Yadav 107).

With the right communication, these people could have been told to stay away from the beach; hence, cutting on the number of fatalities that occurred (Patra 362). As it has been proven time and again, an effective system of coordination and communication is essential in saving lives during times of natural disasters (Andrew 23).

Natural disasters will always remain a part of us during our stay on planet earth. So as to minimize the loss of lives during natural disasters, it is useful for man to utilize all resources at his disposal to preserve lives during the occurrence of natural disasters.

Such a direction would involve understanding the science of natural disasters like tsunamis, and thus develop measures that can help in warning, planning and rescue programs when natural disasters strike. The 2004 tsunami has especially provided us with important lessons on the direction that we can adopt to prevent massive deaths during such disasters.

Adopting programs that are helpful in preparing for such disasters can be fruitful in saving many lives.

Such a direction would involve developing an efficient system of communication, designing and implementing a standby resource of manpower and machinery for emergencies, using technological systems to warn for possible oncoming disasters, and importantly, developing a system of peaceful coexistence with our natural environment (such as the planting of mangrove forests along coastlines to help in subduing tsunamis).

Works Cited

Abek, Kennedy, “Physical Size of Tsunamigenic Earthquakes from Tsunami Data.” Journal of Geography Research , volume 84.1 (2006): 1561-1568. Print

Andrew, John, “Tsunami Generated Forms.” Science of Tsunami Hazards 10.1 (2003): 21-34. Print

Damen, Michiel, What are Tsunamis? New York: McGraw, 2008. Print

Dawson, Foster “The Identification of Tsunami Deposits in Coastal Sediments.” Science of Tsunami Hazards 9.4 (2000): 206-423. Print

Hanson, Briggs, Sea level Change in North Iceland , London: McMillan, 2004. Print

Hassain, Kundsen, Effects of the 2004 Tsunami in India , Mumbai: McGraw, 2007 Print

Liu, Fearn, History of Tsunami Catastrophes , Beijing: McMillan, 2009. Print

Meihde, Mark, Characteristics of Tsunamis . New York: International Institute For geo-information Science Press, 2006 Print

Moore, Normark, Giant Hawaiian Landslides , New York: McMillan, 2005. Print

Patra, Singh, Agrochemical , Mumbai: McMillan, 1996. Print

UNEP, Early Warning Systems, New York: UNEP Press, 2011. Print Van, Frank, the Science of Tsunamis, New York: International Institute For geo-information Science Press, 2006 Print

Vitarana, Tissa, Sri Lanka after the Indian Ocean Tsunami New York: International Institute for geo-information Science Press, 2006 Print

Yadav, Agarwal, Soil, Water Conservation . Mumbai: Oxford, 2007. Print

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Article Contents

1 introduction, 2 numerical modelling, 3 data used in this study, 5 conclusion.

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The 2004 December 26 Indian Ocean tsunami impact on Sri Lanka: cascade modelling from ocean to city scales

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B. Poisson, M. Garcin, R. Pedreros, The 2004 December 26 Indian Ocean tsunami impact on Sri Lanka: cascade modelling from ocean to city scales, Geophysical Journal International , Volume 177, Issue 3, June 2009, Pages 1080–1090, https://doi.org/10.1111/j.1365-246X.2009.04106.x

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The 2004 December 26 Indian Ocean tsunami severely hit Sri Lanka. Although it was not in the direct path of the initial tsunami waves, the western coast was struck by diffracted waves that caused much damage. The numerical model GEOWAVE is used to compute tsunami generation, propagation and inundation from the earthquake source to the Sri Lankan coast. A nested grid system is constructed to increase the resolution until Galle Bay, on the southwestern coast, where a 20 m-grid is used. The six nested topobathymetric grids are interpolated from ETOPO2 and high resolution data, at sea as onshore. Simulation results are compared with tsunami height data from National Oceanic and Atmospheric Administration (NOAA; US) and Geological Survey & Mines Bureau (GSMB; Sri Lanka). When the grid resolution increases, the discrepancy between the model and the data remains, on average, good, whereas its spread increases. We then conclude that the order of magnitude of the tsunami height is consistent from the 180 m-resolution grid, but the spatial imprecision is too high to locally predict reliable water heights. Nevertheless, the comparison between computed time-series of sea surface elevation at the Colombo tide station and tide-gauge data shows a very good agreement as both amplitude, and arrival time of the first wave are well reproduced. When focusing onshore, the modelled inundation limit is compared with the limit measured in the field. With its a priori setup, computed inundation spreads much farther behind the field limit. We then integrate more accurate nearshore conditions into the model. Non-linear shallow water equations are chosen instead of fully non-linear Boussinesq equations; the bottom friction on land is increased to a much higher value than at sea; the buildings cover and the low tide conditions are taken into account in the DEM. The resulting high resolution simulation agrees better with field data, even if discrepancies are still locally observed in places of DEM imprecision and in a river valley. This simulation, however, demonstrates that taking into account nearshore and onshore features may significantly improve tsunami impact assessment.

The 2004 December 26 Sumatra—Andaman earthquake was one of the most disastrous recorded earthquakes, due, in part, to its large magnitude, M w between 9.1 and 9.3 ( Ammon et al. 2005 ; Park et al. 2005 ; Stein & Okal 2005 ). The earthquake generated a tsunami, which propagated through the Indian Ocean and caused extreme inundation and destruction along the surroundings coasts ( Chanson 2005 ). The resulting death toll is estimated to be between 200 000 and 300 000. The most devastated country was Indonesia, as it lies near the subduction zone where the earthquake occurred, but the tsunami also severely struck Sri Lanka, where more than 30 000 people were killed ( Liu et al. 2005 ; Goff et al. 2006 ; Inoue et al. 2007 ). The tsunami hit the island because it was on the main propagation axis, and then diffracted around it, so that the southwestern zone was as greatly damaged as the eastern zone.

Since 2005, numerous studies have attempted to model the tsunami propagation: some focused on the seismic source to discriminate between inverted slip distributions ( Pietrzak et al. 2007 ) or to directly invert the slip distribution ( Grilli et al. 2007 ; Piatanesi & Lorito 2007 ; Sladen & Hébert 2008 ); others attempted to model the tsunami impact on local targets like Thailand or the Mascarene Islands ( Ioualalen et al. 2007 ; Hébert et al. 2007 ); and one study examined the propagation of the tsunami over the entire world and the spatial transport of energy in all oceans ( Kowalik et al. 2007 ).

The potential influence of nearshore propagation effects on local tsunami height at the shoreline and onshore is well known. Most tsunami models, however, only operate on rough bathymetric data ( Dao & Tkalich 2007 ; Geist et al. 2007 ; Ioualalen et al. 2007 ). The difficulty is to obtain high resolution data and to handle the whole extent of tsunami propagation, for example, with an irregular mesh or a nested system ( Pietrzak et al. 2007 ; Hébert et al. 2007 ). Without such accurate data, numerical modelling may only compute tsunami amplitude at sea and cannot appraise the tsunami impact on the coasts ( Geist et al. 2007 ).

In this study, we extend the global model of Grilli et al. (2007) , with focus on the impact on southwestern Sri Lanka. We built a simulation chain to increase the resolution of the tsunami calculation in this area. Along the process, computed tsunami heights on Sri Lanka shore are compared with several sets of observations. The last step of the simulation chain is a focus on the Bay of Galle. There, model results are compared with field data of inundation limits. At this stage, we run several calculations with different options and parameters in order to refine the model so that it agrees, as well as possible, with the data.

2.1 Tsunami model

In this study, we used the tsunami generation, propagation and inundation model GEOWAVE ( Watts et al. 2003 ). The initial tsunami (surface water deformation and velocity) is computed from the vertical displacements of seafloor produced by an elastic dislocation simulating the earthquake ( Okada 1985 ). Then, propagation and inundation of tsunami waves are computed with FUNWAVE, a model based on fully non-linear Boussinesq equations, accounting for frequency dispersion ( Wei et al. 1995 ). The methodology of GEOWAVE is described in Grilli et al. (2007) .

2.2 Nesting system

The aim is to compute tsunami waves until they reach the Sri Lankan coast. In a first stage, the most important thing is to describe the tsunami wavelength well, which is related to the source characteristics. For an earthquake-generated tsunami, the wavelength generally reaches a few hundred kilometres if the concerned fault is long. For the 2004 Sumatra earthquake, the wavelength is estimated, from the first wave measured by the Jason-1 satellite altimeter, as around 500–600 km ( Gower 2005 ). The spatial resolution of the computational grid must be chosen according to this wavelength. In our case, a grid resolution of a few kilometres is enough to model tsunami propagation across the deep part of the Indian Ocean. However, when the tsunami comes near the coast, its wavelength decreases due to the decreasing depth, so that the resolution of the computational grid must increase. As GEOWAVE can solve tsunami propagation only on a regular square grid, we had to implement a system of nested grids. Considering the layout of the affected zone, a one-way nested scheme appears to be appropriate. Such systems are commonly used in tsunami modelling with non-linear shallow water equations ( Koshimura & Mofjeld 2001 ; Titov et al. 2005 ), but this approach was not previously applied to Boussinesq-type models. In such a one-way nested grid scheme, wave propagation is first computed on the entire domain of the coarse grid. Information (velocity and water height) are extracted from the coarse grid simulation at the boundaries of the included fine grid and then used for the calculation on the fine grid. Information are not returned to the coarse grid from the fine one. Indeed, in the case of the December 2004 tsunami, the waves propagate in one preferential direction, they enter the Sri Lanka region and then exit without coming back again. The trapped waves are fully computed in the finer grids. It appears that some waves are reflected by the Maldives and then come back towards Sri Lanka, but the simulation shows that they have a negligible amplitude compared with direct and trapped waves (see Fig. 3 ). In practice, a first complete run is performed on grid of δ x 0 -uniform spacing, during which water height and velocities are recorded at the limits of a second domain included within the first. Then, these conditions are interpolated on the limits of second domain, according to the chosen nested grid resolution δ x 1 < δ x 0 and used to compute through a new run more accurate tsunami propagation on this finer grid.

Shaded relief map of sea surface height 4 hr after the earthquake (S0). White arrows indicate the first wave reflected from Sri Lanka, whereas black ones mark the wave reflected from the Maldives.

In our simulations, we chose to use a nested ratio of 3, so that δ x 1 = δ x 0 /3. As we intend to reach a local scale around the Bay of Galle in Sri Lanka, the nesting process is iterated as needed. The resolution of all grids composing the nesting system are reported in Table 1 . The first three simulations comprise the whole of Sri Lanka ( Fig. 1 ). The last three are centred on the southwestern part of the island and then on the Bay of Galle.

Mesh and domain size for the six nested grids used in this study.

Location of the six nested grids used in our simulation chain (coordinates are in Mercator projection).

2.3 Source parameters

The source parameters are taken from Grilli et al. (2007) , who adjusted a five-segment fault model to satellite track records and some tide gauge data around the most devastasted part of the Indian Ocean coasts. We also use the same time sequence spanning 1200 s, which could be debated as it largely exceeds measured rupture duration ( Ammon et al. 2005 ; Ni et al. 2005 ; Park et al. 2005 ). Grilli et al. (2007) defend their choice by referring to rising time effects, low shear-wave speed in the accretionary prism and since other tsunami modelling also assumed reduced rupture speed (e.g. Fujii & Satake 2007 ).

2.4 Outputs

The output of each calculation consists of time-series of gridded sea surface elevation (SSE), maximum SSE on the domain ( z max ) and, eventually, time-series of SSE at selected points taken as numerical gauges. The time-series results are important as they help to constrain the arrival time of the tsunami at the shore, and they also enable tsunami period estimation.

3.1 Bathymetric and topographic data

Bathymetric grids are derived from several data sets of various resolutions.

The first grid S0 is derived from the ETOPO2 data set ( U.S. Department of Commerce & Atmospheric Administration 2006 ). ETOPO2 is made from several data sources, mainly, satellite altimetry and has a 2′× 2′ spatial resolution.

A loose data set of sounding points on the complete Sri Lanka margin was provided by the DEOCOM project (Delimitation of the Outer Edge of the Continental Margin of Sri Lanka under the UNCLOS; Fig. 2 ). Nearshore detailed bathymetric profiles were provided by the Coastal Conservation Department (CCD), but only a few kilometres along north of Galle. Around Galle Bay and Weligama Bay, fine resolution data were provided by the National Hydrographic Office (NHO) of Sri Lanka ( Garcin et al. 2007 ). These data have been checked and corrected before computing topobathymetric grids at various resolutions up to 20 m ( Garcin et al. 2007 , 2008 ).

Bathymetric data points around the southwestern Sri Lankan coast.

Topography is needed to model inundation and tsunami reflection when the tsunami hits the coast. It, thus, mainly plays a role in high-resolution simulations, that is S4 and S5. We used a fine topographical data set on a 3–4-km wide coastal strip, where the main part is below 10 m elevation. A 20 m specific DEM was interpolated from the Survey Department of Sri Lanka data (elevation points and contour lines) and covers the coastal strip from Beruwala to Weligama Bay (thus covering the extent of S4 extent; Garcin et al. 2007 ). Another DEM made by the United Nations University (UNU) from a 1 : 5000 map digitizing and kinematic GPS data covers the Galle urban area with a 5 m resolution. Both these DEMs helped to build S4 and S5 topographical grids through subsampling. Elsewhere, SRTM elevation data with a 3″× 3″ resolution are used but have almost no influence on the tsunami calculation. Because of their limited extent, fine topographical data are integrated only in S3 and subsequent grids.

We use the Mercator projection to build a nesting system of six rectangular uniform grids from topobathymetric data with spatial resolutions from 4860 to 20 m ( Table 1 ).

3.2 Tsunami observation data: tsunami height and inundation

As in the NOAA data set, what we call here ‘tsunami height’ may refer to the maximum elevation reached by the water either at sea (in case of tide gauges) or onshore (when talking about run-up).

Tsunami height data sets from several sources were used in this study. We first retrieved NOAA data ( http://www.ngdc.noaa.gov/hazard/tsu.shtml ), which is well spatially distributed data for the entire global domain of calculation. NOAA data for Sri Lanka are used to particularly check S0 to S2 simulations. When focusing on southern Sri Lanka (S2 and S3), we use GSMB data collected in the field a few days after the tsunami disaster (2004 December 30; Siriwardana et al. 2005 ).

Then, when zooming on the Bay of Galle, we resorted to NOAA data because the GSMB data are too sparse in this zone (S4).

The nearest tide gauge to our zone of interest is located at Colombo, around the middle of the western coast of Sri Lanka. This tide gauge recorded the first half of the tsunami wave but then malfunctioned. This record, however, is precious, as it precisely indicates the time of tsunami arrival and the height of the first wave (2.1 m), even if the arriving wave at Colombo had been somewhat disturbed and weakened when moving around half the island.

Around Galle, we have the maximum inundation limits as it was estimated, interpretated and localized by GPS in the field from witnesses testimonies ( Garcin et al. 2007 ). This field inundation limit (FIL) extends from Weligama Bay in the south to Beruwala in the north, so that the S4 domain is entirely covered.

Topographic data on simulations S0–S2 are derived only from SRTM data set, inducing a higher shore relief in the main part of southwestern Sri Lanka. Because of this artefact, the simulated tsunami at these resolutions cannot inundate the coastal zone as it should. In consequence, a lot of points that were actually inundated are modelled as always dry. This happens also on the S3–S5 grids, probably because of imprecision in data localization and/or in topographical modelling. For this reason, we relocalized data points for each simulation within a 1-pixel radius for S0 and S1 and 2-pixel radius for S2–S4 in such a way that we can test an average agreement with NOAA data, regardless of details. At the same time, we compare data at its original location with the result, as a control.

All simulations compute tsunami propagation and inundation until 5 hr 40 min after the beginning of rupture, corresponding to at least 2 hr 30 min after the first tsunami impact on Sri Lanka.

In the following, a parallel between model results and data is drawn and discussed simulation after simulation.

4.1 First simulations: S0 and S1

The simulation of tsunami propagation throughout the Indian Ocean from Sumatra to the Maldives distinctly reproduces two major reflections of tsunami waves ( Fig. 3 ). The first obstacle hit by the tsunami is Sri Lanka. Here, diffraction and reflection of the wave occur at the same moment. The diffracted wave rolls around the island and runs along the shore until it is on the western part. The reflected wave moved radially away around the island. The second obstacle was the Maldives, at the southwest of southern India. There was some diffraction on the atoll islands, but the first and highest wave was also reflected from the archipelago and moved back, mostly northeastwards. When looking in detail at the simulation in the area between Sri Lanka and Maldives, the waves reflected by the Maldives archipelago are clearly visible in the open sea, not because of their amplitude but because the open sea is no longer disturbed by direct incident waves ( Fig. 3 ). As the reflected wave front propagates towards the Sri Lankan shore, their contribution to simulated wave height appears completely dwarfed by the trapped waves coming from diffraction along the southern coast. In a real system, however, the dissipation of trapped waves should have been more efficient, so that the reflected waves may have contributed somewhat to the latest waves impacting southwestern Sri Lanka, about 2.5 hr after the first wave struck.

Watts et al. (2005) made the initial simulation, from which Grilli et al. (2007) elaborated their source model, and they already compared the simulated propagation of the tsunami with available satellite altimetry track records on a domain similar to our S0. They found that salient features of the Jason-1 satellite transect were reproduced, although details differ somewhat.

Maximal SSE (m) on S1 grid (1620 m resolution), with NOAA points for checking S0 and S1 results around Sri Lanka.

NOAA check points for S0 and S1, in southern India (IN) and Sri Lanka (SL).

The polarity of the first wave is positive at each point of Table 2 , as was observed on tide-gauge data throughout the S1 domain ( Nagarajan et al. 2006 ). The polarity is directly determined by the source mechanism, but it is important to check that diffraction along the Sri Lankan coast is correctly modelled and does not alter the shape of tsunami waves with respect to observations.

We observe that in the northern part of Sri Lanka, a coastal segment was saved from tsunami impact, thanks to two bathymetric thresholds located on both sides ( Fig. 4 ). Just opposite, the corresponding coast segment in India was similarly saved. Both thresholds standing in the way of the tsunami efficiently stopped the waves. This result of the modelling is consistent with inundation extent observed in northern Sri Lanka ( Wijetunge 2006 ).

4.2 Intermediate simulations: S2 and S3

S2 is the last simulation stage comprising the whole of Sri Lanka. It shows more clearly than S1 how the whole eastern coast of the island is severely impacted by the tsunami, with probable far-penetrating inundation. On the western side, the most harshly affected zone lies south of Colombo. This part of the coast was reached by diffracted waves having hit the eastern coast and also later by waves reflected from the Maldives.

As S2 grid includes Colombo, we put a numerical gauge at the location of the tide-gauge in the harbour. Fig. 5 shows the comparison between detided sea level data and the model results. Data are only available for the first 20 min of the tsunami demonstration at Colombo, corresponding to the sea surface rise and subsequent fall. Although Colombo lies across the island with respect to the side facing the tsunami, the model result agrees well with the rise and is only a bit shifted during the fall. Both arrival time and maximum amplitude of this first wave are very well reproduced. This consistency confirms the model validity, even in calculating diffracted waves evolution.

Tide-gauge data at Colombo superimposed on the corresponding time-series extracted from S2 results.

Maximal SSE on coastal part of the S3 grid (180 m resolution), with GSMB data superimposed.

Frequency distribution of z max to NOAA measured tsunami height ratio, for S2 and S3 simulations (of respective resolutions 540 and 180 m).

Frequency distribution of z max to GSMB measured tsunami height ratio, for S2 and S3 simulations (of respective resolutions 540 and 180 m).

On Fig. 6 the FIL measured by Garcin et al. (2008) is also plotted. This limit is clearly overestimated by the model in many places where the computed tsunami penetrates further 3 km.

The z max map for S3 exhibits a patch of run-up higher than 7 m on a section of the coastline (red spot on the Fig. 6 ). This is the place where a train was swept off by the tsunami in Pereliya, killing more than 1500 people. The calculated inundation limit there agrees quite well with data. When looking at the DEM, we see that a topographical bank stops the tsunami penetration, contrary to other locations where the model calculates too great an inundation.

4.3 Final simulations: S4 and S5

S4 and S5 finally focus on the Galle Bay and neighbourhood ( Fig. 9 ).

Maximal SSE on the S4 grid (60 m resolution), with NOAA points around Galle. White crosses indicate the NOAA data locations.

First, the z max map for S4 shows that the highest wave at the shoreline on this coastal part ranges from 4 m at Weligama to 5 m at Galle ( Fig. 9 ). Onshore, the calculated tsunami run-up increases, yet up to 1 m more. z max increase where the coast inundated is well predicted, but if we compare the FIL with the computed inundation limit, the rush of water inland appears to be actually inflated. The excessive flooding in the simulation occurs in relatively flat zones of the coast (from east to west): Weligama, Ahangama, Galle and further afield. At Weligama, the topography may have stopped the real tsunami, whereas the DEM is too flat to have the same stopping effect. At Ahangama, the inundated zone behind the FIL corresponds almost exactly to the Koggala Lake, which is described as land in the DEM because of its positive elevation. At Galle, a flat zone on the back of the town is inundated during the simulation, although it stayed dry in reality. This takes place in the S5 zone, so it will be discussed later. The 5-m elevation contour for the used DEM is plotted on Fig. 9 for indication. The frontal part of the contour line matches remarkably well with the computed inundation limit. This agreement suggests that in our simulations, the only way to stop the tsunami propagation even onshore is a topographic bank. In the case of such a powerful tsunami, energy dissipation in FUNWAVE is insufficient to stop inundation within a 1–1.5 km wide coastal strip, as it should.

Frequency distribution of computed z max to NOAA measured tsunami height ratio, for S4 simulation (of resolution 60 m).

The S5 simulation on a 20-m resolution grid intends to refine the calculation on the Bay of Galle. One can first note that the tsunami magnitude computed in S3 over the Galle Bay does not change in S4 and S5 ( Fig. 11 ). S3 appears to be adequate for computing tsunami heights at the shoreline, as it clearly improves the tsunami propagation model when water depth gradually decreases in this zone. Then, S4 and S5 improve the precision of the result and slightly improve the calculation of inundation limit, but the spatial distribution of tsunami height does not change any more. The stabilisation of tsunami height with increasing resolution validates the simulation chain, as we are certain not to miss the amplification effect at shallow depth near the coast.

S2–S5 maximal computed SSE on S5 around Galle. Coastline and 5 m contour are extracted from the S5 topobathymetric grid. The dashed lines indicate the track of the Fig. 12 profiles.

Instantaneous sea surface height profiles are extracted from S3, S4 and S5 outputs across Galle Bay and until computed inundation limit ( Fig. 12 ). They confirm the same amplitude and shape of tsunami waves whatever the simulation. S4 and S5 are particularly concordant, except for minor details on shore.

Instantaneous SSE profiles through Galle Bay and the town, at the maximum of the first and second wave (see location on the Fig. 11 ). Three profiles are extracted for each wave from S3, S4 and S5 outputs, for comparison. The topographical profile is also reported from the S5 grid.

We extracted the SSE time-series at a numerical gauge corresponding to the SL19 NOAA point, at the Bay entrance ( Fig. 13 ). This time-series indicates that the highest wave at Galle was the second. This result is consistent with eye witness testimonies ( Garcin et al. 2007 ). In addition, we can observe on this graph that subsequent waves are still significant, as they exceed half the highest wave. This could be due to the influence of waves that were reflected back from the Maldives. These waves indeed came back to the southwestern coast of Sri Lanka around 1hr 50 min after the first arrival, but the numerical gauge signal shows that they only had a negligible effect on the SSE ( Fig. 13 ). The wave sequence could also have been influenced by trapped waves.

SSE time-series at the SL19 numerical gauge, off the Galle Fort (simulation S5).

As was noted already in previous simulations, the computed inundation limit outruns by far the FIL ( Fig. 11 ). We then tried to improve the final computation of tsunami propagation and inundation by taking into account the following previously ignored considerations:

In simulations S0–S4, we chose in FUNWAVE to use fully non-linear Boussinesq equations from Wei et al. (1995) . It may be, however, better to use the non-linear shallow water equations (NLSWE) option for the S5 domain, as it is physically more appropriate for very shallow water. The wavelength indeed remains very high as the depth greatly decreases, so that the depth to wavelength ratio is very small nearshore, thus allowing to use NLSWE.

The bottom friction coefficient had been set as its default value, 0.0025. As this value is much lower than the rough surface onshore, we try to consider a more realistic bottom friction coefficient on the topography. 0.2–0.3 bottom friction coefficients are assigned to coral reefs contexts ( Lugo-Fernandez et al. 1998 ; Lowe et al. 2005 ). Besides, this coefficient may reach 0.1–0.2 when estimated on rough bed during storm events ( Trembanis et al. 2004 ). We then tested several bottom friction values on land: 0.1, 0.2 and 0.5.

Inundation, wave-breaking and stopping of tsunami waves depend on obstacles in its way. At Galle, the seafront consists of many, often connected, buildings rather high compared with the incoming waves. Thanks to the Geographic Information System for Coastal Hazards in Sri Lanka project, we have available high-resolution vectorized data for buildings, which had been extracted from maps and data of the Survey Department of Sri Lanka ( Garcin et al. 2007 , 2008 ). Topographical data used until then represented only a digital terrain model, so that the buildings cover will help to refine the S5 topographical grid into an approximate digital surface model. Although the pixel size exceeds most of buildings length, elevation is noteably raised in areas with many connected buildings. As individual building heights were unknown, uniform heights of 3 and 8 m were tested.

The tide had not been taken into account until now, as it has only an influence close to the shore. The tsunami indeed arrived on the western coast of Sri Lanka just at low tide. To take this factor into account, we added 0.4 m to our topobathymetric grid, corresponding to the 0.4 m negative amplitude of the considered tide.

We made serial test simulations by modifying underlined parameters first one by one, then by combining several effects at once. The aim is to move the computed inundation limit as near to the FIL as possible. To better assess the difference between the model result and inundation data, we now consider maximal water height on land ( h max w ) instead of z max . h max w is computed by subtracting the positive topography from z max . Fig. 14 shows maps of h max w in four of the performed tests. In the default simulation (test 0), h max w appears to exceed 4 m until 2 km beyond the FIL, where the computed inundation limit lies. When choosing NLSWE option (test 1), h max w drops by 1 m against test 0 almost everywhere. In test 2, we resume test 1 settings by adding the buildings cover to the initial DEM. A uniform height of 3 m was first imposed, which corresponds mostly to residential sector, behind the seafront. Then we chose another uniform height of 8 m, to represent the high buildings on the seafront (with at least three levels for the main part). Both options lead to almost the same h max w map, except that buildings get submerged for a 3 m height and not for a 8 m height. Anyway, h max w in test 2 seems to be markedly reduced behind the FIL. Inundation still extends too far, but where h max w had reached 3 m in test 0, it is now restricted to an average value of 1 m. As we add a high bottom friction coefficient in test 3, two changes in h max w are perceptible: first, h max w decreases when moving away from the FIL because of enhanced friction; and second, the strip of h max w of more than 3 m along the seafront appears to be paradoxically wider than in test 2. The reason for this is that enhanced friction does not allow the incoming mass of water to spread as greatly as for a very low friction. The waves then do not flow quickly away after having ran over the first seafront buildings.

Maximal water height map for four S5 simulation tests with modified parameters.

The ultimate test on S5 simulation consists of combining together NLSWE, 8 m buildings cover, high bottom friction coefficient of 0.5 and the 0.4 m tidal shift of the DEM ( Fig. 15 ). The resulting inundation limit is now consistent with the FIL in many places, in the west of the domain and in the east of Galle Bay. Places of remaining discrepancy may be interpreted one by one. A points on a river valley, where the high bottom friction coefficient artificially prevents the propagation of tsunami flow as it should overrun the river. B indicates a very flat area that is too low to actually stop the flow propagation (2 m elevation on almost the whole dark blue zone). At last, topographical data at Unawatuna (C) exhibits much steeper slopes than the actual topography, so, that it does not allow the water flow to reach the FIL. It must be noted that in the last test, calculated inundation limit steps aside the 5-m contour line more than in any other simulation, whereas it comes closer to the FIL. This assessment suggests that by default, the modelled tsunami flow on land only stops when a topographical obstacle is in its way.

Maximal water height map for the last test on the S5 simulation. Black arrows and letters indicate places where the discrepancy between model and FIL is larger.

The 2004 December 26 tsunami severely impacted the coast of Sri Lanka. To numerically model this impact, it has been necessary to implement a nesting grid system, with six nested grids of increasing resolution. From 4860 to 540 m mesh size, the nesting process supports the improvement of tsunami propagation calculation as it approaches the coast. Then, from 180 m to 20 m mesh sizes, increasing resolution helps mostly to refine the model at sea and to improve the tsunami processes at the shoreline and onshore.

Successive nested simulations are checked with several distinct data sets. Maximum water elevation is compared with NOAA tsunami height data in simulations S0–S4. The model results are consistent, on average, with these NOAA data, for simulations S2–S4. However, when the spatial resolution of calculation increases, the distribution of model-data difference also increases, showing growing spatial dispersion. The observed discrepancy may likely be due to the lack of detailed bathymetric and/or topographical data that may induce very localized effects. Another tsunami height data set helps to check the model consistency. GSMB data are compared with S2 and S3 results in the southern part of Sri Lanka. While some data points are neglected because the modelled tsunami does not reach them, the comparison between data and model results at the remaining points leads to a good agreement. In case of NOAA data, as for GSMB data, we had to relocalized data points within a 1 km radius before making the comparison. The same comparison between S4 model results and NOAA data leads us to require a similar relocalization. Considering this technique, we can conclude that the model is only consistent in average. This is not surprising, as the tsunami evolution and run-up depend a lot on very local details like buildings, structures and ground roughness. Results must then be considered by sector, not by punctual values.

Apart from tsunami height data, we used tide-gauge data from the Colombo station to check arrival time and height of the first tsunami wave on the western coast. The time-series of SSE at this point agrees very well with available measurements. This agreement is important in confirming that the model succeeds in calculating the tsunami wave diffraction along an about 300 km-long coastal segment before it reaches Colombo.

The last simulation, S5, has a pixel size of 20 m and focuses on Galle Bay. As too few tsunami height data are available at this place, S5 model results are tested from the inundation point of view. The maximum inundation limit is taken from field work of Garcin et al. (2008) . It concerns mainly the S5 domain. As the computed limit extends far beyond the FIL, we try to improve the description of high resolution context and processes by changing some parameters and inputs in the model. We computed test simulations by taking into account NLSWE theory for very shallow water propagation in the Bay, much higher bottom friction onshore than at sea, buildings cover and tidal shift of elevation. A last test with all changes together shows a much better concordance between the resulting inundation limit and the FIL. The remaining discrepancy is located in places where the DEM seems to be distorted, or in a river valley where the local water is not taken into account by the model. The focus of the tsunami model on a small high resolution domain brings out the great importance of taking into account detailed characteristics of the coast, otherwise the computed tsunami may be significantly overestimated.

Acknowledgments

This study was funded by BRGM research projects. Philip Watts (Applied Fluids Engineering, Inc.) provided the initial GEOWAVE code version that we modified and used in this work. The authors are very grateful to the GSMB team for their useful help in field investigations and for providing tsunami heights data. They thank the University of Hawai'i Sea Level Center for making available tide-gauge data from the Colombo sea level station. John Douglas and Thomas Dewez helped to improve the English of the manuscript. Two anonymous reviewers provided useful comments that helped us to improve the quality of the manuscript.

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Sri lanka, the tsunami and the evolution of disaster response.

COLOMBO, 24 December 2014 (IRIN) - On the morning of 26 December 2004, Mohideen Ajeemal, a fish distributor from Sainathimaruthu, a village on Sri Lanka’s eastern coast, hurriedly climbed a coconut tree to escape rapidly rising seawater. As he did so he saw his young daughter and son struggling to save themselves. “I found both bodies later that afternoon. My son’s body was swept away about a mile, my daughter’s had got stuck in a fence,” he said.

A decade later, the 45-year-old said he finally feels safer next to the sea.

“Now I check the weather regularly, I have SMS alerts on my phone that warn me of possible dangers,” he said. On 15 November 2014 when a 7.3 magnitude earthquake was reported 150km northeast of the Indonesian island of Maluku, an SMS alerted Ajeemal that there was no danger to Sri Lanka.

“In 2004 we did not know anything about an earthquake or a tsunami. Now we know better and all thanks to the tsunami,” said Mohamed Iqbal, Ajeemal’s neighbour who also survived the tsunami.

The tsunami took a dramatic toll on unsuspecting Sri Lankans - 35,322 were killed, half a million were displaced, and more than 100,000 houses were destroyed. Half of the damage struck areas had been hard-hit by a 21-year armed conflict , making access complex and politically charged. The country was left with a reconstruction bill of over US$3 billion and an unprecedented humanitarian challenge.

When the tsunami struck, a 32-month ceasefire between the Sri Lankan government and separatist Liberation Tigers of Tamil Eelam (LTTE) was on the verge of collapse.

Some analysts believe that had it not been for the tsunami, the final, bloody phase of the conflict in which violence reached unprecedented levels would have erupted earlier.

“LTTE leader Velupillai Prabhakaran would have gone to war but for the tsunami, so on the positive side the tsunami did delay LTTE's walkout of ceasefire,” Ramani Hariharan, former intelligence head of the Indian peacekeeping force in Sri Lanka, told IRIN.

Hariharan argued that the tsunami offered a chance for the government and the Tigers to work together with renewed international goodwill and funding commitments. But both sides made hardline demands to control tsunami reconstruction efforts in rebel-held areas.

A former high ranking official with the government’s Reconstruction and Development Agency (RADA), who asked not to be named, explained: “The problem we had was there was no mutual trust between the LTTE and the government at any level. It was very difficult to get even simple things like [knowledge of] how many people needed toilets in areas under Tiger rule tabulated because the Tigers would never allow any government representative to assess that without their purview. When that happened, government officials always felt the numbers were cooked.”

According to researchers writing in the Overseas Development Institute’s (ODI) Humanitarian Practice Network magazine, within a year of the tsunami, the government and LTTE camps had polarized further, which had a direct impact on how aid was allowed to be distributed.

Whether or not the tsunami delayed the war’s finale, it did change how the country deals with natural disasters, said S. M. Mohamed, secretary to the Ministry of Disaster Management. “It was eye-opener for the entire nation on disaster preparedness.”

Before 2004, Sri Lanka had no early warning mechanisms or disaster preparedness programmes. Following any natural disaster, whichever ministry handled specific social services would take over relief operations.

Disaster data before 2004 are not readily available. The last major natural disaster before the tsunami was the November 1978 cyclone that hit the eastern part of the country killing around 1,000 and displacing around a million. Another large flood was reported in 1986, killing over 300. A major drought was reported in 2001, affecting over 300,000. Three months before the tsunami, over 200 people were killed in a landslide.

Six months after the tsunami, in May 2005, the Disaster Management Act established the first ever National Disaster Management Council headed by the president. In August 2005, the Disaster Management Centre (DMC) was founded with offices in each of the country’s 25 districts to oversee all disaster preparedness, early warning and relief work.

The DMC’s power is firmly established. Invested with the authority to disseminate all early warning information, the agency has unilateral access to one of the country’s largest mobile networks with a subscriber base of over eight million. It can send out a warning without consulting the service provider. The agency also uses the armed forces, the police as well as networks like the Sri Lanka Red Cross Society (SLRC) to send out warnings.

One of the largest post-tsunami actions took place in April 2012 when over a million people were successfully evacuated from the coast stretching from west to north after a tsunami warning .

Sri Lanka has also invested in disaster preparedness: In March 2014 the Ministry of Disaster Management launched a five-year $233 million Comprehensive Disaster Management Programme . In September the government unveiled a four year $110 million climate resilience programme funded by a World Bank loan . The government has also signed an agreement with the Bank that will allow it to draw from a $102 million loan facility for disaster assistance within 48 hours of a natural disaster that has been declared a national emergency.

“There has never been this much emphasis on disaster resilience ever,” DMC’s Kumara said.

Localized gaps persist

Despite such efforts and millions in investments, there are still deadly gaps in Sri Lanka’s disaster preparedness levels. The biggest concern has been delivering timely warnings to people in danger and getting them to act on them.

A landslide in the village of Meeriyabedda in the southeastern district of Badulla killed 12 people last October. According to R. M. S. Bandara, head of the landside risk research and management unit at the National Building Resources Organisation (NBRO), warnings of possible landslide were issued but there was no proper structure at the village level to act on them.

Similar early warning failures cost 29 lives in November 2011, in Southern Province when gale force winds unexpectedly struck. In July 2013, over 70 were killed in the same region when there was no warning about the onset of the annual South West Monsoon that moved faster than anticipated.

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Lessons Learned from the Sri Lanka’s Tsunami Reconstruction

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Published online 9 May 2006 | Nature | doi:10.1038/news060508-5

Sri Lanka's water still plagued by tsunami

Wells flooded with salt and pollution, survey finds.

Quirin Schiermeier

The devastating tsunami of 26 December 2004 is still making itself felt in Sri Lanka; though more than 16 months have passed, the well water in many coastal areas of that country is still undrinkable, a study has found.

The Indian Ocean tsunami, created by an earthquake off the coast of Sumatra, Indonesia, killed more than 25,000 people in Sri Lanka. The flood also destroyed, or contaminated with sea water and pollutants, almost all the wells within reach of the waves, which in places penetrated up to 1.5 kilometres inland. Most of the rural or semi-urban population of Sri Lanka's southern and eastern coastlines rely on groundwater taken from some 40,000 shallow wells, each of which serves several families.

Problems with this well water still persist today, according to a 14-strong team of scientists. The team, including geologists and water engineers from Sri Lanka, the United States and Denmark, began surveying the problem in February 2005. They found that in most places the aquifer, which is normally fed and replenished by monsoon rainfall, shows little, if any, sign of recovery 1 .

About 67-100% of wells that had been flooded, and 17-50% of those that escaped the floodwaters, were still not suitable for drinking 7 months after the tsunami, notes Karen Villholth of the International Water Management Institute in Colombo, Sri Lanka. Vilhoth is a co-author of the paper, and leads a well recovery initiative in Sri Lanka's Batticaloa and Ampara districts.

"Levels of salinity were still extraordinarily high in September, as was cross-contamination with pesticides and insecticides from upland fields," says Jayantha Obeysekera, a Sri Lanka-born hydrologist at South Florida Water Management District in West Palm Beach. "What makes the situation worse is that there exists no water-control monitoring system," he says.

Here comes the rain

One reason for the continued saltwater contamination is that Sri Lanka received relatively little monsoon rain in the first months after the tsunami. The monsoon was substantial again in December last year. But the researchers say it will take several more monsoon seasons until rainwater will have sufficiently flushed the aquifer in some of the most-affected regions.

Local people's attempts to clean the wells after the flood have sometimes done more harm than good, the team found. Excessive pumping, for example, only allowed more sea water to enter the aquifer from below and caused numerous wells to collapse. Besides, polluted water that was pumped out of wells was often disposed of improperly, allowing contaminants to seep back into the aquifer. Sand mining, and enhanced beach erosion caused by the tsunami, are also adding to the slow rate of recovery, the scientists say.

"It would be unfair to say that the locals or helpers were doing anything wrong," cautions Tissa Illangasekare, a groundwater hydrologist at the Colorado School of Mines in Golden, and lead author of the study. "Given the enormity of the disaster it is no wonder that there was just no sufficient level of preparedness."

Be prepared

Hundreds of thousands of households in Sri Lanka have become accustomed to using clean water trucked in by the government or supplied by various relief organizations. Plans are now underway to pipe water to many coastal villages, and expand centralized sewage collection. However, fights flaring up between Sri Lankan government forces and Tamil separatists could hamper relief efforts in some of the most severely affected villages on the island's east coast.

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The researchers hope that their study of the hydrogeological impact of the tsunami could be used as a first step towards developing internationally recognized emergency guidelines for many other areas threatened by hurricanes, storm surges and sea-level rise. Illangasekare is now organizing a NATO-funded workshop on water security, which will be held in Croatia in autumn.

"Tsunamis don't happen too often," says Illangasekare. "So when you have the opportunity you should look as closely as possible at the problems that can arise, and try to learn the lessons."

Visit our lankas water still plagued.html">newsblog to read and post comments about this story.  

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Essay on Tsunami for Students and Children

500+ words essay on tsunami.

Tsunami is a phenomenon where a series of strong waves that are responsible for the surge in water sometimes reach the heights in many meters. This is a natural disaster that is caused due to the volcano eruption in the ocean beds. Also, a phenomenon like landslides and earthquakes contributes to reasons for a tsunami. Like other natural disasters, the impact of the tsunami is also huge. It has been seen throughout history how disastrous the tsunami is. The essay on tsunami talks about various factors that contribute to the tsunami and the damage it causes to mankind. 

Essay on Tsunami

The disaster that is caused due to waves generated in the ocean because of the earthquake and whose main point is under the water is known as ‘Tsunami’. Also, the term tsunami is associated with tidal waves. Thus, a tsunami is also called as the series of ocean waves that have a very long wavelength. Because of the tsunami, there are strong waves of water is formed and this moves landwards. So, this causes inland movement of water which is very high and lasts for a long time. Thus, the impact of these waves is also very high. 

Greeks were the first people on Earth to claim the effects of the tsunami. They claim that tsunami is just like land earthquakes. Also, the only difference between tsunami and earthquake is that tsunami is caused in oceans. Thus, the scale and ferocity of the tsunami are almost impossible to control. 

Get the huge list of more than 500 Essay Topics and Ideas

The History of Tsunami

The highest ever recorded tsunami was on 9th July 1958 in the record books. It took place in a bay which was located in the ligula bay along the coasts of Alaska. After the quake, a massive mass of rock fell into the bay waters from the cliff nearby. Thus, this created an impact and produced a wave that reached a height of 524 meters. Also, this is regarded as one of the highest recorded tsunami waves ever. 

The destructive waves responsible for the occurrence of tsunami is also produced in waters of bays or lakes. As this water approached the coast, it grows larger. However, the size of this wave is very low in deep-sea areas. Tsunami waves that are generated in the lakes or bays do not travel for a long distance. Thus, they are not as destructive as the ones produced in the ocean waters. There are various directions in which tsunami can travel from the main point. 

One similar devastating tsunami was experienced in India in 2004. However, the origin of this tsunami was located near Indonesia. Because of the tsunami, it was expected that a total of 2 lakh people lost their lives. The waves traveled extensively thousands of kilometers in countries like Thailand, India, Indonesia, Sri Lanka, Bangladesh, and the Maldives. 

Tsunamis occur mainly in the Pacific Ocean. There are very chances that they take place in the area where there are larger bodies. Coastlines and open bays next to very deep waters may help tsunami further into a step-like wave. 

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Post-tsunami recovery: issues and challenges in Sri Lanka

2005, Tokyo: Asian …

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Tokyo, Asian …

Sisira Jayasuriya

International Journal of Strategic Property Management

Roshani Palliyaguru

The “construction output shock” that was generated by the massive reconstruction programmes after the Indian Ocean tsunami in Sri Lanka spread through the economic system and was reflected in economic indicators due to the multiple linkages that the construction industry has with other economic sectors. On the ground, it was observed that the prices of construction materials, labour and plant rose suddenly in the affected regions making the reconstruction effort more costly. This paper considers the responses of major economic and construction indicators to the construction shock due to post‐tsunami reconstruction and the amount of time needed before they returned to an equilibrium state. Empirical results indicate that the construction output shock had a significant impact on material, labour and equipment price indices in the short run but other indicators showed only a very marginal response indicating that the remoteness of the disaster would have delayed the propagation. Santru...

Richard Haigh

ABSTRACT: This paper reviews the reconstruction efforts in Sri Lanka to ascertain the efficiency of post-disaster reconstruction through identifying the barriers. Even though the immediate humanitarian relief found to be more effective, long-term reconstruction was ineffective due to lack of funding, lack of knowledge to support local community, institutional constraints, and absence of coordinated management.

Eric Rasmussen

Ten days after the 26 December 2004 tsunami killed several hundred thousand people around the Bay of Bengal, I led a small team into Indonesia to lend support to both the US military relief operations aboard the aircraft carrier USS Abraham Lincoln and the UN relief agencies working on the devastated beaches of Banda Aceh in northern Sumatra. The emergency response task was enormous. The earthquake and tsunami just west of the Sumatran coast had affected a large portion of the island's coastline, from Padang on the low western coast, up and around the northern tip past Lhokseumawe into the Strait of Malacca. The earthquake, at magnitude 9.3, was the second largest ever recorded by a seismograph (second only to a 9.5 in Chile in 1960). It occurred along a fault line that was later found to be nearly 800 miles long, arching from Indonesia north toward Bangladesh. The instantaneous vertical slip of nearly 30 feet along the Burma plate was large enough to cause massive tsunamis that propagated in an east-west direction. Waves came crashing ashore on Sumatra at heights of up to 100 feet mere minutes after the earthquake. The low-lying areas in the Bay of Bengal to the north and south (Bangladesh and Diego Garcia, for example) were largely spared, but the waves destroyed large swaths of coastal settlement in Indonesia, Thailand, Sri Lanka, and south India, and they caused measurable damage on the east coast of Africa nearly five-thousand miles from the epicenter. A large portion of the Indonesian loss was in the northern Special Territory of Aceh, a region that had been closed to outsiders for more than twenty years due to a civil insurgency that arose in 1977. Initial casualty reports from late December 2004 put the deaths in the Aceh area in the many tens of thousands with at least as many displaced. As the later reports came in the death toll in Aceh Province alone rose to well over 100,000. All told more than 200,000 South Asians died that day, making the event one of the worst natural disasters in recorded human history. But even beyond the heavy toll on human lives, the Bay of Bengal earthquake and tsunami caused an enormous environmental impact which will affect the region for many years to come. There are initial survey reports that the Boxing Day Tsunami inflicted severe damage on multiple complex ecosystems including mangrove swamps, coral reefs, coastal hardwood forests, coastal wetlands, sand dunes, coastal river systems, and tidal rock formations. Further environmental threats include the spread of solid and liquid waste, with industrial chemical dispersion causing further groundwater pollution, plus the destruction of sewage collectors and treatment plants. These second-order effects have serious consequences in the rebuilding of the affected societies, including the economic cost incurred with the loss of several thousand banana and mango farms. That discussion must be left to another essay. Disclaimer: The opinions expressed in this article are mine, though they may have been developed in concert with numerous colleagues. I am solely responsible for errors and omissions and I do not speak for the Department of the Navy or the Department of Defense.

Abstract Sri Lanka was found to be a disaster prone country in the recent past. The impact is more severe when developing countries are faced to various natural or man-made disasters. Impact appears in many forms; loss of lives and property, economic impact, social impact etc. As a developing country, Sri Lanka is much more concerned with the country's economic development.

Andrew Butt

The Indian Ocean tsunami of December 2004 resulted in over 35,000 deaths and damage to almost 100,000 houses in coastal Sri Lanka. In the aftermath a significant program of international aid resulted in the reconstruction of over 65,000 housing units-some work is ongoing. The priorities for reconstruction focussed on the immediacy of the post-disaster recovery process, and this has been subject to criticism in relation to the quality and suitability of rebuilding and of newly built housing and the regional priorities for development funding. Less consideration has been given to the longer-term consequences of the settlement systems that have been created – specifically the creation of small settlement clusters that are distant to employment and services, which in some instances have proved unpopular for these reasons. This paper provides analysis of the established housing projects in the tsunami-affected and post-conflict Trincomalee region on Sri Lanka&#39;s east coast and conside...

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Dramatic increase of economic losses from Natural disasters derail economic and human development in many places. This dissertation sheds light on natural disaster risk and short-term and long-term household wellbeing after disasters. It is composed of three empirical studies of Sri Lanka. The first study examines the impacts of frequently occurring extreme weather events on individual health and health care cost using national household data. The analysis shows that local floods and droughts impose a significant risk to health when individuals are exposed directly and their communities indirectly to these hazards. These risks are associated with the land-use in the affected regions and the status of access to sanitation and hygiene. Health risks due to flood and drought cause a considerable economic burden on the private and public health care sectors. Finally, we learn that recurring extreme weather events may potentially be sources of significant health risk and economic cost to ...

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Why Time Is Running Out Across the Maldives’ Lovely Little Islands

Global tourism brought a modern economy to the country’s thousand islands. For many Maldivians, the teeming capital beckons.

Teenagers gathering to watch the sun set in Nolhivaranfaru, a remote island in the Maldives. Credit...

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By Alex Travelli and Maahil Mohamed

Photographs by Elke Scholiers

Reporting from Malé, its nearby islands and Nolhivaranfaru in the Maldives

• April 6, 2024

To live in the Maldives is to live in one of two worlds. Either you belong to the capital — Malé, a micro-Manhattan in the Indian Ocean — or you are out in “the islands,” among the quietest and most remote villages this side of the Arctic tundra.

It is in these places — far from the archipelago’s walled-garden resort atolls, where no Maldivians actually dwell — that the country is picking between two visions of its future, like much of the rest of Asia, but more so.

The outer islands are steadily depopulating, as the appeal of making a life through tuna fishing and coconut farming along their crushed-coral seashores shrinks. The splendid isolation may be what attracts visitors, but it seems incompatible with islanders’ aspirations in a nation modernized by global tourism.

As Maldivians give up on island life, the government feels compelled to keep building up Malé, the country’s one real city. But Malé is already pressed up hard against the limits of human habitation. By some measures, it is the most densely populated island on earth, with over a third of the country’s 520,000 people on a landmass that can be crossed by foot in about 20 minutes.

If more Maldivians are going to move there, its physical structure will need to be radically reworked. In the meantime, it is sprawling outward wherever it can: The government is surrounding Malé with sea bridges to artificial islands packed with housing projects financed by China and India .

On Jan. 22, President Mohamed Muizzu announced his otherworldly vision for an undersea tunnel between Malé proper and a land-reclamation project where Chinese investors will help build 65,000 housing units on what is now barely a sandbar.

Mr. Muizzu, a civil engineer by training, said the tunnel would “provide beautiful views of the sea” as commuters passed through it. (Feasibility to be determined.)

Humay Ghafoor, a researcher who campaigns against environmental degradation, said that “nobody does any assessments” before commissioning “massive infrastructure” projects. This allows an airport, for instance, to be built over a mangrove, destroying a whole island’s freshwater supply.

The Maldives consists of a thousand islands stretched along a 550-mile axis, each one a bit of exposed coral that grew from the rims of a prehistoric range of undersea volcanoes. These form rings called atolls — a word that comes to English from the native Dhivehi language. Most of the 188 inhabited islands have fewer than 1,000 residents.

The resorts — those airy villas floating over turquoise seas — are all on technically “uninhabited” islands. The guests are foreign, and most of the staff is, too, mainly from India and Bangladesh. In some ways, the resorts are like offshore oil rigs, pumping out nearly all of the country’s income. By design, they are divorced from Maldivian culture and abstracted from their South Asian location.

Bay of Bengal

Nolhivaranfaru

The typical inhabited island is likewise rich in sunshine and warmth and has access to a shallow lagoon, palm trees and maybe a mangrove forest. The inhabitants are highly literate, many are English-speaking and they are connected to the rest of the world by the internet, mobile data and long ferry routes.

Their traditions survive, still. Perhaps every island except Malé has a holhuashi, a covered seating platform at its harbor, sometimes circled by hanging woven chairs. Men gather to rest at midday and exchange gossip.

There is little doubt that climate change will eventually bring doom to this country, most of which is just a meter or two above sea level. But that catastrophe is thought to be a century or more away.

Instead, Maldivians are leaving the islands for the sake of their children, looking to Malé and the world beyond. When it comes to education and health care, there is no substitute for city life.

Nolhivaranfaru, a fishhook-shaped bit of powdery white sand, with a green and fertile core between its beaches, is like many of the Maldives’ inhabited islands. Flowering frangipani stand over an Islamic cemetery near its piers, centered around a centuries-old shrine to an Arab pilgrim. It takes 25 minutes by speedboat to reach the nearest landmass and two airplanes from there to get to neighboring India.

That is a journey that Maryam Asima, a 30-year-old mother of twins, made at great cost and personal hardship. She and her husband, the captain of a tourist yacht that docks 175 miles away, near Malé, had been unable to conceive. Two years ago, Ms. Asima and her sister, who was in a similar position, traveled to Kochi, India, a city of 2.1 million, where they made do on their own during 11 months of IVF treatment.

Health care remains rudimentary even on the better connected of the outer islands. Staff at the local clinic scoff at the idea of someday providing IVF. They say quietly that even most emergency care is beyond them: Any patient who needs a ventilator must be flown hundreds of miles away.

Ms. Asima, now back on the island with her 6-month-old twins, says she is satisfied with the results of her ordeal. Her sister has given her a nephew, too. With her encouragement, two other women from the island have become pregnant in the same way. The government has started offering $500 subsidies and the possibility of free air travel for families that need to go abroad for IVF.

She likes the “home feeling” of her island and hopes to send her children to school there, even if they need to travel to a nearby island to see a pediatrician. But this is not her first home: Ms. Asima was born on an even smaller island, Maavaidhoo, which was abandoned after being swamped by the Indian Ocean tsunami of 2004.

Many Maldivians have been on the move for a generation or more, leaving smaller communities for larger ones. More than anywhere else, those who can afford it go to Malé.

Thirty years ago, it was not unusual for families to send unaccompanied minors on long ferry journeys, of 20 hours or more, to live in Malé. They would stay with distant relatives or even strangers and work as pint-size housekeepers to pay for their room and board as they attended one of the country’s better schools.

Island families still send their children to study in Malé, but usually now they travel as teenagers; better primary schooling is available even in remote places.

The cramped conditions of the capital are the first challenge they face. A compact grid of streets jams pedestrians, motorbikes, workshops and luxury perfumers together like a miniature version of central Hong Kong. One-bedroom apartments rent for five times the starting salary of a government office worker.

Ajuvad, a nervous, soft-spoken 23-year-old, came to Malé at 16 to join his older siblings, six people crammed into three bedrooms. They are all professionals, with jobs as teachers and technicians. But they were raised in another world, a 36-hour ferry ride away. There, the beach was a five-minute walk away with no roads and no motorbikes, and their home was a four-bedroom house that their father, a fisherman, built himself. Their mother made fish paste and sold it to neighbors.

Ajuvad, who asked that his last name be withheld to protect his privacy, remembers the transition as being “quite a challenge.” Having to live without his parents, and without an inch of space to study alone in quiet, he said, “I thought my world had collapsed.”

Ahmed Abbas, a 39-year-old hardware salesman, had an easier time moving into Malé’s urban sprawl from a distant southern island 12 years ago. His family of six shares a two-bedroom apartment in a complex built by Chinese developers, across a sea bridge from the city proper. They spend only half of their income on rent, and he drives to the city, 25 minutes each way, twice a day.

Mr. Abbas studied and worked around South India for many years before settling down. He has seen enough of the world to appreciate his family’s perch, which they share with two love birds: Small, exotic pets are a big business in little Malé.

But he still misses the island life. Back home, it was “nice because the people are nice,” he said, “normal country people, all smiling.”

Alex Travelli is a correspondent for The Times based in New Delhi, covering business and economic matters in India and the rest of South Asia. He previously worked as an editor and correspondent for The Economist. More about Alex Travelli

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Shopping through online platforms is a highly controversial issue. Naturally, you cannot be completely sure when placing an order through an unfamiliar site, with which you have never cooperated. That is why we recommend that people contact trusted companies that have hundreds of positive reviews.

As for buying essays through sites, then you need to be as careful as possible and carefully check every detail. Read company reviews on third-party sources or ask a question on the forum. Check out the guarantees given by the specialists and discuss cooperation with the company manager. Do not transfer money to someone else's account until they send you a document with an essay for review.

Good online platforms provide certificates and some personal data so that the client can have the necessary information about the service manual. Service employees should immediately calculate the cost of the order for you and in the process of work are not entitled to add a percentage to this amount, if you do not make additional edits and preferences.

From a high school essay to university term paper or even a PHD thesis

Emery Evans

Pricing depends on the type of task you wish to be completed, the number of pages, and the due date. The longer the due date you put in, the bigger discount you get!