The Impact on Bottom Sediments and Ostracods in the Khlong Thom River Mouth Following the 2004 Indian Ocean Tsunami

2014 ◽  
Vol 18 (2) ◽  
pp. 104-117 ◽  
Author(s):  
Katsura Yamada ◽  
Miyabi Terakura ◽  
Shinji Tsukawaki
Keyword(s):  
Indian Ocean ◽  
Bottom Sediments ◽  
River Mouth ◽  
Indian Ocean Tsunami ◽  
2004 Indian Ocean Tsunami ◽  
The Impact

The Impact of the December 2004 Indian Ocean Tsunami on Tourism in Thailand

2006 ◽  
Vol 22 (3_suppl) ◽  
pp. 889-900 ◽  
Author(s):  
Thomas A. Birkland ◽  
Pannapa Herabat ◽  
Richard G. Little ◽  
William A. Wallace
Keyword(s):  
Indian Ocean ◽  
Perceived Risk ◽  
Tourism Industry ◽  
Indian Ocean Tsunami ◽  
2004 Indian Ocean Tsunami ◽  
Southern Thailand ◽  
Physical Infrastructure ◽  
Vertical Evacuation ◽  
Response And Recovery ◽  
The Impact

The 26 December 2004 Indian Ocean tsunami appears to have reduced tourist visits to southern Thailand and particularly to the provinces of Phuket and Phang Nga. In Thailand, a much higher proportion of the tsunami victims were tourists than in other affected nations. Also, the tourism industry, which is a major source of foreign exchange, is very sensitive to the perception of risk created by disasters like this tsunami. Although revenues may remain depressed for some time, it is likely that tourism will rebound in this region because of the attractiveness of the physical amenity and the value it offers for European tourists. Damage to the physical infrastructure did not serve as a substantial impediment to response and recovery. Information and warning systems, together with buildings that afford vertical evacuation, will protect lives and reduce perceived risk.

A synoptic picture of the impact of the 26th December 2004 Indian Ocean tsunami on the coast of Sri Lanka

2010 ◽  
Vol 25 (12) ◽  
pp. 1874-1880 ◽  
Author(s):  
M. Ioualalen ◽  
W. Rentería ◽  
K. Ilayaraja ◽  
M. Chlieh ◽  
P. Arreaga-Vargas
Keyword(s):  
Sri Lanka ◽  
Indian Ocean ◽  
Indian Ocean Tsunami ◽  
2004 Indian Ocean Tsunami ◽  
The Impact

Coastal and River Mouth Morphology Change in Sri Lanka Due to the 2004 Indian Ocean Tsunami

2007 ◽  
Author(s):  
Hitoshi Tanaka ◽  
Kazuo Ishino ◽  
Bandara Nawarathna ◽  
Hajime Nakagawa ◽  
Shinichiro Yano
Keyword(s):  
Sri Lanka ◽  
Indian Ocean ◽  
River Mouth ◽  
Indian Ocean Tsunami ◽  
2004 Indian Ocean Tsunami ◽  
Morphology Change

Maldives Field Survey after the December 2004 Indian Ocean Tsunami

2006 ◽  
Vol 22 (3_suppl) ◽  
pp. 137-154 ◽  
Author(s):  
Hermann M. Fritz ◽  
Costas E. Synolakis ◽  
Brian G. McAdoo
Keyword(s):  
Sri Lanka ◽  
Indian Ocean ◽  
Global Positioning System ◽  
Field Survey ◽  
Indian Ocean Tsunami ◽  
Positioning System ◽  
2004 Indian Ocean Tsunami ◽  
Global Positioning ◽  
The Impact

The tsunami of 26 December 2004 severely affected the Maldives at a distance of 2,500 km from the epicenter of the magnitude 9.0 earthquake. The Maldives provide an opportunity to assess the impact of a tsunami on coral atolls. Two international tsunami survey teams (ITSTs) surveyed a total of 13 heavily damaged islands. The islands were visited by seaplane on 14–15 and 18–19 January 2005. We recorded tsunami heights of up to 4 m on Vilufushi on the basis of the location of debris in trees and watermarks on buildings. Each watermark was localized by means of a global positioning system (GPS) and was photographed. Numerous eyewitness interviews were recorded on video. The significantly lower tsunami impact on the Maldives as compared with Sri Lanka is largely due to the topography and bathymetry of the atoll chain.

scholarly journals Geoinformatics in mangrove monitoring: damage and recovery after the 2004 Indian Ocean tsunami in Phang Nga, Thailand

2011 ◽  
Vol 11 (7) ◽  
pp. 1851-1862 ◽  
Author(s):  
D. Kamthonkiat ◽  
C. Rodfai ◽  
A. Saiwanrungkul ◽  
S. Koshimura ◽  
M. Matsuoka
Keyword(s):  
Indian Ocean ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Agricultural Land ◽  
Likelihood Method ◽  
Indian Ocean Tsunami ◽  
Western Coast ◽  
2004 Indian Ocean Tsunami ◽  
The Impact ◽  
Mangrove Trees

Abstract. In the aftermath of the 2004 Indian Ocean Tsunami, it has been proven that mangrove ecosystems provide protection against coastal disasters by acting as bioshields. Satellite data have been effectively used to detect, assess, and monitor the changes in mangroves during the pre- and post- tsunami periods. However, not much information regarding mangrove restoration or reforestation is available. Rather than undertaking time-consuming fieldwork, this study proposed using geoinformatic technologies such as Remote Sensing (RS), Geographic Information System (GIS), and Global Positioning System (GPS) to monitor the mangrove recovery. The analysis focused only on the tsunami-impacted mangrove areas along the western coast of the Tai Muang, Takuapa and Khuraburi Districts of Phang Nga Province, southern region of Thailand. The results consisted of 2 parts, first: the supervised classification of main land uses, namely forest, mangrove, agricultural land, built-up area, bare soil, water body, and miscellaneous covers in ASTER images, was conducted using the maximum likelihood method with higher than 75 % for overall accuracy. Once the confusion between classes was improved in post-processing, the accuracy of mangrove class was greater than 85 % for all dates. The results showed that the mangrove area in 2005 was reduced by approximately 5 % (1054.5 ha) from 2003 due to the impact of the 2004 Indian Ocean Tsunami. Although the recovery program (replacing the same species of dead mangrove trees, mainly the Rhizophora apiculata Bl and Rhizophora mucronata Poir, in situ) had started by mid-2005, the areas gradually decreased to approximately 7–8 % in 2006 and 2010 compared with the reference year of 2003. Second, the recovery trend was observed in the Normalized Difference Vegetation Index (NDVI) fluctuation curve and the supporting field survey data. The recovery patterns were summarized into 2 categories: (i) gradually recovery, and (ii) fluctuating recovery. The gradually recovery category that implied the homogeneous pattern or uniform reforestation was observed in the seriously damaged area where most of the mangrove trees were swept away during the tsunami. This pattern covered approximately 50.35 % of the total reforested area. The NDVI time series of the uniform or homogeneous reforested mangrove at the sampled plots has gradually increased after 2005. The fluctuating recovery category that implied the heterogeneous pattern or non-uniform reforestation was observed in partially damaged areas where some of the mangrove trees were swept away and broken but still some trees were remained in the area. The heterogeneous patterns covered approximately 49.65 % of the total reforested area.

Geological and Geomorphological Tsunami Hazard Analysis for the Maldives Using an Integrated WE Method and a LR Model

2016 ◽  
Vol 10 (01) ◽  
pp. 1650003
Author(s):  
Mahmood Riyaz ◽  
Anawat Suppasri
Keyword(s):  
Indian Ocean ◽  
Hazard Analysis ◽  
Model Development ◽  
Tsunami Hazard ◽  
Indian Ocean Tsunami ◽  
Weight Of Evidence ◽  
2004 Indian Ocean Tsunami ◽  
Geomorphological Parameters ◽  
Hazard And Risk ◽  
The Impact

This study presents a tsunami hazard analysis for the Maldives using integrated statistical approaches, such as the WE (weight of evidence) method and a LR (logistic regression) model, using historical flooding records from the Maldives following the 2004 Indian Ocean Tsunami. The data with respect to the geological and geomorphological parameters of the islands and reefs, which were collected from 202 inhabited islands and seven resorts in the Maldives, were weighted by the presence/absence of evidence from the impacted islands. The tsunami hazard and risk were evaluated using spatial weights calculated for each variable. The predicted tsunami risk was compared with the impact of the 2004 Indian Ocean Tsunami. The results show that for the three cases, the success rate of the estimated hazard and risk prediction ranged between 74% and 90% for the low and high impact islands, respectively. However, the predictability for medium impact islands in the three cases was within the range of 52–58%. The results of this study can be applied to hazard and risk assessments, are useful for tsunami behavior model development for coral islands and can be used to identify islands that are naturally protected, sheltered or resilient against natural disasters, such as tsunamis.

Medieval forewarning of the 2004 Indian Ocean tsunami in Thailand

Nature ◽  
2008 ◽  
Vol 455 (7217) ◽  
pp. 1228-1231 ◽  
Author(s):  
Kruawun Jankaew ◽  
Brian F. Atwater ◽  
Yuki Sawai ◽  
Montri Choowong ◽  
Thasinee Charoentitirat ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Indian Ocean Tsunami ◽  
2004 Indian Ocean Tsunami

A snapshot of the 2004 Indian Ocean tsunami: societal impacts and consequences

2006 ◽  
Vol 15 (1) ◽  
pp. 163-177 ◽  
Author(s):  
Havidan Rodriguez ◽  
Tricia Wachtendorf ◽  
James Kendra ◽  
Joseph Trainor
Keyword(s):  
Indian Ocean ◽  
Indian Ocean Tsunami ◽  
2004 Indian Ocean Tsunami ◽  
Societal Impacts

scholarly journals Developing tsunami fragility curves based on the satellite remote sensing and the numerical modeling of the 2004 Indian Ocean tsunami in Thailand

2011 ◽  
Vol 11 (1) ◽  
pp. 173-189 ◽  
Author(s):  
A. Suppasri ◽  
S. Koshimura ◽  
F. Imamura
Keyword(s):  
Indian Ocean ◽  
Building Materials ◽  
Fragility Curves ◽  
Distribution Functions ◽  
Least Square ◽  
Indian Ocean Tsunami ◽  
2004 Indian Ocean Tsunami ◽  
Inundation Depth ◽  
High Resolution Satellite Images ◽  
Tsunami Fragility

Abstract. The 2004 Indian Ocean tsunami damaged and destroyed numerous buildings and houses in Thailand. Estimation of tsunami impact to buildings from this event and evaluation of the potential risks are important but still in progress. The tsunami fragility curve is a function used to estimate the structural fragility against tsunami hazards. This study was undertaken to develop fragility curves using visual inspection of high-resolution satellite images (IKONOS) taken before and after tsunami events to classify whether the buildings were destroyed or not based on the remaining roof. Then, a tsunami inundation model is created to reconstruct the tsunami features such as inundation depth, current velocity, and hydrodynamic force of the event. It is assumed that the fragility curves are expressed as normal or lognormal distribution functions and the estimation of the median and log-standard deviation is performed using least square fitting. From the results, the developed fragility curves for different types of building materials (mixed type, reinforced concrete and wood) show consistent performance in damage probability and when compared to the existing curves for other locations.

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