Assessment of Tsunami hazard in Coastal Area of Pakistan

                                     Assessment of Tsunami hazard in Coastal Area of Pakistan

Author Name: Sohail Ahmed (Geology)

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1.      Introduction

            Tsunamis are a disaster risk in coastal areas, and their natural potential is increased by a variety of issues. A continually increasing population growth rate is one example. Coastal areas are home to a significant part of the earth’s population. The abundance of biological and non-biological coastal resources is driving this population pressure. According to the United Nations Development Program, as the world's population rises, disaster risk will rise as a result of economic expansion and rising living standards (social improvement). To reduce risk in catastrophe management, boost capacity or support disaster mitigation activities. All structural and non-structural procedures taken to avoid or mitigate the impact of a disaster are included in disaster management. One component that contributes to its effectiveness is careful, directed, and coordinated planning (Ervita et al., 2019).

            Coastal zones makeup around 4% of planet and are home to 1\3rd of planet’s inhabitants. According to a 2006 report by World Conservation Monitoring Centre of United Nations Environment Program, coastal zones are expected to double in population within fifteen years (Wijetunge. 2009; Jaffe et al. 2009). Furthermore, world's seaside zones are home to a varied range of ecologies with great biotic and financial value. Because lagoons, dunes, coral reefs, mangrove forests, seagrass beds, and other coastal vegetation can be found in coastal environments (Srinivasalu et al. 2007; Yan et al. 2009). Natural calamities, such as tsunamis, may be a threat to this environment. Seismic activity is continuing in Pakistan, which could result in a tsunami in the region. Approximately 100 significant earthquakes have rocked Pakistan in the last century, from 1915 to 2015. (Irtem et al. 2009). A large number of tsunamis along Pakistan's coastal line have been caused by tectonic events in Makran area and along the diverging borders of the Indo-Australian plates. An extensive range of shore areas in Pakistan, comprising of Karachi, Gwadar, Badin and Makran are expected to be susceptible towards tsunamis based on historical data (Alberico et al., 2015). Several studies on tsunami transmission models, geomorphologic variations produced by tsunamis, and the impact of tsunamis on the natural environment, as well as ecological defence systems against tsunami impairment, have been done. (Cocharda et al. 2008; Kathiresan and Rajendran 2005). The Tsunami of 2004 in Indian Ocean was one of the most horrible and damaging ever, with a large-scale area-wise spread and 163,978 victims. Recent researchers have employed weighted mean to investigate tsunami vulnerability by assessing numerous factors & generating vulnerability index which can regulate tsunami destruction in the most extreme conditions; however, (Omira et al. 2009; Dominey-Howes et al. 2009). Comparatively, the Analytic hierarchy process as an additional appropriate weighing method was used by Dall'Osso et al. (2009). This inquiry, however, entails a mild investigation of tsunami vulnerability, however present study entails an assessment (incorporating multiple criteria) Geographic Information Systems based methodology regarding tsunami vulnerability for Pakistan's coast i.e. from Gwadar to Karachi. Different geographical characteristics such as topography and tsunami direction, as well as coastline vicinity and profile, are also given importance in this study. Previous studies looked at physical elements like structures, but latest research takes local factors into account to create a 30-meter grid tsunami vulnerability map. The weighting system was created with the Analytical hierarchy process technique in mind (Aslam et al., 2020).

            This study used remote sensing technology in the form of small format aerial photography (FUFK) obtained from UAV recording by using drones to produce detailed scale Digital Elevation Model (DEM) and some physical parameters that influenced the occurrence of tsunamis to determine the level of tsunami susceptibility in coastal areas. A similar study was carried out. At Parangtritis Beach, DI Yogyakarta, researchers conducted tsunami modelling with a specific run-up scenario to estimate the magnitude of losses, and tsunami modelling using DEM data retrieved from UAV recording results. The study and application of some physical factors to determine the amount of tsunami susceptibility using a weighted and scoring mechanism. The weighting and scoring method's results will be used to examine a variety of physical criteria that are important in assessing the level of tsunami hazard in their study area (Ervita et al., 2019).

1.1.Research Questions

The research questions of this study are:

·         Which coastal areas are most vulnerable to the tsunami?

·         How to mitigate the risks and reduce damage in case of a Tsunami?

1.2.Objectives of the study

The aims and objectives are:

·         To evaluate possible tsunami disasters & their impact on the Pakistan coast.

·         To develop practical technologies to mitigate tsunami risks.

·         Implementation of the strategic plans for disaster mitigation of government.

·         Contributions to tsunami disaster mitigation strategies.

·         To form a Geographic Information Systems based tool for assessing the tsunami hazard effects on the shoreline.

2.      Significance of the study

            Karachi is one of Pakistan's major metropolises, located at 24°51 N and 67°02 E. It has the undisputed distinction of being Pakistan's economic centre, and hence hosts a significant share of the country's economic activities. With around 23.5 million humans (according to 2013 census) and a land area of approximately 3527 km2 (1362 mi2), Karachi has a density of over 6000 people per square kilometre. Similarly, Gwadar is another important city on Pakistan's coast that is growing as an economic powerhouse in the future. Southeast Asia is home to a tectonically active region. Tsunamis occur along the coasts of Pakistan and India, and while they are less common, they are not unheard of. Tsunamis and deep-sea earthquakes have happened in Northern Arabian Sea at various times during the entire history, according to a systematic analysis of geological history that includes recent times. As seen in Fig. 1, this is the case. However, only a few of these occurrences have been properly considered and recognised. Devastating tsunamis struck western part of India because of earthquakes in 1524 and 1819, respectively, in Kutch area. Several earthquakes that have occurred in the last century indicate that the region is seismically active (Fig. 1). Massive earthquakes have triggered deadly tsunamis along Pakistan's Makran coastlines' subduction zone. Although there is no evidence to back this up, it is thought that tsunamis like these were exceedingly severe and massive in Pakistan, India, Iran, and Oman's coastal districts (Attary et al., 2017). These tsunamis had a huge impact on the Indian Ocean's bordering countries and islands. The earthquake of November 28, 1945, was one of the most powerful, resulting in a tsunami that caused tremendous destruction and human casualties. Tsunami's buildup height ranged from 1 to 13 metres. Earliest identified tsunami in this region was most likely caused by a big scale earthquake in Indus region around 326 before Christ. (Murty and Bapat 1999). According to the literature, an earthquake in Indian Ocean triggered a tsunami, which decimated Alexander the great’s caravan form Macedonia, when it was returning back to Greece after Sub-continental expedition. Findings show that Pakistani shorelines are extremely vulnerable to major earthquakes that result in destructive tsunamis. As shown in Fig. 1, the Makran seashore is prone to earthquakes occurring in Indian Ocean. Given recent seismic activity, it would not be unreasonable to forecast that a major magnitude earthquake will occur on a point west of location of incident which occurred in 1945 (Jacob et al. 1978). This type of earthquake can result in a devastating tsunami. Indian Ocean and rifting point of the Eurasian & Australian plates are both in front of Pakistan's shore. As a result, if these rifts become active, metropolises such as Karachi and Gwadar might face massive devastation. In history, earthquakes of extremely high magnitude and intensity have happened in this location, and such earthquakes may occur again in the future. An approximation of tsunami sensitivity with corresponding environmental characteristics might influence greatly in this area to aid ineffectual administration and dealing with potential and predicted tragedies. (Aslam et al., 2020)

            To provide a stepping stone in disaster management planning and process, a research of tsunami catastrophe susceptibility in Pakistan’s coastal districts is required. Gawadar, as one of the developing cities, has a high level of activity. The need for space usage in coastal areas is increasing as the population grows and mobilises. As a result, the government has started reclaiming land in various areas. Nonetheless, meeting demand does not always coincide with threat assessment, particularly in the case of tsunamis. As a result, the number of tsunami-vulnerable elements in Pakistan’s coastal districts continues to rise (Supriatna et al., 2018).

3.      Methodology

            Following a thorough analysis of the literature, a comprehensive approach has been reviewed, with 5 factors or parameters that may affect Tsunami threat factored into the hazard assessment. Topographic elevation, coastal shape and proximity, tsunami direction and topographic slope are the five criteria. After these parameters had been finalised, they were ranked using the analytical hierarchy technique (AHP). The tsunami hazard was calculated using a weighted overly function once the weightages of each dataset were calculated. Finally, tsunami vulnerability was connected with area's land usage to determine the likely danger to the land. The methodological flowchart used in this investigation is shown in Figure 2. Methodology of research is inspired from Sinaga et al. 2011 and Aslam et al. 2017. Tsunami threat is investigated and mapped by employing Analytical hierarchy process. A pairwise correlation was used to determine the importance of each element AHP by allocating specific values according to the examination of connected factors (Althuwaynee et al. 2014). The Analytical Hierarchy Processing technique is divided into 3 phases: establishing a hierarchy of purposes, comparing criteria couple wise, and then calculating weightages for all datasets (Aslam et al., 2020).

3.1. Procurement of airborne photos

            The research began with the preparation of UAV's path and installation of markers for ground control point calculation. At an altitude of roughly 300 meters above the ground, two flying courses were selected to document coastline zones. Topographic layout of area was indicated by 10 pre-marks spread in the field. The coordinates were determined using Real Time Kinematic approach and a geodetic Global Positioning System. The UAV was piloted semi-automatically in the air, with an autopilot component guiding it toward the specified flight path. The pilot only had control over it during takeoff and landing, while the copilot supervised the flight trail and location from the Control Station on ground. The instrument aboard the Unmanned Aerial Vehicle was Canon Powershot A2500 pocket camera whose resolution was 16Mpix. In order to avoid fuzzy shots, camera was programmed to take pictures every 2 seconds with 1/2000 shutter speed. In March of 2017, the aerial photograph data acquisition was completed. The altitude & the land cover data are used to provide very precise investigation on the inundation model and damage assessment by giving the latest Digital Elevation Model data based on UAV photographs (Watanabe & Kawahara, 2016).

3.2. DEM generation

            The deepness of tidal floods and impacted zones, were determined using a GIS technique using a Digital Elevation Model (Schneider et al., 2016). The availability of comprehensive DEM is frequently limited, making modelling difficult. As analysis of inundations generated by tidal & river floods, lahars and tsunamis is particularly dependent on elevation differences, this work used Unmanned Aerial Vehicle expertise to acquire a comprehensive DEM. A DEM can be used to describe a region's topography condition. The more information you provide, the accurate and more precise the outcomes will be. Altitude information stored in the aerial images taken by the UAV is reproduced in every pixel value. As a result, these photographs can be used to create topographic data (DEM) for the research area. Images which are partly overlying on one another constitute an image array with a Ground Sample Distance of 9 centimeters per pixel as a result of UAV assisted recording. The detail that UAV photography provides to objects in comparatively elevated regions is undeniable. As a result, it is projected to make it easier to analyse the spatial data needed for modelling. Agisoft Photoscan software was used to process the data collected from UAV imagery. Digital Elevation Model of study area was generated by combining pixels with related values together to create point clouds. Dense clouds were then used to create georeferenced three-dimensional (3D) data. This sequential operation resulted in topographic data (DEM). Put the figure as near as conceivable to the point in the text where it is primarily mentioned. It may be required to insert some figures and tables before their text citations if there are a lot of them. In case a table or figure is too big to be placed in one column, it can be centered at the upper or lowest part of the page across both columns (Teh et al., 2019).  

3.3. Loss estimation

            This study employed the tsunami vulnerability map, which was made up of multiple flooding scenarios, to assess the losses caused by tsunamis (depths of four, six, eight, ten and twelve meters). Map was created using a Digital Evaluation Model and GIS modelling. In certain cases, it was covered with present land cover map in order to assess the kind & degree of tsunami-attacked land cover. Land cover map was created by interpreting aerial pictures taken by a UAV that had outstanding resolution and could be used as a base map for a 1:5,000 land cover map. If a tsunami hits & ruins shore, land usage shows the potential economic loss per hectare. In every form of land cover, this particular value signifies projected market worth of buildings and other properties per hectare. The largest possible loss in the research region was represented by the outcomes of the overlay procedure of tsunami susceptibility map & land cover map. Trading centers, agronomic land, fish farms, uniform/ non-uniform settlement, and unused space were all classified as land uses in this study. Cottages on agronomic land were thought to be worth similar as non-uniform settlements. Residents with a medium to upper wages own uniform settlement, whereas those with a middle to lower wages possess non-uniform settlement. Uniform settlement had an estimated economic value per hectare of €1,200,000, non-uniform settlement had an estimated value of €1,000,000, farm had an estimated value of €80,000, fish pond had an estimated value of €95,000, business centre had an estimated value of €2,500,000, and open land had an estimated value of €1,700. These figures were built on authentic information at the time of investigation as well as scientifically verified conclusions from parallel researches, therefore they were accurate in describing region’s state. Although loss estimates in this study are approximate, they are useful for risk evaluation and creation of tsunami catastrophe alleviation strategies (Marfai et al., 2019).

4.      Conclusion

            The use of spatial functions, such as topography, establishment of buffer area, locality computation, reclassification into raster format, map algebra and crossing procedures, in GIS-based analysis is critical for disaster evaluation. This sort of policy can assist in regional development for administration, as well as provide a means of mitigating the ramification of natural catastrophes like tsunamis. However, availability of data needed to quantify the threat of environmental hazards may limit these evaluations. We have to employ 5 geographical variables that encompass appropriate data to comprehend calamity incidences and reparations. A persistent potential danger is what development of an appropriate weighting scheme entails. The tsunami vulnerability maps could be extremely useful in attempts to manage and respond to disasters in Karachi and Gwadar, especially in light of the recent tsunami tragedies in the Arabian Sea (Aslam et al., 2020).

            According to the tsunami susceptibility study, some coastal locations in the south are more vulnerable to tsunamis than those in the southwest. Coastal material roughness and slope gradient are thought to have a significant impact on assessment outcomes (Ervita et al., 2019).

5.      Future Recommendations and Implications

            The study's recommendations for a mitigation plan include disaster risk reduction strategies such as spreading tsunami information and knowledge, improving community capacity and preparedness to deal with tsunamis, preparing and installing tsunami early warning system instruments, planning of temporary shelter areas and evacuation routes, and science and technology advancement. The study area can be extended further to monitor and assess more coastal area. More parameters which are relevant should be involved to get more accurate results and analysis. The study's practical conclusion will be the adoption of tsunami threat evaluation, which will include the efficiency of attenuation. The resultant map obtained can be for planning a tsunami evacuation course as well as a tsunami evacuation building. To construct a resultant tsunami hazard map, it is important to assess tsunami susceptibility zones, which requires the physical and social factors of vulnerability.

References

Alberico, I., Di Fiore, V., Iavarone, R., Petrosino, P., Piemontese, L., Tarallo, D., Punzo, M., & Marsella, E. (2015). The tsunami vulnerability assessment of urban environments through freely available datasets: The case study of napoli city (southern italy). Journal of Marine Science and Engineering, 3(3), 981–1005. https://doi.org/10.3390/jmse3030981

Althuwaynee OF, Pradhan B, Park HJ, Lee JH (2014) A novel ensemble bivariate statistical         evidential belief function with knowledge-based analytical hierarchy process and     multivariate statistical logistic regression for landslide susceptibility mapping. Catena        114:21–36

Aslam, B., Ismail, S., & Maqsoom, A. (2020). Geospatial mapping of tsunami susceptibility          of Karachi to Gwadar Coastal area of Pakistan. Arabian Journal of Geosciences, 13(17).             https://doi.org/10.1007/s12517-020-05916-4

Aslam, B., J, M., ZI, M., A, G., & IA, Q. (2017). GIS mapping of tsunami susceptibility: Case     study of the karachi city in Sindh, Pakistan. Journal of Geography & Natural Disasters,            07(01). https://doi.org/10.4172/2167-0587.1000187

Attary, N., Unnikrishnan, V. U., van de Lindt, J. W., Cox, D. T., & Barbosa, A. R. (2017). Performance-based Tsunami Engineering Methodology for risk assessment of structures. Engineering Structures, 141, 676–686. https://doi.org/10.1016/j.engstruct.2017.03.071

Browning, J., & Thomas, N. (2016). An assessment of the tsunami risk in Muscat and Salalah, Oman, based on estimations of probable maximum loss. International Journal of Disaster Risk Reduction, 16, 75–87. https://doi.org/10.1016/j.ijdrr.2016.02.002

Buchhorn M, Smets B, Bertels L, Lesiv M, Tsendbazar N-E, Herold M, Fritz S (2019) Copernicus           Global Land Service: Land Cover 100m, epoch "2015", Globe (Version V2.0.2) [Data set].           Zenodo. https:// doi.org/10.5281/zenodo.3243508

Cankaya, Z. C., Suzen, M. L., Yalciner, A. C., Kolat, C., Zaytsev, A., & Aytore, B. (2016). A new GIS-based Tsunami Risk Evaluation: Methuva (METU tsunami human vulnerability assessment) at Yenikapı, Istanbul. Earth, Planets and Space, 68(1). https://doi.org/10.1186/s40623-016-0507-0

Cocharda R, Ranamukhaarachchi SL, Shivakoti GP, Shipin OV, Edwards PJ, Seeland KT (2008)            The 2004 tsunami in Aceh and Southern Thailand: a review on coastal ecosystems, wave       hazards and vulnerability, perspectives in plant ecology. Evol Syst 10(1):3– 40

Dias, F., Dutykh, D., O’Brien, L., Renzi, E., & Stefanakis, T. (2014). On the modelling of Tsunami Generation and tsunami inundation. Procedia IUTAM, 10, 338–355. https://doi.org/10.1016/j.piutam.2014.01.029

Eckert, S., Jelinek, R., Zeug, G., & Krausmann, E. (2012). Remote Sensing-based assessment       of tsunami vulnerability and risk in Alexandria, Egypt. Applied Geography, 32(2), 714– 723. https://doi.org/10.1016/j.apgeog.2011.08.003

Ervita, K., Marfai, M. A., Khakhim, N., & Mei, E. T. (2019). Tsunami susceptibility mapping        in the coastal area of Ternate Island. Sixth International Symposium on LAPAN-IPB       Satellite. https://doi.org/10.1117/12.2541843

Farhan, A., & Akhyar, H. (2017). Analysis of Tsunami Disaster Map by Geographic          Information System (GIS): Aceh Singkil-Indonesia. IOP Conference Series: Earth and     Environmental Science, 56, 012002. https://doi.org/10.1088/1755-1315/56/1/012002

Irtem E,Gedik N, Kabdasli MS, YasNE(2009)Coastal foresteffectson tsunami run-up heights.      Ocean Eng 36(3-4):313–320

Jaffe BE, Gelfenbuam G (2009) A simple model for calculating tsunami flow speed from tsunami            deposits. Sediment Geol 200(3-4):347– 361

Jaimes, M. A., Reinoso, E., Ordaz, M., Huerta, B., Silva, R., Mendoza, E., & Rodríguez, J. C. (2016). A new approach to probabilistic earthquake-induced tsunami risk assessment. Ocean & Coastal Management, 119, 68–75. https://doi.org/10.1016/j.ocecoaman.2015.10.007

Kathiresan K, Rajendran N (2005) Coastal mangrove forests mitigated tsunami. Estuar Coast Shelf          Sci 65(3):601–606

Marfai, M. A., Sunarto, Khakim, N., Fatchurohman, H., Cahyadi, A., Wibowo, Y. A., & Rosaji,             F. S. (2019). Tsunami hazard mapping and loss estimation using geographic        information system in Drini Beach, gunungkidul coastal area, Yogyakarta, Indonesia. E3S         Web of Conferences, 76, 03010. https://doi.org/10.1051/e3sconf/20197603010

Murty T, Bapat A (1999) Tsunamis on the coastlines of India. International Tsunami Symposium.

Papadopoulos, G. A., Gràcia, E., Urgeles, R., Sallares, V., De Martini, P. M., Pantosti, D., González, M., Yalciner, A. C., Mascle, J., Sakellariou, D., Salamon, A., Tinti, S., Karastathis, V., Fokaefs, A., Camerlenghi, A., Novikova, T., & Papageorgiou, A. (2014). Historical and pre-historical tsunamis in the Mediterranean and its connected seas: Geological signatures, generation mechanisms and coastal impacts. Marine Geology, 354, 81–109. https://doi.org/10.1016/j.margeo.2014.04.014

Papathoma M, Dominey-Howes D (2003) Tsunami vulnerability assessment and its implications   for coastal hazard analysis and disaster management planning, Gulf of Corinth, Greece.          Nat Hazards Earth Syst Sci 3(6):733–747

Papathoma, M., & Dominey-Howes, D. (2003). Tsunami vulnerability assessment and its implications for coastal Hazard Analysis and Disaster Management Planning, gulf of           corinth, Greece. Natural Hazards and Earth System Sciences, 3(6), 733–747.         https://doi.org/10.5194/nhess-3-733-2003

Rani, M., Rehman, S., Sajjad, H., Chaudhary, B. S., Sharma, J., Bhardwaj, S., & Kumar, P. (2018). Assessing coastal landscape vulnerability using geospatial techniques along vizianagaram–srikakulam coast of Andhra Pradesh, India. Natural Hazards, 94(2), 711–725. https://doi.org/10.1007/s11069-018-3414-9

Schneider, B., Hoffmann, G., & Reicherter, K. (2016). Scenario-based tsunami risk assessment using a static flooding approach and high-resolution digital elevation data: An example from Muscat in Oman. Global and Planetary Change, 139, 183–194. https://doi.org/10.1016/j.gloplacha.2016.02.005

Sinaga, T. P., Nugroho, A., Lee, Y.-W., & Suh, Y. (2011). GIS mapping of tsunami          vulnerability: Case study of the jembrana regency in Bali, Indonesia. KSCE Journal of Civil Engineering, 15(3), 537–543. https://doi.org/10.1007/s12205-011-0741-8

Srinivasalu S, Thangadurai N, Switzer AD, Mohan VR, Ayyamperumal T (2007) Erosion and       sedimentation in Kalpakkam (N Tamil Nadu, India) from the 26th December 2004 tsunami.             Mar Geol 240(1-4): 65–75

Supriatna, S., Sobirin, S., & Pratiwi, A. N. (2018). Spatial model of vulnerability towards tsunami in Bayah coastal area, Banten, Indonesia. https://doi.org/10.1063/1.5064175

Teh, S. Y., Koh, H. L., & Lim, Y. H. (2019). High-resolution digital elevation and bathymetry model for tsunami run-up and inundation simulation in Penang. Journal of Earthquake and Tsunami, 13(05n06), 1941001. https://doi.org/10.1142/s179343111941001x

Tinti, S. (1991). Assessment of tsunami hazard in the Italian Seas. Tsunami Hazard, 267–283.    https://doi.org/10.1007/978-94-011-3362-3_11

Watanabe, Y., & Kawahara, Y. (2016). UAV photogrammetry for monitoring changes in river topography and vegetation. Procedia Engineering, 154, 317–325. https://doi.org/10.1016/j.proeng.2016.07.482

Wijetunge JJ (2009) Field measurements and numerical simulations of             the2004tsunamiimpactonthesouthcoastofSriLanka.OceanEng 36(12-13):960–973

Yan Z, Tang D (2009) Changes in suspended sediments associated with 2004 Indian Ocean         tsunami. Adv Space Res 43(1):89–95