Tsunami Warning System Research Articles

Hybrid Duplex Medium Access Control Protocol for Tsunami Early Warning Systems in Underwater Networks

Tsunamis are devastating natural phenomena that cause extensive damage to both human life and infrastructure. To mitigate such impacts, tsunami early warning systems have been deployed globally. South Korea has also initiated a project to install a tsunami warning system to monitor its surrounding seas. To ensure reliable warning decisions, various types of data must be combined, but efficiently transmitting heterogeneous data poses a challenge due to the unique characteristics of underwater acoustic communication. Therefore, this paper proposes a Hybrid Duplex Medium Access Control (HDMAC) protocol designed for a tsunami warning system, with a specific focus on heterogeneous data transmission. HDMAC efficiently handles both seismic and environmental data by utilizing hybrid duplexing, which combines frequency duplex for seismic data with time duplex for environmental data. The protocol addresses the distinct transmission requirements for each data type by optimizing channel utilization through a group Automatic Repeat request (ARQ) scheme and packet size adjustment. Theoretical analysis predicts that HDMAC can achieve a channel utilization of up to 0.91 in smaller networks and 0.64 in larger networks. HDMAC is validated through simulations, and the simulation results closely match these predictions. The simulation results demonstrate the efficiency of HDMAC in supporting real-time submarine earthquake monitoring systems.

A Review of Tsunamis Generated by Volcanoes (TGV) Source Mechanism, Modelling, Monitoring and Warning Systems

Tsunamis generated by volcanic eruptions have risen to prominence since the December 2018 tsunami generated by the flank collapse of Anak Krakatau during a moderate eruption and then the global tsunami generated by the explosive eruption of the Hunga volcano in the Tongan Archipelago in January 2022. Both events cause fatalities and highlight the lack in tsunami warning systems to detect and warn for tsunamis induced by volcanic mechanisms. Following the Hunga Tonga—Hunga Ha’apai eruption and tsunami, an ad hoc working group on Tsunamis Generated by Volcanoes was formed by the Intergovernmental Oceanographic Commission of UNESCO. Volcanic tsunamis differ from seismic tsunamis in that there are a wide range of source mechanisms that can generate the tsunamis waves and this makes understanding, modelling and monitoring volcanic tsunamis much more difficult than seismic tsunamis. This paper provides a review of both the mechanisms behind volcanic tsunamis and the variety of modelling techniques that can be used to simulate their effects for tsunami hazard assessment and forecasting. It gives an example of a volcanic tsunami risk assessment undertaken for Stromboli, outlines the requirement of volcanic monitoring to warn for tsunami hazard and provides examples of volcanic tsunami warning systems in Italy, the Hawaiian Island (USA), Tonga and Indonesia. The paper finishes by highlighting the need for implementing monitoring and warning systems for volcanic tsunamis for locations with submarine volcanoes or near-shore volcanoes which could potentially generate tsunamis.

Disaster cultures and the Indonesia Tsunami Early Warning System: (mis)alignments revealed by the 2018 non-tectonic tsunami in Labuan

This study examined the (mis)alignments between multiple disaster cultures, the Indonesia Tsunami Early Warning System (InaTEWS) and local risk profiles as revealed by the 2018 non-tectonic tsunami triggered by the Anak Krakatau Volcanic eruption in Labuan. The research assessed to what extent the tsunami warning system was adapted to local needs and characteristics. Based on qualitative data collection, this study showed that before the 2018 tsunami (and notwithstanding memories of the 1883 Krakatau tsunami) different local groups shared a strikingly homogeneous understanding of tsunamis as exclusively triggered by earthquakes. After the 2018 tsunami, study participants reported increased awareness of different tsunami types and earthquake risks. However, this rarely translated into practical actions and changes at the local level as structural and cultural factors significantly hampered the work of local government and emergency management agencies. This research identified steps to improve alignment, for example by involving community members in warning technology maintenance, tailoring awareness-raising materials to local hazard profiles and connecting awareness-raising with local cultural traditions. However, a reform of the InaTEWS is necessary, including overcoming sectoral silos and incorporating local knowledge and experiences into policymaking. By addressing these (mis)alignments, authorities can better support communities to understand tsunami risks and respond to future event, ultimately enhancing preparedness.

A review of tsunami early warning at the local level - Key actors, dissemination pathways, and remaining challenges

At the International Symposium on the Lessons Learnt from the 2018 Tsunamis in Palu and Sunda Strait, organised by IOC-UNESCO, experts identified the need for a critical dialogue on the future direction of tsunami warning systems, especially for events other than tectonic origins and with short warning times. This first phase of a longer-term study was initiated to develop a better understanding of tsunami early warning at the local level in countries exposed to a tsunami risk. This desk study involved a systematic literature review of academic publications from 2005 to 2021, and a narrative literature review to examine policy and guidance reports on tsunami early warning. Seven guiding research questions were identified to set the boundaries of the research and provide a clear framework for the review. The draft questions were reviewed and, through several iterations, refined by academics and representatives of working groups and technical agencies involved in tsunami early warning. The findings reveal the characteristics of the literature on tsunami early warning at the local level, as well as insights into how early warning approaches vary across Ocean Basins and between countries. The results revealed substantial challenges that hinder effective tsunami early warning at the local level, such as inadequate coordination of actors. Among several areas for improvement, the need to promote a culture for self-evacuation has been identified as a practical solution to face nearfield tsunamis. The results of this analysis will inform further detailed empirical investigations to provide contemporary insights on the status of tsunami early warning at the local level in different countries.

Modeling of the solitary waves trajectories in “shallow water” environme

Solitary waves in the “shallow water” environment are considered and approximate approach to calculation and prediction of trajectories of these waves is proposed. We use an approach which was developed in recent years and was called the T-representations method. This method allows to move away from the exact solutions of the corresponding systems of differential equations and construct the trajectory approximately, in fact estimating only the point of maximum of the wave amplitude and its displacement. An appropriate mathematical simplification makes efficient computer simulations possible. In recent years, software packages have been actively used to monitor and predict a number of dangerous natural phenomena, such as Earth Alerts, Pacific Tsunami Warning System, Wave Monitoring Sites and others. But these systems do not take solitary waves into account. Using our approach, it is possible to refine the parameters of solitary waves in a specific environment, investigate whether such waves exist there at all and create functions taking into account solitary waves in monitoring systems.

DEEP LEARNING TO PREDICT TSUNAMI HEIGHT AT THE SHORELINE USING OCEAN BOTTOM PRESSURE DATA

Real-time tsunami prediction is a required component of a tsunami warning system. Several advances have been made to improve prediction in the tsunami warning process, including precomputed databases and the assimilation of deep-ocean observations (DART buoys) into numerical modeling (Bernard and Titov, 2015). These improvements aim to accurately and quickly predict the time and height of the tsunami wave impact. Here, two deep learning models (DLM) are developed to predict the maximum tsunami height at a local/long shoreline from four time series observations of ocean bottom pressure data.

Are eruptions reliable precursors to marine volcano collapses?

Volcanoes are sources of numerous threats including lava flows, pyroclastic flows, ash dispersal and landslides or sector collapses. In addition to these commonly known volcanic hazards, volcano-induced tsunamis can occur in the marine environment, introducing a major hazard that can affect populations located far away from the volcanoes. Existing tsunami warning systems generally do not account for volcano-generated tsunamis, due to the multiple source mechanisms that can cause such tsunamis, a limited understanding of precursory signals for these events, and the need for local detection rather than remote sensing. Among these source mechanisms of volcanic tsunamis, sector and lateral collapses are at the high risk-low frequency extreme of risk matrices. Marine volcanoes grow in specific environments, with factors like marine clays, constant full saturation, sediment transport and remobilization, interaction with ocean dynamics, and sea level changes that may impact edifice stability in distinct ways. The majority of historically documented marine volcano collapses occurred at erupting volcanoes, suggesting that eruptions could serve as a remotely detectable warning signal for collapses. However, careful examination of temporal sequences of these examples reveals that collapses do not always follow eruptions. Consequently, there is a need for identifying other, more robust precursors to volcano collapse, in particular in the marine environment, where the consequences of collapses may be widespread.

Crowdsourcing data interpretation for the response to the first public tsunami alert in the Mediterranean sea, after the October 30th, 2020 earthquake (Mw7.0), Samos, Greece

On October 30, 2020, 11:51 UTC, a large shallow earthquake of moment magnitude Mw7.0 ruptured the eastern Aegean Sea area and affected several Greek islands, mainly Samos, as well as the Izmir area, western Turkey. A moderate damaging tsunami followed the earthquake and inundated many coastal zones in the area. At 12:15 UTC, the Greek Civil Protection sent to the residents of the eastern Aegean Sea Greek islands a tsunami alert via SMS through the single European emergency phone number 112. It has been the first time that a tsunami warning was publicly issued in the frame of the North-East Atlantic and Mediterranean Tsunami Warning System (NEAMTWS/IOC/UNESCO) since the system became operational during the summer of 2012. Through an on-line questionnaire survey conducted soon after the event we investigated the impact the tsunami 112 alert message had among the population. The aim of this study is to assess the effectiveness of the 112 number as a tool for tsunami early warning, to look after possible weaknesses that may need future improvements and to better understand the level of tsunami risk awareness among the population. The response received from 344 citizens clearly indicates that the particular tsunami alert in general had positive impact to the message recipients, but highlights several weaknesses of the tsunami emergency management chain in Greece and demonstrates some aspects of the 112 system that need improvement.

Evacuation Decisions during the Great East Japan Earthquake

We analyse evacuation decisions with data from a survey among 10,384 survivers of the 2011 Great East Japan earthquake. The decisions of individuals and families to evacuate or stay are influenced by the Tsunami warning system as well as the behaviour of the surrounding population which is modelled as the percentage of persons evacuating from a city. We formulate binary choice models with “field effects” where we try to control for the endogeneity with a 2-stage model approach. Our results quantify the field effect and suggest that with each minute the Tsunami warning arrives later, on average 3% less of the population are evacuating and surviving. We also show the importance of other variables, in particular the preparedness measures such as signage and evacuation drills.

Numerical validation of an effective slender fault source solution for past tsunami scenarios

To estimate tsunami hazards, it is first necessary to have reliable data relating to the rupture characteristics, such as epicenter, fault geometry, uplift speed, and duration. We made use of a mathematical model that combines analytical and machine learning technique capable of retrieving rupture characteristics from acoustic data. The model was applied with short computational times to data recorded by the comprehensive nuclear-test-Ban Treaty organization hydrophones during four tectonic events that were reported to trigger tsunami waves. The presented inverse problem model for acoustic waves with adequate tsunami propagation tools can be used as a complementary technique alongside tsunami warning systems due to the high propagating speeds of the sound in the ocean. In this paper, the validity of the solutions provided by the inverse problem model is tested by using the calculated earthquake parameters as input to the Cornell multi-grid coupled tsunami numerical model, which, in turn, output surface wave elevations (tsunami) to be compared against deep-ocean assessment and reporting of tsunamis buoy data.

Landslide Induced Tsunami Hazard at Volcanoes: the Case of Santorini

The destructive tsunami on 22 December 2018 due to the flank collapse of the Anak Krakatau volcano was a bitter reminder of large tsunami risks and of the shortcomings of the existing tsunami warning systems for atypical sources (tsunamis generated by non-seismic and complex sources). In the Mediterranean, several tsunamis were generated by landslides associated with volcanic systems in the past.The volcanic unrest experienced in 2011–2012 on the Santorini volcanic island in the Southern Aegean Sea pointed out the need to identify and quantify tsunami hazard and risk due to possible flank instability which may be triggered as a result of volcanic unrest or nearby seismotectonic activities. Inspired from this need, in this study we examined three possible landslide scenarios in Santorini Island with tsunamigenic potential. The results show that the scenarios considered in our study are able to generate significant local tsunamis impacting Santorini and the nearby islands, as well as producing significant impact along the coasts of the Southern Aegean Sea. While maximum tsunami amplitudes/arrival time ranges are 1.2 m/30-90 min for locations in the Greek-Turkish coasts in the far field, they are in the order of ≈60 m/1-2 min for some locations at the Santorini Island. The extreme tsunami amplitudes and short arrival times for locations inside the Santorini Island is a major challenge in terms of tsunami hazard warning and mitigation. As an effort to address this challenge, a discussion on the requirements for local tsunami warning system addressing atypical sources in the context of multi-hazard disaster risk reduction is also provided.

Architecture-Oriented Agent-Based Simulations and Machine Learning Solution: The Case of Tsunami Emergency Analysis for Local Decision Makers

Tsunamis are a perilous natural phenomenon endangering growing coastal populations and tourists in many seaside resorts. Failures in responding to recent tsunami events stresses the importance of further research in building a robust tsunami warning system, especially in the “last mile” component. The lack of detail, unification and standardisation in information processing and decision support hampers wider implementation of reusable information technology solutions among local authorities and officials. In this paper, the architecture of a tsunami emergency solution is introduced. The aim of the research is to present a tsunami emergency solution for local authorities and officials responsible for preparing tsunami response and evacuation plans. The solution is based on a combination of machine learning techniques and agent-based modelling, enabling analysis of both real and simulated datasets. The solution is designed and developed based on the principles of enterprise architecture development. The data exploration follows the practices for data mining and big data analyses. The architecture of the solution is depicted using the standardised notation and includes components that can be exploited by responsible local authorities to test various tsunami impact scenarios and prepare plans for appropriate response measures.

Tsunami risk perception, a state-of-the-art review with a focus in the NEAM region

Large-scale coastal urban sprawl, development of tourist accommodations and industrial maritime poles have highly increased the tsunami risk to people living and/or traveling along the coasts of our planet. The disastrous tsunamis in the Indian Ocean (2004) and in the Pacific Ocean (2011), as well as a suite of other damaging events worldwide, have encouraged International Institutions, first of all UNESCO Intergovernmental Oceanographic Commission, National Governments and Local Communities to implement Tsunami Warning Systems (TWS), to raise awareness on tsunami risk, and to create a multilevel risk governance. In this framework, research on tsunami risk perception plays a key role. The results of these studies should be taken into account in designing risk mitigation programs and tools (such as drills, activities with local communities, emergency plans, etc.). This paper presents a review of such studies, carried out in several countries worldwide through many thousands of interviews performed with different techniques. Most tsunami risk perception studies were carried out in the regions where the Indian Ocean Tsunami Warning System and the Pacific Ocean one (PTWS) operate. In the NEAMTWS (North-East Atlantic, Mediterranean and connected seas Tsunami Warning Systems) region, only few specific studies were conducted, mostly within the EU-funded ASTARTE project (2013–2017) and more recently in a few extensive surveys on tsunami risk perception conducted in Italy between 2019 and 2021. Although the twenty-three studies analyzed in our review show a strong heterogeneity of methodological approaches and population samples, they allow us to outline some general considerations on tsunami risk as perceived by people in the different regions of the world. With the help of a table, we schematically summarized the emerging strengths, weaknesses and lessons learned in the twenty-three papers, noting an increase in the number of such studies in the last 5 years. The surveys were mostly concentrated in high-risk areas and focused on local residents. Some differences emerged depending on the memory of past tsunamis, education level, and local cultures. This provides useful hints for sound citizen-based tsunami risk reduction actions, including improved risk communication aimed at increasing the resilience of tsunami-prone populations. The need for increasing the assessment of tourists’ tsunami risk perception, and for a more homogeneous survey strategy also emerge from our analysis.

A Fuzzy-Logic Approach for Optimized and Cost-Effective Early Warning System for Tsunami Detection

With the economic crisis going around the world, a new approach, “build back better”, has been adopted as a recovery package for various systems. The tsunami detection and warning system is one such system, crucial for saving human lives and infrastructure. While designing a tsunami detection system, the social, economic, and geographical circumstances are considered to be vital. This research is focused on designing a low-cost early warning system mainly for underdeveloped countries, which are more prone to tsunami damage due to a lack of any reliable early warning and detection systems. Such countries require proper cost-effective solutions to address these issues. Previous research has shown that the existing systems are either very costly or hard to implement and manage. In this study, we present a wireless sensor networking model, which is an optimized model in terms of cost, delay, and energy consumption. This research contemplates the techniques and advantages of the intelligence of marine animals. We propose a fuzzy logic-based approach for early tsunami detection, using electromagnetic and pressure sensors, based on the behavioral attributes of turtles and real-time values of earthquakes and water levels.

Limb Sounders Tracking Tsunami-Induced Perturbations from the Stratosphere to the Ionosphere

In this study, we employ three types of satellite data from two different limb sounders: the FORMOSAT-3/COSMIC (F3/C) radio occultation (RO) technique and the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument to study the vertical coupling of the 16-09-2015 Chile tsunami-induced perturbations from the stratosphere to the ionosphere. All three types of datasets, including temperature profiles from 10 to 55 km and 16 to 107 km, and electron density profiles from 120 to 550 km, recognized perturbations of different scales at different heights after the Chile tsunami. The vertical scales identified by the wavelet analysis are from 1–2 km, 5–9 km, and 25–50 km in the stratosphere, mesosphere, and ionosphere, respectively. Meanwhile, as a comparison and validation of the reliability, we also revisited the 11-03-2011 Tohoku earthquake/tsunami-related perturbations from the stratosphere to the ionosphere using the same data. It is believed that the two tsunamis both disturbed the whole atmosphere space, and the scale of these signals gradually increases with the increase in altitude but decreases with time. In addition, the tsunami-related ionospheric gravity wavefronts are examined by the F3/C observations. Another interesting point is that the temperature perturbations recorded by the SABER from 70–100 km altitude are found to arrive earlier than the 2015 tsunami wavefront. The findings in this study suggest that the limb-sounding technique is a useful instrument for detecting the tsunami-coupling gravity wave and benefits the tsunami warning system.

An Egyptian Tsunami Contingency Plan

Tsunami is one of the most destructive natural hazards that threatens coastal communities worldwide. Until the famous 2004 tsunami, the phenomenon has rarely been regarded as an essential issue facing coastal communities, following the incident that resulted in over 400 thousand human casualties and left over 750000 homeless people after completely demolishing their homes. The world's consciousness has changed regarding the real threat that tsunami imposes on the existence of coastal communities around the world. The UNESCO's initiated a program, Northeast Atlantic Mediterranean Tsunami Warning System NEAMTWS, to raise all the required mitigation measures to ensure that residents of coastal communities know the kind and degree of threats they are exposed to and how to deal with them. The project identified Egypt's Mediterranean coast as an area of high vulnerability when exposed to tsunami hazards. Although the project was set to achieve its objective by 2011, none of its objectives have been achieved. There is a total ignorance among the local community, authorities, and planners regarding the imposed threat. There is an urgent need for an innovative approach that could mitigate the impact and threats of tsunamis and increase the resilience of coastal communities facing them. The paper aims to innovate a contingency plan for the Egyptian coastal communities as an effective tool for increasing the resilience of coastal communities against tsunamis hazards. A contingency plan is to be proposed based on profound theoretical and analytical analysis of the literature review. Furthermore, based on the findings of an empirical study, the most effective knowledge transfer tool of the proposed plan is to be tested targeting the local community participants. Finally, the paper is to induct an 'Egyptian Tsunami Contingency Plan' (ETCP) that can be applied within the context and limitations of Egyptian coastal communities' conditions.

Tsunami Triggered by the Lamb Wave From the 2022 Tonga Volcanic Eruption and Transition in the Offshore Japan Region

AbstractThe 2022 volcanic eruption in Tonga caused an unusually large tsunami around the Pacific. It travels with a faster apparent velocity and has larger amplitudes at long distances than what would be expected from a conventional tsunami from the volcanic source. This tsunami was generated by the moving atmospheric Lamb wave and traveled at the speed of the Lamb wave (0.31 km/s). Japanese data showed the amplitude of this first tsunami becomes small when approaching the coast, due to the weaker air‐sea coupling at the shallow depth. This wave split when passing the continental slope, and traveled at the speed of the ocean gravity wave. Therefore, the tsunami observed at the coast is delayed by thousands of seconds from the passage of the Lamb wave. Tsunamis generated by this atmospheric mechanism have not been previously observed by modern digital recording systems and should be considered in the tsunami warning systems.

Analysis of Faster-Than-Real-Time (FTRT) Tsunami Simulations for the Spanish Tsunami Warning System for the Atlantic

Real-time local tsunami warnings embody uncertainty from unknowns in the source definition within the first minutes after the tsunami generates. In general, Tsunami Warning Systems (TWS) provide a quick estimate for tsunami action from deterministic simulations of a single event. In this study, variability in tsunami source parameters has been included by running 135 tsunami simulations; besides this, four different computational domains in the northeastern Atlantic ocean have been considered, resulting in 540 simulations associated with a single event. This was done for tsunamis generated by earthquakes in the Gulf of Cadiz with impact in the western Iberian peninsula and the Canary Islands. A first answer is provided after one minute, and 7 min are required to perform all the simulations in the four computational domains. The fast computation allows alert levels all along the coast to be incorporated into the Spanish National Tsunami Early Warning System. The main findings are that the use of a set of scenarios that account for the uncertainty in source parameters can produce higher tsunami warnings in certain coastal areas than those obtained from a single deterministic reference scenario. Therefore, this work shows that considering uncertainties in tsunami source parameters helps to avoid possible tsunami warning level underestimations. Furthermore, this study demonstrates that this is possible to do in real time in an actual TWS with the use of high-performance computing resources.

Preliminary Observations and Impact in Japan of the Tsunami Caused by the Tonga Volcanic Eruption on January 15, 2022

The tsunami caused by the Tonga submarine volcanic eruption that occurred at 13:15 Japan Time (JST) on January 15, 2022, exposed a blind spot in Japan’s tsunami monitoring and warning system, which was established in 1952 for local tsunamis and expanded to distant tsunamis after the 1960 Chile tsunami. This paper summarizes how the warning system responded to the unprecedented tsunami, the actual evacuation process, and the damage it caused in Japan. Initially, the tsunami from the volcanic eruption was expected to arrive at approximately midnight with amplitudes of less than 20 cm. However, a series of short waves arrived at approximately 21:00, a few hours earlier than expected. The early arrival of these sea waves coincided with a rapid increase in atmospheric pressure; then, the short-period component was predominant, and the wave height was amplified while forming wave groups. After a 1.2 m tsunami was observed in Amami City in southern Japan at 23:55 JST, the Japan Meteorological Agency issued a tsunami warning/advisory. The tsunami continued, and all advisories were cleared at 14:00 JST on January 16. Information about this tsunami and the response to it are summarized here, including the characteristics and issues of the actual tsunami evacuation situation in each region. There were no casualties, but the issues that emerged included difficulty evacuating on a winter night and traffic congestion due to evacuation by car and under the conditions of the COVID-19 pandemic. In the coastal area, damage to fishing boats and aquaculture facilities was reported due to the flow of the tsunami. In addition, damage to aquaculture facilities, including those producing oysters, scallops, seaweed and other marine products, decreased the supply of marine products, and the economic impact is likely to increase in the future.

Hardware/Software Solution for Low Power Evaluation of Tsunami Danger

Carbon footprint reduction issues have been drawing more and more attention these days. Reducing the energy consumption is among the basic directions along this line. In the paper, a low-energy approach to tsunami danger evaluation is concerned. After several disaster tsunamis of the XXIst century, the question arises whether is it possible to evaluate in a couple of minutes the tsunami wave parameters, expected at the particular geo location. The point is that it takes around 20 min for the wave to approach the nearest coast after a seismic event offshore of Japan. Currently, the main tool for studying tsunamis is computer modeling. In particular, the expected tsunami height near the coastline, when a major underwater earthquake is detected, can be estimated by a series of numerical experiments of various scenarios of generation and the following wave propagation. Reducing the calculation time of such scenarios and the necessary energy consumption for this is the scope of this study. Moreover, in case of the major earthquake, the electric power shutdown is possible (e.g., the accident at the Fukushima nuclear power station in Japan on 11 May 2011), so the solution should be of low energy-consuming, preferably based at regular personal computers (PCs) or laptops. The way to achieve the requested performance of numerical modeling at the PC platform is a combination of efficient algorithms and their hardware acceleration. Following this strategy, a solution for the fast numerical simulation of tsunami wave propagation has been proposed. Most of tsunami researchers use the shallow-water approximation to simulate tsunami wave propagation at deep water areas. For software implementation, the MacCormack finite-difference scheme has been chosen, as it is suitable for pipelining. For hardware code acceleration, a special processor, that is, the calculator, has been designed at a field-programmable gate array (FPGA) platform. This combination was tested in terms of precision by comparison with the reference code and with the exact solutions (known for some special cases of the bottom profile). The achieved performance made it possible to calculate the wave propagation over a 1000 × 500 km water area in 1 min (the mesh size was compared to 250 m). It was nearly 300 times faster compared to that of a regular PC and 10 times faster compared to the use of a central processing unit (CPU). This result, being implemented into tsunami warning systems, will make it possible to reduce human casualties and economy losses for the so-called near-field tsunamis. The presented paper discussed the new aspect of such implementation, namely low energy consumption. The corresponding measurements for three platforms (PC and two types of FPGA) have been performed, and a comparison of the obtained results of energy consumption was given. As the numerical simulation of numerous tsunami propagation scenarios from different sources are needed for the purpose of coastal tsunami zoning, the integrated amount of the saving energy is expected to be really valuable. For the time being, tsunami researchers have not used the FPGA-based acceleration of computer code execution. Perhaps, the energy-saving aspect is able to promote the use of FPGAs in tsunami researches. The approach to designing special FPGA-based processors for the fast solution of various engineering problems using a PC could be extended to other areas, such as bioinformatics (motif search in DNA sequences and other algorithms of genome analysis and molecular dynamics) and seismic data processing (three-dimensional (3D) wave package decomposition, data compression, noise suppression, etc.).